diff --git a/01_Python_Jupyter/code/00/einfuehrung_jupyter.ipynb b/01_Python_Jupyter/code/00/einfuehrung_jupyter.ipynb
index ae802a3..f82ae3d 100644
--- a/01_Python_Jupyter/code/00/einfuehrung_jupyter.ipynb
+++ b/01_Python_Jupyter/code/00/einfuehrung_jupyter.ipynb
@@ -744,46 +744,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'alcohol'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[1;32mc:\\Users\\tikaiz.DESKTOP-N3LM399\\.conda\\envs\\dsai\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'alcohol'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[46], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124malcohol\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mash\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmagnesium\u001b[39m\u001b[38;5;124m'\u001b[39m ] \u001b[38;5;66;03m# all columns\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mplot_pairplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtarget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdeep\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[1;32mIn[45], line 3\u001b[0m, in \u001b[0;36mplot_pairplot\u001b[1;34m(data, hue, palette, columns)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_pairplot\u001b[39m(data, hue, palette, columns):\n\u001b[1;32m----> 3\u001b[0m     \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpairplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\tikaiz.DESKTOP-N3LM399\\.conda\\envs\\dsai\\lib\\site-packages\\seaborn\\axisgrid.py:2153\u001b[0m, in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[0;32m   2151\u001b[0m     diag_kws\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfill\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   2152\u001b[0m     diag_kws\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn_singular\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m-> 2153\u001b[0m     grid\u001b[38;5;241m.\u001b[39mmap_diag(kdeplot, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdiag_kws)\n\u001b[0;32m   2155\u001b[0m \u001b[38;5;66;03m# Maybe plot on the off-diagonals\u001b[39;00m\n\u001b[0;32m   2156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m diag_kind \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\tikaiz.DESKTOP-N3LM399\\.conda\\envs\\dsai\\lib\\site-packages\\seaborn\\axisgrid.py:1496\u001b[0m, in \u001b[0;36mPairGrid.map_diag\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1493\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1494\u001b[0m     plt\u001b[38;5;241m.\u001b[39msca(ax)\n\u001b[1;32m-> 1496\u001b[0m vector \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvar\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m   1497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hue_var \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1498\u001b[0m     hue \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hue_var]\n",
-      "File \u001b[1;32mc:\\Users\\tikaiz.DESKTOP-N3LM399\\.conda\\envs\\dsai\\lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
-      "File \u001b[1;32mc:\\Users\\tikaiz.DESKTOP-N3LM399\\.conda\\envs\\dsai\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'alcohol'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 750x750 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "columns = none# all columns\n",
+    "columns = None# all columns\n",
     "\n",
     "plot_pairplot(data, hue=\"target\", palette=\"deep\", columns=columns) # other palettes: \"viridis\", \"deep\", \"muted\", \"pastel\", \"bright\", \"dark\", \"colorblind\""
    ]
diff --git a/02_daten_tabellarisch/code/daten_tabellarisch_1.ipynb b/02_daten_tabellarisch/code/daten_tabellarisch_1.ipynb
new file mode 100644
index 0000000..c5a9eca
--- /dev/null
+++ b/02_daten_tabellarisch/code/daten_tabellarisch_1.ipynb
@@ -0,0 +1,7692 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"\n",
+    "    border: 2px solid #4CAF50; \n",
+    "    padding: 15px; \n",
+    "    background-color: #f4f4f4; \n",
+    "    border-radius: 10px; \n",
+    "    align-items: center;\">\n",
+    "\n",
+    "<h1 style=\"margin: 0; color: #4CAF50;\">Einführung tabellarische Daten</h1>\n",
+    "<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
+    "<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
+    "\n",
+    "<div style=\"flex-shrink: 0;\">\n",
+    "    <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
+    "</div>\n",
+    "<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
+    "</div>\n",
+    "<div style=\"flex: 1;\">\n",
+    "</div>   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Allgemeines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Viele Daten sind in tabellarischer Form\n",
+    "* Gespeichert oft in csv, txt oder in zBsp. Datenbank\n",
+    "* Einfach zu interpretieren\n",
+    "* Gute Skalierbarkeit\n",
+    "* Einfacher Austausch zwischen verschiedenen Systemen und Plattformen\n",
+    "* Einfache Sortierung, Filterung, Aggregierung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Aufbau und Eigenschaften"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Eigenschaften von Tabellen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Hat immer ein <em> \"Länge mal Breite </em> Format\"\n",
+    "* Wird oft als Matrix modelliert (für numerische Daten zBsp mit $\\mathbb{R}^{m\\times n}$)\n",
+    "* (Normalerweise) unabhängige Einträge (i.i.d.)\n",
+    "* Verschiedene Datentypen: numerisch (float, int), text (string, char), leer (None, null, NaN, \"\")\n",
+    "* Jede Spalte ist ein Feature -> Was ist ein Feature?\n",
+    "* Jede Zeile ist ein Datenpunkt -> Was ist ein Datenpunkt?\n",
+    "* Jede Spalte <em>kann </em> ein Label sein -> Was ist ein Label?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Spalte des tabellarischen Datasets\n",
+    "* Eine <em> Eigenschaft </em>\n",
+    "* Label ist <em> kein </em> Feature\n",
+    "* Kann/Muss oft normalisiert, aggregiert oder vorverarbeitet (preprocessing) werden, dadurch kann die Qualität des Features verbessert werden (kann aber auch schlechter werden)\n",
+    "* Kann in Beziehung zu anderen Features stehen und durch Korrelationen oder Abhängigkeiten beeinflusst werden (ZBsp Feature \"Preis\" in einem Datensatz über Autos hängt meistens direkt mit der Leistung des Autos zusammen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datenpunkt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Zeile des tabellarischen Datasets\n",
+    "* Steht für einen Eintrag\n",
+    "* Je mehr Zeilen, desto mehr Daten\n",
+    "* Quantität $\\neq$ Qualität:\n",
+    "    * Können Duplikate beinhalten\n",
+    "    * Daten modellieren vielleicht die \"echte Situation\" nicht gut\n",
+    "    * Können leere Felder haben\n",
+    "    * Können falsch sein (Umfragen etc.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Label ist quasi ein Namensschild bzw. eine Zugehörigkeit\n",
+    "* Zbsp in diesem Titanic Dataset könnte man <em> survived </em> verwenden, um zu analysieren, welche Unterschiede zwischen Überlebenden und Nicht-Überlebenden bestehen/bestanden\n",
+    "* Anderes Beispiel wäre aber auch Alter (Frauen und Kinder zuerst?), oder vielleicht eine geometrische Untersuchung mit den Cabin-Nummern (Vielleicht auf einer Seite der Kabine mehr Mortalitätsrate?)\n",
+    "* Kann auch eine reelle Zahl (float) sein (Regression)\n",
+    "* Analysen können auch ohne Label durchgeführt werden:\n",
+    "    * Gruppierungen\n",
+    "    * Dimensions-Reduzierung (zBsp.: PCA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Einführung in Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Way to Go für Tabellarische Daten\n",
+    "* Daten können aus verschiedenen Quellen eingelesen und ggf. auch wieder gespeichert werden (txt, csv, sql)\n",
+    "* Eingelesene Daten werden in sogenannten <em>DataFrames</em> gespeichert\n",
+    "* Es gibt auch <em>Series</em> als Datenstruktur für Listen- oder Array-ähnlichen Datenstrukturen\n",
+    "* Pandas bietet eine flexible Indizierungsmöglichkeit\n",
+    "* Viele statistische Größen (Mittelwert, Median, etc.) können einfach berechnet werden\n",
+    "* Gute Integration mit weiteren Python-Bibliotheken (NumPy, Matplotlib, Scikit-Learn, etc.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Im folgenden wird nun Pandas anhand von einem Beispiel eingeführt. Dabei wird dieses Dataset in diesem Notebook überall verwendet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importieren von Pandas\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Erstellung und Indizierung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir erstellen ein Dataframe aus einer Liste ohne Index Spalte:\n",
+    "\n",
+    "df = pd.DataFrame({'Name'        : [\"Peter\", \"Karla\", \"Anne\", \"Nino\", \"Andrzej\"],\n",
+    "                   'Geschlecht'  : ['M',pd.NA,'W','M','M'],\n",
+    "                   'Alter'       : [45, 53, 16, 22, 61],\n",
+    "                   'Größe'       : [1.77,1.72, 1.82, 1.71, 1.68],\n",
+    "                   'Nationalität': [\"deutsch\", \"schweizerisch\", \"deutsch\", \"italienisch\", \"polnisch\"],\n",
+    "                   'Gehalt'      : [3400, 4000, 0, pd.NA, 2300]\n",
+    "                   }\n",
+    "                  )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "0    Peter          M     45   1.77        deutsch   3400\n",
+       "1    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "2     Anne          W     16   1.82        deutsch      0\n",
+       "3     Nino          M     22   1.71    italienisch   <NA>\n",
+       "4  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Name               Anne\n",
+       "Geschlecht            W\n",
+       "Alter                16\n",
+       "Größe              1.82\n",
+       "Nationalität    deutsch\n",
+       "Gehalt                0\n",
+       "Name: 2, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Indizierung mit loc\n",
+    "# Wir wählen die Zeile mit dem Index 2 aus\n",
+    "df.loc[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Name              Peter\n",
+       "Geschlecht            M\n",
+       "Alter                45\n",
+       "Größe              1.77\n",
+       "Nationalität    deutsch\n",
+       "Gehalt             3400\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Indizierung mit iloc\n",
+    "# Wir wählen die erste Zeile aus\n",
+    "df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Nun erstellen wir ein Dataframe aus einer Liste mit Index Spalte:\n",
+    "\n",
+    "df = pd.DataFrame({'Name'        : [\"Peter\", \"Karla\", \"Anne\", \"Nino\", \"Andrzej\"],\n",
+    "                     'Geschlecht'  : ['M',pd.NA,'W','M','M'],\n",
+    "                     'Alter'       : [45, 53, 16, 22, 61],\n",
+    "                     'Größe'       : [1.77,1.72, 1.82, 1.71, 1.68],\n",
+    "                     'Nationalität': [\"deutsch\", \"schweizerisch\", \"deutsch\", \"italienisch\", \"polnisch\"],\n",
+    "                     'Gehalt'      : [3400, 4000, 0, pd.NA, 2300]\n",
+    "                     },\n",
+    "                    index = ['A', 'B', 'C', 'D', 'E']\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "C     Anne          W     16   1.82        deutsch      0\n",
+       "D     Nino          M     22   1.71    italienisch   <NA>\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Index ist nun selber gewählt, vorher numerisch (0-4), nun (A-E)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Name              Peter\n",
+       "Geschlecht            M\n",
+       "Alter                45\n",
+       "Größe              1.77\n",
+       "Nationalität    deutsch\n",
+       "Gehalt             3400\n",
+       "Name: A, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Indizierung mit Index\n",
+    "\n",
+    "df.loc['A']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Name              Peter\n",
+       "Geschlecht            M\n",
+       "Alter                45\n",
+       "Größe              1.77\n",
+       "Nationalität    deutsch\n",
+       "Gehalt             3400\n",
+       "Name: A, dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# iloc indizierung nach wie vor möglich um die 1. Zeile auszuwählen\n",
+    "df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Peter'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Wir können auch nur bestimmte Spalten auswählen\n",
+    "df.loc['A', 'Name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht\n",
+       "A    Peter          M\n",
+       "B    Karla       <NA>\n",
+       "C     Anne          W\n",
+       "D     Nino          M\n",
+       "E  Andrzej          M"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Es ist auch die Indizierung von mehreren Spalten möglich\n",
+    "df.loc[:, ['Name', 'Geschlecht']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name Geschlecht\n",
+       "A  Peter          M\n",
+       "B  Karla       <NA>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bzw. auch inklusive Zeilen\n",
+    "df.loc[['A', 'B'], ['Name', 'Geschlecht']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ebenso kann über eine Maske indiziert werden\n",
+    "temp = df.loc[df['Alter'] > 20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "D     Nino          M     22   1.71    italienisch   <NA>\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mehrere Bedingungen können auch kombiniert werden\n",
+    "temp = df.loc[(df['Alter'] > 20) & (df['Gehalt'] > 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Das Dataframe kann auch mittels SQL ähnlicher Syntax gefiltert werden\n",
+    "temp = df.query('Alter > 20 & Größe > 1.71')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "A  Peter          M     45   1.77        deutsch   3400\n",
+       "B  Karla       <NA>     53   1.72  schweizerisch   4000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Die Ergebnisse sind Peter bzw. Peter\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Eine andere Art der Indizierung ist die Verwendung von at und iat\n",
+    "# at und iat sind schneller als loc und iloc, da sie nur einen Wert zurückgeben\n",
+    "\n",
+    "# Beispiel:\n",
+    "a = df.at['A', 'Name']\n",
+    "b = df.iat[0, 0]\n",
+    "\n",
+    "print(f'Die Ergebnisse sind {a} bzw. {b}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+      "A  Peter Müller          M     45   1.77        deutsch   3400\n",
+      "B         Karla       <NA>     53   1.72  schweizerisch   4000\n",
+      "C          Anne          W     16   1.82        deutsch      0\n",
+      "D          Nino          M     22   1.71    italienisch   <NA>\n",
+      "E       Andrzej          M     61   1.68       polnisch   2300\n",
+      "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+      "A    Peter          M     45   1.77        deutsch   3400\n",
+      "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+      "C     Anne          W     16   1.82        deutsch      0\n",
+      "D     Nino          M     22   1.71    italienisch   <NA>\n",
+      "E  Andrzej          M     61   1.68       polnisch   2300\n"
+     ]
+    }
+   ],
+   "source": [
+    "# at und iat können auch für das Setzen von Werten verwendet werden\n",
+    "df.at['A', 'Name'] = 'Peter Müller'\n",
+    "print(df)\n",
+    "df.iat[0, 0] = 'Peter'\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iat und at liefert einen Fehler, wenn mehere Werte ausgegeben werden sollen\n",
+    "# df.at[['A', 'B'], ['Name']] # Fehler: Richtig wäre die nächste Zeile\n",
+    "# df.loc[['A', 'B'], ['Name']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Außerdem kann auch einfach wie bei einer Liste eine (oder mehrere) Spalte(n) ausgewählt werden\n",
+    "names = df['Name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A      Peter\n",
+      "B      Karla\n",
+      "C       Anne\n",
+      "D       Nino\n",
+      "E    Andrzej\n",
+      "Name: Name, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(names)) # Das Ergebnis ist nun eine Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gleiches Ergebnis liefert die Adressierung via dem Punkt \".\" (Ähnlich wie bei Objekten)\n",
+    "names = df.Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A      Peter\n",
+      "B      Karla\n",
+      "C       Anne\n",
+      "D       Nino\n",
+      "E    Andrzej\n",
+      "Name: Name, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(names)) # Der Typ ist also auch gleich"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spalten und Datentypen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Datentypen von Python und Pandas sind ähnlich, müssen aber dennoch unterschieden werden. Kann Pandas einen Datentyp nicht eindeutig zuordnen wird dieser automatisch als `object` angenommen. In numerischen Spalten ist dies meist ein Zeichen das Daten fehlen und diese zuerst bearbeitet (zBsp. beseitigt) werden müssen.\n",
+    "\n",
+    "\n",
+    "| Pandas Type | Native Python Type  | Beschreibung          |\n",
+    "|-------------|---------------------|-----------------------|\n",
+    "| object      | string              | Zahlen und Strings    |\n",
+    "| int64       | int                 | Ganzzahlen            |\n",
+    "| float64     | float               | Gleitkomma           |\n",
+    "| datetime64  | N/A                 | Datum und Zeit        |\n",
+    "\n",
+    "Verwende `info`() um Informationen über Datentypen und Spalten anzuzeigen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 5 entries, A to E\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   Name          5 non-null      object \n",
+      " 1   Geschlecht    4 non-null      object \n",
+      " 2   Alter         5 non-null      int64  \n",
+      " 3   Größe         5 non-null      float64\n",
+      " 4   Nationalität  5 non-null      object \n",
+      " 5   Gehalt        4 non-null      object \n",
+      "dtypes: float64(1), int64(1), object(4)\n",
+      "memory usage: 452.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Informationen über das Dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "Index: 5 entries, A to E\n",
+      "Series name: Name\n",
+      "Non-Null Count  Dtype \n",
+      "--------------  ----- \n",
+      "5 non-null      object\n",
+      "dtypes: object(1)\n",
+      "memory usage: 252.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Für beliebige Spalten kann folgendes verwendet werden\n",
+    "df['Name'].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In der Info wird auch angegeben, wie viele Werte None (=null) sind."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NaN-Values und None/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Geschlecht  Alter  Größe  Nationalität  Gehalt\n",
+       "A  False       False  False  False         False   False\n",
+       "B  False        True  False  False         False   False\n",
+       "C  False       False  False  False         False   False\n",
+       "D  False       False  False  False         False    True\n",
+       "E  False       False  False  False         False   False"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Um die Felder anzuzeigen, welche fehlende Werte enthalten, verwenden wir den folgenden Befehl\n",
+    "df.isna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Name  Geschlecht  Alter  Größe  Nationalität  Gehalt\n",
+       "A  True        True   True   True          True    True\n",
+       "B  True       False   True   True          True    True\n",
+       "C  True        True   True   True          True    True\n",
+       "D  True        True   True   True          True   False\n",
+       "E  True        True   True   True          True    True"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ähnlich können wir auch die Felder anzeigen, welche keine fehlenden Werte enthalten\n",
+    "df.notna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warum ist es wichtig, solche Werte zu überprüfen bzw. sich mit sochen Werten zu befassen?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Um nan-Werte zu löschen, verwenden wir den folgenden Befehl\n",
+    "cleaned_df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe Nationalität Gehalt\n",
+       "A    Peter          M     45   1.77      deutsch   3400\n",
+       "C     Anne          W     16   1.82      deutsch      0\n",
+       "E  Andrzej          M     61   1.68     polnisch   2300"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dies kann auch mit einzelnen Spalten gemacht werden\n",
+    "cleaned_df_2 = df.dropna(subset=['Gehalt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "C     Anne          W     16   1.82        deutsch      0\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_df_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NULL percent of column Geschlecht:  20.0 %\n",
+      "NULL absolute of column Geschlecht:  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "column = 'Geschlecht'\n",
+    "\n",
+    "amount_null = df.filter([column]).isnull().sum() / len(df) * 100\n",
+    "amount_null_count = df.filter([column]).isnull().sum()\n",
+    "\n",
+    "\n",
+    "print(f'NULL percent of column {column}: ', amount_null.iloc[0], '%')\n",
+    "print(f'NULL absolute of column {column}: ', amount_null_count.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sortieren und Filtern"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verwende zum Sortieren des Dataframes die Methode `sort_values('Spalte')`. Verwende dabei `inplace`, damit die Änderungen direkt im aktuellen Dataframe durchgeführt werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir sortieren zuerst nach dem Alter\n",
+    "df.sort_values('Alter', inplace=True) # Wir verwenden inplace=True um das Dataframe zu überschreiben"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "C     Anne          W     16   1.82        deutsch      0\n",
+       "D     Nino          M     22   1.71    italienisch   <NA>\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir können auch nach mehreren Spalten sortieren\n",
+    "df.sort_values(['Geschlecht', 'Alter'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "D     Nino          M     22   1.71    italienisch   <NA>\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300\n",
+       "C     Anne          W     16   1.82        deutsch      0\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir können auch die Sortierung umkehren für jede der Spalten\n",
+    "df.sort_values(['Geschlecht', 'Alter'], ascending=[False, False], inplace=True) # gleich wie ascending=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "      <th>Größe</th>\n",
+       "      <th>Nationalität</th>\n",
+       "      <th>Gehalt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>polnisch</td>\n",
+       "      <td>2300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1.77</td>\n",
+       "      <td>deutsch</td>\n",
+       "      <td>3400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>italienisch</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>schweizerisch</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter  Größe   Nationalität Gehalt\n",
+       "C     Anne          W     16   1.82        deutsch      0\n",
+       "E  Andrzej          M     61   1.68       polnisch   2300\n",
+       "A    Peter          M     45   1.77        deutsch   3400\n",
+       "D     Nino          M     22   1.71    italienisch   <NA>\n",
+       "B    Karla       <NA>     53   1.72  schweizerisch   4000"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Um zu filtern, können wir auch den folgenden Befehl verwenden, dieser ist sehr ähnlich zu der normalen Indizierung\n",
+    "df_filtered = df.filter(['Name', 'Geschlecht', 'Alter'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Geschlecht</th>\n",
+       "      <th>Alter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Anne</td>\n",
+       "      <td>W</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>Andrzej</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>M</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Nino</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Karla</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name Geschlecht  Alter\n",
+       "C     Anne          W     16\n",
+       "E  Andrzej          M     61\n",
+       "A    Peter          M     45\n",
+       "D     Nino          M     22\n",
+       "B    Karla       <NA>     53"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "C    False\n",
+       "E    False\n",
+       "A     True\n",
+       "D    False\n",
+       "B     True\n",
+       "Name: Name, dtype: bool"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Um herauszufinden ob ein Wert in einer Spalte vorkommt, können wir folgenden Code verwenden\n",
+    "\n",
+    "df['Name'].isin(['Peter', 'Karla'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Datenimport und Datenexport in Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas stellt umfangreiche Methoden zum importieren, bearbeiten und speichern von Daten zur Verfügung.\n",
+    "\n",
+    "* Es werden zahlreiche Datenformate (.csv, .xlsx, .json, ...) unterstützt\n",
+    "* Daten können Lokal oder OnLine gespeichert werden\n",
+    "* Header können angepasst werden\n",
+    "* Datentypen können angepasst werden\n",
+    "* Spalten und Zeilen können verändert werden\n",
+    "* Fehlende Werte können bearbeitet werden\n",
+    "* ...\n",
+    "\n",
+    "<h3>Formate, welche unter anderem von Pandas unterstützt werden</h3>\n",
+    "\n",
+    "| Data Format |        Read       |            Save |\n",
+    "| ------------ | :---------------: | --------------: |\n",
+    "| csv          |  `pd.read_csv()`  |   `df.to_csv()` |\n",
+    "| json         |  `pd.read_json()` |  `df.to_json()` |\n",
+    "| excel        | `pd.read_excel()` | `df.to_excel()` |\n",
+    "| hdf          |  `pd.read_hdf()`  |   `df.to_hdf()` |\n",
+    "| sql          |  `pd.read_sql()`  |   `df.to_sql()` |\n",
+    "| ...          |        ...        |             ... |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Laden aus einer CSV Datei"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir verwenden das *Titanic* Dataset. Dieses ist nun gespeichert im Ordner `_data/titanic.csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wir verwenden dafür die Pakete os und pandas\n",
+    "\n",
+    "import os\n",
+    "import pandas as pd # bereits vorher importiert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset saved to path ..\\..\\_data\\titanic.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Hilfsmethode\n",
+    "\n",
+    "path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.csv\")\n",
+    "\n",
+    "if not os.path.exists(path):\n",
+    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
+    "    df = pd.read_csv(\"https://raw.githubusercontent.com/rolandmueller/titanic/main/titanic3.csv\")\n",
+    "    # df = sns.load_dataset(\"titanic\")\n",
+    "    df.to_csv(path, index=False)\n",
+    "    print(f\"Dataset saved to path {path}\")\n",
+    "else:\n",
+    "    print(\"Dataset is already existing\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.csv\")\n",
+    "\n",
+    "titanic = pd.read_csv(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wie wir sehen haben wir 1309 Zeilen (Datenpunkte) und 14 Spalten.\n",
+    "\n",
+    "Was ist hier das Label?\n",
+    "\n",
+    "Datasets können aber auch genau umgekehrt aufgebaut sein."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transpornieren des Dataframes\n",
+    "\n",
+    "transposed_titanic = titanic.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>1299</th>\n",
+       "      <th>1300</th>\n",
+       "      <th>1301</th>\n",
+       "      <th>1302</th>\n",
+       "      <th>1303</th>\n",
+       "      <th>1304</th>\n",
+       "      <th>1305</th>\n",
+       "      <th>1306</th>\n",
+       "      <th>1307</th>\n",
+       "      <th>1308</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>pclass</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>survived</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>Anderson, Mr. Harry</td>\n",
+       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
+       "      <td>Andrews, Mr. Thomas Jr</td>\n",
+       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
+       "      <td>Artagaveytia, Mr. Ramon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yasbeck, Mr. Antoni</td>\n",
+       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
+       "      <td>Youseff, Mr. Gerious</td>\n",
+       "      <td>Yousif, Mr. Wazli</td>\n",
+       "      <td>Yousseff, Mr. Gerious</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>...</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "      <td>female</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>45.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sibsp</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parch</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ticket</th>\n",
+       "      <td>24160</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>19952</td>\n",
+       "      <td>13502</td>\n",
+       "      <td>112050</td>\n",
+       "      <td>11769</td>\n",
+       "      <td>PC 17609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2659</td>\n",
+       "      <td>2659</td>\n",
+       "      <td>2628</td>\n",
+       "      <td>2647</td>\n",
+       "      <td>2627</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>315082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fare</th>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>26.55</td>\n",
+       "      <td>77.9583</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>51.4792</td>\n",
+       "      <td>49.5042</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>14.4583</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>7.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cabin</th>\n",
+       "      <td>B5</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>E12</td>\n",
+       "      <td>D7</td>\n",
+       "      <td>A36</td>\n",
+       "      <td>C101</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>embarked</th>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>S</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>boat</th>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>body</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>home.dest</th>\n",
+       "      <td>St Louis, MO</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>New York, NY</td>\n",
+       "      <td>Hudson, NY</td>\n",
+       "      <td>Belfast, NI</td>\n",
+       "      <td>Bayside, Queens, NY</td>\n",
+       "      <td>Montevideo, Uruguay</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14 rows × 1309 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    0                                1     \\\n",
+       "pclass                                 1                                1   \n",
+       "survived                               1                                1   \n",
+       "name       Allen, Miss. Elisabeth Walton   Allison, Master. Hudson Trevor   \n",
+       "sex                               female                             male   \n",
+       "age                                 29.0                           0.9167   \n",
+       "sibsp                                  0                                1   \n",
+       "parch                                  0                                2   \n",
+       "ticket                             24160                           113781   \n",
+       "fare                            211.3375                           151.55   \n",
+       "cabin                                 B5                          C22 C26   \n",
+       "embarked                               S                                S   \n",
+       "boat                                   2                               11   \n",
+       "body                                 NaN                              NaN   \n",
+       "home.dest                   St Louis, MO  Montreal, PQ / Chesterville, ON   \n",
+       "\n",
+       "                                      2     \\\n",
+       "pclass                                   1   \n",
+       "survived                                 0   \n",
+       "name          Allison, Miss. Helen Loraine   \n",
+       "sex                                 female   \n",
+       "age                                    2.0   \n",
+       "sibsp                                    1   \n",
+       "parch                                    2   \n",
+       "ticket                              113781   \n",
+       "fare                                151.55   \n",
+       "cabin                              C22 C26   \n",
+       "embarked                                 S   \n",
+       "boat                                   NaN   \n",
+       "body                                   NaN   \n",
+       "home.dest  Montreal, PQ / Chesterville, ON   \n",
+       "\n",
+       "                                           3     \\\n",
+       "pclass                                        1   \n",
+       "survived                                      0   \n",
+       "name       Allison, Mr. Hudson Joshua Creighton   \n",
+       "sex                                        male   \n",
+       "age                                        30.0   \n",
+       "sibsp                                         1   \n",
+       "parch                                         2   \n",
+       "ticket                                   113781   \n",
+       "fare                                     151.55   \n",
+       "cabin                                   C22 C26   \n",
+       "embarked                                      S   \n",
+       "boat                                        NaN   \n",
+       "body                                      135.0   \n",
+       "home.dest       Montreal, PQ / Chesterville, ON   \n",
+       "\n",
+       "                                                      4     \\\n",
+       "pclass                                                   1   \n",
+       "survived                                                 0   \n",
+       "name       Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "sex                                                 female   \n",
+       "age                                                   25.0   \n",
+       "sibsp                                                    1   \n",
+       "parch                                                    2   \n",
+       "ticket                                              113781   \n",
+       "fare                                                151.55   \n",
+       "cabin                                              C22 C26   \n",
+       "embarked                                                 S   \n",
+       "boat                                                   NaN   \n",
+       "body                                                   NaN   \n",
+       "home.dest                  Montreal, PQ / Chesterville, ON   \n",
+       "\n",
+       "                          5                                  6     \\\n",
+       "pclass                       1                                  1   \n",
+       "survived                     1                                  1   \n",
+       "name       Anderson, Mr. Harry  Andrews, Miss. Kornelia Theodosia   \n",
+       "sex                       male                             female   \n",
+       "age                       48.0                               63.0   \n",
+       "sibsp                        0                                  1   \n",
+       "parch                        0                                  0   \n",
+       "ticket                   19952                              13502   \n",
+       "fare                     26.55                            77.9583   \n",
+       "cabin                      E12                                 D7   \n",
+       "embarked                     S                                  S   \n",
+       "boat                         3                                 10   \n",
+       "body                       NaN                                NaN   \n",
+       "home.dest         New York, NY                         Hudson, NY   \n",
+       "\n",
+       "                             7     \\\n",
+       "pclass                          1   \n",
+       "survived                        0   \n",
+       "name       Andrews, Mr. Thomas Jr   \n",
+       "sex                          male   \n",
+       "age                          39.0   \n",
+       "sibsp                           0   \n",
+       "parch                           0   \n",
+       "ticket                     112050   \n",
+       "fare                          0.0   \n",
+       "cabin                         A36   \n",
+       "embarked                        S   \n",
+       "boat                          NaN   \n",
+       "body                          NaN   \n",
+       "home.dest             Belfast, NI   \n",
+       "\n",
+       "                                                    8     \\\n",
+       "pclass                                                 1   \n",
+       "survived                                               1   \n",
+       "name       Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
+       "sex                                               female   \n",
+       "age                                                 53.0   \n",
+       "sibsp                                                  2   \n",
+       "parch                                                  0   \n",
+       "ticket                                             11769   \n",
+       "fare                                             51.4792   \n",
+       "cabin                                               C101   \n",
+       "embarked                                               S   \n",
+       "boat                                                   D   \n",
+       "body                                                 NaN   \n",
+       "home.dest                            Bayside, Queens, NY   \n",
+       "\n",
+       "                              9     ...                 1299  \\\n",
+       "pclass                           1  ...                    3   \n",
+       "survived                         0  ...                    0   \n",
+       "name       Artagaveytia, Mr. Ramon  ...  Yasbeck, Mr. Antoni   \n",
+       "sex                           male  ...                 male   \n",
+       "age                           71.0  ...                 27.0   \n",
+       "sibsp                            0  ...                    1   \n",
+       "parch                            0  ...                    0   \n",
+       "ticket                    PC 17609  ...                 2659   \n",
+       "fare                       49.5042  ...              14.4542   \n",
+       "cabin                          NaN  ...                  NaN   \n",
+       "embarked                         C  ...                    C   \n",
+       "boat                           NaN  ...                    C   \n",
+       "body                          22.0  ...                  NaN   \n",
+       "home.dest      Montevideo, Uruguay  ...                  NaN   \n",
+       "\n",
+       "                                              1300                  1301  \\\n",
+       "pclass                                           3                     3   \n",
+       "survived                                         1                     0   \n",
+       "name       Yasbeck, Mrs. Antoni (Selini Alexander)  Youseff, Mr. Gerious   \n",
+       "sex                                         female                  male   \n",
+       "age                                           15.0                  45.5   \n",
+       "sibsp                                            1                     0   \n",
+       "parch                                            0                     0   \n",
+       "ticket                                        2659                  2628   \n",
+       "fare                                       14.4542                 7.225   \n",
+       "cabin                                          NaN                   NaN   \n",
+       "embarked                                         C                     C   \n",
+       "boat                                           NaN                   NaN   \n",
+       "body                                           NaN                 312.0   \n",
+       "home.dest                                      NaN                   NaN   \n",
+       "\n",
+       "                        1302                   1303                  1304  \\\n",
+       "pclass                     3                      3                     3   \n",
+       "survived                   0                      0                     0   \n",
+       "name       Yousif, Mr. Wazli  Yousseff, Mr. Gerious  Zabour, Miss. Hileni   \n",
+       "sex                     male                   male                female   \n",
+       "age                      NaN                    NaN                  14.5   \n",
+       "sibsp                      0                      0                     1   \n",
+       "parch                      0                      0                     0   \n",
+       "ticket                  2647                   2627                  2665   \n",
+       "fare                   7.225                14.4583               14.4542   \n",
+       "cabin                    NaN                    NaN                   NaN   \n",
+       "embarked                   C                      C                     C   \n",
+       "boat                     NaN                    NaN                   NaN   \n",
+       "body                     NaN                    NaN                 328.0   \n",
+       "home.dest                NaN                    NaN                   NaN   \n",
+       "\n",
+       "                            1305                       1306  \\\n",
+       "pclass                         3                          3   \n",
+       "survived                       0                          0   \n",
+       "name       Zabour, Miss. Thamine  Zakarian, Mr. Mapriededer   \n",
+       "sex                       female                       male   \n",
+       "age                          NaN                       26.5   \n",
+       "sibsp                          1                          0   \n",
+       "parch                          0                          0   \n",
+       "ticket                      2665                       2656   \n",
+       "fare                     14.4542                      7.225   \n",
+       "cabin                        NaN                        NaN   \n",
+       "embarked                       C                          C   \n",
+       "boat                         NaN                        NaN   \n",
+       "body                         NaN                      304.0   \n",
+       "home.dest                    NaN                        NaN   \n",
+       "\n",
+       "                          1307                1308  \n",
+       "pclass                       3                   3  \n",
+       "survived                     0                   0  \n",
+       "name       Zakarian, Mr. Ortin  Zimmerman, Mr. Leo  \n",
+       "sex                       male                male  \n",
+       "age                       27.0                29.0  \n",
+       "sibsp                        0                   0  \n",
+       "parch                        0                   0  \n",
+       "ticket                    2670              315082  \n",
+       "fare                     7.225               7.875  \n",
+       "cabin                      NaN                 NaN  \n",
+       "embarked                     C                   S  \n",
+       "boat                       NaN                 NaN  \n",
+       "body                       NaN                 NaN  \n",
+       "home.dest                  NaN                 NaN  \n",
+       "\n",
+       "[14 rows x 1309 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transposed_titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es gibt einige andere Parameter, die beim Einlesen der Daten angepasst werden können. So kann explizit angegeben werden, welches Trennzeichen verwendet wird oder ob die erste Zeile als Header verwendet werden soll. Ebenso kann die Indexspalte explizit angegeben werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Einlesen der Daten ohne Header\n",
+    "titanic = pd.read_csv(path, header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
+       "      <th>13</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pclass</td>\n",
+       "      <td>survived</td>\n",
+       "      <td>name</td>\n",
+       "      <td>sex</td>\n",
+       "      <td>age</td>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>parch</td>\n",
+       "      <td>ticket</td>\n",
+       "      <td>fare</td>\n",
+       "      <td>cabin</td>\n",
+       "      <td>embarked</td>\n",
+       "      <td>boat</td>\n",
+       "      <td>body</td>\n",
+       "      <td>home.dest</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.55</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1309</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1310 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1                                     2       3       4   \\\n",
+       "0     pclass  survived                                  name     sex     age   \n",
+       "1          1         1         Allen, Miss. Elisabeth Walton  female    29.0   \n",
+       "2          1         1        Allison, Master. Hudson Trevor    male  0.9167   \n",
+       "3          1         0          Allison, Miss. Helen Loraine  female     2.0   \n",
+       "4          1         0  Allison, Mr. Hudson Joshua Creighton    male    30.0   \n",
+       "...      ...       ...                                   ...     ...     ...   \n",
+       "1305       3         0                  Zabour, Miss. Hileni  female    14.5   \n",
+       "1306       3         0                 Zabour, Miss. Thamine  female     NaN   \n",
+       "1307       3         0             Zakarian, Mr. Mapriededer    male    26.5   \n",
+       "1308       3         0                   Zakarian, Mr. Ortin    male    27.0   \n",
+       "1309       3         0                    Zimmerman, Mr. Leo    male    29.0   \n",
+       "\n",
+       "         5      6       7         8        9         10    11     12  \\\n",
+       "0     sibsp  parch  ticket      fare    cabin  embarked  boat   body   \n",
+       "1         0      0   24160  211.3375       B5         S     2    NaN   \n",
+       "2         1      2  113781    151.55  C22 C26         S    11    NaN   \n",
+       "3         1      2  113781    151.55  C22 C26         S   NaN    NaN   \n",
+       "4         1      2  113781    151.55  C22 C26         S   NaN  135.0   \n",
+       "...     ...    ...     ...       ...      ...       ...   ...    ...   \n",
+       "1305      1      0    2665   14.4542      NaN         C   NaN  328.0   \n",
+       "1306      1      0    2665   14.4542      NaN         C   NaN    NaN   \n",
+       "1307      0      0    2656     7.225      NaN         C   NaN  304.0   \n",
+       "1308      0      0    2670     7.225      NaN         C   NaN    NaN   \n",
+       "1309      0      0  315082     7.875      NaN         S   NaN    NaN   \n",
+       "\n",
+       "                                   13  \n",
+       "0                           home.dest  \n",
+       "1                        St Louis, MO  \n",
+       "2     Montreal, PQ / Chesterville, ON  \n",
+       "3     Montreal, PQ / Chesterville, ON  \n",
+       "4     Montreal, PQ / Chesterville, ON  \n",
+       "...                               ...  \n",
+       "1305                              NaN  \n",
+       "1306                              NaN  \n",
+       "1307                              NaN  \n",
+       "1308                              NaN  \n",
+       "1309                              NaN  \n",
+       "\n",
+       "[1310 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Einlesen der Daten mit expliziter Angabe der Indexspalte\n",
+    "titanic = pd.read_csv(path, index_col=\"age\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>29.0000</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.9167</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0000</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0000</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0000</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14.5000</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.5000</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0000</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0000</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         pclass  survived                                             name  \\\n",
+       "age                                                                          \n",
+       "29.0000       1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "0.9167        1         1                   Allison, Master. Hudson Trevor   \n",
+       "2.0000        1         0                     Allison, Miss. Helen Loraine   \n",
+       "30.0000       1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "25.0000       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...         ...       ...                                              ...   \n",
+       "14.5000       3         0                             Zabour, Miss. Hileni   \n",
+       "NaN           3         0                            Zabour, Miss. Thamine   \n",
+       "26.5000       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "27.0000       3         0                              Zakarian, Mr. Ortin   \n",
+       "29.0000       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "            sex  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "age                                                                             \n",
+       "29.0000  female      0      0   24160  211.3375       B5        S    2    NaN   \n",
+       "0.9167     male      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
+       "2.0000   female      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "30.0000    male      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
+       "25.0000  female      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "...         ...    ...    ...     ...       ...      ...      ...  ...    ...   \n",
+       "14.5000  female      1      0    2665   14.4542      NaN        C  NaN  328.0   \n",
+       "NaN      female      1      0    2665   14.4542      NaN        C  NaN    NaN   \n",
+       "26.5000    male      0      0    2656    7.2250      NaN        C  NaN  304.0   \n",
+       "27.0000    male      0      0    2670    7.2250      NaN        C  NaN    NaN   \n",
+       "29.0000    male      0      0  315082    7.8750      NaN        S  NaN    NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "age                                       \n",
+       "29.0000                     St Louis, MO  \n",
+       "0.9167   Montreal, PQ / Chesterville, ON  \n",
+       "2.0000   Montreal, PQ / Chesterville, ON  \n",
+       "30.0000  Montreal, PQ / Chesterville, ON  \n",
+       "25.0000  Montreal, PQ / Chesterville, ON  \n",
+       "...                                  ...  \n",
+       "14.5000                              NaN  \n",
+       "NaN                                  NaN  \n",
+       "26.5000                              NaN  \n",
+       "27.0000                              NaN  \n",
+       "29.0000                              NaN  \n",
+       "\n",
+       "[1309 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
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+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Bowerman, Miss. Elsie Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>113505</td>\n",
+       "      <td>55.0000</td>\n",
+       "      <td>E33</td>\n",
+       "      <td>S</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Leonards-on-Sea, England Ohio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cleaver, Miss. Alice</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Frolicher, Miss. Hedwig Margaritha</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13568</td>\n",
+       "      <td>49.5000</td>\n",
+       "      <td>B39</td>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Zurich, Switzerland</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>112378</td>\n",
+       "      <td>59.4000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ostby, Miss. Helene Ragnhild</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>113509</td>\n",
+       "      <td>61.9792</td>\n",
+       "      <td>B36</td>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Providence, RI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113776</td>\n",
+       "      <td>66.6000</td>\n",
+       "      <td>C2</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Isleworth, England</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ringhini, Mr. Sante</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>248738</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bangkok, Thailand / Roseville, IL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cook, Mrs. (Selena Rogers)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>W./C. 14266</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>F33</td>\n",
+       "      <td>S</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Pennsylvania</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Jefferys, Mr. Ernest Wilfred</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C.A. 31029</td>\n",
+       "      <td>31.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Guernsey / Elizabeth, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Karnes, Mrs. J Frank (Claire Bennett)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>F.C.C. 13534</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>India / Pittsburgh, PA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Laroche, Mrs. Joseph (Juliette Marie Louise La...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SC/Paris 2123</td>\n",
+       "      <td>41.5792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Paris / Haiti</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Oxenham, Mr. Percy Thomas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>W./C. 14260</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Pondersend, England / New Durham, NJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Barton, Mr. David John</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>324669</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>England New York, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Berglund, Mr. Karl Ivar Sven</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PP 4348</td>\n",
+       "      <td>9.3500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tranvik, Finland New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Bradley, Miss. Bridget Delia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>334914</td>\n",
+       "      <td>7.7250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kingwilliamstown, Co Cork, Ireland Glens Falls...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bridgerule, Devon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Brobeck, Mr. Karl Rudolf</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350045</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sweden Worcester, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Connolly, Miss. Kate</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370373</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Ireland</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7552</td>\n",
+       "      <td>10.5167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Norrlot, Sweden Chicago, IL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Davies, Mr. Evan</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SC/A4 23568</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Dennis, Mr. Samuel</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21172</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judi...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347072</td>\n",
+       "      <td>13.9000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>West Haven, CT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Gilinski, Mr. Eliezer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14973</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Hellstrom, Miss. Hilda Maria</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7548</td>\n",
+       "      <td>8.9625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Johansson, Mr. Erik</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350052</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Karlsson, Mr. Nils August</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350060</td>\n",
+       "      <td>7.5208</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kink, Miss. Maria</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315152</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Landergren, Miss. Aurora Adelia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C 7077</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347065</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Leeni, Mr. Fahim (\"Philip Zenni\")</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2620</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Maenpaa, Mr. Matti Alexanteri</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101275</td>\n",
+       "      <td>7.1250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Naidenoff, Mr. Penko</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349206</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Nysten, Miss. Anna Sofia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347081</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ohman, Miss. Velin</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347085</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Perkin, Mr. John Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21174</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Riihivouri, Miss. Susanna Juhantytar \"Sanni\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3101295</td>\n",
+       "      <td>39.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sirayanian, Mr. Orsen</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2669</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Strandberg, Miss. Ida Sofia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7553</td>\n",
+       "      <td>9.8375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vartanian, Mr. David</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2658</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>13 15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Vovk, Mr. Janko</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349252</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Waelens, Mr. Achille</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>345767</td>\n",
+       "      <td>9.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                               name  \\\n",
+       "age                                                                         \n",
+       "22.0       1         1                        Bowerman, Miss. Elsie Edith   \n",
+       "22.0       1         1                               Cleaver, Miss. Alice   \n",
+       "22.0       1         1                 Frolicher, Miss. Hedwig Margaritha   \n",
+       "22.0       1         1                     Gibson, Miss. Dorothy Winifred   \n",
+       "22.0       1         1                       Ostby, Miss. Helene Ragnhild   \n",
+       "22.0       1         1                  Pears, Mrs. Thomas (Edith Wearne)   \n",
+       "22.0       1         0                                Ringhini, Mr. Sante   \n",
+       "22.0       2         1  Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...   \n",
+       "22.0       2         1                         Cook, Mrs. (Selena Rogers)   \n",
+       "22.0       2         0                       Jefferys, Mr. Ernest Wilfred   \n",
+       "22.0       2         0              Karnes, Mrs. J Frank (Claire Bennett)   \n",
+       "22.0       2         1  Laroche, Mrs. Joseph (Juliette Marie Louise La...   \n",
+       "22.0       2         1                          Oxenham, Mr. Percy Thomas   \n",
+       "22.0       3         0                             Barton, Mr. David John   \n",
+       "22.0       3         0                       Berglund, Mr. Karl Ivar Sven   \n",
+       "22.0       3         1                       Bradley, Miss. Bridget Delia   \n",
+       "22.0       3         0                            Braund, Mr. Owen Harris   \n",
+       "22.0       3         0                           Brobeck, Mr. Karl Rudolf   \n",
+       "22.0       3         1                               Connolly, Miss. Kate   \n",
+       "22.0       3         0                       Dahlberg, Miss. Gerda Ulrika   \n",
+       "22.0       3         0                                   Davies, Mr. Evan   \n",
+       "22.0       3         0                                 Dennis, Mr. Samuel   \n",
+       "22.0       3         1  Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judi...   \n",
+       "22.0       3         0                              Gilinski, Mr. Eliezer   \n",
+       "22.0       3         1                       Hellstrom, Miss. Hilda Maria   \n",
+       "22.0       3         1       Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
+       "22.0       3         0                                Johansson, Mr. Erik   \n",
+       "22.0       3         0                          Karlsson, Mr. Nils August   \n",
+       "22.0       3         0                                  Kink, Miss. Maria   \n",
+       "22.0       3         1                    Landergren, Miss. Aurora Adelia   \n",
+       "22.0       3         0                     Larsson-Rondberg, Mr. Edvard A   \n",
+       "22.0       3         1                  Leeni, Mr. Fahim (\"Philip Zenni\")   \n",
+       "22.0       3         0                      Maenpaa, Mr. Matti Alexanteri   \n",
+       "22.0       3         0                               Naidenoff, Mr. Penko   \n",
+       "22.0       3         1                           Nysten, Miss. Anna Sofia   \n",
+       "22.0       3         1                                 Ohman, Miss. Velin   \n",
+       "22.0       3         0                             Perkin, Mr. John Henry   \n",
+       "22.0       3         0       Riihivouri, Miss. Susanna Juhantytar \"Sanni\"   \n",
+       "22.0       3         0                              Sirayanian, Mr. Orsen   \n",
+       "22.0       3         0                        Strandberg, Miss. Ida Sofia   \n",
+       "22.0       3         1                               Vartanian, Mr. David   \n",
+       "22.0       3         0                                    Vovk, Mr. Janko   \n",
+       "22.0       3         0                               Waelens, Mr. Achille   \n",
+       "\n",
+       "         sex  sibsp  parch             ticket      fare cabin embarked   boat  \\\n",
+       "age                                                                             \n",
+       "22.0  female      0      1             113505   55.0000   E33        S      6   \n",
+       "22.0  female      0      0             113781  151.5500   NaN        S     11   \n",
+       "22.0  female      0      2              13568   49.5000   B39        C      5   \n",
+       "22.0  female      0      1             112378   59.4000   NaN        C      7   \n",
+       "22.0  female      0      1             113509   61.9792   B36        C      5   \n",
+       "22.0  female      1      0             113776   66.6000    C2        S      8   \n",
+       "22.0    male      0      0           PC 17760  135.6333   NaN        C    NaN   \n",
+       "22.0  female      1      1             248738   29.0000   NaN        S     13   \n",
+       "22.0  female      0      0        W./C. 14266   10.5000   F33        S     14   \n",
+       "22.0    male      2      0         C.A. 31029   31.5000   NaN        S    NaN   \n",
+       "22.0  female      0      0       F.C.C. 13534   21.0000   NaN        S    NaN   \n",
+       "22.0  female      1      2      SC/Paris 2123   41.5792   NaN        C     14   \n",
+       "22.0    male      0      0        W./C. 14260   10.5000   NaN        S     13   \n",
+       "22.0    male      0      0             324669    8.0500   NaN        S    NaN   \n",
+       "22.0    male      0      0            PP 4348    9.3500   NaN        S    NaN   \n",
+       "22.0  female      0      0             334914    7.7250   NaN        Q     13   \n",
+       "22.0    male      1      0          A/5 21171    7.2500   NaN        S    NaN   \n",
+       "22.0    male      0      0             350045    7.7958   NaN        S    NaN   \n",
+       "22.0  female      0      0             370373    7.7500   NaN        Q     13   \n",
+       "22.0  female      0      0               7552   10.5167   NaN        S    NaN   \n",
+       "22.0    male      0      0        SC/A4 23568    8.0500   NaN        S    NaN   \n",
+       "22.0    male      0      0          A/5 21172    7.2500   NaN        S    NaN   \n",
+       "22.0  female      1      0             347072   13.9000   NaN        S     16   \n",
+       "22.0    male      0      0              14973    8.0500   NaN        S    NaN   \n",
+       "22.0  female      0      0               7548    8.9625   NaN        S      C   \n",
+       "22.0  female      1      1            3101298   12.2875   NaN        S     15   \n",
+       "22.0    male      0      0             350052    7.7958   NaN        S    NaN   \n",
+       "22.0    male      0      0             350060    7.5208   NaN        S    NaN   \n",
+       "22.0  female      2      0             315152    8.6625   NaN        S    NaN   \n",
+       "22.0  female      0      0             C 7077    7.2500   NaN        S     13   \n",
+       "22.0    male      0      0             347065    7.7750   NaN        S    NaN   \n",
+       "22.0    male      0      0               2620    7.2250   NaN        C      6   \n",
+       "22.0    male      0      0  STON/O 2. 3101275    7.1250   NaN        S    NaN   \n",
+       "22.0    male      0      0             349206    7.8958   NaN        S    NaN   \n",
+       "22.0  female      0      0             347081    7.7500   NaN        S     13   \n",
+       "22.0  female      0      0             347085    7.7750   NaN        S      C   \n",
+       "22.0    male      0      0          A/5 21174    7.2500   NaN        S    NaN   \n",
+       "22.0  female      0      0            3101295   39.6875   NaN        S    NaN   \n",
+       "22.0    male      0      0               2669    7.2292   NaN        C    NaN   \n",
+       "22.0  female      0      0               7553    9.8375   NaN        S    NaN   \n",
+       "22.0    male      0      0               2658    7.2250   NaN        C  13 15   \n",
+       "22.0    male      0      0             349252    7.8958   NaN        S    NaN   \n",
+       "22.0    male      0      0             345767    9.0000   NaN        S    NaN   \n",
+       "\n",
+       "       body                                          home.dest  \n",
+       "age                                                             \n",
+       "22.0    NaN                   St Leonards-on-Sea, England Ohio  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                Zurich, Switzerland  \n",
+       "22.0    NaN                                       New York, NY  \n",
+       "22.0    NaN                                     Providence, RI  \n",
+       "22.0    NaN                                 Isleworth, England  \n",
+       "22.0  232.0                                                NaN  \n",
+       "22.0    NaN                  Bangkok, Thailand / Roseville, IL  \n",
+       "22.0    NaN                                       Pennsylvania  \n",
+       "22.0    NaN                           Guernsey / Elizabeth, NJ  \n",
+       "22.0    NaN                             India / Pittsburgh, PA  \n",
+       "22.0    NaN                                      Paris / Haiti  \n",
+       "22.0    NaN               Pondersend, England / New Durham, NJ  \n",
+       "22.0    NaN                               England New York, NY  \n",
+       "22.0    NaN                          Tranvik, Finland New York  \n",
+       "22.0    NaN  Kingwilliamstown, Co Cork, Ireland Glens Falls...  \n",
+       "22.0    NaN                                  Bridgerule, Devon  \n",
+       "22.0    NaN                               Sweden Worcester, MA  \n",
+       "22.0    NaN                                            Ireland  \n",
+       "22.0    NaN                        Norrlot, Sweden Chicago, IL  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                     West Haven, CT  \n",
+       "22.0   47.0                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0  156.0                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                                                NaN  \n",
+       "22.0    NaN                     Antwerp, Belgium / Stanton, OH  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Achtung: Index ist nicht mehr eindeutig\n",
+    "\n",
+    "# Hier filtern wir nach allen Personen dem Alter von 22 Jahren\n",
+    "titanic.loc[22]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Nun verwenden wir das Geschlecht als Index\n",
+    "titanic = pd.read_csv(path, index_col='sex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        pclass  survived                                             name  \\\n",
+       "sex                                                                         \n",
+       "female       1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "male         1         1                   Allison, Master. Hudson Trevor   \n",
+       "female       1         0                     Allison, Miss. Helen Loraine   \n",
+       "male         1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "female       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...        ...       ...                                              ...   \n",
+       "female       3         0                             Zabour, Miss. Hileni   \n",
+       "female       3         0                            Zabour, Miss. Thamine   \n",
+       "male         3         0                        Zakarian, Mr. Mapriededer   \n",
+       "male         3         0                              Zakarian, Mr. Ortin   \n",
+       "male         3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "            age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "sex                                                                             \n",
+       "female  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
+       "male     0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
+       "female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "male    30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
+       "female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "...         ...    ...    ...     ...       ...      ...      ...  ...    ...   \n",
+       "female  14.5000      1      0    2665   14.4542      NaN        C  NaN  328.0   \n",
+       "female      NaN      1      0    2665   14.4542      NaN        C  NaN    NaN   \n",
+       "male    26.5000      0      0    2656    7.2250      NaN        C  NaN  304.0   \n",
+       "male    27.0000      0      0    2670    7.2250      NaN        C  NaN    NaN   \n",
+       "male    29.0000      0      0  315082    7.8750      NaN        S  NaN    NaN   \n",
+       "\n",
+       "                              home.dest  \n",
+       "sex                                      \n",
+       "female                     St Louis, MO  \n",
+       "male    Montreal, PQ / Chesterville, ON  \n",
+       "female  Montreal, PQ / Chesterville, ON  \n",
+       "male    Montreal, PQ / Chesterville, ON  \n",
+       "female  Montreal, PQ / Chesterville, ON  \n",
+       "...                                 ...  \n",
+       "female                              NaN  \n",
+       "female                              NaN  \n",
+       "male                                NaN  \n",
+       "male                                NaN  \n",
+       "male                                NaN  \n",
+       "\n",
+       "[1309 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
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+       "      <th>body</th>\n",
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+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13502</td>\n",
+       "      <td>77.9583</td>\n",
+       "      <td>D7</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hudson, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11769</td>\n",
+       "      <td>51.4792</td>\n",
+       "      <td>C101</td>\n",
+       "      <td>S</td>\n",
+       "      <td>D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bayside, Queens, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2688</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2659</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>466 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        pclass  survived                                             name  \\\n",
+       "sex                                                                         \n",
+       "female       1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "female       1         0                     Allison, Miss. Helen Loraine   \n",
+       "female       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "female       1         1                Andrews, Miss. Kornelia Theodosia   \n",
+       "female       1         1    Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
+       "...        ...       ...                                              ...   \n",
+       "female       3         1  Whabee, Mrs. George Joseph (Shawneene Abi-Saab)   \n",
+       "female       3         1                 Wilkes, Mrs. James (Ellen Needs)   \n",
+       "female       3         1          Yasbeck, Mrs. Antoni (Selini Alexander)   \n",
+       "female       3         0                             Zabour, Miss. Hileni   \n",
+       "female       3         0                            Zabour, Miss. Thamine   \n",
+       "\n",
+       "         age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "sex                                                                          \n",
+       "female  29.0      0      0   24160  211.3375       B5        S    2    NaN   \n",
+       "female   2.0      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "female  25.0      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "female  63.0      1      0   13502   77.9583       D7        S   10    NaN   \n",
+       "female  53.0      2      0   11769   51.4792     C101        S    D    NaN   \n",
+       "...      ...    ...    ...     ...       ...      ...      ...  ...    ...   \n",
+       "female  38.0      0      0    2688    7.2292      NaN        C    C    NaN   \n",
+       "female  47.0      1      0  363272    7.0000      NaN        S  NaN    NaN   \n",
+       "female  15.0      1      0    2659   14.4542      NaN        C  NaN    NaN   \n",
+       "female  14.5      1      0    2665   14.4542      NaN        C  NaN  328.0   \n",
+       "female   NaN      1      0    2665   14.4542      NaN        C  NaN    NaN   \n",
+       "\n",
+       "                              home.dest  \n",
+       "sex                                      \n",
+       "female                     St Louis, MO  \n",
+       "female  Montreal, PQ / Chesterville, ON  \n",
+       "female  Montreal, PQ / Chesterville, ON  \n",
+       "female                       Hudson, NY  \n",
+       "female              Bayside, Queens, NY  \n",
+       "...                                 ...  \n",
+       "female                              NaN  \n",
+       "female                              NaN  \n",
+       "female                              NaN  \n",
+       "female                              NaN  \n",
+       "female                              NaN  \n",
+       "\n",
+       "[466 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.loc['female']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Einlesen der Daten mit expliziter Angabe des Trennzeichens\n",
+    "titanic = pd.read_csv(path, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In manchen Fällen kann das Trennzeichen (zBsp \",\") Probleme machen (zBsp Tausender-Trennzeichen oder als Gleitkomma für floats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In so einem Fall kann dann die eigene Option decimal verwendet werden\n",
+    "titanic = pd.read_csv(path, sep=\",\", decimal=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Falls es keinen Header gibt, kann dieser selber erstellt werden mit dem `names` argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>own_class</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      own_class  survived                                             name  \\\n",
+       "0             1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1             1         1                   Allison, Master. Hudson Trevor   \n",
+       "2             1         0                     Allison, Miss. Helen Loraine   \n",
+       "3             1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4             1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...         ...       ...                                              ...   \n",
+       "1304          3         0                             Zabour, Miss. Hileni   \n",
+       "1305          3         0                            Zabour, Miss. Thamine   \n",
+       "1306          3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307          3         0                              Zakarian, Mr. Ortin   \n",
+       "1308          3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "own_header_titanic = pd.read_csv(path, header=0, names=['own_class', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'])\n",
+    "\n",
+    "own_header_titanic # würde auch funktionieren wenn kein header gegeben wäre"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In unserem Fall wollen wir die Namen beibehalten, sprich wir verwenden die gleichen Spaltennamen wie im originalen Datenset. Um die Spaltennamen des ursprünglichen Datensets zu erhalten verwenden wir den `columns()` Befehl."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Alle Spaltennamen erhalten\n",
+    "columns = titanic.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
+       "       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   int64  \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1046 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  745 non-null    object \n",
+      "dtypes: float64(3), int64(4), object(7)\n",
+      "memory usage: 143.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ausgeben lassen der Informationen über die Spalten inklusive Null-Count:\n",
+    "titanic.info()\n",
+    "\n",
+    "# Weitere Infos findet man oft auf der Webseite des Datesets, in diesem Fall: https://www.kaggle.com/c/titanic/data (Achtung, nicht genau das gleiche Dataset, in diesem Fall bereits bereinigt und aufgteilt in train und test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir sehen, manche Spalten sind als Typ `object`, obwohl sie zbsp als string durchgehen könnten. Richtige Datentypen sind wichtig, da so dann mehr Methoden zur Verfügung stehen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Für manche Spalten ist es aber dann Vorteilhaft, die <em>null</em> bzw. <em>None</em> Werte davor zu befüllen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Betrachten wir die Spalte home.dest:\n",
+    "# Die Daten sind eigentlich Werte des Typs string, aber es wird als object interpretiert -> Dies ändern wir nun\n",
+    "\n",
+    "# Füllen der Daten mit dem Wert '' (leerer String)\n",
+    "titanic['home.dest'] = titanic['home.dest'].fillna(value='')\n",
+    "\n",
+    "\n",
+    "titanic['home.dest'] = titanic[\"home.dest\"].astype(\"string\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   int64  \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1046 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  1309 non-null   string \n",
+      "dtypes: float64(3), int64(4), object(6), string(1)\n",
+      "memory usage: 143.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                          St Louis, MO\n",
+       "1       Montreal, PQ / Chesterville, ON\n",
+       "2       Montreal, PQ / Chesterville, ON\n",
+       "3       Montreal, PQ / Chesterville, ON\n",
+       "4       Montreal, PQ / Chesterville, ON\n",
+       "                     ...               \n",
+       "1304                                   \n",
+       "1305                                   \n",
+       "1306                                   \n",
+       "1307                                   \n",
+       "1308                                   \n",
+       "Name: home.dest, Length: 1309, dtype: string"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic['home.dest']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wenn im Vorhinein die Datentypen schon feststehen, können auch diese beim Einlesen verwendet werden. Dafür muss ein Dictionary für das Argument `dtype` übergeben werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Einlesen der Daten mit home.dest als String\n",
+    "titanic = pd.read_csv(path, dtype={\"home.dest\": 'string'}, header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   int64  \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1046 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  745 non-null    string \n",
+      "dtypes: float64(3), int64(4), object(6), string(1)\n",
+      "memory usage: 143.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                          St Louis, MO\n",
+       "1       Montreal, PQ / Chesterville, ON\n",
+       "2       Montreal, PQ / Chesterville, ON\n",
+       "3       Montreal, PQ / Chesterville, ON\n",
+       "4       Montreal, PQ / Chesterville, ON\n",
+       "                     ...               \n",
+       "1304                               <NA>\n",
+       "1305                               <NA>\n",
+       "1306                               <NA>\n",
+       "1307                               <NA>\n",
+       "1308                               <NA>\n",
+       "Name: home.dest, Length: 1309, dtype: string"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic['home.dest']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es können auch sogenannte na-filter verwendet (und adaptiert) bzw. deaktiviert werden, welche Pandas ggf. unterbinden, leere String und dergleichen mit einen <b>NaN</b>-Symbol zu ersetzen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Einlesen mit na_filter = False\n",
+    "titanic = pd.read_csv(path, na_filter=False, header=0, dtype={\"home.dest\": 'string'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                          St Louis, MO\n",
+       "1       Montreal, PQ / Chesterville, ON\n",
+       "2       Montreal, PQ / Chesterville, ON\n",
+       "3       Montreal, PQ / Chesterville, ON\n",
+       "4       Montreal, PQ / Chesterville, ON\n",
+       "                     ...               \n",
+       "1304                                   \n",
+       "1305                                   \n",
+       "1306                                   \n",
+       "1307                                   \n",
+       "1308                                   \n",
+       "Name: home.dest, Length: 1309, dtype: string"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic['home.dest']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speichern eines Dataframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wie am Anfang erwähnt können die Dataframes auch in einer Datei gespeichert werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# speichern des dataframes als json\n",
+    "\n",
+    "path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.json\")\n",
+    "\n",
+    "titanic.to_json(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Laden der json Datei:\n",
+    "titanic = pd.read_json(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked  boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S     2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S    11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...   ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542     None        C  None   \n",
+       "1305  female      NaN      1      0    2665   14.4542     None        C  None   \n",
+       "1306    male  26.5000      0      0    2656    7.2250     None        C  None   \n",
+       "1307    male  27.0000      0      0    2670    7.2250     None        C  None   \n",
+       "1308    male  29.0000      0      0  315082    7.8750     None        S  None   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                             None  \n",
+       "1305    NaN                             None  \n",
+       "1306  304.0                             None  \n",
+       "1307    NaN                             None  \n",
+       "1308    NaN                             None  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   int64  \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1046 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  745 non-null    object \n",
+      "dtypes: float64(3), int64(4), object(7)\n",
+      "memory usage: 153.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can delete the created json again\n",
+    "os.remove(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.csv\")\n",
+    "\n",
+    "# In the case of a csv file, which occurs most often, we have a lot of possibilities\n",
+    "titanic.to_csv(path, sep=',', decimal='.', index=False, header=True) # and many more"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Aufgabe:\n",
+    "Laden Sie das *Titanic* Dataset erneut (Falls Link benötigt wird: https://raw.githubusercontent.com/rolandmueller/titanic/main/titanic3.csv).\n",
+    "\n",
+    "Bearbeiten Sie nun folgende Punkte! Dokumentieren Sie dabei ihre Schritte indem Sie ggf. auch Fehlversuche als Zellen zu belassen (um zu zeigen, dass etwas nicht funktioniert). \n",
+    "\n",
+    "* Wie ist das Dataset aufgebaut?\n",
+    "\n",
+    "    Ein Datensatz hat (name, alter, kabine, etc.) und ob diese person überlebt hat.\n",
+    "* Was bedeuten die einzelnen Features?\n",
+    "\n",
+    "    Es gibt personen mit (name, alter, etc.) und ob diese Personen überlebt haben.\n",
+    "* Extrahieren Sie Informationen über das Dataframe über die `info()` Methoden\n",
+    "\n",
+    "    Namen sind Objects und bei body gibt es die wenigsten non-nulls\n",
+    "* Analysieren Sie die Datentypen und auch die Anzahl der leeren Zellen in jeder Spalte. Wie viele Prozent sind leer/fehlend?\n",
+    "\n",
+    "    In body gibt es 90.7% NaN werte\n",
+    "* Filtern sie das Dataframe, sodass es nur Zeilen beinhaltet, bei der die `Geschlecht`-Spalte und bei der das `Alter` nicht leer ist\n",
+    "\n",
+    "    Fertig!\n",
+    "* Sortieren Sie das Dataframe absteigend nach der Spalte `Alter`\n",
+    "\n",
+    "    Fertig!\n",
+    "* Erstellen Sie ein neues Dataframe (`inplace=False`), wo Sie den Namen außerdem noch alphabetisch sortieren (aufsteigend). Es soll also zuerst absteigend nach `Alter`, danach aufsteigend nach `Name` sortiert werden.\n",
+    "\n",
+    "    Fertig!\n",
+    "* Speichern Sie dieses neue Dataframe unter einem neuen Namen ab und verwenden Sie ab jetzt nur noch dieses Dataset.\n",
+    "\n",
+    "    Fertig!\n",
+    "* Verwenden Sie die anschließend einen Befehl um die Ticket-Klasse `pclass` der jüngste(n) Passagier(in) herauszufinden und außerdem, ob diese Person überlebt hat. Sollte dieser Wert <em>leer</em> sein, so dürfen Sie diese Zeilen entfernen. Wie alt war diese Person?\n",
+    "\n",
+    "    Age: 0.1667\n",
+    "    pclass: 3\n",
+    "    survived: 1\n",
+    "* Bonus: Finden Sie, unabhängig von den vorigen Aufgaben, den längsten Namen (Anzahl der Charakter). Wie viele Zeichen hat dieser Name?\n",
+    "\n",
+    "    Length: 82\n",
+    "    Name: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   int64  \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1046 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  745 non-null    object \n",
+      "dtypes: float64(3), int64(4), object(7)\n",
+      "memory usage: 153.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Aufgabe 3\n",
+    "titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NULL percent of column body:  90.75630252100841 %\n",
+      "NULL absolute of column body:  1188\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Aufgabe 4\n",
+    "column = 'body'\n",
+    "\n",
+    "amount_null = titanic.filter([column]).isnull().sum() / len(titanic) * 100\n",
+    "amount_null_count = titanic.filter([column]).isnull().sum()\n",
+    "\n",
+    "\n",
+    "print(f'NULL percent of column {column}: ', amount_null.iloc[0], '%')\n",
+    "print(f'NULL absolute of column {column}: ', amount_null_count.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Youseff, Mr. Gerious</td>\n",
+       "      <td>male</td>\n",
+       "      <td>45.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2628</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>312.0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1046 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1301       3         0                             Youseff, Mr. Gerious   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked  boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S     2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S    11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...   ...   \n",
+       "1301    male  45.5000      0      0    2628    7.2250     None        C  None   \n",
+       "1304  female  14.5000      1      0    2665   14.4542     None        C  None   \n",
+       "1306    male  26.5000      0      0    2656    7.2250     None        C  None   \n",
+       "1307    male  27.0000      0      0    2670    7.2250     None        C  None   \n",
+       "1308    male  29.0000      0      0  315082    7.8750     None        S  None   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1301  312.0                             None  \n",
+       "1304  328.0                             None  \n",
+       "1306  304.0                             None  \n",
+       "1307    NaN                             None  \n",
+       "1308    NaN                             None  \n",
+       "\n",
+       "[1046 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Aufgabe 5\n",
+    "cleaned_df_2 = titanic.dropna(subset=['sex', 'age'])\n",
+    "cleaned_df_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Aufgabe 6\n",
+    "titanic.sort_values('age', ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
+       "      <td>male</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27042</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>A23</td>\n",
+       "      <td>S</td>\n",
+       "      <td>B</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hessle, Yorks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cavendish, Mrs. Tyrell William (Julia Florence...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19877</td>\n",
+       "      <td>78.8500</td>\n",
+       "      <td>C46</td>\n",
+       "      <td>S</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Little Onn Hall, Staffs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1235</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Svensson, Mr. Johan</td>\n",
+       "      <td>male</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347060</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Artagaveytia, Mr. Ramon</td>\n",
+       "      <td>male</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17609</td>\n",
+       "      <td>49.5042</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>Montevideo, Uruguay</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Goldschmidt, Mr. George B</td>\n",
+       "      <td>male</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17754</td>\n",
+       "      <td>34.6542</td>\n",
+       "      <td>A5</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yousif, Mr. Wazli</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2647</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yousseff, Mr. Gerious</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2627</td>\n",
+       "      <td>14.4583</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>None</td>\n",
+       "      <td>C</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1262</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>van Billiard, Master. James William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A/5. 851</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1268</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>van Melkebeke, Mr. Philemon</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>345777</td>\n",
+       "      <td>9.5000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                               name  \\\n",
+       "14         1         1               Barkworth, Mr. Algernon Henry Wilson   \n",
+       "61         1         1  Cavendish, Mrs. Tyrell William (Julia Florence...   \n",
+       "1235       3         0                                Svensson, Mr. Johan   \n",
+       "9          1         0                            Artagaveytia, Mr. Ramon   \n",
+       "135        1         0                          Goldschmidt, Mr. George B   \n",
+       "...      ...       ...                                                ...   \n",
+       "1302       3         0                                  Yousif, Mr. Wazli   \n",
+       "1303       3         0                              Yousseff, Mr. Gerious   \n",
+       "1305       3         0                              Zabour, Miss. Thamine   \n",
+       "1262       3         0                van Billiard, Master. James William   \n",
+       "1268       3         0                        van Melkebeke, Mr. Philemon   \n",
+       "\n",
+       "         sex   age  sibsp  parch    ticket     fare cabin embarked  boat  \\\n",
+       "14      male  80.0      0      0     27042  30.0000   A23        S     B   \n",
+       "61    female  76.0      1      0     19877  78.8500   C46        S     6   \n",
+       "1235    male  74.0      0      0    347060   7.7750  None        S  None   \n",
+       "9       male  71.0      0      0  PC 17609  49.5042  None        C  None   \n",
+       "135     male  71.0      0      0  PC 17754  34.6542    A5        C  None   \n",
+       "...      ...   ...    ...    ...       ...      ...   ...      ...   ...   \n",
+       "1302    male   NaN      0      0      2647   7.2250  None        C  None   \n",
+       "1303    male   NaN      0      0      2627  14.4583  None        C  None   \n",
+       "1305  female   NaN      1      0      2665  14.4542  None        C  None   \n",
+       "1262    male   NaN      1      1  A/5. 851  14.5000  None        S  None   \n",
+       "1268    male   NaN      0      0    345777   9.5000  None        S  None   \n",
+       "\n",
+       "      body                home.dest  \n",
+       "14     NaN            Hessle, Yorks  \n",
+       "61     NaN  Little Onn Hall, Staffs  \n",
+       "1235   NaN                     None  \n",
+       "9     22.0      Montevideo, Uruguay  \n",
+       "135    NaN             New York, NY  \n",
+       "...    ...                      ...  \n",
+       "1302   NaN                     None  \n",
+       "1303   NaN                     None  \n",
+       "1305   NaN                     None  \n",
+       "1262   NaN                     None  \n",
+       "1268   NaN                     None  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Aufgabe 7\n",
+    "titanic_sorted_name = titanic.sort_values(['age', 'name'], ascending=[False, True], inplace=False)\n",
+    "titanic_sorted_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\flori\\AppData\\Local\\Temp\\ipykernel_8412\\4067016626.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.sort_values('age', ascending=True, inplace=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "pclass                                             3\n",
+       "survived                                           1\n",
+       "name         Dean, Miss. Elizabeth Gladys \"Millvina\"\n",
+       "sex                                           female\n",
+       "age                                           0.1667\n",
+       "sibsp                                              1\n",
+       "parch                                              2\n",
+       "ticket                                     C.A. 2315\n",
+       "fare                                          20.575\n",
+       "cabin                                           None\n",
+       "embarked                                           S\n",
+       "boat                                              10\n",
+       "body                                             NaN\n",
+       "home.dest                 Devon, England Wichita, KS\n",
+       "Name: 763, dtype: object"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Aufgabe 8\n",
+    "temp = titanic_sorted_name.dropna(subset=['age'])\n",
+    "temp.sort_values('age', ascending=True, inplace=True)\n",
+    "temp.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Bonus\n",
+    "lengths = titanic_sorted_name['name'].apply(len)\n",
+    "longest_name = titanic_sorted_name.loc[lengths.idxmax(), 'name']\n",
+    "longest_name_length = lengths.max()\n",
+    "longest_name_length\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/02_daten_tabellarisch/code/daten_tabellarisch_2.ipynb b/02_daten_tabellarisch/code/daten_tabellarisch_2.ipynb
new file mode 100644
index 0000000..1e02bb4
--- /dev/null
+++ b/02_daten_tabellarisch/code/daten_tabellarisch_2.ipynb
@@ -0,0 +1,5991 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"\n",
+    "    border: 2px solid #4CAF50; \n",
+    "    padding: 15px; \n",
+    "    background-color: #f4f4f4; \n",
+    "    border-radius: 10px; \n",
+    "    align-items: center;\">\n",
+    "\n",
+    "<h1 style=\"margin: 0; color: #4CAF50;\">Datenverarbeitung in Pandas</h1>\n",
+    "<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
+    "<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
+    "\n",
+    "<div style=\"flex-shrink: 0;\">\n",
+    "    <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
+    "</div>\n",
+    "<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
+    "</div>\n",
+    "<div style=\"flex: 1;\">\n",
+    "</div>   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ersten Manipulationen von Dataframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Beginnen wir mit dem Hinzufügen von Daten.\n",
+    "Wir arbeiten dabei wieder hauptsächlich mit dem *Titanic* Dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir fügen nun einen fiktionalen Datensatz hinzu\n",
+    "path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.csv\")\n",
+    "titanic = pd.read_csv(path)\n",
+    "\n",
+    "new_row = {'pclass': 1, 'survived': 1, 'name': 'Alice Burger', 'sex': 'female', 'age': 35, 'sibsp': 0, 'parch': 0, 'ticket': '12345', 'fare': 50.0, 'cabin': 'C10', 'embarked': 'S', 'home.dest': 'New York'}\n",
+    "new_df = pd.DataFrame(new_row, index=[0])\n",
+    "titanic = pd.concat([titanic, new_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Für mehrere Daten zum hinzufügen können wir auch eine Liste von Dictionaries verwenden\n",
+    "new_row_1 = {'pclass': 1, 'survived': 1, 'name': 'Alice Burger', 'sex': 'female', 'age': 35, 'sibsp': 0, 'parch': 0, 'ticket': '12345', 'fare': 50.0, 'cabin': 'C10', 'embarked': 'S', 'home.dest': 'New York'} # sind zBsp Zwillinge :D\n",
+    "new_row_2 = {'pclass': 1, 'survived': 1, 'name': 'Bob Burger', 'sex': 'male', 'age': 35, 'sibsp': 0, 'parch': 0, 'ticket': '12345', 'fare': 50.0, 'cabin': 'C10', 'embarked': 'S', 'home.dest': 'New York'} # sind zBsp Zwillinge :D\n",
+    "\n",
+    "new_data = [new_row_1, new_row_2]\n",
+    "new_df = pd.DataFrame(new_data, index=[0, 1])\n",
+    "\n",
+    "titanic = pd.concat([titanic, new_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Duplikate können auch entfernt werden"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic.drop_duplicates(inplace=True, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nun betrachten wir die eindeutigen Zeilenwerte in einem Dataframe. Dafür benutzen wir den Befehl `unique()` bzw. `nunique()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Anzahl der eindeutige Werte in allen Spalten anzeigen lassen\n",
+    "titanic.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Eindeutige Werte in einer Spalte anzeigen lassen\n",
+    "\n",
+    "column = 'sibsp' # Andere interessante Fälle: 'boat'\n",
+    "titanic[column].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir können auch die eindeutigen Werte und deren Anzahl anzeigen lassen\n",
+    "\n",
+    "column = 'sibsp'\n",
+    "\n",
+    "titanic[column].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In vielen Fällen sind nicht alle Spalten relevant, diese können mit dem Befehl `drop()` entfernt werden. Welche Spalten soll man entfernen, welche Spalten sollte man nicht entfernen?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop columns\n",
+    "titanic.drop(columns=['boat', 'body'], inplace=True) # erneut inplace Option um Speicherplatz zu sparen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wie bereits gesehen können aber auch Zeilen \"gedroppt\" werden, dies passiert mittels geschickter indizierung. Für den Fall, dass <em>leere</em> Werte gelöscht werden sollen, gibt es nach wie vor den Befehl `dropna()`. Dieses mal spezifizieren wir dabei eine spezielle Achse. Merksatz in diesem Fall (und auch bei Matrizen): \"Zeilen zuerst, Spalten später\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Spalten, welche leere Werte enthalten, können auch auch mit dem dropna() Befehl entfernt werden, dazu braucht es das Argument axis=1\n",
+    "titanic.dropna(axis=1, inplace=True) # Vergleiche mit letzter Woche: dropna() entfernt Zeilen mit leeren Werten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir laden nun das Dataset erneut um die bisherigen Änderungen zu verwerfen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wollen wir Daten aggregieren, so können wir den `agg()` Befehl verwenden. Wo kann dies nützlich sein? Für welche Datentypen funktionieren welche Aggregierungsmöglichkeiten?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agg_df = titanic.agg(['count', 'size', 'nunique'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agg_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Weitere Möglichkeiten sind `sum`, `mean`, `median`, `std`, `var`, `min`, `max`, etc. --> Wir werden später genauer auf Statistiken eingehen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Eine weitere Möglichkeit ist es, Funktionen auf die Daten anzuwenden\n",
+    "titanic['increased_age'] = titanic['age'].apply(lambda x: x + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic.drop(columns=['increased_age'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wir können groupby verwenden, um die Daten zu gruppieren\n",
+    "grouped_df = titanic.groupby('pclass')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir können nun mit dem gruppierten Dataframe `grouped_df` arbeiten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_values = grouped_df.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name, group in grouped_df:    \n",
+    "    print(name)\n",
+    "    print(group)\n",
+    "    print(\"*\"*50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survival_rate = titanic.groupby('pclass')['survived'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survival_rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fehlende Werte/Datenpunkte"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* In vielen Fällen werden Daten nicht perfekt sein\n",
+    "* Daten können:\n",
+    "    * fehlerhaft/falsch sein bzw. mit Rauschen überlagert\n",
+    "    * \"unmöglich\" sein (Zum Beispiel: männlich und schwanger)\n",
+    "    * fehlen\n",
+    "\n",
+    "Es gibt verschiedene Möglichkeiten, wie diese Probleme behandelt werden können."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sanity Checks. Wir laden zuerst ein modifiziertes Dataset\n",
+    "\n",
+    "modified_titanic = pd.read_csv(path.replace(\"titanic\", \"modified_titanic\"), header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male   NaN      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Welche(n) Fehler vermuten wir hier? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Es gibt negative Werte für das Alter\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Testen, ob alter \"age\" negativ ist:\n",
+    "negative_age = modified_titanic['age'] < 0\n",
+    "\n",
+    "if negative_age.any():\n",
+    "    print(\"Es gibt negative Werte für das Alter\")\n",
+    "else:\n",
+    "    print(\"Alle Werte in der Spalte Alter sind nicht-negativ (positiv oder 0)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1       False\n",
+       "2       False\n",
+       "3       False\n",
+       "4       False\n",
+       "        ...  \n",
+       "1304    False\n",
+       "1305    False\n",
+       "1306    False\n",
+       "1307    False\n",
+       "1308    False\n",
+       "Name: age, Length: 1309, dtype: bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Wie sieht das Objekt negative_age aus:\n",
+    "negative_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Anzahl der negativen Werte für das Alter: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wir können auch die genaue Anzahl der negativen Werte anzeigen lassen\n",
+    "negative_age_count = negative_age.sum()\n",
+    "print(f\"Anzahl der negativen Werte für das Alter: {negative_age_count}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1309 non-null   object \n",
+      " 1   survived   1309 non-null   int64  \n",
+      " 2   name       1309 non-null   object \n",
+      " 3   sex        1309 non-null   object \n",
+      " 4   age        1037 non-null   float64\n",
+      " 5   sibsp      1309 non-null   int64  \n",
+      " 6   parch      1309 non-null   int64  \n",
+      " 7   ticket     1309 non-null   object \n",
+      " 8   fare       1308 non-null   float64\n",
+      " 9   cabin      295 non-null    object \n",
+      " 10  embarked   1307 non-null   object \n",
+      " 11  boat       486 non-null    object \n",
+      " 12  body       121 non-null    float64\n",
+      " 13  home.dest  745 non-null    object \n",
+      "dtypes: float64(3), int64(3), object(8)\n",
+      "memory usage: 143.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Series name: age\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "1037 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 10.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic['age'].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nun müssen wir etwas mit diesen falschen/fehlerhaften Daten unternehmen. Wir können:\n",
+    "\n",
+    "* NaN oder anderen wiedererkennbaren Wert vergeben (0, -1) (Vorteile? Nachteile?)\n",
+    "* Mittelwert vergeben (Vorteile? Nachteile?)\n",
+    "* Werte der Zeilen geben, die sich darüber/darunter befinden (Vorteile? Nachteile?)\n",
+    "* Diese Daten=Zeilen entfernen (Vorteile? Nachteile?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NaN vergeben für die negativen Werte\n",
+    "modified_titanic.loc[negative_age, 'age'] = None # negative_age = modified_titanic['age'] < 0 (von vorher)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male   NaN      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Series name: age\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "1028 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 10.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic['age'].info() # nun sind in der Spalte \"age\" weniger Werte vorhanden (mehr Werte sind NaN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ersetzen von negativen Alter mit Mittelwert\n",
+    "mean_age = modified_titanic['age'].mean()\n",
+    "modified_titanic.loc[negative_age, 'age'] = mean_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29.885619357976655\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mean_age)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male   NaN      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Series name: age\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "1037 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 10.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic['age'].info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modified_titanic['age'] = modified_titanic['age'].bfill() # Ersetzt alle fehldenden Werte der Spalte age mit dem nächsten vorhandenen Wert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male  20.0      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Series name: age\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "1309 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 10.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic['age'].info() # nun gibt es keine NaN Werte mehr. WICHTIG: Es wurde kein negatives Alter hier ersetzt sondern nur NaN Werte (bisherige negative Werte sind aber aktuell auf mean_age gesetzt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Erneutes Laden des manipulierten Datensets\n",
+    "modified_titanic = pd.read_csv(path.replace(\"titanic\", \"modified_titanic\"), header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Eine weitere sehr praktische Variante ist es, die Methode fillna() zu verwenden\n",
+    "modified_titanic['age'] = modified_titanic['age'].fillna(mean_age)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.885619</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex        age  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male  29.885619   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.000000   \n",
+       "2                                Head, Mr. Christopher    male  42.000000   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.000000   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.000000   \n",
+       "...                                                ...     ...        ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.000000   \n",
+       "1305                              Lurette, Miss. Elise  female  58.000000   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.000000   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.000000   \n",
+       "1308                                Otter, Mr. Richard    male  39.000000   \n",
+       "\n",
+       "      sibsp  parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         1      0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         1      0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0      0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1      1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         1      0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...    ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Series name: age\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "1309 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 10.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic['age'].info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Erneutes Laden des manipulierten Datensets\n",
+    "modified_titanic = pd.read_csv(path.replace(\"titanic\", \"modified_titanic\"), header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Zeilen entfernen, die negative Werte für das Alter enthalten, NaN Werte bleiben erhalten\n",
+    "modified_titanic = modified_titanic[modified_titanic['age'].ge(0) | modified_titanic['age'].isnull()] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
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+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1300 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male   NaN      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1300 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1300 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1300 non-null   object \n",
+      " 1   survived   1300 non-null   int64  \n",
+      " 2   name       1300 non-null   object \n",
+      " 3   sex        1300 non-null   object \n",
+      " 4   age        1028 non-null   float64\n",
+      " 5   sibsp      1300 non-null   int64  \n",
+      " 6   parch      1300 non-null   int64  \n",
+      " 7   ticket     1300 non-null   object \n",
+      " 8   fare       1299 non-null   float64\n",
+      " 9   cabin      293 non-null    object \n",
+      " 10  embarked   1298 non-null   object \n",
+      " 11  boat       484 non-null    object \n",
+      " 12  body       120 non-null    float64\n",
+      " 13  home.dest  740 non-null    object \n",
+      "dtypes: float64(3), int64(3), object(8)\n",
+      "memory usage: 152.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# andere Möglichkeit mit der Query Methode\n",
+    "modified_titanic = pd.read_csv(path.replace(\"titanic\", \"modified_titanic\"), header=0, sep=',', index_col=False, decimal='.')\n",
+    "\n",
+    "modified_titanic = modified_titanic.query('age >= 0 or age.isnull()') # Achtung: lässt NaN Werte bestehen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Thorneycroft, Mr. Percival</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376564</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lindqvist, Mr. Eino William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O 2. 3101285</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Head, Mr. Christopher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113038</td>\n",
+       "      <td>42.5000</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London / Middlesex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>250651</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Minneapolis, MN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11753</td>\n",
+       "      <td>52.5542</td>\n",
+       "      <td>D19</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Boston, MA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>White, Mrs. John Stuart (Ella Holmes)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17760</td>\n",
+       "      <td>135.6333</td>\n",
+       "      <td>C32</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY / Briarcliff Manor NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>First Class</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lurette, Miss. Elise</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Celotti, Mr. Francesco</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343275</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Third Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350036</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Second Class</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Otter, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28213</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Middleburg Heights, OH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1300 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass  survived  \\\n",
+       "0      Third Class         0   \n",
+       "1      Third Class         1   \n",
+       "2      First Class         0   \n",
+       "3     Second Class         0   \n",
+       "4      First Class         1   \n",
+       "...            ...       ...   \n",
+       "1304   First Class         1   \n",
+       "1305   First Class         1   \n",
+       "1306   Third Class         0   \n",
+       "1307   Third Class         0   \n",
+       "1308  Second Class         0   \n",
+       "\n",
+       "                                                  name     sex   age  sibsp  \\\n",
+       "0                           Thorneycroft, Mr. Percival    male   NaN      1   \n",
+       "1                          Lindqvist, Mr. Eino William    male  20.0      1   \n",
+       "2                                Head, Mr. Christopher    male  42.0      0   \n",
+       "3                Lahtinen, Mrs. William (Anna Sylfven)  female  26.0      1   \n",
+       "4     Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)  female  45.0      1   \n",
+       "...                                                ...     ...   ...    ...   \n",
+       "1304             White, Mrs. John Stuart (Ella Holmes)  female  55.0      0   \n",
+       "1305                              Lurette, Miss. Elise  female  58.0      0   \n",
+       "1306                            Celotti, Mr. Francesco    male  24.0      0   \n",
+       "1307                   Fischer, Mr. Eberhard Thelander    male  18.0      0   \n",
+       "1308                                Otter, Mr. Richard    male  39.0      0   \n",
+       "\n",
+       "      parch             ticket      fare cabin embarked boat  body  \\\n",
+       "0         0             376564   16.1000   NaN        S  NaN   NaN   \n",
+       "1         0  STON/O 2. 3101285    7.9250   NaN        S   15   NaN   \n",
+       "2         0             113038   42.5000   B11        S  NaN   NaN   \n",
+       "3         1             250651   26.0000   NaN        S  NaN   NaN   \n",
+       "4         0              11753   52.5542   D19        S    5   NaN   \n",
+       "...     ...                ...       ...   ...      ...  ...   ...   \n",
+       "1304      0           PC 17760  135.6333   C32        C    8   NaN   \n",
+       "1305      0           PC 17569  146.5208   B80        C  NaN   NaN   \n",
+       "1306      0             343275    8.0500   NaN        S  NaN   NaN   \n",
+       "1307      0             350036    7.7958   NaN        S  NaN   NaN   \n",
+       "1308      0              28213   13.0000   NaN        S  NaN   NaN   \n",
+       "\n",
+       "                               home.dest  \n",
+       "0                                    NaN  \n",
+       "1                                    NaN  \n",
+       "2                     London / Middlesex  \n",
+       "3                        Minneapolis, MN  \n",
+       "4                             Boston, MA  \n",
+       "...                                  ...  \n",
+       "1304  New York, NY / Briarcliff Manor NY  \n",
+       "1305                                 NaN  \n",
+       "1306                              London  \n",
+       "1307                                 NaN  \n",
+       "1308              Middleburg Heights, OH  \n",
+       "\n",
+       "[1300 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modified_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1300 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   pclass     1300 non-null   object \n",
+      " 1   survived   1300 non-null   int64  \n",
+      " 2   name       1300 non-null   object \n",
+      " 3   sex        1300 non-null   object \n",
+      " 4   age        1028 non-null   float64\n",
+      " 5   sibsp      1300 non-null   int64  \n",
+      " 6   parch      1300 non-null   int64  \n",
+      " 7   ticket     1300 non-null   object \n",
+      " 8   fare       1299 non-null   float64\n",
+      " 9   cabin      293 non-null    object \n",
+      " 10  embarked   1298 non-null   object \n",
+      " 11  boat       484 non-null    object \n",
+      " 12  body       120 non-null    float64\n",
+      " 13  home.dest  740 non-null    object \n",
+      "dtypes: float64(3), int64(3), object(8)\n",
+      "memory usage: 152.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "modified_titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Normalisierung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In vielen Fällen sind die gegebenen Daten nicht normalisiert. Dies kann erhebliche Nachteile mit sich bringen. Wann kann es nachteilig sein, Daten zu normalisieren? (Ausreißer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die folgende Abbildung zeigt, warum Normalisierung so wichtig ist. "
+   ]
+  },
+  {
+   "attachments": {
+    "Untitled-2.png": {
+     "image/png": "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"
+    },
+    "Untitled.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Untitled.png](attachment:Untitled.png)![Untitled-2.png](attachment:Untitled-2.png)\n",
+    "\n",
+    "(von https://python-graph-gallery.com/94-use-normalization-on-seaborn-heatmap/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es gibt verschiedene Möglichkeiten, Daten zu normalisieren. Was sind die Vorteile und Nachteile? Wann eignen sich besondere Normalisierungsmethoden besonders gut bzw. schlecht? Was ist der Wertebereich von den neuen Daten?\n",
+    "* Min-Max Normalisierung $x' = \\frac{x-\\min x}{\\max x-\\min x}$\n",
+    "* z-Wert Normalisierung $x'=\\frac{x-\\mu}{\\sigma}$\n",
+    "* Relative Skalierung: $x' = \\frac{x}{\\Sigma x}$ (Aufpassen auf negative Werte (Division durch 0?))\n",
+    "* Logarithmische Transformation: $x' = \\log(x)$ bzw. $x'=\\log(x+1)$\n",
+    "* $\\ell_2$-Normalisierung (Längennormalisierung) $x'=\\frac{x}{\\sqrt{\\Sigma_i x_i^2}}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es kann aber das Normalisieren oft auch einfach nur eine Konvertierung der Werte in eine andere Einheit bedeuten."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Durch das Normalisieren geht oftmals der Bezug zu den Werten/Daten verloren, auf dies muss bei Visualisierungen oder späteren Datenverarbeitung, insbesondere in Machine Learning Modellen berücksichtigt werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.') # Achtung jetzt wieder normales Titanic Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# min-max normalization\n",
+    "def min_max_normalization(df, columns):\n",
+    "    for column in columns:\n",
+    "        df[column] = (df[column] - df[column].min()) / (df[column].max() - df[column].min())\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = ['age', 'fare']\n",
+    "titanic = min_max_normalization(titanic, columns) # Die übergebenen Spalten sind nun im Bereich [-1, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.361169</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>0.412503</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.009395</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.022964</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.373695</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.311064</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.179540</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.329854</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>0.014102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.336117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>0.014102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.361169</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>0.015371</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex       age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  0.361169      0      0   24160  0.412503       B5        S    2   \n",
+       "1       male  0.009395      1      2  113781  0.295806  C22 C26        S   11   \n",
+       "2     female  0.022964      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "3       male  0.373695      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "4     female  0.311064      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "...      ...       ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  0.179540      1      0    2665  0.028213      NaN        C  NaN   \n",
+       "1305  female       NaN      1      0    2665  0.028213      NaN        C  NaN   \n",
+       "1306    male  0.329854      0      0    2656  0.014102      NaN        C  NaN   \n",
+       "1307    male  0.336117      0      0    2670  0.014102      NaN        C  NaN   \n",
+       "1308    male  0.361169      0      0  315082  0.015371      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ebenso möglich ist die Normalisierung mittels `sklearn`. Es kann installiert werden mit `conda install scikit-learn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "titanic[['age', 'fare']] = MinMaxScaler().fit_transform(titanic[['age', 'fare']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.361169</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>0.412503</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.009395</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.022964</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.373695</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.311064</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.179540</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.329854</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>0.014102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.336117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>0.014102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.361169</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>0.015371</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex       age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  0.361169      0      0   24160  0.412503       B5        S    2   \n",
+       "1       male  0.009395      1      2  113781  0.295806  C22 C26        S   11   \n",
+       "2     female  0.022964      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "3       male  0.373695      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "4     female  0.311064      1      2  113781  0.295806  C22 C26        S  NaN   \n",
+       "...      ...       ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  0.179540      1      0    2665  0.028213      NaN        C  NaN   \n",
+       "1305  female       NaN      1      0    2665  0.028213      NaN        C  NaN   \n",
+       "1306    male  0.329854      0      0    2656  0.014102      NaN        C  NaN   \n",
+       "1307    male  0.336117      0      0    2670  0.014102      NaN        C  NaN   \n",
+       "1308    male  0.361169      0      0  315082  0.015371      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# genauso bei der z-Score Normalisierung\n",
+    "\n",
+    "def z_score_normalization(df, columns):\n",
+    "    for column in columns:\n",
+    "        df[column] = (df[column] - df[column].mean()) / df[column].std()\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = ['age', 'fare']\n",
+    "titanic = z_score_normalization(titanic, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.061133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>3.439849</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-2.009535</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.284729</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-1.934376</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.284729</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.008247</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.284729</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.338650</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.284729</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-1.067134</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>-0.364022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>-0.364022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.234581</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>-0.503693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.199891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>-0.503693</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.061133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>-0.491135</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex       age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female -0.061133      0      0   24160  3.439849       B5        S    2   \n",
+       "1       male -2.009535      1      2  113781  2.284729  C22 C26        S   11   \n",
+       "2     female -1.934376      1      2  113781  2.284729  C22 C26        S  NaN   \n",
+       "3       male  0.008247      1      2  113781  2.284729  C22 C26        S  NaN   \n",
+       "4     female -0.338650      1      2  113781  2.284729  C22 C26        S  NaN   \n",
+       "...      ...       ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female -1.067134      1      0    2665 -0.364022      NaN        C  NaN   \n",
+       "1305  female       NaN      1      0    2665 -0.364022      NaN        C  NaN   \n",
+       "1306    male -0.234581      0      0    2656 -0.503693      NaN        C  NaN   \n",
+       "1307    male -0.199891      0      0    2670 -0.503693      NaN        C  NaN   \n",
+       "1308    male -0.061133      0      0  315082 -0.491135      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Auch hierfür gibt es eine Funktion in `sklearn`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# auch hier gibt es eine Funktion in sklearn\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "titanic[['age', 'fare']] = StandardScaler().fit_transform(titanic[['age', 'fare']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.061162</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>3.441165</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-2.010496</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.285603</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-1.935302</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.285603</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.008251</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.285603</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.338812</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>2.285603</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-1.067644</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>-0.364161</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>-0.364161</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.234693</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>-0.503886</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.199987</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>-0.503886</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.061162</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>-0.491323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex       age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female -0.061162      0      0   24160  3.441165       B5        S    2   \n",
+       "1       male -2.010496      1      2  113781  2.285603  C22 C26        S   11   \n",
+       "2     female -1.935302      1      2  113781  2.285603  C22 C26        S  NaN   \n",
+       "3       male  0.008251      1      2  113781  2.285603  C22 C26        S  NaN   \n",
+       "4     female -0.338812      1      2  113781  2.285603  C22 C26        S  NaN   \n",
+       "...      ...       ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female -1.067644      1      0    2665 -0.364161      NaN        C  NaN   \n",
+       "1305  female       NaN      1      0    2665 -0.364161      NaN        C  NaN   \n",
+       "1306    male -0.234693      0      0    2656 -0.503886      NaN        C  NaN   \n",
+       "1307    male -0.199987      0      0    2670 -0.503886      NaN        C  NaN   \n",
+       "1308    male -0.061162      0      0  315082 -0.491323      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Datenskalen und deren Transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Viele Datenskalen sind nicht numerisch. Dies erschwert in manchen Fällen die weitere Verarbeitung. Grundsätzlich unterscheidet man 3 Typen von Daten:\n",
+    "1. Quantitative Daten\n",
+    "2. Ordinale Daten\n",
+    "3. Nominelle Daten"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Quantitative Daten</b>\n",
+    "\n",
+    "* Numerische Typen\n",
+    "* Kann unterschieden werden in diskret und stetig\n",
+    "* Haben normalerweise eine (physikalische) Einheit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Ordinale Daten</b>\n",
+    "\n",
+    "* Kategorien\n",
+    "* Es gibt eine **sinnvolle Ordnung**\n",
+    "* **Keine** **Relation**/Verhältnis, zBsp. Kleidungsgröße L ist <b>nicht</b> $n$-mal so groß wie Kleidungsgröße M\n",
+    "* Beispiele: Kleidungsgrößen, Schulnoten, Job-Klassifizierung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Nominale Daten</b>\n",
+    "* Kategorien\n",
+    "* **Keine** Reihenfolge/Ordnung\n",
+    "* Beispiele: Geschlecht, Farbe, Name, $\\ldots$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Welche Spalten sind hier quantitativ, ordinal bzw. nominal?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warum kann das numerisieren von Daten wichtig sein? In welchen Fällen ergibt sich welcher Vorteil/Nachteil?\n",
+    "Sollen nun die Daten \"ver-numerisiert\" werden, so gibt es folgende Möglichkeiten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wir verwenden nun folgende Methoden um die Daten zu quantifizieren. Dafür verwenden wir die Methoden von sklearn\n",
+    "\n",
+    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')\n",
+    "titanic_ordinal = titanic.copy(deep=True)\n",
+    "\n",
+    "titanic_ordinal[['sex']] = OrdinalEncoder(dtype=int).fit_transform(titanic_ordinal[['sex']]) # \"sex\" ist nun ein \"0/1\" (binary) Attribut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  sex  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton    0   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor    1   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine    0   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton    1   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)    0   \n",
+       "...      ...       ...                                              ...  ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni    0   \n",
+       "1305       3         0                            Zabour, Miss. Thamine    0   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer    1   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin    1   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo    1   \n",
+       "\n",
+       "          age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "0     29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
+       "1      0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
+       "2      2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "3     30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
+       "4     25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "...       ...    ...    ...     ...       ...      ...      ...  ...    ...   \n",
+       "1304  14.5000      1      0    2665   14.4542      NaN        C  NaN  328.0   \n",
+       "1305      NaN      1      0    2665   14.4542      NaN        C  NaN    NaN   \n",
+       "1306  26.5000      0      0    2656    7.2250      NaN        C  NaN  304.0   \n",
+       "1307  27.0000      0      0    2670    7.2250      NaN        C  NaN    NaN   \n",
+       "1308  29.0000      0      0  315082    7.8750      NaN        S  NaN    NaN   \n",
+       "\n",
+       "                            home.dest  \n",
+       "0                        St Louis, MO  \n",
+       "1     Montreal, PQ / Chesterville, ON  \n",
+       "2     Montreal, PQ / Chesterville, ON  \n",
+       "3     Montreal, PQ / Chesterville, ON  \n",
+       "4     Montreal, PQ / Chesterville, ON  \n",
+       "...                               ...  \n",
+       "1304                              NaN  \n",
+       "1305                              NaN  \n",
+       "1306                              NaN  \n",
+       "1307                              NaN  \n",
+       "1308                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_ordinal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Im vorigen Beispiel wurden doppelte eckige Klammern verwendet, um ein Dataframe zu erhalten, ansonsten wird ein Series Objekt zurückgegeben, siehe folgenden Code\n",
+    "a = titanic[['sex']]\n",
+    "b = titanic['sex']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         sex\n",
+       "0     female\n",
+       "1       male\n",
+       "2     female\n",
+       "3       male\n",
+       "4     female\n",
+       "...      ...\n",
+       "1304  female\n",
+       "1305  female\n",
+       "1306    male\n",
+       "1307    male\n",
+       "1308    male\n",
+       "\n",
+       "[1309 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       female\n",
+       "1         male\n",
+       "2       female\n",
+       "3         male\n",
+       "4       female\n",
+       "         ...  \n",
+       "1304    female\n",
+       "1305    female\n",
+       "1306      male\n",
+       "1307      male\n",
+       "1308      male\n",
+       "Name: sex, Length: 1309, dtype: object"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Falls das Objekt gespeichert wird, kann es auch wieder zurück transformiert werden\n",
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')\n",
+    "titanic_ordinal = titanic.copy(deep=True)\n",
+    "\n",
+    "OE = OrdinalEncoder(dtype=int)\n",
+    "\n",
+    "titanic_ordinal[['sex']] = OE.fit_transform(titanic_ordinal[['sex']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  sex  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton    0   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor    1   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine    0   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton    1   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)    0   \n",
+       "...      ...       ...                                              ...  ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni    0   \n",
+       "1305       3         0                            Zabour, Miss. Thamine    0   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer    1   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin    1   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo    1   \n",
+       "\n",
+       "          age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "0     29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
+       "1      0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
+       "2      2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "3     30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
+       "4     25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "...       ...    ...    ...     ...       ...      ...      ...  ...    ...   \n",
+       "1304  14.5000      1      0    2665   14.4542      NaN        C  NaN  328.0   \n",
+       "1305      NaN      1      0    2665   14.4542      NaN        C  NaN    NaN   \n",
+       "1306  26.5000      0      0    2656    7.2250      NaN        C  NaN  304.0   \n",
+       "1307  27.0000      0      0    2670    7.2250      NaN        C  NaN    NaN   \n",
+       "1308  29.0000      0      0  315082    7.8750      NaN        S  NaN    NaN   \n",
+       "\n",
+       "                            home.dest  \n",
+       "0                        St Louis, MO  \n",
+       "1     Montreal, PQ / Chesterville, ON  \n",
+       "2     Montreal, PQ / Chesterville, ON  \n",
+       "3     Montreal, PQ / Chesterville, ON  \n",
+       "4     Montreal, PQ / Chesterville, ON  \n",
+       "...                               ...  \n",
+       "1304                              NaN  \n",
+       "1305                              NaN  \n",
+       "1306                              NaN  \n",
+       "1307                              NaN  \n",
+       "1308                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_ordinal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To demonstrate inverse transform\n",
+    "titanic_ordinal[['sex']] = OE.inverse_transform(titanic_ordinal[['sex']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                                             name  \\\n",
+       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
+       "1          1         1                   Allison, Master. Hudson Trevor   \n",
+       "2          1         0                     Allison, Miss. Helen Loraine   \n",
+       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
+       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
+       "...      ...       ...                                              ...   \n",
+       "1304       3         0                             Zabour, Miss. Hileni   \n",
+       "1305       3         0                            Zabour, Miss. Thamine   \n",
+       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
+       "1307       3         0                              Zakarian, Mr. Ortin   \n",
+       "1308       3         0                               Zimmerman, Mr. Leo   \n",
+       "\n",
+       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
+       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
+       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
+       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
+       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
+       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
+       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
+       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
+       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
+       "\n",
+       "       body                        home.dest  \n",
+       "0       NaN                     St Louis, MO  \n",
+       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
+       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
+       "...     ...                              ...  \n",
+       "1304  328.0                              NaN  \n",
+       "1305    NaN                              NaN  \n",
+       "1306  304.0                              NaN  \n",
+       "1307    NaN                              NaN  \n",
+       "1308    NaN                              NaN  \n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_ordinal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')\n",
+    "titanic_onehot = titanic.copy(deep=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OneHotEncoder\n",
+    "encoder = OneHotEncoder(sparse_output=False, dtype=int)\n",
+    "encoded_sex = encoder.fit_transform(titanic_onehot[['sex']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0],\n",
+       "       [0, 1],\n",
+       "       [1, 0],\n",
+       "       ...,\n",
+       "       [0, 1],\n",
+       "       [0, 1],\n",
+       "       [0, 1]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_sex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_sex_df = pd.DataFrame(encoded_sex, columns=encoder.get_feature_names_out(['sex'])) # erstellt die Spalten Sex_male und Sex_female"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_sex_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic_onehot.drop(columns=['sex'], inplace=True)\n",
+    "titanic_onehot = pd.concat([titanic_onehot, encoded_sex_df], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic_onehot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Verknüpfen von Dataframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es gibt mehrere Befehle, um Dataframes miteinander zu verknüpfen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`merge()`:2 Objekte verknüpfen und join über die Spalten.\n",
+    "\n",
+    "`join()`: 2 Objekte verknüpfen und Join über die Indizes.\n",
+    "\n",
+    "`concat()`: 2 oder mehr Dataframes werden miteinander verknüpft. (Bereits verwendet)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')\n",
+    "\n",
+    "homes = titanic[['home.dest']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic.drop(columns=['home.dest'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfmerged = titanic.merge(right=homes, left_index=True, right_index=True) # wenn nicht der Index verwendet wird, dann left_on='column_name' und right_on='column_name'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfmerged"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')\n",
+    "\n",
+    "homes = titanic[['home.dest']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic.drop(columns=['home.dest'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Join\n",
+    "dfjoined = titanic.join(homes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfjoined"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Nahezu) sämtliche bisher durchgemachte Schritte sind im Normalfall möglicherweise notwendig, um ein eine vernünftige Datenananlyse zu beginnen. Nicht jeder Schritt ist dabei bei jedem Dataset anwendbar, jedoch muss sich mit diesen Schritten auseinander gesetzt werden."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Weitere Möglichkeiten werden in den folgenden Wochen erläutert (Dimensionsreduzierung, Feature Selection, Smoothing methods, etc.). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Da es auch andere Formen von Datasets gibt (Bilder, etc. - werden später durchgemacht), gibt es dann noch ein paar Datenset-spezifische Vorverarbeitungsschritte."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](attachment:image.png)\n",
+    "\n",
+    "(adaptiert von Seminar Künstliche Intelligenz 16. und 17. Juni St.Polten/Online)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_data/modified_titanic.csv b/_data/modified_titanic.csv
new file mode 100644
index 0000000..f30d94a
--- /dev/null
+++ b/_data/modified_titanic.csv
@@ -0,0 +1,1310 @@
+pclass,survived,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked,boat,body,home.dest
+Third Class,0,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S,,,
+Third Class,1,"Lindqvist, Mr. Eino William",male,20.0,1,0,STON/O 2. 3101285,7.925,,S,15,,
+First Class,0,"Head, Mr. Christopher",male,42.0,0,0,113038,42.5,B11,S,,,London / Middlesex
+Second Class,0,"Lahtinen, Mrs. William (Anna Sylfven)",female,26.0,1,1,250651,26.0,,S,,,"Minneapolis, MN"
+First Class,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45.0,1,0,11753,52.5542,D19,S,5,,"Boston, MA"
+Second Class,1,"Becker, Miss. Ruth Elizabeth",female,12.0,2,1,230136,39.0,F4,S,13,,"Guntur, India / Benton Harbour, MI"
+First Class,0,"Holverson, Mr. Alexander Oskar",male,42.0,1,0,113789,52.0,,S,,38.0,"New York, NY"
+Third Class,1,"Landergren, Miss. Aurora Adelia",female,22.0,0,0,C 7077,7.25,,S,13,,
+Third Class,0,"Kalvik, Mr. Johannes Halvorsen",male,21.0,0,0,8475,8.4333,,S,,,
+Second Class,1,"Wells, Miss. Joan",female,4.0,1,1,29103,23.0,,S,14,,"Cornwall / Akron, OH"
+First Class,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48.0,1,0,PC 17761,106.425,C86,C,2,,"Deephaven, MN / Cedar Rapids, IA"
+Third Class,0,"Bengtsson, Mr. John Viktor",male,26.0,0,0,347068,7.775,,S,,,"Krakudden, Sweden Moune, IL"
+Third Class,0,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S,,14.0,
+Third Class,0,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C,,,"Syria Kent, ON"
+Third Class,0,"Chronopoulos, Mr. Demetrios",male,18.0,1,0,2680,14.4542,,C,,,Greece
+Third Class,0,"Kelly, Mr. James",male,44.0,0,0,363592,8.05,,S,,,
+Third Class,0,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S,,,
+Third Class,0,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q,,68.0,"Aughnacliff, Co Longford, Ireland New York, NY"
+First Class,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35.0,1,0,36973,83.475,C83,S,D,,"New York, NY"
+Second Class,0,"Hickman, Mr. Lewis",male,32.0,2,0,S.O.C. 14879,73.5,,S,,256.0,"West Hampstead, London / Neepawa, MB"
+Second Class,0,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21.0,,S,,,"Upper Burma, India Pittsburgh, PA"
+Third Class,0,"Markun, Mr. Johann",male,33.0,0,0,349257,7.8958,,S,,,
+Second Class,0,"Sweet, Mr. George Frederick",male,14.0,0,0,220845,65.0,,S,,,"Somerset / Bernardsville, NJ"
+Third Class,0,"Johnston, Master. William Arthur ""Willie""",male,,1,2,W./C. 6607,23.45,,S,,,
+Third Class,0,"Goodwin, Miss. Jessie Allis",female,10.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+First Class,1,"Ward, Miss. Anna",female,35.0,0,0,PC 17755,512.3292,,C,3,,
+Third Class,0,"van Billiard, Master. James William",male,,1,1,A/5. 851,14.5,,S,,,
+Third Class,0,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C,,,
+Third Class,1,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24.0,0,3,2666,19.2583,,C,C,,"Syria New York, NY"
+Third Class,0,"Mionoff, Mr. Stoytcho",male,28.0,0,0,349207,7.8958,,S,,,
+Third Class,0,"Davies, Mr. Joseph",male,17.0,2,0,A/4 48873,8.05,,S,,,"West Bromwich, England Pontiac, MI"
+First Class,0,"Ryerson, Mr. Arthur Larned",male,61.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C,,,"Haverford, PA / Cooperstown, NY"
+Third Class,1,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C,15,,
+Third Class,0,"Vovk, Mr. Janko",male,22.0,0,0,349252,7.8958,,S,,,
+Third Class,0,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47.0,1,0,A/5. 3337,14.5,,S,,7.0,
+First Class,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,C,4,,"New York, NY"
+Third Class,1,"McCoy, Miss. Alicia",female,,2,0,367226,23.25,,Q,16,,
+First Class,1,"Carter, Miss. Lucile Polk",female,14.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+Second Class,0,"Sedgwick, Mr. Charles Frederick Waddington",male,25.0,0,0,244361,13.0,,S,,,Liverpool
+Third Class,1,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q,16,,
+Third Class,0,"Karaic, Mr. Milan",male,30.0,0,0,349246,7.8958,,S,,,
+Second Class,0,"Fox, Mr. Stanley Hubert",male,36.0,0,0,229236,13.0,,S,,236.0,"Rochester, NY"
+First Class,1,"Eustis, Miss. Elizabeth Mussey",female,54.0,1,0,36947,78.2667,D20,C,4,,"Brookline, MA"
+Third Class,0,"Hendekovic, Mr. Ignjac",male,28.0,0,0,349243,7.8958,,S,,306.0,
+Third Class,0,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S,,,
+First Class,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18.0,1,0,113773,53.1,D30,S,10,,"New York, NY"
+Third Class,0,"Ekstrom, Mr. Johan",male,45.0,0,0,347061,6.975,,S,,,"Effington Rut, SD"
+Second Class,1,"West, Miss. Barbara J",female,0.9167,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+Third Class,0,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C,,181.0,
+Third Class,0,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S,,,
+Third Class,0,"Lievens, Mr. Rene Aime",male,24.0,0,0,345781,9.5,,S,,,
+Second Class,1,"West, Miss. Constance Mirium",female,5.0,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+Third Class,0,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18.0,0,0,347078,7.75,,S,,,
+Third Class,1,"Krekorian, Mr. Neshan",male,25.0,0,0,2654,7.2292,F E57,C,10,,
+Third Class,0,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q,,,
+Third Class,1,"Badman, Miss. Emily Louisa",female,18.0,0,0,A/4 31416,8.05,,S,C,,"London Skanteales, NY"
+First Class,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40.0,1,1,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+Third Class,1,"Ayoub, Miss. Banoura",female,13.0,0,0,2687,7.2292,,C,C,,"Syria Youngstown, OH"
+Third Class,1,"Albimona, Mr. Nassef Cassem",male,26.0,0,0,2699,18.7875,,C,15,,"Syria Fredericksburg, VA"
+Third Class,0,"Conlon, Mr. Thomas Henry",male,31.0,0,0,21332,7.7333,,Q,,,"Philadelphia, PA"
+Second Class,0,"Beauchamp, Mr. Henry James",male,28.0,0,0,244358,26.0,,S,,,England
+First Class,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+Third Class,0,"Johnson, Mr. Malkolm Joackim",male,33.0,0,0,347062,7.775,,S,,37.0,
+First Class,1,"Minahan, Miss. Daisy E",female,33.0,1,0,19928,90.0,C78,Q,14,,"Green Bay, WI"
+Third Class,0,"Cor, Mr. Liudevit",male,19.0,0,0,349231,7.8958,,S,,,Austria
+Third Class,1,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3,0,3101278,15.85,,S,,,"Ruotsinphytaa, Finland New York, NY"
+Third Class,0,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C,,,
+Second Class,0,"Reeves, Mr. David",male,36.0,0,0,C.A. 17248,10.5,,S,,,"Brighton, Sussex"
+Third Class,0,"McNamee, Mrs. Neal (Eileen O'Leary)",female,19.0,1,0,376566,16.1,,S,,53.0,
+Third Class,0,"Karlsson, Mr. Nils August",male,22.0,0,0,350060,7.5208,,S,,,
+Third Class,1,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q,13,,
+First Class,0,"Case, Mr. Howard Brown",male,49.0,0,0,19924,26.0,,S,,,"Ascot, Berkshire / Rochester, NY"
+Second Class,1,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33.0,,S,11,,"London / Chicago, IL"
+Second Class,0,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39.0,0,0,250655,26.0,,S,,,
+First Class,0,"Blackwell, Mr. Stephen Weart",male,45.0,0,0,113784,35.5,T,S,,,"Trenton, NJ"
+Third Class,1,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S,13,,
+Third Class,1,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q,16,,
+Second Class,0,"Bracken, Mr. James H",male,27.0,0,0,220367,13.0,,S,,,"Lake Arthur, Chavez County, NM"
+Third Class,0,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C,,328.0,
+Second Class,0,"Knight, Mr. Robert J",male,,0,0,239855,0.0,,S,,,Belfast
+Third Class,0,"Bourke, Mrs. John (Catherine)",female,32.0,1,1,364849,15.5,,Q,,,"Ireland Chicago, IL"
+Third Class,0,"Stanley, Mr. Edward Roland",male,21.0,0,0,A/4 45380,8.05,,S,,,
+Third Class,0,"Thomas, Mr. Tannous",male,,0,0,2684,7.225,,C,,,
+First Class,1,"Spedden, Master. Robert Douglas",male,6.0,0,2,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+Third Class,0,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S,,,
+Third Class,0,"Linehan, Mr. Michael",male,,0,0,330971,7.8792,,Q,,,
+Second Class,1,"Ware, Mrs. John James (Florence Louise Long)",female,31.0,0,0,CA 31352,21.0,,S,10,,"Bristol, England / New Britain, CT"
+Third Class,0,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S,,,
+Third Class,0,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,S,,,"Lower Clapton, Middlesex or Erdington, Birmingham"
+First Class,1,"Bonnell, Miss. Elizabeth",female,58.0,0,0,113783,26.55,C103,S,8,,"Birkdale, England Cleveland, Ohio"
+Third Class,0,"Panula, Mr. Ernesti Arvid",male,16.0,4,1,3101295,39.6875,,S,,,
+Third Class,0,"Berglund, Mr. Karl Ivar Sven",male,22.0,0,0,PP 4348,9.35,,S,,,"Tranvik, Finland New York"
+Third Class,0,"Carlsson, Mr. Carl Robert",male,24.0,0,0,350409,7.8542,,S,,,"Goteborg, Sweden Huntley, IL"
+Third Class,0,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q,,,
+Third Class,0,"Warren, Mr. Charles William",male,,0,0,C.A. 49867,7.55,,S,,,
+First Class,0,"Walker, Mr. William Anderson",male,47.0,0,0,36967,34.0208,D46,S,,,"East Orange, NJ"
+First Class,0,"Sutton, Mr. Frederick",male,61.0,0,0,36963,32.3208,D50,S,,46.0,"Haddenfield, NJ"
+Third Class,0,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S,,,
+Third Class,0,"Sivola, Mr. Antti Wilhelm",male,21.0,0,0,STON/O 2. 3101280,7.925,,S,,,
+Third Class,1,"Nicola-Yarred, Miss. Jamila",female,14.0,1,0,2651,11.2417,,C,C,,
+Third Class,0,"Braf, Miss. Elin Ester Maria",female,20.0,0,0,347471,7.8542,,S,,,"Medeltorp, Sweden Chicago, IL"
+First Class,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51.0,1,0,13502,77.9583,D11,S,10,,"Hudson, NY"
+Third Class,0,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S,,,"Bulgaria Coon Rapids, IA"
+First Class,0,"McCaffry, Mr. Thomas Francis",male,46.0,0,0,13050,75.2417,C6,C,,292.0,"Vancouver, BC"
+Third Class,1,"Sheerlinck, Mr. Jan Baptist",male,29.0,0,0,345779,9.5,,S,11,,
+Second Class,0,"Pengelly, Mr. Frederick William",male,19.0,0,0,28665,10.5,,S,,,"Gunnislake, England / Butte, MT"
+Third Class,0,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C,,,
+Second Class,1,"Toomey, Miss. Ellen",female,50.0,0,0,F.C.C. 13531,10.5,,S,9,,"Indianapolis, IN"
+Third Class,0,"Palsson, Master. Paul Folke",male,6.0,3,1,349909,21.075,,S,,,
+Second Class,0,"Karnes, Mrs. J Frank (Claire Bennett)",female,22.0,0,0,F.C.C. 13534,21.0,,S,,,"India / Pittsburgh, PA"
+Third Class,0,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S,,,
+First Class,0,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C,,,"Chicago, IL"
+First Class,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)",female,53.0,0,0,PC 17606,27.4458,,C,6,,"Washington, DC"
+Third Class,0,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S,,,
+First Class,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C,6,,"Paris, France"
+First Class,1,"Beckwith, Mr. Richard Leonard",male,37.0,1,1,11751,52.5542,D35,S,5,,"New York, NY"
+Third Class,0,"Moen, Mr. Sigurd Hansen",male,25.0,0,0,348123,7.65,F G73,S,,309.0,
+Third Class,0,"Sage, Miss. Ada",female,,8,2,CA. 2343,69.55,,S,,,
+Third Class,0,"Johnston, Mrs. Andrew G (Elizabeth ""Lily"" Watson)",female,,1,2,W./C. 6607,23.45,,S,,,
+Second Class,0,"Collyer, Mr. Harvey",male,31.0,1,1,C.A. 31921,26.25,,S,,,"Bishopstoke, Hants / Fayette Valley, ID"
+Third Class,1,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q,D,,
+Third Class,0,"Sawyer, Mr. Frederick Charles",male,-24.5,0,0,342826,8.05,,S,,284.0,
+Second Class,0,"Sobey, Mr. Samuel James Hayden",male,25.0,0,0,C.A. 29178,13.0,,S,,,"Cornwall / Houghton, MI"
+Second Class,0,"Lahtinen, Rev. William",male,30.0,1,1,250651,26.0,,S,,,"Minneapolis, MN"
+Second Class,0,"Renouf, Mr. Peter Henry",male,34.0,1,0,31027,21.0,,S,12,,"Elizabeth, NJ"
+Third Class,0,"Foley, Mr. Joseph",male,26.0,0,0,330910,7.8792,,Q,,,"Ireland Chicago, IL"
+First Class,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,,1,1,11751,52.5542,D35,S,5,,"New York, NY"
+First Class,1,"Calderhead, Mr. Edward Pennington",male,42.0,0,0,PC 17476,26.2875,E24,S,5,,"New York, NY"
+Third Class,0,"Dantcheff, Mr. Ristiu",male,25.0,0,0,349203,7.8958,,S,,,"Bulgaria Chicago, IL"
+Second Class,1,"Watt, Miss. Bertha J",female,12.0,0,0,C.A. 33595,15.75,,S,9,,"Aberdeen / Portland, OR"
+First Class,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54.0,1,0,PC 17603,59.4,,C,6,,"New York, NY"
+Third Class,1,"Sap, Mr. Julius",male,25.0,0,0,345768,9.5,,S,11,,
+Third Class,0,"Patchett, Mr. George",male,-19.0,0,0,358585,14.5,,S,,,
+Third Class,1,"Daly, Mr. Eugene Patrick",male,29.0,0,0,382651,7.75,,Q,13 15 B,,"Co Athlone, Ireland New York, NY"
+Second Class,1,"Becker, Master. Richard F",male,1.0,2,1,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+Second Class,1,"Garside, Miss. Ethel",female,34.0,0,0,243880,13.0,,S,12,,"Brooklyn, NY"
+Third Class,1,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q,,,
+Third Class,1,"de Mulder, Mr. Theodore",male,30.0,0,0,345774,9.5,,S,11,,"Belgium Detroit, MI"
+Third Class,0,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q,,,
+First Class,1,"Kimball, Mr. Edwin Nelson Jr",male,42.0,1,0,11753,52.5542,D19,S,5,,"Boston, MA"
+Third Class,0,"Petroff, Mr. Nedelio",male,19.0,0,0,349212,7.8958,,S,,,
+Second Class,0,"Bateman, Rev. Robert James",male,51.0,0,0,S.O.P. 1166,12.525,,S,,174.0,"Jacksonville, FL"
+Second Class,0,"Chapman, Mr. John Henry",male,37.0,1,0,SC/AH 29037,26.0,,S,,17.0,"Cornwall / Spokane, WA"
+Third Class,0,"Matinoff, Mr. Nicola",male,,0,0,349255,7.8958,,C,,,
+First Class,1,"Daly, Mr. Peter Denis ",male,51.0,0,0,113055,26.55,E17,S,5 9,,"Lima, Peru"
+Second Class,0,"Gill, Mr. John William",male,24.0,0,0,233866,13.0,,S,,155.0,"Clevedon, England"
+Third Class,0,"MacKay, Mr. George William",male,,0,0,C.A. 42795,7.55,,S,,,
+Third Class,0,"Gustafsson, Mr. Anders Vilhelm",male,37.0,2,0,3101276,7.925,,S,,98.0,"Ruotsinphytaa, Finland New York, NY"
+Second Class,0,"Abelson, Mr. Samuel",male,30.0,1,0,P/PP 3381,24.0,,C,,,"Russia New York, NY"
+First Class,0,"Clifford, Mr. George Quincy",male,,0,0,110465,52.0,A14,S,,,"Stoughton, MA"
+Third Class,1,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15.0,1,0,2659,14.4542,,C,,,
+Third Class,1,"Johansson Palmquist, Mr. Oskar Leander",male,26.0,0,0,347070,7.775,,S,15,,
+Second Class,0,"Lingane, Mr. John",male,61.0,0,0,235509,12.35,,Q,,,
+First Class,1,"Bishop, Mr. Dickinson H",male,25.0,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI"
+Second Class,1,"Harris, Mr. George",male,62.0,0,0,S.W./PP 752,10.5,,S,15,,London
+Second Class,1,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,0,0,C.A. 29395,10.5,F33,S,11,,"Folkstone, Kent / New York, NY"
+First Class,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35.0,1,0,113789,52.0,,S,8,,"New York, NY"
+Third Class,1,"Asplund, Mr. Johan Charles",male,23.0,0,0,350054,7.7958,,S,13,,"Oskarshamn, Sweden Minneapolis, MN"
+Third Class,0,"Goodwin, Miss. Lillian Amy",female,16.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+First Class,1,"Sagesser, Mlle. Emma",female,24.0,0,0,PC 17477,69.3,B35,C,9,,
+Third Class,0,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S,,,
+Third Class,1,"Cribb, Miss. Laura Alice",female,17.0,0,1,371362,16.1,,S,12,,"Bournemouth, England Newark, NJ"
+Third Class,0,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S,,,
+First Class,1,"Madill, Miss. Georgette Alexandra",female,15.0,0,1,24160,211.3375,B5,S,2,,"St Louis, MO"
+First Class,0,"Foreman, Mr. Benjamin Laventall",male,30.0,0,0,113051,27.75,C111,C,,,"New York, NY"
+First Class,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C,6,,"New York, NY"
+First Class,0,"Moore, Mr. Clarence Bloomfield",male,47.0,0,0,113796,42.4,,S,,,"Washington, DC"
+First Class,0,"Newell, Mr. Arthur Webster",male,58.0,0,2,35273,113.275,D48,C,,122.0,"Lexington, MA"
+Third Class,0,"Petranec, Miss. Matilda",female,28.0,0,0,349245,7.8958,,S,,,
+Third Class,0,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C,,,
+Third Class,1,"Abelseth, Mr. Olaus Jorgensen",male,25.0,0,0,348122,7.65,F G63,S,A,,"Perkins County, SD"
+Second Class,1,"Collett, Mr. Sidney C Stuart",male,24.0,0,0,28034,10.5,,S,9,,"London / Fort Byron, NY"
+Third Class,0,"Ware, Mr. Frederick",male,,0,0,359309,8.05,,S,,,
+Second Class,1,"Hocking, Miss. Ellen ""Nellie""",female,20.0,2,1,29105,23.0,,S,4,,"Cornwall / Akron, OH"
+First Class,1,"Williams, Mr. Richard Norris II",male,21.0,0,1,PC 17597,61.3792,,C,A,,"Geneva, Switzerland / Radnor, PA"
+Third Class,0,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42.0,0,0,348121,7.65,F G63,S,,120.0,
+First Class,0,"Kenyon, Mr. Frederick R",male,41.0,1,0,17464,51.8625,D21,S,,,"Southington / Noank, CT"
+First Class,0,"Molson, Mr. Harry Markland",male,55.0,0,0,113787,30.5,C30,S,,,"Montreal, PQ"
+Third Class,0,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q,,,
+First Class,0,"Millet, Mr. Francis Davis",male,65.0,0,0,13509,26.55,E38,S,,249.0,"East Bridgewater, MA"
+Third Class,0,"Assaf, Mr. Gerios",male,21.0,0,0,2692,7.225,,C,,,"Ottawa, ON"
+Third Class,1,"Dahl, Mr. Karl Edwart",male,45.0,0,0,7598,8.05,,S,15,,"Australia Fingal, ND"
+Second Class,0,"Ware, Mr. William Jeffery",male,23.0,1,0,28666,10.5,,S,,,
+Third Class,0,"Andersson, Miss. Ebba Iris Alfrida",female,6.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+First Class,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51.0,0,1,PC 17592,39.4,D28,S,9,,"Paris, France"
+Third Class,0,"Jensen, Mr. Niels Peder",male,48.0,0,0,350047,7.8542,,S,,,
+Third Class,0,"Bourke, Mr. John",male,40.0,1,1,364849,15.5,,Q,,,"Ireland Chicago, IL"
+First Class,1,"Lindstrom, Mrs. Carl Johan (Sigrid Posse)",female,55.0,0,0,112377,27.7208,,C,6,,"Stockholm, Sweden"
+Second Class,0,"Jefferys, Mr. Ernest Wilfred",male,22.0,2,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+Third Class,0,"Sadowitz, Mr. Harry",male,,0,0,LP 1588,7.575,,S,,,
+First Class,0,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0.0,,S,,,Belfast
+Third Class,0,"Green, Mr. George Henry",male,51.0,0,0,21440,8.05,,S,,,"Dorking, Surrey, England"
+Third Class,0,"Rice, Master. Eugene",male,2.0,4,1,382652,29.125,,Q,,,
+Third Class,0,"Haas, Miss. Aloisia",female,24.0,0,0,349236,8.85,,S,,,
+Third Class,0,"Adams, Mr. John",male,26.0,0,0,341826,8.05,,S,,103.0,"Bournemouth, England"
+Second Class,0,"Fahlstrom, Mr. Arne Jonas",male,18.0,0,0,236171,13.0,,S,,,"Oslo, Norway Bayonne, NJ"
+Third Class,0,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S,,50.0,
+Third Class,0,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C,,,
+Second Class,1,"Wilhelms, Mr. Charles",male,31.0,0,0,244270,13.0,,S,9,,"London, England"
+First Class,1,"Compton, Miss. Sara Rebecca",female,39.0,1,1,PC 17756,83.1583,E49,C,14,,"Lakewood, NJ"
+Third Class,0,"Odahl, Mr. Nils Martin",male,23.0,0,0,7267,9.225,,S,,,
+Second Class,0,"McCrae, Mr. Arthur Gordon",male,32.0,0,0,237216,13.5,,S,,209.0,"Sydney, Australia"
+Third Class,0,"Wittevrongel, Mr. Camille",male,36.0,0,0,345771,9.5,,S,,,
+Third Class,0,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S,,,
+Third Class,1,"Stranden, Mr. Juho",male,31.0,0,0,STON/O 2. 3101288,7.925,,S,9,,
+Third Class,0,"Attalah, Miss. Malake",female,17.0,0,0,2627,14.4583,,C,,,
+Second Class,0,"Maybery, Mr. Frank Hubert",male,40.0,0,0,239059,16.0,,S,,,"Weston-Super-Mare / Moose Jaw, SK"
+Third Class,1,"Assaf Khalil, Mrs. Mariana (""Miriam"")",female,45.0,0,0,2696,7.225,,C,C,,"Ottawa, ON"
+Third Class,0,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C,,,Syria
+Third Class,0,"de Pelsmaeker, Mr. Alfons",male,16.0,0,0,345778,9.5,,S,,,
+Third Class,0,"Augustsson, Mr. Albert",male,23.0,0,0,347468,7.8542,,S,,,"Krakoryd, Sweden Bloomington, IL"
+Third Class,1,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q,16,,
+Third Class,0,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S,,201.0,
+Third Class,1,"Hansen, Mrs. Claus Peter (Jennie L Howard)",female,45.0,1,0,350026,14.1083,,S,11,,
+Third Class,1,"McGowan, Miss. Anna ""Annie""",female,15.0,0,0,330923,8.0292,,Q,,,
+First Class,0,"Straus, Mr. Isidor",male,67.0,1,0,PC 17483,221.7792,C55 C57,S,,96.0,"New York, NY"
+Third Class,1,"Asplund, Miss. Lillian Gertrud",female,5.0,4,2,347077,31.3875,,S,15,,"Sweden Worcester, MA"
+Third Class,0,"Hansen, Mr. Henrik Juul",male,26.0,1,0,350025,7.8542,,S,,,
+Third Class,0,"Ilmakangas, Miss. Ida Livija",female,27.0,1,0,STON/O2. 3101270,7.925,,S,,,
+Second Class,1,"Harper, Miss. Annie Jessie ""Nina""",female,6.0,0,1,248727,33.0,,S,11,,"Denmark Hill, Surrey / Chicago"
+Third Class,0,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C,,188.0,
+Third Class,0,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q,,,
+First Class,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55.0,2,0,11770,25.7,C101,S,2,,"New York, NY"
+Third Class,0,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S,,,
+Third Class,0,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S,,,
+First Class,1,"Behr, Mr. Karl Howell",male,26.0,0,0,111369,30.0,C148,C,5,,"New York, NY"
+First Class,1,"Dick, Mr. Albert Adrian",male,31.0,1,0,17474,57.0,B20,S,3,,"Calgary, AB"
+Third Class,1,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q,16,,
+Third Class,0,"Vander Cruyssen, Mr. Victor",male,47.0,0,0,345765,9.0,,S,,,
+First Class,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33.0,0,0,110152,86.5,B77,S,8,,"London  Vancouver, BC"
+Third Class,0,"Ford, Mr. Edward Watson",male,18.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+Third Class,0,"Caram, Mr. Joseph",male,,1,0,2689,14.4583,,C,,,"Ottawa, ON"
+Third Class,0,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q,,,
+Third Class,0,"McGowan, Miss. Katherine",female,35.0,0,0,9232,7.75,,Q,,,
+First Class,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19.0,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI"
+Third Class,0,"Jensen, Mr. Svend Lauritz",male,17.0,1,0,350048,7.0542,,S,,,
+Third Class,1,"Karlsson, Mr. Einar Gervasius",male,21.0,0,0,350053,7.7958,,S,13,,
+First Class,0,"Goldschmidt, Mr. George B",male,71.0,0,0,PC 17754,34.6542,A5,C,,,"New York, NY"
+Second Class,0,"Eitemiller, Mr. George Floyd",male,23.0,0,0,29751,13.0,,S,,,"England / Detroit, MI"
+Third Class,0,"Pokrnic, Mr. Mate",male,17.0,0,0,315095,8.6625,,S,,,
+First Class,1,"Salomon, Mr. Abraham L",male,,0,0,111163,26.0,,S,1,,"New York, NY"
+Third Class,0,"Aronsson, Mr. Ernst Axel Algot",male,24.0,0,0,349911,7.775,,S,,,"Sweden Joliet, IL"
+Third Class,0,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S,,,
+Third Class,0,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S,,,
+First Class,0,"Weir, Col. John",male,60.0,0,0,113800,26.55,,S,,,"England Salt Lake City, Utah"
+Second Class,1,"Wells, Master. Ralph Lester",male,2.0,1,1,29103,23.0,,S,14,,"Cornwall / Akron, OH"
+Second Class,1,"Becker, Miss. Marion Louise",female,4.0,2,1,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+Second Class,0,"Moraweck, Dr. Ernest",male,-54.0,0,0,29011,14.0,,S,,,"Frankfort, KY"
+Second Class,0,"Gilbert, Mr. William",male,47.0,0,0,C.A. 30769,10.5,,S,,,Cornwall
+First Class,1,"Omont, Mr. Alfred Fernand",male,,0,0,F.C. 12998,25.7417,,C,7,,"Paris, France"
+Third Class,0,"Torber, Mr. Ernst William",male,44.0,0,0,364511,8.05,,S,,,
+Third Class,0,"Naughton, Miss. Hannah",female,,0,0,365237,7.75,,Q,,,
+Third Class,1,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q,16,,
+Third Class,1,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31.0,1,1,363291,20.525,,S,C D,,"Strood, Kent, England Detroit, MI"
+Third Class,0,"Balkic, Mr. Cerin",male,26.0,0,0,349248,7.8958,,S,,,
+Third Class,0,"Peacock, Master. Alfred Edward",male,-0.75,1,1,SOTON/O.Q. 3101315,13.775,,S,,,
+Second Class,0,"Swane, Mr. George",male,18.5,0,0,248734,13.0,F,S,,294.0,
+Third Class,0,"Ali, Mr. Ahmed",male,24.0,0,0,SOTON/O.Q. 3101311,7.05,,S,,,
+Third Class,0,"Petersen, Mr. Marius",male,24.0,0,0,342441,8.05,,S,,,
+Third Class,0,"Gustafsson, Mr. Johan Birger",male,28.0,2,0,3101277,7.925,,S,,,"Ruotsinphytaa, Finland New York, NY"
+First Class,0,"Natsch, Mr. Charles H",male,37.0,0,1,PC 17596,29.7,C118,C,,,"Brooklyn, NY"
+Second Class,0,"Sjostedt, Mr. Ernst Adolf",male,59.0,0,0,237442,13.5,,S,,,"Sault St Marie, ON"
+Third Class,0,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q,,,
+Third Class,1,"Lulic, Mr. Nikola",male,27.0,0,0,315098,8.6625,,S,15,,
+Third Class,0,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S,,,
+First Class,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17.0,1,0,PC 17758,108.9,C65,C,8,,"Madrid, Spain"
+Third Class,0,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C,,9.0,
+First Class,1,"Young, Miss. Marie Grice",female,36.0,0,0,PC 17760,135.6333,C32,C,8,,"New York, NY / Washington, DC"
+Third Class,0,"Lithman, Mr. Simon",male,,0,0,S.O./P.P. 251,7.55,,S,,,
+Second Class,0,"Ware, Mr. John James",male,30.0,1,0,CA 31352,21.0,,S,,,"Bristol, England / New Britain, CT"
+Second Class,1,"Collyer, Miss. Marjorie ""Lottie""",female,8.0,0,2,C.A. 31921,26.25,,S,14,,"Bishopstoke, Hants / Fayette Valley, ID"
+Second Class,0,"Corbett, Mrs. Walter H (Irene Colvin)",female,30.0,0,0,237249,13.0,,S,,,"Provo, UT"
+Second Class,1,"Brown, Miss. Edith Eileen",female,15.0,0,2,29750,39.0,,S,14,,"Cape Town, South Africa / Seattle, WA"
+Third Class,1,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q,,,
+First Class,1,"Frolicher-Stehli, Mr. Maxmillian",male,60.0,1,1,13567,79.2,B41,C,5,,"Zurich, Switzerland"
+Second Class,1,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C,9,,"Spain / Havana, Cuba"
+First Class,0,"Loring, Mr. Joseph Holland",male,30.0,0,0,113801,45.5,,S,,,"London / New York, NY"
+First Class,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33.0,1,0,113806,53.1,E8,S,5,,"New York, NY / Ithaca, NY"
+Third Class,0,"Zimmerman, Mr. Leo",male,29.0,0,0,315082,7.875,,S,,,
+Second Class,0,"Givard, Mr. Hans Kristensen",male,30.0,0,0,250646,13.0,,S,,305.0,
+Third Class,0,"Allum, Mr. Owen George",male,18.0,0,0,2223,8.3,,S,,259.0,"Windsor, England New York, NY"
+Second Class,0,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0.0,,S,,,Belfast
+Second Class,1,"Rugg, Miss. Emily",female,21.0,0,0,C.A. 31026,10.5,,S,12,,"Guernsey / Wilmington, DE"
+Third Class,0,"Wiklund, Mr. Jakob Alfred",male,18.0,1,0,3101267,6.4958,,S,,314.0,
+Third Class,1,"Kink-Heilmann, Mr. Anton",male,29.0,3,1,315153,22.025,,S,2,,
+First Class,0,"Wright, Mr. George",male,62.0,0,0,113807,26.55,,S,,,"Halifax, NS"
+Third Class,0,"Kink, Mr. Vincenz",male,26.0,2,0,315151,8.6625,,S,,,
+First Class,1,"Hippach, Miss. Jean Gertrude",female,16.0,0,1,111361,57.9792,B18,C,4,,"Chicago, IL"
+First Class,0,"Andrews, Mr. Thomas Jr",male,39.0,0,0,112050,0.0,A36,S,,,"Belfast, NI"
+Third Class,0,"Asplund, Master. Filip Oscar",male,13.0,4,2,347077,31.3875,,S,,,"Sweden Worcester, MA"
+Third Class,0,"Betros, Mr. Tannous",male,20.0,0,0,2648,4.0125,,C,,,Syria
+Second Class,0,"Faunthorpe, Mr. Harry",male,40.0,1,0,2926,26.0,,S,,286.0,"England / Philadelphia, PA"
+First Class,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S,8,,"Southington / Noank, CT"
+Third Class,1,"Bradley, Miss. Bridget Delia",female,22.0,0,0,334914,7.725,,Q,13,,"Kingwilliamstown, Co Cork, Ireland Glens Falls, NY"
+First Class,1,"LeRoy, Miss. Bertha",female,30.0,0,0,PC 17761,106.425,,C,2,,
+Third Class,0,"Barbara, Mrs. (Catherine David)",female,45.0,0,1,2691,14.4542,,C,,,"Syria Ottawa, ON"
+Third Class,0,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q,,,
+Third Class,0,"Duane, Mr. Frank",male,65.0,0,0,336439,7.75,,Q,,,
+First Class,1,"Perreault, Miss. Anne",female,30.0,0,0,12749,93.5,B73,S,3,,
+Third Class,0,"Reed, Mr. James George",male,,0,0,362316,7.25,,S,,,
+Third Class,0,"Alhomaki, Mr. Ilmari Rudolf",male,20.0,0,0,SOTON/O2 3101287,7.925,,S,,,"Salo, Finland Astoria, OR"
+Third Class,0,"Vendel, Mr. Olof Edvin",male,20.0,0,0,350416,7.8542,,S,,,
+Second Class,0,"Weisz, Mr. Leopold",male,27.0,1,0,228414,26.0,,S,,293.0,"Bromsgrove, England / Montreal, PQ"
+Third Class,1,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S,C,,
+First Class,0,"Fry, Mr. Richard",male,,0,0,112058,0.0,B102,S,,,
+Second Class,1,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54.0,1,3,29105,23.0,,S,4,,"Cornwall / Akron, OH"
+Third Class,0,"Van Impe, Miss. Catharina",female,10.0,0,2,345773,24.15,,S,,,
+Third Class,0,"Abbott, Mr. Rossmore Edward",male,16.0,1,1,C.A. 2673,20.25,,S,,190.0,"East Providence, RI"
+Third Class,0,"Karlsson, Mr. Julius Konrad Eugen",male,33.0,0,0,347465,7.8542,,S,,,
+Third Class,0,"Charters, Mr. David",male,21.0,0,0,A/5. 13032,7.7333,,Q,,,"Ireland New York, NY"
+First Class,0,"Allison, Miss. Helen Loraine",female,,1,2,113781,151.55,C22 C26,S,,,"Montreal, PQ / Chesterville, ON"
+Third Class,0,"Goldsmith, Mr. Frank John",male,33.0,1,1,363291,20.525,,S,,,"Strood, Kent, England Detroit, MI"
+Third Class,0,"Cook, Mr. Jacob",male,43.0,0,0,A/5 3536,8.05,,S,,,
+Third Class,0,"Makinen, Mr. Kalle Edvard",male,29.0,0,0,STON/O 2. 3101268,7.925,,S,,,
+Third Class,0,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C,,,
+Third Class,1,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C,C,,
+Second Class,0,"Sharp, Mr. Percival James R",male,27.0,0,0,244358,26.0,,S,,,"Hornsey, England"
+First Class,0,"Gee, Mr. Arthur H",male,47.0,0,0,111320,38.5,E63,S,,275.0,"St Anne's-on-Sea, Lancashire"
+Third Class,0,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C,,,
+First Class,0,"Allison, Mr. Hudson Joshua Creighton",male,30.0,1,2,113781,151.55,C22 C26,S,,135.0,"Montreal, PQ / Chesterville, ON"
+Third Class,0,"Olsson, Mr. Nils Johan Goransson",male,28.0,0,0,347464,7.8542,,S,,,
+Third Class,1,"Ohman, Miss. Velin",female,22.0,0,0,347085,7.775,,S,C,,
+Third Class,0,"Johnson, Mr. William Cahoone Jr",male,19.0,0,0,LINE,0.0,,S,,,
+Second Class,1,"Wright, Miss. Marion",female,26.0,0,0,220844,13.5,,S,9,,"Yoevil, England / Cottage Grove, OR"
+Third Class,0,"Oreskovic, Miss. Marija",female,20.0,0,0,315096,8.6625,,S,,,
+Second Class,0,"Ashby, Mr. John",male,57.0,0,0,244346,13.0,,S,,,"West Hoboken, NJ"
+Third Class,0,"Elias, Mr. Joseph",male,39.0,0,2,2675,7.2292,,C,,,"Syria Ottawa, ON"
+First Class,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44.0,0,1,111361,57.9792,B18,C,4,,"Chicago, IL"
+Third Class,1,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24.0,0,2,PP 9549,16.7,G6,S,13,,
+Third Class,0,"Nilsson, Mr. August Ferdinand",male,21.0,0,0,350410,7.8542,,S,,,
+Third Class,0,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q,,,
+Second Class,0,"Gale, Mr. Shadrach",male,34.0,1,0,28664,21.0,,S,,,"Cornwall / Clear Creek, CO"
+Second Class,0,"Clarke, Mr. Charles Valentine",male,29.0,1,0,2003,26.0,,S,,,"England / San Francisco, CA"
+Second Class,1,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45.0,1,1,F.C.C. 13529,26.25,,S,14,,"Ilford, Essex / Winnipeg, MB"
+First Class,0,"Julian, Mr. Henry Forbes",male,50.0,0,0,113044,26.0,E60,S,,,London
+Third Class,0,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q,,171.0,
+Third Class,0,"Andersson, Miss. Ellis Anna Maria",female,2.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+Third Class,1,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q,,,
+Third Class,0,"Boulos, Master. Akar",male,6.0,1,1,2678,15.2458,,C,,,"Syria Kent, ON"
+Second Class,1,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+Third Class,0,"Ling, Mr. Lee",male,28.0,0,0,1601,56.4958,,S,,,
+First Class,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48.0,0,0,17466,25.9292,D17,S,8,,"Brooklyn, NY"
+First Class,1,"Goldenberg, Mr. Samuel L",male,49.0,1,0,17453,89.1042,C92,C,5,,"Paris, France / New York, NY"
+Third Class,0,"Sage, Mr. John George",male,,1,9,CA. 2343,69.55,,S,,,
+Third Class,0,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C,,312.0,
+Third Class,0,"Salonen, Mr. Johan Werner",male,39.0,0,0,3101296,7.925,,S,,,
+Third Class,0,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.225,,C,,304.0,
+First Class,0,"Harris, Mr. Henry Birkhardt",male,45.0,1,0,36973,83.475,C83,S,,,"New York, NY"
+Third Class,1,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C,C,,
+Third Class,0,"Palsson, Master. Gosta Leonard",male,2.0,3,1,349909,21.075,,S,,4.0,
+First Class,0,"Penasco y Castellana, Mr. Victor de Satode",male,18.0,1,0,PC 17758,108.9,C65,C,,,"Madrid, Spain"
+Third Class,0,"Eklund, Mr. Hans Linus",male,16.0,0,0,347074,7.775,,S,,,"Karberg, Sweden Jerome Junction, AZ"
+Third Class,1,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C,D,,
+Second Class,0,"Enander, Mr. Ingvar",male,21.0,0,0,236854,13.0,,S,,,"Goteborg, Sweden / Rockford, IL"
+Third Class,0,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q,,,
+Third Class,0,"Arnold-Franchi, Mr. Josef",male,25.0,1,0,349237,17.8,,S,,,"Altdorf, Switzerland"
+Third Class,0,"Elias, Mr. Tannous",male,15.0,1,1,2695,7.2292,,C,,,Syria
+First Class,0,"Fortune, Mr. Charles Alexander",male,19.0,3,2,19950,263.0,C23 C25 C27,S,,,"Winnipeg, MB"
+Second Class,0,"Brown, Mr. Thomas William Solomon",male,60.0,1,1,29750,39.0,,S,,,"Cape Town, South Africa / Seattle, WA"
+Third Class,1,"Karun, Miss. Manca",female,4.0,0,1,349256,13.4167,,C,15,,
+Third Class,1,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)",female,38.0,0,0,2688,7.2292,,C,C,,
+Third Class,0,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C,,,
+Second Class,1,"Caldwell, Mr. Albert Francis",male,26.0,1,1,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+Third Class,0,"Betros, Master. Seman",male,,0,0,2622,7.2292,,C,,,
+Second Class,1,"Mellinger, Miss. Madeleine Violet",female,13.0,0,1,250644,19.5,,S,14,,"England / Bennington, VT"
+Third Class,0,"Davies, Mr. Evan",male,22.0,0,0,SC/A4 23568,8.05,,S,,,
+First Class,0,"Evans, Miss. Edith Corse",female,36.0,0,0,PC 17531,31.6792,A29,C,,,"New York, NY"
+Third Class,0,"Ford, Mr. William Neal",male,16.0,1,3,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+Second Class,1,"Davies, Master. John Morgan Jr",male,8.0,1,1,C.A. 33112,36.75,,S,14,,"St Ives, Cornwall / Hancock, MI"
+Third Class,1,"Najib, Miss. Adele Kiamie ""Jane""",female,15.0,0,0,2667,7.225,,C,C,,
+First Class,0,"Taussig, Mr. Emil",male,52.0,1,1,110413,79.65,E67,S,,,"New York, NY"
+Third Class,1,"Aks, Mrs. Sam (Leah Rosen)",female,18.0,0,1,392091,9.35,,S,13,,"London, England Norfolk, VA"
+Second Class,0,"Baimbrigge, Mr. Charles Robert",male,-23.0,0,0,C.A. 31030,10.5,,S,,,Guernsey
+First Class,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49.0,1,0,PC 17485,56.9292,A20,C,1,,London / Paris
+Third Class,0,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q,,,
+First Class,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62.0,0,0,113572,80.0,B28,,6,,"Cincinatti, OH"
+Second Class,1,"Slayter, Miss. Hilda Mary",female,30.0,0,0,234818,12.35,,Q,13,,"Halifax, NS"
+Third Class,0,"Braund, Mr. Lewis Richard",male,29.0,1,0,3460,7.0458,,S,,,"Bridgerule, Devon"
+Second Class,0,"Phillips, Mr. Escott Robert",male,43.0,0,1,S.O./P.P. 2,21.0,,S,,,"Ilfracombe, Devon"
+Third Class,0,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S,,,
+Third Class,0,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q,,,
+Third Class,1,"Touma, Master. Georges Youssef",male,7.0,1,1,2650,15.2458,,C,C,,
+Third Class,0,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S,,,
+First Class,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35.0,1,0,19943,90.0,C93,S,D,,"New York, NY /  Stamford CT"
+Second Class,0,"Hood, Mr. Ambrose Jr",male,21.0,0,0,S.O.C. 14879,73.5,,S,,,"New Forest, England"
+Second Class,0,"Pain, Dr. Alfred",male,23.0,0,0,244278,10.5,,S,,,"Hamilton, ON"
+First Class,1,"Gibson, Miss. Dorothy Winifred",female,22.0,0,1,112378,59.4,,C,7,,"New York, NY"
+Third Class,0,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S,,,
+Third Class,1,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22.0,1,0,347072,13.9,,S,16,,"West Haven, CT"
+Second Class,1,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,C,,,"New York, NY"
+Third Class,0,"Cacic, Miss. Manda",female,21.0,0,0,315087,8.6625,,S,,,
+First Class,0,"Long, Mr. Milton Clyde",male,29.0,0,0,113501,30.0,D6,S,,126.0,"Springfield, MA"
+First Class,1,"Frauenthal, Dr. Henry William",male,50.0,2,0,PC 17611,133.65,,S,5,,"New York, NY"
+Second Class,0,"Jefferys, Mr. Clifford Thomas",male,24.0,2,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+First Class,0,"Brandeis, Mr. Emil",male,48.0,0,0,PC 17591,50.4958,B10,C,,208.0,"Omaha, NE"
+Third Class,0,"Hassan, Mr. Houssein G N",male,11.0,0,0,2699,18.7875,,C,,,
+Third Class,1,"Honkanen, Miss. Eliina",female,27.0,0,0,STON/O2. 3101283,7.925,,S,,,
+Third Class,1,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,1,5,347077,31.3875,,S,15,,"Sweden  Worcester, MA"
+First Class,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17.0,1,0,17474,57.0,B20,S,3,,"Calgary, AB"
+Third Class,0,"Nancarrow, Mr. William Henry",male,33.0,0,0,A./5. 3338,8.05,,S,,,
+Third Class,1,"Finoli, Mr. Luigi",male,,0,0,SOTON/O.Q. 3101308,7.05,,S,15,,"Italy Philadelphia, PA"
+Second Class,1,"Troutt, Miss. Edwina Celia ""Winnie""",female,27.0,0,0,34218,10.5,E101,S,16,,"Bath, England / Massachusetts"
+Second Class,0,"Wheadon, Mr. Edward H",male,66.0,0,0,C.A. 24579,10.5,,S,,,"Guernsey, England / Edgewood, RI"
+Third Class,1,"Duquemin, Mr. Joseph",male,24.0,0,0,S.O./P.P. 752,7.55,,S,D,,"England Albion, NY"
+Third Class,1,"Vartanian, Mr. David",male,22.0,0,0,2658,7.225,,C,13 15,,
+Third Class,1,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q,16,,
+Third Class,1,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+Third Class,0,"Holm, Mr. John Fredrik Alexander",male,43.0,0,0,C 7075,6.45,,S,,,
+Second Class,1,"Cook, Mrs. (Selena Rogers)",female,22.0,0,0,W./C. 14266,10.5,F33,S,14,,Pennsylvania
+Second Class,1,"Parrish, Mrs. (Lutie Davis)",female,50.0,0,1,230433,26.0,,S,12,,"Woodford County, KY"
+First Class,1,"Chambers, Mr. Norman Campbell",male,27.0,1,0,113806,53.1,E8,S,5,,"New York, NY / Ithaca, NY"
+Third Class,1,"Turkula, Mrs. (Hedwig)",female,63.0,0,0,4134,9.5875,,S,15,,
+First Class,1,"Ismay, Mr. Joseph Bruce",male,49.0,0,0,112058,0.0,B52 B54 B56,S,C,,Liverpool
+First Class,0,"Dulles, Mr. William Crothers",male,39.0,0,0,PC 17580,29.7,A18,C,,133.0,"Philadelphia, PA"
+First Class,1,"Fortune, Miss. Alice Elizabeth",female,24.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+Third Class,0,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S,,,
+Third Class,0,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q,,,
+Third Class,0,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C,,,
+Second Class,1,"Bentham, Miss. Lilian W",female,19.0,0,0,28404,13.0,,S,12,,"Rochester, NY"
+Third Class,0,"Angheloff, Mr. Minko",male,26.0,0,0,349202,7.8958,,S,,,"Bulgaria Chicago, IL"
+First Class,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S,D,,"London, England"
+Third Class,0,"Peacock, Miss. Treasteall",female,3.0,1,1,SOTON/O.Q. 3101315,13.775,,S,,,
+Third Class,1,"Thomas, Master. Assad Alexander",male,0.4167,0,1,2625,8.5167,,C,16,,
+Third Class,0,"Gilinski, Mr. Eliezer",male,22.0,0,0,14973,8.05,,S,,47.0,
+Third Class,0,"Dika, Mr. Mirko",male,17.0,0,0,349232,7.8958,,S,,,
+First Class,0,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C,,45.0,
+First Class,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43.0,1,0,11778,55.4417,C116,C,5,,"Newark, NJ"
+Second Class,1,"Brown, Miss. Amelia ""Mildred""",female,24.0,0,0,248733,13.0,F33,S,11,,"London / Montreal, PQ"
+Second Class,1,"Williams, Mr. Charles Eugene",male,,0,0,244373,13.0,,S,14,,"Harrow, England"
+First Class,0,"Giglio, Mr. Victor",male,24.0,0,0,PC 17593,79.2,B86,C,,,
+Third Class,0,"Goncalves, Mr. Manuel Estanslas",male,38.0,0,0,SOTON/O.Q. 3101306,7.05,,S,,,Portugal
+Third Class,0,"Stankovic, Mr. Ivan",male,33.0,0,0,349239,8.6625,,C,,,
+Third Class,0,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19.0,0,0,348124,7.65,F G73,S,,,
+Second Class,0,"Byles, Rev. Thomas Roussel Davids",male,42.0,0,0,244310,13.0,,S,,,London
+First Class,0,"White, Mr. Richard Frasar",male,21.0,0,1,35281,77.2875,D26,S,,169.0,"Brunswick, ME"
+Second Class,0,"McKane, Mr. Peter David",male,46.0,0,0,28403,26.0,,S,,,"Rochester, NY"
+Third Class,0,"Rice, Master. Albert",male,10.0,4,1,382652,29.125,,Q,,,
+Second Class,0,"Davies, Mr. Charles Henry",male,18.0,0,0,S.O.C. 14879,73.5,,S,,,"Lyndhurst, England"
+First Class,1,"Newell, Miss. Marjorie",female,23.0,1,0,35273,113.275,D36,C,6,,"Lexington, MA"
+Third Class,1,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22.0,1,1,3101298,12.2875,,S,15,,
+Third Class,0,"Baccos, Mr. Raffull",male,20.0,0,0,2679,7.225,,C,,,
+First Class,1,"Geiger, Miss. Amalie",female,35.0,0,0,113503,211.5,C130,C,4,,
+Third Class,0,"Asplund, Master. Clarence Gustaf Hugo",male,9.0,4,2,347077,31.3875,,S,,,"Sweden Worcester, MA"
+Second Class,0,"Myles, Mr. Thomas Francis",male,62.0,0,0,240276,9.6875,,Q,,,"Cambridge, MA"
+Third Class,0,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S,,,
+Second Class,0,"Laroche, Mr. Joseph Philippe Lemercier",male,25.0,1,2,SC/Paris 2123,41.5792,,C,,,Paris / Haiti
+Second Class,1,"Pinsky, Mrs. (Rosa)",female,32.0,0,0,234604,13.0,,S,9,,Russia
+Third Class,1,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33.0,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+Third Class,0,"Larsson, Mr. Bengt Edvin",male,29.0,0,0,347067,7.775,,S,,,
+First Class,1,"Bazzani, Miss. Albina",female,32.0,0,0,11813,76.2917,D15,C,8,,
+First Class,1,"Lesurer, Mr. Gustave J",male,35.0,0,0,PC 17755,512.3292,B101,C,3,,
+First Class,0,"Douglas, Mr. Walter Donald",male,50.0,1,0,PC 17761,106.425,C86,C,,62.0,"Deephaven, MN / Cedar Rapids, IA"
+Third Class,0,"Panula, Mr. Jaako Arnold",male,14.0,4,1,3101295,39.6875,,S,,,
+Second Class,1,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36.0,1,0,226875,26.0,,S,11,,"Warwick, England"
+Third Class,0,"Windelov, Mr. Einar",male,21.0,0,0,SOTON/OQ 3101317,7.25,,S,,,
+First Class,1,"Tucker, Mr. Gilbert Milligan Jr",male,31.0,0,0,2543,28.5375,C53,C,7,,"Albany, NY"
+Third Class,0,"Strom, Miss. Telma Matilda",female,2.0,0,1,347054,10.4625,G6,S,,,
+First Class,1,"Newell, Miss. Madeleine",female,31.0,1,0,35273,113.275,D36,C,6,,"Lexington, MA"
+First Class,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S,7,,"New York, NY"
+First Class,1,"Crosby, Miss. Harriet R",female,36.0,0,2,WE/P 5735,71.0,B22,S,7,,"Milwaukee, WI"
+Second Class,1,"Davis, Miss. Mary",female,28.0,0,0,237668,13.0,,S,13,,"London / Staten Island, NY"
+Third Class,0,"Pasic, Mr. Jakob",male,21.0,0,0,315097,8.6625,,S,,,
+Second Class,1,"Pallas y Castello, Mr. Emilio",male,29.0,0,0,SC/PARIS 2147,13.8583,,C,9,,"Spain / Havana, Cuba"
+First Class,0,"Spencer, Mr. William Augustus",male,57.0,1,0,PC 17569,146.5208,B78,C,,,"Paris, France"
+Third Class,0,"Hakkarainen, Mr. Pekka Pietari",male,28.0,1,0,STON/O2. 3101279,15.85,,S,,,
+Third Class,0,"Oreskovic, Mr. Luka",male,20.0,0,0,315094,8.6625,,S,,,
+First Class,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76.0,1,0,19877,78.85,C46,S,6,,"Little Onn Hall, Staffs"
+First Class,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58.0,0,1,PC 17582,153.4625,C125,S,3,,"Greenwich, CT"
+Third Class,0,"Adahl, Mr. Mauritz Nils Martin",male,30.0,0,0,C 7076,7.25,,S,,72.0,"Asarum, Sweden Brooklyn, NY"
+Third Class,1,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q,D,,
+Third Class,0,"Laitinen, Miss. Kristina Sofia",female,37.0,0,0,4135,9.5875,,S,,,
+First Class,0,"Cairns, Mr. Alexander",male,,0,0,113798,31.0,,S,,,
+First Class,1,"Gracie, Col. Archibald IV",male,53.0,0,0,113780,28.5,C51,C,B,,"Washington, DC"
+Second Class,1,"Sinkkonen, Miss. Anna",female,30.0,0,0,250648,13.0,,S,10,,"Finland / Washington, DC"
+Third Class,1,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C,,,
+First Class,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56.0,0,1,11767,83.1583,C50,C,7,,"Mt Airy, Philadelphia, PA"
+Third Class,0,"Johanson, Mr. Jakob Alfred",male,34.0,0,0,3101264,6.4958,,S,,143.0,
+First Class,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,S,D,,"Scituate, MA"
+Third Class,0,"Skoog, Master. Harald",male,4.0,3,2,347088,27.9,,S,,,
+Second Class,0,"Hickman, Mr. Stanley George",male,,2,0,S.O.C. 14879,73.5,,S,,,"West Hampstead, London / Neepawa, MB"
+First Class,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64.0,1,1,112901,26.55,B26,S,7,,"Milwaukee, WI"
+Second Class,0,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29.0,1,0,SC/AH 29037,26.0,,S,,,"Cornwall / Spokane, WA"
+Second Class,0,"Parker, Mr. Clifford Richard",male,28.0,0,0,SC 14888,10.5,,S,,,"St Andrews, Guernsey"
+Third Class,0,"Svensson, Mr. Johan",male,74.0,0,0,347060,7.775,,S,,,
+First Class,0,"Payne, Mr. Vivian Ponsonby",male,23.0,0,0,12749,93.5,B24,S,,,"Montreal, PQ"
+Second Class,0,"Giles, Mr. Frederick Edward",male,21.0,1,0,28134,11.5,,S,,,"Cornwall / Camden, NJ"
+First Class,1,"Sloper, Mr. William Thompson",male,28.0,0,0,113788,35.5,A6,S,7,,"New Britain, CT"
+First Class,1,"Oliva y Ocana, Dona. Fermina",female,39.0,0,0,PC 17758,108.9,C105,C,8,,
+Second Class,0,"Malachard, Mr. Noel",male,,0,0,237735,15.0458,D,C,,,Paris
+Third Class,0,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q,,,
+Third Class,1,"Tornquist, Mr. William Henry",male,25.0,0,0,LINE,0.0,,S,15,,
+Second Class,0,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0.0,,S,,,Belfast
+Third Class,0,"Nysveen, Mr. Johan Hansen",male,61.0,0,0,345364,6.2375,,S,,,
+Third Class,1,"Baclini, Miss. Marie Catherine",female,5.0,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+First Class,0,"Graham, Mr. George Edward",male,38.0,0,1,PC 17582,153.4625,C91,S,,147.0,"Winnipeg, MB"
+Second Class,0,"Matthews, Mr. William John",male,30.0,0,0,28228,13.0,,S,,,"St Austall, Cornwall"
+Second Class,1,"Hamalainen, Master. Viljo",male,0.6667000000000001,1,1,250649,14.5,,S,4,,"Detroit, MI"
+Third Class,0,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S,,,
+First Class,0,"Maguire, Mr. John Edward",male,30.0,0,0,110469,26.0,C106,S,,,"Brockton, MA"
+Second Class,0,"Levy, Mr. Rene Jacques",male,36.0,0,0,SC/Paris 2163,12.875,D,C,,,"Montreal, PQ"
+Third Class,0,"Yasbeck, Mr. Antoni",male,27.0,1,0,2659,14.4542,,C,C,,
+First Class,0,"Carrau, Mr. Jose Pedro",male,17.0,0,0,113059,47.1,,S,,,"Montevideo, Uruguay"
+Third Class,0,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q,,299.0,"Co Cork, Ireland Roxbury, MA"
+Third Class,1,"Emanuel, Miss. Virginia Ethel",female,5.0,0,0,364516,12.475,,S,13,,"New York, NY"
+Third Class,0,"Ali, Mr. William",male,25.0,0,0,SOTON/O.Q. 3101312,7.05,,S,,79.0,Argentina
+Third Class,0,"Saad, Mr. Khalil",male,25.0,0,0,2672,7.225,,C,,,
+First Class,0,"Thayer, Mr. John Borland",male,49.0,1,1,17421,110.8833,C68,C,,,"Haverford, PA"
+Third Class,0,"Cor, Mr. Ivan",male,27.0,0,0,349229,7.8958,,S,,,Austria
+Third Class,0,"Vander Planke, Miss. Augusta Maria",female,18.0,2,0,345764,18.0,,S,,,
+Third Class,1,"Niskanen, Mr. Juha",male,39.0,0,0,STON/O 2. 3101289,7.925,,S,9,,
+Third Class,0,"Ford, Miss. Robina Maggie ""Ruby""",female,9.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+Third Class,0,"Klasen, Mr. Klas Albin",male,18.0,1,1,350404,7.8542,,S,,,
+Second Class,0,"Montvila, Rev. Juozas",male,27.0,0,0,211536,13.0,,S,,,"Worcester, MA"
+Third Class,0,"Rekic, Mr. Tido",male,38.0,0,0,349249,7.8958,,S,,,
+Third Class,0,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C,,,
+First Class,1,"Taussig, Miss. Ruth",female,18.0,0,2,110413,79.65,E68,S,8,,"New York, NY"
+First Class,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52.0,1,1,12749,93.5,B69,S,3,,"Montreal, PQ"
+Third Class,0,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30.0,1,1,345773,24.15,,S,,,
+Third Class,1,"Connolly, Miss. Kate",female,22.0,0,0,370373,7.75,,Q,13,,Ireland
+First Class,1,"Dodge, Master. Washington",male,4.0,0,2,33638,81.8583,A34,S,5,,"San Francisco, CA"
+First Class,0,"Wick, Mr. George Dennick",male,57.0,1,1,36928,164.8667,,S,,,"Youngstown, OH"
+Third Class,0,"Reynolds, Mr. Harold J",male,21.0,0,0,342684,8.05,,S,,,
+Second Class,0,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0.0,,S,,,Belfast
+First Class,1,"Flynn, Mr. John Irwin (""Irving"")",male,36.0,0,0,PC 17474,26.3875,E25,S,5,,"Brooklyn, NY"
+Third Class,0,"Calic, Mr. Petar",male,17.0,0,0,315086,8.6625,,S,,,
+Third Class,0,"Drazenoic, Mr. Jozef",male,33.0,0,0,349241,7.8958,,C,,51.0,"Austria Niagara Falls, NY"
+Second Class,1,"Oxenham, Mr. Percy Thomas",male,22.0,0,0,W./C. 14260,10.5,,S,13,,"Pondersend, England / New Durham, NJ"
+Second Class,0,"Harbeck, Mr. William H",male,44.0,0,0,248746,13.0,,S,,35.0,"Seattle, WA / Toledo, OH"
+First Class,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27.0,1,2,F.C. 12750,52.0,B71,S,3,,"Montreal, PQ"
+Third Class,0,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q,,,
+Third Class,0,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q,,70.0,
+Third Class,1,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q,14,,
+Third Class,0,"Vander Planke, Mr. Leo Edmondus",male,16.0,2,0,345764,18.0,,S,,,
+Third Class,1,"Barah, Mr. Hanna Assi",male,20.0,0,0,2663,7.2292,,C,15,,
+Second Class,1,"Hamalainen, Mrs. William (Anna)",female,24.0,0,2,250649,14.5,,S,4,,"Detroit, MI"
+First Class,0,"Cavendish, Mr. Tyrell William",male,36.0,1,0,19877,78.85,C46,S,,172.0,"Little Onn Hall, Staffs"
+Third Class,0,"Lockyer, Mr. Edward",male,,0,0,1222,7.8792,,S,,153.0,
+Second Class,1,"Drew, Master. Marshall Brines",male,8.0,0,2,28220,32.5,,S,10,,"Greenport, NY"
+Third Class,0,"Andersson, Mr. Johan Samuel",male,26.0,0,0,347075,7.775,,S,,,"Hartford, CT"
+Second Class,0,"Veal, Mr. James",male,40.0,0,0,28221,13.0,,S,,,"Barre, Co Washington, VT"
+Third Class,1,"Gilnagh, Miss. Katherine ""Katie""",female,16.0,0,0,35851,7.7333,,Q,16,,"Co Longford, Ireland New York, NY"
+Second Class,1,"Hold, Mrs. Stephen (Annie Margaret Hill)",female,29.0,1,0,26707,26.0,,S,10,,"England / Sacramento, CA"
+First Class,1,"Carter, Mr. William Ernest",male,36.0,1,2,113760,120.0,B96 B98,S,C,,"Bryn Mawr, PA"
+First Class,0,"Nicholson, Mr. Arthur Ernest",male,64.0,0,0,693,26.0,,S,,263.0,"Isle of Wight, England"
+Second Class,0,"Rogers, Mr. Reginald Harry",male,19.0,0,0,28004,10.5,,S,,,
+First Class,0,"Smith, Mr. Lucien Philip",male,24.0,1,0,13695,60.0,C31,S,,,"Huntington, WV"
+Third Class,1,"Buckley, Mr. Daniel",male,21.0,0,0,330920,7.8208,,Q,13,,"Kingwilliamstown, Co Cork, Ireland New York, NY"
+Third Class,0,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q,,,
+Second Class,0,"Gillespie, Mr. William Henry",male,34.0,0,0,12233,13.0,,S,,,"Vancouver, BC"
+Second Class,1,"Reynaldo, Ms. Encarnacion",female,28.0,0,0,230434,13.0,,S,9,,Spain
+Third Class,0,"Waelens, Mr. Achille",male,22.0,0,0,345767,9.0,,S,,,"Antwerp, Belgium / Stanton, OH"
+Third Class,0,"Sivic, Mr. Husein",male,40.0,0,0,349251,7.8958,,S,,,
+Second Class,1,"Lehmann, Miss. Bertha",female,17.0,0,0,SC 1748,12.0,,C,12,,"Berne, Switzerland / Central City, IA"
+Third Class,1,"Wilkes, Mrs. James (Ellen Needs)",female,47.0,1,0,363272,7.0,,S,,,
+Third Class,0,"Panula, Master. Eino Viljami",male,1.0,4,1,3101295,39.6875,,S,,,
+Third Class,0,"Morley, Mr. William",male,34.0,0,0,364506,8.05,,S,,,
+Third Class,0,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S,,,
+First Class,1,"Harder, Mr. George Achilles",male,25.0,1,0,11765,55.4417,E50,C,5,,"Brooklyn, NY"
+Third Class,0,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S,,,
+Second Class,0,"Drew, Mr. James Vivian",male,42.0,1,1,28220,32.5,,S,,,"Greenport, NY"
+First Class,1,"Blank, Mr. Henry",male,40.0,0,0,112277,31.0,A31,C,7,,"Glen Ridge, NJ"
+Third Class,0,"Mineff, Mr. Ivan",male,24.0,0,0,349233,7.8958,,S,,,
+Third Class,0,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S,,,
+Third Class,0,"Risien, Mrs. Samuel (Emma)",female,,0,0,364498,14.5,,S,,,
+Third Class,0,"Coleff, Mr. Satio",male,24.0,0,0,349209,7.4958,,S,,,
+First Class,1,"McGough, Mr. James Robert",male,36.0,0,0,PC 17473,26.2875,E25,S,7,,"Philadelphia, PA"
+Third Class,0,"Hagardon, Miss. Kate",female,17.0,0,0,AQ/3. 30631,7.7333,,Q,,,
+Third Class,0,"Goldsmith, Mr. Nathan",male,41.0,0,0,SOTON/O.Q. 3101263,7.85,,S,,,"Philadelphia, PA"
+First Class,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22.0,1,0,113776,66.6,C2,S,8,,"Isleworth, England"
+First Class,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S,3,,"Manchester, England"
+First Class,0,"Carrau, Mr. Francisco M",male,28.0,0,0,113059,47.1,,S,,,"Montevideo, Uruguay"
+First Class,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35.0,0,0,111426,26.55,,C,15,,"Indianapolis, IN"
+Second Class,1,"Herman, Miss. Kate",female,24.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+First Class,1,"Longley, Miss. Gretchen Fiske",female,21.0,0,0,13502,77.9583,D9,S,10,,"Hudson, NY"
+Third Class,1,"Dean, Master. Bertram Vere",male,1.0,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+First Class,0,"Ringhini, Mr. Sante",male,22.0,0,0,PC 17760,135.6333,,C,,232.0,
+Second Class,0,"Carbines, Mr. William",male,19.0,0,0,28424,13.0,,S,,18.0,"St Ives, Cornwall / Calumet, MI"
+Third Class,1,"Hyman, Mr. Abraham",male,,0,0,3470,7.8875,,S,C,,
+First Class,0,"Widener, Mr. Harry Elkins",male,27.0,0,2,113503,211.5,C82,C,,,"Elkins Park, PA"
+Third Class,1,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C,C,,
+First Class,1,"Fortune, Miss. Ethel Flora",female,28.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+Second Class,1,"Sincock, Miss. Maude",female,20.0,0,0,C.A. 33112,36.75,,S,11,,"Cornwall / Hancock, MI"
+Third Class,0,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q,,,
+Third Class,0,"McNeill, Miss. Bridget",female,,0,0,370368,7.75,,Q,,,
+Third Class,0,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C,,,
+Third Class,1,"Johnson, Miss. Eleanor Ileen",female,1.0,1,1,347742,11.1333,,S,15,,
+Third Class,0,"Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)",female,30.0,1,0,349910,15.55,,S,A,,
+Third Class,1,"Coutts, Master. William Loch ""William""",male,3.0,1,1,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+Third Class,0,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S,,,
+Second Class,0,"Stanton, Mr. Samuel Ward",male,41.0,0,0,237734,15.0458,,C,,,"New York, NY"
+Second Class,1,"Navratil, Master. Edmond Roger",male,2.0,1,1,230080,26.0,F2,S,D,,"Nice, France"
+Third Class,1,"Hirvonen, Miss. Hildur E",female,2.0,0,1,3101298,12.2875,,S,15,,
+Third Class,0,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q,,,
+Third Class,0,"Calic, Mr. Jovo",male,17.0,0,0,315093,8.6625,,S,,,
+Third Class,0,"Mahon, Miss. Bridget Delia",female,,0,0,330924,7.8792,,Q,,,
+Third Class,1,"Olsson, Mr. Oscar Wilhelm",male,32.0,0,0,347079,7.775,,S,A,,
+First Class,1,"Chevre, Mr. Paul Romaine",male,45.0,0,0,PC 17594,29.7,A9,C,7,,"Paris, France"
+First Class,1,"Kreuchen, Miss. Emilie",female,39.0,0,0,24160,211.3375,,S,2,,
+Second Class,1,"Ball, Mrs. (Ada E Hall)",female,36.0,0,0,28551,13.0,D,S,10,,"Bristol, Avon / Jacksonville, FL"
+Second Class,1,"Beane, Mrs. Edward (Ethel Clarke)",female,19.0,1,0,2908,26.0,,S,13,,"Norwich / New York, NY"
+Second Class,0,"Kirkland, Rev. Charles Leonard",male,57.0,0,0,219533,12.35,,Q,,,"Glasgow / Bangor, ME"
+Third Class,0,"Andersson, Miss. Sigrid Elisabeth",female,11.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+Third Class,0,"Everett, Mr. Thomas James",male,40.5,0,0,C.A. 6212,15.1,,S,,187.0,
+First Class,0,"Compton, Mr. Alexander Taylor Jr",male,37.0,1,1,PC 17756,83.1583,E52,C,,,"Lakewood, NJ"
+Third Class,0,"Cacic, Mr. Luka",male,38.0,0,0,315089,8.6625,,S,,,Croatia
+Second Class,0,"Gavey, Mr. Lawrence",male,26.0,0,0,31028,10.5,,S,,,"Guernsey / Elizabeth, NJ"
+First Class,0,"Isham, Miss. Ann Elizabeth",female,-50.0,0,0,PC 17595,28.7125,C49,C,,,"Paris, France New York, NY"
+Third Class,1,"Dowdell, Miss. Elizabeth",female,30.0,0,0,364516,12.475,,S,13,,"Union Hill, NJ"
+Third Class,0,"Vande Velde, Mr. Johannes Joseph",male,33.0,0,0,345780,9.5,,S,,,
+Third Class,1,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S,B,,
+Third Class,1,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q,,,
+First Class,1,"Thayer, Mr. John Borland Jr",male,17.0,0,2,17421,110.8833,C70,C,B,,"Haverford, PA"
+Second Class,0,"Gale, Mr. Harry",male,38.0,1,0,28664,21.0,,S,,,"Cornwall / Clear Creek, CO"
+Second Class,1,"Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ",female,48.0,0,2,C.A. 33112,36.75,,S,14,,"St Ives, Cornwall / Hancock, MI"
+Third Class,0,"Rush, Mr. Alfred George John",male,16.0,0,0,A/4. 20589,8.05,,S,,,
+Second Class,1,"Smith, Miss. Marion Elsie",female,40.0,0,0,31418,13.0,,S,9,,
+First Class,1,"Carter, Master. William Thornton II",male,11.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+Second Class,0,"Bowenur, Mr. Solomon",male,42.0,0,0,211535,13.0,,S,,,London
+First Class,0,"Beattie, Mr. Thomson",male,36.0,0,0,13050,75.2417,C6,C,A,,"Winnipeg, MN"
+Third Class,0,"Maenpaa, Mr. Matti Alexanteri",male,22.0,0,0,STON/O 2. 3101275,7.125,,S,,,
+Second Class,1,"Phillips, Miss. Alice Frances Louisa",female,21.0,0,1,S.O./P.P. 2,21.0,,S,12,,"Ilfracombe, Devon"
+Third Class,1,"Coutts, Mrs. William (Winnie ""Minnie"" Treanor)",female,36.0,0,2,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+First Class,0,"Carlsson, Mr. Frans Olof",male,33.0,0,0,695,5.0,B51 B53 B55,S,,,"New York, NY"
+Second Class,0,"Banfield, Mr. Frederick James",male,28.0,0,0,C.A./SOTON 34068,10.5,,S,,,"Plymouth, Dorset / Houghton, MI"
+Third Class,0,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S,,,
+First Class,1,"Cherry, Miss. Gladys",female,30.0,0,0,110152,86.5,B77,S,8,,"London, England"
+Third Class,1,"Johnson, Master. Harold Theodor",male,,1,1,347742,11.1333,,S,15,,
+Third Class,1,"Nysten, Miss. Anna Sofia",female,22.0,0,0,347081,7.75,,S,13,,
+Third Class,0,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S,,,
+Third Class,0,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S,,,
+First Class,1,"Wilson, Miss. Helen Alice",female,31.0,0,0,16966,134.5,E39 E41,C,3,,
+First Class,1,"Serepeca, Miss. Augusta",female,30.0,0,0,113798,31.0,,C,4,,
+Second Class,1,"Nourney, Mr. Alfred (""Baron von Drachstedt"")",male,20.0,0,0,SC/PARIS 2166,13.8625,D38,C,7,,"Cologne, Germany"
+First Class,1,"Wick, Miss. Mary Natalie",female,31.0,0,2,36928,164.8667,C7,S,8,,"Youngstown, OH"
+First Class,0,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35.0,C128,S,,,"London, England"
+Third Class,1,"Carr, Miss. Helen ""Ellen""",female,16.0,0,0,367231,7.75,,Q,16,,"Co Longford, Ireland New York, NY"
+Third Class,0,"Bowen, Mr. David John ""Dai""",male,21.0,0,0,54636,16.1,,S,,,"Treherbert, Cardiff, Wales"
+First Class,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C,D,,"New York, NY"
+First Class,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30.0,D45,S,3,,"Kingston, Surrey"
+Third Class,0,"Ibrahim Shawah, Mr. Yousseff",male,30.0,0,0,2685,7.2292,,C,,,
+Third Class,0,"Markoff, Mr. Marin",male,35.0,0,0,349213,7.8958,,C,,,
+First Class,0,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S,,,"Brighton, MA"
+Third Class,0,"Lennon, Miss. Mary",female,,1,0,370371,15.5,,Q,,,
+Third Class,0,"Davies, Mr. Alfred J",male,24.0,2,0,A/4 48871,24.15,,S,,,"West Bromwich, England Pontiac, MI"
+Second Class,1,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28.0,1,0,2003,26.0,,S,14,,"England / San Francisco, CA"
+Third Class,0,"Salander, Mr. Karl Johan",male,24.0,0,0,7266,9.325,,S,,,
+Third Class,0,"Dintcheff, Mr. Valtcho",male,43.0,0,0,349226,7.8958,,S,,,
+Third Class,0,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29.0,0,4,349909,21.075,,S,,206.0,
+First Class,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,1,0,PC 17572,76.7292,D33,C,3,,"New York, NY"
+Second Class,1,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25.0,0,1,230433,26.0,,S,12,,"Deer Lodge, MT"
+First Class,1,"Anderson, Mr. Harry",male,48.0,0,0,19952,26.55,E12,S,3,,"New York, NY"
+Third Class,0,"Willer, Mr. Aaron (""Abi Weller"")",male,,0,0,3410,8.7125,,S,,,
+Third Class,1,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24.0,1,0,STON/O2. 3101279,15.85,,S,15,,
+Second Class,1,"Bystrom, Mrs. (Karolina)",female,42.0,0,0,236852,13.0,,S,,,"New York, NY"
+Third Class,1,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+First Class,0,"Lindeberg-Lind, Mr. Erik Gustaf (""Mr Edward Lingrey"")",male,42.0,0,0,17475,26.55,,S,,,"Stockholm, Sweden"
+First Class,1,"Cleaver, Miss. Alice",female,22.0,0,0,113781,151.55,,S,11,,
+First Class,0,"White, Mr. Percival Wayland",male,54.0,0,1,35281,77.2875,D26,S,,,"Brunswick, ME"
+Third Class,1,"Smyth, Miss. Julia",female,,0,0,335432,7.7333,,Q,13,,
+Second Class,1,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33.0,0,2,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+Third Class,0,"Coelho, Mr. Domingos Fernandeo",male,20.0,0,0,SOTON/O.Q. 3101307,7.05,,S,,,Portugal
+Second Class,0,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C,,,"New York, NY"
+First Class,1,"Aubart, Mme. Leontine Pauline",female,24.0,0,0,PC 17477,69.3,B35,C,9,,"Paris, France"
+Third Class,1,"Nakid, Mrs. Said (Waika ""Mary"" Mowad)",female,19.0,1,1,2653,15.7417,,C,C,,
+Third Class,0,"Goodwin, Mr. Charles Frederick",male,40.0,1,6,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+Third Class,0,"Fleming, Miss. Honora",female,,0,0,364859,7.75,,Q,,,
+First Class,0,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S,,,"Westcliff-on-Sea, Essex"
+Second Class,1,"Navratil, Master. Michel M",male,3.0,1,1,230080,26.0,F2,S,D,,"Nice, France"
+Third Class,1,"Chip, Mr. Chang",male,32.0,0,0,1601,56.4958,,S,C,,"Hong Kong New York, NY"
+Third Class,1,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q,13,,
+First Class,0,"Rowe, Mr. Alfred G",male,33.0,0,0,113790,26.55,,S,,109.0,London
+Third Class,0,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43.0,1,6,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+First Class,0,"Smart, Mr. John Montgomery",male,56.0,0,0,113792,26.55,,S,,,"New York, NY"
+First Class,0,"Guggenheim, Mr. Benjamin",male,46.0,0,0,PC 17593,79.2,B82 B84,C,,,"New York, NY"
+Third Class,0,"Harmer, Mr. Abraham (David Lishin)",male,25.0,0,0,374887,7.25,,S,B,,
+First Class,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47.0,1,0,W.E.P. 5734,61.175,E31,S,4,,"Amenia, ND"
+First Class,1,"Chaudanson, Miss. Victorine",female,36.0,0,0,PC 17608,262.375,B61,C,4,,
+Third Class,0,"Gustafsson, Mr. Alfred Ossian",male,20.0,0,0,7534,9.8458,,S,,,"Waukegan, Chicago, IL"
+Third Class,1,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q,15,,
+Third Class,0,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S,,,
+First Class,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C,5,,"Paris, France / New York, NY"
+Third Class,0,"Vanden Steen, Mr. Leo Peter",male,28.0,0,0,345783,9.5,,S,,,
+Second Class,1,"Caldwell, Master. Alden Gates",male,0.8333,0,2,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+Second Class,1,"Walcroft, Miss. Nellie",female,31.0,0,0,F.C.C. 13528,21.0,,S,14,,"Mamaroneck, NY"
+First Class,0,"Birnbaum, Mr. Jakob",male,25.0,0,0,13905,26.0,,C,,148.0,"San Francisco, CA"
+Third Class,0,"Pavlovic, Mr. Stefo",male,32.0,0,0,349242,7.8958,,S,,,
+Third Class,1,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S,10,,
+Third Class,0,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S,,,
+Second Class,1,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28.0,1,0,P/PP 3381,24.0,,C,10,,"Russia New York, NY"
+First Class,0,"Colley, Mr. Edward Pomeroy",male,47.0,0,0,5727,25.5875,E58,S,,,"Victoria, BC"
+Third Class,1,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q,16,,
+Third Class,0,"Rice, Mrs. William (Margaret Norton)",female,39.0,0,5,382652,29.125,,Q,,327.0,
+First Class,0,"Reuchlin, Jonkheer. John George",male,38.0,0,0,19972,0.0,,S,,,"Rotterdam, Netherlands"
+Third Class,0,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S,,,
+First Class,1,"Barkworth, Mr. Algernon Henry Wilson",male,80.0,0,0,27042,30.0,A23,S,B,,"Hessle, Yorks"
+First Class,0,"Baxter, Mr. Quigg Edmond",male,24.0,0,1,PC 17558,247.5208,B58 B60,C,,,"Montreal, PQ"
+First Class,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,-59.0,2,0,11769,51.4792,C101,S,D,,"Belmont, MA"
+Third Class,0,"Danbom, Mr. Ernst Gilbert",male,34.0,1,1,347080,14.4,,S,,197.0,"Stanton, IA"
+Third Class,0,"Danoff, Mr. Yoto",male,27.0,0,0,349219,7.8958,,S,,,"Bulgaria Chicago, IL"
+Second Class,0,"Howard, Mrs. Benjamin (Ellen Truelove Arman)",female,60.0,1,0,24065,26.0,,S,,,"Swindon, England"
+First Class,0,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C,,,
+First Class,1,"Hays, Miss. Margaret Bechstein",female,,0,0,11767,83.1583,C54,C,7,,"New York, NY"
+Third Class,0,"Peacock, Mrs. Benjamin (Edith Nile)",female,26.0,0,2,SOTON/O.Q. 3101315,13.775,,S,,,
+Third Class,0,"Assam, Mr. Ali",male,23.0,0,0,SOTON/O.Q. 3101309,7.05,,S,,,
+Third Class,0,"Elias, Mr. Joseph Jr",male,17.0,1,1,2690,7.2292,,C,,,
+First Class,1,"Icard, Miss. Amelie",female,38.0,0,0,113572,80.0,B28,,6,,
+Third Class,0,"Kallio, Mr. Nikolai Erland",male,17.0,0,0,STON/O 2. 3101274,7.125,,S,,,
+Second Class,0,"Angle, Mr. William A",male,34.0,1,0,226875,26.0,,S,,,"Warwick, England"
+Third Class,0,"Jussila, Miss. Katriina",female,20.0,1,0,4136,9.825,,S,,,
+Second Class,0,"Howard, Mr. Benjamin",male,63.0,1,0,24065,26.0,,S,,,"Swindon, England"
+Third Class,0,"Badt, Mr. Mohamed",male,40.0,0,0,2623,7.225,,C,,,
+Third Class,0,"Franklin, Mr. Charles (Charles Fardon)",male,,0,0,SOTON/O.Q. 3101314,7.25,,S,,,
+Third Class,0,"Olsen, Mr. Henry Margido",male,28.0,0,0,C 4001,22.525,,S,,173.0,
+Third Class,0,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S,,,
+First Class,1,"Bissette, Miss. Amelia",female,35.0,0,0,PC 17760,135.6333,C99,S,8,,
+Second Class,0,"Mack, Mrs. (Mary)",female,57.0,0,0,S.O./P.P. 3,10.5,E77,S,,52.0,"Southampton / New York, NY"
+Third Class,0,"Goodwin, Master. William Frederick",male,11.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+Second Class,1,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23.0,0,0,SC/AH Basle 541,13.7917,D,C,11,,"New York, NY"
+Second Class,0,"Coleridge, Mr. Reginald Charles",male,29.0,0,0,W./C. 14263,10.5,,S,,,"Hartford, Huntingdonshire"
+Third Class,0,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S,,,
+Third Class,0,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29.0,1,1,347054,10.4625,G6,S,,,
+Second Class,0,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C,,43.0,"New York, NY"
+Third Class,1,"Jansson, Mr. Carl Olof",male,21.0,0,0,350034,7.7958,,S,A,,
+Third Class,0,"Spinner, Mr. Henry John",male,32.0,0,0,STON/OQ. 369943,8.05,,S,,,
+Third Class,0,"Sage, Master. William Henry",male,14.5,8,2,CA. 2343,69.55,,S,,67.0,
+Third Class,0,"Elsbury, Mr. William James",male,47.0,0,0,A/5 3902,7.25,,S,,,"Illinois, USA"
+Third Class,0,"Andersson, Miss. Ida Augusta Margareta",female,38.0,4,2,347091,7.775,,S,,,"Vadsbro, Sweden Ministee, MI"
+First Class,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55.0,E33,S,6,,"St Leonards-on-Sea, England Ohio"
+Third Class,0,"Alexander, Mr. William",male,26.0,0,0,3474,7.8875,,S,,,"England Albion, NY"
+Second Class,0,"Butler, Mr. Reginald Fenton",male,25.0,0,0,234686,13.0,,S,,97.0,"Southsea, Hants"
+Third Class,0,"Cacic, Miss. Marija",female,30.0,0,0,315084,8.6625,,S,,,
+First Class,1,"Harper, Mr. Henry Sleeper",male,48.0,1,0,PC 17572,76.7292,D33,C,3,,"New York, NY"
+Third Class,0,"Brobeck, Mr. Karl Rudolf",male,22.0,0,0,350045,7.7958,,S,,,"Sweden Worcester, MA"
+First Class,1,"Fortune, Miss. Mabel Helen",female,23.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+Third Class,0,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q,,,
+Third Class,1,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27.0,0,0,350043,7.7958,,S,A,,
+First Class,1,"Bird, Miss. Ellen",female,29.0,0,0,PC 17483,221.7792,C97,S,8,,
+First Class,1,"Andrews, Miss. Kornelia Theodosia",female,63.0,1,0,13502,77.9583,D7,S,10,,"Hudson, NY"
+Second Class,0,"Norman, Mr. Robert Douglas",male,28.0,0,0,218629,13.5,,S,,287.0,Glasgow
+First Class,0,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,S,,175.0,"Dorchester, MA"
+Third Class,0,"Asim, Mr. Adola",male,35.0,0,0,SOTON/O.Q. 3101310,7.05,,S,,,
+Third Class,0,"Bostandyeff, Mr. Guentcho",male,26.0,0,0,349224,7.8958,,S,,,"Bulgaria Chicago, IL"
+Third Class,0,"Johansson, Mr. Gustaf Joel",male,33.0,0,0,7540,8.6542,,S,,285.0,
+First Class,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50.0,1,1,113503,211.5,C80,C,4,,"Elkins Park, PA"
+Third Class,0,"Jonkoff, Mr. Lalio",male,23.0,0,0,349204,7.8958,,S,,,
+First Class,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18.0,1,0,13695,60.0,C31,S,6,,"Huntington, WV"
+Third Class,0,"Turcin, Mr. Stjepan",male,36.0,0,0,349247,7.8958,,S,,,
+First Class,1,"Gibson, Mrs. Leonard (Pauline C Boeson)",female,45.0,0,1,112378,59.4,,C,7,,"New York, NY"
+Second Class,1,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42.0,1,0,SC/AH 3085,26.0,,S,,,"Weston-Super-Mare, Somerset"
+Third Class,0,"Lefebre, Mrs. Frank (Frances)",female,,0,4,4133,25.4667,,S,,,
+Third Class,0,"Johnson, Mr. Alfred",male,49.0,0,0,LINE,0.0,,S,,,
+Third Class,0,"Pearce, Mr. Ernest",male,,0,0,343271,7.0,,S,,,
+Third Class,0,"Skoog, Miss. Mabel",female,9.0,3,2,347088,27.9,,S,,,
+First Class,0,"Pears, Mr. Thomas Clinton",male,29.0,1,0,113776,66.6,C2,S,,,"Isleworth, England"
+Second Class,1,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24.0,1,0,SC/PARIS 2167,27.7208,,C,12,,"Lucca, Italy / California"
+Third Class,1,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,S,,,
+First Class,1,"Frauenthal, Mr. Isaac Gerald",male,43.0,1,0,17765,27.7208,D40,C,5,,"New York, NY"
+First Class,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39.0,1,0,13507,55.9,E44,S,11,,"Duluth, MN"
+Second Class,1,"Lemore, Mrs. (Amelia Milley)",female,34.0,0,0,C.A. 34260,10.5,F33,S,14,,"Chicago, IL"
+Second Class,1,"Kelly, Mrs. Florence ""Fannie""",female,45.0,0,0,223596,13.5,,S,9,,"London / New York, NY"
+First Class,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C,4,,
+Second Class,0,"McCrie, Mr. James Matthew",male,30.0,0,0,233478,13.0,,S,,,"Sarnia, ON"
+Third Class,1,"Jussila, Mr. Eiriik",male,32.0,0,0,STON/O 2. 3101286,7.925,,S,15,,
+Third Class,0,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41.0,0,5,3101295,39.6875,,S,,,
+Third Class,0,"Doyle, Miss. Elizabeth",female,24.0,0,0,368702,7.75,,Q,,,"Ireland New York, NY"
+Third Class,1,"de Messemaeker, Mr. Guillaume Joseph",male,36.5,1,0,345572,17.4,,S,15,,"Tampico, MT"
+First Class,1,"Stengel, Mr. Charles Emil Henry",male,54.0,1,0,11778,55.4417,C116,C,1,,"Newark, NJ"
+Third Class,0,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45.0,1,4,347088,27.9,,S,,,
+Third Class,0,"Andersson, Mr. Anders Johan",male,39.0,1,5,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+First Class,0,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S,,,
+Third Class,1,"Pickard, Mr. Berk (Berk Trembisky)",male,32.0,0,0,SOTON/O.Q. 392078,8.05,E10,S,9,,
+Second Class,1,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34.0,1,1,28220,32.5,,S,10,,"Greenport, NY"
+Third Class,1,"Lundin, Miss. Olga Elida",female,23.0,0,0,347469,7.8542,,S,10,,
+Third Class,0,"Thomson, Mr. Alexander Morrison",male,,0,0,32302,8.05,,S,,,
+Third Class,1,"Nilsson, Miss. Berta Olivia",female,18.0,0,0,347066,7.775,,S,D,,
+Third Class,0,"van Billiard, Master. Walter John",male,11.5,1,1,A/5. 851,14.5,,S,,1.0,
+First Class,1,"Hassab, Mr. Hammad",male,27.0,0,0,PC 17572,76.7292,D49,C,3,,
+Third Class,0,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S,,,
+Second Class,1,"Duran y More, Miss. Florentina",female,30.0,1,0,SC/PARIS 2148,13.8583,,C,12,,"Barcelona, Spain / Havana, Cuba"
+First Class,1,"Daniels, Miss. Sarah",female,33.0,0,0,113781,151.55,,S,8,,
+Third Class,0,"Lindell, Mr. Edvard Bengtsson",male,36.0,1,0,349910,15.55,,S,A,,
+Second Class,0,"Funk, Miss. Annie Clemmer",female,38.0,0,0,237671,13.0,,S,,,"Janjgir, India / Pennsylvania"
+Third Class,0,"Olsen, Mr. Karl Siegwart Andreas",male,42.0,0,1,4579,8.4042,,S,,,
+Second Class,1,"Beane, Mr. Edward",male,32.0,1,0,2908,26.0,,S,13,,"Norwich / New York, NY"
+First Class,0,"Cumings, Mr. John Bradley",male,39.0,1,0,PC 17599,71.2833,C85,C,,,"New York, NY"
+Third Class,1,"Lundstrom, Mr. Thure Edvin",male,32.0,0,0,350403,7.5792,,S,15,,
+Third Class,0,"Canavan, Miss. Mary",female,21.0,0,0,364846,7.75,,Q,,,
+Third Class,1,"Goldsmith, Master. Frank John William ""Frankie""",male,9.0,0,2,363291,20.525,,S,C D,,"Strood, Kent, England Detroit, MI"
+Third Class,1,"McCarthy, Miss. Catherine ""Katie""",female,,0,0,383123,7.75,,Q,15 16,,
+Third Class,0,"Bjorklund, Mr. Ernst Herbert",male,18.0,0,0,347090,7.75,,S,,,"Stockholm, Sweden New York"
+Third Class,0,"Dean, Mr. Bertram Frank",male,26.0,1,2,C.A. 2315,20.575,,S,,,"Devon, England Wichita, KS"
+First Class,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23.0,1,0,21228,82.2667,B45,S,7,,"Minneapolis, MN"
+First Class,0,"Ostby, Mr. Engelhart Cornelius",male,65.0,0,1,113509,61.9792,B30,C,,234.0,"Providence, RI"
+Third Class,0,"Heininen, Miss. Wendla Maria",female,23.0,0,0,STON/O2. 3101290,7.925,,S,,,
+Third Class,0,"Olsson, Miss. Elina",female,31.0,0,0,350407,7.8542,,S,,,
+Third Class,0,"Gustafsson, Mr. Karl Gideon",male,19.0,0,0,347069,7.775,,S,,,"Myren, Sweden New York, NY"
+Third Class,1,"Sjoblom, Miss. Anna Sofia",female,18.0,0,0,3101265,7.4958,,S,16,,
+Third Class,0,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C,,,
+Third Class,0,"Lane, Mr. Patrick",male,,0,0,7935,7.75,,Q,,,
+First Class,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45.0,1,1,36928,164.8667,,S,8,,"Youngstown, OH"
+First Class,0,"Brady, Mr. John Bertram",male,41.0,0,0,113054,30.5,A21,S,,,"Pomeroy, WA"
+Second Class,0,"Jacobsohn, Mr. Sidney Samuel",male,42.0,1,0,243847,27.0,,S,,,London
+Third Class,0,"Skoog, Master. Karl Thorsten",male,10.0,3,2,347088,27.9,,S,,,
+Second Class,1,"Herman, Mrs. Samuel (Jane Laver)",female,48.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+Second Class,0,"Ponesell, Mr. Martin",male,34.0,0,0,250647,13.0,,S,,,"Denmark / New York, NY"
+First Class,1,"Daniel, Mr. Robert Williams",male,27.0,0,0,113804,30.5,,S,3,,"Philadelphia, PA"
+Second Class,1,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33.0,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+Third Class,0,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C,,,
+Second Class,0,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C,,,
+Third Class,0,"Burke, Mr. Jeremiah",male,19.0,0,0,365222,6.75,,Q,,,"Co Cork, Ireland Charlestown, MA"
+Third Class,0,"Burns, Miss. Mary Delia",female,18.0,0,0,330963,7.8792,,Q,,,"Co Sligo, Ireland New York, NY"
+Third Class,0,"Abbing, Mr. Anthony",male,42.0,0,0,C.A. 5547,7.55,,S,,,
+Third Class,0,"Skoog, Miss. Margit Elizabeth",female,2.0,3,2,347088,27.9,,S,,,
+Second Class,0,"West, Mr. Edwy Arthur",male,36.0,1,2,C.A. 34651,27.75,,S,,,"Bournmouth, England"
+First Class,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45.0,0,0,111428,26.55,,S,9,,"New York, NY"
+Third Class,1,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q,D,,
+Third Class,0,"Goodwin, Mr. Charles Edward",male,14.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+Second Class,0,"Peruschitz, Rev. Joseph Maria",male,41.0,0,0,237393,13.0,,S,,,
+Third Class,0,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S,,,
+Third Class,0,"Sage, Mrs. John (Annie Bullen)",female,,1,9,CA. 2343,69.55,,S,,,
+Third Class,0,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q,,,
+Second Class,0,"Troupiansky, Mr. Moses Aaron",male,23.0,0,0,233639,13.0,,S,,,
+Third Class,1,"Riordan, Miss. Johanna ""Hannah""",female,,0,0,334915,7.7208,,Q,13,,
+Second Class,0,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q,,,
+Second Class,0,"Schmidt, Mr. August",male,26.0,0,0,248659,13.0,,S,,,"Newark, NJ"
+Second Class,0,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,1,0,11668,21.0,,S,,,"Plymouth, England"
+Second Class,0,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25.0,1,0,236853,26.0,,S,,,"Skara, Sweden / Rockford, IL"
+Second Class,0,"Hunt, Mr. George Henry",male,33.0,0,0,SCO/W 1585,12.275,,S,,,"Philadelphia, PA"
+Third Class,0,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S,,,
+Second Class,1,"Christy, Mrs. (Alice Frances)",female,45.0,0,2,237789,30.0,,S,12,,London
+Second Class,1,"Hosono, Mr. Masabumi",male,42.0,0,0,237798,13.0,,S,10,,"Tokyo, Japan"
+Second Class,1,"Duran y More, Miss. Asuncion",female,27.0,1,0,SC/PARIS 2149,13.8583,,C,12,,"Barcelona, Spain / Havana, Cuba"
+Third Class,0,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31.0,1,0,345763,18.0,,S,,,
+Third Class,0,"Wenzel, Mr. Linhart",male,32.5,0,0,345775,9.5,,S,,298.0,
+Third Class,0,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S,,,
+Second Class,1,"Beesley, Mr. Lawrence",male,34.0,0,0,248698,13.0,D56,S,13,,London
+First Class,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60.0,0,0,11813,76.2917,D15,C,8,,"Philadelphia, PA"
+Third Class,0,"Strilic, Mr. Ivan",male,27.0,0,0,315083,8.6625,,S,,,
+Second Class,0,"Meyer, Mr. August",male,39.0,0,0,248723,13.0,,S,,,"Harrow-on-the-Hill, Middlesex"
+Third Class,0,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C,,,
+First Class,1,"Allen, Miss. Elisabeth Walton",female,29.0,0,0,24160,211.3375,B5,S,2,,"St Louis, MO"
+First Class,1,"Bowerman, Miss. Elsie Edith",female,22.0,0,1,113505,55.0,E33,S,6,,"St Leonards-on-Sea, England Ohio"
+Third Class,0,"Wirz, Mr. Albert",male,27.0,0,0,315154,8.6625,,S,,131.0,
+Third Class,0,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S,,,
+Third Class,0,"Connolly, Miss. Kate",female,30.0,0,0,330972,7.6292,,Q,,,Ireland
+Third Class,0,"Johansson, Mr. Karl Johan",male,31.0,0,0,347063,7.775,,S,,,
+Third Class,1,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35.0,1,1,C.A. 2673,20.25,,S,A,,"East Providence, RI"
+Third Class,0,"Andersson, Master. Sigvard Harald Elias",male,4.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+Second Class,1,"Herman, Miss. Alice",female,24.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+First Class,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27.0,1,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ"
+Third Class,0,"Dimic, Mr. Jovan",male,42.0,0,0,315088,8.6625,,S,,,
+Third Class,1,"Lang, Mr. Fang",male,26.0,0,0,1601,56.4958,,S,14,,
+First Class,0,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S,,,"Roachdale, IN"
+Third Class,0,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q,,61.0,
+First Class,0,"Widener, Mr. George Dunton",male,50.0,1,1,113503,211.5,C80,C,,,"Elkins Park, PA"
+Second Class,0,"del Carlo, Mr. Sebastiano",male,29.0,1,0,SC/PARIS 2167,27.7208,,C,,295.0,"Lucca, Italy / California"
+Second Class,1,"Richards, Master. George Sibley",male,0.8333,1,1,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+First Class,0,"Smith, Mr. James Clinch",male,56.0,0,0,17764,30.6958,A7,C,,,"St James, Long Island, NY"
+Third Class,0,"Svensson, Mr. Olof",male,24.0,0,0,350035,7.7958,,S,,,
+Second Class,1,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40.0,1,1,29750,39.0,,S,14,,"Cape Town, South Africa / Seattle, WA"
+Third Class,1,"Andersson, Miss. Erna Alexandra",female,17.0,4,2,3101281,7.925,,S,D,,"Ruotsinphyhtaa, Finland New York, NY"
+Third Class,0,"Johansson, Mr. Nils",male,29.0,0,0,347467,7.8542,,S,,,
+Second Class,1,"Hart, Miss. Eva Miriam",female,7.0,0,2,F.C.C. 13529,26.25,,S,14,,"Ilford, Essex / Winnipeg, MB"
+Third Class,0,"Delalic, Mr. Redjo",male,25.0,0,0,349250,7.8958,,S,,,
+Third Class,0,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C,,,
+First Class,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C,7,,"Paris, France"
+Second Class,0,"Nesson, Mr. Israel",male,26.0,0,0,244368,13.0,F2,S,,,"Boston, MA"
+Third Class,0,"Andersen, Mr. Albert Karvin",male,32.0,0,0,C 4001,22.525,,S,,260.0,"Bergen, Norway"
+First Class,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25.0,1,2,113781,151.55,C22 C26,S,,,"Montreal, PQ / Chesterville, ON"
+Second Class,0,"Fillbrook, Mr. Joseph Charles",male,18.0,0,0,C.A. 15185,10.5,,S,,,"Cornwall / Houghton, MI"
+Third Class,0,"Jussila, Miss. Mari Aina",female,21.0,1,0,4137,9.825,,S,,,
+Third Class,1,"Peter, Master. Michael J",male,,1,1,2668,22.3583,,C,C,,
+Third Class,0,"Barbara, Miss. Saiide",female,18.0,0,1,2691,14.4542,,C,,,"Syria Ottawa, ON"
+First Class,0,"Fortune, Mr. Mark",male,64.0,1,4,19950,263.0,C23 C25 C27,S,,,"Winnipeg, MB"
+Third Class,0,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S,,,
+Third Class,1,"Touma, Miss. Maria Youssef",female,9.0,1,1,2650,15.2458,,C,C,,
+Third Class,1,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S,C,,
+First Class,1,"Stahelin-Maeglin, Dr. Max",male,32.0,0,0,13214,30.5,B50,C,3,,"Basel, Switzerland"
+Third Class,0,"Connaghton, Mr. Michael",male,31.0,0,0,335097,7.75,,Q,,,"Ireland Brooklyn, NY"
+Third Class,0,"Hampe, Mr. Leon",male,20.0,0,0,345769,9.5,,S,,,
+Second Class,1,"Mallet, Master. Andre",male,1.0,0,2,S.C./PARIS 2079,37.0042,,C,10,,"Paris / Montreal, PQ"
+First Class,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53.0,2,0,11769,51.4792,C101,S,D,,"Bayside, Queens, NY"
+Third Class,0,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S,,,
+Third Class,0,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S,,,
+Second Class,0,"Wheeler, Mr. Edwin ""Frederick""",male,,0,0,SC/PARIS 2159,12.875,,S,,,
+Third Class,0,"Lyntakoff, Mr. Stanko",male,,0,0,349235,7.8958,,S,,,
+Third Class,0,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S,,,
+Second Class,1,"Quick, Miss. Winifred Vera",female,8.0,1,1,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+First Class,1,"Simonius-Blumer, Col. Oberst Alfons",male,56.0,0,0,13213,35.5,A26,C,3,,"Basel, Switzerland"
+First Class,1,"Maioni, Miss. Roberta",female,16.0,0,0,110152,86.5,B79,S,8,,
+Third Class,0,"Nosworthy, Mr. Richard Cater",male,21.0,0,0,A/4. 39886,7.8,,S,,,
+First Class,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23.0,0,1,11767,83.1583,C54,C,7,,"Mt Airy, Philadelphia, PA"
+Third Class,0,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q,,,
+Third Class,0,"Nasr, Mr. Mustafa",male,,0,0,2652,7.2292,,C,,,
+Third Class,1,"Kink-Heilmann, Miss. Luise Gretchen",female,4.0,0,2,315153,22.025,,S,2,,
+Third Class,0,"Rintamaki, Mr. Matti",male,35.0,0,0,STON/O 2. 3101273,7.125,,S,,,
+Third Class,0,"Storey, Mr. Thomas",male,60.5,0,0,3701,,,S,,261.0,
+First Class,0,"Jones, Mr. Charles Cresson",male,46.0,0,0,694,26.0,,S,,80.0,"Bennington, VT"
+Second Class,0,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24.0,0,0,248747,13.0,,S,,,Paris
+Third Class,0,"Larsson-Rondberg, Mr. Edvard A",male,22.0,0,0,347065,7.775,,S,,,
+Third Class,1,"Kink-Heilmann, Mrs. Anton (Luise Heilmann)",female,26.0,1,1,315153,22.025,,S,2,,
+First Class,0,"Futrelle, Mr. Jacques Heath",male,37.0,1,0,113803,53.1,C123,S,,,"Scituate, MA"
+Third Class,0,"Tikkanen, Mr. Juho",male,32.0,0,0,STON/O 2. 3101293,7.925,,S,,,
+First Class,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18.0,1,0,PC 17757,227.525,C62 C64,C,4,,"New York, NY"
+Third Class,0,"Dakic, Mr. Branko",male,19.0,0,0,349228,10.1708,,S,,,Austria
+First Class,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39.0,1,1,110413,79.65,E67,S,8,,"New York, NY"
+Third Class,1,"Daly, Miss. Margaret Marcella ""Maggie""",female,30.0,0,0,382650,6.95,,Q,15,,"Co Athlone, Ireland New York, NY"
+Third Class,0,"Henriksson, Miss. Jenny Lovisa",female,28.0,0,0,347086,7.775,,S,,,
+Third Class,0,"Leinonen, Mr. Antti Gustaf",male,32.0,0,0,STON/O 2. 3101292,7.925,,S,,,
+First Class,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50.0,0,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ"
+Second Class,1,"Ridsdale, Miss. Lucy",female,50.0,0,0,W./C. 14258,10.5,,S,13,,"London, England / Marietta, Ohio and Milwaukee, WI"
+Second Class,0,"Keane, Mr. Daniel",male,35.0,0,0,233734,12.35,,Q,,,
+Third Class,1,"Aks, Master. Philip Frank",male,0.8333,0,1,392091,9.35,,S,11,,"London, England Norfolk, VA"
+First Class,1,"Snyder, Mr. John Pillsbury",male,24.0,1,0,21228,82.2667,B45,S,7,,"Minneapolis, MN"
+Third Class,0,"Khalil, Mrs. Betros (Zahie ""Maria"" Elias)",female,,1,0,2660,14.4542,,C,,,
+First Class,0,"Kent, Mr. Edward Austin",male,58.0,0,0,11771,29.7,B37,C,,258.0,"Buffalo, NY"
+First Class,0,"Rothschild, Mr. Martin",male,55.0,1,0,PC 17603,59.4,,C,,,"New York, NY"
+First Class,0,"Williams, Mr. Charles Duane",male,51.0,0,1,PC 17597,61.3792,,C,,,"Geneva, Switzerland / Radnor, PA"
+Third Class,0,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q,,,
+Second Class,1,"Richards, Master. William Rowe",male,3.0,1,1,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+First Class,0,"Stead, Mr. William Thomas",male,62.0,0,0,113514,26.55,C87,S,,,"Wimbledon Park, London / Hayling Island, Hants"
+Second Class,1,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,0,0,248706,16.0,,S,13,,"India / Rapid City, SD"
+Second Class,1,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,-29.0,1,0,228414,26.0,,S,10,,"Bromsgrove, England / Montreal, PQ"
+Third Class,0,"Pettersson, Miss. Ellen Natalia",female,18.0,0,0,347087,7.775,,S,,,
+First Class,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52.0,C126,S,5 7,,"London /  East Orange, NJ"
+First Class,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43.0,0,1,24160,211.3375,B3,S,2,,"St Louis, MO"
+Third Class,0,"Thomas, Mr. Charles P",male,,1,0,2621,6.4375,,C,,,
+Third Class,0,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S,,,
+Third Class,0,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S,,,
+Third Class,0,"Hegarty, Miss. Hanora ""Nora""",female,18.0,0,0,365226,6.75,,Q,,,
+Third Class,0,"Panula, Master. Urho Abraham",male,2.0,4,1,3101295,39.6875,,S,,,
+Second Class,0,"Hocking, Mr. Samuel James Metcalfe",male,36.0,0,0,242963,13.0,,S,,,"Devonport, England"
+Third Class,0,"Niklasson, Mr. Samuel",male,28.0,0,0,363611,8.05,,S,,,
+Third Class,0,"Katavelas, Mr. Vassilios (""Catavelas Vassilios"")",male,18.5,0,0,2682,7.2292,,C,,58.0,
+Second Class,1,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40.0,0,0,C.A. 33595,15.75,,S,9,,"Aberdeen / Portland, OR"
+Third Class,0,"Gallagher, Mr. Martin",male,25.0,0,0,36864,7.7417,,Q,,,"New York, NY"
+Third Class,0,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S,A,,
+Third Class,1,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29.0,0,2,2650,15.2458,,C,C,,
+Third Class,0,"Brocklebank, Mr. William Alfred",male,35.0,0,0,364512,8.05,,S,,,"Broomfield, Chelmsford, England"
+Second Class,0,"Collander, Mr. Erik Gustaf",male,28.0,0,0,248740,13.0,,S,,,"Helsinki, Finland Ashtabula, Ohio"
+Third Class,0,"Coleff, Mr. Peju",male,36.0,0,0,349210,7.4958,,S,,,"Bulgaria Chicago, IL"
+First Class,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44.0,0,0,PC 17610,27.7208,B4,C,6,,"Denver, CO"
+Second Class,1,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41.0,0,1,250644,19.5,,S,14,,"England / Bennington, VT"
+Second Class,1,"Laroche, Miss. Louise",female,1.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+Third Class,0,"Dahlberg, Miss. Gerda Ulrika",female,22.0,0,0,7552,10.5167,,S,,,"Norrlot, Sweden Chicago, IL"
+First Class,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+First Class,0,"Meyer, Mr. Edgar Joseph",male,28.0,1,0,PC 17604,82.1708,,C,,,"New York, NY"
+Third Class,0,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S,,,
+Second Class,0,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26.0,F2,S,,15.0,"Nice, France"
+First Class,0,"Borebank, Mr. John James",male,42.0,0,0,110489,26.55,D22,S,,,"London / Winnipeg, MB"
+Third Class,1,"Coutts, Master. Eden Leslie ""Neville""",male,9.0,1,1,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+Third Class,0,"Cribb, Mr. John Hatfield",male,44.0,0,1,371362,16.1,,S,,,"Bournemouth, England Newark, NJ"
+Third Class,1,"Karun, Mr. Franz",male,39.0,0,1,349256,13.4167,,C,15,,
+Second Class,0,"Hiltunen, Miss. Marta",female,18.0,1,1,250650,13.0,,S,,,"Kontiolahti, Finland / Detroit, MI"
+Third Class,1,"Persson, Mr. Ernst Ulrik",male,25.0,1,0,347083,7.775,,S,15,,
+Third Class,0,"Zakarian, Mr. Ortin",male,27.0,0,0,2670,7.225,,C,,,
+First Class,1,"Mock, Mr. Philipp Edmund",male,30.0,1,0,13236,57.75,C78,C,11,,"New York, NY"
+Third Class,0,"Ford, Mr. Arthur",male,,0,0,A/5 1478,8.05,,S,,,"Bridgwater, Somerset, England"
+First Class,0,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S,,166.0,"Surbiton Hill, Surrey"
+Third Class,0,"Panula, Master. Juha Niilo",male,7.0,4,1,3101295,39.6875,,S,,,
+First Class,1,"Barber, Miss. Ellen ""Nellie""",female,26.0,0,0,19877,78.85,,S,6,,
+Third Class,1,"Moubarek, Mrs. George (Omine ""Amenia"" Alexander)",female,,0,2,2661,15.2458,,C,C,,
+First Class,0,"Uruchurtu, Don. Manuel E",male,40.0,0,0,PC 17601,27.7208,,C,,,"Mexico City, Mexico"
+First Class,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C,5,,"New York, NY"
+Third Class,0,"Rosblom, Mr. Viktor Richard",male,18.0,1,1,370129,20.2125,,S,,,
+First Class,1,"Francatelli, Miss. Laura Mabel",female,30.0,0,0,PC 17485,56.9292,E36,C,1,,
+Third Class,0,"Nieminen, Miss. Manta Josefina",female,29.0,0,0,3101297,7.925,,S,,,
+First Class,0,"Marvin, Mr. Daniel Warner",male,19.0,1,0,113773,53.1,D30,S,,,"New York, NY"
+Third Class,1,"Thomas, Mrs. Alexander (Thamine ""Thelma"")",female,16.0,1,1,2625,8.5167,,C,14,,
+First Class,1,"Bonnell, Miss. Caroline",female,30.0,0,0,36928,164.8667,C7,S,8,,"Youngstown, OH"
+First Class,0,"Harrison, Mr. William",male,40.0,0,0,112059,0.0,B94,S,,110.0,
+Second Class,0,"Giles, Mr. Edgar",male,21.0,1,0,28133,11.5,,S,,,"Cornwall / Camden, NJ"
+First Class,1,"Spedden, Mr. Frederic Oakley",male,45.0,1,1,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+Third Class,0,"Dennis, Mr. William",male,36.0,0,0,A/5 21175,7.25,,S,,,
+First Class,1,"Seward, Mr. Frederic Kimber",male,34.0,0,0,113794,26.55,,S,7,,"New York, NY"
+Second Class,0,"Denbury, Mr. Herbert",male,25.0,0,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+First Class,0,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63.0,1,0,PC 17483,221.7792,C55 C57,S,,,"New York, NY"
+First Class,1,"Ryerson, Miss. Emily Borie",female,18.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+Third Class,1,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19.0,1,0,350046,7.8542,,S,16,,
+First Class,1,"Greenfield, Mr. William Bertram",male,23.0,0,1,PC 17759,63.3583,D10 D12,C,7,,"New York, NY"
+Second Class,0,"Mitchell, Mr. Henry Michael",male,70.0,0,0,C.A. 24580,10.5,,S,,,"Guernsey / Montclair, NJ and/or Toledo, Ohio"
+Third Class,1,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q,13,,
+Third Class,0,"Jensen, Mr. Hans Peder",male,20.0,0,0,350050,7.8542,,S,,,
+Third Class,0,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q,,,
+Third Class,0,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41.0,0,2,370129,20.2125,,S,,,
+Third Class,0,"Lindahl, Miss. Agda Thorilda Viktoria",female,25.0,0,0,347071,7.775,,S,,,
+First Class,0,"Porter, Mr. Walter Chamberlain",male,47.0,0,0,110465,52.0,C110,S,,207.0,"Worcester, MA"
+Third Class,0,"Sirayanian, Mr. Orsen",male,22.0,0,0,2669,7.2292,,C,,,
+Third Class,0,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39.0,1,5,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+Third Class,0,"Barry, Miss. Julia",female,27.0,0,0,330844,7.8792,,Q,,,"New York, NY"
+Second Class,0,"Campbell, Mr. William",male,,0,0,239853,0.0,,S,,,Belfast
+Second Class,0,"Chapman, Mr. Charles Henry",male,52.0,0,0,248731,13.5,,S,,130.0,"Bronx, NY"
+Second Class,0,"Jarvis, Mr. John Denzil",male,47.0,0,0,237565,15.0,,S,,,"North Evington, England"
+First Class,0,"Warren, Mr. Frank Manley",male,64.0,1,0,110813,75.25,D37,C,,,"Portland, OR"
+Second Class,1,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q,10,,"Harrisburg, PA"
+Third Class,1,"Nakid, Miss. Maria (""Mary"")",female,1.0,0,2,2653,15.7417,,C,C,,
+First Class,0,"Clark, Mr. Walter Miller",male,27.0,1,0,13508,136.7792,C89,C,,,"Los Angeles, CA"
+First Class,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45.0,0,1,PC 17759,63.3583,D10 D12,C,7,,"New York, NY"
+Second Class,0,"Mallet, Mr. Albert",male,31.0,1,1,S.C./PARIS 2079,37.0042,,C,,,"Paris / Montreal, PQ"
+Third Class,0,"Gronnestad, Mr. Daniel Danielsen",male,32.0,0,0,8471,8.3625,,S,,,"Foresvik, Norway Portland, ND"
+Third Class,0,"Lobb, Mr. William Arthur",male,30.0,1,0,A/5. 3336,16.1,,S,,,
+First Class,0,"Chaffee, Mr. Herbert Fuller",male,46.0,1,0,W.E.P. 5734,61.175,E31,S,,,"Amenia, ND"
+Second Class,0,"Slemen, Mr. Richard James",male,35.0,0,0,28206,10.5,,S,,,Cornwall
+Second Class,0,"Herman, Mr. Samuel",male,49.0,1,2,220845,65.0,,S,,,"Somerset / Bernardsville, NJ"
+Third Class,1,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q,16,,
+Third Class,0,"Somerton, Mr. Francis William",male,30.0,0,0,A.5. 18509,8.05,,S,,,
+Second Class,0,"Bailey, Mr. Percy Andrew",male,18.0,0,0,29108,11.5,,S,,,"Penzance, Cornwall / Akron, OH"
+Third Class,1,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36.0,1,0,345572,17.4,,S,13,,"Tampico, MT"
+Third Class,0,"Naidenoff, Mr. Penko",male,22.0,0,0,349206,7.8958,,S,,,
+Second Class,0,"Dibden, Mr. William",male,18.0,0,0,S.O.C. 14879,73.5,,S,,,"New Forest, England"
+Third Class,0,"Pokrnic, Mr. Tome",male,24.0,0,0,315092,8.6625,,S,,,
+Third Class,1,"Cohen, Mr. Gurshon ""Gus""",male,18.0,0,0,A/5 3540,8.05,,S,12,,"London Brooklyn, NY"
+Third Class,1,"Abrahamsson, Mr. Abraham August Johannes",male,20.0,0,0,SOTON/O2 3101284,7.925,,S,15,,"Taalintehdas, Finland Hoboken, NJ"
+Second Class,0,"Milling, Mr. Jacob Christian",male,48.0,0,0,234360,13.0,,S,,271.0,"Copenhagen, Denmark"
+Third Class,0,"Johansson, Mr. Erik",male,22.0,0,0,350052,7.7958,,S,,156.0,
+Third Class,1,"Svensson, Mr. Johan Cervin",male,14.0,0,0,7538,9.225,,S,13,,
+Second Class,1,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+First Class,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48.0,1,0,11755,39.6,A16,C,1,,London / Paris
+Third Class,0,"Goodwin, Master. Sidney Leonard",male,1.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+First Class,0,"Chisholm, Mr. Roderick Robert Crispin",male,,0,0,112051,0.0,,S,,,"Liverpool, England / Belfast"
+Third Class,0,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q,,,
+Second Class,1,"Bryhl, Miss. Dagmar Jenny Ingeborg ",female,20.0,1,0,236853,26.0,,S,12,,"Skara, Sweden / Rockford, IL"
+Third Class,0,"Kink, Miss. Maria",female,22.0,2,0,315152,8.6625,,S,,,
+First Class,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25.0,1,0,11765,55.4417,E50,C,5,,"Brooklyn, NY"
+Third Class,0,"Vande Walle, Mr. Nestor Cyriel",male,28.0,0,0,345770,9.5,,S,,,
+Second Class,0,"Leyson, Mr. Robert William Norman",male,24.0,0,0,C.A. 29566,10.5,,S,,108.0,
+Second Class,1,"Mellors, Mr. William John",male,19.0,0,0,SW/PP 751,10.5,,S,B,,"Chelsea, London"
+Third Class,0,"Cann, Mr. Ernest Charles",male,21.0,0,0,A./5. 2152,8.05,,S,,,
+Second Class,0,"Carter, Rev. Ernest Courtenay",male,54.0,1,0,244252,26.0,,S,,,London
+Third Class,0,"Lindblom, Miss. Augusta Charlotta",female,45.0,0,0,347073,7.75,,S,,,
+Third Class,1,"Hellstrom, Miss. Hilda Maria",female,22.0,0,0,7548,8.9625,,S,C,,
+First Class,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+Third Class,0,"Dyker, Mr. Adolf Fredrik",male,23.0,1,0,347072,13.9,,S,,,"West Haven, CT"
+Third Class,0,"Edvardsson, Mr. Gustaf Hjalmar",male,18.0,0,0,349912,7.775,,S,,,"Tofta, Sweden Joliet, IL"
+Second Class,1,"Silven, Miss. Lyyli Karoliina",female,,0,2,250652,13.0,,S,16,,"Finland / Minneapolis, MN"
+First Class,0,"Silvey, Mr. William Baird",male,50.0,1,0,13507,55.9,E44,S,,,"Duluth, MN"
+Third Class,1,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q,,,
+First Class,0,"Van der hoef, Mr. Wyckoff",male,61.0,0,0,111240,33.5,B19,S,,245.0,"Brooklyn, NY"
+First Class,0,"Hipkins, Mr. William Edward",male,55.0,0,0,680,50.0,C39,S,,,London / Birmingham
+Third Class,0,"Hansen, Mr. Claus Peter",male,41.0,2,0,350026,14.1083,,S,,,
+Second Class,1,"Wells, Mrs. Arthur Henry (""Addie"" Dart Trevaskis)",female,29.0,0,2,29103,23.0,,S,14,,"Cornwall / Akron, OH"
+Third Class,0,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S,,,
+Third Class,0,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q,,,
+Third Class,0,"Canavan, Mr. Patrick",male,21.0,0,0,364858,7.75,,Q,,,"Ireland Philadelphia, PA"
+Third Class,1,"Sundman, Mr. Johan Julian",male,44.0,0,0,STON/O 2. 3101269,7.925,,S,15,,
+First Class,1,"Allison, Master. Hudson Trevor",male,0.9167,1,2,113781,151.55,C22 C26,S,11,,"Montreal, PQ / Chesterville, ON"
+Third Class,0,"Foley, Mr. William",male,,0,0,365235,7.75,,Q,,,Ireland
+Third Class,0,"Torfa, Mr. Assad",male,,0,0,2673,7.2292,,C,,,
+Third Class,0,"Palsson, Miss. Torborg Danira",female,8.0,3,1,349909,21.075,,S,,,
+First Class,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48.0,1,1,13567,79.2,B41,C,5,,"Zurich, Switzerland"
+Second Class,1,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,0,1,231919,23.0,,S,,,Southampton
+Third Class,0,"Jonsson, Mr. Nils Hilding",male,27.0,0,0,350408,7.8542,,S,,,
+Third Class,0,"Theobald, Mr. Thomas Leonard",male,34.0,0,0,363294,8.05,,S,,176.0,
+Third Class,0,"Rouse, Mr. Richard Henry",male,50.0,0,0,A/5 3594,8.05,,S,,,
+Third Class,1,"Dean, Miss. Elizabeth Gladys ""Millvina""",female,0.1667,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+Third Class,0,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28.0,1,1,347080,14.4,,S,,,"Stanton, IA"
+Third Class,1,"Moor, Mrs. (Beila)",female,27.0,0,1,392096,12.475,E121,S,14,,
+Third Class,0,"Pekoniemi, Mr. Edvard",male,21.0,0,0,STON/O 2. 3101294,7.925,,S,,,
+Third Class,0,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S,,,
+Second Class,1,"Ilett, Miss. Bertha",female,17.0,0,0,SO/C 14885,10.5,,S,,,Guernsey
+Second Class,1,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19.0,0,0,250655,26.0,,S,11,,"Worcester, England"
+First Class,0,"Ross, Mr. John Hugo",male,36.0,0,0,13049,40.125,A10,C,,,"Winnipeg, MB"
+Second Class,1,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24.0,2,1,243847,27.0,,S,12,,London
+Second Class,1,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24.0,1,0,244367,26.0,,S,12,,"Moscow / Bronx, NY"
+Third Class,0,"Cor, Mr. Bartol",male,35.0,0,0,349230,7.8958,,S,,,Austria
+First Class,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60.0,1,0,110813,75.25,D37,C,5,,"Portland, OR"
+Second Class,0,"Downton, Mr. William James",male,54.0,0,0,28403,26.0,,S,,,"Holley, NY"
+Third Class,0,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,1,0,7546,9.475,,S,,,"Sweden Akeley, MN"
+Third Class,0,"Chronopoulos, Mr. Apostolos",male,26.0,1,0,2680,14.4542,,C,,,Greece
+Third Class,1,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22.0,0,0,2620,7.225,,C,6,,
+Third Class,0,"Rice, Master. George Hugh",male,8.0,4,1,382652,29.125,,Q,,,
+Third Class,0,"Mahon, Mr. John",male,,0,0,AQ/4 3130,7.75,,Q,,,
+Third Class,1,"Madsen, Mr. Fridtjof Arne",male,24.0,0,0,C 17369,7.1417,,S,13,,
+Third Class,1,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S,C,,
+Third Class,1,"Dorking, Mr. Edward Arthur",male,19.0,0,0,A/5. 10482,8.05,,S,B,,"England Oglesby, IL"
+Third Class,0,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S,,,
+Second Class,1,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36.0,0,3,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+Third Class,0,"Christmann, Mr. Emil",male,29.0,0,0,343276,8.05,,S,,,
+Second Class,0,"Giles, Mr. Ralph",male,24.0,0,0,248726,13.5,,S,,297.0,"West Kensington, London"
+Second Class,0,"Harris, Mr. Walter",male,30.0,0,0,W/C 14208,10.5,,S,,,"Walthamstow, England"
+Third Class,0,"Widegren, Mr. Carl/Charles Peter",male,51.0,0,0,347064,7.75,,S,,,
+Second Class,0,"Hocking, Mr. Richard George",male,23.0,2,1,29104,11.5,,S,,,"Cornwall / Akron, OH"
+Third Class,0,"Abbott, Master. Eugene Joseph",male,13.0,0,2,C.A. 2673,20.25,,S,,,"East Providence, RI"
+Third Class,0,"Culumovic, Mr. Jeso",male,17.0,0,0,315090,8.6625,,S,,,Austria-Hungary
+First Class,1,"Frolicher, Miss. Hedwig Margaritha",female,22.0,0,2,13568,49.5,B39,C,5,,"Zurich, Switzerland"
+Third Class,0,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,0,0,350406,7.8542,,S,,,
+Third Class,1,"Stanley, Miss. Amy Zillah Elsie",female,23.0,0,0,CA. 2314,7.55,,S,C,,
+Second Class,0,"Botsford, Mr. William Hull",male,26.0,0,0,237670,13.0,,S,,,"Elmira, NY / Orange, NJ"
+First Class,1,"Rosenbaum, Miss. Edith Louise",female,33.0,0,0,PC 17613,27.7208,A11,C,11,,"Paris, France"
+First Class,1,"Dodge, Dr. Washington",male,53.0,1,1,33638,81.8583,A34,S,13,,"San Francisco, CA"
+Third Class,1,"Midtsjo, Mr. Karl Albert",male,21.0,0,0,345501,7.775,,S,15,,
+Third Class,1,"Jonsson, Mr. Carl",male,32.0,0,0,350417,7.8542,,S,15,,
+First Class,0,"Roebling, Mr. Washington Augustus II",male,31.0,0,0,PC 17590,50.4958,A24,S,,,"Trenton, NJ"
+Third Class,0,"Skoog, Mr. Wilhelm",male,40.0,1,4,347088,27.9,,S,,,
+First Class,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54.0,1,1,33638,81.8583,A34,S,5,,"San Francisco, CA"
+Third Class,1,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C,D,,
+First Class,1,"Endres, Miss. Caroline Louise",female,38.0,0,0,PC 17757,227.525,C45,C,4,,"New York, NY"
+Third Class,0,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C,,,
+First Class,0,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C,14,,"New York, NY"
+First Class,0,"Butt, Major. Archibald Willingham",male,45.0,0,0,113050,26.55,B38,S,,,"Washington, DC"
+Third Class,0,"Beavan, Mr. William Thomas",male,19.0,0,0,323951,8.05,,S,,,England
+First Class,0,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C,,,"Gallipolis, Ohio / ? Paris / New York"
+Second Class,1,"Quick, Miss. Phyllis May",female,2.0,1,1,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+First Class,1,"Bowen, Miss. Grace Scott",female,45.0,0,0,PC 17608,262.375,,C,4,,"Cooperstown, NY"
+First Class,0,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C,,189.0,"?Havana, Cuba"
+Third Class,0,"Rice, Master. Arthur",male,4.0,4,1,382652,29.125,,Q,,,
+First Class,0,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S,,,
+Third Class,0,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q,,,"Ireland Chicago, IL"
+First Class,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S,9,,"Los Angeles, CA"
+Second Class,0,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0.0,,S,,,Belfast
+Third Class,0,"Palsson, Miss. Stina Viola",female,3.0,3,1,349909,21.075,,S,,,
+First Class,1,"Peuchen, Major. Arthur Godfrey",male,52.0,0,0,113786,30.5,C104,S,6,,"Toronto, ON"
+Second Class,0,"Hart, Mr. Benjamin",male,43.0,1,1,F.C.C. 13529,26.25,,S,,,"Ilford, Essex / Winnipeg, MB"
+Second Class,0,"Hodges, Mr. Henry Price",male,50.0,0,0,250643,13.0,,S,,149.0,Southampton
+First Class,0,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S,,,"New York, NY"
+First Class,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58.0,0,1,PC 17755,512.3292,B51 B53 B55,C,3,,"Germantown, Philadelphia, PA"
+Third Class,0,"Dooley, Mr. Patrick",male,32.0,0,0,370376,7.75,,Q,,,"Ireland New York, NY"
+Second Class,1,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,1,0,2926,26.0,,S,16,,
+First Class,0,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S,,,"Portland, OR"
+Third Class,1,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q,13,,"Co Clare, Ireland Washington, DC"
+First Class,0,"Rosenshine, Mr. George (""Mr George Thorne"")",male,46.0,0,0,PC 17585,79.2,,C,,16.0,"New York, NY"
+Second Class,0,"Mudd, Mr. Thomas Charles",male,16.0,0,0,S.O./P.P. 3,10.5,,S,,,"Halesworth, England"
+Third Class,1,"Murphy, Miss. Nora",female,,0,0,36568,15.5,,Q,16,,
+First Class,1,"Hoyt, Mr. Frederick Maxfield",male,38.0,1,0,19943,90.0,C93,S,D,,"New York, NY /  Stamford CT"
+Second Class,1,"Christy, Miss. Julie Rachel",female,25.0,1,1,237789,30.0,,S,12,,London
+Third Class,0,"Coxon, Mr. Daniel",male,,0,0,364500,7.25,,S,,,"Merrill, WI"
+Third Class,1,"Sandstrom, Miss. Beatrice Irene",female,1.0,1,1,PP 9549,16.7,G6,S,13,,
+Third Class,0,"Lester, Mr. James",male,39.0,0,0,A/4 48871,24.15,,S,,,
+Second Class,0,"Hickman, Mr. Leonard Mark",male,24.0,2,0,S.O.C. 14879,73.5,,S,,,"West Hampstead, London / Neepawa, MB"
+Third Class,1,"O'Keefe, Mr. Patrick",male,,0,0,368402,7.75,,Q,B,,
+Third Class,0,"Wiklund, Mr. Karl Johan",male,21.0,1,0,3101266,6.4958,,S,,,
+First Class,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39.0,1,1,17421,110.8833,C68,C,4,,"Haverford, PA"
+First Class,1,"Newsom, Miss. Helen Monypeny",female,19.0,0,2,11752,26.2833,D47,S,5,,"New York, NY"
+Second Class,0,"Turpin, Mr. William John Robert",male,29.0,1,0,11668,21.0,,S,,,"Plymouth, England"
+Second Class,1,"Richards, Mrs. Sidney (Emily Hocking)",female,24.0,2,3,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+Second Class,0,"Stokes, Mr. Philip Joseph",male,25.0,0,0,F.C.C. 13540,10.5,,S,,81.0,"Catford, Kent / Detroit, MI"
+Third Class,0,"Rice, Master. Eric",male,7.0,4,1,382652,29.125,,Q,,,
+First Class,1,"Leader, Dr. Alice (Farnham)",female,49.0,0,0,17465,25.9292,D17,S,8,,"New York, NY"
+Third Class,0,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C,,,
+Third Class,0,"McNamee, Mr. Neal",male,24.0,1,0,376566,16.1,,S,,,
+Third Class,0,"Holthen, Mr. Johan Martin",male,28.0,0,0,C 4001,22.525,,S,,,
+Third Class,0,"Hansen, Mr. Henry Damsgaard",male,21.0,0,0,350029,7.8542,,S,,69.0,
+Third Class,0,"Ilmakangas, Miss. Pieta Sofia",female,25.0,1,0,STON/O2. 3101271,7.925,,S,,,
+Third Class,0,"Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)",female,36.0,0,2,350405,12.1833,,S,,,
+First Class,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52.0,1,0,36947,78.2667,D20,C,4,,"Haverford, PA"
+Third Class,0,"Crease, Mr. Ernest James",male,19.0,0,0,S.P. 3464,8.1583,,S,,,"Bristol, England Cleveland, OH"
+Second Class,0,"Gaskell, Mr. Alfred",male,16.0,0,0,239865,26.0,,S,,,"Liverpool / Montreal, PQ"
+Third Class,0,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,S,,,"Bridgerule, Devon"
+Third Class,1,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18.0,0,0,2657,7.2292,,C,C,,"Greensburg, PA"
+First Class,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28.0,0,0,110564,26.55,C52,S,D,,"Stockholm, Sweden / Washington, DC"
+Third Class,0,"Strandberg, Miss. Ida Sofia",female,22.0,0,0,7553,9.8375,,S,,,
+Third Class,0,"Samaan, Mr. Hanna",male,,2,0,2662,21.6792,,C,,,
+First Class,1,"Cardeza, Mr. Thomas Drake Martinez",male,36.0,0,1,PC 17755,512.3292,B51 B53 B55,C,3,,"Austria-Hungary / Germantown, Philadelphia, PA"
+Second Class,1,"Webber, Miss. Susan",female,32.5,0,0,27267,13.0,E101,S,12,,"England / Hartford, CT"
+Second Class,0,"Greenberg, Mr. Samuel",male,52.0,0,0,250647,13.0,,S,,19.0,"Bronx, NY"
+First Class,1,"Shutes, Miss. Elizabeth W",female,40.0,0,0,PC 17582,153.4625,C125,S,3,,"New York, NY / Greenwich CT"
+Third Class,0,"Andreasson, Mr. Paul Edvin",male,20.0,0,0,347466,7.8542,,S,,,"Sweden Chicago, IL"
+Third Class,0,"O'Connor, Mr. Patrick",male,,0,0,366713,7.75,,Q,,,
+Third Class,0,"Saade, Mr. Jean Nassr",male,,0,0,2676,7.225,,C,,,
+Third Class,0,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26.0,1,0,A/5. 3336,16.1,,S,,,
+Third Class,0,"Asplund, Master. Carl Edgar",male,5.0,4,2,347077,31.3875,,S,,,"Sweden  Worcester, MA"
+Third Class,1,"Asplund, Master. Edvin Rojj Felix",male,3.0,4,2,347077,31.3875,,S,15,,"Sweden Worcester, MA"
+Third Class,0,"Backstrom, Mr. Karl Alfred",male,32.0,1,0,3101278,15.85,,S,D,,"Ruotsinphytaa, Finland New York, NY"
+Third Class,0,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S,,,
+First Class,1,"Ostby, Miss. Helene Ragnhild",female,22.0,0,1,113509,61.9792,B36,C,5,,"Providence, RI"
+Third Class,0,"Rosblom, Miss. Salli Helena",female,2.0,1,1,370129,20.2125,,S,,,
+Third Class,0,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S,,,
+First Class,1,"Schabert, Mrs. Paul (Emma Mock)",female,35.0,1,0,13236,57.75,C28,C,11,,"New York, NY"
+Third Class,0,"Boulos, Miss. Nourelain",female,9.0,1,1,2678,15.2458,,C,,,"Syria Kent, ON"
+Second Class,0,"Pulbaum, Mr. Franz",male,27.0,0,0,SC/PARIS 2168,15.0333,,C,,,Paris
+Third Class,0,"Colbert, Mr. Patrick",male,24.0,0,0,371109,7.25,,Q,,,"Co Limerick, Ireland Sherbrooke, PQ"
+Third Class,0,"Olsvigen, Mr. Thor Anderson",male,20.0,0,0,6563,9.225,,S,,89.0,"Oslo, Norway Cameron, WI"
+Second Class,1,"Portaluppi, Mr. Emilio Ilario Giuseppe",male,30.0,0,0,C.A. 34644,12.7375,,C,14,,"Milford, NH"
+Third Class,0,"Stoytcheff, Mr. Ilia",male,19.0,0,0,349205,7.8958,,S,,,
+Third Class,1,"Mulvihill, Miss. Bertha E",female,24.0,0,0,382653,7.75,,Q,15,,
+Third Class,0,"Danbom, Master. Gilbert Sigvard Emanuel",male,0.3333,0,2,347080,14.4,,S,,,"Stanton, IA"
+Third Class,0,"Lundahl, Mr. Johan Svensson",male,51.0,0,0,347743,7.0542,,S,,,
+Third Class,0,"Birkeland, Mr. Hans Martin Monsen",male,21.0,0,0,312992,7.775,,S,,,"Brennes, Norway New York"
+Second Class,0,"Deacon, Mr. Percy William",male,17.0,0,0,S.O.C. 14879,73.5,,S,,,
+First Class,0,"Crosby, Capt. Edward Gifford",male,70.0,1,1,WE/P 5735,71.0,B22,S,,269.0,"Milwaukee, WI"
+Second Class,0,"Hold, Mr. Stephen",male,44.0,1,0,26707,26.0,,S,,,"England / Sacramento, CA"
+First Class,1,"Bidois, Miss. Rosalie",female,,0,0,PC 17757,227.525,,C,4,,
+First Class,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64.0,0,2,PC 17756,83.1583,E45,C,14,,"Lakewood, NJ"
+Third Class,1,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S,13,,"Hong Kong New York, NY"
+Third Class,0,"Nirva, Mr. Iisakki Antino Aijo",male,41.0,0,0,SOTON/O2 3101272,7.125,,S,,,"Finland Sudbury, ON"
+Third Class,0,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C,,,
+Third Class,0,"Peltomaki, Mr. Nikolai Johannes",male,25.0,0,0,STON/O 2. 3101291,7.925,,S,,,
+Second Class,1,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22.0,1,1,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+Third Class,0,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S,,255.0,
+Third Class,0,"Saundercock, Mr. William Henry",male,20.0,0,0,A/5. 2151,8.05,,S,,,
+Second Class,0,"de Brito, Mr. Jose Joaquim",male,32.0,0,0,244360,13.0,,S,,,"Portugal / Sau Paulo, Brazil"
+Third Class,0,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S,,,"Liverpool, England Bedford, OH"
+Third Class,1,"Tenglin, Mr. Gunnar Isidor",male,25.0,0,0,350033,7.7958,,S,13 15,,
+Third Class,0,"Rasmussen, Mrs. (Lena Jacobsen Solvang)",female,,0,0,65305,8.1125,,S,,,
+Third Class,1,"Howard, Miss. May Elizabeth",female,,0,0,A. 2. 39186,8.05,,S,C,,
+Second Class,1,"Trout, Mrs. William H (Jessie L)",female,28.0,0,0,240929,12.65,,S,,,"Columbus, OH"
+Third Class,0,"Attalah, Mr. Sleiman",male,30.0,0,0,2694,7.225,,C,,,"Ottawa, ON"
+Third Class,1,"Olsen, Master. Artur Karl",male,9.0,0,1,C 17368,3.1708,,S,13,,
+Second Class,0,"Aldworth, Mr. Charles Augustus",male,30.0,0,0,248744,13.0,,S,,,"Bryn Mawr, PA, USA"
+First Class,0,"Hays, Mr. Charles Melville",male,55.0,1,1,12749,93.5,B69,S,,307.0,"Montreal, PQ"
+Second Class,1,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31.0,1,1,C.A. 31921,26.25,,S,14,,"Bishopstoke, Hants / Fayette Valley, ID"
+Third Class,1,"Drapkin, Miss. Jennie",female,23.0,0,0,SOTON/OQ 392083,8.05,,S,,,"London New York, NY"
+Second Class,0,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44.0,1,0,244252,26.0,,S,,,London
+First Class,1,"Taylor, Mr. Elmer Zebley",male,48.0,1,0,19996,52.0,C126,S,5 7,,"London /  East Orange, NJ"
+First Class,1,"Graham, Miss. Margaret Edith",female,19.0,0,0,112053,30.0,B42,S,3,,"Greenwich, CT"
+Third Class,1,"Osman, Mrs. Mara",female,31.0,0,0,349244,8.6833,,S,,,
+Second Class,0,"Hale, Mr. Reginald",male,30.0,0,0,250653,13.0,,S,,75.0,"Auburn, NY"
+First Class,1,"Silverthorne, Mr. Spencer Victor",male,35.0,0,0,PC 17475,26.2875,E24,S,5,,"St Louis, MO"
+Second Class,1,"Doling, Miss. Elsie",female,18.0,0,1,231919,23.0,,S,,,Southampton
+Third Class,0,"Spector, Mr. Woolf",male,,0,0,A.5. 3236,8.05,,S,,,
+First Class,0,"Astor, Col. John Jacob",male,47.0,1,0,PC 17757,227.525,C62 C64,C,,124.0,"New York, NY"
+Second Class,1,"Mallet, Mrs. Albert (Antoinette Magnin)",female,24.0,1,1,S.C./PARIS 2079,37.0042,,C,10,,"Paris / Montreal, PQ"
+Third Class,0,"Carlsson, Mr. August Sigfrid",male,28.0,0,0,350042,7.7958,,S,,,"Dagsas, Sweden Fower, MN"
+Third Class,0,"Goodwin, Master. Harold Victor",male,9.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+Second Class,0,"Harper, Rev. John",male,28.0,0,1,248727,33.0,,S,,,"Denmark Hill, Surrey / Chicago"
+Third Class,1,"Hedman, Mr. Oskar Arvid",male,27.0,0,0,347089,6.975,,S,15,,
+First Class,1,"Willard, Miss. Constance",female,21.0,0,0,113795,26.55,,S,8 10,,"Duluth, MN"
+First Class,0,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50.0,A32,S,,,"Seattle, WA"
+Third Class,0,"Perkin, Mr. John Henry",male,22.0,0,0,A/5 21174,7.25,,S,,,
+Third Class,0,"Mardirosian, Mr. Sarkis",male,,0,0,2655,7.2292,F E46,C,,,
+Third Class,0,"Ford, Miss. Doolina Margaret ""Daisy""",female,21.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+Third Class,0,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40.0,1,5,347077,31.3875,,S,,142.0,"Sweden  Worcester, MA"
+Third Class,0,"Carr, Miss. Jeannie",female,37.0,0,0,368364,7.75,,Q,,,"Co Sligo, Ireland Hartford, CT"
+Third Class,1,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S,16,,"Liverpool, England Bedford, OH"
+Third Class,1,"Sunderland, Mr. Victor Francis",male,16.0,0,0,SOTON/OQ 392089,8.05,,S,B,,
+Third Class,0,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C,,196.0,
+Third Class,1,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q,16,,
+Third Class,0,"Klasen, Miss. Gertrud Emilia",female,1.0,1,1,350405,12.1833,,S,,,
+Third Class,0,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S,,,
+Third Class,0,"Cacic, Mr. Jego Grga",male,18.0,0,0,315091,8.6625,,S,,,
+Third Class,0,"Petterson, Mr. Johan Emil",male,25.0,1,0,347076,7.775,,S,,,
+Third Class,0,"Vander Planke, Mr. Julius",male,31.0,3,0,345763,18.0,,S,,,
+Third Class,0,"Fox, Mr. Patrick",male,,0,0,368573,7.75,,Q,,,"Ireland New York, NY"
+Second Class,0,"Louch, Mr. Charles Alexander",male,50.0,1,0,SC/AH 3085,26.0,,S,,121.0,"Weston-Super-Mare, Somerset"
+Third Class,0,"Andersson, Miss. Ingeborg Constanzia",female,9.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+Third Class,0,"Osen, Mr. Olaf Elon",male,16.0,0,0,7534,9.2167,,S,,,
+First Class,1,"Rheims, Mr. George Alexander Lucien",male,,0,0,PC 17607,39.6,,S,A,,"Paris /  New York, NY"
+Third Class,0,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,1,0,349237,17.8,,S,,,"Altdorf, Switzerland"
+Third Class,1,"Bing, Mr. Lee",male,32.0,0,0,1601,56.4958,,S,C,,"Hong Kong New York, NY"
+Third Class,0,"Riihivouri, Miss. Susanna Juhantytar ""Sanni""",female,22.0,0,0,3101295,39.6875,,S,,,
+Second Class,0,"Jenkin, Mr. Stephen Curnow",male,32.0,0,0,C.A. 33111,10.5,,S,,,"St Ives, Cornwall / Houghton, MI"
+First Class,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S,5,,"New York, NY"
+First Class,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60.0,1,4,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+Second Class,0,"Berriman, Mr. William John",male,23.0,0,0,28425,13.0,,S,,,"St Ives, Cornwall / Calumet, MI"
+Third Class,1,"Turja, Miss. Anna Sofia",female,18.0,0,0,4138,9.8417,,S,15,,
+Second Class,0,"Nicholls, Mr. Joseph Charles",male,19.0,1,1,C.A. 33112,36.75,,S,,101.0,"Cornwall / Hancock, MI"
+Third Class,0,"Oreskovic, Miss. Jelka",female,23.0,0,0,315085,8.6625,,S,,,
+Second Class,0,"Andrew, Mr. Edgardo Samuel",male,18.0,0,0,231945,11.5,,S,,,"Buenos Aires, Argentina / New Jersey, NJ"
+Third Class,0,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S,,,
+Third Class,0,"Saether, Mr. Simon Sivertsen",male,38.5,0,0,SOTON/O.Q. 3101262,7.25,,S,,32.0,
+Third Class,0,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q,,,
+Third Class,0,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S,,,
+Third Class,0,"Davies, Mr. John Samuel",male,21.0,2,0,A/4 48871,24.15,,S,,,"West Bromwich, England Pontiac, MI"
+First Class,0,"Artagaveytia, Mr. Ramon",male,71.0,0,0,PC 17609,49.5042,,C,,22.0,"Montevideo, Uruguay"
+First Class,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37.0,1,0,19928,90.0,C78,Q,14,,"Fond du Lac, WI"
+First Class,0,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C,,,"Philadelphia, PA"
+Third Class,0,"Dennis, Mr. Samuel",male,22.0,0,0,A/5 21172,7.25,,S,,,
+Second Class,0,"Kantor, Mr. Sinai",male,34.0,1,0,244367,26.0,,S,,283.0,"Moscow / Bronx, NY"
+Second Class,0,"Richard, Mr. Emile",male,23.0,0,0,SC/PARIS 2133,15.0458,,C,,,"Paris / Montreal, PQ"
+Third Class,0,"Corn, Mr. Harry",male,30.0,0,0,SOTON/OQ 392090,8.05,,S,,,London
+Third Class,1,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q,16,,
+First Class,0,"Minahan, Dr. William Edward",male,44.0,2,0,19928,90.0,C78,Q,,230.0,"Fond du Lac, WI"
+First Class,1,"Lines, Miss. Mary Conover",female,16.0,0,1,PC 17592,39.4,D28,S,9,,"Paris, France"
+Third Class,1,"Jalsevac, Mr. Ivan",male,29.0,0,0,349240,7.8958,,C,15,,
+Third Class,0,"Leonard, Mr. Lionel",male,36.0,0,0,LINE,0.0,,S,,,
+First Class,0,"Smith, Mr. Richard William",male,,0,0,113056,26.0,A19,S,,,"Streatham, Surrey"
+Third Class,1,"Nicola-Yarred, Master. Elias",male,12.0,1,0,2651,11.2417,,C,C,,
+Third Class,1,"Sandstrom, Miss. Marguerite Rut",female,4.0,1,1,PP 9549,16.7,G6,S,13,,
+Second Class,0,"Kvillner, Mr. Johan Henrik Johannesson",male,31.0,0,0,C.A. 18723,10.5,,S,,165.0,"Sweden / Arlington, NJ"
+Third Class,0,"Barton, Mr. David John",male,22.0,0,0,324669,8.05,,S,,,"England New York, NY"
+First Class,1,"Ryerson, Master. John Borie",male,13.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+Third Class,0,"Larsson, Mr. August Viktor",male,29.0,0,0,7545,9.4833,,S,,,
+Third Class,0,"Robins, Mr. Alexander A",male,50.0,1,0,A/5. 3337,14.5,,S,,119.0,
+Third Class,1,"Nilsson, Miss. Helmina Josefina",female,26.0,0,0,347470,7.8542,,S,13,,
+Second Class,1,"Buss, Miss. Kate",female,,0,0,27849,13.0,,S,9,,"Sittingbourne, England / San Diego, CA"
+Third Class,0,"Minkoff, Mr. Lazar",male,-21.0,0,0,349211,7.8958,,S,,,
+Third Class,0,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q,,,
+First Class,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26.0,1,0,13508,136.7792,C89,C,4,,"Los Angeles, CA"
+Second Class,0,"Andrew, Mr. Frank Thomas",male,25.0,0,0,C.A. 34050,10.5,,S,,,"Cornwall, England Houghton, MI"
+Third Class,1,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,0,2,347742,11.1333,,S,15,,
+Third Class,0,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S,,,
+Third Class,1,"Devaney, Miss. Margaret Delia",female,19.0,0,0,330958,7.8792,,Q,C,,"Kilmacowen, Co Sligo, Ireland New York, NY"
+Third Class,0,"Carver, Mr. Alfred John",male,28.0,0,0,392095,7.25,,S,,,"St Denys, Southampton, Hants"
+Third Class,0,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q,,,
+Third Class,0,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S,,,
+Second Class,0,"Fynney, Mr. Joseph J",male,35.0,0,0,239865,26.0,,S,,322.0,"Liverpool / Montreal, PQ"
+Third Class,0,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C,,,"Ottawa, ON"
+Third Class,0,"Van Impe, Mr. Jean Baptiste",male,36.0,1,1,345773,24.15,,S,,,
+First Class,1,"Burns, Miss. Elizabeth Margaret",female,41.0,0,0,16966,134.5,E40,C,3,,
+Third Class,1,"Nakid, Mr. Sahid",male,20.0,1,1,2653,15.7417,,C,C,,
+Second Class,1,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30.0,3,0,31027,21.0,,S,,,"Elizabeth, NJ"
+Third Class,0,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48.0,1,3,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+Third Class,0,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S,,,
+Second Class,1,"Cameron, Miss. Clear Annie",female,35.0,0,0,F.C.C. 13528,21.0,,S,14,,"Mamaroneck, NY"
+Third Class,1,"Moor, Master. Meier",male,6.0,0,1,392096,12.475,E121,S,14,,
+First Class,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24.0,0,0,PC 17482,49.5042,C90,C,6,,"Belgium  Montreal, PQ"
+Third Class,0,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S,,,
+First Class,0,"Davidson, Mr. Thornton",male,31.0,1,0,F.C. 12750,52.0,B71,S,,,"Montreal, PQ"
+Third Class,1,"Abelseth, Miss. Karen Marie",female,16.0,0,0,348125,7.65,,S,16,,"Norway Los Angeles, CA"
+Third Class,0,"Sutehall, Mr. Henry Jr",male,25.0,0,0,SOTON/OQ 392076,7.05,,S,,,
+Third Class,0,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q,,,
+Second Class,0,"Cotterill, Mr. Henry ""Harry""",male,21.0,0,0,29107,11.5,,S,,,"Penzance, Cornwall / Akron, OH"
+Third Class,0,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S,,,
+Third Class,1,"Salkjelsvik, Miss. Anna Kristine",female,21.0,0,0,343120,7.65,,S,C,,
+First Class,1,"White, Mrs. John Stuart (Ella Holmes)",female,55.0,0,0,PC 17760,135.6333,C32,C,8,,"New York, NY / Briarcliff Manor NY"
+First Class,1,"Lurette, Miss. Elise",female,58.0,0,0,PC 17569,146.5208,B80,C,,,
+Third Class,0,"Celotti, Mr. Francesco",male,24.0,0,0,343275,8.05,,S,,,London
+Third Class,0,"Fischer, Mr. Eberhard Thelander",male,18.0,0,0,350036,7.7958,,S,,,
+Second Class,0,"Otter, Mr. Richard",male,39.0,0,0,28213,13.0,,S,,,"Middleburg Heights, OH"
diff --git a/_data/titanic.csv b/_data/titanic.csv
new file mode 100644
index 0000000..cd739e8
--- /dev/null
+++ b/_data/titanic.csv
@@ -0,0 +1,1310 @@
+pclass,survived,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked,boat,body,home.dest
+1,1,"Allen, Miss. Elisabeth Walton",female,29.0,0,0,24160,211.3375,B5,S,2,,"St Louis, MO"
+1,1,"Allison, Master. Hudson Trevor",male,0.9167000000000001,1,2,113781,151.55,C22 C26,S,11,,"Montreal, PQ / Chesterville, ON"
+1,0,"Allison, Miss. Helen Loraine",female,2.0,1,2,113781,151.55,C22 C26,S,,,"Montreal, PQ / Chesterville, ON"
+1,0,"Allison, Mr. Hudson Joshua Creighton",male,30.0,1,2,113781,151.55,C22 C26,S,,135.0,"Montreal, PQ / Chesterville, ON"
+1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25.0,1,2,113781,151.55,C22 C26,S,,,"Montreal, PQ / Chesterville, ON"
+1,1,"Anderson, Mr. Harry",male,48.0,0,0,19952,26.55,E12,S,3,,"New York, NY"
+1,1,"Andrews, Miss. Kornelia Theodosia",female,63.0,1,0,13502,77.9583,D7,S,10,,"Hudson, NY"
+1,0,"Andrews, Mr. Thomas Jr",male,39.0,0,0,112050,0.0,A36,S,,,"Belfast, NI"
+1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53.0,2,0,11769,51.4792,C101,S,D,,"Bayside, Queens, NY"
+1,0,"Artagaveytia, Mr. Ramon",male,71.0,0,0,PC 17609,49.5042,,C,,22.0,"Montevideo, Uruguay"
+1,0,"Astor, Col. John Jacob",male,47.0,1,0,PC 17757,227.525,C62 C64,C,,124.0,"New York, NY"
+1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18.0,1,0,PC 17757,227.525,C62 C64,C,4,,"New York, NY"
+1,1,"Aubart, Mme. Leontine Pauline",female,24.0,0,0,PC 17477,69.3,B35,C,9,,"Paris, France"
+1,1,"Barber, Miss. Ellen ""Nellie""",female,26.0,0,0,19877,78.85,,S,6,,
+1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80.0,0,0,27042,30.0,A23,S,B,,"Hessle, Yorks"
+1,0,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S,,,"New York, NY"
+1,0,"Baxter, Mr. Quigg Edmond",male,24.0,0,1,PC 17558,247.5208,B58 B60,C,,,"Montreal, PQ"
+1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50.0,0,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ"
+1,1,"Bazzani, Miss. Albina",female,32.0,0,0,11813,76.2917,D15,C,8,,
+1,0,"Beattie, Mr. Thomson",male,36.0,0,0,13050,75.2417,C6,C,A,,"Winnipeg, MN"
+1,1,"Beckwith, Mr. Richard Leonard",male,37.0,1,1,11751,52.5542,D35,S,5,,"New York, NY"
+1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47.0,1,1,11751,52.5542,D35,S,5,,"New York, NY"
+1,1,"Behr, Mr. Karl Howell",male,26.0,0,0,111369,30.0,C148,C,5,,"New York, NY"
+1,1,"Bidois, Miss. Rosalie",female,42.0,0,0,PC 17757,227.525,,C,4,,
+1,1,"Bird, Miss. Ellen",female,29.0,0,0,PC 17483,221.7792,C97,S,8,,
+1,0,"Birnbaum, Mr. Jakob",male,25.0,0,0,13905,26.0,,C,,148.0,"San Francisco, CA"
+1,1,"Bishop, Mr. Dickinson H",male,25.0,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI"
+1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19.0,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI"
+1,1,"Bissette, Miss. Amelia",female,35.0,0,0,PC 17760,135.6333,C99,S,8,,
+1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28.0,0,0,110564,26.55,C52,S,D,,"Stockholm, Sweden / Washington, DC"
+1,0,"Blackwell, Mr. Stephen Weart",male,45.0,0,0,113784,35.5,T,S,,,"Trenton, NJ"
+1,1,"Blank, Mr. Henry",male,40.0,0,0,112277,31.0,A31,C,7,,"Glen Ridge, NJ"
+1,1,"Bonnell, Miss. Caroline",female,30.0,0,0,36928,164.8667,C7,S,8,,"Youngstown, OH"
+1,1,"Bonnell, Miss. Elizabeth",female,58.0,0,0,113783,26.55,C103,S,8,,"Birkdale, England Cleveland, Ohio"
+1,0,"Borebank, Mr. John James",male,42.0,0,0,110489,26.55,D22,S,,,"London / Winnipeg, MB"
+1,1,"Bowen, Miss. Grace Scott",female,45.0,0,0,PC 17608,262.375,,C,4,,"Cooperstown, NY"
+1,1,"Bowerman, Miss. Elsie Edith",female,22.0,0,1,113505,55.0,E33,S,6,,"St Leonards-on-Sea, England Ohio"
+1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S,9,,"Los Angeles, CA"
+1,0,"Brady, Mr. John Bertram",male,41.0,0,0,113054,30.5,A21,S,,,"Pomeroy, WA"
+1,0,"Brandeis, Mr. Emil",male,48.0,0,0,PC 17591,50.4958,B10,C,,208.0,"Omaha, NE"
+1,0,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C,,,"Philadelphia, PA"
+1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44.0,0,0,PC 17610,27.7208,B4,C,6,,"Denver, CO"
+1,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,59.0,2,0,11769,51.4792,C101,S,D,,"Belmont, MA"
+1,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60.0,0,0,11813,76.2917,D15,C,8,,"Philadelphia, PA"
+1,1,"Burns, Miss. Elizabeth Margaret",female,41.0,0,0,16966,134.5,E40,C,3,,
+1,0,"Butt, Major. Archibald Willingham",male,45.0,0,0,113050,26.55,B38,S,,,"Washington, DC"
+1,0,"Cairns, Mr. Alexander",male,,0,0,113798,31.0,,S,,,
+1,1,"Calderhead, Mr. Edward Pennington",male,42.0,0,0,PC 17476,26.2875,E24,S,5,,"New York, NY"
+1,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)",female,53.0,0,0,PC 17606,27.4458,,C,6,,"Washington, DC"
+1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36.0,0,1,PC 17755,512.3292,B51 B53 B55,C,3,,"Austria-Hungary / Germantown, Philadelphia, PA"
+1,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58.0,0,1,PC 17755,512.3292,B51 B53 B55,C,3,,"Germantown, Philadelphia, PA"
+1,0,"Carlsson, Mr. Frans Olof",male,33.0,0,0,695,5.0,B51 B53 B55,S,,,"New York, NY"
+1,0,"Carrau, Mr. Francisco M",male,28.0,0,0,113059,47.1,,S,,,"Montevideo, Uruguay"
+1,0,"Carrau, Mr. Jose Pedro",male,17.0,0,0,113059,47.1,,S,,,"Montevideo, Uruguay"
+1,1,"Carter, Master. William Thornton II",male,11.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+1,1,"Carter, Miss. Lucile Polk",female,14.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+1,1,"Carter, Mr. William Ernest",male,36.0,1,2,113760,120.0,B96 B98,S,C,,"Bryn Mawr, PA"
+1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36.0,1,2,113760,120.0,B96 B98,S,4,,"Bryn Mawr, PA"
+1,0,"Case, Mr. Howard Brown",male,49.0,0,0,19924,26.0,,S,,,"Ascot, Berkshire / Rochester, NY"
+1,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C,5,,"New York, NY"
+1,0,"Cavendish, Mr. Tyrell William",male,36.0,1,0,19877,78.85,C46,S,,172.0,"Little Onn Hall, Staffs"
+1,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76.0,1,0,19877,78.85,C46,S,6,,"Little Onn Hall, Staffs"
+1,0,"Chaffee, Mr. Herbert Fuller",male,46.0,1,0,W.E.P. 5734,61.175,E31,S,,,"Amenia, ND"
+1,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47.0,1,0,W.E.P. 5734,61.175,E31,S,4,,"Amenia, ND"
+1,1,"Chambers, Mr. Norman Campbell",male,27.0,1,0,113806,53.1,E8,S,5,,"New York, NY / Ithaca, NY"
+1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33.0,1,0,113806,53.1,E8,S,5,,"New York, NY / Ithaca, NY"
+1,1,"Chaudanson, Miss. Victorine",female,36.0,0,0,PC 17608,262.375,B61,C,4,,
+1,1,"Cherry, Miss. Gladys",female,30.0,0,0,110152,86.5,B77,S,8,,"London, England"
+1,1,"Chevre, Mr. Paul Romaine",male,45.0,0,0,PC 17594,29.7,A9,C,7,,"Paris, France"
+1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55.0,E33,S,6,,"St Leonards-on-Sea, England Ohio"
+1,0,"Chisholm, Mr. Roderick Robert Crispin",male,,0,0,112051,0.0,,S,,,"Liverpool, England / Belfast"
+1,0,"Clark, Mr. Walter Miller",male,27.0,1,0,13508,136.7792,C89,C,,,"Los Angeles, CA"
+1,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26.0,1,0,13508,136.7792,C89,C,4,,"Los Angeles, CA"
+1,1,"Cleaver, Miss. Alice",female,22.0,0,0,113781,151.55,,S,11,,
+1,0,"Clifford, Mr. George Quincy",male,,0,0,110465,52.0,A14,S,,,"Stoughton, MA"
+1,0,"Colley, Mr. Edward Pomeroy",male,47.0,0,0,5727,25.5875,E58,S,,,"Victoria, BC"
+1,1,"Compton, Miss. Sara Rebecca",female,39.0,1,1,PC 17756,83.1583,E49,C,14,,"Lakewood, NJ"
+1,0,"Compton, Mr. Alexander Taylor Jr",male,37.0,1,1,PC 17756,83.1583,E52,C,,,"Lakewood, NJ"
+1,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64.0,0,2,PC 17756,83.1583,E45,C,14,,"Lakewood, NJ"
+1,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55.0,2,0,11770,25.7,C101,S,2,,"New York, NY"
+1,0,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S,,,"Roachdale, IN"
+1,0,"Crosby, Capt. Edward Gifford",male,70.0,1,1,WE/P 5735,71.0,B22,S,,269.0,"Milwaukee, WI"
+1,1,"Crosby, Miss. Harriet R",female,36.0,0,2,WE/P 5735,71.0,B22,S,7,,"Milwaukee, WI"
+1,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64.0,1,1,112901,26.55,B26,S,7,,"Milwaukee, WI"
+1,0,"Cumings, Mr. John Bradley",male,39.0,1,0,PC 17599,71.2833,C85,C,,,"New York, NY"
+1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,C,4,,"New York, NY"
+1,1,"Daly, Mr. Peter Denis ",male,51.0,0,0,113055,26.55,E17,S,5 9,,"Lima, Peru"
+1,1,"Daniel, Mr. Robert Williams",male,27.0,0,0,113804,30.5,,S,3,,"Philadelphia, PA"
+1,1,"Daniels, Miss. Sarah",female,33.0,0,0,113781,151.55,,S,8,,
+1,0,"Davidson, Mr. Thornton",male,31.0,1,0,F.C. 12750,52.0,B71,S,,,"Montreal, PQ"
+1,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27.0,1,2,F.C. 12750,52.0,B71,S,3,,"Montreal, PQ"
+1,1,"Dick, Mr. Albert Adrian",male,31.0,1,0,17474,57.0,B20,S,3,,"Calgary, AB"
+1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17.0,1,0,17474,57.0,B20,S,3,,"Calgary, AB"
+1,1,"Dodge, Dr. Washington",male,53.0,1,1,33638,81.8583,A34,S,13,,"San Francisco, CA"
+1,1,"Dodge, Master. Washington",male,4.0,0,2,33638,81.8583,A34,S,5,,"San Francisco, CA"
+1,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54.0,1,1,33638,81.8583,A34,S,5,,"San Francisco, CA"
+1,0,"Douglas, Mr. Walter Donald",male,50.0,1,0,PC 17761,106.425,C86,C,,62.0,"Deephaven, MN / Cedar Rapids, IA"
+1,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27.0,1,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ"
+1,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48.0,1,0,PC 17761,106.425,C86,C,2,,"Deephaven, MN / Cedar Rapids, IA"
+1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48.0,1,0,11755,39.6,A16,C,1,,London / Paris
+1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49.0,1,0,PC 17485,56.9292,A20,C,1,,London / Paris
+1,0,"Dulles, Mr. William Crothers",male,39.0,0,0,PC 17580,29.7,A18,C,,133.0,"Philadelphia, PA"
+1,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23.0,0,1,11767,83.1583,C54,C,7,,"Mt Airy, Philadelphia, PA"
+1,1,"Endres, Miss. Caroline Louise",female,38.0,0,0,PC 17757,227.525,C45,C,4,,"New York, NY"
+1,1,"Eustis, Miss. Elizabeth Mussey",female,54.0,1,0,36947,78.2667,D20,C,4,,"Brookline, MA"
+1,0,"Evans, Miss. Edith Corse",female,36.0,0,0,PC 17531,31.6792,A29,C,,,"New York, NY"
+1,0,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S,,,
+1,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S,7,,"New York, NY"
+1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C,4,,
+1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36.0,0,0,PC 17474,26.3875,E25,S,5,,"Brooklyn, NY"
+1,0,"Foreman, Mr. Benjamin Laventall",male,30.0,0,0,113051,27.75,C111,C,,,"New York, NY"
+1,1,"Fortune, Miss. Alice Elizabeth",female,24.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+1,1,"Fortune, Miss. Ethel Flora",female,28.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+1,1,"Fortune, Miss. Mabel Helen",female,23.0,3,2,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+1,0,"Fortune, Mr. Charles Alexander",male,19.0,3,2,19950,263.0,C23 C25 C27,S,,,"Winnipeg, MB"
+1,0,"Fortune, Mr. Mark",male,64.0,1,4,19950,263.0,C23 C25 C27,S,,,"Winnipeg, MB"
+1,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60.0,1,4,19950,263.0,C23 C25 C27,S,10,,"Winnipeg, MB"
+1,1,"Francatelli, Miss. Laura Mabel",female,30.0,0,0,PC 17485,56.9292,E36,C,1,,
+1,0,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S,,,"Westcliff-on-Sea, Essex"
+1,1,"Frauenthal, Dr. Henry William",male,50.0,2,0,PC 17611,133.65,,S,5,,"New York, NY"
+1,1,"Frauenthal, Mr. Isaac Gerald",male,43.0,1,0,17765,27.7208,D40,C,5,,"New York, NY"
+1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S,5,,"New York, NY"
+1,1,"Frolicher, Miss. Hedwig Margaritha",female,22.0,0,2,13568,49.5,B39,C,5,,"Zurich, Switzerland"
+1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60.0,1,1,13567,79.2,B41,C,5,,"Zurich, Switzerland"
+1,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48.0,1,1,13567,79.2,B41,C,5,,"Zurich, Switzerland"
+1,0,"Fry, Mr. Richard",male,,0,0,112058,0.0,B102,S,,,
+1,0,"Futrelle, Mr. Jacques Heath",male,37.0,1,0,113803,53.1,C123,S,,,"Scituate, MA"
+1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,S,D,,"Scituate, MA"
+1,0,"Gee, Mr. Arthur H",male,47.0,0,0,111320,38.5,E63,S,,275.0,"St Anne's-on-Sea, Lancashire"
+1,1,"Geiger, Miss. Amalie",female,35.0,0,0,113503,211.5,C130,C,4,,
+1,1,"Gibson, Miss. Dorothy Winifred",female,22.0,0,1,112378,59.4,,C,7,,"New York, NY"
+1,1,"Gibson, Mrs. Leonard (Pauline C Boeson)",female,45.0,0,1,112378,59.4,,C,7,,"New York, NY"
+1,0,"Giglio, Mr. Victor",male,24.0,0,0,PC 17593,79.2,B86,C,,,
+1,1,"Goldenberg, Mr. Samuel L",male,49.0,1,0,17453,89.1042,C92,C,5,,"Paris, France / New York, NY"
+1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C,5,,"Paris, France / New York, NY"
+1,0,"Goldschmidt, Mr. George B",male,71.0,0,0,PC 17754,34.6542,A5,C,,,"New York, NY"
+1,1,"Gracie, Col. Archibald IV",male,53.0,0,0,113780,28.5,C51,C,B,,"Washington, DC"
+1,1,"Graham, Miss. Margaret Edith",female,19.0,0,0,112053,30.0,B42,S,3,,"Greenwich, CT"
+1,0,"Graham, Mr. George Edward",male,38.0,0,1,PC 17582,153.4625,C91,S,,147.0,"Winnipeg, MB"
+1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58.0,0,1,PC 17582,153.4625,C125,S,3,,"Greenwich, CT"
+1,1,"Greenfield, Mr. William Bertram",male,23.0,0,1,PC 17759,63.3583,D10 D12,C,7,,"New York, NY"
+1,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45.0,0,1,PC 17759,63.3583,D10 D12,C,7,,"New York, NY"
+1,0,"Guggenheim, Mr. Benjamin",male,46.0,0,0,PC 17593,79.2,B82 B84,C,,,"New York, NY"
+1,1,"Harder, Mr. George Achilles",male,25.0,1,0,11765,55.4417,E50,C,5,,"Brooklyn, NY"
+1,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25.0,1,0,11765,55.4417,E50,C,5,,"Brooklyn, NY"
+1,1,"Harper, Mr. Henry Sleeper",male,48.0,1,0,PC 17572,76.7292,D33,C,3,,"New York, NY"
+1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,1,0,PC 17572,76.7292,D33,C,3,,"New York, NY"
+1,0,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S,,,
+1,0,"Harris, Mr. Henry Birkhardt",male,45.0,1,0,36973,83.475,C83,S,,,"New York, NY"
+1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35.0,1,0,36973,83.475,C83,S,D,,"New York, NY"
+1,0,"Harrison, Mr. William",male,40.0,0,0,112059,0.0,B94,S,,110.0,
+1,1,"Hassab, Mr. Hammad",male,27.0,0,0,PC 17572,76.7292,D49,C,3,,
+1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30.0,D45,S,3,,"Kingston, Surrey"
+1,1,"Hays, Miss. Margaret Bechstein",female,24.0,0,0,11767,83.1583,C54,C,7,,"New York, NY"
+1,0,"Hays, Mr. Charles Melville",male,55.0,1,1,12749,93.5,B69,S,,307.0,"Montreal, PQ"
+1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52.0,1,1,12749,93.5,B69,S,3,,"Montreal, PQ"
+1,0,"Head, Mr. Christopher",male,42.0,0,0,113038,42.5,B11,S,,,London / Middlesex
+1,0,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S,,,"Brighton, MA"
+1,0,"Hipkins, Mr. William Edward",male,55.0,0,0,680,50.0,C39,S,,,London / Birmingham
+1,1,"Hippach, Miss. Jean Gertrude",female,16.0,0,1,111361,57.9792,B18,C,4,,"Chicago, IL"
+1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44.0,0,1,111361,57.9792,B18,C,4,,"Chicago, IL"
+1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51.0,1,0,13502,77.9583,D11,S,10,,"Hudson, NY"
+1,0,"Holverson, Mr. Alexander Oskar",male,42.0,1,0,113789,52.0,,S,,38.0,"New York, NY"
+1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35.0,1,0,113789,52.0,,S,8,,"New York, NY"
+1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35.0,0,0,111426,26.55,,C,15,,"Indianapolis, IN"
+1,1,"Hoyt, Mr. Frederick Maxfield",male,38.0,1,0,19943,90.0,C93,S,D,,"New York, NY /  Stamford CT"
+1,0,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C,14,,"New York, NY"
+1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35.0,1,0,19943,90.0,C93,S,D,,"New York, NY /  Stamford CT"
+1,1,"Icard, Miss. Amelie",female,38.0,0,0,113572,80.0,B28,,6,,
+1,0,"Isham, Miss. Ann Elizabeth",female,50.0,0,0,PC 17595,28.7125,C49,C,,,"Paris, France New York, NY"
+1,1,"Ismay, Mr. Joseph Bruce",male,49.0,0,0,112058,0.0,B52 B54 B56,S,C,,Liverpool
+1,0,"Jones, Mr. Charles Cresson",male,46.0,0,0,694,26.0,,S,,80.0,"Bennington, VT"
+1,0,"Julian, Mr. Henry Forbes",male,50.0,0,0,113044,26.0,E60,S,,,London
+1,0,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C,,45.0,
+1,0,"Kent, Mr. Edward Austin",male,58.0,0,0,11771,29.7,B37,C,,258.0,"Buffalo, NY"
+1,0,"Kenyon, Mr. Frederick R",male,41.0,1,0,17464,51.8625,D21,S,,,"Southington / Noank, CT"
+1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S,8,,"Southington / Noank, CT"
+1,1,"Kimball, Mr. Edwin Nelson Jr",male,42.0,1,0,11753,52.5542,D19,S,5,,"Boston, MA"
+1,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45.0,1,0,11753,52.5542,D19,S,5,,"Boston, MA"
+1,0,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S,,,"Portland, OR"
+1,1,"Kreuchen, Miss. Emilie",female,39.0,0,0,24160,211.3375,,S,2,,
+1,1,"Leader, Dr. Alice (Farnham)",female,49.0,0,0,17465,25.9292,D17,S,8,,"New York, NY"
+1,1,"LeRoy, Miss. Bertha",female,30.0,0,0,PC 17761,106.425,,C,2,,
+1,1,"Lesurer, Mr. Gustave J",male,35.0,0,0,PC 17755,512.3292,B101,C,3,,
+1,0,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C,,,"Chicago, IL"
+1,0,"Lindeberg-Lind, Mr. Erik Gustaf (""Mr Edward Lingrey"")",male,42.0,0,0,17475,26.55,,S,,,"Stockholm, Sweden"
+1,1,"Lindstrom, Mrs. Carl Johan (Sigrid Posse)",female,55.0,0,0,112377,27.7208,,C,6,,"Stockholm, Sweden"
+1,1,"Lines, Miss. Mary Conover",female,16.0,0,1,PC 17592,39.4,D28,S,9,,"Paris, France"
+1,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51.0,0,1,PC 17592,39.4,D28,S,9,,"Paris, France"
+1,0,"Long, Mr. Milton Clyde",male,29.0,0,0,113501,30.0,D6,S,,126.0,"Springfield, MA"
+1,1,"Longley, Miss. Gretchen Fiske",female,21.0,0,0,13502,77.9583,D9,S,10,,"Hudson, NY"
+1,0,"Loring, Mr. Joseph Holland",male,30.0,0,0,113801,45.5,,S,,,"London / New York, NY"
+1,1,"Lurette, Miss. Elise",female,58.0,0,0,PC 17569,146.5208,B80,C,,,
+1,1,"Madill, Miss. Georgette Alexandra",female,15.0,0,1,24160,211.3375,B5,S,2,,"St Louis, MO"
+1,0,"Maguire, Mr. John Edward",male,30.0,0,0,110469,26.0,C106,S,,,"Brockton, MA"
+1,1,"Maioni, Miss. Roberta",female,16.0,0,0,110152,86.5,B79,S,8,,
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+1,0,"Marvin, Mr. Daniel Warner",male,19.0,1,0,113773,53.1,D30,S,,,"New York, NY"
+1,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18.0,1,0,113773,53.1,D30,S,10,,"New York, NY"
+1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24.0,0,0,PC 17482,49.5042,C90,C,6,,"Belgium  Montreal, PQ"
+1,0,"McCaffry, Mr. Thomas Francis",male,46.0,0,0,13050,75.2417,C6,C,,292.0,"Vancouver, BC"
+1,0,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,S,,175.0,"Dorchester, MA"
+1,1,"McGough, Mr. James Robert",male,36.0,0,0,PC 17473,26.2875,E25,S,7,,"Philadelphia, PA"
+1,0,"Meyer, Mr. Edgar Joseph",male,28.0,1,0,PC 17604,82.1708,,C,,,"New York, NY"
+1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C,6,,"New York, NY"
+1,0,"Millet, Mr. Francis Davis",male,65.0,0,0,13509,26.55,E38,S,,249.0,"East Bridgewater, MA"
+1,0,"Minahan, Dr. William Edward",male,44.0,2,0,19928,90.0,C78,Q,,230.0,"Fond du Lac, WI"
+1,1,"Minahan, Miss. Daisy E",female,33.0,1,0,19928,90.0,C78,Q,14,,"Green Bay, WI"
+1,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37.0,1,0,19928,90.0,C78,Q,14,,"Fond du Lac, WI"
+1,1,"Mock, Mr. Philipp Edmund",male,30.0,1,0,13236,57.75,C78,C,11,,"New York, NY"
+1,0,"Molson, Mr. Harry Markland",male,55.0,0,0,113787,30.5,C30,S,,,"Montreal, PQ"
+1,0,"Moore, Mr. Clarence Bloomfield",male,47.0,0,0,113796,42.4,,S,,,"Washington, DC"
+1,0,"Natsch, Mr. Charles H",male,37.0,0,1,PC 17596,29.7,C118,C,,,"Brooklyn, NY"
+1,1,"Newell, Miss. Madeleine",female,31.0,1,0,35273,113.275,D36,C,6,,"Lexington, MA"
+1,1,"Newell, Miss. Marjorie",female,23.0,1,0,35273,113.275,D36,C,6,,"Lexington, MA"
+1,0,"Newell, Mr. Arthur Webster",male,58.0,0,2,35273,113.275,D48,C,,122.0,"Lexington, MA"
+1,1,"Newsom, Miss. Helen Monypeny",female,19.0,0,2,11752,26.2833,D47,S,5,,"New York, NY"
+1,0,"Nicholson, Mr. Arthur Ernest",male,64.0,0,0,693,26.0,,S,,263.0,"Isle of Wight, England"
+1,1,"Oliva y Ocana, Dona. Fermina",female,39.0,0,0,PC 17758,108.9,C105,C,8,,
+1,1,"Omont, Mr. Alfred Fernand",male,,0,0,F.C. 12998,25.7417,,C,7,,"Paris, France"
+1,1,"Ostby, Miss. Helene Ragnhild",female,22.0,0,1,113509,61.9792,B36,C,5,,"Providence, RI"
+1,0,"Ostby, Mr. Engelhart Cornelius",male,65.0,0,1,113509,61.9792,B30,C,,234.0,"Providence, RI"
+1,0,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C,,189.0,"?Havana, Cuba"
+1,0,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0.0,,S,,,Belfast
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+1,0,"Payne, Mr. Vivian Ponsonby",male,23.0,0,0,12749,93.5,B24,S,,,"Montreal, PQ"
+1,0,"Pears, Mr. Thomas Clinton",male,29.0,1,0,113776,66.6,C2,S,,,"Isleworth, England"
+1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22.0,1,0,113776,66.6,C2,S,8,,"Isleworth, England"
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+1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17.0,1,0,PC 17758,108.9,C65,C,8,,"Madrid, Spain"
+1,1,"Perreault, Miss. Anne",female,30.0,0,0,12749,93.5,B73,S,3,,
+1,1,"Peuchen, Major. Arthur Godfrey",male,52.0,0,0,113786,30.5,C104,S,6,,"Toronto, ON"
+1,0,"Porter, Mr. Walter Chamberlain",male,47.0,0,0,110465,52.0,C110,S,,207.0,"Worcester, MA"
+1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56.0,0,1,11767,83.1583,C50,C,7,,"Mt Airy, Philadelphia, PA"
+1,0,"Reuchlin, Jonkheer. John George",male,38.0,0,0,19972,0.0,,S,,,"Rotterdam, Netherlands"
+1,1,"Rheims, Mr. George Alexander Lucien",male,,0,0,PC 17607,39.6,,S,A,,"Paris /  New York, NY"
+1,0,"Ringhini, Mr. Sante",male,22.0,0,0,PC 17760,135.6333,,C,,232.0,
+1,0,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C,,,
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+1,0,"Roebling, Mr. Washington Augustus II",male,31.0,0,0,PC 17590,50.4958,A24,S,,,"Trenton, NJ"
+1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45.0,0,0,111428,26.55,,S,9,,"New York, NY"
+1,0,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50.0,A32,S,,,"Seattle, WA"
+1,1,"Rosenbaum, Miss. Edith Louise",female,33.0,0,0,PC 17613,27.7208,A11,C,11,,"Paris, France"
+1,0,"Rosenshine, Mr. George (""Mr George Thorne"")",male,46.0,0,0,PC 17585,79.2,,C,,16.0,"New York, NY"
+1,0,"Ross, Mr. John Hugo",male,36.0,0,0,13049,40.125,A10,C,,,"Winnipeg, MB"
+1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33.0,0,0,110152,86.5,B77,S,8,,"London  Vancouver, BC"
+1,0,"Rothschild, Mr. Martin",male,55.0,1,0,PC 17603,59.4,,C,,,"New York, NY"
+1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54.0,1,0,PC 17603,59.4,,C,6,,"New York, NY"
+1,0,"Rowe, Mr. Alfred G",male,33.0,0,0,113790,26.55,,S,,109.0,London
+1,1,"Ryerson, Master. John Borie",male,13.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+1,1,"Ryerson, Miss. Emily Borie",female,18.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+1,0,"Ryerson, Mr. Arthur Larned",male,61.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C,,,"Haverford, PA / Cooperstown, NY"
+1,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C,4,,"Haverford, PA / Cooperstown, NY"
+1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S,3,,"Manchester, England"
+1,1,"Sagesser, Mlle. Emma",female,24.0,0,0,PC 17477,69.3,B35,C,9,,
+1,1,"Salomon, Mr. Abraham L",male,,0,0,111163,26.0,,S,1,,"New York, NY"
+1,1,"Schabert, Mrs. Paul (Emma Mock)",female,35.0,1,0,13236,57.75,C28,C,11,,"New York, NY"
+1,1,"Serepeca, Miss. Augusta",female,30.0,0,0,113798,31.0,,C,4,,
+1,1,"Seward, Mr. Frederic Kimber",male,34.0,0,0,113794,26.55,,S,7,,"New York, NY"
+1,1,"Shutes, Miss. Elizabeth W",female,40.0,0,0,PC 17582,153.4625,C125,S,3,,"New York, NY / Greenwich CT"
+1,1,"Silverthorne, Mr. Spencer Victor",male,35.0,0,0,PC 17475,26.2875,E24,S,5,,"St Louis, MO"
+1,0,"Silvey, Mr. William Baird",male,50.0,1,0,13507,55.9,E44,S,,,"Duluth, MN"
+1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39.0,1,0,13507,55.9,E44,S,11,,"Duluth, MN"
+1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56.0,0,0,13213,35.5,A26,C,3,,"Basel, Switzerland"
+1,1,"Sloper, Mr. William Thompson",male,28.0,0,0,113788,35.5,A6,S,7,,"New Britain, CT"
+1,0,"Smart, Mr. John Montgomery",male,56.0,0,0,113792,26.55,,S,,,"New York, NY"
+1,0,"Smith, Mr. James Clinch",male,56.0,0,0,17764,30.6958,A7,C,,,"St James, Long Island, NY"
+1,0,"Smith, Mr. Lucien Philip",male,24.0,1,0,13695,60.0,C31,S,,,"Huntington, WV"
+1,0,"Smith, Mr. Richard William",male,,0,0,113056,26.0,A19,S,,,"Streatham, Surrey"
+1,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18.0,1,0,13695,60.0,C31,S,6,,"Huntington, WV"
+1,1,"Snyder, Mr. John Pillsbury",male,24.0,1,0,21228,82.2667,B45,S,7,,"Minneapolis, MN"
+1,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23.0,1,0,21228,82.2667,B45,S,7,,"Minneapolis, MN"
+1,1,"Spedden, Master. Robert Douglas",male,6.0,0,2,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+1,1,"Spedden, Mr. Frederic Oakley",male,45.0,1,1,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40.0,1,1,16966,134.5,E34,C,3,,"Tuxedo Park, NY"
+1,0,"Spencer, Mr. William Augustus",male,57.0,1,0,PC 17569,146.5208,B78,C,,,"Paris, France"
+1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C,6,,"Paris, France"
+1,1,"Stahelin-Maeglin, Dr. Max",male,32.0,0,0,13214,30.5,B50,C,3,,"Basel, Switzerland"
+1,0,"Stead, Mr. William Thomas",male,62.0,0,0,113514,26.55,C87,S,,,"Wimbledon Park, London / Hayling Island, Hants"
+1,1,"Stengel, Mr. Charles Emil Henry",male,54.0,1,0,11778,55.4417,C116,C,1,,"Newark, NJ"
+1,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43.0,1,0,11778,55.4417,C116,C,5,,"Newark, NJ"
+1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52.0,1,0,36947,78.2667,D20,C,4,,"Haverford, PA"
+1,0,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C,,,"Gallipolis, Ohio / ? Paris / New York"
+1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62.0,0,0,113572,80.0,B28,,6,,"Cincinatti, OH"
+1,0,"Straus, Mr. Isidor",male,67.0,1,0,PC 17483,221.7792,C55 C57,S,,96.0,"New York, NY"
+1,0,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63.0,1,0,PC 17483,221.7792,C55 C57,S,,,"New York, NY"
+1,0,"Sutton, Mr. Frederick",male,61.0,0,0,36963,32.3208,D50,S,,46.0,"Haddenfield, NJ"
+1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48.0,0,0,17466,25.9292,D17,S,8,,"Brooklyn, NY"
+1,1,"Taussig, Miss. Ruth",female,18.0,0,2,110413,79.65,E68,S,8,,"New York, NY"
+1,0,"Taussig, Mr. Emil",male,52.0,1,1,110413,79.65,E67,S,,,"New York, NY"
+1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39.0,1,1,110413,79.65,E67,S,8,,"New York, NY"
+1,1,"Taylor, Mr. Elmer Zebley",male,48.0,1,0,19996,52.0,C126,S,5 7,,"London /  East Orange, NJ"
+1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52.0,C126,S,5 7,,"London /  East Orange, NJ"
+1,0,"Thayer, Mr. John Borland",male,49.0,1,1,17421,110.8833,C68,C,,,"Haverford, PA"
+1,1,"Thayer, Mr. John Borland Jr",male,17.0,0,2,17421,110.8833,C70,C,B,,"Haverford, PA"
+1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39.0,1,1,17421,110.8833,C68,C,4,,"Haverford, PA"
+1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C,D,,"New York, NY"
+1,1,"Tucker, Mr. Gilbert Milligan Jr",male,31.0,0,0,2543,28.5375,C53,C,7,,"Albany, NY"
+1,0,"Uruchurtu, Don. Manuel E",male,40.0,0,0,PC 17601,27.7208,,C,,,"Mexico City, Mexico"
+1,0,"Van der hoef, Mr. Wyckoff",male,61.0,0,0,111240,33.5,B19,S,,245.0,"Brooklyn, NY"
+1,0,"Walker, Mr. William Anderson",male,47.0,0,0,36967,34.0208,D46,S,,,"East Orange, NJ"
+1,1,"Ward, Miss. Anna",female,35.0,0,0,PC 17755,512.3292,,C,3,,
+1,0,"Warren, Mr. Frank Manley",male,64.0,1,0,110813,75.25,D37,C,,,"Portland, OR"
+1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60.0,1,0,110813,75.25,D37,C,5,,"Portland, OR"
+1,0,"Weir, Col. John",male,60.0,0,0,113800,26.55,,S,,,"England Salt Lake City, Utah"
+1,0,"White, Mr. Percival Wayland",male,54.0,0,1,35281,77.2875,D26,S,,,"Brunswick, ME"
+1,0,"White, Mr. Richard Frasar",male,21.0,0,1,35281,77.2875,D26,S,,169.0,"Brunswick, ME"
+1,1,"White, Mrs. John Stuart (Ella Holmes)",female,55.0,0,0,PC 17760,135.6333,C32,C,8,,"New York, NY / Briarcliff Manor NY"
+1,1,"Wick, Miss. Mary Natalie",female,31.0,0,2,36928,164.8667,C7,S,8,,"Youngstown, OH"
+1,0,"Wick, Mr. George Dennick",male,57.0,1,1,36928,164.8667,,S,,,"Youngstown, OH"
+1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45.0,1,1,36928,164.8667,,S,8,,"Youngstown, OH"
+1,0,"Widener, Mr. George Dunton",male,50.0,1,1,113503,211.5,C80,C,,,"Elkins Park, PA"
+1,0,"Widener, Mr. Harry Elkins",male,27.0,0,2,113503,211.5,C82,C,,,"Elkins Park, PA"
+1,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50.0,1,1,113503,211.5,C80,C,4,,"Elkins Park, PA"
+1,1,"Willard, Miss. Constance",female,21.0,0,0,113795,26.55,,S,8 10,,"Duluth, MN"
+1,0,"Williams, Mr. Charles Duane",male,51.0,0,1,PC 17597,61.3792,,C,,,"Geneva, Switzerland / Radnor, PA"
+1,1,"Williams, Mr. Richard Norris II",male,21.0,0,1,PC 17597,61.3792,,C,A,,"Geneva, Switzerland / Radnor, PA"
+1,0,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35.0,C128,S,,,"London, England"
+1,1,"Wilson, Miss. Helen Alice",female,31.0,0,0,16966,134.5,E39 E41,C,3,,
+1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S,D,,"London, England"
+1,0,"Wright, Mr. George",male,62.0,0,0,113807,26.55,,S,,,"Halifax, NS"
+1,1,"Young, Miss. Marie Grice",female,36.0,0,0,PC 17760,135.6333,C32,C,8,,"New York, NY / Washington, DC"
+2,0,"Abelson, Mr. Samuel",male,30.0,1,0,P/PP 3381,24.0,,C,,,"Russia New York, NY"
+2,1,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28.0,1,0,P/PP 3381,24.0,,C,10,,"Russia New York, NY"
+2,0,"Aldworth, Mr. Charles Augustus",male,30.0,0,0,248744,13.0,,S,,,"Bryn Mawr, PA, USA"
+2,0,"Andrew, Mr. Edgardo Samuel",male,18.0,0,0,231945,11.5,,S,,,"Buenos Aires, Argentina / New Jersey, NJ"
+2,0,"Andrew, Mr. Frank Thomas",male,25.0,0,0,C.A. 34050,10.5,,S,,,"Cornwall, England Houghton, MI"
+2,0,"Angle, Mr. William A",male,34.0,1,0,226875,26.0,,S,,,"Warwick, England"
+2,1,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36.0,1,0,226875,26.0,,S,11,,"Warwick, England"
+2,0,"Ashby, Mr. John",male,57.0,0,0,244346,13.0,,S,,,"West Hoboken, NJ"
+2,0,"Bailey, Mr. Percy Andrew",male,18.0,0,0,29108,11.5,,S,,,"Penzance, Cornwall / Akron, OH"
+2,0,"Baimbrigge, Mr. Charles Robert",male,23.0,0,0,C.A. 31030,10.5,,S,,,Guernsey
+2,1,"Ball, Mrs. (Ada E Hall)",female,36.0,0,0,28551,13.0,D,S,10,,"Bristol, Avon / Jacksonville, FL"
+2,0,"Banfield, Mr. Frederick James",male,28.0,0,0,C.A./SOTON 34068,10.5,,S,,,"Plymouth, Dorset / Houghton, MI"
+2,0,"Bateman, Rev. Robert James",male,51.0,0,0,S.O.P. 1166,12.525,,S,,174.0,"Jacksonville, FL"
+2,1,"Beane, Mr. Edward",male,32.0,1,0,2908,26.0,,S,13,,"Norwich / New York, NY"
+2,1,"Beane, Mrs. Edward (Ethel Clarke)",female,19.0,1,0,2908,26.0,,S,13,,"Norwich / New York, NY"
+2,0,"Beauchamp, Mr. Henry James",male,28.0,0,0,244358,26.0,,S,,,England
+2,1,"Becker, Master. Richard F",male,1.0,2,1,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+2,1,"Becker, Miss. Marion Louise",female,4.0,2,1,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+2,1,"Becker, Miss. Ruth Elizabeth",female,12.0,2,1,230136,39.0,F4,S,13,,"Guntur, India / Benton Harbour, MI"
+2,1,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36.0,0,3,230136,39.0,F4,S,11,,"Guntur, India / Benton Harbour, MI"
+2,1,"Beesley, Mr. Lawrence",male,34.0,0,0,248698,13.0,D56,S,13,,London
+2,1,"Bentham, Miss. Lilian W",female,19.0,0,0,28404,13.0,,S,12,,"Rochester, NY"
+2,0,"Berriman, Mr. William John",male,23.0,0,0,28425,13.0,,S,,,"St Ives, Cornwall / Calumet, MI"
+2,0,"Botsford, Mr. William Hull",male,26.0,0,0,237670,13.0,,S,,,"Elmira, NY / Orange, NJ"
+2,0,"Bowenur, Mr. Solomon",male,42.0,0,0,211535,13.0,,S,,,London
+2,0,"Bracken, Mr. James H",male,27.0,0,0,220367,13.0,,S,,,"Lake Arthur, Chavez County, NM"
+2,1,"Brown, Miss. Amelia ""Mildred""",female,24.0,0,0,248733,13.0,F33,S,11,,"London / Montreal, PQ"
+2,1,"Brown, Miss. Edith Eileen",female,15.0,0,2,29750,39.0,,S,14,,"Cape Town, South Africa / Seattle, WA"
+2,0,"Brown, Mr. Thomas William Solomon",male,60.0,1,1,29750,39.0,,S,,,"Cape Town, South Africa / Seattle, WA"
+2,1,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40.0,1,1,29750,39.0,,S,14,,"Cape Town, South Africa / Seattle, WA"
+2,1,"Bryhl, Miss. Dagmar Jenny Ingeborg ",female,20.0,1,0,236853,26.0,,S,12,,"Skara, Sweden / Rockford, IL"
+2,0,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25.0,1,0,236853,26.0,,S,,,"Skara, Sweden / Rockford, IL"
+2,1,"Buss, Miss. Kate",female,36.0,0,0,27849,13.0,,S,9,,"Sittingbourne, England / San Diego, CA"
+2,0,"Butler, Mr. Reginald Fenton",male,25.0,0,0,234686,13.0,,S,,97.0,"Southsea, Hants"
+2,0,"Byles, Rev. Thomas Roussel Davids",male,42.0,0,0,244310,13.0,,S,,,London
+2,1,"Bystrom, Mrs. (Karolina)",female,42.0,0,0,236852,13.0,,S,,,"New York, NY"
+2,1,"Caldwell, Master. Alden Gates",male,0.8333,0,2,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+2,1,"Caldwell, Mr. Albert Francis",male,26.0,1,1,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+2,1,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22.0,1,1,248738,29.0,,S,13,,"Bangkok, Thailand / Roseville, IL"
+2,1,"Cameron, Miss. Clear Annie",female,35.0,0,0,F.C.C. 13528,21.0,,S,14,,"Mamaroneck, NY"
+2,0,"Campbell, Mr. William",male,,0,0,239853,0.0,,S,,,Belfast
+2,0,"Carbines, Mr. William",male,19.0,0,0,28424,13.0,,S,,18.0,"St Ives, Cornwall / Calumet, MI"
+2,0,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44.0,1,0,244252,26.0,,S,,,London
+2,0,"Carter, Rev. Ernest Courtenay",male,54.0,1,0,244252,26.0,,S,,,London
+2,0,"Chapman, Mr. Charles Henry",male,52.0,0,0,248731,13.5,,S,,130.0,"Bronx, NY"
+2,0,"Chapman, Mr. John Henry",male,37.0,1,0,SC/AH 29037,26.0,,S,,17.0,"Cornwall / Spokane, WA"
+2,0,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29.0,1,0,SC/AH 29037,26.0,,S,,,"Cornwall / Spokane, WA"
+2,1,"Christy, Miss. Julie Rachel",female,25.0,1,1,237789,30.0,,S,12,,London
+2,1,"Christy, Mrs. (Alice Frances)",female,45.0,0,2,237789,30.0,,S,12,,London
+2,0,"Clarke, Mr. Charles Valentine",male,29.0,1,0,2003,26.0,,S,,,"England / San Francisco, CA"
+2,1,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28.0,1,0,2003,26.0,,S,14,,"England / San Francisco, CA"
+2,0,"Coleridge, Mr. Reginald Charles",male,29.0,0,0,W./C. 14263,10.5,,S,,,"Hartford, Huntingdonshire"
+2,0,"Collander, Mr. Erik Gustaf",male,28.0,0,0,248740,13.0,,S,,,"Helsinki, Finland Ashtabula, Ohio"
+2,1,"Collett, Mr. Sidney C Stuart",male,24.0,0,0,28034,10.5,,S,9,,"London / Fort Byron, NY"
+2,1,"Collyer, Miss. Marjorie ""Lottie""",female,8.0,0,2,C.A. 31921,26.25,,S,14,,"Bishopstoke, Hants / Fayette Valley, ID"
+2,0,"Collyer, Mr. Harvey",male,31.0,1,1,C.A. 31921,26.25,,S,,,"Bishopstoke, Hants / Fayette Valley, ID"
+2,1,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31.0,1,1,C.A. 31921,26.25,,S,14,,"Bishopstoke, Hants / Fayette Valley, ID"
+2,1,"Cook, Mrs. (Selena Rogers)",female,22.0,0,0,W./C. 14266,10.5,F33,S,14,,Pennsylvania
+2,0,"Corbett, Mrs. Walter H (Irene Colvin)",female,30.0,0,0,237249,13.0,,S,,,"Provo, UT"
+2,0,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21.0,,S,,,"Upper Burma, India Pittsburgh, PA"
+2,0,"Cotterill, Mr. Henry ""Harry""",male,21.0,0,0,29107,11.5,,S,,,"Penzance, Cornwall / Akron, OH"
+2,0,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0.0,,S,,,Belfast
+2,1,"Davies, Master. John Morgan Jr",male,8.0,1,1,C.A. 33112,36.75,,S,14,,"St Ives, Cornwall / Hancock, MI"
+2,0,"Davies, Mr. Charles Henry",male,18.0,0,0,S.O.C. 14879,73.5,,S,,,"Lyndhurst, England"
+2,1,"Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ",female,48.0,0,2,C.A. 33112,36.75,,S,14,,"St Ives, Cornwall / Hancock, MI"
+2,1,"Davis, Miss. Mary",female,28.0,0,0,237668,13.0,,S,13,,"London / Staten Island, NY"
+2,0,"de Brito, Mr. Jose Joaquim",male,32.0,0,0,244360,13.0,,S,,,"Portugal / Sau Paulo, Brazil"
+2,0,"Deacon, Mr. Percy William",male,17.0,0,0,S.O.C. 14879,73.5,,S,,,
+2,0,"del Carlo, Mr. Sebastiano",male,29.0,1,0,SC/PARIS 2167,27.7208,,C,,295.0,"Lucca, Italy / California"
+2,1,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24.0,1,0,SC/PARIS 2167,27.7208,,C,12,,"Lucca, Italy / California"
+2,0,"Denbury, Mr. Herbert",male,25.0,0,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+2,0,"Dibden, Mr. William",male,18.0,0,0,S.O.C. 14879,73.5,,S,,,"New Forest, England"
+2,1,"Doling, Miss. Elsie",female,18.0,0,1,231919,23.0,,S,,,Southampton
+2,1,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,0,1,231919,23.0,,S,,,Southampton
+2,0,"Downton, Mr. William James",male,54.0,0,0,28403,26.0,,S,,,"Holley, NY"
+2,1,"Drew, Master. Marshall Brines",male,8.0,0,2,28220,32.5,,S,10,,"Greenport, NY"
+2,0,"Drew, Mr. James Vivian",male,42.0,1,1,28220,32.5,,S,,,"Greenport, NY"
+2,1,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34.0,1,1,28220,32.5,,S,10,,"Greenport, NY"
+2,1,"Duran y More, Miss. Asuncion",female,27.0,1,0,SC/PARIS 2149,13.8583,,C,12,,"Barcelona, Spain / Havana, Cuba"
+2,1,"Duran y More, Miss. Florentina",female,30.0,1,0,SC/PARIS 2148,13.8583,,C,12,,"Barcelona, Spain / Havana, Cuba"
+2,0,"Eitemiller, Mr. George Floyd",male,23.0,0,0,29751,13.0,,S,,,"England / Detroit, MI"
+2,0,"Enander, Mr. Ingvar",male,21.0,0,0,236854,13.0,,S,,,"Goteborg, Sweden / Rockford, IL"
+2,0,"Fahlstrom, Mr. Arne Jonas",male,18.0,0,0,236171,13.0,,S,,,"Oslo, Norway Bayonne, NJ"
+2,0,"Faunthorpe, Mr. Harry",male,40.0,1,0,2926,26.0,,S,,286.0,"England / Philadelphia, PA"
+2,1,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,1,0,2926,26.0,,S,16,,
+2,0,"Fillbrook, Mr. Joseph Charles",male,18.0,0,0,C.A. 15185,10.5,,S,,,"Cornwall / Houghton, MI"
+2,0,"Fox, Mr. Stanley Hubert",male,36.0,0,0,229236,13.0,,S,,236.0,"Rochester, NY"
+2,0,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0.0,,S,,,Belfast
+2,0,"Funk, Miss. Annie Clemmer",female,38.0,0,0,237671,13.0,,S,,,"Janjgir, India / Pennsylvania"
+2,0,"Fynney, Mr. Joseph J",male,35.0,0,0,239865,26.0,,S,,322.0,"Liverpool / Montreal, PQ"
+2,0,"Gale, Mr. Harry",male,38.0,1,0,28664,21.0,,S,,,"Cornwall / Clear Creek, CO"
+2,0,"Gale, Mr. Shadrach",male,34.0,1,0,28664,21.0,,S,,,"Cornwall / Clear Creek, CO"
+2,1,"Garside, Miss. Ethel",female,34.0,0,0,243880,13.0,,S,12,,"Brooklyn, NY"
+2,0,"Gaskell, Mr. Alfred",male,16.0,0,0,239865,26.0,,S,,,"Liverpool / Montreal, PQ"
+2,0,"Gavey, Mr. Lawrence",male,26.0,0,0,31028,10.5,,S,,,"Guernsey / Elizabeth, NJ"
+2,0,"Gilbert, Mr. William",male,47.0,0,0,C.A. 30769,10.5,,S,,,Cornwall
+2,0,"Giles, Mr. Edgar",male,21.0,1,0,28133,11.5,,S,,,"Cornwall / Camden, NJ"
+2,0,"Giles, Mr. Frederick Edward",male,21.0,1,0,28134,11.5,,S,,,"Cornwall / Camden, NJ"
+2,0,"Giles, Mr. Ralph",male,24.0,0,0,248726,13.5,,S,,297.0,"West Kensington, London"
+2,0,"Gill, Mr. John William",male,24.0,0,0,233866,13.0,,S,,155.0,"Clevedon, England"
+2,0,"Gillespie, Mr. William Henry",male,34.0,0,0,12233,13.0,,S,,,"Vancouver, BC"
+2,0,"Givard, Mr. Hans Kristensen",male,30.0,0,0,250646,13.0,,S,,305.0,
+2,0,"Greenberg, Mr. Samuel",male,52.0,0,0,250647,13.0,,S,,19.0,"Bronx, NY"
+2,0,"Hale, Mr. Reginald",male,30.0,0,0,250653,13.0,,S,,75.0,"Auburn, NY"
+2,1,"Hamalainen, Master. Viljo",male,0.6667000000000001,1,1,250649,14.5,,S,4,,"Detroit, MI"
+2,1,"Hamalainen, Mrs. William (Anna)",female,24.0,0,2,250649,14.5,,S,4,,"Detroit, MI"
+2,0,"Harbeck, Mr. William H",male,44.0,0,0,248746,13.0,,S,,35.0,"Seattle, WA / Toledo, OH"
+2,1,"Harper, Miss. Annie Jessie ""Nina""",female,6.0,0,1,248727,33.0,,S,11,,"Denmark Hill, Surrey / Chicago"
+2,0,"Harper, Rev. John",male,28.0,0,1,248727,33.0,,S,,,"Denmark Hill, Surrey / Chicago"
+2,1,"Harris, Mr. George",male,62.0,0,0,S.W./PP 752,10.5,,S,15,,London
+2,0,"Harris, Mr. Walter",male,30.0,0,0,W/C 14208,10.5,,S,,,"Walthamstow, England"
+2,1,"Hart, Miss. Eva Miriam",female,7.0,0,2,F.C.C. 13529,26.25,,S,14,,"Ilford, Essex / Winnipeg, MB"
+2,0,"Hart, Mr. Benjamin",male,43.0,1,1,F.C.C. 13529,26.25,,S,,,"Ilford, Essex / Winnipeg, MB"
+2,1,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45.0,1,1,F.C.C. 13529,26.25,,S,14,,"Ilford, Essex / Winnipeg, MB"
+2,1,"Herman, Miss. Alice",female,24.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+2,1,"Herman, Miss. Kate",female,24.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+2,0,"Herman, Mr. Samuel",male,49.0,1,2,220845,65.0,,S,,,"Somerset / Bernardsville, NJ"
+2,1,"Herman, Mrs. Samuel (Jane Laver)",female,48.0,1,2,220845,65.0,,S,9,,"Somerset / Bernardsville, NJ"
+2,1,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,0,0,248706,16.0,,S,13,,"India / Rapid City, SD"
+2,0,"Hickman, Mr. Leonard Mark",male,24.0,2,0,S.O.C. 14879,73.5,,S,,,"West Hampstead, London / Neepawa, MB"
+2,0,"Hickman, Mr. Lewis",male,32.0,2,0,S.O.C. 14879,73.5,,S,,256.0,"West Hampstead, London / Neepawa, MB"
+2,0,"Hickman, Mr. Stanley George",male,21.0,2,0,S.O.C. 14879,73.5,,S,,,"West Hampstead, London / Neepawa, MB"
+2,0,"Hiltunen, Miss. Marta",female,18.0,1,1,250650,13.0,,S,,,"Kontiolahti, Finland / Detroit, MI"
+2,1,"Hocking, Miss. Ellen ""Nellie""",female,20.0,2,1,29105,23.0,,S,4,,"Cornwall / Akron, OH"
+2,0,"Hocking, Mr. Richard George",male,23.0,2,1,29104,11.5,,S,,,"Cornwall / Akron, OH"
+2,0,"Hocking, Mr. Samuel James Metcalfe",male,36.0,0,0,242963,13.0,,S,,,"Devonport, England"
+2,1,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54.0,1,3,29105,23.0,,S,4,,"Cornwall / Akron, OH"
+2,0,"Hodges, Mr. Henry Price",male,50.0,0,0,250643,13.0,,S,,149.0,Southampton
+2,0,"Hold, Mr. Stephen",male,44.0,1,0,26707,26.0,,S,,,"England / Sacramento, CA"
+2,1,"Hold, Mrs. Stephen (Annie Margaret Hill)",female,29.0,1,0,26707,26.0,,S,10,,"England / Sacramento, CA"
+2,0,"Hood, Mr. Ambrose Jr",male,21.0,0,0,S.O.C. 14879,73.5,,S,,,"New Forest, England"
+2,1,"Hosono, Mr. Masabumi",male,42.0,0,0,237798,13.0,,S,10,,"Tokyo, Japan"
+2,0,"Howard, Mr. Benjamin",male,63.0,1,0,24065,26.0,,S,,,"Swindon, England"
+2,0,"Howard, Mrs. Benjamin (Ellen Truelove Arman)",female,60.0,1,0,24065,26.0,,S,,,"Swindon, England"
+2,0,"Hunt, Mr. George Henry",male,33.0,0,0,SCO/W 1585,12.275,,S,,,"Philadelphia, PA"
+2,1,"Ilett, Miss. Bertha",female,17.0,0,0,SO/C 14885,10.5,,S,,,Guernsey
+2,0,"Jacobsohn, Mr. Sidney Samuel",male,42.0,1,0,243847,27.0,,S,,,London
+2,1,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24.0,2,1,243847,27.0,,S,12,,London
+2,0,"Jarvis, Mr. John Denzil",male,47.0,0,0,237565,15.0,,S,,,"North Evington, England"
+2,0,"Jefferys, Mr. Clifford Thomas",male,24.0,2,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+2,0,"Jefferys, Mr. Ernest Wilfred",male,22.0,2,0,C.A. 31029,31.5,,S,,,"Guernsey / Elizabeth, NJ"
+2,0,"Jenkin, Mr. Stephen Curnow",male,32.0,0,0,C.A. 33111,10.5,,S,,,"St Ives, Cornwall / Houghton, MI"
+2,1,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23.0,0,0,SC/AH Basle 541,13.7917,D,C,11,,"New York, NY"
+2,0,"Kantor, Mr. Sinai",male,34.0,1,0,244367,26.0,,S,,283.0,"Moscow / Bronx, NY"
+2,1,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24.0,1,0,244367,26.0,,S,12,,"Moscow / Bronx, NY"
+2,0,"Karnes, Mrs. J Frank (Claire Bennett)",female,22.0,0,0,F.C.C. 13534,21.0,,S,,,"India / Pittsburgh, PA"
+2,1,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q,10,,"Harrisburg, PA"
+2,0,"Keane, Mr. Daniel",male,35.0,0,0,233734,12.35,,Q,,,
+2,1,"Kelly, Mrs. Florence ""Fannie""",female,45.0,0,0,223596,13.5,,S,9,,"London / New York, NY"
+2,0,"Kirkland, Rev. Charles Leonard",male,57.0,0,0,219533,12.35,,Q,,,"Glasgow / Bangor, ME"
+2,0,"Knight, Mr. Robert J",male,,0,0,239855,0.0,,S,,,Belfast
+2,0,"Kvillner, Mr. Johan Henrik Johannesson",male,31.0,0,0,C.A. 18723,10.5,,S,,165.0,"Sweden / Arlington, NJ"
+2,0,"Lahtinen, Mrs. William (Anna Sylfven)",female,26.0,1,1,250651,26.0,,S,,,"Minneapolis, MN"
+2,0,"Lahtinen, Rev. William",male,30.0,1,1,250651,26.0,,S,,,"Minneapolis, MN"
+2,0,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q,,,
+2,1,"Laroche, Miss. Louise",female,1.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+2,1,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+2,0,"Laroche, Mr. Joseph Philippe Lemercier",male,25.0,1,2,SC/Paris 2123,41.5792,,C,,,Paris / Haiti
+2,1,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22.0,1,2,SC/Paris 2123,41.5792,,C,14,,Paris / Haiti
+2,1,"Lehmann, Miss. Bertha",female,17.0,0,0,SC 1748,12.0,,C,12,,"Berne, Switzerland / Central City, IA"
+2,1,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33.0,,S,11,,"London / Chicago, IL"
+2,1,"Lemore, Mrs. (Amelia Milley)",female,34.0,0,0,C.A. 34260,10.5,F33,S,14,,"Chicago, IL"
+2,0,"Levy, Mr. Rene Jacques",male,36.0,0,0,SC/Paris 2163,12.875,D,C,,,"Montreal, PQ"
+2,0,"Leyson, Mr. Robert William Norman",male,24.0,0,0,C.A. 29566,10.5,,S,,108.0,
+2,0,"Lingane, Mr. John",male,61.0,0,0,235509,12.35,,Q,,,
+2,0,"Louch, Mr. Charles Alexander",male,50.0,1,0,SC/AH 3085,26.0,,S,,121.0,"Weston-Super-Mare, Somerset"
+2,1,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42.0,1,0,SC/AH 3085,26.0,,S,,,"Weston-Super-Mare, Somerset"
+2,0,"Mack, Mrs. (Mary)",female,57.0,0,0,S.O./P.P. 3,10.5,E77,S,,52.0,"Southampton / New York, NY"
+2,0,"Malachard, Mr. Noel",male,,0,0,237735,15.0458,D,C,,,Paris
+2,1,"Mallet, Master. Andre",male,1.0,0,2,S.C./PARIS 2079,37.0042,,C,10,,"Paris / Montreal, PQ"
+2,0,"Mallet, Mr. Albert",male,31.0,1,1,S.C./PARIS 2079,37.0042,,C,,,"Paris / Montreal, PQ"
+2,1,"Mallet, Mrs. Albert (Antoinette Magnin)",female,24.0,1,1,S.C./PARIS 2079,37.0042,,C,10,,"Paris / Montreal, PQ"
+2,0,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C,,,"New York, NY"
+2,0,"Matthews, Mr. William John",male,30.0,0,0,28228,13.0,,S,,,"St Austall, Cornwall"
+2,0,"Maybery, Mr. Frank Hubert",male,40.0,0,0,239059,16.0,,S,,,"Weston-Super-Mare / Moose Jaw, SK"
+2,0,"McCrae, Mr. Arthur Gordon",male,32.0,0,0,237216,13.5,,S,,209.0,"Sydney, Australia"
+2,0,"McCrie, Mr. James Matthew",male,30.0,0,0,233478,13.0,,S,,,"Sarnia, ON"
+2,0,"McKane, Mr. Peter David",male,46.0,0,0,28403,26.0,,S,,,"Rochester, NY"
+2,1,"Mellinger, Miss. Madeleine Violet",female,13.0,0,1,250644,19.5,,S,14,,"England / Bennington, VT"
+2,1,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41.0,0,1,250644,19.5,,S,14,,"England / Bennington, VT"
+2,1,"Mellors, Mr. William John",male,19.0,0,0,SW/PP 751,10.5,,S,B,,"Chelsea, London"
+2,0,"Meyer, Mr. August",male,39.0,0,0,248723,13.0,,S,,,"Harrow-on-the-Hill, Middlesex"
+2,0,"Milling, Mr. Jacob Christian",male,48.0,0,0,234360,13.0,,S,,271.0,"Copenhagen, Denmark"
+2,0,"Mitchell, Mr. Henry Michael",male,70.0,0,0,C.A. 24580,10.5,,S,,,"Guernsey / Montclair, NJ and/or Toledo, Ohio"
+2,0,"Montvila, Rev. Juozas",male,27.0,0,0,211536,13.0,,S,,,"Worcester, MA"
+2,0,"Moraweck, Dr. Ernest",male,54.0,0,0,29011,14.0,,S,,,"Frankfort, KY"
+2,0,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39.0,0,0,250655,26.0,,S,,,
+2,0,"Mudd, Mr. Thomas Charles",male,16.0,0,0,S.O./P.P. 3,10.5,,S,,,"Halesworth, England"
+2,0,"Myles, Mr. Thomas Francis",male,62.0,0,0,240276,9.6875,,Q,,,"Cambridge, MA"
+2,0,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C,,43.0,"New York, NY"
+2,1,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,C,,,"New York, NY"
+2,1,"Navratil, Master. Edmond Roger",male,2.0,1,1,230080,26.0,F2,S,D,,"Nice, France"
+2,1,"Navratil, Master. Michel M",male,3.0,1,1,230080,26.0,F2,S,D,,"Nice, France"
+2,0,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26.0,F2,S,,15.0,"Nice, France"
+2,0,"Nesson, Mr. Israel",male,26.0,0,0,244368,13.0,F2,S,,,"Boston, MA"
+2,0,"Nicholls, Mr. Joseph Charles",male,19.0,1,1,C.A. 33112,36.75,,S,,101.0,"Cornwall / Hancock, MI"
+2,0,"Norman, Mr. Robert Douglas",male,28.0,0,0,218629,13.5,,S,,287.0,Glasgow
+2,1,"Nourney, Mr. Alfred (""Baron von Drachstedt"")",male,20.0,0,0,SC/PARIS 2166,13.8625,D38,C,7,,"Cologne, Germany"
+2,1,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,0,0,C.A. 29395,10.5,F33,S,11,,"Folkstone, Kent / New York, NY"
+2,0,"Otter, Mr. Richard",male,39.0,0,0,28213,13.0,,S,,,"Middleburg Heights, OH"
+2,1,"Oxenham, Mr. Percy Thomas",male,22.0,0,0,W./C. 14260,10.5,,S,13,,"Pondersend, England / New Durham, NJ"
+2,1,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C,9,,"Spain / Havana, Cuba"
+2,0,"Pain, Dr. Alfred",male,23.0,0,0,244278,10.5,,S,,,"Hamilton, ON"
+2,1,"Pallas y Castello, Mr. Emilio",male,29.0,0,0,SC/PARIS 2147,13.8583,,C,9,,"Spain / Havana, Cuba"
+2,0,"Parker, Mr. Clifford Richard",male,28.0,0,0,SC 14888,10.5,,S,,,"St Andrews, Guernsey"
+2,0,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0.0,,S,,,Belfast
+2,1,"Parrish, Mrs. (Lutie Davis)",female,50.0,0,1,230433,26.0,,S,12,,"Woodford County, KY"
+2,0,"Pengelly, Mr. Frederick William",male,19.0,0,0,28665,10.5,,S,,,"Gunnislake, England / Butte, MT"
+2,0,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C,,,
+2,0,"Peruschitz, Rev. Joseph Maria",male,41.0,0,0,237393,13.0,,S,,,
+2,1,"Phillips, Miss. Alice Frances Louisa",female,21.0,0,1,S.O./P.P. 2,21.0,,S,12,,"Ilfracombe, Devon"
+2,1,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19.0,0,0,250655,26.0,,S,11,,"Worcester, England"
+2,0,"Phillips, Mr. Escott Robert",male,43.0,0,1,S.O./P.P. 2,21.0,,S,,,"Ilfracombe, Devon"
+2,1,"Pinsky, Mrs. (Rosa)",female,32.0,0,0,234604,13.0,,S,9,,Russia
+2,0,"Ponesell, Mr. Martin",male,34.0,0,0,250647,13.0,,S,,,"Denmark / New York, NY"
+2,1,"Portaluppi, Mr. Emilio Ilario Giuseppe",male,30.0,0,0,C.A. 34644,12.7375,,C,14,,"Milford, NH"
+2,0,"Pulbaum, Mr. Franz",male,27.0,0,0,SC/PARIS 2168,15.0333,,C,,,Paris
+2,1,"Quick, Miss. Phyllis May",female,2.0,1,1,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+2,1,"Quick, Miss. Winifred Vera",female,8.0,1,1,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+2,1,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33.0,0,2,26360,26.0,,S,11,,"Plymouth, Devon / Detroit, MI"
+2,0,"Reeves, Mr. David",male,36.0,0,0,C.A. 17248,10.5,,S,,,"Brighton, Sussex"
+2,0,"Renouf, Mr. Peter Henry",male,34.0,1,0,31027,21.0,,S,12,,"Elizabeth, NJ"
+2,1,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30.0,3,0,31027,21.0,,S,,,"Elizabeth, NJ"
+2,1,"Reynaldo, Ms. Encarnacion",female,28.0,0,0,230434,13.0,,S,9,,Spain
+2,0,"Richard, Mr. Emile",male,23.0,0,0,SC/PARIS 2133,15.0458,,C,,,"Paris / Montreal, PQ"
+2,1,"Richards, Master. George Sibley",male,0.8333,1,1,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+2,1,"Richards, Master. William Rowe",male,3.0,1,1,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+2,1,"Richards, Mrs. Sidney (Emily Hocking)",female,24.0,2,3,29106,18.75,,S,4,,"Cornwall / Akron, OH"
+2,1,"Ridsdale, Miss. Lucy",female,50.0,0,0,W./C. 14258,10.5,,S,13,,"London, England / Marietta, Ohio and Milwaukee, WI"
+2,0,"Rogers, Mr. Reginald Harry",male,19.0,0,0,28004,10.5,,S,,,
+2,1,"Rugg, Miss. Emily",female,21.0,0,0,C.A. 31026,10.5,,S,12,,"Guernsey / Wilmington, DE"
+2,0,"Schmidt, Mr. August",male,26.0,0,0,248659,13.0,,S,,,"Newark, NJ"
+2,0,"Sedgwick, Mr. Charles Frederick Waddington",male,25.0,0,0,244361,13.0,,S,,,Liverpool
+2,0,"Sharp, Mr. Percival James R",male,27.0,0,0,244358,26.0,,S,,,"Hornsey, England"
+2,1,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25.0,0,1,230433,26.0,,S,12,,"Deer Lodge, MT"
+2,1,"Silven, Miss. Lyyli Karoliina",female,18.0,0,2,250652,13.0,,S,16,,"Finland / Minneapolis, MN"
+2,1,"Sincock, Miss. Maude",female,20.0,0,0,C.A. 33112,36.75,,S,11,,"Cornwall / Hancock, MI"
+2,1,"Sinkkonen, Miss. Anna",female,30.0,0,0,250648,13.0,,S,10,,"Finland / Washington, DC"
+2,0,"Sjostedt, Mr. Ernst Adolf",male,59.0,0,0,237442,13.5,,S,,,"Sault St Marie, ON"
+2,1,"Slayter, Miss. Hilda Mary",female,30.0,0,0,234818,12.35,,Q,13,,"Halifax, NS"
+2,0,"Slemen, Mr. Richard James",male,35.0,0,0,28206,10.5,,S,,,Cornwall
+2,1,"Smith, Miss. Marion Elsie",female,40.0,0,0,31418,13.0,,S,9,,
+2,0,"Sobey, Mr. Samuel James Hayden",male,25.0,0,0,C.A. 29178,13.0,,S,,,"Cornwall / Houghton, MI"
+2,0,"Stanton, Mr. Samuel Ward",male,41.0,0,0,237734,15.0458,,C,,,"New York, NY"
+2,0,"Stokes, Mr. Philip Joseph",male,25.0,0,0,F.C.C. 13540,10.5,,S,,81.0,"Catford, Kent / Detroit, MI"
+2,0,"Swane, Mr. George",male,18.5,0,0,248734,13.0,F,S,,294.0,
+2,0,"Sweet, Mr. George Frederick",male,14.0,0,0,220845,65.0,,S,,,"Somerset / Bernardsville, NJ"
+2,1,"Toomey, Miss. Ellen",female,50.0,0,0,F.C.C. 13531,10.5,,S,9,,"Indianapolis, IN"
+2,0,"Troupiansky, Mr. Moses Aaron",male,23.0,0,0,233639,13.0,,S,,,
+2,1,"Trout, Mrs. William H (Jessie L)",female,28.0,0,0,240929,12.65,,S,,,"Columbus, OH"
+2,1,"Troutt, Miss. Edwina Celia ""Winnie""",female,27.0,0,0,34218,10.5,E101,S,16,,"Bath, England / Massachusetts"
+2,0,"Turpin, Mr. William John Robert",male,29.0,1,0,11668,21.0,,S,,,"Plymouth, England"
+2,0,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,1,0,11668,21.0,,S,,,"Plymouth, England"
+2,0,"Veal, Mr. James",male,40.0,0,0,28221,13.0,,S,,,"Barre, Co Washington, VT"
+2,1,"Walcroft, Miss. Nellie",female,31.0,0,0,F.C.C. 13528,21.0,,S,14,,"Mamaroneck, NY"
+2,0,"Ware, Mr. John James",male,30.0,1,0,CA 31352,21.0,,S,,,"Bristol, England / New Britain, CT"
+2,0,"Ware, Mr. William Jeffery",male,23.0,1,0,28666,10.5,,S,,,
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+2,0,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0.0,,S,,,Belfast
+2,1,"Watt, Miss. Bertha J",female,12.0,0,0,C.A. 33595,15.75,,S,9,,"Aberdeen / Portland, OR"
+2,1,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40.0,0,0,C.A. 33595,15.75,,S,9,,"Aberdeen / Portland, OR"
+2,1,"Webber, Miss. Susan",female,32.5,0,0,27267,13.0,E101,S,12,,"England / Hartford, CT"
+2,0,"Weisz, Mr. Leopold",male,27.0,1,0,228414,26.0,,S,,293.0,"Bromsgrove, England / Montreal, PQ"
+2,1,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29.0,1,0,228414,26.0,,S,10,,"Bromsgrove, England / Montreal, PQ"
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+2,1,"Wells, Miss. Joan",female,4.0,1,1,29103,23.0,,S,14,,"Cornwall / Akron, OH"
+2,1,"Wells, Mrs. Arthur Henry (""Addie"" Dart Trevaskis)",female,29.0,0,2,29103,23.0,,S,14,,"Cornwall / Akron, OH"
+2,1,"West, Miss. Barbara J",female,0.9167000000000001,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+2,1,"West, Miss. Constance Mirium",female,5.0,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+2,0,"West, Mr. Edwy Arthur",male,36.0,1,2,C.A. 34651,27.75,,S,,,"Bournmouth, England"
+2,1,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33.0,1,2,C.A. 34651,27.75,,S,10,,"Bournmouth, England"
+2,0,"Wheadon, Mr. Edward H",male,66.0,0,0,C.A. 24579,10.5,,S,,,"Guernsey, England / Edgewood, RI"
+2,0,"Wheeler, Mr. Edwin ""Frederick""",male,,0,0,SC/PARIS 2159,12.875,,S,,,
+2,1,"Wilhelms, Mr. Charles",male,31.0,0,0,244270,13.0,,S,9,,"London, England"
+2,1,"Williams, Mr. Charles Eugene",male,,0,0,244373,13.0,,S,14,,"Harrow, England"
+2,1,"Wright, Miss. Marion",female,26.0,0,0,220844,13.5,,S,9,,"Yoevil, England / Cottage Grove, OR"
+2,0,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24.0,0,0,248747,13.0,,S,,,Paris
+3,0,"Abbing, Mr. Anthony",male,42.0,0,0,C.A. 5547,7.55,,S,,,
+3,0,"Abbott, Master. Eugene Joseph",male,13.0,0,2,C.A. 2673,20.25,,S,,,"East Providence, RI"
+3,0,"Abbott, Mr. Rossmore Edward",male,16.0,1,1,C.A. 2673,20.25,,S,,190.0,"East Providence, RI"
+3,1,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35.0,1,1,C.A. 2673,20.25,,S,A,,"East Providence, RI"
+3,1,"Abelseth, Miss. Karen Marie",female,16.0,0,0,348125,7.65,,S,16,,"Norway Los Angeles, CA"
+3,1,"Abelseth, Mr. Olaus Jorgensen",male,25.0,0,0,348122,7.65,F G63,S,A,,"Perkins County, SD"
+3,1,"Abrahamsson, Mr. Abraham August Johannes",male,20.0,0,0,SOTON/O2 3101284,7.925,,S,15,,"Taalintehdas, Finland Hoboken, NJ"
+3,1,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18.0,0,0,2657,7.2292,,C,C,,"Greensburg, PA"
+3,0,"Adahl, Mr. Mauritz Nils Martin",male,30.0,0,0,C 7076,7.25,,S,,72.0,"Asarum, Sweden Brooklyn, NY"
+3,0,"Adams, Mr. John",male,26.0,0,0,341826,8.05,,S,,103.0,"Bournemouth, England"
+3,0,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,1,0,7546,9.475,,S,,,"Sweden Akeley, MN"
+3,1,"Aks, Master. Philip Frank",male,0.8333,0,1,392091,9.35,,S,11,,"London, England Norfolk, VA"
+3,1,"Aks, Mrs. Sam (Leah Rosen)",female,18.0,0,1,392091,9.35,,S,13,,"London, England Norfolk, VA"
+3,1,"Albimona, Mr. Nassef Cassem",male,26.0,0,0,2699,18.7875,,C,15,,"Syria Fredericksburg, VA"
+3,0,"Alexander, Mr. William",male,26.0,0,0,3474,7.8875,,S,,,"England Albion, NY"
+3,0,"Alhomaki, Mr. Ilmari Rudolf",male,20.0,0,0,SOTON/O2 3101287,7.925,,S,,,"Salo, Finland Astoria, OR"
+3,0,"Ali, Mr. Ahmed",male,24.0,0,0,SOTON/O.Q. 3101311,7.05,,S,,,
+3,0,"Ali, Mr. William",male,25.0,0,0,SOTON/O.Q. 3101312,7.05,,S,,79.0,Argentina
+3,0,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,S,,,"Lower Clapton, Middlesex or Erdington, Birmingham"
+3,0,"Allum, Mr. Owen George",male,18.0,0,0,2223,8.3,,S,,259.0,"Windsor, England New York, NY"
+3,0,"Andersen, Mr. Albert Karvin",male,32.0,0,0,C 4001,22.525,,S,,260.0,"Bergen, Norway"
+3,1,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19.0,1,0,350046,7.8542000000000005,,S,16,,
+3,0,"Andersson, Master. Sigvard Harald Elias",male,4.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,0,"Andersson, Miss. Ebba Iris Alfrida",female,6.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,0,"Andersson, Miss. Ellis Anna Maria",female,2.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,1,"Andersson, Miss. Erna Alexandra",female,17.0,4,2,3101281,7.925,,S,D,,"Ruotsinphyhtaa, Finland New York, NY"
+3,0,"Andersson, Miss. Ida Augusta Margareta",female,38.0,4,2,347091,7.775,,S,,,"Vadsbro, Sweden Ministee, MI"
+3,0,"Andersson, Miss. Ingeborg Constanzia",female,9.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,0,"Andersson, Miss. Sigrid Elisabeth",female,11.0,4,2,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,0,"Andersson, Mr. Anders Johan",male,39.0,1,5,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,1,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27.0,0,0,350043,7.7958,,S,A,,
+3,0,"Andersson, Mr. Johan Samuel",male,26.0,0,0,347075,7.775,,S,,,"Hartford, CT"
+3,0,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39.0,1,5,347082,31.275,,S,,,"Sweden Winnipeg, MN"
+3,0,"Andreasson, Mr. Paul Edvin",male,20.0,0,0,347466,7.8542000000000005,,S,,,"Sweden Chicago, IL"
+3,0,"Angheloff, Mr. Minko",male,26.0,0,0,349202,7.8958,,S,,,"Bulgaria Chicago, IL"
+3,0,"Arnold-Franchi, Mr. Josef",male,25.0,1,0,349237,17.8,,S,,,"Altdorf, Switzerland"
+3,0,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,1,0,349237,17.8,,S,,,"Altdorf, Switzerland"
+3,0,"Aronsson, Mr. Ernst Axel Algot",male,24.0,0,0,349911,7.775,,S,,,"Sweden Joliet, IL"
+3,0,"Asim, Mr. Adola",male,35.0,0,0,SOTON/O.Q. 3101310,7.05,,S,,,
+3,0,"Asplund, Master. Carl Edgar",male,5.0,4,2,347077,31.3875,,S,,,"Sweden  Worcester, MA"
+3,0,"Asplund, Master. Clarence Gustaf Hugo",male,9.0,4,2,347077,31.3875,,S,,,"Sweden Worcester, MA"
+3,1,"Asplund, Master. Edvin Rojj Felix",male,3.0,4,2,347077,31.3875,,S,15,,"Sweden Worcester, MA"
+3,0,"Asplund, Master. Filip Oscar",male,13.0,4,2,347077,31.3875,,S,,,"Sweden Worcester, MA"
+3,1,"Asplund, Miss. Lillian Gertrud",female,5.0,4,2,347077,31.3875,,S,15,,"Sweden Worcester, MA"
+3,0,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40.0,1,5,347077,31.3875,,S,,142.0,"Sweden  Worcester, MA"
+3,1,"Asplund, Mr. Johan Charles",male,23.0,0,0,350054,7.7958,,S,13,,"Oskarshamn, Sweden Minneapolis, MN"
+3,1,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,1,5,347077,31.3875,,S,15,,"Sweden  Worcester, MA"
+3,1,"Assaf Khalil, Mrs. Mariana (""Miriam"")",female,45.0,0,0,2696,7.225,,C,C,,"Ottawa, ON"
+3,0,"Assaf, Mr. Gerios",male,21.0,0,0,2692,7.225,,C,,,"Ottawa, ON"
+3,0,"Assam, Mr. Ali",male,23.0,0,0,SOTON/O.Q. 3101309,7.05,,S,,,
+3,0,"Attalah, Miss. Malake",female,17.0,0,0,2627,14.4583,,C,,,
+3,0,"Attalah, Mr. Sleiman",male,30.0,0,0,2694,7.225,,C,,,"Ottawa, ON"
+3,0,"Augustsson, Mr. Albert",male,23.0,0,0,347468,7.8542000000000005,,S,,,"Krakoryd, Sweden Bloomington, IL"
+3,1,"Ayoub, Miss. Banoura",female,13.0,0,0,2687,7.2292,,C,C,,"Syria Youngstown, OH"
+3,0,"Baccos, Mr. Raffull",male,20.0,0,0,2679,7.225,,C,,,
+3,0,"Backstrom, Mr. Karl Alfred",male,32.0,1,0,3101278,15.85,,S,D,,"Ruotsinphytaa, Finland New York, NY"
+3,1,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3,0,3101278,15.85,,S,,,"Ruotsinphytaa, Finland New York, NY"
+3,1,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+3,1,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+3,1,"Baclini, Miss. Marie Catherine",female,5.0,2,1,2666,19.2583,,C,C,,"Syria New York, NY"
+3,1,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24.0,0,3,2666,19.2583,,C,C,,"Syria New York, NY"
+3,1,"Badman, Miss. Emily Louisa",female,18.0,0,0,A/4 31416,8.05,,S,C,,"London Skanteales, NY"
+3,0,"Badt, Mr. Mohamed",male,40.0,0,0,2623,7.225,,C,,,
+3,0,"Balkic, Mr. Cerin",male,26.0,0,0,349248,7.8958,,S,,,
+3,1,"Barah, Mr. Hanna Assi",male,20.0,0,0,2663,7.2292,,C,15,,
+3,0,"Barbara, Miss. Saiide",female,18.0,0,1,2691,14.4542,,C,,,"Syria Ottawa, ON"
+3,0,"Barbara, Mrs. (Catherine David)",female,45.0,0,1,2691,14.4542,,C,,,"Syria Ottawa, ON"
+3,0,"Barry, Miss. Julia",female,27.0,0,0,330844,7.8792,,Q,,,"New York, NY"
+3,0,"Barton, Mr. David John",male,22.0,0,0,324669,8.05,,S,,,"England New York, NY"
+3,0,"Beavan, Mr. William Thomas",male,19.0,0,0,323951,8.05,,S,,,England
+3,0,"Bengtsson, Mr. John Viktor",male,26.0,0,0,347068,7.775,,S,,,"Krakudden, Sweden Moune, IL"
+3,0,"Berglund, Mr. Karl Ivar Sven",male,22.0,0,0,PP 4348,9.35,,S,,,"Tranvik, Finland New York"
+3,0,"Betros, Master. Seman",male,,0,0,2622,7.2292,,C,,,
+3,0,"Betros, Mr. Tannous",male,20.0,0,0,2648,4.0125,,C,,,Syria
+3,1,"Bing, Mr. Lee",male,32.0,0,0,1601,56.4958,,S,C,,"Hong Kong New York, NY"
+3,0,"Birkeland, Mr. Hans Martin Monsen",male,21.0,0,0,312992,7.775,,S,,,"Brennes, Norway New York"
+3,0,"Bjorklund, Mr. Ernst Herbert",male,18.0,0,0,347090,7.75,,S,,,"Stockholm, Sweden New York"
+3,0,"Bostandyeff, Mr. Guentcho",male,26.0,0,0,349224,7.8958,,S,,,"Bulgaria Chicago, IL"
+3,0,"Boulos, Master. Akar",male,6.0,1,1,2678,15.2458,,C,,,"Syria Kent, ON"
+3,0,"Boulos, Miss. Nourelain",female,9.0,1,1,2678,15.2458,,C,,,"Syria Kent, ON"
+3,0,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C,,,Syria
+3,0,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C,,,"Syria Kent, ON"
+3,0,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q,,,"Ireland Chicago, IL"
+3,0,"Bourke, Mr. John",male,40.0,1,1,364849,15.5,,Q,,,"Ireland Chicago, IL"
+3,0,"Bourke, Mrs. John (Catherine)",female,32.0,1,1,364849,15.5,,Q,,,"Ireland Chicago, IL"
+3,0,"Bowen, Mr. David John ""Dai""",male,21.0,0,0,54636,16.1,,S,,,"Treherbert, Cardiff, Wales"
+3,1,"Bradley, Miss. Bridget Delia",female,22.0,0,0,334914,7.725,,Q,13,,"Kingwilliamstown, Co Cork, Ireland Glens Falls, NY"
+3,0,"Braf, Miss. Elin Ester Maria",female,20.0,0,0,347471,7.8542000000000005,,S,,,"Medeltorp, Sweden Chicago, IL"
+3,0,"Braund, Mr. Lewis Richard",male,29.0,1,0,3460,7.0458,,S,,,"Bridgerule, Devon"
+3,0,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,S,,,"Bridgerule, Devon"
+3,0,"Brobeck, Mr. Karl Rudolf",male,22.0,0,0,350045,7.7958,,S,,,"Sweden Worcester, MA"
+3,0,"Brocklebank, Mr. William Alfred",male,35.0,0,0,364512,8.05,,S,,,"Broomfield, Chelmsford, England"
+3,0,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q,,299.0,"Co Cork, Ireland Roxbury, MA"
+3,1,"Buckley, Mr. Daniel",male,21.0,0,0,330920,7.8208,,Q,13,,"Kingwilliamstown, Co Cork, Ireland New York, NY"
+3,0,"Burke, Mr. Jeremiah",male,19.0,0,0,365222,6.75,,Q,,,"Co Cork, Ireland Charlestown, MA"
+3,0,"Burns, Miss. Mary Delia",female,18.0,0,0,330963,7.8792,,Q,,,"Co Sligo, Ireland New York, NY"
+3,0,"Cacic, Miss. Manda",female,21.0,0,0,315087,8.6625,,S,,,
+3,0,"Cacic, Miss. Marija",female,30.0,0,0,315084,8.6625,,S,,,
+3,0,"Cacic, Mr. Jego Grga",male,18.0,0,0,315091,8.6625,,S,,,
+3,0,"Cacic, Mr. Luka",male,38.0,0,0,315089,8.6625,,S,,,Croatia
+3,0,"Calic, Mr. Jovo",male,17.0,0,0,315093,8.6625,,S,,,
+3,0,"Calic, Mr. Petar",male,17.0,0,0,315086,8.6625,,S,,,
+3,0,"Canavan, Miss. Mary",female,21.0,0,0,364846,7.75,,Q,,,
+3,0,"Canavan, Mr. Patrick",male,21.0,0,0,364858,7.75,,Q,,,"Ireland Philadelphia, PA"
+3,0,"Cann, Mr. Ernest Charles",male,21.0,0,0,A./5. 2152,8.05,,S,,,
+3,0,"Caram, Mr. Joseph",male,,1,0,2689,14.4583,,C,,,"Ottawa, ON"
+3,0,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C,,,"Ottawa, ON"
+3,0,"Carlsson, Mr. August Sigfrid",male,28.0,0,0,350042,7.7958,,S,,,"Dagsas, Sweden Fower, MN"
+3,0,"Carlsson, Mr. Carl Robert",male,24.0,0,0,350409,7.8542000000000005,,S,,,"Goteborg, Sweden Huntley, IL"
+3,1,"Carr, Miss. Helen ""Ellen""",female,16.0,0,0,367231,7.75,,Q,16,,"Co Longford, Ireland New York, NY"
+3,0,"Carr, Miss. Jeannie",female,37.0,0,0,368364,7.75,,Q,,,"Co Sligo, Ireland Hartford, CT"
+3,0,"Carver, Mr. Alfred John",male,28.0,0,0,392095,7.25,,S,,,"St Denys, Southampton, Hants"
+3,0,"Celotti, Mr. Francesco",male,24.0,0,0,343275,8.05,,S,,,London
+3,0,"Charters, Mr. David",male,21.0,0,0,A/5. 13032,7.7333,,Q,,,"Ireland New York, NY"
+3,1,"Chip, Mr. Chang",male,32.0,0,0,1601,56.4958,,S,C,,"Hong Kong New York, NY"
+3,0,"Christmann, Mr. Emil",male,29.0,0,0,343276,8.05,,S,,,
+3,0,"Chronopoulos, Mr. Apostolos",male,26.0,1,0,2680,14.4542,,C,,,Greece
+3,0,"Chronopoulos, Mr. Demetrios",male,18.0,1,0,2680,14.4542,,C,,,Greece
+3,0,"Coelho, Mr. Domingos Fernandeo",male,20.0,0,0,SOTON/O.Q. 3101307,7.05,,S,,,Portugal
+3,1,"Cohen, Mr. Gurshon ""Gus""",male,18.0,0,0,A/5 3540,8.05,,S,12,,"London Brooklyn, NY"
+3,0,"Colbert, Mr. Patrick",male,24.0,0,0,371109,7.25,,Q,,,"Co Limerick, Ireland Sherbrooke, PQ"
+3,0,"Coleff, Mr. Peju",male,36.0,0,0,349210,7.4958,,S,,,"Bulgaria Chicago, IL"
+3,0,"Coleff, Mr. Satio",male,24.0,0,0,349209,7.4958,,S,,,
+3,0,"Conlon, Mr. Thomas Henry",male,31.0,0,0,21332,7.7333,,Q,,,"Philadelphia, PA"
+3,0,"Connaghton, Mr. Michael",male,31.0,0,0,335097,7.75,,Q,,,"Ireland Brooklyn, NY"
+3,1,"Connolly, Miss. Kate",female,22.0,0,0,370373,7.75,,Q,13,,Ireland
+3,0,"Connolly, Miss. Kate",female,30.0,0,0,330972,7.6292,,Q,,,Ireland
+3,0,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q,,171.0,
+3,0,"Cook, Mr. Jacob",male,43.0,0,0,A/5 3536,8.05,,S,,,
+3,0,"Cor, Mr. Bartol",male,35.0,0,0,349230,7.8958,,S,,,Austria
+3,0,"Cor, Mr. Ivan",male,27.0,0,0,349229,7.8958,,S,,,Austria
+3,0,"Cor, Mr. Liudevit",male,19.0,0,0,349231,7.8958,,S,,,Austria
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+3,1,"Coutts, Master. Eden Leslie ""Neville""",male,9.0,1,1,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+3,1,"Coutts, Master. William Loch ""William""",male,3.0,1,1,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+3,1,"Coutts, Mrs. William (Winnie ""Minnie"" Treanor)",female,36.0,0,2,C.A. 37671,15.9,,S,2,,"England Brooklyn, NY"
+3,0,"Coxon, Mr. Daniel",male,59.0,0,0,364500,7.25,,S,,,"Merrill, WI"
+3,0,"Crease, Mr. Ernest James",male,19.0,0,0,S.P. 3464,8.1583,,S,,,"Bristol, England Cleveland, OH"
+3,1,"Cribb, Miss. Laura Alice",female,17.0,0,1,371362,16.1,,S,12,,"Bournemouth, England Newark, NJ"
+3,0,"Cribb, Mr. John Hatfield",male,44.0,0,1,371362,16.1,,S,,,"Bournemouth, England Newark, NJ"
+3,0,"Culumovic, Mr. Jeso",male,17.0,0,0,315090,8.6625,,S,,,Austria-Hungary
+3,0,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C,,9.0,
+3,1,"Dahl, Mr. Karl Edwart",male,45.0,0,0,7598,8.05,,S,15,,"Australia Fingal, ND"
+3,0,"Dahlberg, Miss. Gerda Ulrika",female,22.0,0,0,7552,10.5167,,S,,,"Norrlot, Sweden Chicago, IL"
+3,0,"Dakic, Mr. Branko",male,19.0,0,0,349228,10.1708,,S,,,Austria
+3,1,"Daly, Miss. Margaret Marcella ""Maggie""",female,30.0,0,0,382650,6.95,,Q,15,,"Co Athlone, Ireland New York, NY"
+3,1,"Daly, Mr. Eugene Patrick",male,29.0,0,0,382651,7.75,,Q,13 15 B,,"Co Athlone, Ireland New York, NY"
+3,0,"Danbom, Master. Gilbert Sigvard Emanuel",male,0.33330000000000004,0,2,347080,14.4,,S,,,"Stanton, IA"
+3,0,"Danbom, Mr. Ernst Gilbert",male,34.0,1,1,347080,14.4,,S,,197.0,"Stanton, IA"
+3,0,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28.0,1,1,347080,14.4,,S,,,"Stanton, IA"
+3,0,"Danoff, Mr. Yoto",male,27.0,0,0,349219,7.8958,,S,,,"Bulgaria Chicago, IL"
+3,0,"Dantcheff, Mr. Ristiu",male,25.0,0,0,349203,7.8958,,S,,,"Bulgaria Chicago, IL"
+3,0,"Davies, Mr. Alfred J",male,24.0,2,0,A/4 48871,24.15,,S,,,"West Bromwich, England Pontiac, MI"
+3,0,"Davies, Mr. Evan",male,22.0,0,0,SC/A4 23568,8.05,,S,,,
+3,0,"Davies, Mr. John Samuel",male,21.0,2,0,A/4 48871,24.15,,S,,,"West Bromwich, England Pontiac, MI"
+3,0,"Davies, Mr. Joseph",male,17.0,2,0,A/4 48873,8.05,,S,,,"West Bromwich, England Pontiac, MI"
+3,0,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S,,,"Liverpool, England Bedford, OH"
+3,1,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S,16,,"Liverpool, England Bedford, OH"
+3,1,"de Messemaeker, Mr. Guillaume Joseph",male,36.5,1,0,345572,17.4,,S,15,,"Tampico, MT"
+3,1,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36.0,1,0,345572,17.4,,S,13,,"Tampico, MT"
+3,1,"de Mulder, Mr. Theodore",male,30.0,0,0,345774,9.5,,S,11,,"Belgium Detroit, MI"
+3,0,"de Pelsmaeker, Mr. Alfons",male,16.0,0,0,345778,9.5,,S,,,
+3,1,"Dean, Master. Bertram Vere",male,1.0,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+3,1,"Dean, Miss. Elizabeth Gladys ""Millvina""",female,0.16670000000000001,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+3,0,"Dean, Mr. Bertram Frank",male,26.0,1,2,C.A. 2315,20.575,,S,,,"Devon, England Wichita, KS"
+3,1,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33.0,1,2,C.A. 2315,20.575,,S,10,,"Devon, England Wichita, KS"
+3,0,"Delalic, Mr. Redjo",male,25.0,0,0,349250,7.8958,,S,,,
+3,0,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S,,,
+3,0,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S,,,"Bulgaria Coon Rapids, IA"
+3,0,"Dennis, Mr. Samuel",male,22.0,0,0,A/5 21172,7.25,,S,,,
+3,0,"Dennis, Mr. William",male,36.0,0,0,A/5 21175,7.25,,S,,,
+3,1,"Devaney, Miss. Margaret Delia",female,19.0,0,0,330958,7.8792,,Q,C,,"Kilmacowen, Co Sligo, Ireland New York, NY"
+3,0,"Dika, Mr. Mirko",male,17.0,0,0,349232,7.8958,,S,,,
+3,0,"Dimic, Mr. Jovan",male,42.0,0,0,315088,8.6625,,S,,,
+3,0,"Dintcheff, Mr. Valtcho",male,43.0,0,0,349226,7.8958,,S,,,
+3,0,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C,,,
+3,0,"Dooley, Mr. Patrick",male,32.0,0,0,370376,7.75,,Q,,,"Ireland New York, NY"
+3,1,"Dorking, Mr. Edward Arthur",male,19.0,0,0,A/5. 10482,8.05,,S,B,,"England Oglesby, IL"
+3,1,"Dowdell, Miss. Elizabeth",female,30.0,0,0,364516,12.475,,S,13,,"Union Hill, NJ"
+3,0,"Doyle, Miss. Elizabeth",female,24.0,0,0,368702,7.75,,Q,,,"Ireland New York, NY"
+3,1,"Drapkin, Miss. Jennie",female,23.0,0,0,SOTON/OQ 392083,8.05,,S,,,"London New York, NY"
+3,0,"Drazenoic, Mr. Jozef",male,33.0,0,0,349241,7.8958,,C,,51.0,"Austria Niagara Falls, NY"
+3,0,"Duane, Mr. Frank",male,65.0,0,0,336439,7.75,,Q,,,
+3,1,"Duquemin, Mr. Joseph",male,24.0,0,0,S.O./P.P. 752,7.55,,S,D,,"England Albion, NY"
+3,0,"Dyker, Mr. Adolf Fredrik",male,23.0,1,0,347072,13.9,,S,,,"West Haven, CT"
+3,1,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22.0,1,0,347072,13.9,,S,16,,"West Haven, CT"
+3,0,"Edvardsson, Mr. Gustaf Hjalmar",male,18.0,0,0,349912,7.775,,S,,,"Tofta, Sweden Joliet, IL"
+3,0,"Eklund, Mr. Hans Linus",male,16.0,0,0,347074,7.775,,S,,,"Karberg, Sweden Jerome Junction, AZ"
+3,0,"Ekstrom, Mr. Johan",male,45.0,0,0,347061,6.975,,S,,,"Effington Rut, SD"
+3,0,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C,,,
+3,0,"Elias, Mr. Joseph",male,39.0,0,2,2675,7.2292,,C,,,"Syria Ottawa, ON"
+3,0,"Elias, Mr. Joseph Jr",male,17.0,1,1,2690,7.2292,,C,,,
+3,0,"Elias, Mr. Tannous",male,15.0,1,1,2695,7.2292,,C,,,Syria
+3,0,"Elsbury, Mr. William James",male,47.0,0,0,A/5 3902,7.25,,S,,,"Illinois, USA"
+3,1,"Emanuel, Miss. Virginia Ethel",female,5.0,0,0,364516,12.475,,S,13,,"New York, NY"
+3,0,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C,,,
+3,0,"Everett, Mr. Thomas James",male,40.5,0,0,C.A. 6212,15.1,,S,,187.0,
+3,0,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q,,68.0,"Aughnacliff, Co Longford, Ireland New York, NY"
+3,1,"Finoli, Mr. Luigi",male,,0,0,SOTON/O.Q. 3101308,7.05,,S,15,,"Italy Philadelphia, PA"
+3,0,"Fischer, Mr. Eberhard Thelander",male,18.0,0,0,350036,7.7958,,S,,,
+3,0,"Fleming, Miss. Honora",female,,0,0,364859,7.75,,Q,,,
+3,0,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q,,,
+3,0,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q,,,
+3,0,"Foley, Mr. Joseph",male,26.0,0,0,330910,7.8792,,Q,,,"Ireland Chicago, IL"
+3,0,"Foley, Mr. William",male,,0,0,365235,7.75,,Q,,,Ireland
+3,1,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S,13,,"Hong Kong New York, NY"
+3,0,"Ford, Miss. Doolina Margaret ""Daisy""",female,21.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+3,0,"Ford, Miss. Robina Maggie ""Ruby""",female,9.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+3,0,"Ford, Mr. Arthur",male,,0,0,A/5 1478,8.05,,S,,,"Bridgwater, Somerset, England"
+3,0,"Ford, Mr. Edward Watson",male,18.0,2,2,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+3,0,"Ford, Mr. William Neal",male,16.0,1,3,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+3,0,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48.0,1,3,W./C. 6608,34.375,,S,,,"Rotherfield, Sussex, England Essex Co, MA"
+3,0,"Fox, Mr. Patrick",male,,0,0,368573,7.75,,Q,,,"Ireland New York, NY"
+3,0,"Franklin, Mr. Charles (Charles Fardon)",male,,0,0,SOTON/O.Q. 3101314,7.25,,S,,,
+3,0,"Gallagher, Mr. Martin",male,25.0,0,0,36864,7.7417,,Q,,,"New York, NY"
+3,0,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S,,,
+3,0,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C,,,
+3,0,"Gilinski, Mr. Eliezer",male,22.0,0,0,14973,8.05,,S,,47.0,
+3,1,"Gilnagh, Miss. Katherine ""Katie""",female,16.0,0,0,35851,7.7333,,Q,16,,"Co Longford, Ireland New York, NY"
+3,1,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q,13,,"Co Clare, Ireland Washington, DC"
+3,1,"Goldsmith, Master. Frank John William ""Frankie""",male,9.0,0,2,363291,20.525,,S,C D,,"Strood, Kent, England Detroit, MI"
+3,0,"Goldsmith, Mr. Frank John",male,33.0,1,1,363291,20.525,,S,,,"Strood, Kent, England Detroit, MI"
+3,0,"Goldsmith, Mr. Nathan",male,41.0,0,0,SOTON/O.Q. 3101263,7.85,,S,,,"Philadelphia, PA"
+3,1,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31.0,1,1,363291,20.525,,S,C D,,"Strood, Kent, England Detroit, MI"
+3,0,"Goncalves, Mr. Manuel Estanslas",male,38.0,0,0,SOTON/O.Q. 3101306,7.05,,S,,,Portugal
+3,0,"Goodwin, Master. Harold Victor",male,9.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Master. Sidney Leonard",male,1.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Master. William Frederick",male,11.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Miss. Jessie Allis",female,10.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Miss. Lillian Amy",female,16.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Mr. Charles Edward",male,14.0,5,2,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Mr. Charles Frederick",male,40.0,1,6,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43.0,1,6,CA 2144,46.9,,S,,,"Wiltshire, England Niagara Falls, NY"
+3,0,"Green, Mr. George Henry",male,51.0,0,0,21440,8.05,,S,,,"Dorking, Surrey, England"
+3,0,"Gronnestad, Mr. Daniel Danielsen",male,32.0,0,0,8471,8.3625,,S,,,"Foresvik, Norway Portland, ND"
+3,0,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S,,,
+3,0,"Gustafsson, Mr. Alfred Ossian",male,20.0,0,0,7534,9.8458,,S,,,"Waukegan, Chicago, IL"
+3,0,"Gustafsson, Mr. Anders Vilhelm",male,37.0,2,0,3101276,7.925,,S,,98.0,"Ruotsinphytaa, Finland New York, NY"
+3,0,"Gustafsson, Mr. Johan Birger",male,28.0,2,0,3101277,7.925,,S,,,"Ruotsinphytaa, Finland New York, NY"
+3,0,"Gustafsson, Mr. Karl Gideon",male,19.0,0,0,347069,7.775,,S,,,"Myren, Sweden New York, NY"
+3,0,"Haas, Miss. Aloisia",female,24.0,0,0,349236,8.85,,S,,,
+3,0,"Hagardon, Miss. Kate",female,17.0,0,0,AQ/3. 30631,7.7333,,Q,,,
+3,0,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S,,,
+3,0,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S,,,
+3,0,"Hakkarainen, Mr. Pekka Pietari",male,28.0,1,0,STON/O2. 3101279,15.85,,S,,,
+3,1,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24.0,1,0,STON/O2. 3101279,15.85,,S,15,,
+3,0,"Hampe, Mr. Leon",male,20.0,0,0,345769,9.5,,S,,,
+3,0,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C,,188.0,
+3,0,"Hansen, Mr. Claus Peter",male,41.0,2,0,350026,14.1083,,S,,,
+3,0,"Hansen, Mr. Henrik Juul",male,26.0,1,0,350025,7.8542000000000005,,S,,,
+3,0,"Hansen, Mr. Henry Damsgaard",male,21.0,0,0,350029,7.8542000000000005,,S,,69.0,
+3,1,"Hansen, Mrs. Claus Peter (Jennie L Howard)",female,45.0,1,0,350026,14.1083,,S,11,,
+3,0,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S,,,
+3,0,"Harmer, Mr. Abraham (David Lishin)",male,25.0,0,0,374887,7.25,,S,B,,
+3,0,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q,,,
+3,0,"Hassan, Mr. Houssein G N",male,11.0,0,0,2699,18.7875,,C,,,
+3,1,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q,16,,
+3,1,"Hedman, Mr. Oskar Arvid",male,27.0,0,0,347089,6.975,,S,15,,
+3,1,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S,C,,
+3,0,"Hegarty, Miss. Hanora ""Nora""",female,18.0,0,0,365226,6.75,,Q,,,
+3,1,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,S,,,
+3,0,"Heininen, Miss. Wendla Maria",female,23.0,0,0,STON/O2. 3101290,7.925,,S,,,
+3,1,"Hellstrom, Miss. Hilda Maria",female,22.0,0,0,7548,8.9625,,S,C,,
+3,0,"Hendekovic, Mr. Ignjac",male,28.0,0,0,349243,7.8958,,S,,306.0,
+3,0,"Henriksson, Miss. Jenny Lovisa",female,28.0,0,0,347086,7.775,,S,,,
+3,0,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q,,,
+3,1,"Hirvonen, Miss. Hildur E",female,2.0,0,1,3101298,12.2875,,S,15,,
+3,1,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22.0,1,1,3101298,12.2875,,S,15,,
+3,0,"Holm, Mr. John Fredrik Alexander",male,43.0,0,0,C 7075,6.45,,S,,,
+3,0,"Holthen, Mr. Johan Martin",male,28.0,0,0,C 4001,22.525,,S,,,
+3,1,"Honkanen, Miss. Eliina",female,27.0,0,0,STON/O2. 3101283,7.925,,S,,,
+3,0,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q,,,
+3,1,"Howard, Miss. May Elizabeth",female,,0,0,A. 2. 39186,8.05,,S,C,,
+3,0,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42.0,0,0,348121,7.65,F G63,S,,120.0,
+3,1,"Hyman, Mr. Abraham",male,,0,0,3470,7.8875,,S,C,,
+3,0,"Ibrahim Shawah, Mr. Yousseff",male,30.0,0,0,2685,7.2292,,C,,,
+3,0,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S,,,
+3,0,"Ilmakangas, Miss. Ida Livija",female,27.0,1,0,STON/O2. 3101270,7.925,,S,,,
+3,0,"Ilmakangas, Miss. Pieta Sofia",female,25.0,1,0,STON/O2. 3101271,7.925,,S,,,
+3,0,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S,,,
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