5992 lines
581 KiB
Plaintext
5992 lines
581 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div style=\"\n",
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" border: 2px solid #4CAF50; \n",
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" padding: 15px; \n",
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" background-color: #f4f4f4; \n",
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" border-radius: 10px; \n",
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" align-items: center;\">\n",
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"\n",
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"<h1 style=\"margin: 0; color: #4CAF50;\">Datenverarbeitung in Pandas</h1>\n",
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"<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
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"<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
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"\n",
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"<div style=\"flex-shrink: 0;\">\n",
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" <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",
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"</div>\n",
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"<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
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"</div>\n",
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"<div style=\"flex: 1;\">\n",
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"</div> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Ersten Manipulationen von Dataframes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Beginnen wir mit dem Hinzufügen von Daten.\n",
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"Wir arbeiten dabei wieder hauptsächlich mit dem *Titanic* Dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Wir fügen nun einen fiktionalen Datensatz hinzu\n",
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"path = os.path.join(\"..\", \"..\", \"_data\", \"titanic.csv\")\n",
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"titanic = pd.read_csv(path)\n",
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"\n",
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"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",
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"new_df = pd.DataFrame(new_row, index=[0])\n",
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"titanic = pd.concat([titanic, new_df], ignore_index=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"titanic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Für mehrere Daten zum hinzufügen können wir auch eine Liste von Dictionaries verwenden\n",
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"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",
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"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",
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"\n",
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"new_data = [new_row_1, new_row_2]\n",
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"new_df = pd.DataFrame(new_data, index=[0, 1])\n",
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"\n",
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"titanic = pd.concat([titanic, new_df], ignore_index=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"titanic"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Duplikate können auch entfernt werden"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"titanic.drop_duplicates(inplace=True, ignore_index=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"titanic"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Nun betrachten wir die eindeutigen Zeilenwerte in einem Dataframe. Dafür benutzen wir den Befehl `unique()` bzw. `nunique()`"
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]
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},
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{
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||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Anzahl der eindeutige Werte in allen Spalten anzeigen lassen\n",
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"titanic.nunique()"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"# Eindeutige Werte in einer Spalte anzeigen lassen\n",
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"\n",
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"column = 'sibsp' # Andere interessante Fälle: 'boat'\n",
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"titanic[column].unique()"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"# Wir können auch die eindeutigen Werte und deren Anzahl anzeigen lassen\n",
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"\n",
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"column = 'sibsp'\n",
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"\n",
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"titanic[column].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"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?"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Drop columns\n",
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"titanic.drop(columns=['boat', 'body'], inplace=True) # erneut inplace Option um Speicherplatz zu sparen"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"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\""
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 12,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"# Spalten, welche leere Werte enthalten, können auch auch mit dem dropna() Befehl entfernt werden, dazu braucht es das Argument axis=1\n",
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"titanic.dropna(axis=1, inplace=True) # Vergleiche mit letzter Woche: dropna() entfernt Zeilen mit leeren Werten"
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]
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"titanic"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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||
"Wir laden nun das Dataset erneut um die bisherigen Änderungen zu verwerfen."
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 14,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"titanic = pd.read_csv(path, header=0, sep=',', index_col=False, decimal='.')"
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||
]
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},
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{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"Wollen wir Daten aggregieren, so können wir den `agg()` Befehl verwenden. Wo kann dies nützlich sein? Für welche Datentypen funktionieren welche Aggregierungsmöglichkeiten?"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 15,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"agg_df = titanic.agg(['count', 'size', 'nunique'])"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"agg_df"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"Weitere Möglichkeiten sind `sum`, `mean`, `median`, `std`, `var`, `min`, `max`, etc. --> Wir werden später genauer auf Statistiken eingehen."
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 17,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"# Eine weitere Möglichkeit ist es, Funktionen auf die Daten anzuwenden\n",
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||
"titanic['increased_age'] = titanic['age'].apply(lambda x: x + 1)"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"titanic"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 19,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"titanic.drop(columns=['increased_age'], inplace=True)"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 20,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"# Wir können groupby verwenden, um die Daten zu gruppieren\n",
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||
"grouped_df = titanic.groupby('pclass')"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"Wir können nun mit dem gruppierten Dataframe `grouped_df` arbeiten"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 21,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"count_values = grouped_df.size()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"count_values"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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||
"for name, group in grouped_df: \n",
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" print(name)\n",
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" print(group)\n",
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" print(\"*\"*50)"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 24,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"survival_rate = titanic.groupby('pclass')['survived'].mean()"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"survival_rate"
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]
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},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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"---"
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||
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||
"## Fehlende Werte/Datenpunkte"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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"* In vielen Fällen werden Daten nicht perfekt sein\n",
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"* Daten können:\n",
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" * fehlerhaft/falsch sein bzw. mit Rauschen überlagert\n",
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" * \"unmöglich\" sein (Zum Beispiel: männlich und schwanger)\n",
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" * fehlen\n",
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"\n",
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||
"Es gibt verschiedene Möglichkeiten, wie diese Probleme behandelt werden können."
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]
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||
},
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||
{
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||
"cell_type": "code",
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"execution_count": 5,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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||
"# Sanity Checks. Wir laden zuerst ein modifiziertes Dataset\n",
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"\n",
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"modified_titanic = pd.read_csv(path.replace(\"titanic\", \"modified_titanic\"), header=0, sep=',', index_col=False, decimal='.')"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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"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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
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|
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|
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|
||
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
"modified_titanic['age'] = modified_titanic['age'].bfill() # Ersetzt alle fehldenden Werte der Spalte age mit dem nächsten vorhandenen Wert"
|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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||
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||
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|
||
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|
||
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|
||
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Third Class</td>\n",
|
||
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|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>Middleburg Heights, OH</td>\n",
|
||
" </tr>\n",
|
||
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|
||
"</table>\n",
|
||
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|
||
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|
||
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|
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|
||
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|
||
"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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"... ... ... ... ... \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",
|
||
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|
||
"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]"
|
||
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|
||
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|
||
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|
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|
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|
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|
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"modified_titanic"
|
||
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|
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|
||
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|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
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|
||
"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": [
|
||
{
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>20.000000</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>2</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <th>3</th>\n",
|
||
" <td>Second Class</td>\n",
|
||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
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|
||
" <td>female</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>First Class</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
" <td>female</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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": 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": {
|
||
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"
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},
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"Untitled.png": {
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||
"image/png": 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"
|
||
}
|
||
},
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\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",
|
||
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|
||
" <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",
|
||
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|
||
" <td>Allison, Miss. Helen Loraine</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>0.022964</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>113781</td>\n",
|
||
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|
||
" <td>C22 C26</td>\n",
|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <td>0.311064</td>\n",
|
||
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||
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|
||
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|
||
" <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",
|
||
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|
||
" <tr>\n",
|
||
" <th>1304</th>\n",
|
||
" <td>3</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <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": {
|
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|
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||
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|
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|
||
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|
||
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|
||
" <th></th>\n",
|
||
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|
||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
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|
||
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|
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"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",
|
||
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|
||
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|
||
"... ... ... ... \n",
|
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|
||
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||
"1307 3 0 Zakarian, Mr. Ortin \n",
|
||
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|
||
"\n",
|
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" sex age sibsp parch ticket fare cabin embarked boat \\\n",
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|
||
"... ... ... ... ... ... ... ... ... ... \n",
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||
"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",
|
||
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|
||
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|
||
"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",
|
||
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|
||
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|
||
"1308 NaN NaN \n",
|
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"\n",
|
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"[1309 rows x 14 columns]"
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|
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|
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|
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|
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|
||
"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",
|
||
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|
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|
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|
||
" 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",
|
||
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|
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"... ... ... ... \n",
|
||
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|
||
"1305 3 0 Zabour, Miss. Thamine \n",
|
||
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|
||
"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",
|
||
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|
||
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|
||
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|
||
"... ... ... ... ... ... ... ... ... ... \n",
|
||
"1304 female -1.067134 1 0 2665 -0.364022 NaN C NaN \n",
|
||
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||
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||
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|
||
"\n",
|
||
" body home.dest \n",
|
||
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|
||
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|
||
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|
||
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|
||
"4 NaN Montreal, PQ / Chesterville, ON \n",
|
||
"... ... ... \n",
|
||
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|
||
"1305 NaN NaN \n",
|
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|
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||
"1308 NaN NaN \n",
|
||
"\n",
|
||
"[1309 rows x 14 columns]"
|
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|
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|
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|
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|
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|
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"titanic"
|
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]
|
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|
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{
|
||
"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']])"
|
||
]
|
||
},
|
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{
|
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"cell_type": "code",
|
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"metadata": {},
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
" <td>-0.061162</td>\n",
|
||
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|
||
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|
||
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|
||
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||
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|
||
" <td>S</td>\n",
|
||
" <td>2</td>\n",
|
||
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|
||
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|
||
" </tr>\n",
|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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||
" <td>C22 C26</td>\n",
|
||
" <td>S</td>\n",
|
||
" <td>11</td>\n",
|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>S</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Montreal, PQ / Chesterville, ON</td>\n",
|
||
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|
||
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|
||
" <th>4</th>\n",
|
||
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|
||
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|
||
" <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
|
||
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|
||
" <td>-0.338812</td>\n",
|
||
" <td>1</td>\n",
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||
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|
||
" <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",
|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
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||
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|
||
" <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",
|
||
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|
||
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|
||
" <th>1304</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
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|
||
" <td>C</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>328.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>1305</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Zabour, Miss. Thamine</td>\n",
|
||
" <td>female</td>\n",
|
||
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|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>C</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <tr>\n",
|
||
" <th>1308</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Zimmerman, Mr. Leo</td>\n",
|
||
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|
||
" <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": [
|
||
{
|
||
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||
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||
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||
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||
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||
" <td>C22 C26</td>\n",
|
||
" <td>S</td>\n",
|
||
" <td>11</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>NaN</td>\n",
|
||
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|
||
" <td>Montreal, PQ / Chesterville, ON</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
" <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": {
|
||
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|
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|
||
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|
||
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|
||
" <thead>\n",
|
||
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|
||
" <th></th>\n",
|
||
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|
||
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|
||
" <th>name</th>\n",
|
||
" <th>sex</th>\n",
|
||
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|
||
" <th>sibsp</th>\n",
|
||
" <th>parch</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
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|
||
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|
||
" <td>B5</td>\n",
|
||
" <td>S</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <td>Montreal, PQ / Chesterville, ON</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </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",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>1305</th>\n",
|
||
" <td>3</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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",
|
||
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|
||
" <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",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
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|
||
" <td>7.2250</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>C</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>1308</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Zimmerman, Mr. Leo</td>\n",
|
||
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|
||
" <td>29.0000</td>\n",
|
||
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|
||
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|
||
" <td>315082</td>\n",
|
||
" <td>7.8750</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
" </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",
|
||
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|
||
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|
||
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|
||
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|
||
"... ... ... ... ... \n",
|
||
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|
||
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|
||
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|
||
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|
||
"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",
|
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|
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|
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|
||
"... ... ... ... ... ... ... ... ... ... \n",
|
||
"1304 14.5000 1 0 2665 14.4542 NaN C NaN 328.0 \n",
|
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|
||
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|
||
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|
||
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|
||
"\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"4 Montreal, PQ / Chesterville, ON \n",
|
||
"... ... \n",
|
||
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|
||
"1305 NaN \n",
|
||
"1306 NaN \n",
|
||
"1307 NaN \n",
|
||
"1308 NaN \n",
|
||
"\n",
|
||
"[1309 rows x 14 columns]"
|
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|
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
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|
||
"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",
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|
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|
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|
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|
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"data": {
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|
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"0 female\n",
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"1 male\n",
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|
||
"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",
|
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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|
||
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||
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|
||
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||
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||
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||
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||
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|
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
"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",
|
||
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|
||
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|
||
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|
||
"... ... ... ... ... \n",
|
||
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|
||
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|
||
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|
||
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|
||
"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",
|
||
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|
||
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|
||
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|
||
"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": [
|
||
"\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
|
||
}
|