979 lines
51 KiB
Plaintext
979 lines
51 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "935dd393-0cdb-470f-b5e7-cf2af42855fd",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"url = \"https://pixees.fr/informatiquelycee/n_site/asset/titanic.csv\""
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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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"id": "a4ff45d8-079a-4a2e-a220-87760831d4ba",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#Importation des données \n",
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"import pandas as pd\n",
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"data = pd.io.parsers.read_csv(url)"
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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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"id": "3f9f39d2-aced-4bba-acf7-b00505f93a21",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>PassengerId</th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Name</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Ticket</th>\n",
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" <th>Fare</th>\n",
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" <th>Cabin</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Braund, Mr. Owen Harris</td>\n",
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" <td>male</td>\n",
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" <td>22.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>A/5 21171</td>\n",
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" <td>7.2500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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" <td>female</td>\n",
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" <td>38.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>PC 17599</td>\n",
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" <td>71.2833</td>\n",
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" <td>C85</td>\n",
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" <td>C</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>Heikkinen, Miss. Laina</td>\n",
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" <td>female</td>\n",
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" <td>26.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>STON/O2. 3101282</td>\n",
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" <td>7.9250</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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" <td>female</td>\n",
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" <td>35.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>113803</td>\n",
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" <td>53.1000</td>\n",
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" <td>C123</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>Allen, Mr. William Henry</td>\n",
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" <td>male</td>\n",
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" <td>35.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>373450</td>\n",
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" <td>8.0500</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" PassengerId Survived Pclass \\\n",
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"0 1 0 3 \n",
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"1 2 1 1 \n",
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"2 3 1 3 \n",
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"3 4 1 1 \n",
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"4 5 0 3 \n",
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"\n",
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" Name Sex Age SibSp \\\n",
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"0 Braund, Mr. Owen Harris male 22.0 1 \n",
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"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
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"2 Heikkinen, Miss. Laina female 26.0 0 \n",
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"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
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"4 Allen, Mr. William Henry male 35.0 0 \n",
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"\n",
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" Parch Ticket Fare Cabin Embarked \n",
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"0 0 A/5 21171 7.2500 NaN S \n",
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"1 0 PC 17599 71.2833 C85 C \n",
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"2 0 STON/O2. 3101282 7.9250 NaN S \n",
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"3 0 113803 53.1000 C123 S \n",
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"4 0 373450 8.0500 NaN S "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.head()"
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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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"id": "f4b6f349-e8d5-406b-84f0-ee876df95e1f",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>PassengerId</th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Name</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Ticket</th>\n",
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" <th>Fare</th>\n",
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" <th>Cabin</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>886</th>\n",
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" <td>887</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>Montvila, Rev. Juozas</td>\n",
|
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" <td>male</td>\n",
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" <td>27.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>211536</td>\n",
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" <td>13.00</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>887</th>\n",
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" <td>888</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Graham, Miss. Margaret Edith</td>\n",
|
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" <td>female</td>\n",
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" <td>19.0</td>\n",
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" <td>0</td>\n",
|
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" <td>0</td>\n",
|
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" <td>112053</td>\n",
|
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" <td>30.00</td>\n",
|
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" <td>B42</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>888</th>\n",
|
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" <td>889</td>\n",
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" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
|
|
" <td>female</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>1</td>\n",
|
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" <td>2</td>\n",
|
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" <td>W./C. 6607</td>\n",
|
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" <td>23.45</td>\n",
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" <td>NaN</td>\n",
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" <td>S</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>889</th>\n",
|
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" <td>890</td>\n",
|
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
|
" <td>Behr, Mr. Karl Howell</td>\n",
|
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" <td>male</td>\n",
|
|
" <td>26.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>111369</td>\n",
|
|
" <td>30.00</td>\n",
|
|
" <td>C148</td>\n",
|
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" <td>C</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>890</th>\n",
|
|
" <td>891</td>\n",
|
|
" <td>0</td>\n",
|
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" <td>3</td>\n",
|
|
" <td>Dooley, Mr. Patrick</td>\n",
|
|
" <td>male</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>370376</td>\n",
|
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" <td>7.75</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>Q</td>\n",
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" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" PassengerId Survived Pclass Name \\\n",
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"886 887 0 2 Montvila, Rev. Juozas \n",
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"887 888 1 1 Graham, Miss. Margaret Edith \n",
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"888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
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"889 890 1 1 Behr, Mr. Karl Howell \n",
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"890 891 0 3 Dooley, Mr. Patrick \n",
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"\n",
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" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
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"886 male 27.0 0 0 211536 13.00 NaN S \n",
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"887 female 19.0 0 0 112053 30.00 B42 S \n",
|
|
"888 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
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"889 male 26.0 0 0 111369 30.00 C148 C \n",
|
|
"890 male 32.0 0 0 370376 7.75 NaN Q "
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]
|
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.tail()"
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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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"id": "86cc0139-bc42-49eb-9261-946db3ce517e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"data = data.drop(['Name', 'PassengerId', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=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": 6,
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"id": "84dcd03a-9f74-467d-92f8-720fd5dc57a2",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
|
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" <td>0</td>\n",
|
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" <td>3</td>\n",
|
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" <td>male</td>\n",
|
|
" <td>22.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>1</th>\n",
|
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
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" <td>female</td>\n",
|
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" <td>38.0</td>\n",
|
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" </tr>\n",
|
|
" <tr>\n",
|
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" <th>2</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>female</td>\n",
|
|
" <td>26.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>3</th>\n",
|
|
" <td>1</td>\n",
|
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" <td>1</td>\n",
|
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" <td>female</td>\n",
|
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" <td>35.0</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>4</th>\n",
|
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" <td>0</td>\n",
|
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" <td>3</td>\n",
|
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" <td>male</td>\n",
|
|
" <td>35.0</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
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" Survived Pclass Sex Age\n",
|
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"0 0 3 male 22.0\n",
|
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"1 1 1 female 38.0\n",
|
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"2 1 3 female 26.0\n",
|
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"3 1 1 female 35.0\n",
|
|
"4 0 3 male 35.0"
|
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]
|
|
},
|
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"data.head()"
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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": 7,
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"id": "8fa36ab8-f947-46b2-9b26-5c608058fd48",
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"metadata": {
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"tags": []
|
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},
|
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"outputs": [],
|
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"source": [
|
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"data = data.dropna(axis=0)"
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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": 8,
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"id": "b4eeb852-30d2-43b6-82f6-23d1c5db4139",
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"metadata": {
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"tags": []
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},
|
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(714, 4)\n"
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]
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}
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],
|
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"source": [
|
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"print(data.shape)"
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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": 9,
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"id": "3a91513f-f8e5-4ef3-b0d8-967f98539a50",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style scoped>\n",
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|
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"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Survived</th>\n",
|
|
" <th>Pclass</th>\n",
|
|
" <th>Age</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>714.000000</td>\n",
|
|
" <td>714.000000</td>\n",
|
|
" <td>714.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>0.406162</td>\n",
|
|
" <td>2.236695</td>\n",
|
|
" <td>29.699118</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.491460</td>\n",
|
|
" <td>0.838250</td>\n",
|
|
" <td>14.526497</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.420000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>20.125000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>28.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>38.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>80.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Survived Pclass Age\n",
|
|
"count 714.000000 714.000000 714.000000\n",
|
|
"mean 0.406162 2.236695 29.699118\n",
|
|
"std 0.491460 0.838250 14.526497\n",
|
|
"min 0.000000 1.000000 0.420000\n",
|
|
"25% 0.000000 1.000000 20.125000\n",
|
|
"50% 0.000000 2.000000 28.000000\n",
|
|
"75% 1.000000 3.000000 38.000000\n",
|
|
"max 1.000000 3.000000 80.000000"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "5e52ca0e-69fe-47eb-b4f9-0ae4f1d38a27",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"3 355\n",
|
|
"1 186\n",
|
|
"2 173\n",
|
|
"Name: Pclass, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data['Pclass'].value_counts()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "61a70d41-bea2-4872-8372-7a999a8e73ac",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: >"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"data['Pclass'].value_counts().plot.bar()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "f77de794-b854-4697-811a-c404b14eb680",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: >"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
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"data": {
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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
|
|
],
|
|
"source": [
|
|
"data['Age'].hist()"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "124a3f8b-2884-40b9-ab56-3a3366c499ee",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
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{
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"data": {
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|
"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Survived</th>\n",
|
|
" <th>Pclass</th>\n",
|
|
" <th>Age</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
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" <th>Sex</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>female</th>\n",
|
|
" <td>0.754789</td>\n",
|
|
" <td>2.065134</td>\n",
|
|
" <td>27.915709</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>male</th>\n",
|
|
" <td>0.205298</td>\n",
|
|
" <td>2.335541</td>\n",
|
|
" <td>30.726645</td>\n",
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|
" </tr>\n",
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|
" </tbody>\n",
|
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"</table>\n",
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"</div>"
|
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],
|
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"text/plain": [
|
|
" Survived Pclass Age\n",
|
|
"Sex \n",
|
|
"female 0.754789 2.065134 27.915709\n",
|
|
"male 0.205298 2.335541 30.726645"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data.groupby(['Sex']).mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "895a8e68-0e8d-4580-bfd0-be8f287d6342",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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|
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|
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|
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|
|
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" Survived Age\n",
|
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"Sex Pclass \n",
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"female 1 0.964706 34.611765\n",
|
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" 2 0.918919 28.722973\n",
|
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|
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" Survived Age\n",
|
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"Sex Pclass \n",
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|
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|
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