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Athena/titanic.ipynb
atarata-ensae f9ed14cfe6 bateau
2023-05-16 12:01:50 +00:00

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{
"cells": [
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"cell_type": "code",
"execution_count": 1,
"id": "add9990c-3373-481a-9bea-97b5e4fecd89",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"url = \"https://pixees.fr/informatiquelycee/n_site/asset/titanic.csv\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "427faad8-02c5-4089-8f4f-038d507fd5f2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as plt\n",
"titanic = pd.io.parsers.read_csv(url)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "24ff8e61-fba1-4537-8254-63778db832ff",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
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"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "67ae5a45-2a88-4d62-868b-bafd7f5aafb4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 891 non-null int64 \n",
" 1 Survived 891 non-null int64 \n",
" 2 Pclass 891 non-null int64 \n",
" 3 Name 891 non-null object \n",
" 4 Sex 891 non-null object \n",
" 5 Age 714 non-null float64\n",
" 6 SibSp 891 non-null int64 \n",
" 7 Parch 891 non-null int64 \n",
" 8 Ticket 891 non-null object \n",
" 9 Fare 891 non-null float64\n",
" 10 Cabin 204 non-null object \n",
" 11 Embarked 889 non-null object \n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.7+ KB\n"
]
}
],
"source": [
"titanic.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9acfd690-f13d-49bb-b3fb-88b32e7790e1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"titanic = titanic.drop(['Name', 'PassengerId', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7e7a7e67-da94-42eb-b382-35626cad511b",
"metadata": {
"tags": []
},
"outputs": [
{
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" Survived Pclass Sex Age\n",
"0 0 3 male 22.0\n",
"1 1 1 female 38.0\n",
"2 1 3 female 26.0\n",
"3 1 1 female 35.0\n",
"4 0 3 male 35.0"
]
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"execution_count": 6,
"metadata": {},
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"source": [
"titanic.head()"
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