{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 2018-09-18 - Rappels sur pandas et maplotlib\n", "\n", "Manipulation de donn\u00e9es autour du jeu des passagers du Titanic qu'on peut r\u00e9cup\u00e9rer sur [opendatasoft](https://public.opendatasoft.com/explore/dataset/titanic-passengers/?flg=fr) ou [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets/tree/master/Datasets)."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import pandas"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"scrolled": false}, "outputs": [], "source": ["df = pandas.read_csv(\"titanic.csv/titanic.csv\")"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/plain": ["pandas.core.frame.DataFrame"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["type(df)"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/html": ["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
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"], "text/plain": [" PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \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", "\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 "]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["df.head(n=2)"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/html": ["
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PassengerIdSurvivedPclass
0103
1211
\n", "
"], "text/plain": [" PassengerId Survived Pclass\n", "0 1 0 3\n", "1 2 1 1"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["subset = df[ [\"PassengerId\", \"Survived\", \"Pclass\"] ]\n", "subset.head(n=2)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["survived = subset[ [\"Survived\", \"Pclass\"] ].groupby([\"Pclass\"]).sum()\n", "compte = subset[ [\"Survived\", \"Pclass\"] ].groupby([\"Pclass\"]).count()\n", "compte.columns = ['total']"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Survived
Pclass
1136
287
3119
\n", "
"], "text/plain": [" Survived\n", "Pclass \n", "1 136\n", "2 87\n", "3 119"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["survived"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/html": ["
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total
Pclass
1216
2184
3491
\n", "
"], "text/plain": [" total\n", "Pclass \n", "1 216\n", "2 184\n", "3 491"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["compte"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Survivedtotal
Pclass
1136216
287184
3119491
\n", "
"], "text/plain": [" Survived total\n", "Pclass \n", "1 136 216\n", "2 87 184\n", "3 119 491"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["jointure = survived.join(compte)\n", "jointure"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Survivedtotalsurvie
Pclass
11362160.629630
2871840.472826
31194910.242363
\n", "
"], "text/plain": [" Survived total survie\n", "Pclass \n", "1 136 216 0.629630\n", "2 87 184 0.472826\n", "3 119 491 0.242363"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["jointure[\"survie\"] = jointure['Survived'] / jointure.total\n", "jointure"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ax = jointure[['survie']].plot(kind=\"bar\", figsize=(2, 2))\n", "ax.set_title(\"Titanic\")\n", "ax.set_ylabel(\"%\");"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"image/png": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(1, 2, figsize=(8,3))\n", "jointure[['survie']].plot(kind=\"bar\", ax=ax[0])\n", "ax[0].set_title(\"Titanic\")\n", "ax[0].set_ylabel(\"%\");\n", "jointure.drop('survie', axis=1).plot(kind=\"bar\", ax=ax[1])\n", "ax[1].set_title(\"Titanic\")\n", "ax[1].set_ylabel(\"%\");"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": ["jointure.to_excel(\"titanic.xlsx\")"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/html": ["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
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"], "text/plain": [" PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \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", "\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 "]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["df.head(n=2)"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": ["mat = df[['Survived', 'Age']].values"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 0., 1., 1., ..., 0., 1., 0.],\n", " [22., 38., 26., ..., nan, 26., 32.]])"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["mat.T"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[342., nan],\n", " [ nan, nan]])"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["mat.T @ mat"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 484., 836., 572., ..., nan, 572., 704.],\n", " [ 836., 1445., 989., ..., nan, 989., 1216.],\n", " [ 572., 989., 677., ..., nan, 677., 832.],\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ 572., 989., 677., ..., nan, 677., 832.],\n", " [ 704., 1216., 832., ..., nan, 832., 1024.]])"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["mat @ mat.T"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/html": ["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
\n", "
"], "text/plain": [" PassengerId Survived Pclass Name Sex Age SibSp \\\n", "889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 \n", "890 891 0 3 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "889 0 111369 30.00 C148 C \n", "890 0 370376 7.75 NaN Q "]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["df.tail(n=2)"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["'Braund, Mr. Owen Harris'"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["names = list(df['Name'])\n", "names\n", "nom = names[0]\n", "nom"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/plain": ["'mr'"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["nom.split(',')[1].split('.')[0].strip().lower()"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": ["mr = []\n", "for nom in names:\n", " mr.append(nom.split(',')[1].split('.')[0].strip().lower())"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": ["df['mr'] = mr"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": ["gr = df[ ['Sex', \"mr\", \"PassengerId\"] ].groupby(['Sex', \"mr\"], as_index=False).count()"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"text/html": ["
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SexmrPassengerId
0femaledr1
1femalelady1
2femalemiss182
3femalemlle2
4femalemme1
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"], "text/plain": [" Sex mr PassengerId\n", "0 female dr 1\n", "1 female lady 1\n", "2 female miss 182\n", "3 female mlle 2\n", "4 female mme 1"]}, "execution_count": 27, "metadata": {}, "output_type": "execute_result"}], "source": ["gr.head()"]}, {"cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Sexfemalemale
mr
captNaN1.0
colNaN2.0
donNaN1.0
dr1.06.0
jonkheerNaN1.0
lady1.0NaN
majorNaN2.0
masterNaN40.0
miss182.0NaN
mlle2.0NaN
mme1.0NaN
mrNaN517.0
mrs125.0NaN
ms1.0NaN
revNaN6.0
sirNaN1.0
the countess1.0NaN
\n", "
"], "text/plain": ["Sex female male\n", "mr \n", "capt NaN 1.0\n", "col NaN 2.0\n", "don NaN 1.0\n", "dr 1.0 6.0\n", "jonkheer NaN 1.0\n", "lady 1.0 NaN\n", "major NaN 2.0\n", "master NaN 40.0\n", "miss 182.0 NaN\n", "mlle 2.0 NaN\n", "mme 1.0 NaN\n", "mr NaN 517.0\n", "mrs 125.0 NaN\n", "ms 1.0 NaN\n", "rev NaN 6.0\n", "sir NaN 1.0\n", "the countess 1.0 NaN"]}, "execution_count": 28, "metadata": {}, "output_type": "execute_result"}], "source": ["gr.pivot(\"mr\", \"Sex\", \"PassengerId\")"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "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": "3.7.0"}}, "nbformat": 4, "nbformat_minor": 2}