{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# pip3 install pandas xlrd matplotlib\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Code géographiqueNbre de ménages fiscauxNbre de personnes dans les ménages fiscauxNbre d'unités de consommation dans les ménages fiscaux1er quartile (€)Médiane (€)3e quartile (€)Écart interquartile (€)1er décile (€)2e décile (€)...dont part des indemnités chômage (%)dont part des salaires, traitements hors chômage (%)dont part des revenus des activités non salariées (%)Part des pensions, retraites et rentes (%)Part des revenus du patrimoine et autres revenus (%)Part de l'ensemble des prestations sociales (%)dont part des prestations familiales (%)dont part des minima sociaux (%)dont part des prestations logement (%)Part des impôts (%)
Libellé géographique
L'Abergement-Clémenciat01001308801.0527.10NaN22228.000000NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
L'Abergement-de-Varey01002100245.5162.45NaN22883.333333NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ambérieu-en-Bugey01004621613917.09543.7014314.019735.20000026194.66666711880.66666710398.57142913079.0...3.365.63.926.99.56.72.72.02.0-15.8
Ambérieux-en-Dombes010056241671.51081.95NaN23182.666667NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Ambronay0100710442635.51732.7517564.021986.50000027836.66666710272.66666713620.66666716568.0...2.571.22.626.78.14.02.40.80.8-15.1
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5 rows × 30 columns

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" ], "text/plain": [ " Code géographique Nbre de ménages fiscaux \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat 01001 308 \n", "L'Abergement-de-Varey 01002 100 \n", "Ambérieu-en-Bugey 01004 6216 \n", "Ambérieux-en-Dombes 01005 624 \n", "Ambronay 01007 1044 \n", "\n", " Nbre de personnes dans les ménages fiscaux \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat 801.0 \n", "L'Abergement-de-Varey 245.5 \n", "Ambérieu-en-Bugey 13917.0 \n", "Ambérieux-en-Dombes 1671.5 \n", "Ambronay 2635.5 \n", "\n", " Nbre d'unités de consommation dans les ménages fiscaux \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat 527.10 \n", "L'Abergement-de-Varey 162.45 \n", "Ambérieu-en-Bugey 9543.70 \n", "Ambérieux-en-Dombes 1081.95 \n", "Ambronay 1732.75 \n", "\n", " 1er quartile (€) Médiane (€) 3e quartile (€) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN 22228.000000 NaN \n", "L'Abergement-de-Varey NaN 22883.333333 NaN \n", "Ambérieu-en-Bugey 14314.0 19735.200000 26194.666667 \n", "Ambérieux-en-Dombes NaN 23182.666667 NaN \n", "Ambronay 17564.0 21986.500000 27836.666667 \n", "\n", " Écart interquartile (€) 1er décile (€) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN NaN \n", "L'Abergement-de-Varey NaN NaN \n", "Ambérieu-en-Bugey 11880.666667 10398.571429 \n", "Ambérieux-en-Dombes NaN NaN \n", "Ambronay 10272.666667 13620.666667 \n", "\n", " 2e décile (€) ... \\\n", "Libellé géographique ... \n", "L'Abergement-Clémenciat NaN ... \n", "L'Abergement-de-Varey NaN ... \n", "Ambérieu-en-Bugey 13079.0 ... \n", "Ambérieux-en-Dombes NaN ... \n", "Ambronay 16568.0 ... \n", "\n", " dont part des indemnités chômage (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 3.3 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 2.5 \n", "\n", " dont part des salaires, traitements hors chômage (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 65.6 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 71.2 \n", "\n", " dont part des revenus des activités non salariées (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 3.9 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 2.6 \n", "\n", " Part des pensions, retraites et rentes (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 26.9 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 26.7 \n", "\n", " Part des revenus du patrimoine et autres revenus (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 9.5 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 8.1 \n", "\n", " Part de l'ensemble des prestations sociales (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 6.7 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 4.0 \n", "\n", " dont part des prestations familiales (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 2.7 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 2.4 \n", "\n", " dont part des minima sociaux (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 2.0 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 0.8 \n", "\n", " dont part des prestations logement (%) \\\n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey 2.0 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay 0.8 \n", "\n", " Part des impôts (%) \n", "Libellé géographique \n", "L'Abergement-Clémenciat NaN \n", "L'Abergement-de-Varey NaN \n", "Ambérieu-en-Bugey -15.8 \n", "Ambérieux-en-Dombes NaN \n", "Ambronay -15.1 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel(\n", " \"FILO_DISP_COM.xls\", \n", " sheet_name=\"ENSEMBLE\",\n", " header=0,\n", " index_col=1,\n", " skiprows=[0,1,2,3,5]\n", ")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Nbre de ménages fiscauxNbre de personnes dans les ménages fiscauxNbre d'unités de consommation dans les ménages fiscaux1er quartile (€)Médiane (€)3e quartile (€)Écart interquartile (€)1er décile (€)2e décile (€)3e décile (€)...dont part des indemnités chômage (%)dont part des salaires, traitements hors chômage (%)dont part des revenus des activités non salariées (%)Part des pensions, retraites et rentes (%)Part des revenus du patrimoine et autres revenus (%)Part de l'ensemble des prestations sociales (%)dont part des prestations familiales (%)dont part des minima sociaux (%)dont part des prestations logement (%)Part des impôts (%)
count3.225200e+043.225200e+043.225200e+045257.00000032252.0000005257.0000005257.0000005257.0000005257.0000005257.000000...5257.0000005257.0000005257.0000005257.0000005257.0000005257.0000005257.0000005257.0000005257.0000005257.000000
mean9.002475e+022.080761e+031.418240e+0316089.86303020633.44427027794.04188411704.17885412064.30904714928.42899617177.716755...3.16747262.5936475.15487929.87907610.0534725.1962532.2513411.6040711.340517-16.046224
std7.293511e+031.518587e+041.074782e+042558.1718422927.0469555340.5107923567.0731831977.9347592379.7345012729.879074...0.74691611.1169352.2050297.5377364.6685552.7583180.8426861.3220090.8782733.155266
min3.200000e+017.500000e+016.150000e+018075.66425110157.08333315384.3333336390.0000005712.5000007452.7586218512.828000...1.30000013.6000000.6000007.5000002.8000000.6000000.3000000.1000000.100000-39.600000
25%1.040000e+022.490000e+021.681500e+0214355.00000018797.61428624420.0000009709.50000010623.33333313318.00000015354.000000...2.60000055.5000003.60000024.9000007.7000003.4000001.7000000.8000000.700000-17.600000
50%2.120000e+025.190000e+023.475000e+0215961.60000020198.21428626568.46153810861.00000012088.00000014852.00000017022.173913...3.10000063.3000004.90000029.1000009.3000004.6000002.2000001.2000001.100000-15.500000
75%5.080000e+021.241000e+038.318125e+0217628.00000021938.89881029843.60000012561.73333313455.18518516433.46666718772.903226...3.60000070.5000006.30000034.00000011.3000006.2000002.7000002.0000001.700000-14.000000
max1.030953e+062.096399e+061.500666e+0628807.00000046156.00000073721.66666749888.80952420274.76190526500.00000032830.800000...9.00000096.10000026.40000068.10000070.60000028.0000008.10000014.5000008.400000-4.700000
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8 rows × 29 columns

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" ], "text/plain": [ " Nbre de ménages fiscaux Nbre de personnes dans les ménages fiscaux \\\n", "count 3.225200e+04 3.225200e+04 \n", "mean 9.002475e+02 2.080761e+03 \n", "std 7.293511e+03 1.518587e+04 \n", "min 3.200000e+01 7.500000e+01 \n", "25% 1.040000e+02 2.490000e+02 \n", "50% 2.120000e+02 5.190000e+02 \n", "75% 5.080000e+02 1.241000e+03 \n", "max 1.030953e+06 2.096399e+06 \n", "\n", " Nbre d'unités de consommation dans les ménages fiscaux \\\n", "count 3.225200e+04 \n", "mean 1.418240e+03 \n", "std 1.074782e+04 \n", "min 6.150000e+01 \n", "25% 1.681500e+02 \n", "50% 3.475000e+02 \n", "75% 8.318125e+02 \n", "max 1.500666e+06 \n", "\n", " 1er quartile (€) Médiane (€) 3e quartile (€) \\\n", "count 5257.000000 32252.000000 5257.000000 \n", "mean 16089.863030 20633.444270 27794.041884 \n", "std 2558.171842 2927.046955 5340.510792 \n", "min 8075.664251 10157.083333 15384.333333 \n", "25% 14355.000000 18797.614286 24420.000000 \n", "50% 15961.600000 20198.214286 26568.461538 \n", "75% 17628.000000 21938.898810 29843.600000 \n", "max 28807.000000 46156.000000 73721.666667 \n", "\n", " Écart interquartile (€) 1er décile (€) 2e décile (€) 3e décile (€) \\\n", "count 5257.000000 5257.000000 5257.000000 5257.000000 \n", "mean 11704.178854 12064.309047 14928.428996 17177.716755 \n", "std 3567.073183 1977.934759 2379.734501 2729.879074 \n", "min 6390.000000 5712.500000 7452.758621 8512.828000 \n", "25% 9709.500000 10623.333333 13318.000000 15354.000000 \n", "50% 10861.000000 12088.000000 14852.000000 17022.173913 \n", "75% 12561.733333 13455.185185 16433.466667 18772.903226 \n", "max 49888.809524 20274.761905 26500.000000 32830.800000 \n", "\n", " ... dont part des indemnités chômage (%) \\\n", "count ... 5257.000000 \n", "mean ... 3.167472 \n", "std ... 0.746916 \n", "min ... 1.300000 \n", "25% ... 2.600000 \n", "50% ... 3.100000 \n", "75% ... 3.600000 \n", "max ... 9.000000 \n", "\n", " dont part des salaires, traitements hors chômage (%) \\\n", "count 5257.000000 \n", "mean 62.593647 \n", "std 11.116935 \n", "min 13.600000 \n", "25% 55.500000 \n", "50% 63.300000 \n", "75% 70.500000 \n", "max 96.100000 \n", "\n", " dont part des revenus des activités non salariées (%) \\\n", "count 5257.000000 \n", "mean 5.154879 \n", "std 2.205029 \n", "min 0.600000 \n", "25% 3.600000 \n", "50% 4.900000 \n", "75% 6.300000 \n", "max 26.400000 \n", "\n", " Part des pensions, retraites et rentes (%) \\\n", "count 5257.000000 \n", "mean 29.879076 \n", "std 7.537736 \n", "min 7.500000 \n", "25% 24.900000 \n", "50% 29.100000 \n", "75% 34.000000 \n", "max 68.100000 \n", "\n", " Part des revenus du patrimoine et autres revenus (%) \\\n", "count 5257.000000 \n", "mean 10.053472 \n", "std 4.668555 \n", "min 2.800000 \n", "25% 7.700000 \n", "50% 9.300000 \n", "75% 11.300000 \n", "max 70.600000 \n", "\n", " Part de l'ensemble des prestations sociales (%) \\\n", "count 5257.000000 \n", "mean 5.196253 \n", "std 2.758318 \n", "min 0.600000 \n", "25% 3.400000 \n", "50% 4.600000 \n", "75% 6.200000 \n", "max 28.000000 \n", "\n", " dont part des prestations familiales (%) \\\n", "count 5257.000000 \n", "mean 2.251341 \n", "std 0.842686 \n", "min 0.300000 \n", "25% 1.700000 \n", "50% 2.200000 \n", "75% 2.700000 \n", "max 8.100000 \n", "\n", " dont part des minima sociaux (%) \\\n", "count 5257.000000 \n", "mean 1.604071 \n", "std 1.322009 \n", "min 0.100000 \n", "25% 0.800000 \n", "50% 1.200000 \n", "75% 2.000000 \n", "max 14.500000 \n", "\n", " dont part des prestations logement (%) Part des impôts (%) \n", "count 5257.000000 5257.000000 \n", "mean 1.340517 -16.046224 \n", "std 0.878273 3.155266 \n", "min 0.100000 -39.600000 \n", "25% 0.700000 -17.600000 \n", "50% 1.100000 -15.500000 \n", "75% 1.700000 -14.000000 \n", "max 8.400000 -4.700000 \n", "\n", "[8 rows x 29 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1er décile (€) 12064.309047\n", "2e décile (€) 14928.428996\n", "3e décile (€) 17177.716755\n", "4e décile (€) 19237.312999\n", "6e décile (€) 23515.999045\n", "7e décile (€) 26163.756935\n", "8e décile (€) 29782.823174\n", "9e décile (€) 36217.930962\n", "dtype: float64\n" ] } ], "source": [ "print(df.iloc[:,8:16].mean())" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AxesSubplot(0.125,0.125;0.775x0.755)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(df.iloc[:,8:16].mean().plot(kind=\"bar\", title=\"revenus annuels\", grid=True))" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AxesSubplot(0.125,0.125;0.775x0.755)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print((df.iloc[:,8:16].mean()/12).plot(kind=\"bar\", title=\"revenus mensuels\", grid=True))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Libellé géographique\n", "Paris 7e Arrondissement 0.529859\n", "Paris 6e Arrondissement 0.494262\n", "Paris 16e Arrondissement 0.492540\n", "Paris 8e Arrondissement 0.480449\n", "Neuilly-sur-Seine 0.477613\n", "Paris 1er Arrondissement 0.462938\n", "Ferney-Voltaire 0.440434\n", "Paris 17e Arrondissement 0.439884\n", "Paris 3e Arrondissement 0.437138\n", "Paris 2e Arrondissement 0.436749\n", "Paris 4e Arrondissement 0.429267\n", "Prévessin-Moëns 0.428811\n", "Paris 0.425874\n", "Veyrier-du-Lac 0.425221\n", "Ramatuelle 0.423017\n", "Le Vésinet 0.411753\n", "Paris 5e Arrondissement 0.410132\n", "Ornex 0.409172\n", "Saint-Genis-Pouilly 0.408779\n", "Le Touquet-Paris-Plage 0.408160\n", "Gaillard 0.407640\n", "Saint-Tropez 0.404223\n", "Divonne-les-Bains 0.403497\n", "Paris 9e Arrondissement 0.401696\n", "Saint-Julien-en-Genevois 0.401373\n", "Saint-Cyr-au-Mont-d'Or 0.394604\n", "Thoiry 0.393348\n", "Cassis 0.389627\n", "Collonges-sous-Salève 0.388632\n", "Ambilly 0.387890\n", "Name: Indice de Gini, dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.sort_values(by=\"Indice de Gini\", ascending=False)\n", "df2.iloc[0:30,][\"Indice de Gini\"]" ] }, { "cell_type": "code", "execution_count": null, "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }