From e449ac6661472c99db4726994e0f48f5a3d9d66d Mon Sep 17 00:00:00 2001 From: Sami GHEBRID Date: Thu, 4 May 2023 11:01:42 +0200 Subject: [PATCH] Untitled --- .../Untitled-checkpoint.ipynb | 2583 +++++++++++++++++ ProjetStats/Untitled.ipynb | 2194 +++++++++++--- 2 files changed, 4412 insertions(+), 365 deletions(-) create mode 100644 ProjetStats/.ipynb_checkpoints/Untitled-checkpoint.ipynb diff --git a/ProjetStats/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ProjetStats/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..d814db6 --- /dev/null +++ b/ProjetStats/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,2583 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "a9b74e9b", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"~/stats/Report_2021.csv\", encoding=\"latin-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4cf63957", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryISO CodeRegionPosition 2021Position 2020Global ScoreWith AbusesWithout AbusesJournalist KilledMedia Workers KilledJournalist ImprisonedMedia Workers ImprisonedSituation
0AfghanistanAFGAsia Pacific12212259.8136.7265.603300Difficult
1AlbaniaALBEurope838469.4176.0269.410000Problematic
2AlgeriaDZAArab States14614652.7464.4552.740010Difficult
3AndorraANDEurope393776.68100.0076.680000Satisfactory
4AngolaAGOAfrica10310665.9474.3565.940000Problematic
..........................................
175VenezuelaVENSouth America14814752.4045.7153.840000Difficult
176VietnamVNMAsia Pacific17517521.5431.9624.8200240Very Serious
177YemenYEMMiddle East16916737.6546.6737.654050Very Serious
178ZambiaZMBAfrica11512061.79100.0061.790000Difficult
179ZimbabweZWEAfrica13012656.8865.3456.880010Difficult
\n", + "

180 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Country ISO Code Region Position 2021 Position 2020 \\\n", + "0 Afghanistan AFG Asia Pacific 122 122 \n", + "1 Albania ALB Europe 83 84 \n", + "2 Algeria DZA Arab States 146 146 \n", + "3 Andorra AND Europe 39 37 \n", + "4 Angola AGO Africa 103 106 \n", + ".. ... ... ... ... ... \n", + "175 Venezuela VEN South America 148 147 \n", + "176 Vietnam VNM Asia Pacific 175 175 \n", + "177 Yemen YEM Middle East 169 167 \n", + "178 Zambia ZMB Africa 115 120 \n", + "179 Zimbabwe ZWE Africa 130 126 \n", + "\n", + " Global Score With Abuses Without Abuses Journalist Killed \\\n", + "0 59.81 36.72 65.60 3 \n", + "1 69.41 76.02 69.41 0 \n", + "2 52.74 64.45 52.74 0 \n", + "3 76.68 100.00 76.68 0 \n", + "4 65.94 74.35 65.94 0 \n", + ".. ... ... ... ... \n", + "175 52.40 45.71 53.84 0 \n", + "176 21.54 31.96 24.82 0 \n", + "177 37.65 46.67 37.65 4 \n", + "178 61.79 100.00 61.79 0 \n", + "179 56.88 65.34 56.88 0 \n", + "\n", + " Media Workers Killed Journalist Imprisoned Media Workers Imprisoned \\\n", + "0 3 0 0 \n", + "1 0 0 0 \n", + "2 0 1 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + ".. ... ... ... \n", + "175 0 0 0 \n", + "176 0 24 0 \n", + "177 0 5 0 \n", + "178 0 0 0 \n", + "179 0 1 0 \n", + "\n", + " Situation \n", + "0 Difficult \n", + "1 Problematic \n", + "2 Difficult \n", + "3 Satisfactory \n", + "4 Problematic \n", + ".. ... \n", + "175 Difficult \n", + "176 Very Serious \n", + "177 Very Serious \n", + "178 Difficult \n", + "179 Difficult \n", + "\n", + "[180 rows x 13 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c04f98fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "64.91827777777779\n" + ] + } + ], + "source": [ + "print(df['Global Score'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a75a371", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 6., 3., 4., 9., 22., 25., 39., 39., 21., 12.]),\n", + " array([18.55 , 26.023, 33.496, 40.969, 48.442, 55.915, 63.388, 70.861,\n", + " 78.334, 85.807, 93.28 ]),\n", + " )" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(df['Global Score'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3ef8e5ae", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CountryISO CodeRegionPosition 2021Position 2020Global ScoreWith AbusesWithout AbusesJournalist KilledMedia Workers KilledJournalist ImprisonedMedia Workers ImprisonedSituation
3AndorraANDEurope393776.68100.0076.680000Satisfactory
7AustraliaAUSAsia Pacific252680.21100.0080.210000Satisfactory
8AustriaAUTEurope171883.6689.0183.660000Satisfactory
13BelgiumBELEurope111288.3193.0788.310000Good
19BotswanaBWAAfrica383976.7593.0776.750000Satisfactory
23Burkina FasoBFAAfrica373876.8393.0776.832000Satisfactory
25Cabo VerdeCPVAfrica272579.91100.0079.910000Satisfactory
28CanadaCANNorth America141684.75100.0084.750000Satisfactory
36Costa RicaCRISouth America5791.2489.0191.790000Good
39CyprusCYPEurope262780.1583.9180.150000Satisfactory
40Czech RepublicCZEEurope404076.62100.0076.620000Satisfactory
42DenmarkDNKEurope4391.43100.0091.430000Good
50EstoniaESTEurope151484.75100.0084.750000Satisfactory
54FinlandFINEurope2293.01100.0093.010000Good
55FranceFRAEurope343477.4058.1082.110000Satisfactory
59GermanyDEUEurope131184.7656.6991.750000Satisfactory
60GhanaGHAAfrica303078.6782.0878.670010Satisfactory
70IcelandISLEurope161584.63100.0084.630000Satisfactory
75IrelandIRLEurope121388.09100.0088.090000Good
77ItalyITAEurope414176.6172.2777.370000Satisfactory
79JamaicaJAMSouth America7690.04100.0090.040000Good
88LatviaLVAEurope222280.74100.0080.740000Satisfactory
93LiechtensteinLIEEurope232480.51100.0080.510000Satisfactory
94LithuaniaLTUEurope282879.85100.0079.850000Satisfactory
95LuxembourgLUXEurope201782.44100.0082.440000Satisfactory
111NamibiaNAMAfrica242380.2889.0180.280000Satisfactory
113NetherlandsNLDEurope6590.3386.1491.261000Good
114New ZealandNZLAsia Pacific8989.96100.0089.960000Good
121NorwayNOREurope1193.28100.0093.280000Good
122OECSNaNNaN454476.02100.0076.030000Satisfactory
127Papua New GuineaPNGAsia Pacific474675.12100.0075.120000Satisfactory
132PortugalPRTEurope91089.89100.0089.890000Good
134RomaniaROUEurope484875.09100.0075.090000Satisfactory
137SamoaWSMAsia Pacific212180.76100.0080.760000Satisfactory
144SlovakiaSVKEurope353376.98100.0076.980000Satisfactory
145SloveniaSVNEurope363276.9093.0776.900000Satisfactory
147South AfricaZAFAfrica323178.4154.3684.390000Satisfactory
148South KoreaKORAsia Pacific424276.57100.0076.570000Satisfactory
150SpainESPEurope292979.5676.0280.300000Satisfactory
153SurinameSURSouth America192083.05100.0083.050000Satisfactory
154SwedenSWEEurope3492.76100.0092.760000Good
155SwitzerlandCHEEurope10889.4593.0789.450000Good
157TaiwanTWNAsia Pacific434376.14100.0076.140000Satisfactory
163TongaTONAsia Pacific465075.41100.0075.410000Satisfactory
164Trinidad and TobagoTTOSouth America313678.45100.0078.450000Satisfactory
171United KingdomGBREurope333578.4186.1478.350000Satisfactory
172United StatesUSANorth America444576.0760.3079.970000Satisfactory
173UruguayURYSouth America181983.62100.0083.620000Satisfactory
\n", + "
" + ], + "text/plain": [ + " Country ISO Code Region Position 2021 \\\n", + "3 Andorra AND Europe 39 \n", + "7 Australia AUS Asia Pacific 25 \n", + "8 Austria AUT Europe 17 \n", + "13 Belgium BEL Europe 11 \n", + "19 Botswana BWA Africa 38 \n", + "23 Burkina Faso BFA Africa 37 \n", + "25 Cabo Verde CPV Africa 27 \n", + "28 Canada CAN North America 14 \n", + "36 Costa Rica CRI South America 5 \n", + "39 Cyprus CYP Europe 26 \n", + "40 Czech Republic CZE Europe 40 \n", + "42 Denmark DNK Europe 4 \n", + "50 Estonia EST Europe 15 \n", + "54 Finland FIN Europe 2 \n", + "55 France FRA Europe 34 \n", + "59 Germany DEU Europe 13 \n", + "60 Ghana GHA Africa 30 \n", + "70 Iceland ISL Europe 16 \n", + "75 Ireland IRL Europe 12 \n", + "77 Italy ITA Europe 41 \n", + "79 Jamaica JAM South America 7 \n", + "88 Latvia LVA Europe 22 \n", + "93 Liechtenstein LIE Europe 23 \n", + "94 Lithuania LTU Europe 28 \n", + "95 Luxembourg LUX Europe 20 \n", + "111 Namibia NAM Africa 24 \n", + "113 Netherlands NLD Europe 6 \n", + "114 New Zealand NZL Asia Pacific 8 \n", + "121 Norway NOR Europe 1 \n", + "122 OECS NaN NaN 45 \n", + "127 Papua New Guinea PNG Asia Pacific 47 \n", + "132 Portugal PRT Europe 9 \n", + "134 Romania ROU Europe 48 \n", + "137 Samoa WSM Asia Pacific 21 \n", + "144 Slovakia SVK Europe 35 \n", + "145 Slovenia SVN Europe 36 \n", + "147 South Africa ZAF Africa 32 \n", + "148 South Korea KOR Asia Pacific 42 \n", + "150 Spain ESP Europe 29 \n", + "153 Suriname SUR South America 19 \n", + "154 Sweden SWE Europe 3 \n", + "155 Switzerland CHE Europe 10 \n", + "157 Taiwan TWN Asia Pacific 43 \n", + "163 Tonga TON Asia Pacific 46 \n", + "164 Trinidad and Tobago TTO South America 31 \n", + "171 United Kingdom GBR Europe 33 \n", + "172 United States USA North America 44 \n", + "173 Uruguay URY South America 18 \n", + "\n", + " Position 2020 Global Score With Abuses Without Abuses \\\n", + "3 37 76.68 100.00 76.68 \n", + "7 26 80.21 100.00 80.21 \n", + "8 18 83.66 89.01 83.66 \n", + "13 12 88.31 93.07 88.31 \n", + "19 39 76.75 93.07 76.75 \n", + "23 38 76.83 93.07 76.83 \n", + "25 25 79.91 100.00 79.91 \n", + "28 16 84.75 100.00 84.75 \n", + "36 7 91.24 89.01 91.79 \n", + "39 27 80.15 83.91 80.15 \n", + "40 40 76.62 100.00 76.62 \n", + "42 3 91.43 100.00 91.43 \n", + "50 14 84.75 100.00 84.75 \n", + "54 2 93.01 100.00 93.01 \n", + "55 34 77.40 58.10 82.11 \n", + "59 11 84.76 56.69 91.75 \n", + "60 30 78.67 82.08 78.67 \n", + "70 15 84.63 100.00 84.63 \n", + "75 13 88.09 100.00 88.09 \n", + "77 41 76.61 72.27 77.37 \n", + "79 6 90.04 100.00 90.04 \n", + "88 22 80.74 100.00 80.74 \n", + "93 24 80.51 100.00 80.51 \n", + "94 28 79.85 100.00 79.85 \n", + "95 17 82.44 100.00 82.44 \n", + "111 23 80.28 89.01 80.28 \n", + "113 5 90.33 86.14 91.26 \n", + "114 9 89.96 100.00 89.96 \n", + "121 1 93.28 100.00 93.28 \n", + "122 44 76.02 100.00 76.03 \n", + "127 46 75.12 100.00 75.12 \n", + "132 10 89.89 100.00 89.89 \n", + "134 48 75.09 100.00 75.09 \n", + "137 21 80.76 100.00 80.76 \n", + "144 33 76.98 100.00 76.98 \n", + "145 32 76.90 93.07 76.90 \n", + "147 31 78.41 54.36 84.39 \n", + "148 42 76.57 100.00 76.57 \n", + "150 29 79.56 76.02 80.30 \n", + "153 20 83.05 100.00 83.05 \n", + "154 4 92.76 100.00 92.76 \n", + "155 8 89.45 93.07 89.45 \n", + "157 43 76.14 100.00 76.14 \n", + "163 50 75.41 100.00 75.41 \n", + "164 36 78.45 100.00 78.45 \n", + "171 35 78.41 86.14 78.35 \n", + "172 45 76.07 60.30 79.97 \n", + "173 19 83.62 100.00 83.62 \n", + "\n", + " Journalist Killed Media Workers Killed Journalist Imprisoned \\\n", + "3 0 0 0 \n", + "7 0 0 0 \n", + "8 0 0 0 \n", + "13 0 0 0 \n", + "19 0 0 0 \n", + "23 2 0 0 \n", + "25 0 0 0 \n", + "28 0 0 0 \n", + "36 0 0 0 \n", + "39 0 0 0 \n", + "40 0 0 0 \n", + "42 0 0 0 \n", + "50 0 0 0 \n", + "54 0 0 0 \n", + "55 0 0 0 \n", + "59 0 0 0 \n", + "60 0 0 1 \n", + "70 0 0 0 \n", + "75 0 0 0 \n", + "77 0 0 0 \n", + "79 0 0 0 \n", + "88 0 0 0 \n", + "93 0 0 0 \n", + "94 0 0 0 \n", + "95 0 0 0 \n", + "111 0 0 0 \n", + "113 1 0 0 \n", + "114 0 0 0 \n", + "121 0 0 0 \n", + "122 0 0 0 \n", + "127 0 0 0 \n", + "132 0 0 0 \n", + "134 0 0 0 \n", + "137 0 0 0 \n", + "144 0 0 0 \n", + "145 0 0 0 \n", + "147 0 0 0 \n", + "148 0 0 0 \n", + "150 0 0 0 \n", + "153 0 0 0 \n", + "154 0 0 0 \n", + "155 0 0 0 \n", + "157 0 0 0 \n", + "163 0 0 0 \n", + "164 0 0 0 \n", + "171 0 0 0 \n", + "172 0 0 0 \n", + "173 0 0 0 \n", + "\n", + " Media Workers Imprisoned Situation \n", + "3 0 Satisfactory \n", + "7 0 Satisfactory \n", + "8 0 Satisfactory \n", + "13 0 Good \n", + "19 0 Satisfactory \n", + "23 0 Satisfactory \n", + "25 0 Satisfactory \n", + "28 0 Satisfactory \n", + "36 0 Good \n", + "39 0 Satisfactory \n", + "40 0 Satisfactory \n", + "42 0 Good \n", + "50 0 Satisfactory \n", + "54 0 Good \n", + "55 0 Satisfactory \n", + "59 0 Satisfactory \n", + "60 0 Satisfactory \n", + "70 0 Satisfactory \n", + "75 0 Good \n", + "77 0 Satisfactory \n", + "79 0 Good \n", + "88 0 Satisfactory \n", + "93 0 Satisfactory \n", + "94 0 Satisfactory \n", + "95 0 Satisfactory \n", + "111 0 Satisfactory \n", + "113 0 Good \n", + "114 0 Good \n", + "121 0 Good \n", + "122 0 Satisfactory \n", + "127 0 Satisfactory \n", + "132 0 Good \n", + "134 0 Satisfactory \n", + "137 0 Satisfactory \n", + "144 0 Satisfactory \n", + "145 0 Satisfactory \n", + "147 0 Satisfactory \n", + "148 0 Satisfactory \n", + "150 0 Satisfactory \n", + "153 0 Satisfactory \n", + "154 0 Good \n", + "155 0 Good \n", + "157 0 Satisfactory \n", + "163 0 Satisfactory \n", + "164 0 Satisfactory \n", + "171 0 Satisfactory \n", + "172 0 Satisfactory \n", + "173 0 Satisfactory " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paysbon est toujours un DataFrame : \n" + ] + } + ], + "source": [ + "Score=df[\"Global Score\"] # df[label_colonne] sélectionne une colonne (renvoie la Series correspondante)\n", + "Paysbon = df.loc[Score > 75] # df.loc[critère] sélectionne un sous-échantillon de lignes.\n", + " # Le critère de sélection doit lui-même être calculé à partir d'une Series.\n", + "display(Paysbon)\n", + "print(\"Paysbon est toujours un DataFrame : \", type(Paysbon))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fb2a2980", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "moyenne des Score: 64.91827777777779\n", + "écart-type des Score: 15.831010824369084\n", + "quantiles des prix:\n" + ] + }, + { + "data": { + "text/plain": [ + "0.10 44.4750\n", + "0.25 56.1800\n", + "0.50 68.3100\n", + "0.75 75.5625\n", + "0.90 83.1070\n", + "Name: Global Score, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Score=df[\"Global Score\"]\n", + "print(\"moyenne des Score:\",Score.mean())\n", + "print(\"écart-type des Score:\",Score.std())\n", + "print(\"quantiles des prix:\")\n", + "display(df['Global Score'].quantile([0.1,0.25,0.5,0.75,0.90]))\n", + "Score.hist(bins=50)\n", + "plt.title(\"Score Global\")\n", + "plt.xlabel(\"Score sur 100\")\n", + "plt.ylabel(\"Nombres de pays ayant le même score\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "177f7309", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryISO CodeRegionPosition 2021Position 2020Global ScoreWith AbusesWithout AbusesJournalist KilledMedia Workers KilledJournalist ImprisonedMedia Workers ImprisonedSituation
3AndorraANDEurope393776.68100.0076.680000Satisfactory
7AustraliaAUSAsia Pacific252680.21100.0080.210000Satisfactory
8AustriaAUTEurope171883.6689.0183.660000Satisfactory
13BelgiumBELEurope111288.3193.0788.310000Good
19BotswanaBWAAfrica383976.7593.0776.750000Satisfactory
23Burkina FasoBFAAfrica373876.8393.0776.832000Satisfactory
25Cabo VerdeCPVAfrica272579.91100.0079.910000Satisfactory
28CanadaCANNorth America141684.75100.0084.750000Satisfactory
36Costa RicaCRISouth America5791.2489.0191.790000Good
39CyprusCYPEurope262780.1583.9180.150000Satisfactory
40Czech RepublicCZEEurope404076.62100.0076.620000Satisfactory
42DenmarkDNKEurope4391.43100.0091.430000Good
50EstoniaESTEurope151484.75100.0084.750000Satisfactory
54FinlandFINEurope2293.01100.0093.010000Good
55FranceFRAEurope343477.4058.1082.110000Satisfactory
59GermanyDEUEurope131184.7656.6991.750000Satisfactory
60GhanaGHAAfrica303078.6782.0878.670010Satisfactory
70IcelandISLEurope161584.63100.0084.630000Satisfactory
75IrelandIRLEurope121388.09100.0088.090000Good
77ItalyITAEurope414176.6172.2777.370000Satisfactory
79JamaicaJAMSouth America7690.04100.0090.040000Good
88LatviaLVAEurope222280.74100.0080.740000Satisfactory
93LiechtensteinLIEEurope232480.51100.0080.510000Satisfactory
94LithuaniaLTUEurope282879.85100.0079.850000Satisfactory
95LuxembourgLUXEurope201782.44100.0082.440000Satisfactory
111NamibiaNAMAfrica242380.2889.0180.280000Satisfactory
113NetherlandsNLDEurope6590.3386.1491.261000Good
114New ZealandNZLAsia Pacific8989.96100.0089.960000Good
121NorwayNOREurope1193.28100.0093.280000Good
122OECSNaNNaN454476.02100.0076.030000Satisfactory
127Papua New GuineaPNGAsia Pacific474675.12100.0075.120000Satisfactory
132PortugalPRTEurope91089.89100.0089.890000Good
134RomaniaROUEurope484875.09100.0075.090000Satisfactory
137SamoaWSMAsia Pacific212180.76100.0080.760000Satisfactory
139SenegalSENAfrica494774.7871.6775.380000Problematic
144SlovakiaSVKEurope353376.98100.0076.980000Satisfactory
145SloveniaSVNEurope363276.9093.0776.900000Satisfactory
147South AfricaZAFAfrica323178.4154.3684.390000Satisfactory
148South KoreaKORAsia Pacific424276.57100.0076.570000Satisfactory
150SpainESPEurope292979.5676.0280.300000Satisfactory
153SurinameSURSouth America192083.05100.0083.050000Satisfactory
154SwedenSWEEurope3492.76100.0092.760000Good
155SwitzerlandCHEEurope10889.4593.0789.450000Good
157TaiwanTWNAsia Pacific434376.14100.0076.140000Satisfactory
163TongaTONAsia Pacific465075.41100.0075.410000Satisfactory
164Trinidad and TobagoTTOSouth America313678.45100.0078.450000Satisfactory
171United KingdomGBREurope333578.4186.1478.350000Satisfactory
172United StatesUSANorth America444576.0760.3079.970000Satisfactory
173UruguayURYSouth America181983.62100.0083.620000Satisfactory
\n", + "
" + ], + "text/plain": [ + " Country ISO Code Region Position 2021 \\\n", + "3 Andorra AND Europe 39 \n", + "7 Australia AUS Asia Pacific 25 \n", + "8 Austria AUT Europe 17 \n", + "13 Belgium BEL Europe 11 \n", + "19 Botswana BWA Africa 38 \n", + "23 Burkina Faso BFA Africa 37 \n", + "25 Cabo Verde CPV Africa 27 \n", + "28 Canada CAN North America 14 \n", + "36 Costa Rica CRI South America 5 \n", + "39 Cyprus CYP Europe 26 \n", + "40 Czech Republic CZE Europe 40 \n", + "42 Denmark DNK Europe 4 \n", + "50 Estonia EST Europe 15 \n", + "54 Finland FIN Europe 2 \n", + "55 France FRA Europe 34 \n", + "59 Germany DEU Europe 13 \n", + "60 Ghana GHA Africa 30 \n", + "70 Iceland ISL Europe 16 \n", + "75 Ireland IRL Europe 12 \n", + "77 Italy ITA Europe 41 \n", + "79 Jamaica JAM South America 7 \n", + "88 Latvia LVA Europe 22 \n", + "93 Liechtenstein LIE Europe 23 \n", + "94 Lithuania LTU Europe 28 \n", + "95 Luxembourg LUX Europe 20 \n", + "111 Namibia NAM Africa 24 \n", + "113 Netherlands NLD Europe 6 \n", + "114 New Zealand NZL Asia Pacific 8 \n", + "121 Norway NOR Europe 1 \n", + "122 OECS NaN NaN 45 \n", + "127 Papua New Guinea PNG Asia Pacific 47 \n", + "132 Portugal PRT Europe 9 \n", + "134 Romania ROU Europe 48 \n", + "137 Samoa WSM Asia Pacific 21 \n", + "139 Senegal SEN Africa 49 \n", + "144 Slovakia SVK Europe 35 \n", + "145 Slovenia SVN Europe 36 \n", + "147 South Africa ZAF Africa 32 \n", + "148 South Korea KOR Asia Pacific 42 \n", + "150 Spain ESP Europe 29 \n", + "153 Suriname SUR South America 19 \n", + "154 Sweden SWE Europe 3 \n", + "155 Switzerland CHE Europe 10 \n", + "157 Taiwan TWN Asia Pacific 43 \n", + "163 Tonga TON Asia Pacific 46 \n", + "164 Trinidad and Tobago TTO South America 31 \n", + "171 United Kingdom GBR Europe 33 \n", + "172 United States USA North America 44 \n", + "173 Uruguay URY South America 18 \n", + "\n", + " Position 2020 Global Score With Abuses Without Abuses \\\n", + "3 37 76.68 100.00 76.68 \n", + "7 26 80.21 100.00 80.21 \n", + "8 18 83.66 89.01 83.66 \n", + "13 12 88.31 93.07 88.31 \n", + "19 39 76.75 93.07 76.75 \n", + "23 38 76.83 93.07 76.83 \n", + "25 25 79.91 100.00 79.91 \n", + "28 16 84.75 100.00 84.75 \n", + "36 7 91.24 89.01 91.79 \n", + "39 27 80.15 83.91 80.15 \n", + "40 40 76.62 100.00 76.62 \n", + "42 3 91.43 100.00 91.43 \n", + "50 14 84.75 100.00 84.75 \n", + "54 2 93.01 100.00 93.01 \n", + "55 34 77.40 58.10 82.11 \n", + "59 11 84.76 56.69 91.75 \n", + "60 30 78.67 82.08 78.67 \n", + "70 15 84.63 100.00 84.63 \n", + "75 13 88.09 100.00 88.09 \n", + "77 41 76.61 72.27 77.37 \n", + "79 6 90.04 100.00 90.04 \n", + "88 22 80.74 100.00 80.74 \n", + "93 24 80.51 100.00 80.51 \n", + "94 28 79.85 100.00 79.85 \n", + "95 17 82.44 100.00 82.44 \n", + "111 23 80.28 89.01 80.28 \n", + "113 5 90.33 86.14 91.26 \n", + "114 9 89.96 100.00 89.96 \n", + "121 1 93.28 100.00 93.28 \n", + "122 44 76.02 100.00 76.03 \n", + "127 46 75.12 100.00 75.12 \n", + "132 10 89.89 100.00 89.89 \n", + "134 48 75.09 100.00 75.09 \n", + "137 21 80.76 100.00 80.76 \n", + "139 47 74.78 71.67 75.38 \n", + "144 33 76.98 100.00 76.98 \n", + "145 32 76.90 93.07 76.90 \n", + "147 31 78.41 54.36 84.39 \n", + "148 42 76.57 100.00 76.57 \n", + "150 29 79.56 76.02 80.30 \n", + "153 20 83.05 100.00 83.05 \n", + "154 4 92.76 100.00 92.76 \n", + "155 8 89.45 93.07 89.45 \n", + "157 43 76.14 100.00 76.14 \n", + "163 50 75.41 100.00 75.41 \n", + "164 36 78.45 100.00 78.45 \n", + "171 35 78.41 86.14 78.35 \n", + "172 45 76.07 60.30 79.97 \n", + "173 19 83.62 100.00 83.62 \n", + "\n", + " Journalist Killed Media Workers Killed Journalist Imprisoned \\\n", + "3 0 0 0 \n", + "7 0 0 0 \n", + "8 0 0 0 \n", + "13 0 0 0 \n", + "19 0 0 0 \n", + "23 2 0 0 \n", + "25 0 0 0 \n", + "28 0 0 0 \n", + "36 0 0 0 \n", + "39 0 0 0 \n", + "40 0 0 0 \n", + "42 0 0 0 \n", + "50 0 0 0 \n", + "54 0 0 0 \n", + "55 0 0 0 \n", + "59 0 0 0 \n", + "60 0 0 1 \n", + "70 0 0 0 \n", + "75 0 0 0 \n", + "77 0 0 0 \n", + "79 0 0 0 \n", + "88 0 0 0 \n", + "93 0 0 0 \n", + "94 0 0 0 \n", + "95 0 0 0 \n", + "111 0 0 0 \n", + "113 1 0 0 \n", + "114 0 0 0 \n", + "121 0 0 0 \n", + "122 0 0 0 \n", + "127 0 0 0 \n", + "132 0 0 0 \n", + "134 0 0 0 \n", + "137 0 0 0 \n", + "139 0 0 0 \n", + "144 0 0 0 \n", + "145 0 0 0 \n", + "147 0 0 0 \n", + "148 0 0 0 \n", + "150 0 0 0 \n", + "153 0 0 0 \n", + "154 0 0 0 \n", + "155 0 0 0 \n", + "157 0 0 0 \n", + "163 0 0 0 \n", + "164 0 0 0 \n", + "171 0 0 0 \n", + "172 0 0 0 \n", + "173 0 0 0 \n", + "\n", + " Media Workers Imprisoned Situation \n", + "3 0 Satisfactory \n", + "7 0 Satisfactory \n", + "8 0 Satisfactory \n", + "13 0 Good \n", + "19 0 Satisfactory \n", + "23 0 Satisfactory \n", + "25 0 Satisfactory \n", + "28 0 Satisfactory \n", + "36 0 Good \n", + "39 0 Satisfactory \n", + "40 0 Satisfactory \n", + "42 0 Good \n", + "50 0 Satisfactory \n", + "54 0 Good \n", + "55 0 Satisfactory \n", + "59 0 Satisfactory \n", + "60 0 Satisfactory \n", + "70 0 Satisfactory \n", + "75 0 Good \n", + "77 0 Satisfactory \n", + "79 0 Good \n", + "88 0 Satisfactory \n", + "93 0 Satisfactory \n", + "94 0 Satisfactory \n", + "95 0 Satisfactory \n", + "111 0 Satisfactory \n", + "113 0 Good \n", + "114 0 Good \n", + "121 0 Good \n", + "122 0 Satisfactory \n", + "127 0 Satisfactory \n", + "132 0 Good \n", + "134 0 Satisfactory \n", + "137 0 Satisfactory \n", + "139 0 Problematic \n", + "144 0 Satisfactory \n", + "145 0 Satisfactory \n", + "147 0 Satisfactory \n", + "148 0 Satisfactory \n", + "150 0 Satisfactory \n", + "153 0 Satisfactory \n", + "154 0 Good \n", + "155 0 Good \n", + "157 0 Satisfactory \n", + "163 0 Satisfactory \n", + "164 0 Satisfactory \n", + "171 0 Satisfactory \n", + "172 0 Satisfactory \n", + "173 0 Satisfactory " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "position est toujours un DataFrame : \n" + ] + } + ], + "source": [ + "Pos = df[\"Position 2021\"] # df[label_colonne] sélectionne une colonne (renvoie la Series correspondante)\n", + "Position = df.loc[Pos < 50] # df.loc[critère] sélectionne un sous-échantillon de lignes. # Le critère de sélection doit lui-même être calculé à partir d'une Series.\n", + "display(Position)\n", + "print(\"position est toujours un DataFrame : \", type(Position))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2812820d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e593f8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ProjetStats/Untitled.ipynb b/ProjetStats/Untitled.ipynb index ad70408..f1fe2ba 100644 --- a/ProjetStats/Untitled.ipynb +++ b/ProjetStats/Untitled.ipynb @@ -3,144 +3,344 @@ { "cell_type": "code", "execution_count": 2, - "id": "117db053", + "id": "a9b74e9b", "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"~/stats/Report_2021.csv\", encoding=\"latin-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4cf63957", + "metadata": { + "scrolled": true + }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "la taille de notre échantillon est : (50,)\n" - ] - }, { "data": { - "image/png": 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CountryISO CodeRegionPosition 2021Position 2020Global ScoreWith AbusesWithout AbusesJournalist KilledMedia Workers KilledJournalist ImprisonedMedia Workers ImprisonedSituation
0AfghanistanAFGAsia Pacific12212259.8136.7265.603300Difficult
1AlbaniaALBEurope838469.4176.0269.410000Problematic
2AlgeriaDZAArab States14614652.7464.4552.740010Difficult
3AndorraANDEurope393776.68100.0076.680000Satisfactory
4AngolaAGOAfrica10310665.9474.3565.940000Problematic
..........................................
175VenezuelaVENSouth America14814752.4045.7153.840000Difficult
176VietnamVNMAsia Pacific17517521.5431.9624.8200240Very Serious
177YemenYEMMiddle East16916737.6546.6737.654050Very Serious
178ZambiaZMBAfrica11512061.79100.0061.790000Difficult
179ZimbabweZWEAfrica13012656.8865.3456.880010Difficult
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" + " Country ISO Code Region Position 2021 Position 2020 \\\n", + "0 Afghanistan AFG Asia Pacific 122 122 \n", + "1 Albania ALB Europe 83 84 \n", + "2 Algeria DZA Arab States 146 146 \n", + "3 Andorra AND Europe 39 37 \n", + "4 Angola AGO Africa 103 106 \n", + ".. ... ... ... ... ... \n", + "175 Venezuela VEN South America 148 147 \n", + "176 Vietnam VNM Asia Pacific 175 175 \n", + "177 Yemen YEM Middle East 169 167 \n", + "178 Zambia ZMB Africa 115 120 \n", + "179 Zimbabwe ZWE Africa 130 126 \n", + "\n", + " Global Score With Abuses Without Abuses Journalist Killed \\\n", + "0 59.81 36.72 65.60 3 \n", + "1 69.41 76.02 69.41 0 \n", + "2 52.74 64.45 52.74 0 \n", + "3 76.68 100.00 76.68 0 \n", + "4 65.94 74.35 65.94 0 \n", + ".. ... ... ... ... \n", + "175 52.40 45.71 53.84 0 \n", + "176 21.54 31.96 24.82 0 \n", + "177 37.65 46.67 37.65 4 \n", + "178 61.79 100.00 61.79 0 \n", + "179 56.88 65.34 56.88 0 \n", + "\n", + " Media Workers Killed Journalist Imprisoned Media Workers Imprisoned \\\n", + "0 3 0 0 \n", + "1 0 0 0 \n", + "2 0 1 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + ".. ... ... ... \n", + "175 0 0 0 \n", + "176 0 24 0 \n", + "177 0 5 0 \n", + "178 0 0 0 \n", + "179 0 1 0 \n", + "\n", + " Situation \n", + "0 Difficult \n", + "1 Problematic \n", + "2 Difficult \n", + "3 Satisfactory \n", + "4 Problematic \n", + ".. ... \n", + "175 Difficult \n", + "176 Very Serious \n", + "177 Very Serious \n", + "178 Difficult \n", + "179 Difficult \n", + "\n", + "[180 rows x 13 columns]" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# un exemple simple x réel et y aussi\n", - "rng = np.random.RandomState(42) #pour générer les mêmes données\n", - "#constituer un exmple de points aléatoires\n", - "x = 10 * rng.rand(50)\n", - "print('la taille de notre échantillon est :',x.shape)\n", - "y=2*x-1 + rng.randn(50) # définir une relation entre x et y + bruit\n", - "#afficher data y=f(x) [y en fonction de x] comme un nuage de points\n", - "plt.scatter(x, y);\n", - "plt.show()" + "display(df)" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "5c7f44f9", + "execution_count": 4, + "id": "c04f98fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "la tailles des entrées est : (50, 1)\n", - "----- la solution -----\n", - "la valeur trouvée de a est : 1.9776566003853107\n", - "la valeur trouvée de b est : -0.9033107255311146\n", - "[4.04083078]\n", - "[-2.88096733 -0.02435224 2.83226285 5.68887794 8.54549303 11.40210812\n", - " 14.25872321 17.1153383 19.97195339 22.82856848]\n" + "64.91827777777779\n" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - " # On peut résoudre ce problème de régression linéaire avec sklearn\n", - "# on choisit et on charge le modèle\n", - "from sklearn.linear_model import LinearRegression\n", - "\n", - "X = x[:, np.newaxis]\n", - "print('la tailles des entrées est :',X.shape)\n", - "\n", - "models = LinearRegression(fit_intercept=True)\n", - "models.fit(X, y)\n", - "\n", - "a=models.coef_\n", - "print('-'*5,'la solution','-'*5)\n", - "print('la valeur trouvée de a est : ', a[0])\n", - "\n", - "b=models.intercept_\n", - "print('la valeur trouvée de b est : ', b)\n", - "\n", - "#solution pour un seul point\n", - "xnew=np.array([2.50])\n", - "ynew = models.predict(xnew.reshape(-1, 1))\n", - "print(ynew)\n", - "\n", - "#solution pour un tableau de points\n", - "xnew=np.linspace(-1,12,10)\n", - "#s'assurer d'avoir le bon format\n", - "xnew=xnew[:, np.newaxis]\n", - "ynew = models.predict(xnew)\n", - "print(ynew)\n", - "\n", - "plt.scatter(x, y,color='k');# données apprentissage en noir\n", - "plt.scatter(xnew, np.zeros(xnew.shape[0]),color='b');# x_i non observés en bleu\n", - "plt.scatter(xnew, ynew,color='r', marker='*');# y_i prédit ave la régression␣\n", - "\n", - "\n", - "\n", - " " + "print(df['Global Score'].mean())" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "bb29179b", + "execution_count": 5, + "id": "2a75a371", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50,)\n", - "Biais ou erreur en chaque point : \n", - "\n" - ] - }, { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "(array([ 6., 3., 4., 9., 22., 25., 39., 39., 21., 12.]),\n", + " array([18.55 , 26.023, 33.496, 40.969, 48.442, 55.915, 63.388, 70.861,\n", + " 78.334, 85.807, 93.28 ]),\n", + " )" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -149,40 +349,35 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L'erreur globale peut être donnée l'erreur quadratique moyenne : 0.8230711437486881\n" - ] } ], "source": [ - " #on peut aussi afficher la fonction f\n", - "plt.scatter(x, y,color='k');\n", - "#plt.scatter(xnew, ynew);\n", - "plt.plot(xnew, ynew,'r');\n", - "#l'erreur est donnée par la somme cumulée des distances\n", - "#entre les points en noir et la droite en rouge\n", - "ypred=models.predict(X)\n", - "print(ypred.shape)\n", - "print('Biais ou erreur en chaque point : \\n')\n", - "plt.figure()\n", - "plt.plot(x, (y-ypred), 'g*')\n", - "plt.show()\n", - "print('L\\'erreur globale peut être donnée l\\'erreur quadratique moyenne : ',np.mean((y-ypred)**2))" + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(df['Global Score'])" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "505d3fb3", - "metadata": {}, + "execution_count": 6, + "id": "3ef8e5ae", + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" ] @@ -194,128 +389,1110 @@ } ], "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "#constituer un exmple de data\n", - "x = np.array(10 * rng.rand(100,2))\n", - "y=2*np.inner(np.array([-1,1]), x)+ 2*rng.randn(x.shape[0])\n", - "fig=plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.scatter(x[:,0], x[:,1],y,c='b', marker='o');\n", - "ax.set_xlabel('valeur de x[:,0]')\n", - "ax.set_ylabel('aleur de x[:,1]')\n", - "ax.set_zlabel('valeur de y ')\n", - "model = LinearRegression(fit_intercept=True)\n", - "model.fit(x, y)\n", - "xnew = np.array(10 * rng.rand(1000,2))\n", - "ynew = model.predict(xnew)\n", - "plt.show()" + "df['Global Score'].plot.box()" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "79d4e56e", - "metadata": {}, + "execution_count": 7, + "id": "a588fc72", + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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CountryISO CodeRegionPosition 2021Position 2020Global ScoreWith AbusesWithout AbusesJournalist KilledMedia Workers KilledJournalist ImprisonedMedia Workers ImprisonedSituation
3AndorraANDEurope393776.68100.0076.680000Satisfactory
7AustraliaAUSAsia Pacific252680.21100.0080.210000Satisfactory
8AustriaAUTEurope171883.6689.0183.660000Satisfactory
13BelgiumBELEurope111288.3193.0788.310000Good
19BotswanaBWAAfrica383976.7593.0776.750000Satisfactory
23Burkina FasoBFAAfrica373876.8393.0776.832000Satisfactory
25Cabo VerdeCPVAfrica272579.91100.0079.910000Satisfactory
28CanadaCANNorth America141684.75100.0084.750000Satisfactory
36Costa RicaCRISouth America5791.2489.0191.790000Good
39CyprusCYPEurope262780.1583.9180.150000Satisfactory
40Czech RepublicCZEEurope404076.62100.0076.620000Satisfactory
42DenmarkDNKEurope4391.43100.0091.430000Good
50EstoniaESTEurope151484.75100.0084.750000Satisfactory
54FinlandFINEurope2293.01100.0093.010000Good
55FranceFRAEurope343477.4058.1082.110000Satisfactory
59GermanyDEUEurope131184.7656.6991.750000Satisfactory
60GhanaGHAAfrica303078.6782.0878.670010Satisfactory
70IcelandISLEurope161584.63100.0084.630000Satisfactory
75IrelandIRLEurope121388.09100.0088.090000Good
77ItalyITAEurope414176.6172.2777.370000Satisfactory
79JamaicaJAMSouth America7690.04100.0090.040000Good
88LatviaLVAEurope222280.74100.0080.740000Satisfactory
93LiechtensteinLIEEurope232480.51100.0080.510000Satisfactory
94LithuaniaLTUEurope282879.85100.0079.850000Satisfactory
95LuxembourgLUXEurope201782.44100.0082.440000Satisfactory
111NamibiaNAMAfrica242380.2889.0180.280000Satisfactory
113NetherlandsNLDEurope6590.3386.1491.261000Good
114New ZealandNZLAsia Pacific8989.96100.0089.960000Good
121NorwayNOREurope1193.28100.0093.280000Good
122OECSNaNNaN454476.02100.0076.030000Satisfactory
127Papua New GuineaPNGAsia Pacific474675.12100.0075.120000Satisfactory
132PortugalPRTEurope91089.89100.0089.890000Good
134RomaniaROUEurope484875.09100.0075.090000Satisfactory
137SamoaWSMAsia Pacific212180.76100.0080.760000Satisfactory
144SlovakiaSVKEurope353376.98100.0076.980000Satisfactory
145SloveniaSVNEurope363276.9093.0776.900000Satisfactory
147South AfricaZAFAfrica323178.4154.3684.390000Satisfactory
148South KoreaKORAsia Pacific424276.57100.0076.570000Satisfactory
150SpainESPEurope292979.5676.0280.300000Satisfactory
153SurinameSURSouth America192083.05100.0083.050000Satisfactory
154SwedenSWEEurope3492.76100.0092.760000Good
155SwitzerlandCHEEurope10889.4593.0789.450000Good
157TaiwanTWNAsia Pacific434376.14100.0076.140000Satisfactory
163TongaTONAsia Pacific465075.41100.0075.410000Satisfactory
164Trinidad and TobagoTTOSouth America313678.45100.0078.450000Satisfactory
171United KingdomGBREurope333578.4186.1478.350000Satisfactory
172United StatesUSANorth America444576.0760.3079.970000Satisfactory
173UruguayURYSouth America181983.62100.0083.620000Satisfactory
\n", + "
" + ], "text/plain": [ - "
" + " Country ISO Code Region Position 2021 \\\n", + "3 Andorra AND Europe 39 \n", + "7 Australia AUS Asia Pacific 25 \n", + "8 Austria AUT Europe 17 \n", + "13 Belgium BEL Europe 11 \n", + "19 Botswana BWA Africa 38 \n", + "23 Burkina Faso BFA Africa 37 \n", + "25 Cabo Verde CPV Africa 27 \n", + "28 Canada CAN North America 14 \n", + "36 Costa Rica CRI South America 5 \n", + "39 Cyprus CYP Europe 26 \n", + "40 Czech Republic CZE Europe 40 \n", + "42 Denmark DNK Europe 4 \n", + "50 Estonia EST Europe 15 \n", + "54 Finland FIN Europe 2 \n", + "55 France FRA Europe 34 \n", + "59 Germany DEU Europe 13 \n", + "60 Ghana GHA Africa 30 \n", + "70 Iceland ISL Europe 16 \n", + "75 Ireland IRL Europe 12 \n", + "77 Italy ITA Europe 41 \n", + "79 Jamaica JAM South America 7 \n", + "88 Latvia LVA Europe 22 \n", + "93 Liechtenstein LIE Europe 23 \n", + "94 Lithuania LTU Europe 28 \n", + "95 Luxembourg LUX Europe 20 \n", + "111 Namibia NAM Africa 24 \n", + "113 Netherlands NLD Europe 6 \n", + "114 New Zealand NZL Asia Pacific 8 \n", + "121 Norway NOR Europe 1 \n", + "122 OECS NaN NaN 45 \n", + "127 Papua New Guinea PNG Asia Pacific 47 \n", + "132 Portugal PRT Europe 9 \n", + "134 Romania ROU Europe 48 \n", + "137 Samoa WSM Asia Pacific 21 \n", + "144 Slovakia SVK Europe 35 \n", + "145 Slovenia SVN Europe 36 \n", + "147 South Africa ZAF Africa 32 \n", + "148 South Korea KOR Asia Pacific 42 \n", + "150 Spain ESP Europe 29 \n", + "153 Suriname SUR South America 19 \n", + "154 Sweden SWE Europe 3 \n", + "155 Switzerland CHE Europe 10 \n", + "157 Taiwan TWN Asia Pacific 43 \n", + "163 Tonga TON Asia Pacific 46 \n", + "164 Trinidad and Tobago TTO South America 31 \n", + "171 United Kingdom GBR Europe 33 \n", + "172 United States USA North America 44 \n", + "173 Uruguay URY South America 18 \n", + "\n", + " Position 2020 Global Score With Abuses Without Abuses \\\n", + "3 37 76.68 100.00 76.68 \n", + "7 26 80.21 100.00 80.21 \n", + "8 18 83.66 89.01 83.66 \n", + "13 12 88.31 93.07 88.31 \n", + "19 39 76.75 93.07 76.75 \n", + "23 38 76.83 93.07 76.83 \n", + "25 25 79.91 100.00 79.91 \n", + "28 16 84.75 100.00 84.75 \n", + "36 7 91.24 89.01 91.79 \n", + "39 27 80.15 83.91 80.15 \n", + "40 40 76.62 100.00 76.62 \n", + "42 3 91.43 100.00 91.43 \n", + "50 14 84.75 100.00 84.75 \n", + "54 2 93.01 100.00 93.01 \n", + "55 34 77.40 58.10 82.11 \n", + "59 11 84.76 56.69 91.75 \n", + "60 30 78.67 82.08 78.67 \n", + "70 15 84.63 100.00 84.63 \n", + "75 13 88.09 100.00 88.09 \n", + "77 41 76.61 72.27 77.37 \n", + "79 6 90.04 100.00 90.04 \n", + "88 22 80.74 100.00 80.74 \n", + "93 24 80.51 100.00 80.51 \n", + "94 28 79.85 100.00 79.85 \n", + "95 17 82.44 100.00 82.44 \n", + "111 23 80.28 89.01 80.28 \n", + "113 5 90.33 86.14 91.26 \n", + "114 9 89.96 100.00 89.96 \n", + "121 1 93.28 100.00 93.28 \n", + "122 44 76.02 100.00 76.03 \n", + "127 46 75.12 100.00 75.12 \n", + "132 10 89.89 100.00 89.89 \n", + "134 48 75.09 100.00 75.09 \n", + "137 21 80.76 100.00 80.76 \n", + "144 33 76.98 100.00 76.98 \n", + "145 32 76.90 93.07 76.90 \n", + "147 31 78.41 54.36 84.39 \n", + "148 42 76.57 100.00 76.57 \n", + "150 29 79.56 76.02 80.30 \n", + "153 20 83.05 100.00 83.05 \n", + "154 4 92.76 100.00 92.76 \n", + "155 8 89.45 93.07 89.45 \n", + "157 43 76.14 100.00 76.14 \n", + "163 50 75.41 100.00 75.41 \n", + "164 36 78.45 100.00 78.45 \n", + "171 35 78.41 86.14 78.35 \n", + "172 45 76.07 60.30 79.97 \n", + "173 19 83.62 100.00 83.62 \n", + "\n", + " Journalist Killed Media Workers Killed Journalist Imprisoned \\\n", + "3 0 0 0 \n", + "7 0 0 0 \n", + "8 0 0 0 \n", + "13 0 0 0 \n", + "19 0 0 0 \n", + "23 2 0 0 \n", + "25 0 0 0 \n", + "28 0 0 0 \n", + "36 0 0 0 \n", + "39 0 0 0 \n", + "40 0 0 0 \n", + "42 0 0 0 \n", + "50 0 0 0 \n", + "54 0 0 0 \n", + "55 0 0 0 \n", + "59 0 0 0 \n", + "60 0 0 1 \n", + "70 0 0 0 \n", + "75 0 0 0 \n", + "77 0 0 0 \n", + "79 0 0 0 \n", + "88 0 0 0 \n", + "93 0 0 0 \n", + "94 0 0 0 \n", + "95 0 0 0 \n", + "111 0 0 0 \n", + "113 1 0 0 \n", + "114 0 0 0 \n", + "121 0 0 0 \n", + "122 0 0 0 \n", + "127 0 0 0 \n", + "132 0 0 0 \n", + "134 0 0 0 \n", + "137 0 0 0 \n", + "144 0 0 0 \n", + "145 0 0 0 \n", + "147 0 0 0 \n", + "148 0 0 0 \n", + "150 0 0 0 \n", + "153 0 0 0 \n", + "154 0 0 0 \n", + "155 0 0 0 \n", + "157 0 0 0 \n", + "163 0 0 0 \n", + "164 0 0 0 \n", + "171 0 0 0 \n", + "172 0 0 0 \n", + "173 0 0 0 \n", + "\n", + " Media Workers Imprisoned Situation \n", + "3 0 Satisfactory \n", + "7 0 Satisfactory \n", + "8 0 Satisfactory \n", + "13 0 Good \n", + "19 0 Satisfactory \n", + "23 0 Satisfactory \n", + "25 0 Satisfactory \n", + "28 0 Satisfactory \n", + "36 0 Good \n", + "39 0 Satisfactory \n", + "40 0 Satisfactory \n", + "42 0 Good \n", + "50 0 Satisfactory \n", + "54 0 Good \n", + "55 0 Satisfactory \n", + "59 0 Satisfactory \n", + "60 0 Satisfactory \n", + "70 0 Satisfactory \n", + "75 0 Good \n", + "77 0 Satisfactory \n", + "79 0 Good \n", + "88 0 Satisfactory \n", + "93 0 Satisfactory \n", + "94 0 Satisfactory \n", + "95 0 Satisfactory \n", + "111 0 Satisfactory \n", + "113 0 Good \n", + "114 0 Good \n", + "121 0 Good \n", + "122 0 Satisfactory \n", + "127 0 Satisfactory \n", + "132 0 Good \n", + "134 0 Satisfactory \n", + "137 0 Satisfactory \n", + "144 0 Satisfactory \n", + "145 0 Satisfactory \n", + "147 0 Satisfactory \n", + "148 0 Satisfactory \n", + "150 0 Satisfactory \n", + "153 0 Satisfactory \n", + "154 0 Good \n", + "155 0 Good \n", + "157 0 Satisfactory \n", + "163 0 Satisfactory \n", + "164 0 Satisfactory \n", + "171 0 Satisfactory \n", + "172 0 Satisfactory \n", + "173 0 Satisfactory " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "fig=plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.scatter(x[:,0], x[:,1],y,c='b', marker='o');\n", - "ax.scatter(x[:,0], x[:,1],y,c='b', marker='o');\n", - "ax.set_xlabel('valeur de x[:,0]')\n", - "ax.set_ylabel('aleur de x[:,1]')\n", - "ax.set_zlabel('valeur de y ')\n", - "ax.scatter(xnew[:,0], xnew[:,1],ynew,c='r', marker='*');\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "d5f87251", - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "5473.375\n", - "65.44444444444444\n", - "5178883.696428572\n", - "115.77777777777777\n" + "Paysbon est toujours un DataFrame : \n" ] } ], "source": [ - "import statistics\n", - "\n", - "PIB=[8802,5872,4775,5680,7964,5680,3072,1942]\n", - "scolarisation=[83,69,63,63,62,81,62,56,50]\n", - "\n", - "moyPIB=statistics.mean(PIB)\n", - "moyScolar=statistics.mean(scolarisation)\n", - "print(moyPIB)\n", - "print(moyScolar)\n", - "print(statistics.variance(PIB))\n", - "print(statistics.variance(scolarisation))" + "Score=df[\"Global Score\"] # df[label_colonne] sélectionne une colonne (renvoie la Series correspondante)\n", + "Paysbon = df.loc[Score > 75] # df.loc[critère] sélectionne un sous-échantillon de lignes.\n", + " # Le critère de sélection doit lui-même être calculé à partir d'une Series.\n", + "display(Paysbon)\n", + "print(\"Paysbon est toujours un DataFrame : \", type(Paysbon))" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "152d59f6", + "execution_count": 8, + "id": "fb2a2980", "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "positional argument follows keyword argument (148137705.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [1]\u001b[0;36m\u001b[0m\n\u001b[0;31m cov1=np.stack((PIB,scolarisation), axis=0,\"k*\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "moyenne des Score: 64.91827777777779\n", + "écart-type des Score: 15.831010824369084\n", + "quantiles des prix:\n" ] + }, + { + "data": { + "text/plain": [ + "0.10 44.4750\n", + "0.25 56.1800\n", + "0.50 68.3100\n", + "0.75 75.5625\n", + "0.90 83.1070\n", + "Name: Global Score, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "cov1=np.stack((PIB,scolarisation), axis=0,)\n", - "print(np.cov(cov1))" + "Score=df[\"Global Score\"]\n", + "print(\"moyenne des Score:\",Score.mean())\n", + "print(\"écart-type des Score:\",Score.std())\n", + "print(\"quantiles des prix:\")\n", + "display(df['Global Score'].quantile([0.1,0.25,0.5,0.75,0.90]))\n", + "Score.hist(bins=50)\n", + "plt.title(\"Score Global\")\n", + "plt.xlabel(\"Score sur 100\")\n", + "plt.ylabel(\"Nombres de pays ayant le même score\")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 36, - "id": "be2e880b", - "metadata": {}, + "execution_count": 11, + "id": "177f7309", + "metadata": { + "scrolled": true + }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "(150, 4)\n", - "[0 1 2]\n", - "(150, 2)\n", - "[0 1]\n", - "(4,)\n", - "la taille : (414, 4)\n", - "Avec : 414 lignes\n", - "Avec : 4 colonnes\n", - "****************************************\n" - ] - }, { "data": { "text/html": [ @@ -337,199 +1514,486 @@ " \n", " \n", " \n", - " age\n", - " distance métro\n", - " magasins proches\n", - " prix au m2\n", + " Country\n", + " ISO Code\n", + " Region\n", + " Position 2021\n", + " Position 2020\n", + " Global Score\n", + " With Abuses\n", + " Without Abuses\n", + " Journalist Killed\n", + " Media Workers Killed\n", + " Journalist Imprisoned\n", + " Media Workers Imprisoned\n", + " Situation\n", " \n", " \n", " \n", " \n", - " count\n", - " 414.000000\n", - " 414.000000\n", - " 414.000000\n", - " 414.000000\n", + " 9\n", + " Azerbaijan\n", + " AZE\n", + " Asia Pacific\n", + " 167\n", + " 168\n", + " 41.23\n", + " 49.76\n", + " 41.23\n", + " 2\n", + " 0\n", + " 1\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 10\n", + " Bahrain\n", + " BHR\n", + " Arab States\n", + " 168\n", + " 169\n", + " 38.90\n", + " 35.09\n", + " 39.89\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 32\n", + " China\n", + " CHN\n", + " Asia Pacific\n", + " 177\n", + " 177\n", + " 21.28\n", + " 18.23\n", + " 21.90\n", + " 1\n", + " 1\n", + " 3\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 38\n", + " Cuba\n", + " CUB\n", + " South America\n", + " 171\n", + " 171\n", + " 36.06\n", + " 100.00\n", + " 36.07\n", + " 0\n", + " 0\n", + " 2\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 43\n", + " Djibouti\n", + " DJI\n", + " Arab States\n", + " 176\n", + " 176\n", + " 21.38\n", + " 89.01\n", + " 21.38\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 46\n", + " Egypt\n", + " EGY\n", + " Middle East\n", + " 166\n", + " 166\n", + " 43.83\n", + " 35.87\n", + " 45.33\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 48\n", + " Equatorial Guinea\n", + " GNQ\n", + " Africa\n", + " 164\n", + " 165\n", + " 44.33\n", + " 100.00\n", + " 44.33\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 49\n", + " Eritrea\n", + " ERI\n", + " Africa\n", + " 180\n", + " 178\n", + " 18.55\n", + " 26.95\n", + " 17.95\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 73\n", + " Iran\n", + " IRN\n", + " Middle East\n", + " 174\n", + " 173\n", + " 27.30\n", + " 32.61\n", + " 29.89\n", + " 0\n", + " 0\n", + " 3\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 74\n", + " Iraq\n", + " IRQ\n", + " Middle East\n", + " 163\n", + " 162\n", + " 44.43\n", + " 35.29\n", + " 46.43\n", + " 0\n", + " 0\n", + " 2\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 87\n", + " Laos\n", + " LAO\n", + " Asia Pacific\n", + " 172\n", + " 172\n", + " 29.44\n", + " 42.47\n", + " 29.44\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " mean\n", - " 17.712560\n", - " 1083.885689\n", - " 4.094203\n", - " 37.980193\n", + " 92\n", + " Libya\n", + " LBY\n", + " Middle East\n", + " 165\n", + " 164\n", + " 44.27\n", + " 53.95\n", + " 44.27\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " std\n", - " 11.392485\n", - " 1262.109595\n", - " 2.945562\n", - " 13.606488\n", + " 118\n", + " North Korea\n", + " PRK\n", + " Asia Pacific\n", + " 179\n", + " 180\n", + " 18.72\n", + " 48.07\n", + " 18.72\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " min\n", - " 0.000000\n", - " 23.382840\n", - " 0.000000\n", - " 7.600000\n", + " 138\n", + " Saudi Arabia\n", + " SAU\n", + " Middle East\n", + " 170\n", + " 170\n", + " 37.27\n", + " 28.93\n", + " 38.85\n", + " 0\n", + " 0\n", + " 2\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " 25%\n", - " 9.025000\n", - " 289.324800\n", - " 1.000000\n", - " 27.700000\n", + " 143\n", + " Singapore\n", + " SGP\n", + " Asia Pacific\n", + " 160\n", + " 158\n", + " 44.80\n", + " 100.00\n", + " 44.80\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " 50%\n", - " 16.100000\n", - " 492.231300\n", - " 4.000000\n", - " 38.450000\n", + " 146\n", + " Somalia\n", + " SOM\n", + " Arab States\n", + " 161\n", + " 163\n", + " 44.53\n", + " 54.57\n", + " 44.53\n", + " 2\n", + " 0\n", + " 3\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " 75%\n", - " 28.150000\n", - " 1454.279000\n", - " 6.000000\n", - " 46.600000\n", + " 156\n", + " Syria\n", + " SYR\n", + " Middle East\n", + " 173\n", + " 174\n", + " 29.37\n", + " 17.41\n", + " 31.89\n", + " 1\n", + " 0\n", + " 10\n", + " 0\n", + " Very Serious\n", " \n", " \n", - " max\n", - " 43.800000\n", - " 6488.021000\n", - " 10.000000\n", - " 117.500000\n", + " 158\n", + " Tajikistan\n", + " TJK\n", + " Asia Pacific\n", + " 162\n", + " 161\n", + " 44.48\n", + " 52.64\n", + " 44.48\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 167\n", + " Turkmenistan\n", + " TKM\n", + " Asia Pacific\n", + " 178\n", + " 179\n", + " 19.97\n", + " 100.00\n", + " 19.97\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 176\n", + " Vietnam\n", + " VNM\n", + " Asia Pacific\n", + " 175\n", + " 175\n", + " 21.54\n", + " 31.96\n", + " 24.82\n", + " 0\n", + " 0\n", + " 24\n", + " 0\n", + " Very Serious\n", + " \n", + " \n", + " 177\n", + " Yemen\n", + " YEM\n", + " Middle East\n", + " 169\n", + " 167\n", + " 37.65\n", + " 46.67\n", + " 37.65\n", + " 4\n", + " 0\n", + " 5\n", + " 0\n", + " Very Serious\n", " \n", " \n", "\n", "" ], "text/plain": [ - " age distance métro magasins proches prix au m2\n", - "count 414.000000 414.000000 414.000000 414.000000\n", - "mean 17.712560 1083.885689 4.094203 37.980193\n", - "std 11.392485 1262.109595 2.945562 13.606488\n", - "min 0.000000 23.382840 0.000000 7.600000\n", - "25% 9.025000 289.324800 1.000000 27.700000\n", - "50% 16.100000 492.231300 4.000000 38.450000\n", - "75% 28.150000 1454.279000 6.000000 46.600000\n", - "max 43.800000 6488.021000 10.000000 117.500000" + " Country ISO Code Region Position 2021 Position 2020 \\\n", + "9 Azerbaijan AZE Asia Pacific 167 168 \n", + "10 Bahrain BHR Arab States 168 169 \n", + "32 China CHN Asia Pacific 177 177 \n", + "38 Cuba CUB South America 171 171 \n", + "43 Djibouti DJI Arab States 176 176 \n", + "46 Egypt EGY Middle East 166 166 \n", + "48 Equatorial Guinea GNQ Africa 164 165 \n", + "49 Eritrea ERI Africa 180 178 \n", + "73 Iran IRN Middle East 174 173 \n", + "74 Iraq IRQ Middle East 163 162 \n", + "87 Laos LAO Asia Pacific 172 172 \n", + "92 Libya LBY Middle East 165 164 \n", + "118 North Korea PRK Asia Pacific 179 180 \n", + "138 Saudi Arabia SAU Middle East 170 170 \n", + "143 Singapore SGP Asia Pacific 160 158 \n", + "146 Somalia SOM Arab States 161 163 \n", + "156 Syria SYR Middle East 173 174 \n", + "158 Tajikistan TJK Asia Pacific 162 161 \n", + "167 Turkmenistan TKM Asia Pacific 178 179 \n", + "176 Vietnam VNM Asia Pacific 175 175 \n", + "177 Yemen YEM Middle East 169 167 \n", + "\n", + " Global Score With Abuses Without Abuses Journalist Killed \\\n", + "9 41.23 49.76 41.23 2 \n", + "10 38.90 35.09 39.89 0 \n", + "32 21.28 18.23 21.90 1 \n", + "38 36.06 100.00 36.07 0 \n", + "43 21.38 89.01 21.38 0 \n", + "46 43.83 35.87 45.33 0 \n", + "48 44.33 100.00 44.33 0 \n", + "49 18.55 26.95 17.95 0 \n", + "73 27.30 32.61 29.89 0 \n", + "74 44.43 35.29 46.43 0 \n", + "87 29.44 42.47 29.44 0 \n", + "92 44.27 53.95 44.27 0 \n", + "118 18.72 48.07 18.72 0 \n", + "138 37.27 28.93 38.85 0 \n", + "143 44.80 100.00 44.80 0 \n", + "146 44.53 54.57 44.53 2 \n", + "156 29.37 17.41 31.89 1 \n", + "158 44.48 52.64 44.48 0 \n", + "167 19.97 100.00 19.97 0 \n", + "176 21.54 31.96 24.82 0 \n", + "177 37.65 46.67 37.65 4 \n", + "\n", + " Media Workers Killed Journalist Imprisoned Media Workers Imprisoned \\\n", + "9 0 1 0 \n", + "10 0 0 0 \n", + "32 1 3 0 \n", + "38 0 2 0 \n", + "43 0 0 0 \n", + "46 0 0 0 \n", + "48 0 0 0 \n", + "49 0 0 0 \n", + "73 0 3 0 \n", + "74 0 2 0 \n", + "87 0 0 0 \n", + "92 0 0 0 \n", + "118 0 0 0 \n", + "138 0 2 0 \n", + "143 0 0 0 \n", + "146 0 3 0 \n", + "156 0 10 0 \n", + "158 0 0 0 \n", + "167 0 0 0 \n", + "176 0 24 0 \n", + "177 0 5 0 \n", + "\n", + " Situation \n", + "9 Very Serious \n", + "10 Very Serious \n", + "32 Very Serious \n", + "38 Very Serious \n", + "43 Very Serious \n", + "46 Very Serious \n", + "48 Very Serious \n", + "49 Very Serious \n", + "73 Very Serious \n", + "74 Very Serious \n", + "87 Very Serious \n", + "92 Very Serious \n", + "118 Very Serious \n", + "138 Very Serious \n", + "143 Very Serious \n", + "146 Very Serious \n", + "156 Very Serious \n", + "158 Very Serious \n", + "167 Very Serious \n", + "176 Very Serious \n", + "177 Very Serious " ] }, - "execution_count": 36, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "position est toujours un DataFrame : \n" + ] } ], "source": [ - "#importer les bibliothèques\n", - "#pour l'affichage (si déjà fait pour np, plt)\n", - "%matplotlib inline\n", - "#charger des datasest de sklearn\n", - "from sklearn import datasets\n", - "#charger la base iris\n", - "iris = datasets.load_iris()\n", - "#vérifier le type de la variable iris\n", - "print(type(iris))\n", - "#vérifier le type de données\n", - "print(type(iris.data))\n", - "#vérifier les dimensions\n", - "print(iris.data.shape)\n", - "#Sur wikipédia chercher la signification de ces données\n", - "X = iris.data[:, :2] # Utiliser les deux premières colonnes afin d'avoir un␣\n", - "print(np.unique(iris.target))\n", - "#on va garder deux classes seulement pour un test simple\n", - "y = (iris.target != 0) * 1 # re-étiquetage des fleurs\n", - "print(X.shape)\n", - "print(np.unique(y))\n", - "#visualisation des données\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='classe 0')\n", - "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='classe 1')\n", - "plt.legend();\n", - "#charger le modèle pour y binaire\n", - "from sklearn.linear_model import LogisticRegression\n", - "model = LogisticRegression(C=1e20) # Régression logistique\n", - "# Entrainement du modèle avec toutes les données\n", - "model.fit(X, y)\n", - "Xnew = np.array([\n", - "[5.5, 2.5],\n", - "[7, 3],\n", - "[3,2],\n", - "[5,3]\n", - "])\n", - "model.predict(Xnew)\n", - "#vérification visuelle\n", - "#visualisation des données\n", - "\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='y= 0')\n", - "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='y= 1')\n", - "s = np.random.rand(*Xnew[:, 0].shape) * 800 + 500\n", - "print(s.shape)\n", - "Color='kygm' #noir jaune vert magneta\n", - "for i in range(Xnew.shape[0]):\n", - " plt.scatter(Xnew[i, 0], Xnew[i, 1],s[i],color=Color[i],marker=r'$\\clubsuit$',)\n", - "plt.legend();\n", - "\n", - " # comme n'importe quelle librarire, il faut commencer par la charger à l'aide␣\n", - "\n", - "import pandas\n", - "# maintenat que c'est fait on peut utiliser son contenu\n", - "# par exemple :vérifier la version installée sur votre machine\n", - "pandas.__version__\n", - " # et si on lui donne un nom pour faciliter les appels\n", - "import pandas as pd\n", - "\n", - "# Lecture d'un fichier de données et le récupérer sous forme de dataframe sous␣\n", - "\n", - "df = pd.read_csv(\"Prix_Appartements.csv\") # à partir d'un csv\n", - "df.head(5)\n", - "## On peut afficher les dimensions (nombre de lignes et de colonnes) ## avec␣\n", - "print('la taille :',df.shape) ## (nb lignes, nb colonnes) print('*'*40)\n", - "print('Avec :',df.shape[0],' lignes') ## (nb lignes, nb colonnes) print('*'*40)\n", - "print('Avec :',df.shape[1],' colonnes') ## (nb lignes, nb colonnes)␣\n", - "print('*'*40)\n", - "## La commande df.head(n) permet d'afficher uniquement les n premiers éléments␣\n", - "# car la taille de la dataframe est grande avec 4622 lignes\n", - "df.head(6) # les 6 premières lignes de 0 à 5 = 6-1\n", - "\n", - " ## De même df.tail(n) affiche les n=3 derniers éléments\n", - "df.tail(3)\n", - " # La commande describe() est très utile. Elle permet d'obtenir, en une seule␣\n", - "\n", - "# des statistiques des colonnes (UNIQUEMENT pour les colonnes de type numérique)\n", - "df.describe()" + "Pos = df[\"Global Score\"] # df[label_colonne] sélectionne une colonne (renvoie la Series correspondante)\n", + "Position = df.loc[Pos < 45] # df.loc[critère] sélectionne un sous-échantillon de lignes. # Le critère de sélection doit lui-même être calculé à partir d'une Series.\n", + "display(Position)\n", + "print(\"position est toujours un DataFrame : \", type(Position))" ] }, { "cell_type": "code", "execution_count": null, - "id": "ea50541a", + "id": "2812820d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e593f8", "metadata": {}, "outputs": [], "source": []