{ "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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\n", "text/plain": [ "
" ] }, "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 }