diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 785007e037b2c119016392d60030fd0491b2d214..ecc2d746be45e7ce78025f407d02b3fe8083f3d5 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -5,15 +5,15 @@ "metadata": {}, "source": [ "# Document Computationnel : Sujet 7 - Autour du SARS-CoV-2 (Covid-19)\n", - "- Dernière modification : *29/05/2020*\n", + "- Dernière modification : *30/05/2020*\n", "- Langage utilisé : *Python*\n", "\n", "## Table des matières \n", "\n", "1. [Résumé / *abstract*](#résumé)\n", "2. [Importation des données](#importation-des-données)\n", - "3. Formatage des données\n", - "4. Traitement des données\n", + "3. [Formatage des données](#formatage-des-données)\n", + "4. [Traitement des données](#traitement-des-données)\n", "5. Visualisation\n", "6. Conclusion\n", "\n", @@ -1907,16 +1907,20 @@ "* Royaume-Unis\n", "* États-Unis\n", "\n", + "---\n", + "\n", + "# Formatage des données\n", + "\n", "## Regroupement des données à inclure dans l'étude\n", "\n", "Ici nous utilisons la méthode [*loc*](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) de pandas pour extraire des données brutes les lignes correspondantes aux pays cités ci-dessus.\n", "\n", - "Afin de ne pas rendre le *code* illisible le processus est divisé en de multiples étapes. (toutes ces étapes peuvent être regroupé en une expression logique." + "Afin de ne pas rendre le *code* illisible le processus est divisé en de multiples étapes. (toutes ces étapes peuvent être regroupées)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2006,7 +2010,7 @@ "[1 rows x 132 columns]" ] }, - "execution_count": 4, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -2021,7 +2025,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2138,7 +2142,7 @@ "[2 rows x 132 columns]" ] }, - "execution_count": 5, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2161,10 +2165,36 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 37, "metadata": { "scrolled": true }, + "outputs": [], + "source": [ + "countries_list= list(['Germany', 'Iran', 'Italy', 'Japan', 'Korea, South', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US'])\n", + "#print(countries_list)\n", + "\n", + "for country in countries_list : \n", + " dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == country) & (raw_data['Province/State'].isnull())])\n", + "\n", + "# Manualy adding Hong-Kong \n", + "dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] == 'Hong Kong')]) \n", + "\n", + "#Uncomment to see the dataframe\n", + "#dataCountries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour éviter que deux lignes correspondent au même pays, la Chine. On renome *Hong Kong, China* en *Hong Kong, Hong Kong*. Ainsi Nous pourrons ajouter toute les régions de Chine dans une même ligne nommée *China*. Nous utilisons donc la méthode [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) pour remplacer le nom du pays." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { @@ -2499,9 +2529,33 @@ " 1699176\n", " 1721753\n", " \n", + " \n", + " 61\n", + " Hong Kong\n", + " Hong Kong\n", + " 22.3000\n", + " 114.2000\n", + " 0\n", + " 2\n", + " 2\n", + " 5\n", + " 8\n", + " 8\n", + " ...\n", + " 1055\n", + " 1055\n", + " 1055\n", + " 1065\n", + " 1065\n", + " 1065\n", + " 1065\n", + " 1065\n", + " 1066\n", + " 1066\n", + " \n", " \n", "\n", - "

12 rows × 132 columns

\n", + "

13 rows × 132 columns

\n", "" ], "text/plain": [ @@ -2518,6 +2572,7 @@ "201 NaN Spain 40.0000 -4.0000 0 0 \n", "223 NaN United Kingdom 55.3781 -3.4360 0 0 \n", "225 NaN US 37.0902 -95.7129 1 1 \n", + "61 Hong Kong Hong Kong 22.3000 114.2000 0 2 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/19/20 5/20/20 5/21/20 \\\n", "23 0 0 0 0 ... 55791 55983 56235 \n", @@ -2532,6 +2587,7 @@ "201 0 0 0 0 ... 232037 232555 233037 \n", "223 0 0 0 0 ... 248818 248293 250908 \n", "225 2 2 5 5 ... 1528568 1551853 1577147 \n", + "61 2 5 8 8 ... 1055 1055 1055 \n", "\n", " 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \n", "23 56511 56810 57092 57342 57455 57592 57849 \n", @@ -2546,21 +2602,19 @@ "201 234824 235290 235772 235400 236259 236259 237906 \n", "223 254195 257154 259559 261184 265227 267240 269127 \n", "225 1600937 1622612 1643246 1662302 1680913 1699176 1721753 \n", + "61 1065 1065 1065 1065 1065 1066 1066 \n", "\n", - "[12 rows x 132 columns]" + "[13 rows x 132 columns]" ] }, - "execution_count": 6, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "countries_list= list(['China, Hong-Kong', 'Germany', 'Iran', 'Italy', 'Japan', 'Korea, South', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US'])\n", - "#print(countries_list)\n", "\n", - "for country in countries_list : \n", - " dataCountries = dataCountries.append(raw_data.loc[(raw_data['Country/Region'] == country) & (raw_data['Province/State'].isnull())])\n", + "dataCountries[\"Country/Region\"].replace({\"China\": \"Hong Kong\"}, inplace=True)\n", "\n", "dataCountries" ] @@ -2569,12 +2623,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "TODO explain" + "La chine est composée de plusieurs regions. Pour étudier l'ensemble de la Chine moins Hong-kong (voir consigne) nous additionnons le nombre de contaminés par jour dans une nouvelle ligne nommée China avec l'index 1 car non utilisé (orignellement utilisé par l'Afghanistan). Pour se faire nous utilisons les méthodes [at](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html) et [sum](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html).\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2623,707 +2678,203 @@ " \n", " \n", " \n", - " 49\n", - " Anhui\n", - " China\n", - " 31.8257\n", - " 117.2264\n", - " 1.0\n", - " 9.0\n", - " 15.0\n", - " 39.0\n", - " 60.0\n", - " 70.0\n", - " ...\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " 991.0\n", - " \n", - " \n", - " 50\n", - " Beijing\n", - " China\n", - " 40.1824\n", - " 116.4142\n", - " 14.0\n", - " 22.0\n", - " 36.0\n", - " 41.0\n", - " 68.0\n", - " 80.0\n", - " ...\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " 593.0\n", - " \n", - " \n", - " 51\n", - " Chongqing\n", - " China\n", - " 30.0572\n", - " 107.8740\n", - " 6.0\n", - " 9.0\n", - " 27.0\n", - " 57.0\n", - " 75.0\n", - " 110.0\n", - " ...\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " 579.0\n", - " \n", - " \n", - " 52\n", - " Fujian\n", - " China\n", - " 26.0789\n", - " 117.9874\n", - " 1.0\n", - " 5.0\n", - " 10.0\n", - " 18.0\n", - " 35.0\n", - " 59.0\n", - " ...\n", - " 356.0\n", - " 356.0\n", - " 356.0\n", - " 356.0\n", - " 356.0\n", - " 356.0\n", - " 357.0\n", - " 357.0\n", - " 358.0\n", - " 358.0\n", - " \n", - " \n", - " 53\n", - " Gansu\n", - " China\n", - " 37.8099\n", - " 101.0583\n", + " 23\n", + " NaN\n", + " Belgium\n", + " 50.8333\n", + " 4.0000\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " ...\n", + " 55791.0\n", + " 55983.0\n", + " 56235.0\n", + " 56511.0\n", + " 56810.0\n", + " 57092.0\n", + " 57342.0\n", + " 57455.0\n", + " 57592.0\n", + " 57849.0\n", + " \n", + " \n", + " 116\n", + " NaN\n", + " France\n", + " 46.2276\n", + " 2.2137\n", + " 0.0\n", " 0.0\n", " 2.0\n", - " 2.0\n", - " 4.0\n", - " 7.0\n", - " 14.0\n", - " ...\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " 139.0\n", - " \n", - " \n", - " 54\n", - " Guangdong\n", - " China\n", - " 23.3417\n", - " 113.4244\n", - " 26.0\n", - " 32.0\n", - " 53.0\n", - " 78.0\n", - " 111.0\n", - " 151.0\n", - " ...\n", - " 1590.0\n", - " 1590.0\n", - " 1590.0\n", - " 1591.0\n", - " 1592.0\n", - " 1592.0\n", - " 1592.0\n", - " 1592.0\n", - " 1592.0\n", - " 1592.0\n", - " \n", - " \n", - " 55\n", - " Guangxi\n", - " China\n", - " 23.8298\n", - " 108.7881\n", - " 2.0\n", - " 5.0\n", - " 23.0\n", - " 23.0\n", - " 36.0\n", - " 46.0\n", - " ...\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " 254.0\n", - " \n", - " \n", - " 56\n", - " Guizhou\n", - " China\n", - " 26.8154\n", - " 106.8748\n", - " 1.0\n", " 3.0\n", " 3.0\n", - " 4.0\n", - " 5.0\n", - " 7.0\n", + " 3.0\n", " ...\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", - " 147.0\n", + " 178428.0\n", + " 179069.0\n", + " 179306.0\n", + " 179645.0\n", + " 179964.0\n", + " 179859.0\n", + " 180166.0\n", + " 179887.0\n", + " 180044.0\n", + " 183309.0\n", " \n", " \n", - " 57\n", - " Hainan\n", - " China\n", - " 19.1959\n", - " 109.7453\n", - " 4.0\n", - " 5.0\n", - " 8.0\n", - " 19.0\n", - " 22.0\n", - " 33.0\n", - " ...\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " 169.0\n", - " \n", - " \n", - " 58\n", - " Hebei\n", - " China\n", - " 39.5490\n", - " 116.1306\n", - " 1.0\n", - " 1.0\n", - " 2.0\n", - " 8.0\n", - " 13.0\n", - " 18.0\n", - " ...\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " 328.0\n", - " \n", - " \n", - " 59\n", - " Heilongjiang\n", - " China\n", - " 47.8620\n", - " 127.7615\n", + " 120\n", + " NaN\n", + " Germany\n", + " 51.0000\n", + " 9.0000\n", " 0.0\n", - " 2.0\n", - " 4.0\n", - " 9.0\n", - " 15.0\n", - " 21.0\n", - " ...\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " 945.0\n", - " \n", - " \n", - " 60\n", - " Henan\n", - " China\n", - " 33.8820\n", - " 113.6140\n", - " 5.0\n", - " 5.0\n", - " 9.0\n", - " 32.0\n", - " 83.0\n", - " 128.0\n", - " ...\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", - " 1276.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " ...\n", + " 177778.0\n", + " 178473.0\n", + " 179021.0\n", + " 179710.0\n", + " 179986.0\n", + " 180328.0\n", + " 180600.0\n", + " 181200.0\n", + " 181524.0\n", + " 182196.0\n", " \n", " \n", - " 61\n", - " Hong Kong\n", - " China\n", - " 22.3000\n", - " 114.2000\n", + " 133\n", + " NaN\n", + " Iran\n", + " 32.0000\n", + " 53.0000\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " 2.0\n", - " 2.0\n", - " 5.0\n", - " 8.0\n", - " 8.0\n", " ...\n", - " 1055.0\n", - " 1055.0\n", - " 1055.0\n", - " 1065.0\n", - " 1065.0\n", - " 1065.0\n", - " 1065.0\n", - " 1065.0\n", - " 1066.0\n", - " 1066.0\n", + " 124603.0\n", + " 126949.0\n", + " 129341.0\n", + " 131652.0\n", + " 133521.0\n", + " 135701.0\n", + " 137724.0\n", + " 139511.0\n", + " 141591.0\n", + " 143849.0\n", " \n", " \n", - " 62\n", - " Hubei\n", - " China\n", - " 30.9756\n", - " 112.2707\n", - " 444.0\n", - " 444.0\n", - " 549.0\n", - " 761.0\n", - " 1058.0\n", - " 1423.0\n", - " ...\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " 68135.0\n", - " \n", - " \n", - " 63\n", - " Hunan\n", - " China\n", - " 27.6104\n", - " 111.7088\n", - " 4.0\n", - " 9.0\n", - " 24.0\n", - " 43.0\n", - " 69.0\n", - " 100.0\n", - " ...\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " 1019.0\n", - " \n", - " \n", - " 64\n", - " Inner Mongolia\n", - " China\n", - " 44.0935\n", - " 113.9448\n", + " 137\n", + " NaN\n", + " Italy\n", + " 43.0000\n", + " 12.0000\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", - " 7.0\n", - " 7.0\n", - " 11.0\n", " ...\n", - " 216.0\n", - " 216.0\n", - " 216.0\n", - " 217.0\n", - " 217.0\n", - " 227.0\n", - " 232.0\n", - " 232.0\n", - " 232.0\n", - " 232.0\n", + " 226699.0\n", + " 227364.0\n", + " 228006.0\n", + " 228658.0\n", + " 229327.0\n", + " 229858.0\n", + " 230158.0\n", + " 230555.0\n", + " 231139.0\n", + " 231732.0\n", " \n", " \n", - " 65\n", - " Jiangsu\n", - " China\n", - " 32.9711\n", - " 119.4550\n", - " 1.0\n", - " 5.0\n", - " 9.0\n", - " 18.0\n", - " 33.0\n", - " 47.0\n", - " ...\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " 653.0\n", - " \n", - " \n", - " 66\n", - " Jiangxi\n", - " China\n", - " 27.6140\n", - " 115.7221\n", - " 2.0\n", - " 7.0\n", - " 18.0\n", - " 18.0\n", - " 36.0\n", - " 72.0\n", - " ...\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " 937.0\n", - " \n", - " \n", - " 67\n", - " Jilin\n", - " China\n", - " 43.6661\n", - " 126.1923\n", - " 0.0\n", - " 1.0\n", - " 3.0\n", - " 4.0\n", - " 4.0\n", - " 6.0\n", - " ...\n", - " 151.0\n", - " 151.0\n", - " 151.0\n", - " 154.0\n", - " 155.0\n", - " 155.0\n", - " 155.0\n", - " 155.0\n", - " 155.0\n", - " 155.0\n", - " \n", - " \n", - " 68\n", - " Liaoning\n", - " China\n", - " 41.2956\n", - " 122.6085\n", + " 139\n", + " NaN\n", + " Japan\n", + " 36.0000\n", + " 138.0000\n", " 2.0\n", - " 3.0\n", - " 4.0\n", - " 17.0\n", - " 21.0\n", - " 27.0\n", - " ...\n", - 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" 75.0\n", - " 75.0\n", + " 11110.0\n", + " 11122.0\n", + " 11142.0\n", + " 11165.0\n", + " 11190.0\n", + " 11206.0\n", + " 11225.0\n", + " 11265.0\n", + " 11344.0\n", + " 11402.0\n", " \n", " \n", - " 71\n", - " Qinghai\n", - " China\n", - " 35.7452\n", - " 95.9956\n", + " 169\n", + " NaN\n", + " Netherlands\n", + " 52.1326\n", + " 5.2913\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", - " 1.0\n", - " 6.0\n", - " ...\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " 18.0\n", - " \n", - " \n", - " 72\n", - " Shaanxi\n", - " China\n", - " 35.1917\n", - " 108.8701\n", " 0.0\n", - " 3.0\n", - " 5.0\n", - " 15.0\n", - " 22.0\n", - " 35.0\n", - " ...\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " 308.0\n", - " \n", - " \n", - " 73\n", - " Shandong\n", - " China\n", - " 36.3427\n", - " 118.1498\n", - " 2.0\n", - " 6.0\n", - " 15.0\n", - 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" 1.0\n", - " 1.0\n", - " 1.0\n", + " 29432.0\n", + " 29660.0\n", + " 29912.0\n", + " 30200.0\n", + " 30471.0\n", + " 30623.0\n", + " 30788.0\n", + " 31007.0\n", + " 31292.0\n", + " 31596.0\n", " \n", " \n", - " 79\n", - " Xinjiang\n", - " China\n", - " 41.1129\n", - " 85.2401\n", + " 201\n", + " NaN\n", + " Spain\n", + " 40.0000\n", + " -4.0000\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " 2.0\n", - " 2.0\n", - " 3.0\n", - " 4.0\n", - " 5.0\n", " ...\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", - " 76.0\n", + " 232037.0\n", + " 232555.0\n", + " 233037.0\n", + " 234824.0\n", + " 235290.0\n", + " 235772.0\n", + " 235400.0\n", + " 236259.0\n", + " 236259.0\n", + " 237906.0\n", " \n", " \n", - " 80\n", - " Yunnan\n", - " China\n", - " 24.9740\n", - " 101.4870\n", - " 1.0\n", - " 2.0\n", - " 5.0\n", - " 11.0\n", - " 16.0\n", - " 26.0\n", - " ...\n", - " 185.0\n", - " 185.0\n", - " 185.0\n", - 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34 rows × 132 columns

\n", + "

14 rows × 132 columns

\n", "" ], "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", - "49 Anhui China 31.8257 117.2264 1.0 9.0 \n", - "50 Beijing China 40.1824 116.4142 14.0 22.0 \n", - "51 Chongqing China 30.0572 107.8740 6.0 9.0 \n", - "52 Fujian China 26.0789 117.9874 1.0 5.0 \n", - "53 Gansu China 37.8099 101.0583 0.0 2.0 \n", - "54 Guangdong China 23.3417 113.4244 26.0 32.0 \n", - "55 Guangxi China 23.8298 108.7881 2.0 5.0 \n", - "56 Guizhou China 26.8154 106.8748 1.0 3.0 \n", - "57 Hainan China 19.1959 109.7453 4.0 5.0 \n", - "58 Hebei China 39.5490 116.1306 1.0 1.0 \n", - "59 Heilongjiang China 47.8620 127.7615 0.0 2.0 \n", - "60 Henan China 33.8820 113.6140 5.0 5.0 \n", - "61 Hong Kong China 22.3000 114.2000 0.0 2.0 \n", - "62 Hubei China 30.9756 112.2707 444.0 444.0 \n", - "63 Hunan China 27.6104 111.7088 4.0 9.0 \n", - "64 Inner Mongolia China 44.0935 113.9448 0.0 0.0 \n", - "65 Jiangsu China 32.9711 119.4550 1.0 5.0 \n", - "66 Jiangxi China 27.6140 115.7221 2.0 7.0 \n", - "67 Jilin China 43.6661 126.1923 0.0 1.0 \n", - "68 Liaoning China 41.2956 122.6085 2.0 3.0 \n", - "69 Macau China 22.1667 113.5500 1.0 2.0 \n", - "70 Ningxia China 37.2692 106.1655 1.0 1.0 \n", - "71 Qinghai China 35.7452 95.9956 0.0 0.0 \n", - "72 Shaanxi China 35.1917 108.8701 0.0 3.0 \n", - "73 Shandong China 36.3427 118.1498 2.0 6.0 \n", - "74 Shanghai China 31.2020 121.4491 9.0 16.0 \n", - "75 Shanxi China 37.5777 112.2922 1.0 1.0 \n", - "76 Sichuan China 30.6171 102.7103 5.0 8.0 \n", - "77 Tianjin China 39.3054 117.3230 4.0 4.0 \n", - "78 Tibet China 31.6927 88.0924 0.0 0.0 \n", - "79 Xinjiang China 41.1129 85.2401 0.0 2.0 \n", - "80 Yunnan China 24.9740 101.4870 1.0 2.0 \n", - "81 Zhejiang China 29.1832 120.0934 10.0 27.0 \n", - "1 NaN China NaN NaN 548.0 643.0 \n", + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "23 NaN Belgium 50.8333 4.0000 0.0 0.0 \n", + "116 NaN France 46.2276 2.2137 0.0 0.0 \n", + "120 NaN Germany 51.0000 9.0000 0.0 0.0 \n", + "133 NaN Iran 32.0000 53.0000 0.0 0.0 \n", + "137 NaN Italy 43.0000 12.0000 0.0 0.0 \n", + "139 NaN Japan 36.0000 138.0000 2.0 2.0 \n", + "143 NaN Korea, South 36.0000 128.0000 1.0 1.0 \n", + "169 NaN Netherlands 52.1326 5.2913 0.0 0.0 \n", + "184 NaN Portugal 39.3999 -8.2245 0.0 0.0 \n", + "201 NaN Spain 40.0000 -4.0000 0.0 0.0 \n", + "223 NaN United Kingdom 55.3781 -3.4360 0.0 0.0 \n", + "225 NaN US 37.0902 -95.7129 1.0 1.0 \n", + "61 Hong Kong Hong Kong 22.3000 114.2000 0.0 2.0 \n", + "1 NaN China NaN NaN 548.0 641.0 \n", "\n", - 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"[34 rows x 132 columns]" + " 5/27/20 5/28/20 \n", + "23 57592.0 57849.0 \n", + "116 180044.0 183309.0 \n", + "120 181524.0 182196.0 \n", + "133 141591.0 143849.0 \n", + "137 231139.0 231732.0 \n", + "139 16651.0 16598.0 \n", + "143 11344.0 11402.0 \n", + "169 45768.0 45950.0 \n", + "184 31292.0 31596.0 \n", + "201 236259.0 237906.0 \n", + "223 267240.0 269127.0 \n", + "225 1699176.0 1721753.0 \n", + "61 1066.0 1066.0 \n", + "1 83040.0 83040.0 \n", + "\n", + "[14 rows x 132 columns]" ] }, - "execution_count": 14, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For china the data have to be summed between region in order to get the results for the whole country.\n", - "dataChina = raw_data.loc[(raw_data['Country/Region'] == 'China')]\n", + "dataChina = raw_data.loc[((raw_data['Country/Region'] == 'China') & (raw_data['Province/State'] != 'Hong Kong' ))]\n", "\n", "#print(dataChina)\n", "\n", - "#let's use df.sum() to sum rows \n", + "#We want to sum per date and not the regions or the latitude so we remove them from our temporary list of column.\n", "col_list= list(dataChina)\n", "col_list.remove(\"Province/State\")\n", "col_list.remove(\"Country/Region\")\n", @@ -3574,14 +3105,1822 @@ "col_list.remove(\"Long\")\n", "\n", "\n", - "\n", + "#let's use df.sum() to sum rows \n", "for col in col_list: \n", " dataChina.at['1', col] = dataChina[col].sum()\n", "\n", - "\n", + "#Rename the Country in the column we have just created above.\n", "dataChina.at['1', \"Country/Region\"] = \"China\"\n", "\n", - "dataChina" + "dataChina\n", + "#Now add the data to Data Countries\n", + "dataCountries= dataCountries.append(dataChina.loc[(dataChina['Country/Region'] == 'China') & (dataChina['Province/State'].isnull())])\n", + "\n", + "dataCountries\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous avons donc un dataFrame regroupant l'ensemble des données necéssaire nous pouvons encore supprimer les données que nous n'utiliserons pas telles que les provinces et régions ou la latitude et la longitude. Nous utilisons la méthode [drop](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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JoursCountry/Region1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/20...5/19/205/20/205/21/205/22/205/23/205/24/205/25/205/26/205/27/205/28/20
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120Germany0.00.00.00.00.01.04.04.04.0...177778.0178473.0179021.0179710.0179986.0180328.0180600.0181200.0181524.0182196.0
133Iran0.00.00.00.00.00.00.00.00.0...124603.0126949.0129341.0131652.0133521.0135701.0137724.0139511.0141591.0143849.0
137Italy0.00.00.00.00.00.00.00.00.0...226699.0227364.0228006.0228658.0229327.0229858.0230158.0230555.0231139.0231732.0
139Japan2.02.02.02.04.04.07.07.011.0...16367.016367.016424.016513.016536.016550.016581.016623.016651.016598.0
143Korea, South1.01.02.02.03.04.04.04.04.0...11110.011122.011142.011165.011190.011206.011225.011265.011344.011402.0
169Netherlands0.00.00.00.00.00.00.00.00.0...44249.044447.044700.044888.045064.045236.045445.045578.045768.045950.0
184Portugal0.00.00.00.00.00.00.00.00.0...29432.029660.029912.030200.030471.030623.030788.031007.031292.031596.0
201Spain0.00.00.00.00.00.00.00.00.0...232037.0232555.0233037.0234824.0235290.0235772.0235400.0236259.0236259.0237906.0
223United Kingdom0.00.00.00.00.00.00.00.00.0...248818.0248293.0250908.0254195.0257154.0259559.0261184.0265227.0267240.0269127.0
225US1.01.02.02.05.05.05.05.05.0...1528568.01551853.01577147.01600937.01622612.01643246.01662302.01680913.01699176.01721753.0
61Hong Kong0.02.02.05.08.08.08.010.010.0...1055.01055.01055.01065.01065.01065.01065.01065.01066.01066.0
1China548.0641.0918.01401.02067.02869.05501.06077.08131.0...83008.083008.083008.083016.083019.083030.083037.083038.083040.083040.0
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14 rows × 129 columns

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23116120133137139143169184201223225611
Jours
Country/RegionBelgiumFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUnited KingdomUSHong KongChina
1/22/200000021000010548
1/23/200000021000012641
1/24/200200022000022918
1/25/2003000220000251401
1/26/2003000430000582067
1/27/2003100440000582869
1/28/2004400740000585501
1/29/20054007400005106077
1/30/200540011400005108131
1/31/2005502151100027129790
2/1/20068022012001281311878
2/2/200610022015001281516615
2/3/2006120220150012111519701
2/4/2016120222160012111723690
2/5/2016120222190012112127419
2/6/2016120222230012112430563
2/7/2016130325240013112534085
2/8/20111130325240013112636788
2/9/20111140326250023112939800
2/10/20111140326270028113842316
2/11/20111160326280028124944337
2/12/20111160328280029125044709
2/13/20111160328280029135359842
2/14/20111160329280029135666302
2/15/20112160343280029135668357
2/16/20112160359290029135770456
2/17/20112160366300029136072374
2/18/20112160374310029136274149
2/19/20112162384310029136374556
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4/29/204785916509316153993657203591138951076538802245052129171652211.03991e+06103782907
4/30/204851916576416300994640205463140881077439316250452134351712531.06942e+06103782919
5/1/204903216576416407795646207428143051078039791253512152161774541.10346e+06103982920
5/2/204951716697616496796448209328145711079340236251902165821822601.13254e+06103982920
5/3/204990616727216566497424210717148771080140571252822174661865991.15804e+06103982925
5/4/205026716788616615298647211938150781080440770255242180111905841.18038e+06104082926
5/5/205050916893516700799970213013152531080641087257022193291949901.20435e+06104082928
5/6/2050781172465168162101650214457152531081041319261822203252011011.22933e+06104082930
5/7/2051420173040169430103135215858154771082241774267152214472067151.25702e+06104482931
5/8/2052011174318170588104691217185155751084042093272682228572113641.28393e+06104482932
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129 rows × 14 columns

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1.52857e+06 \n", + "5/20/20 44447 29660 232555 248293 1.55185e+06 \n", + "5/21/20 44700 29912 233037 250908 1.57715e+06 \n", + "5/22/20 44888 30200 234824 254195 1.60094e+06 \n", + "5/23/20 45064 30471 235290 257154 1.62261e+06 \n", + "5/24/20 45236 30623 235772 259559 1.64325e+06 \n", + "5/25/20 45445 30788 235400 261184 1.6623e+06 \n", + "5/26/20 45578 31007 236259 265227 1.68091e+06 \n", + "5/27/20 45768 31292 236259 267240 1.69918e+06 \n", + "5/28/20 45950 31596 237906 269127 1.72175e+06 \n", + "\n", + " 61 1 \n", + "Jours \n", + "Country/Region Hong Kong China \n", + "1/22/20 0 548 \n", + "1/23/20 2 641 \n", + "1/24/20 2 918 \n", + "1/25/20 5 1401 \n", + "1/26/20 8 2067 \n", + "1/27/20 8 2869 \n", + "1/28/20 8 5501 \n", + "1/29/20 10 6077 \n", + "1/30/20 10 8131 \n", + "1/31/20 12 9790 \n", + "2/1/20 13 11878 \n", + "2/2/20 15 16615 \n", + "2/3/20 15 19701 \n", + "2/4/20 17 23690 \n", + "2/5/20 21 27419 \n", + "2/6/20 24 30563 \n", + "2/7/20 25 34085 \n", + "2/8/20 26 36788 \n", + 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83037 \n", + "5/26/20 1065 83038 \n", + "5/27/20 1066 83040 \n", + "5/28/20 1066 83040 \n", + "\n", + "[129 rows x 14 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataTransposed =dataCountries.T\n", + "\n", + "dataTransposed\n", + "\n" ] }, {