diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index a5d2ec9c69630fd5a55af12a72e93dc7a6985870..77796bb87442a4e8c04dfd2e74346426d14455d9 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -18,18 +18,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1619869" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data_file_name = \"time_series_covid19_confirmed_global.csv\"\n", "if not(os.path.isfile(data_file_name)):\n", @@ -47,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1820,7 +1809,7 @@ "[289 rows x 1041 columns]" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1832,7 +1821,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -1853,7 +1842,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -3626,14 +3615,14 @@ "[75 rows x 1041 columns]" ] }, - "execution_count": 53, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wanted_regions_names = [\"Belgium\", \"China\", \"France\", \"Germany\", \"Iran\", \"Italy\", \"Japan\", \"Korea, South\", \"Netherlands\", \"Portugal\", \"Spain\", \"United Kingdom\", \"US\"]\n", - "regions_tab = pd.DataFrame(df.loc[[(countries in wanted_regions_names) for countries in df['Country/Region']]])\n", + "regions_tab = pd.DataFrame(raw_data.loc[[(countries in wanted_regions_names) for countries in raw_data['Country/Region']]])\n", "regions_tab" ] }, @@ -3641,12 +3630,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On supprime maintenant les lignes inutiles (où on a 0 cas de virus au total) :" + "On supprime maintenant les lignes inutiles (où on a 0 cas de virus au total), ainsi que les colonnes Lat et Long qui ne nous servent pas non plus :" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -3672,14 +3661,14 @@ " \n", " Province/State\n", " Country/Region\n", - " Lat\n", - " Long\n", " 1/22/20\n", " 1/23/20\n", " 1/24/20\n", " 1/25/20\n", " 1/26/20\n", " 1/27/20\n", + " 1/28/20\n", + " 1/29/20\n", " ...\n", " 11/14/22\n", " 11/15/22\n", @@ -3698,8 +3687,8 @@ " 24\n", " NaN\n", " Belgium\n", - " 50.833300\n", - " 4.469936\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -3722,14 +3711,14 @@ " 59\n", " Anhui\n", " China\n", - " 31.825700\n", - " 117.226400\n", " 1\n", " 9\n", " 15\n", " 39\n", " 60\n", " 70\n", + " 106\n", + " 152\n", " ...\n", " 1559\n", " 1564\n", @@ -3746,14 +3735,14 @@ " 60\n", " Beijing\n", " China\n", - " 40.182400\n", - " 116.414200\n", " 14\n", " 22\n", " 36\n", " 41\n", " 68\n", " 80\n", + " 91\n", + " 111\n", " ...\n", " 5945\n", " 6147\n", @@ -3770,14 +3759,14 @@ " 61\n", " Chongqing\n", " China\n", - " 30.057200\n", - " 107.874000\n", " 6\n", " 9\n", " 27\n", " 57\n", " 75\n", " 110\n", + " 132\n", + " 147\n", " ...\n", " 2512\n", " 2798\n", @@ -3794,14 +3783,14 @@ " 62\n", " Fujian\n", " China\n", - " 26.078900\n", - " 117.987400\n", " 1\n", " 5\n", " 10\n", " 18\n", " 35\n", " 59\n", + " 80\n", + " 84\n", " ...\n", " 5448\n", " 5479\n", @@ -3818,14 +3807,14 @@ " 63\n", " Gansu\n", " China\n", - " 35.751800\n", - " 104.286100\n", " 0\n", " 2\n", " 2\n", " 4\n", " 7\n", " 14\n", + " 19\n", + " 24\n", " ...\n", " 1455\n", " 1468\n", @@ -3842,14 +3831,14 @@ " 64\n", " Guangdong\n", " China\n", - " 23.341700\n", - " 113.424400\n", " 26\n", " 32\n", " 53\n", " 78\n", " 111\n", " 151\n", + " 207\n", + " 277\n", " ...\n", " 18190\n", " 18778\n", @@ -3866,14 +3855,14 @@ " 65\n", " Guangxi\n", " China\n", - " 23.829800\n", - " 108.788100\n", " 2\n", " 5\n", " 23\n", " 23\n", " 36\n", " 46\n", + " 51\n", + " 58\n", " ...\n", " 2350\n", " 2350\n", @@ -3890,14 +3879,14 @@ " 66\n", " Guizhou\n", " China\n", - " 26.815400\n", - " 106.874800\n", " 1\n", " 3\n", " 3\n", " 4\n", " 5\n", " 7\n", + " 9\n", + " 9\n", " ...\n", " 1001\n", " 1011\n", @@ -3914,14 +3903,14 @@ " 67\n", " Hainan\n", " China\n", - " 19.195900\n", - " 109.745300\n", " 4\n", " 5\n", " 8\n", " 19\n", " 22\n", " 33\n", + " 40\n", + " 43\n", " ...\n", " 8984\n", " 8992\n", @@ -3938,14 +3927,14 @@ " 68\n", " Hebei\n", " China\n", - " 39.549000\n", - " 116.130600\n", " 1\n", " 1\n", " 2\n", " 8\n", " 13\n", " 18\n", + " 33\n", + " 48\n", " ...\n", " 2054\n", " 2055\n", @@ -3962,14 +3951,14 @@ " 69\n", " Heilongjiang\n", " China\n", - " 47.862000\n", - " 127.761500\n", " 0\n", " 2\n", " 4\n", " 9\n", " 15\n", " 21\n", + " 33\n", + " 38\n", " ...\n", " 3620\n", " 3655\n", @@ -3986,14 +3975,14 @@ " 70\n", " Henan\n", " China\n", - " 37.895700\n", - " 114.904200\n", " 5\n", " 5\n", " 9\n", " 32\n", " 83\n", " 128\n", + " 168\n", + " 206\n", " ...\n", " 4995\n", " 5195\n", @@ -4010,14 +3999,14 @@ " 71\n", " Hong Kong\n", " China\n", - " 22.300000\n", - " 114.200000\n", " 0\n", " 2\n", " 2\n", " 5\n", " 8\n", " 8\n", + " 8\n", + " 10\n", " ...\n", " 1992218\n", " 1998169\n", @@ -4034,14 +4023,14 @@ " 72\n", " Hubei\n", " China\n", - " 30.975600\n", - " 112.270700\n", " 444\n", " 444\n", " 549\n", " 761\n", " 1058\n", " 1423\n", + " 3554\n", + " 3554\n", " ...\n", " 68448\n", " 68454\n", @@ -4058,14 +4047,14 @@ " 73\n", " Hunan\n", " China\n", - " 27.610400\n", - " 111.708800\n", " 4\n", " 9\n", " 24\n", " 43\n", " 69\n", " 100\n", + " 143\n", + " 221\n", " ...\n", " 1850\n", " 1856\n", @@ -4082,14 +4071,14 @@ " 74\n", " Inner Mongolia\n", " China\n", - " 44.093500\n", - " 113.944800\n", " 0\n", " 0\n", " 1\n", " 7\n", " 7\n", " 11\n", + " 15\n", + " 16\n", " ...\n", " 6055\n", " 6138\n", @@ -4106,14 +4095,14 @@ " 75\n", " Jiangsu\n", " China\n", - " 32.971100\n", - " 119.455000\n", " 1\n", " 5\n", " 9\n", " 18\n", " 33\n", " 47\n", + " 70\n", + " 99\n", " ...\n", " 2832\n", " 2859\n", @@ -4130,14 +4119,14 @@ " 76\n", " Jiangxi\n", " China\n", - " 27.614000\n", - " 115.722100\n", " 2\n", " 7\n", " 18\n", " 18\n", " 36\n", " 72\n", + " 109\n", + " 109\n", " ...\n", " 1506\n", " 1506\n", @@ -4154,14 +4143,14 @@ " 77\n", " Jilin\n", " China\n", - " 43.666100\n", - " 126.192300\n", " 0\n", " 1\n", " 3\n", " 4\n", " 4\n", " 6\n", + " 8\n", + " 9\n", " ...\n", " 40329\n", " 40329\n", @@ -4178,14 +4167,14 @@ " 78\n", " Liaoning\n", " China\n", - " 41.295600\n", - " 122.608500\n", " 2\n", " 3\n", " 4\n", " 17\n", " 21\n", " 27\n", + " 34\n", + " 39\n", " ...\n", " 2080\n", " 2084\n", @@ -4202,14 +4191,14 @@ " 79\n", " Macau\n", " China\n", - " 22.166700\n", - " 113.550000\n", " 1\n", " 2\n", " 2\n", " 2\n", " 5\n", " 6\n", + " 7\n", + " 7\n", " ...\n", " 795\n", " 795\n", @@ -4226,14 +4215,14 @@ " 80\n", " Ningxia\n", " China\n", - " 37.269200\n", - " 106.165500\n", " 1\n", " 1\n", " 2\n", " 3\n", " 4\n", " 7\n", + " 11\n", + " 12\n", " ...\n", " 225\n", " 225\n", @@ -4250,14 +4239,14 @@ " 81\n", " Qinghai\n", " China\n", - " 35.745200\n", - " 95.995600\n", " 0\n", " 0\n", " 0\n", " 1\n", " 1\n", " 6\n", + " 6\n", + " 6\n", " ...\n", " 328\n", " 330\n", @@ -4274,14 +4263,14 @@ " 82\n", " Shaanxi\n", " China\n", - " 35.191700\n", - " 108.870100\n", " 0\n", " 3\n", " 5\n", " 15\n", " 22\n", " 35\n", + " 46\n", + " 56\n", " ...\n", " 4315\n", " 4378\n", @@ -4298,14 +4287,14 @@ " 83\n", " Shandong\n", " China\n", - " 36.342700\n", - " 118.149800\n", " 2\n", " 6\n", " 15\n", " 27\n", " 46\n", " 75\n", + " 95\n", + " 130\n", " ...\n", " 3286\n", " 3299\n", @@ -4322,14 +4311,14 @@ " 84\n", " Shanghai\n", " China\n", - " 31.202000\n", - " 121.449100\n", " 9\n", " 16\n", " 20\n", " 33\n", " 40\n", " 53\n", + " 66\n", + " 96\n", " ...\n", " 64341\n", " 64345\n", @@ -4346,14 +4335,14 @@ " 85\n", " Shanxi\n", " China\n", - " 37.577700\n", - " 112.292200\n", " 1\n", " 1\n", " 1\n", " 6\n", " 9\n", " 13\n", + " 27\n", + " 27\n", " ...\n", " 1923\n", " 1989\n", @@ -4370,14 +4359,14 @@ " 86\n", " Sichuan\n", " China\n", - " 30.617100\n", - " 102.710300\n", " 5\n", " 8\n", " 15\n", " 28\n", " 44\n", " 69\n", + " 90\n", + " 108\n", " ...\n", " 6825\n", " 6889\n", @@ -4394,14 +4383,14 @@ " 87\n", " Tianjin\n", " China\n", - " 39.305400\n", - " 117.323000\n", " 4\n", " 4\n", " 8\n", " 10\n", " 14\n", " 23\n", + " 24\n", + " 27\n", " ...\n", " 2530\n", " 2539\n", @@ -4442,8 +4431,8 @@ " 129\n", " St Martin\n", " France\n", - " 18.070800\n", - " -63.050100\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4466,8 +4455,8 @@ " 130\n", " Wallis and Futuna\n", " France\n", - " -14.293800\n", - " -178.116500\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4490,14 +4479,14 @@ " 131\n", " NaN\n", " France\n", - " 46.227600\n", - " 2.213700\n", " 0\n", " 0\n", " 2\n", " 3\n", " 3\n", " 3\n", + " 4\n", + " 5\n", " ...\n", " 36145223\n", " 36193559\n", @@ -4514,14 +4503,14 @@ " 135\n", " NaN\n", " Germany\n", - " 51.165691\n", - " 10.451526\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 1\n", + " 4\n", + " 4\n", " ...\n", " 36080574\n", " 36119184\n", @@ -4538,8 +4527,8 @@ " 150\n", " NaN\n", " Iran\n", - " 32.427908\n", - " 53.688046\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4562,8 +4551,8 @@ " 154\n", " NaN\n", " Italy\n", - " 41.871940\n", - " 12.567380\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4586,14 +4575,14 @@ " 156\n", " NaN\n", " Japan\n", - " 36.204824\n", - " 138.252924\n", " 2\n", " 2\n", " 2\n", " 2\n", " 4\n", " 4\n", + " 7\n", + " 7\n", " ...\n", " 23241410\n", " 23341236\n", @@ -4610,14 +4599,14 @@ " 162\n", " NaN\n", " Korea, South\n", - " 35.907757\n", - " 127.766922\n", " 1\n", " 1\n", " 2\n", " 2\n", " 3\n", " 4\n", + " 4\n", + " 4\n", " ...\n", " 26290877\n", " 26357464\n", @@ -4634,8 +4623,8 @@ " 196\n", " Aruba\n", " Netherlands\n", - " 12.521100\n", - " -69.968300\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4658,8 +4647,8 @@ " 197\n", " Bonaire, Sint Eustatius and Saba\n", " Netherlands\n", - " 12.178400\n", - " -68.238500\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4682,8 +4671,8 @@ " 198\n", " Curacao\n", " Netherlands\n", - " 12.169600\n", - " -68.990000\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4706,8 +4695,8 @@ " 199\n", " Sint Maarten\n", " Netherlands\n", - " 18.042500\n", - " -63.054800\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4730,8 +4719,8 @@ " 200\n", " NaN\n", " Netherlands\n", - " 52.132600\n", - " 5.291300\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4754,8 +4743,8 @@ " 218\n", " NaN\n", " Portugal\n", - " 39.399900\n", - " -8.224500\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4778,8 +4767,8 @@ " 241\n", " NaN\n", " Spain\n", - " 40.463667\n", - " -3.749220\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4802,14 +4791,14 @@ " 260\n", " NaN\n", " US\n", - " 40.000000\n", - " -100.000000\n", " 1\n", " 1\n", " 2\n", " 2\n", " 5\n", " 5\n", + " 5\n", + " 6\n", " ...\n", " 98054070\n", " 98113463\n", @@ -4826,8 +4815,8 @@ " 264\n", " Anguilla\n", " United Kingdom\n", - " 18.220600\n", - " -63.068600\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4850,8 +4839,8 @@ " 265\n", " Bermuda\n", " United Kingdom\n", - " 32.307800\n", - " -64.750500\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4874,8 +4863,8 @@ " 266\n", " British Virgin Islands\n", " United Kingdom\n", - " 18.420700\n", - " -64.640000\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4898,8 +4887,8 @@ " 267\n", " Cayman Islands\n", " United Kingdom\n", - " 19.313300\n", - " -81.254600\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4922,8 +4911,8 @@ " 269\n", " Falkland Islands (Malvinas)\n", " United Kingdom\n", - " -51.796300\n", - " -59.523600\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4946,8 +4935,8 @@ " 270\n", " Gibraltar\n", " United Kingdom\n", - " 36.140800\n", - " -5.353600\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4970,8 +4959,8 @@ " 271\n", " Guernsey\n", " United Kingdom\n", - " 49.448196\n", - " -2.589490\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -4994,8 +4983,8 @@ " 272\n", " Isle of Man\n", " United Kingdom\n", - " 54.236100\n", - " -4.548100\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5018,8 +5007,8 @@ " 273\n", " Jersey\n", " United Kingdom\n", - " 49.213800\n", - " -2.135800\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5042,8 +5031,8 @@ " 274\n", " Montserrat\n", " United Kingdom\n", - " 16.742498\n", - " -62.187366\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5066,8 +5055,8 @@ " 275\n", " Pitcairn Islands\n", " United Kingdom\n", - " -24.376800\n", - " -128.324200\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5090,8 +5079,8 @@ " 276\n", " Saint Helena, Ascension and Tristan da Cunha\n", " United Kingdom\n", - " -7.946700\n", - " -14.355900\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5114,8 +5103,8 @@ " 277\n", " Turks and Caicos Islands\n", " United Kingdom\n", - " 21.694000\n", - " -71.797900\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5138,8 +5127,8 @@ " 278\n", " NaN\n", " United Kingdom\n", - " 55.378100\n", - " -3.436000\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -5160,272 +5149,2998 @@ " \n", " \n", "\n", - "

74 rows × 1041 columns

\n", + "

74 rows × 1039 columns

\n", "" ], "text/plain": [ - " Province/State Country/Region Lat \\\n", - "24 NaN Belgium 50.833300 \n", - "59 Anhui China 31.825700 \n", - "60 Beijing China 40.182400 \n", - "61 Chongqing China 30.057200 \n", - "62 Fujian China 26.078900 \n", - "63 Gansu China 35.751800 \n", - "64 Guangdong China 23.341700 \n", - "65 Guangxi China 23.829800 \n", - "66 Guizhou China 26.815400 \n", - "67 Hainan China 19.195900 \n", - "68 Hebei China 39.549000 \n", - "69 Heilongjiang China 47.862000 \n", - "70 Henan China 37.895700 \n", - "71 Hong Kong China 22.300000 \n", - "72 Hubei China 30.975600 \n", - "73 Hunan China 27.610400 \n", - "74 Inner Mongolia China 44.093500 \n", - "75 Jiangsu China 32.971100 \n", - "76 Jiangxi China 27.614000 \n", - "77 Jilin China 43.666100 \n", - "78 Liaoning China 41.295600 \n", - "79 Macau China 22.166700 \n", - "80 Ningxia China 37.269200 \n", - "81 Qinghai China 35.745200 \n", - "82 Shaanxi China 35.191700 \n", - "83 Shandong China 36.342700 \n", - "84 Shanghai China 31.202000 \n", - "85 Shanxi China 37.577700 \n", - "86 Sichuan China 30.617100 \n", - "87 Tianjin China 39.305400 \n", - ".. ... ... ... \n", - "129 St Martin France 18.070800 \n", - "130 Wallis and Futuna France -14.293800 \n", - "131 NaN France 46.227600 \n", - "135 NaN Germany 51.165691 \n", - "150 NaN Iran 32.427908 \n", - "154 NaN Italy 41.871940 \n", - "156 NaN Japan 36.204824 \n", - "162 NaN Korea, South 35.907757 \n", - "196 Aruba Netherlands 12.521100 \n", - "197 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", - "198 Curacao Netherlands 12.169600 \n", - "199 Sint Maarten Netherlands 18.042500 \n", - "200 NaN Netherlands 52.132600 \n", - "218 NaN Portugal 39.399900 \n", - "241 NaN Spain 40.463667 \n", - "260 NaN US 40.000000 \n", - "264 Anguilla United Kingdom 18.220600 \n", - "265 Bermuda United Kingdom 32.307800 \n", - "266 British Virgin Islands United Kingdom 18.420700 \n", - "267 Cayman Islands United Kingdom 19.313300 \n", - "269 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", - "270 Gibraltar United Kingdom 36.140800 \n", - "271 Guernsey United Kingdom 49.448196 \n", - "272 Isle of Man United Kingdom 54.236100 \n", - "273 Jersey United Kingdom 49.213800 \n", - "274 Montserrat United Kingdom 16.742498 \n", - "275 Pitcairn Islands United Kingdom -24.376800 \n", - "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom -7.946700 \n", - "277 Turks and Caicos Islands United Kingdom 21.694000 \n", - "278 NaN United Kingdom 55.378100 \n", + " Province/State Country/Region 1/22/20 \\\n", + "24 NaN Belgium 0 \n", + "59 Anhui China 1 \n", + "60 Beijing China 14 \n", + "61 Chongqing China 6 \n", + "62 Fujian China 1 \n", + "63 Gansu China 0 \n", + "64 Guangdong China 26 \n", + "65 Guangxi China 2 \n", + "66 Guizhou China 1 \n", + "67 Hainan China 4 \n", + "68 Hebei China 1 \n", + "69 Heilongjiang China 0 \n", + "70 Henan China 5 \n", + "71 Hong Kong China 0 \n", + "72 Hubei China 444 \n", + "73 Hunan China 4 \n", + "74 Inner Mongolia China 0 \n", + "75 Jiangsu China 1 \n", + "76 Jiangxi China 2 \n", + "77 Jilin China 0 \n", + "78 Liaoning China 2 \n", + "79 Macau China 1 \n", + "80 Ningxia China 1 \n", + "81 Qinghai China 0 \n", + "82 Shaanxi China 0 \n", + "83 Shandong China 2 \n", + "84 Shanghai China 9 \n", + "85 Shanxi China 1 \n", + "86 Sichuan China 5 \n", + "87 Tianjin China 4 \n", + ".. ... ... ... \n", + "129 St Martin France 0 \n", + "130 Wallis and Futuna France 0 \n", + "131 NaN France 0 \n", + "135 NaN Germany 0 \n", + "150 NaN Iran 0 \n", + "154 NaN Italy 0 \n", + "156 NaN Japan 2 \n", + "162 NaN Korea, South 1 \n", + "196 Aruba Netherlands 0 \n", + "197 Bonaire, Sint Eustatius and Saba Netherlands 0 \n", + "198 Curacao Netherlands 0 \n", + "199 Sint Maarten Netherlands 0 \n", + "200 NaN Netherlands 0 \n", + "218 NaN Portugal 0 \n", + "241 NaN Spain 0 \n", + "260 NaN US 1 \n", + "264 Anguilla United Kingdom 0 \n", + "265 Bermuda United Kingdom 0 \n", + "266 British Virgin Islands United Kingdom 0 \n", + "267 Cayman Islands United Kingdom 0 \n", + "269 Falkland Islands (Malvinas) United Kingdom 0 \n", + "270 Gibraltar United Kingdom 0 \n", + "271 Guernsey United Kingdom 0 \n", + "272 Isle of Man United Kingdom 0 \n", + "273 Jersey United Kingdom 0 \n", + "274 Montserrat United Kingdom 0 \n", + "275 Pitcairn Islands United Kingdom 0 \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom 0 \n", + "277 Turks and Caicos Islands United Kingdom 0 \n", + "278 NaN United Kingdom 0 \n", "\n", - 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"[74 rows x 1041 columns]" + "[74 rows x 1039 columns]" ] }, - "execution_count": 57, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regions_tab = pd.DataFrame(regions_tab.loc[regions_tab.iloc[:,-1] != 0])\n", + "regions_tab.drop('Lat', inplace=True, axis=1)\n", + "regions_tab.drop('Long', inplace=True, axis=1)\n", + "regions_tab" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def fusion_regions_with_province_exception_keeping(df, region, exception):\n", + " tab = df.values.tolist()\n", + " lines_len = len(tab[0])\n", + " total = [0] * (lines_len - 2)\n", + " areas_to_delete = []\n", + " for i in range(len(tab)):\n", + " if tab[i][1] == region and (exception == \"\" or exception != tab[i][0]):\n", + " values = tab[i]\n", + " areas_to_delete.append((tab[i][1], tab[i][0]))\n", + " for j in range(2, lines_len):\n", + " total[j-2] += values[j]\n", + " elif exception == tab[i][0]:\n", + " areas_to_delete.append((tab[i][1], tab[i][0]))\n", + " tab.append([\"All\", tab[i][0]] + tab[i][2:])\n", + " for area in areas_to_delete:\n", + " for i in range(len(tab)):\n", + " if (tab[i][1], tab[i][0]) == area:\n", + " del tab[i]\n", + " break\n", + " tab.append([\"All\", region] + total)\n", + " df = pd.DataFrame(tab)\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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31GibraltarUnited Kingdom00000000...20166201662016620166201842018420184201842018420184
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34JerseyUnited Kingdom00000000...62317623176231762317625896258962589625896258962589
35MontserratUnited Kingdom00000000...1403140314031403140314031403140314031403
36Pitcairn IslandsUnited Kingdom00000000...4444444444
37Saint Helena, Ascension and Tristan da CunhaUnited Kingdom00000000...2115215921592159215921592159215921592159
38Turks and Caicos IslandsUnited Kingdom00000000...6431643164316431643164316431643164466446
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41AllChina54864191814012067286955016077...1137641115523611952101197482122138512668471292852132044813224011378017
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exception):\n", + " tab = df.values.tolist()\n", + " lines_len = len(tab[0])\n", + " total = [0] * (lines_len - 2)\n", + " areas_to_delete = []\n", + " for i in range(len(tab)):\n", + " if tab[i][1] == region and (exception == \"\" or (exception != tab[i][0] and (exception != \"*\" or tab[i][0] != tab[i][0]))):\n", + " values = tab[i]\n", + " areas_to_delete.append((tab[i][1], tab[i][0]))\n", + " for j in range(2, lines_len):\n", + " total[j-2] += values[j]\n", + " elif tab[i][1] != region:\n", + " pass\n", + " elif exception == \"*\" and tab[i][0] == tab[i][0]:\n", + " areas_to_delete.append((tab[i][1], tab[i][0]))\n", + " elif exception == tab[i][0]:\n", + " areas_to_delete.append((tab[i][1], tab[i][0]))\n", + " for area in areas_to_delete:\n", + " for i in range(len(tab)):\n", + " if (tab[i][1], tab[i][0]) == area:\n", + " del tab[i]\n", + " break\n", + " tab.append([\"All\", region] + total)\n", + " df = pd.DataFrame(tab)\n", + " return df" + ] + }, + { + "cell_type": "code", + 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5NaNKorea, South11223444...26290877263574642641290126462319265127542655876526581856266547292672505326784142
6ArubaNetherlands00000000...43423434234349443494434944349443494434944349443568
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8CuracaoNetherlands00000000...45507455074553645536455364553645536455364553645559
9Sint MaartenNetherlands00000000...10931109311094310943109431094310943109431094310949
10NaNNetherlands00000000...8528384853131685313168531316853401385340138534013853401385367188536718
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"execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regions_tab = fusion_regions_with_province_exception_deleting(regions_tab, \"France\", \"*\")\n", + "regions_tab" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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3AllNetherlands00000000...8528384853131685313168531316853401385340138534013853401385367188536718
4AllBelgium00000000...4624251462425146242514627186462718646271864627186462718646295524629552
5AllGermany00000144...36080574361191843615249036180077362054053620540536205405362470813628037136318461
6AllIran00000000...7559001755905575590557559164755921175592587559296755936875594167559446
7AllItaly00000000...23823192238231922382319223823192240315382403153824031538240315382403153824031538
8AllUnited Kingdom00000000...23954192239541922395419223977633239776332397763323977633239776332397763323977633
9AllJapan22224477...23241410233412362344893823540544236263182371620523793927238363512395758724090948
10AllKorea, South11223444...26290877263574642641290126462319265127542655876526581856266547292672505326784142
11AllPortugal00000000...5532964553296455329645532964553710855371085537108553710855371085537108
12AllSpain00000000...13551539135515391355153913551539135737211357372113573721135737211357372113573721
13AllUS11225556...98054070981134639819774398251350983069709831157398314841983573989839207698503462
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14 rows × 1039 columns

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