{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import isoweek\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "y = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",index_col=1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "Chine=y.loc[['China'],:]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateLatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/20...6/3/206/4/206/5/206/6/206/7/206/8/206/9/206/10/206/11/206/12/20
Country/Region
ChinaAnhui31.8257117.22641915396070106...991991991991991991991991991991
ChinaBeijing40.1824116.414214223641688091...594594594594594594594594595601
ChinaChongqing30.0572107.874069275775110132...579579579579579579579579579579
ChinaFujian26.0789117.9874151018355980...358358358359359359359360361361
ChinaGansu37.8099101.0583022471419...139139139139139139139139139139
ChinaGuangdong23.3417113.424426325378111151207...1598159816011602160216041604160716071608
ChinaGuangxi23.8298108.7881252323364651...254254254254254254254254254254
ChinaGuizhou26.8154106.87481334579...147147147147147147147147147147
ChinaHainan19.1959109.745345819223340...169169169170170170170170170171
ChinaHebei39.5490116.13061128131833...328328328328328328328328328328
ChinaHeilongjiang47.8620127.76150249152133...947947947947947947947947947947
ChinaHenan33.8820113.61405593283128168...1276127612761276127612761276127612761276
ChinaHong Kong22.3000114.20000225888...1093109911021105110611071107110711071108
ChinaHubei30.9756112.2707444444549761105814233554...68135681356813568135681356813568135681356813568135
ChinaHunan27.6104111.708849244369100143...1019101910191019101910191019101910191019
ChinaInner Mongolia44.0935113.9448001771115...235235235235235235237237237237
ChinaJiangsu32.9711119.455015918334770...653653653653653653653653653653
ChinaJiangxi27.6140115.72212718183672109...932932932932932932932932932932
ChinaJilin43.6661126.19230134468...155155155155155155155155155155
ChinaLiaoning41.2956122.608523417212734...149149149149149149149149149149
ChinaMacau22.1667113.55001222567...45454545454545454545
ChinaNingxia37.2692106.165511234711...75757575757575757575
ChinaQinghai35.745295.99560001166...18181818181818181818
ChinaShaanxi35.1917108.870103515223546...309309309311311311311311311311
ChinaShandong36.3427118.1498261527467595...792792792792792792792792792792
ChinaShanghai31.2020121.44919162033405366...673677677677678678678684689690
ChinaShanxi37.5777112.2922111691327...198198198198198198198198198198
ChinaSichuan30.6171102.7103581528446990...577578578578581582582582582583
ChinaTianjin39.3054117.323044810142324...192192192193193193194195195196
ChinaTibet31.692788.09240000000...1111111111
ChinaXinjiang41.112985.240102234510...76767676767676767676
ChinaYunnan24.9740101.487012511162644...185185185185185185185185185185
ChinaZhejiang29.1832120.093410274362104128173...1268126812681268126812681268126812681268
\n", "

33 rows × 146 columns

\n", "
" ], "text/plain": [ " Province/State Lat Long 1/22/20 1/23/20 1/24/20 \\\n", "Country/Region \n", "China Anhui 31.8257 117.2264 1 9 15 \n", "China Beijing 40.1824 116.4142 14 22 36 \n", "China Chongqing 30.0572 107.8740 6 9 27 \n", "China Fujian 26.0789 117.9874 1 5 10 \n", "China Gansu 37.8099 101.0583 0 2 2 \n", "China Guangdong 23.3417 113.4244 26 32 53 \n", "China Guangxi 23.8298 108.7881 2 5 23 \n", "China Guizhou 26.8154 106.8748 1 3 3 \n", "China Hainan 19.1959 109.7453 4 5 8 \n", "China Hebei 39.5490 116.1306 1 1 2 \n", "China Heilongjiang 47.8620 127.7615 0 2 4 \n", "China Henan 33.8820 113.6140 5 5 9 \n", "China Hong Kong 22.3000 114.2000 0 2 2 \n", "China Hubei 30.9756 112.2707 444 444 549 \n", "China Hunan 27.6104 111.7088 4 9 24 \n", "China Inner Mongolia 44.0935 113.9448 0 0 1 \n", "China Jiangsu 32.9711 119.4550 1 5 9 \n", "China Jiangxi 27.6140 115.7221 2 7 18 \n", "China Jilin 43.6661 126.1923 0 1 3 \n", "China Liaoning 41.2956 122.6085 2 3 4 \n", "China Macau 22.1667 113.5500 1 2 2 \n", "China Ningxia 37.2692 106.1655 1 1 2 \n", "China Qinghai 35.7452 95.9956 0 0 0 \n", "China Shaanxi 35.1917 108.8701 0 3 5 \n", "China Shandong 36.3427 118.1498 2 6 15 \n", "China Shanghai 31.2020 121.4491 9 16 20 \n", "China Shanxi 37.5777 112.2922 1 1 1 \n", "China Sichuan 30.6171 102.7103 5 8 15 \n", "China Tianjin 39.3054 117.3230 4 4 8 \n", "China Tibet 31.6927 88.0924 0 0 0 \n", "China Xinjiang 41.1129 85.2401 0 2 2 \n", "China Yunnan 24.9740 101.4870 1 2 5 \n", "China Zhejiang 29.1832 120.0934 10 27 43 \n", "\n", " 1/25/20 1/26/20 1/27/20 1/28/20 ... 6/3/20 6/4/20 \\\n", "Country/Region ... \n", "China 39 60 70 106 ... 991 991 \n", "China 41 68 80 91 ... 594 594 \n", "China 57 75 110 132 ... 579 579 \n", "China 18 35 59 80 ... 358 358 \n", "China 4 7 14 19 ... 139 139 \n", "China 78 111 151 207 ... 1598 1598 \n", "China 23 36 46 51 ... 254 254 \n", "China 4 5 7 9 ... 147 147 \n", "China 19 22 33 40 ... 169 169 \n", "China 8 13 18 33 ... 328 328 \n", "China 9 15 21 33 ... 947 947 \n", "China 32 83 128 168 ... 1276 1276 \n", "China 5 8 8 8 ... 1093 1099 \n", "China 761 1058 1423 3554 ... 68135 68135 \n", "China 43 69 100 143 ... 1019 1019 \n", "China 7 7 11 15 ... 235 235 \n", "China 18 33 47 70 ... 653 653 \n", "China 18 36 72 109 ... 932 932 \n", "China 4 4 6 8 ... 155 155 \n", "China 17 21 27 34 ... 149 149 \n", "China 2 5 6 7 ... 45 45 \n", "China 3 4 7 11 ... 75 75 \n", "China 1 1 6 6 ... 18 18 \n", "China 15 22 35 46 ... 309 309 \n", "China 27 46 75 95 ... 792 792 \n", "China 33 40 53 66 ... 673 677 \n", "China 6 9 13 27 ... 198 198 \n", "China 28 44 69 90 ... 577 578 \n", "China 10 14 23 24 ... 192 192 \n", "China 0 0 0 0 ... 1 1 \n", "China 3 4 5 10 ... 76 76 \n", "China 11 16 26 44 ... 185 185 \n", "China 62 104 128 173 ... 1268 1268 \n", "\n", " 6/5/20 6/6/20 6/7/20 6/8/20 6/9/20 6/10/20 6/11/20 \\\n", "Country/Region \n", "China 991 991 991 991 991 991 991 \n", "China 594 594 594 594 594 594 595 \n", "China 579 579 579 579 579 579 579 \n", "China 358 359 359 359 359 360 361 \n", "China 139 139 139 139 139 139 139 \n", "China 1601 1602 1602 1604 1604 1607 1607 \n", "China 254 254 254 254 254 254 254 \n", "China 147 147 147 147 147 147 147 \n", "China 169 170 170 170 170 170 170 \n", "China 328 328 328 328 328 328 328 \n", "China 947 947 947 947 947 947 947 \n", "China 1276 1276 1276 1276 1276 1276 1276 \n", "China 1102 1105 1106 1107 1107 1107 1107 \n", "China 68135 68135 68135 68135 68135 68135 68135 \n", "China 1019 1019 1019 1019 1019 1019 1019 \n", "China 235 235 235 235 237 237 237 \n", "China 653 653 653 653 653 653 653 \n", "China 932 932 932 932 932 932 932 \n", "China 155 155 155 155 155 155 155 \n", "China 149 149 149 149 149 149 149 \n", "China 45 45 45 45 45 45 45 \n", "China 75 75 75 75 75 75 75 \n", "China 18 18 18 18 18 18 18 \n", "China 309 311 311 311 311 311 311 \n", "China 792 792 792 792 792 792 792 \n", "China 677 677 678 678 678 684 689 \n", "China 198 198 198 198 198 198 198 \n", "China 578 578 581 582 582 582 582 \n", "China 192 193 193 193 194 195 195 \n", "China 1 1 1 1 1 1 1 \n", "China 76 76 76 76 76 76 76 \n", "China 185 185 185 185 185 185 185 \n", "China 1268 1268 1268 1268 1268 1268 1268 \n", "\n", " 6/12/20 \n", "Country/Region \n", "China 991 \n", "China 601 \n", "China 579 \n", "China 361 \n", "China 139 \n", "China 1608 \n", "China 254 \n", "China 147 \n", "China 171 \n", "China 328 \n", "China 947 \n", "China 1276 \n", "China 1108 \n", "China 68135 \n", "China 1019 \n", "China 237 \n", "China 653 \n", "China 932 \n", "China 155 \n", "China 149 \n", "China 45 \n", "China 75 \n", "China 18 \n", "China 311 \n", "China 792 \n", "China 690 \n", "China 198 \n", "China 583 \n", "China 196 \n", "China 1 \n", "China 76 \n", "China 185 \n", "China 1268 \n", "\n", "[33 rows x 146 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Chine" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m HongKong=Chine.iloc[[12],:]fgdg\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "HongKong=Chine.iloc[[12],:]fgdg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HongKong.loc['Chine', 'Country/Region'] = 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HongKong" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "H=HongKong.replace(\"China\",\"Hong Kong\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(H.dtypes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HongKong.at[0,'Country/Region']= 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HongKong=HongKong.drop(columns='Province/State')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h=H.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h.dtypes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HongKong.astype(np.str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HongKong.replace['China','H']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", sep = '\\t', header = None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Belgique=x.loc[['Belgium'],:]\n", "Chine=x.loc[['China'],:]\n", "France=x.loc[['France'],:]\n", "Allemagne=x.loc[['Germany'],:]\n", "Iran=x.loc[['Iran'],:]\n", "Italie=x.loc[['Italy'],:]\n", "Japon=x.loc[['Japan'],:]\n", "Hollande_et_colonies=x.loc[['Netherlands'],:]\n", "Portugal=x.loc[['Portugal'],:]\n", "Espagne=x.loc[['Spain'],:]\n", "RoyaumeUni_et_colonies=x.loc[['United Kingdom'],:]\n", "CoréeduSud=x.loc[['Korea, South'],:]\n", "EtatsUnis=x.loc[['US'],:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z=x.reindex(columns = ['Country/Region', 'Province/State'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z=x.iloc[[12],:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z=z.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z.astype({12: int})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "z" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv(\"C:/Users/xavier/Documents/Cours ESPE\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }