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mooc-rr
Commit b0437603 authored by dca34f41fee2efa24adbe57b0fcf3009's avatar dca34f41fee2efa24adbe57b0fcf3009
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module3/exo3/exercice.ipynb
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......@@ -1851,274 +1851,12 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Province/State Country/Region 1/22/20 \\\n",
"0 NaN Afghanistan 0 \n",
"1 NaN Albania 0 \n",
"2 NaN Algeria 0 \n",
"3 NaN Andorra 0 \n",
"4 NaN Angola 0 \n",
"5 NaN Antigua and Barbuda 0 \n",
"6 NaN Argentina 0 \n",
"7 NaN Armenia 0 \n",
"8 Australian Capital Territory Australia 0 \n",
"9 New South Wales Australia 0 \n",
"10 Northern Territory Australia 0 \n",
"11 Queensland Australia 0 \n",
"12 South Australia Australia 0 \n",
"13 Tasmania Australia 0 \n",
"14 Victoria Australia 0 \n",
"15 Western Australia Australia 0 \n",
"16 NaN Austria 0 \n",
"17 NaN Azerbaijan 0 \n",
"18 NaN Bahamas 0 \n",
"19 NaN Bahrain 0 \n",
"20 NaN Bangladesh 0 \n",
"21 NaN Barbados 0 \n",
"22 NaN Belarus 0 \n",
"23 NaN Belgium 0 \n",
"24 NaN Benin 0 \n",
"25 NaN Bhutan 0 \n",
"26 NaN Bolivia 0 \n",
"27 NaN Bosnia and Herzegovina 0 \n",
"28 NaN Brazil 0 \n",
"29 NaN Brunei 0 \n",
".. ... ... ... \n",
"236 NaN Timor-Leste 0 \n",
"237 NaN Belize 0 \n",
"238 NaN Laos 0 \n",
"239 NaN Libya 0 \n",
"240 NaN West Bank and Gaza 0 \n",
"241 NaN Guinea-Bissau 0 \n",
"242 NaN Mali 0 \n",
"243 NaN Saint Kitts and Nevis 0 \n",
"244 Northwest Territories Canada 0 \n",
"245 Yukon Canada 0 \n",
"246 NaN Kosovo 0 \n",
"247 NaN Burma 0 \n",
"248 Anguilla United Kingdom 0 \n",
"249 British Virgin Islands United Kingdom 0 \n",
"250 Turks and Caicos Islands United Kingdom 0 \n",
"251 NaN MS Zaandam 0 \n",
"252 NaN Botswana 0 \n",
"253 NaN Burundi 0 \n",
"254 NaN Sierra Leone 0 \n",
"255 Bonaire, Sint Eustatius and Saba Netherlands 0 \n",
"256 NaN Malawi 0 \n",
"257 Falkland Islands (Malvinas) United Kingdom 0 \n",
"258 Saint Pierre and Miquelon France 0 \n",
"259 NaN South Sudan 0 \n",
"260 NaN Western Sahara 0 \n",
"261 NaN Sao Tome and Principe 0 \n",
"262 NaN Yemen 0 \n",
"263 NaN Comoros 0 \n",
"264 NaN Tajikistan 0 \n",
"265 NaN Lesotho 0 \n",
"\n",
" 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... \\\n",
"0 0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 0 ... \n",
"3 0 0 0 0 0 0 0 ... \n",
"4 0 0 0 0 0 0 0 ... \n",
"5 0 0 0 0 0 0 0 ... \n",
"6 0 0 0 0 0 0 0 ... \n",
"7 0 0 0 0 0 0 0 ... \n",
"8 0 0 0 0 0 0 0 ... \n",
"9 0 0 0 3 4 4 4 ... \n",
"10 0 0 0 0 0 0 0 ... \n",
"11 0 0 0 0 0 0 1 ... \n",
"12 0 0 0 0 0 0 0 ... \n",
"13 0 0 0 0 0 0 0 ... \n",
"14 0 0 0 1 1 1 1 ... \n",
"15 0 0 0 0 0 0 0 ... \n",
"16 0 0 0 0 0 0 0 ... \n",
"17 0 0 0 0 0 0 0 ... \n",
"18 0 0 0 0 0 0 0 ... \n",
"19 0 0 0 0 0 0 0 ... \n",
"20 0 0 0 0 0 0 0 ... \n",
"21 0 0 0 0 0 0 0 ... \n",
"22 0 0 0 0 0 0 0 ... \n",
"23 0 0 0 0 0 0 0 ... \n",
"24 0 0 0 0 0 0 0 ... \n",
"25 0 0 0 0 0 0 0 ... \n",
"26 0 0 0 0 0 0 0 ... \n",
"27 0 0 0 0 0 0 0 ... \n",
"28 0 0 0 0 0 0 0 ... \n",
"29 0 0 0 0 0 0 0 ... \n",
".. ... ... ... ... ... ... ... ... \n",
"236 0 0 0 0 0 0 0 ... \n",
"237 0 0 0 0 0 0 0 ... \n",
"238 0 0 0 0 0 0 0 ... \n",
"239 0 0 0 0 0 0 0 ... \n",
"240 0 0 0 0 0 0 0 ... \n",
"241 0 0 0 0 0 0 0 ... \n",
"242 0 0 0 0 0 0 0 ... \n",
"243 0 0 0 0 0 0 0 ... \n",
"244 0 0 0 0 0 0 0 ... \n",
"245 0 0 0 0 0 0 0 ... \n",
"246 0 0 0 0 0 0 0 ... \n",
"247 0 0 0 0 0 0 0 ... \n",
"248 0 0 0 0 0 0 0 ... \n",
"249 0 0 0 0 0 0 0 ... \n",
"250 0 0 0 0 0 0 0 ... \n",
"251 0 0 0 0 0 0 0 ... \n",
"252 0 0 0 0 0 0 0 ... \n",
"253 0 0 0 0 0 0 0 ... \n",
"254 0 0 0 0 0 0 0 ... \n",
"255 0 0 0 0 0 0 0 ... \n",
"256 0 0 0 0 0 0 0 ... \n",
"257 0 0 0 0 0 0 0 ... \n",
"258 0 0 0 0 0 0 0 ... \n",
"259 0 0 0 0 0 0 0 ... \n",
"260 0 0 0 0 0 0 0 ... \n",
"261 0 0 0 0 0 0 0 ... \n",
"262 0 0 0 0 0 0 0 ... \n",
"263 0 0 0 0 0 0 0 ... \n",
"264 0 0 0 0 0 0 0 ... \n",
"265 0 0 0 0 0 0 0 ... \n",
"\n",
" 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n",
"0 14525 15205 15750 16509 17267 18054 18969 19551 20342 \n",
"1 1122 1137 1143 1164 1184 1197 1212 1232 1246 \n",
"2 9267 9394 9513 9626 9733 9831 9935 10050 10154 \n",
"3 764 764 765 844 851 852 852 852 852 \n",
"4 84 86 86 86 86 86 86 88 91 \n",
"5 25 26 26 26 26 26 26 26 26 \n",
"6 16214 16851 17415 18319 19268 20197 21037 22020 22794 \n",
"7 8927 9282 9492 10009 10524 11221 11817 12364 13130 \n",
"8 107 107 107 107 107 107 107 108 108 \n",
"9 3095 3098 3104 3104 3106 3110 3110 3109 3112 \n",
"10 29 29 29 29 29 29 29 29 29 \n",
"11 1058 1058 1059 1059 1060 1060 1061 1061 1062 \n",
"12 440 440 440 440 440 440 440 440 440 \n",
"13 228 228 228 228 228 228 228 228 228 \n",
"14 1649 1653 1663 1670 1678 1681 1681 1685 1687 \n",
"15 586 589 591 592 592 592 596 599 599 \n",
"16 16685 16731 16733 16759 16771 16805 16843 16898 16902 \n",
"17 5246 5494 5662 5935 6260 6522 6860 7239 7553 \n",
"18 102 102 102 102 102 102 102 103 103 \n",
"19 10793 11398 11871 12311 12815 13296 13835 14383 14763 \n",
"20 44608 47153 49534 52445 55140 57563 60391 63026 65769 \n",
"21 92 92 92 92 92 92 92 92 92 \n",
"22 41658 42556 43403 44255 45116 45981 46868 47751 48630 \n",
"23 58186 58381 58517 58615 58685 58767 58907 59072 59226 \n",
"24 224 232 243 244 244 261 261 261 261 \n",
"25 33 43 43 47 47 47 48 48 59 \n",
"26 9592 9982 10531 10991 11638 12245 12728 13358 13643 \n",
"27 2494 2510 2524 2535 2551 2594 2606 2606 2606 \n",
"28 498440 514849 526447 555383 584016 614941 645771 672846 691758 \n",
"29 141 141 141 141 141 141 141 141 141 \n",
".. ... ... ... ... ... ... ... ... ... \n",
"236 24 24 24 24 24 24 24 24 24 \n",
"237 18 18 18 18 18 18 19 19 19 \n",
"238 19 19 19 19 19 19 19 19 19 \n",
"239 130 156 168 182 196 209 239 256 256 \n",
"240 447 448 449 451 457 464 464 464 472 \n",
"241 1256 1256 1339 1339 1339 1339 1368 1368 1368 \n",
"242 1250 1265 1315 1351 1386 1461 1485 1523 1533 \n",
"243 15 15 15 15 15 15 15 15 15 \n",
"244 5 5 5 5 5 5 5 5 5 \n",
"245 11 11 11 11 11 11 11 11 11 \n",
"246 1064 1064 1064 1064 1142 1142 1142 1142 1142 \n",
"247 224 224 228 232 233 236 236 240 242 \n",
"248 3 3 3 3 3 3 3 3 3 \n",
"249 8 8 8 8 8 8 8 8 8 \n",
"250 12 12 12 12 12 12 12 12 12 \n",
"251 9 9 9 9 9 9 9 9 9 \n",
"252 35 35 38 40 40 40 40 40 40 \n",
"253 63 63 63 63 63 63 63 83 83 \n",
"254 852 861 865 896 909 914 929 946 969 \n",
"255 6 6 7 7 7 7 7 7 7 \n",
"256 279 284 336 358 369 393 409 409 438 \n",
"257 13 13 13 13 13 13 13 13 13 \n",
"258 1 1 1 1 1 1 1 1 1 \n",
"259 994 994 994 994 994 994 994 994 1317 \n",
"260 9 9 9 9 9 9 9 9 9 \n",
"261 479 483 484 484 484 485 499 499 513 \n",
"262 310 323 354 399 419 453 469 482 484 \n",
"263 106 106 106 132 132 132 132 141 141 \n",
"264 3807 3930 4013 4100 4191 4289 4370 4453 4529 \n",
"265 2 2 2 2 4 4 4 4 4 \n",
"\n",
" 6/8/20 \n",
"0 20917 \n",
"1 1263 \n",
"2 10265 \n",
"3 852 \n",
"4 92 \n",
"5 26 \n",
"6 23620 \n",
"7 13325 \n",
"8 108 \n",
"9 3114 \n",
"10 29 \n",
"11 1062 \n",
"12 440 \n",
"13 228 \n",
"14 1687 \n",
"15 599 \n",
"16 16968 \n",
"17 7876 \n",
"18 103 \n",
"19 15417 \n",
"20 68504 \n",
"21 92 \n",
"22 49453 \n",
"23 59348 \n",
"24 288 \n",
"25 59 \n",
"26 13949 \n",
"27 2704 \n",
"28 707412 \n",
"29 141 \n",
".. ... \n",
"236 24 \n",
"237 19 \n",
"238 19 \n",
"239 332 \n",
"240 473 \n",
"241 1389 \n",
"242 1547 \n",
"243 15 \n",
"244 5 \n",
"245 11 \n",
"246 1263 \n",
"247 244 \n",
"248 3 \n",
"249 8 \n",
"250 12 \n",
"251 9 \n",
"252 42 \n",
"253 83 \n",
"254 1001 \n",
"255 7 \n",
"256 443 \n",
"257 13 \n",
"258 1 \n",
"259 1604 \n",
"260 9 \n",
"261 513 \n",
"262 496 \n",
"263 141 \n",
"264 4609 \n",
"265 4 \n",
"\n",
"[266 rows x 141 columns]\n"
]
}
],
"outputs": [],
"source": [
"df = pd.DataFrame(raw_data)\n",
"\n",
"df_total=df.drop(columns=['Lat', 'Long'])\n",
"df=df_total\n",
"\n",
"print(df)"
"df=df_total"
]
},
{
......@@ -2132,270 +1870,9 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Province/State Country/Region 1/22/20 1/23/20 \\\n",
"23 NaN Belgium 0 0 \n",
"49 Anhui China 1 9 \n",
"50 Beijing China 14 22 \n",
"51 Chongqing China 6 9 \n",
"52 Fujian China 1 5 \n",
"53 Gansu China 0 2 \n",
"54 Guangdong China 26 32 \n",
"55 Guangxi China 2 5 \n",
"56 Guizhou China 1 3 \n",
"57 Hainan China 4 5 \n",
"58 Hebei China 1 1 \n",
"59 Heilongjiang China 0 2 \n",
"60 Henan China 5 5 \n",
"61 Hong Kong China 0 2 \n",
"62 Hubei China 444 444 \n",
"63 Hunan China 4 9 \n",
"64 Inner Mongolia China 0 0 \n",
"65 Jiangsu China 1 5 \n",
"66 Jiangxi China 2 7 \n",
"67 Jilin China 0 1 \n",
"68 Liaoning China 2 3 \n",
"69 Macau China 1 2 \n",
"70 Ningxia China 1 1 \n",
"71 Qinghai China 0 0 \n",
"72 Shaanxi China 0 3 \n",
"73 Shandong China 2 6 \n",
"74 Shanghai China 9 16 \n",
"75 Shanxi China 1 1 \n",
"76 Sichuan China 5 8 \n",
"77 Tianjin China 4 4 \n",
".. ... ... ... ... \n",
"111 New Caledonia France 0 0 \n",
"112 Reunion France 0 0 \n",
"113 Saint Barthelemy France 0 0 \n",
"114 St Martin France 0 0 \n",
"115 Martinique France 0 0 \n",
"116 NaN France 0 0 \n",
"120 NaN Germany 0 0 \n",
"133 NaN Iran 0 0 \n",
"137 NaN Italy 0 0 \n",
"139 NaN Japan 2 2 \n",
"166 Aruba Netherlands 0 0 \n",
"167 Curacao Netherlands 0 0 \n",
"168 Sint Maarten Netherlands 0 0 \n",
"169 NaN Netherlands 0 0 \n",
"184 NaN Portugal 0 0 \n",
"201 NaN Spain 0 0 \n",
"217 Bermuda United Kingdom 0 0 \n",
"218 Cayman Islands United Kingdom 0 0 \n",
"219 Channel Islands United Kingdom 0 0 \n",
"220 Gibraltar United Kingdom 0 0 \n",
"221 Isle of Man United Kingdom 0 0 \n",
"222 Montserrat United Kingdom 0 0 \n",
"223 NaN United Kingdom 0 0 \n",
"225 NaN US 1 1 \n",
"248 Anguilla United Kingdom 0 0 \n",
"249 British Virgin Islands United Kingdom 0 0 \n",
"250 Turks and Caicos Islands United Kingdom 0 0 \n",
"255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n",
"257 Falkland Islands (Malvinas) United Kingdom 0 0 \n",
"258 Saint Pierre and Miquelon France 0 0 \n",
"\n",
" 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n",
"23 0 0 0 0 0 0 ... 58186 \n",
"49 15 39 60 70 106 152 ... 991 \n",
"50 36 41 68 80 91 111 ... 593 \n",
"51 27 57 75 110 132 147 ... 579 \n",
"52 10 18 35 59 80 84 ... 358 \n",
"53 2 4 7 14 19 24 ... 139 \n",
"54 53 78 111 151 207 277 ... 1593 \n",
"55 23 23 36 46 51 58 ... 254 \n",
"56 3 4 5 7 9 9 ... 147 \n",
"57 8 19 22 33 40 43 ... 169 \n",
"58 2 8 13 18 33 48 ... 328 \n",
"59 4 9 15 21 33 38 ... 945 \n",
"60 9 32 83 128 168 206 ... 1276 \n",
"61 2 5 8 8 8 10 ... 1082 \n",
"62 549 761 1058 1423 3554 3554 ... 68135 \n",
"63 24 43 69 100 143 221 ... 1019 \n",
"64 1 7 7 11 15 16 ... 232 \n",
"65 9 18 33 47 70 99 ... 653 \n",
"66 18 18 36 72 109 109 ... 937 \n",
"67 3 4 4 6 8 9 ... 155 \n",
"68 4 17 21 27 34 39 ... 149 \n",
"69 2 2 5 6 7 7 ... 45 \n",
"70 2 3 4 7 11 12 ... 75 \n",
"71 0 1 1 6 6 6 ... 18 \n",
"72 5 15 22 35 46 56 ... 308 \n",
"73 15 27 46 75 95 130 ... 792 \n",
"74 20 33 40 53 66 96 ... 672 \n",
"75 1 6 9 13 27 27 ... 198 \n",
"76 15 28 44 69 90 108 ... 564 \n",
"77 8 10 14 23 24 27 ... 192 \n",
".. ... ... ... ... ... ... ... ... \n",
"111 0 0 0 0 0 0 ... 19 \n",
"112 0 0 0 0 0 0 ... 471 \n",
"113 0 0 0 0 0 0 ... 6 \n",
"114 0 0 0 0 0 0 ... 41 \n",
"115 0 0 0 0 0 0 ... 200 \n",
"116 2 3 3 3 4 5 ... 185616 \n",
"120 0 0 0 1 4 4 ... 183189 \n",
"133 0 0 0 0 0 0 ... 148950 \n",
"137 0 0 0 0 0 0 ... 232664 \n",
"139 2 2 4 4 7 7 ... 16716 \n",
"166 0 0 0 0 0 0 ... 101 \n",
"167 0 0 0 0 0 0 ... 19 \n",
"168 0 0 0 0 0 0 ... 77 \n",
"169 0 0 0 0 0 0 ... 46257 \n",
"184 0 0 0 0 0 0 ... 32203 \n",
"201 0 0 0 0 0 0 ... 239228 \n",
"217 0 0 0 0 0 0 ... 140 \n",
"218 0 0 0 0 0 0 ... 141 \n",
"219 0 0 0 0 0 0 ... 560 \n",
"220 0 0 0 0 0 0 ... 169 \n",
"221 0 0 0 0 0 0 ... 336 \n",
"222 0 0 0 0 0 0 ... 11 \n",
"223 0 0 0 0 0 0 ... 272826 \n",
"225 2 2 5 5 5 5 ... 1770165 \n",
"248 0 0 0 0 0 0 ... 3 \n",
"249 0 0 0 0 0 0 ... 8 \n",
"250 0 0 0 0 0 0 ... 12 \n",
"255 0 0 0 0 0 0 ... 6 \n",
"257 0 0 0 0 0 0 ... 13 \n",
"258 0 0 0 0 0 0 ... 1 \n",
"\n",
" 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n",
"23 58381 58517 58615 58685 58767 58907 59072 59226 \n",
"49 991 991 991 991 991 991 991 991 \n",
"50 593 593 593 594 594 594 594 594 \n",
"51 579 579 579 579 579 579 579 579 \n",
"52 358 358 358 358 358 358 359 359 \n",
"53 139 139 139 139 139 139 139 139 \n",
"54 1595 1596 1597 1598 1598 1601 1602 1602 \n",
"55 254 254 254 254 254 254 254 254 \n",
"56 147 147 147 147 147 147 147 147 \n",
"57 169 169 169 169 169 169 170 170 \n",
"58 328 328 328 328 328 328 328 328 \n",
"59 945 945 945 947 947 947 947 947 \n",
"60 1276 1276 1276 1276 1276 1276 1276 1276 \n",
"61 1084 1087 1093 1093 1099 1102 1105 1106 \n",
"62 68135 68135 68135 68135 68135 68135 68135 68135 \n",
"63 1019 1019 1019 1019 1019 1019 1019 1019 \n",
"64 235 235 235 235 235 235 235 235 \n",
"65 653 653 653 653 653 653 653 653 \n",
"66 937 937 937 932 932 932 932 932 \n",
"67 155 155 155 155 155 155 155 155 \n",
"68 149 149 149 149 149 149 149 149 \n",
"69 45 45 45 45 45 45 45 45 \n",
"70 75 75 75 75 75 75 75 75 \n",
"71 18 18 18 18 18 18 18 18 \n",
"72 308 309 309 309 309 309 311 311 \n",
"73 792 792 792 792 792 792 792 792 \n",
"74 672 673 673 673 677 677 677 678 \n",
"75 198 198 198 198 198 198 198 198 \n",
"76 575 577 577 577 578 578 578 581 \n",
"77 192 192 192 192 192 192 193 193 \n",
".. ... ... ... ... ... ... ... ... \n",
"111 19 20 20 20 20 20 20 20 \n",
"112 471 473 477 478 479 480 480 480 \n",
"113 6 6 6 6 6 6 6 6 \n",
"114 41 41 41 41 41 41 41 41 \n",
"115 200 200 200 200 200 202 202 202 \n",
"116 185851 185952 184980 188836 185986 186538 187067 187360 \n",
"120 183410 183594 183879 184121 184472 184924 185450 185750 \n",
"133 151466 154445 157562 160696 164270 167156 169425 171789 \n",
"137 232997 233197 233515 233836 234013 234531 234801 234998 \n",
"139 16751 16787 16837 16867 16911 16958 17000 17039 \n",
"166 101 101 101 101 101 101 101 101 \n",
"167 19 19 20 21 21 21 21 21 \n",
"168 77 77 77 77 77 77 77 77 \n",
"169 46442 46545 46647 46733 46942 47152 47335 47574 \n",
"184 32500 32700 32895 33261 33592 33969 34351 34693 \n",
"201 239479 239638 239932 240326 240660 240978 241310 241550 \n",
"217 140 141 141 141 141 141 141 141 \n",
"218 141 150 151 156 160 164 164 164 \n",
"219 560 560 560 561 561 561 563 563 \n",
"220 170 170 172 173 173 174 175 176 \n",
"221 336 336 336 336 336 336 336 336 \n",
"222 11 11 11 11 11 11 11 11 \n",
"223 274762 276332 277985 279856 281661 283311 284868 286194 \n",
"225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n",
"248 3 3 3 3 3 3 3 3 \n",
"249 8 8 8 8 8 8 8 8 \n",
"250 12 12 12 12 12 12 12 12 \n",
"255 6 7 7 7 7 7 7 7 \n",
"257 13 13 13 13 13 13 13 13 \n",
"258 1 1 1 1 1 1 1 1 \n",
"\n",
" 6/8/20 \n",
"23 59348 \n",
"49 991 \n",
"50 594 \n",
"51 579 \n",
"52 359 \n",
"53 139 \n",
"54 1604 \n",
"55 254 \n",
"56 147 \n",
"57 170 \n",
"58 328 \n",
"59 947 \n",
"60 1276 \n",
"61 1107 \n",
"62 68135 \n",
"63 1019 \n",
"64 235 \n",
"65 653 \n",
"66 932 \n",
"67 155 \n",
"68 149 \n",
"69 45 \n",
"70 75 \n",
"71 18 \n",
"72 311 \n",
"73 792 \n",
"74 678 \n",
"75 198 \n",
"76 582 \n",
"77 193 \n",
".. ... \n",
"111 20 \n",
"112 481 \n",
"113 6 \n",
"114 41 \n",
"115 202 \n",
"116 187458 \n",
"120 186109 \n",
"133 173832 \n",
"137 235278 \n",
"139 17060 \n",
"166 101 \n",
"167 21 \n",
"168 77 \n",
"169 47739 \n",
"184 34885 \n",
"201 241717 \n",
"217 141 \n",
"218 171 \n",
"219 564 \n",
"220 176 \n",
"221 336 \n",
"222 11 \n",
"223 287399 \n",
"225 1960897 \n",
"248 3 \n",
"249 8 \n",
"250 12 \n",
"255 7 \n",
"257 13 \n",
"258 1 \n",
"\n",
"[68 rows x 141 columns]\n"
]
}
],
"outputs": [],
"source": [
"df=df.drop(df[(df['Country/Region'] != 'Belgium') & (df['Country/Region'] != 'China') & (df['Country/Region'] != 'France') & (df['Country/Region'] != 'Germany') & (df['Country/Region'] != 'Iran') & (df['Country/Region'] != 'Italy') & (df['Country/Region'] != 'Japan') & (df['Country/Region'] != 'Korea South') & (df['Country/Region'] != 'Netherlands') & (df['Country/Region'] != 'Portugal') & (df['Country/Region'] != 'Spain') & (df['Country/Region'] != 'United Kingdom') & (df['Country/Region'] != 'US')].index)\n",
"print(df)"
"df=df.drop(df[(df['Country/Region'] != 'Belgium') & (df['Country/Region'] != 'China') & (df['Country/Region'] != 'France') & (df['Country/Region'] != 'Germany') & (df['Country/Region'] != 'Iran') & (df['Country/Region'] != 'Italy') & (df['Country/Region'] != 'Japan') & (df['Country/Region'] != 'Korea South') & (df['Country/Region'] != 'Netherlands') & (df['Country/Region'] != 'Portugal') & (df['Country/Region'] != 'Spain') & (df['Country/Region'] != 'United Kingdom') & (df['Country/Region'] != 'US')].index)\n"
]
},
{
......@@ -2410,265 +1887,6 @@
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Province/State Country/Region 1/22/20 1/23/20 \\\n",
"23 NaN Belgium 0 0 \n",
"49 Anhui China 1 9 \n",
"50 Beijing China 14 22 \n",
"51 Chongqing China 6 9 \n",
"52 Fujian China 1 5 \n",
"53 Gansu China 0 2 \n",
"54 Guangdong China 26 32 \n",
"55 Guangxi China 2 5 \n",
"56 Guizhou China 1 3 \n",
"57 Hainan China 4 5 \n",
"58 Hebei China 1 1 \n",
"59 Heilongjiang China 0 2 \n",
"60 Henan China 5 5 \n",
"61 Hong Kong Hong Kong 0 2 \n",
"62 Hubei China 444 444 \n",
"63 Hunan China 4 9 \n",
"64 Inner Mongolia China 0 0 \n",
"65 Jiangsu China 1 5 \n",
"66 Jiangxi China 2 7 \n",
"67 Jilin China 0 1 \n",
"68 Liaoning China 2 3 \n",
"69 Macau China 1 2 \n",
"70 Ningxia China 1 1 \n",
"71 Qinghai China 0 0 \n",
"72 Shaanxi China 0 3 \n",
"73 Shandong China 2 6 \n",
"74 Shanghai China 9 16 \n",
"75 Shanxi China 1 1 \n",
"76 Sichuan China 5 8 \n",
"77 Tianjin China 4 4 \n",
".. ... ... ... ... \n",
"111 New Caledonia France 0 0 \n",
"112 Reunion France 0 0 \n",
"113 Saint Barthelemy France 0 0 \n",
"114 St Martin France 0 0 \n",
"115 Martinique France 0 0 \n",
"116 NaN France 0 0 \n",
"120 NaN Germany 0 0 \n",
"133 NaN Iran 0 0 \n",
"137 NaN Italy 0 0 \n",
"139 NaN Japan 2 2 \n",
"166 Aruba Netherlands 0 0 \n",
"167 Curacao Netherlands 0 0 \n",
"168 Sint Maarten Netherlands 0 0 \n",
"169 NaN Netherlands 0 0 \n",
"184 NaN Portugal 0 0 \n",
"201 NaN Spain 0 0 \n",
"217 Bermuda United Kingdom 0 0 \n",
"218 Cayman Islands United Kingdom 0 0 \n",
"219 Channel Islands United Kingdom 0 0 \n",
"220 Gibraltar United Kingdom 0 0 \n",
"221 Isle of Man United Kingdom 0 0 \n",
"222 Montserrat United Kingdom 0 0 \n",
"223 NaN United Kingdom 0 0 \n",
"225 NaN US 1 1 \n",
"248 Anguilla United Kingdom 0 0 \n",
"249 British Virgin Islands United Kingdom 0 0 \n",
"250 Turks and Caicos Islands United Kingdom 0 0 \n",
"255 Bonaire, Sint Eustatius and Saba Netherlands 0 0 \n",
"257 Falkland Islands (Malvinas) United Kingdom 0 0 \n",
"258 Saint Pierre and Miquelon France 0 0 \n",
"\n",
" 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 \\\n",
"23 0 0 0 0 0 0 ... 58186 \n",
"49 15 39 60 70 106 152 ... 991 \n",
"50 36 41 68 80 91 111 ... 593 \n",
"51 27 57 75 110 132 147 ... 579 \n",
"52 10 18 35 59 80 84 ... 358 \n",
"53 2 4 7 14 19 24 ... 139 \n",
"54 53 78 111 151 207 277 ... 1593 \n",
"55 23 23 36 46 51 58 ... 254 \n",
"56 3 4 5 7 9 9 ... 147 \n",
"57 8 19 22 33 40 43 ... 169 \n",
"58 2 8 13 18 33 48 ... 328 \n",
"59 4 9 15 21 33 38 ... 945 \n",
"60 9 32 83 128 168 206 ... 1276 \n",
"61 2 5 8 8 8 10 ... 1082 \n",
"62 549 761 1058 1423 3554 3554 ... 68135 \n",
"63 24 43 69 100 143 221 ... 1019 \n",
"64 1 7 7 11 15 16 ... 232 \n",
"65 9 18 33 47 70 99 ... 653 \n",
"66 18 18 36 72 109 109 ... 937 \n",
"67 3 4 4 6 8 9 ... 155 \n",
"68 4 17 21 27 34 39 ... 149 \n",
"69 2 2 5 6 7 7 ... 45 \n",
"70 2 3 4 7 11 12 ... 75 \n",
"71 0 1 1 6 6 6 ... 18 \n",
"72 5 15 22 35 46 56 ... 308 \n",
"73 15 27 46 75 95 130 ... 792 \n",
"74 20 33 40 53 66 96 ... 672 \n",
"75 1 6 9 13 27 27 ... 198 \n",
"76 15 28 44 69 90 108 ... 564 \n",
"77 8 10 14 23 24 27 ... 192 \n",
".. ... ... ... ... ... ... ... ... \n",
"111 0 0 0 0 0 0 ... 19 \n",
"112 0 0 0 0 0 0 ... 471 \n",
"113 0 0 0 0 0 0 ... 6 \n",
"114 0 0 0 0 0 0 ... 41 \n",
"115 0 0 0 0 0 0 ... 200 \n",
"116 2 3 3 3 4 5 ... 185616 \n",
"120 0 0 0 1 4 4 ... 183189 \n",
"133 0 0 0 0 0 0 ... 148950 \n",
"137 0 0 0 0 0 0 ... 232664 \n",
"139 2 2 4 4 7 7 ... 16716 \n",
"166 0 0 0 0 0 0 ... 101 \n",
"167 0 0 0 0 0 0 ... 19 \n",
"168 0 0 0 0 0 0 ... 77 \n",
"169 0 0 0 0 0 0 ... 46257 \n",
"184 0 0 0 0 0 0 ... 32203 \n",
"201 0 0 0 0 0 0 ... 239228 \n",
"217 0 0 0 0 0 0 ... 140 \n",
"218 0 0 0 0 0 0 ... 141 \n",
"219 0 0 0 0 0 0 ... 560 \n",
"220 0 0 0 0 0 0 ... 169 \n",
"221 0 0 0 0 0 0 ... 336 \n",
"222 0 0 0 0 0 0 ... 11 \n",
"223 0 0 0 0 0 0 ... 272826 \n",
"225 2 2 5 5 5 5 ... 1770165 \n",
"248 0 0 0 0 0 0 ... 3 \n",
"249 0 0 0 0 0 0 ... 8 \n",
"250 0 0 0 0 0 0 ... 12 \n",
"255 0 0 0 0 0 0 ... 6 \n",
"257 0 0 0 0 0 0 ... 13 \n",
"258 0 0 0 0 0 0 ... 1 \n",
"\n",
" 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n",
"23 58381 58517 58615 58685 58767 58907 59072 59226 \n",
"49 991 991 991 991 991 991 991 991 \n",
"50 593 593 593 594 594 594 594 594 \n",
"51 579 579 579 579 579 579 579 579 \n",
"52 358 358 358 358 358 358 359 359 \n",
"53 139 139 139 139 139 139 139 139 \n",
"54 1595 1596 1597 1598 1598 1601 1602 1602 \n",
"55 254 254 254 254 254 254 254 254 \n",
"56 147 147 147 147 147 147 147 147 \n",
"57 169 169 169 169 169 169 170 170 \n",
"58 328 328 328 328 328 328 328 328 \n",
"59 945 945 945 947 947 947 947 947 \n",
"60 1276 1276 1276 1276 1276 1276 1276 1276 \n",
"61 1084 1087 1093 1093 1099 1102 1105 1106 \n",
"62 68135 68135 68135 68135 68135 68135 68135 68135 \n",
"63 1019 1019 1019 1019 1019 1019 1019 1019 \n",
"64 235 235 235 235 235 235 235 235 \n",
"65 653 653 653 653 653 653 653 653 \n",
"66 937 937 937 932 932 932 932 932 \n",
"67 155 155 155 155 155 155 155 155 \n",
"68 149 149 149 149 149 149 149 149 \n",
"69 45 45 45 45 45 45 45 45 \n",
"70 75 75 75 75 75 75 75 75 \n",
"71 18 18 18 18 18 18 18 18 \n",
"72 308 309 309 309 309 309 311 311 \n",
"73 792 792 792 792 792 792 792 792 \n",
"74 672 673 673 673 677 677 677 678 \n",
"75 198 198 198 198 198 198 198 198 \n",
"76 575 577 577 577 578 578 578 581 \n",
"77 192 192 192 192 192 192 193 193 \n",
".. ... ... ... ... ... ... ... ... \n",
"111 19 20 20 20 20 20 20 20 \n",
"112 471 473 477 478 479 480 480 480 \n",
"113 6 6 6 6 6 6 6 6 \n",
"114 41 41 41 41 41 41 41 41 \n",
"115 200 200 200 200 200 202 202 202 \n",
"116 185851 185952 184980 188836 185986 186538 187067 187360 \n",
"120 183410 183594 183879 184121 184472 184924 185450 185750 \n",
"133 151466 154445 157562 160696 164270 167156 169425 171789 \n",
"137 232997 233197 233515 233836 234013 234531 234801 234998 \n",
"139 16751 16787 16837 16867 16911 16958 17000 17039 \n",
"166 101 101 101 101 101 101 101 101 \n",
"167 19 19 20 21 21 21 21 21 \n",
"168 77 77 77 77 77 77 77 77 \n",
"169 46442 46545 46647 46733 46942 47152 47335 47574 \n",
"184 32500 32700 32895 33261 33592 33969 34351 34693 \n",
"201 239479 239638 239932 240326 240660 240978 241310 241550 \n",
"217 140 141 141 141 141 141 141 141 \n",
"218 141 150 151 156 160 164 164 164 \n",
"219 560 560 560 561 561 561 563 563 \n",
"220 170 170 172 173 173 174 175 176 \n",
"221 336 336 336 336 336 336 336 336 \n",
"222 11 11 11 11 11 11 11 11 \n",
"223 274762 276332 277985 279856 281661 283311 284868 286194 \n",
"225 1790172 1811020 1831821 1851520 1872660 1897380 1920061 1943647 \n",
"248 3 3 3 3 3 3 3 3 \n",
"249 8 8 8 8 8 8 8 8 \n",
"250 12 12 12 12 12 12 12 12 \n",
"255 6 7 7 7 7 7 7 7 \n",
"257 13 13 13 13 13 13 13 13 \n",
"258 1 1 1 1 1 1 1 1 \n",
"\n",
" 6/8/20 \n",
"23 59348 \n",
"49 991 \n",
"50 594 \n",
"51 579 \n",
"52 359 \n",
"53 139 \n",
"54 1604 \n",
"55 254 \n",
"56 147 \n",
"57 170 \n",
"58 328 \n",
"59 947 \n",
"60 1276 \n",
"61 1107 \n",
"62 68135 \n",
"63 1019 \n",
"64 235 \n",
"65 653 \n",
"66 932 \n",
"67 155 \n",
"68 149 \n",
"69 45 \n",
"70 75 \n",
"71 18 \n",
"72 311 \n",
"73 792 \n",
"74 678 \n",
"75 198 \n",
"76 582 \n",
"77 193 \n",
".. ... \n",
"111 20 \n",
"112 481 \n",
"113 6 \n",
"114 41 \n",
"115 202 \n",
"116 187458 \n",
"120 186109 \n",
"133 173832 \n",
"137 235278 \n",
"139 17060 \n",
"166 101 \n",
"167 21 \n",
"168 77 \n",
"169 47739 \n",
"184 34885 \n",
"201 241717 \n",
"217 141 \n",
"218 171 \n",
"219 564 \n",
"220 176 \n",
"221 336 \n",
"222 11 \n",
"223 287399 \n",
"225 1960897 \n",
"248 3 \n",
"249 8 \n",
"250 12 \n",
"255 7 \n",
"257 13 \n",
"258 1 \n",
"\n",
"[68 rows x 141 columns]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
......@@ -2679,8 +1897,7 @@
}
],
"source": [
"df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n",
"print(df)"
"df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n"
]
},
{
......@@ -2694,153 +1911,7 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
"23 NaN Belgium 0 0 0 0 \n",
"49 Anhui China 1 9 15 39 \n",
"50 Beijing China 14 22 36 41 \n",
"51 Chongqing China 6 9 27 57 \n",
"52 Fujian China 1 5 10 18 \n",
"53 Gansu China 0 2 2 4 \n",
"54 Guangdong China 26 32 53 78 \n",
"55 Guangxi China 2 5 23 23 \n",
"56 Guizhou China 1 3 3 4 \n",
"57 Hainan China 4 5 8 19 \n",
"58 Hebei China 1 1 2 8 \n",
"59 Heilongjiang China 0 2 4 9 \n",
"60 Henan China 5 5 9 32 \n",
"61 Hong Kong Hong Kong 0 2 2 5 \n",
"62 Hubei China 444 444 549 761 \n",
"63 Hunan China 4 9 24 43 \n",
"64 Inner Mongolia China 0 0 1 7 \n",
"65 Jiangsu China 1 5 9 18 \n",
"66 Jiangxi China 2 7 18 18 \n",
"67 Jilin China 0 1 3 4 \n",
"68 Liaoning China 2 3 4 17 \n",
"69 Macau China 1 2 2 2 \n",
"70 Ningxia China 1 1 2 3 \n",
"71 Qinghai China 0 0 0 1 \n",
"72 Shaanxi China 0 3 5 15 \n",
"73 Shandong China 2 6 15 27 \n",
"74 Shanghai China 9 16 20 33 \n",
"75 Shanxi China 1 1 1 6 \n",
"76 Sichuan China 5 8 15 28 \n",
"77 Tianjin China 4 4 8 10 \n",
"78 Tibet China 0 0 0 0 \n",
"79 Xinjiang China 0 2 2 3 \n",
"80 Yunnan China 1 2 5 11 \n",
"81 Zhejiang China 10 27 43 62 \n",
"116 NaN France 0 0 2 3 \n",
"120 NaN Germany 0 0 0 0 \n",
"133 NaN Iran 0 0 0 0 \n",
"137 NaN Italy 0 0 0 0 \n",
"139 NaN Japan 2 2 2 2 \n",
"169 NaN Netherlands 0 0 0 0 \n",
"184 NaN Portugal 0 0 0 0 \n",
"201 NaN Spain 0 0 0 0 \n",
"223 NaN United Kingdom 0 0 0 0 \n",
"225 NaN US 1 1 2 2 \n",
"\n",
" 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/30/20 5/31/20 6/1/20 \\\n",
"23 0 0 0 0 ... 58186 58381 58517 \n",
"49 60 70 106 152 ... 991 991 991 \n",
"50 68 80 91 111 ... 593 593 593 \n",
"51 75 110 132 147 ... 579 579 579 \n",
"52 35 59 80 84 ... 358 358 358 \n",
"53 7 14 19 24 ... 139 139 139 \n",
"54 111 151 207 277 ... 1593 1595 1596 \n",
"55 36 46 51 58 ... 254 254 254 \n",
"56 5 7 9 9 ... 147 147 147 \n",
"57 22 33 40 43 ... 169 169 169 \n",
"58 13 18 33 48 ... 328 328 328 \n",
"59 15 21 33 38 ... 945 945 945 \n",
"60 83 128 168 206 ... 1276 1276 1276 \n",
"61 8 8 8 10 ... 1082 1084 1087 \n",
"62 1058 1423 3554 3554 ... 68135 68135 68135 \n",
"63 69 100 143 221 ... 1019 1019 1019 \n",
"64 7 11 15 16 ... 232 235 235 \n",
"65 33 47 70 99 ... 653 653 653 \n",
"66 36 72 109 109 ... 937 937 937 \n",
"67 4 6 8 9 ... 155 155 155 \n",
"68 21 27 34 39 ... 149 149 149 \n",
"69 5 6 7 7 ... 45 45 45 \n",
"70 4 7 11 12 ... 75 75 75 \n",
"71 1 6 6 6 ... 18 18 18 \n",
"72 22 35 46 56 ... 308 308 309 \n",
"73 46 75 95 130 ... 792 792 792 \n",
"74 40 53 66 96 ... 672 672 673 \n",
"75 9 13 27 27 ... 198 198 198 \n",
"76 44 69 90 108 ... 564 575 577 \n",
"77 14 23 24 27 ... 192 192 192 \n",
"78 0 0 0 0 ... 1 1 1 \n",
"79 4 5 10 13 ... 76 76 76 \n",
"80 16 26 44 55 ... 185 185 185 \n",
"81 104 128 173 296 ... 1268 1268 1268 \n",
"116 3 3 4 5 ... 185616 185851 185952 \n",
"120 0 1 4 4 ... 183189 183410 183594 \n",
"133 0 0 0 0 ... 148950 151466 154445 \n",
"137 0 0 0 0 ... 232664 232997 233197 \n",
"139 4 4 7 7 ... 16716 16751 16787 \n",
"169 0 0 0 0 ... 46257 46442 46545 \n",
"184 0 0 0 0 ... 32203 32500 32700 \n",
"201 0 0 0 0 ... 239228 239479 239638 \n",
"223 0 0 0 0 ... 272826 274762 276332 \n",
"225 5 5 5 5 ... 1770165 1790172 1811020 \n",
"\n",
" 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n",
"23 58615 58685 58767 58907 59072 59226 59348 \n",
"49 991 991 991 991 991 991 991 \n",
"50 593 594 594 594 594 594 594 \n",
"51 579 579 579 579 579 579 579 \n",
"52 358 358 358 358 359 359 359 \n",
"53 139 139 139 139 139 139 139 \n",
"54 1597 1598 1598 1601 1602 1602 1604 \n",
"55 254 254 254 254 254 254 254 \n",
"56 147 147 147 147 147 147 147 \n",
"57 169 169 169 169 170 170 170 \n",
"58 328 328 328 328 328 328 328 \n",
"59 945 947 947 947 947 947 947 \n",
"60 1276 1276 1276 1276 1276 1276 1276 \n",
"61 1093 1093 1099 1102 1105 1106 1107 \n",
"62 68135 68135 68135 68135 68135 68135 68135 \n",
"63 1019 1019 1019 1019 1019 1019 1019 \n",
"64 235 235 235 235 235 235 235 \n",
"65 653 653 653 653 653 653 653 \n",
"66 937 932 932 932 932 932 932 \n",
"67 155 155 155 155 155 155 155 \n",
"68 149 149 149 149 149 149 149 \n",
"69 45 45 45 45 45 45 45 \n",
"70 75 75 75 75 75 75 75 \n",
"71 18 18 18 18 18 18 18 \n",
"72 309 309 309 309 311 311 311 \n",
"73 792 792 792 792 792 792 792 \n",
"74 673 673 677 677 677 678 678 \n",
"75 198 198 198 198 198 198 198 \n",
"76 577 577 578 578 578 581 582 \n",
"77 192 192 192 192 193 193 193 \n",
"78 1 1 1 1 1 1 1 \n",
"79 76 76 76 76 76 76 76 \n",
"80 185 185 185 185 185 185 185 \n",
"81 1268 1268 1268 1268 1268 1268 1268 \n",
"116 184980 188836 185986 186538 187067 187360 187458 \n",
"120 183879 184121 184472 184924 185450 185750 186109 \n",
"133 157562 160696 164270 167156 169425 171789 173832 \n",
"137 233515 233836 234013 234531 234801 234998 235278 \n",
"139 16837 16867 16911 16958 17000 17039 17060 \n",
"169 46647 46733 46942 47152 47335 47574 47739 \n",
"184 32895 33261 33592 33969 34351 34693 34885 \n",
"201 239932 240326 240660 240978 241310 241550 241717 \n",
"223 277985 279856 281661 283311 284868 286194 287399 \n",
"225 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n",
"\n",
"[44 rows x 141 columns]\n"
]
}
],
"outputs": [],
"source": [
"fr=df[(df['Country/Region']=='France')]\n",
"fr=fr['Province/State']\n",
......@@ -2856,10 +1927,7 @@
"\n",
"df=df.drop(fr.index)\n",
"df=df.drop(ne.index)\n",
"df=df.drop(uk.index)\n",
"\n",
"\n",
"print(df)\n"
"df=df.drop(uk.index)\n"
]
},
{
......@@ -2873,68 +1941,11 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n",
"Country/Region \n",
"Belgium 0 0 0 0 0 0 0 \n",
"China 548 641 918 1401 2067 2869 5501 \n",
"France 0 0 2 3 3 3 4 \n",
"Germany 0 0 0 0 0 1 4 \n",
"Hong Kong 0 2 2 5 8 8 8 \n",
"Iran 0 0 0 0 0 0 0 \n",
"Italy 0 0 0 0 0 0 0 \n",
"Japan 2 2 2 2 4 4 7 \n",
"Netherlands 0 0 0 0 0 0 0 \n",
"Portugal 0 0 0 0 0 0 0 \n",
"Spain 0 0 0 0 0 0 0 \n",
"US 1 1 2 2 5 5 5 \n",
"United Kingdom 0 0 0 0 0 0 0 \n",
"\n",
" 1/29/20 1/30/20 1/31/20 ... 5/30/20 5/31/20 6/1/20 \\\n",
"Country/Region ... \n",
"Belgium 0 0 0 ... 58186 58381 58517 \n",
"China 6077 8131 9790 ... 83046 83062 83067 \n",
"France 5 5 5 ... 185616 185851 185952 \n",
"Germany 4 4 5 ... 183189 183410 183594 \n",
"Hong Kong 10 10 12 ... 1082 1084 1087 \n",
"Iran 0 0 0 ... 148950 151466 154445 \n",
"Italy 0 0 2 ... 232664 232997 233197 \n",
"Japan 7 11 15 ... 16716 16751 16787 \n",
"Netherlands 0 0 0 ... 46257 46442 46545 \n",
"Portugal 0 0 0 ... 32203 32500 32700 \n",
"Spain 0 0 0 ... 239228 239479 239638 \n",
"US 5 5 7 ... 1770165 1790172 1811020 \n",
"United Kingdom 0 0 2 ... 272826 274762 276332 \n",
"\n",
" 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 \n",
"Country/Region \n",
"Belgium 58615 58685 58767 58907 59072 59226 59348 \n",
"China 83068 83067 83072 83075 83081 83085 83088 \n",
"France 184980 188836 185986 186538 187067 187360 187458 \n",
"Germany 183879 184121 184472 184924 185450 185750 186109 \n",
"Hong Kong 1093 1093 1099 1102 1105 1106 1107 \n",
"Iran 157562 160696 164270 167156 169425 171789 173832 \n",
"Italy 233515 233836 234013 234531 234801 234998 235278 \n",
"Japan 16837 16867 16911 16958 17000 17039 17060 \n",
"Netherlands 46647 46733 46942 47152 47335 47574 47739 \n",
"Portugal 32895 33261 33592 33969 34351 34693 34885 \n",
"Spain 239932 240326 240660 240978 241310 241550 241717 \n",
"US 1831821 1851520 1872660 1897380 1920061 1943647 1960897 \n",
"United Kingdom 277985 279856 281661 283311 284868 286194 287399 \n",
"\n",
"[13 rows x 139 columns]\n"
]
}
],
"outputs": [],
"source": [
"df.drop('Province/State', axis = 1, inplace = True)\n",
"grouped=df.groupby('Country/Region')\n",
"df=grouped.sum()\n",
"print(df)"
"df=grouped.sum()"
]
},
{
......
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