{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The SARS-CoV-2 (Covid-19) epidemic analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path\n", "from os import path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data on the Covid-19 incidence are available [here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv). We download them as a file in CSV format." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data downloaded on 09.06.2020\n", "\n", "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Column name | Description |\n", "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", "| `Province/State` | Province/State |\n", "| `Country/Region` | Country/Region |\n", "| `Lat` | Latitude |\n", "| `Long` | Longitude |\n", "| `1/22/20` | Dates |" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/20...5/30/205/31/206/1/206/2/206/3/206/4/206/5/206/6/206/7/206/8/20
00NaNAfghanistan33.00000065.00000000000...14525152051575016509172671805418969195512034220917
11NaNAlbania41.15330020.16830000000...1122113711431164118411971212123212461263
22NaNAlgeria28.0339001.65960000000...9267939495139626973398319935100501015410265
33NaNAndorra42.5063001.52180000000...764764765844851852852852852852
44NaNAngola-11.20270017.87390000000...84868686868686889192
55NaNAntigua and Barbuda17.060800-61.79640000000...25262626262626262626
66NaNArgentina-38.416100-63.61670000000...16214168511741518319192682019721037220202279423620
77NaNArmenia40.06910045.03820000000...89279282949210009105241122111817123641313013325
88Australian Capital TerritoryAustralia-35.473500149.01240000000...107107107107107107107108108108
99New South WalesAustralia-33.868800151.20930000003...3095309831043104310631103110310931123114
1010Northern TerritoryAustralia-12.463400130.84560000000...29292929292929292929
1111QueenslandAustralia-28.016700153.40000000000...1058105810591059106010601061106110621062
1212South AustraliaAustralia-34.928500138.60070000000...440440440440440440440440440440
1313TasmaniaAustralia-41.454500145.97070000000...228228228228228228228228228228
1414VictoriaAustralia-37.813600144.96310000001...1649165316631670167816811681168516871687
1515Western AustraliaAustralia-31.950500115.86050000000...586589591592592592596599599599
1616NaNAustria47.51620014.55010000000...16685167311673316759167711680516843168981690216968
1717NaNAzerbaijan40.14310047.57690000000...5246549456625935626065226860723975537876
1818NaNBahamas25.034300-77.39630000000...102102102102102102102103103103
1919NaNBahrain26.02750050.55000000000...10793113981187112311128151329613835143831476315417
2020NaNBangladesh23.68500090.35630000000...44608471534953452445551405756360391630266576968504
2121NaNBarbados13.193900-59.54320000000...92929292929292929292
2222NaNBelarus53.70980027.95340000000...41658425564340344255451164598146868477514863049453
2323NaNBelgium50.8333004.00000000000...58186583815851758615586855876758907590725922659348
2424NaNBenin9.3077002.31580000000...224232243244244261261261261288
2525NaNBhutan27.51420090.43360000000...33434347474748485959
2626NaNBolivia-16.290200-63.58870000000...959299821053110991116381224512728133581364313949
2727NaNBosnia and Herzegovina43.91590017.67910000000...2494251025242535255125942606260626062704
2828NaNBrazil-14.235000-51.92530000000...498440514849526447555383584016614941645771672846691758707412
2929NaNBrunei4.535300114.72770000000...141141141141141141141141141141
..................................................................
236236NaNTimor-Leste-8.874217125.72753900000...24242424242424242424
237237NaNBelize13.193900-59.54320000000...18181818181819191919
238238NaNLaos19.856270102.49549600000...19191919191919191919
239239NaNLibya26.33510017.22833100000...130156168182196209239256256332
240240NaNWest Bank and Gaza31.95220035.23320000000...447448449451457464464464472473
241241NaNGuinea-Bissau11.803700-15.18040000000...1256125613391339133913391368136813681389
242242NaNMali17.570692-3.99616600000...1250126513151351138614611485152315331547
243243NaNSaint Kitts and Nevis17.357822-62.78299800000...15151515151515151515
244244Northwest TerritoriesCanada64.825500-124.84570000000...5555555555
245245YukonCanada64.282300-135.00000000000...11111111111111111111
246246NaNKosovo42.60263620.90297700000...1064106410641064114211421142114211421263
247247NaNBurma21.91620095.95600000000...224224228232233236236240242244
248248AnguillaUnited Kingdom18.220600-63.06860000000...3333333333
249249British Virgin IslandsUnited Kingdom18.420700-64.64000000000...8888888888
250250Turks and Caicos IslandsUnited Kingdom21.694000-71.79790000000...12121212121212121212
251251NaNMS Zaandam0.0000000.00000000000...9999999999
252252NaNBotswana-22.32850024.68490000000...35353840404040404042
253253NaNBurundi-3.37310029.91890000000...63636363636363838383
254254NaNSierra Leone8.460555-11.77988900000...8528618658969099149299469691001
255255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.23850000000...6677777777
256256NaNMalawi-13.25430834.30152500000...279284336358369393409409438443
257257Falkland Islands (Malvinas)United Kingdom-51.796300-59.52360000000...13131313131313131313
258258Saint Pierre and MiquelonFrance46.885200-56.31590000000...1111111111
259259NaNSouth Sudan6.87700031.30700000000...99499499499499499499499413171604
260260NaNWestern Sahara24.215500-12.88580000000...9999999999
261261NaNSao Tome and Principe0.1863606.61308100000...479483484484484485499499513513
262262NaNYemen15.55272748.51638800000...310323354399419453469482484496
263263NaNComoros-11.64550043.33330000000...106106106132132132132141141141
264264NaNTajikistan38.86103471.27609300000...3807393040134100419142894370445345294609
265265NaNLesotho-29.60998828.23360800000...2222444444
\n", "

266 rows × 144 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 Province/State Country/Region \\\n", "0 0 NaN Afghanistan \n", "1 1 NaN Albania \n", "2 2 NaN Algeria \n", "3 3 NaN Andorra \n", "4 4 NaN Angola \n", "5 5 NaN Antigua and Barbuda \n", "6 6 NaN Argentina \n", "7 7 NaN Armenia \n", "8 8 Australian Capital Territory Australia \n", "9 9 New South Wales Australia \n", "10 10 Northern Territory Australia \n", "11 11 Queensland Australia \n", "12 12 South Australia Australia \n", "13 13 Tasmania Australia \n", "14 14 Victoria Australia \n", "15 15 Western Australia Australia \n", "16 16 NaN Austria \n", "17 17 NaN Azerbaijan \n", "18 18 NaN Bahamas \n", "19 19 NaN Bahrain \n", "20 20 NaN Bangladesh \n", "21 21 NaN Barbados \n", "22 22 NaN Belarus \n", "23 23 NaN Belgium \n", "24 24 NaN Benin \n", "25 25 NaN Bhutan \n", "26 26 NaN Bolivia \n", "27 27 NaN Bosnia and Herzegovina \n", "28 28 NaN Brazil \n", "29 29 NaN Brunei \n", ".. ... ... ... \n", "236 236 NaN Timor-Leste \n", "237 237 NaN Belize \n", "238 238 NaN Laos \n", "239 239 NaN Libya \n", "240 240 NaN West Bank and Gaza \n", "241 241 NaN Guinea-Bissau \n", "242 242 NaN Mali \n", "243 243 NaN Saint Kitts and Nevis \n", "244 244 Northwest Territories Canada \n", "245 245 Yukon Canada \n", "246 246 NaN Kosovo \n", "247 247 NaN Burma \n", "248 248 Anguilla United Kingdom \n", "249 249 British Virgin Islands United Kingdom \n", "250 250 Turks and Caicos Islands United Kingdom \n", "251 251 NaN MS Zaandam \n", "252 252 NaN Botswana \n", "253 253 NaN Burundi \n", "254 254 NaN Sierra Leone \n", "255 255 Bonaire, Sint Eustatius and Saba Netherlands \n", "256 256 NaN Malawi \n", "257 257 Falkland Islands (Malvinas) United Kingdom \n", "258 258 Saint Pierre and Miquelon France \n", "259 259 NaN South Sudan \n", "260 260 NaN Western Sahara \n", "261 261 NaN Sao Tome and Principe \n", "262 262 NaN Yemen \n", "263 263 NaN Comoros \n", "264 264 NaN Tajikistan \n", "265 265 NaN Lesotho \n", "\n", " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "0 33.000000 65.000000 0 0 0 0 0 \n", "1 41.153300 20.168300 0 0 0 0 0 \n", "2 28.033900 1.659600 0 0 0 0 0 \n", "3 42.506300 1.521800 0 0 0 0 0 \n", "4 -11.202700 17.873900 0 0 0 0 0 \n", "5 17.060800 -61.796400 0 0 0 0 0 \n", "6 -38.416100 -63.616700 0 0 0 0 0 \n", "7 40.069100 45.038200 0 0 0 0 0 \n", "8 -35.473500 149.012400 0 0 0 0 0 \n", "9 -33.868800 151.209300 0 0 0 0 3 \n", "10 -12.463400 130.845600 0 0 0 0 0 \n", "11 -28.016700 153.400000 0 0 0 0 0 \n", "12 -34.928500 138.600700 0 0 0 0 0 \n", "13 -41.454500 145.970700 0 0 0 0 0 \n", "14 -37.813600 144.963100 0 0 0 0 1 \n", "15 -31.950500 115.860500 0 0 0 0 0 \n", "16 47.516200 14.550100 0 0 0 0 0 \n", "17 40.143100 47.576900 0 0 0 0 0 \n", "18 25.034300 -77.396300 0 0 0 0 0 \n", "19 26.027500 50.550000 0 0 0 0 0 \n", "20 23.685000 90.356300 0 0 0 0 0 \n", "21 13.193900 -59.543200 0 0 0 0 0 \n", "22 53.709800 27.953400 0 0 0 0 0 \n", "23 50.833300 4.000000 0 0 0 0 0 \n", "24 9.307700 2.315800 0 0 0 0 0 \n", "25 27.514200 90.433600 0 0 0 0 0 \n", "26 -16.290200 -63.588700 0 0 0 0 0 \n", "27 43.915900 17.679100 0 0 0 0 0 \n", "28 -14.235000 -51.925300 0 0 0 0 0 \n", "29 4.535300 114.727700 0 0 0 0 0 \n", ".. ... ... ... ... ... ... ... \n", "236 -8.874217 125.727539 0 0 0 0 0 \n", "237 13.193900 -59.543200 0 0 0 0 0 \n", "238 19.856270 102.495496 0 0 0 0 0 \n", "239 26.335100 17.228331 0 0 0 0 0 \n", "240 31.952200 35.233200 0 0 0 0 0 \n", "241 11.803700 -15.180400 0 0 0 0 0 \n", "242 17.570692 -3.996166 0 0 0 0 0 \n", "243 17.357822 -62.782998 0 0 0 0 0 \n", "244 64.825500 -124.845700 0 0 0 0 0 \n", "245 64.282300 -135.000000 0 0 0 0 0 \n", "246 42.602636 20.902977 0 0 0 0 0 \n", "247 21.916200 95.956000 0 0 0 0 0 \n", "248 18.220600 -63.068600 0 0 0 0 0 \n", "249 18.420700 -64.640000 0 0 0 0 0 \n", "250 21.694000 -71.797900 0 0 0 0 0 \n", "251 0.000000 0.000000 0 0 0 0 0 \n", "252 -22.328500 24.684900 0 0 0 0 0 \n", "253 -3.373100 29.918900 0 0 0 0 0 \n", "254 8.460555 -11.779889 0 0 0 0 0 \n", "255 12.178400 -68.238500 0 0 0 0 0 \n", "256 -13.254308 34.301525 0 0 0 0 0 \n", "257 -51.796300 -59.523600 0 0 0 0 0 \n", "258 46.885200 -56.315900 0 0 0 0 0 \n", "259 6.877000 31.307000 0 0 0 0 0 \n", "260 24.215500 -12.885800 0 0 0 0 0 \n", "261 0.186360 6.613081 0 0 0 0 0 \n", "262 15.552727 48.516388 0 0 0 0 0 \n", "263 -11.645500 43.333300 0 0 0 0 0 \n", "264 38.861034 71.276093 0 0 0 0 0 \n", "265 -29.609988 28.233608 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 \\\n", "0 ... 14525 15205 15750 16509 17267 18054 18969 19551 \n", "1 ... 1122 1137 1143 1164 1184 1197 1212 1232 \n", "2 ... 9267 9394 9513 9626 9733 9831 9935 10050 \n", "3 ... 764 764 765 844 851 852 852 852 \n", "4 ... 84 86 86 86 86 86 86 88 \n", "5 ... 25 26 26 26 26 26 26 26 \n", "6 ... 16214 16851 17415 18319 19268 20197 21037 22020 \n", "7 ... 8927 9282 9492 10009 10524 11221 11817 12364 \n", "8 ... 107 107 107 107 107 107 107 108 \n", "9 ... 3095 3098 3104 3104 3106 3110 3110 3109 \n", "10 ... 29 29 29 29 29 29 29 29 \n", "11 ... 1058 1058 1059 1059 1060 1060 1061 1061 \n", "12 ... 440 440 440 440 440 440 440 440 \n", "13 ... 228 228 228 228 228 228 228 228 \n", "14 ... 1649 1653 1663 1670 1678 1681 1681 1685 \n", "15 ... 586 589 591 592 592 592 596 599 \n", "16 ... 16685 16731 16733 16759 16771 16805 16843 16898 \n", "17 ... 5246 5494 5662 5935 6260 6522 6860 7239 \n", "18 ... 102 102 102 102 102 102 102 103 \n", "19 ... 10793 11398 11871 12311 12815 13296 13835 14383 \n", "20 ... 44608 47153 49534 52445 55140 57563 60391 63026 \n", "21 ... 92 92 92 92 92 92 92 92 \n", "22 ... 41658 42556 43403 44255 45116 45981 46868 47751 \n", "23 ... 58186 58381 58517 58615 58685 58767 58907 59072 \n", "24 ... 224 232 243 244 244 261 261 261 \n", "25 ... 33 43 43 47 47 47 48 48 \n", "26 ... 9592 9982 10531 10991 11638 12245 12728 13358 \n", "27 ... 2494 2510 2524 2535 2551 2594 2606 2606 \n", "28 ... 498440 514849 526447 555383 584016 614941 645771 672846 \n", "29 ... 141 141 141 141 141 141 141 141 \n", ".. ... ... ... ... ... ... ... ... ... \n", "236 ... 24 24 24 24 24 24 24 24 \n", "237 ... 18 18 18 18 18 18 19 19 \n", "238 ... 19 19 19 19 19 19 19 19 \n", "239 ... 130 156 168 182 196 209 239 256 \n", "240 ... 447 448 449 451 457 464 464 464 \n", "241 ... 1256 1256 1339 1339 1339 1339 1368 1368 \n", "242 ... 1250 1265 1315 1351 1386 1461 1485 1523 \n", "243 ... 15 15 15 15 15 15 15 15 \n", "244 ... 5 5 5 5 5 5 5 5 \n", "245 ... 11 11 11 11 11 11 11 11 \n", "246 ... 1064 1064 1064 1064 1142 1142 1142 1142 \n", "247 ... 224 224 228 232 233 236 236 240 \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", "251 ... 9 9 9 9 9 9 9 9 \n", "252 ... 35 35 38 40 40 40 40 40 \n", "253 ... 63 63 63 63 63 63 63 83 \n", "254 ... 852 861 865 896 909 914 929 946 \n", "255 ... 6 6 7 7 7 7 7 7 \n", "256 ... 279 284 336 358 369 393 409 409 \n", "257 ... 13 13 13 13 13 13 13 13 \n", "258 ... 1 1 1 1 1 1 1 1 \n", "259 ... 994 994 994 994 994 994 994 994 \n", "260 ... 9 9 9 9 9 9 9 9 \n", "261 ... 479 483 484 484 484 485 499 499 \n", "262 ... 310 323 354 399 419 453 469 482 \n", "263 ... 106 106 106 132 132 132 132 141 \n", "264 ... 3807 3930 4013 4100 4191 4289 4370 4453 \n", "265 ... 2 2 2 2 4 4 4 4 \n", "\n", " 6/7/20 6/8/20 \n", "0 20342 20917 \n", "1 1246 1263 \n", "2 10154 10265 \n", "3 852 852 \n", "4 91 92 \n", "5 26 26 \n", "6 22794 23620 \n", "7 13130 13325 \n", "8 108 108 \n", "9 3112 3114 \n", "10 29 29 \n", "11 1062 1062 \n", "12 440 440 \n", "13 228 228 \n", "14 1687 1687 \n", "15 599 599 \n", "16 16902 16968 \n", "17 7553 7876 \n", "18 103 103 \n", "19 14763 15417 \n", "20 65769 68504 \n", "21 92 92 \n", "22 48630 49453 \n", "23 59226 59348 \n", "24 261 288 \n", "25 59 59 \n", "26 13643 13949 \n", "27 2606 2704 \n", "28 691758 707412 \n", "29 141 141 \n", ".. ... ... \n", "236 24 24 \n", "237 19 19 \n", "238 19 19 \n", "239 256 332 \n", "240 472 473 \n", "241 1368 1389 \n", "242 1533 1547 \n", "243 15 15 \n", "244 5 5 \n", "245 11 11 \n", "246 1142 1263 \n", "247 242 244 \n", "248 3 3 \n", "249 8 8 \n", "250 12 12 \n", "251 9 9 \n", "252 40 42 \n", "253 83 83 \n", "254 969 1001 \n", "255 7 7 \n", "256 438 443 \n", "257 13 13 \n", "258 1 1 \n", "259 1317 1604 \n", "260 9 9 \n", "261 513 513 \n", "262 484 496 \n", "263 141 141 \n", "264 4529 4609 \n", "265 4 4 \n", "\n", "[266 rows x 144 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exists = os.path.isfile('statistics.csv')\n", "if exists:\n", " raw_data = pd.read_csv('statistics.csv')\n", " df = pd.DataFrame(raw_data)\n", "else:\n", " raw_data = pd.read_csv(data_url)\n", " df = pd.DataFrame(raw_data)\n", " df.to_csv('statistics.csv')\n", " \n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove Long and Lat columns (just for convenience) and make a spared copy in df_total for the \"world\" graph" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df_total=df.drop(columns=['Lat', 'Long'])\n", "df=df_total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove \"not interesting\" countries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience change China to Hong Kong in the Hong Kong line" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove colonies of France, Netherlands and UK" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "fr=df[(df['Country/Region']=='France')]\n", "fr=fr['Province/State']\n", "fr=fr.dropna()\n", "\n", "ne=df[(df['Country/Region']=='Netherlands')]\n", "ne=ne['Province/State']\n", "ne=ne.dropna()\n", "\n", "uk=df[(df['Country/Region']=='United Kingdom')]\n", "uk=uk['Province/State']\n", "uk=uk.dropna()\n", "\n", "df=df.drop(fr.index)\n", "df=df.drop(ne.index)\n", "df=df.drop(uk.index)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove Province/State column and compute total daily sum for China" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df.drop('Province/State', axis = 1, inplace = True)\n", "grouped=df.groupby('Country/Region')\n", "df=grouped.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct graphs for the countries above" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax=df.transpose().plot()\n", "ax.set_xlabel(\"date\")\n", "ax.set_ylabel(\"confirmed cases\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we make the analogous graph for the Covid-19 incidence in the world" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df=df_total\n", "df.drop('Province/State', axis = 1, inplace = True)\n", "df.drop('Country/Region', axis = 1, inplace = True)\n", "df=df.sum(axis=0)\n", "\n", "ax=df.transpose().plot()\n", "ax.set_xlabel(\"date\")\n", "ax.set_ylabel(\"confirmed cases\")\n", "plt.show()" ] } ], "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": 2 }