diff --git a/module3/exo3/exercice_fr_SARS-CoV-2.ipynb b/module3/exo3/exercice_fr_SARS-CoV-2.ipynb index f6bb8a87564de5d4e8f4ca64fe44bd5f997b067e..72257017e4337dca35968011fcdf521ea5e5aacf 100644 --- a/module3/exo3/exercice_fr_SARS-CoV-2.ipynb +++ b/module3/exo3/exercice_fr_SARS-CoV-2.ipynb @@ -1932,10 +1932,13 @@ ], "source": [ "flag = 0\n", + "table_of_errors = []\n", "for i in raw_data.index : \n", " for d in range(len(columns_to_study[:-1])):\n", " if (int(raw_data.iloc[i,d+4]) > int(raw_data.iloc[i,d+4 +1])):\n", " data_problem = (raw_data.iloc[i, 1:2], columns_to_study[d], columns_to_study[d+1])\n", + " table_of_errors.append((i,d+4))\n", + " table_of_errors.append((i,d+5))\n", " flag = flag +1\n", "print(\"Il y a %s donnees superieurs a celle de la donnee suivante\" % str(flag))" ] @@ -1944,18 +1947,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "--> il y a donc bien des valeurs possiblement incoherentes avec des jours ou le taux cummule de cas decroit par rapport a la veille. \n", - "Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", - "\n", - "Aux vues du faible nombre d'incoherence (362) par rapport au nombre de donnees total (288 lignes de donnees * 1142 comptage = 328896 donnees totales), on peut choisir de passer outre et de conserver la table ainsi\n", - "\n", - "\n", - "\n", - "## 1eres analyses realisees uniquement sur la France\n", + "--> il y a donc bien des valeurs possiblement incoherentes avec des jours ou le taux cummule de cas decroit par rapport a la veille. Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", "\n", - "On commence par ne recuperer que la ligne de donnees correspondant a la France \n", - "\n", - "--> Country/Region = France ET Province/State = Nan" + "Dans le doute, on \"supprime\" les 2 donnees en les fixant a na grace a la sauvegarde des couple (row, col) dans table_of_errors" ] }, { @@ -2009,1171 +2003,995 @@ " \n", " \n", " \n", - " 131\n", + " 0\n", " NaN\n", - " France\n", - " 46.2276\n", - " 2.2137\n", + " Afghanistan\n", + " 33.939110\n", + " 67.709953\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", - " 2\n", - " 3\n", - " 3\n", - " 3\n", " ...\n", - " 38579269\n", - " 38583794\n", - " 38587990\n", - " 38591184\n", - " 38591184\n", - " 38591184\n", - " 38599330\n", - " 38606393\n", - " 38612201\n", - " 38618509\n", + " 209322.0\n", + " 209340.0\n", + " 209358.0\n", + " 209362\n", + " 209369.0\n", + " 209390.0\n", + " 209406.0\n", + " 209436\n", + " 209451\n", + " 209451\n", " \n", - " \n", - "\n", - "

1 rows × 1147 columns

\n", - "" - ], - "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", - "131 NaN France 46.2276 2.2137 0 0 2 \n", - "\n", - " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", - "131 3 3 3 ... 38579269 38583794 38587990 \n", - "\n", - " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", - "131 38591184 38591184 38591184 38599330 38606393 38612201 38618509 \n", - "\n", - "[1 rows x 1147 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_france = raw_data.loc[(raw_data['Country/Region'] == \"France\") & (raw_data['Province/State'].isnull())]\n", - "df_france" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pour le plot, on transpose les donnees en ne conservant que les lignes des incidences cummulees - a partir de la colonne 5 " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "df_france_final = df_france.transpose()[5:]\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pour plus de clarte on change le nom de la colonne pour le nom du pays France" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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France
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" - ], - "text/plain": [ - " France\n", - "1/23/20 0\n", - "1/24/20 2\n", - "1/25/20 3\n", - "1/26/20 3\n", - "1/27/20 3" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_france_final.rename(columns={131: \"France\"}, inplace=True)\n", - "df_france_final.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On change les dates en un format interpretable par pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DatetimeIndex(['2020-01-23', '2020-01-24', '2020-01-25', '2020-01-26',\n", - " '2020-01-27', '2020-01-28', '2020-01-29', '2020-01-30',\n", - " '2020-01-31', '2020-02-01',\n", - " ...\n", - " '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',\n", - " '2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',\n", - " '2023-03-08', '2023-03-09'],\n", - " dtype='datetime64[ns]', length=1142, freq=None)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_dates = pd.to_datetime(df_france_final.index)\n", - "all_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On reinitialise ces dates formattees comme index de la table de donnees " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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France
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2020-01-2429Australian Capital TerritoryAustralia-35.473500149.012400000000...232018.0232018.0232619.0232619232619.0232619.0232619.0232619232619232974
2020-01-2510New South WalesAustralia-33.868800151.209300000034...3900969.03900969.03908129.039081293908129.03908129.03908129.0390812939081293915992
2020-01-26311Northern TerritoryAustralia-12.463400130.845600000000...104931.0104931.0105021.0105021105021.0105021.0105021.0105021105021105111
2020-01-27312QueenslandAustralia-27.469800153.025100000000...1796633.01796633.01800236.018002361800236.01800236.01800236.0180023618002361800236
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" - ], - "text/plain": [ - " France\n", - "2020-01-23 0\n", - "2020-01-24 2\n", - "2020-01-25 3\n", - "2020-01-26 3\n", - "2020-01-27 3" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_france_final.index = all_dates\n", - "df_france_final.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On peut ploter l'incidence en France " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "df_france_final.plot(ax=ax)\n", - "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generalisation aux pays d'interet que sont :\n", - "\n", - "* la Belgique (Belgium)\n", - "* la Chine - toutes les provinces sauf Hong-Kong (China)\n", - "* Hong Kong (China, Hong-Kong)\n", - "* la France métropolitaine (France)\n", - "* l’Allemagne (Germany)\n", - "* l’Iran (Iran)\n", - "* l’Italie (Italy)\n", - "* le Japon (Japan)\n", - "* la Corée du Sud (Korea, South)\n", - "* la Hollande sans les colonies (Netherlands)\n", - "* le Portugal (Portugal)\n", - "* l’Espagne (Spain)\n", - "* le Royaume-Unis sans les colonies (United Kingdom)\n", - "* les États-Unis (US).\n", - "\n", - "\n", - "### Creation d'un pays \"Hong-Kong\" \n", - "Hong-Kong apparait comme une province de la Chine. Pour plus de facilite a recupere les donnees, nous remplacons le pays anciennement \"China\" par Hong Kong pour la province Hong Kong uniquement. \n", - "Je choisis de faire une copie du fichier initial raw_data pour pouvoir y revenir le cas echeant. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", - "71 NaN Hong Kong 22.3 114.2 0 2 2 \n", - "\n", - " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", - "71 5 8 8 ... 2876106 2876106 2876106 2876106 \n", - "\n", - " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", - "71 2876106 2876106 2876106 2876106 2876106 2876106 \n", - "\n", - "[1 rows x 1147 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data= raw_data\n", - "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Country/Region'] = \"Hong Kong\"\n", - "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Province/State'] = np.nan\n", - "new_data.loc[(new_data['Country/Region'] == \"Hong Kong\")]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gestion particuliere de la Chine\n", - "La Chine apparait sous de multiples province que nous allons sommer en un unique pays.\n", - "\n", - "On commence par recuperer toutes les donnees de Chine\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
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59AnhuiChina31.8257117.226415VictoriaAustralia-37.813600144.963100000011915396070...22752275227522752275227522752275227522752874262.02874262.02877260.028772602877260.02877260.02877260.0287726028772602880559
60BeijingChina40.1824116.414214223641688016Western AustraliaAustralia-31.950500115.860500000000...407744077440774407744077440774407744077440774407741291077.01291077.01293461.012934611293461.01293461.01293461.0129346112934611293461
61ChongqingChina30.0572107.874069275775110...14715147151471514715147151471514715147151471514715
62FujianChina26.0789117.9874151018355917NaNAustria47.51620014.550100000000...171221712217122171221712217122171221712217122171225911294.05919616.05926148.059312475936666.05940935.05943417.0594941859558605961143
63GansuChina35.7518104.286118NaNAzerbaijan40.14310047.576900000000224714...1742174217421742174217421742174217421742828548.0828588.0828628.0828648828682.0828721.0828730.0828783828819828825
64GuangdongChina23.3417113.42442632537811115119NaNBahamas25.025885-78.035889000000...10324810324810324810324810324810324810324810324810324810324837491.037491.037491.03749137491.037491.037491.0374913749137491
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66GuizhouChina26.8154106.874813345721NaNBangladesh23.68500090.356300000000...25342534253425342534253425342534253425342037773.02037829.02037829.020378292037829.02037829.02037829.0203782920378712037871
67HainanChina19.1959109.745345819223322NaNBarbados13.193900-59.543200000000...10483104831048310483104831048310483104831048310483106645.0106645.0106645.0106645106645.0106645.0106645.0106645106645106798
68HebeiChina39.5490116.13061128131823NaNBelarus53.70980027.953400000000...3292329232923292329232923292329232923292994037.0994037.0994037.0994037994037.0994037.0994037.0994037994037994037
69HeilongjiangChina47.8620127.761524NaNBelgium50.8333004.4699360000002491521...66036603660366036603660366036603660366034717655.04717655.04727795.047277954727795.04727795.04727795.0472779547277954739365
70HenanChina37.8957114.9042559328312825NaNBelize17.189900-88.497600000000...994899489948994899489948994899489948994870757.070757.070757.07075770757.070757.070757.0707577075770757
72HubeiChina30.9756112.27074444445497611058142326NaNBenin9.3077002.315800000000...7213172131721317213172131721317213172131721317213127990.027990.027990.02799027990.027990.027990.0279992799927999
73HunanChina27.6104111.70884924436910027NaNBhutan27.51420090.433600000000...743774377437743774377437743774377437743762615.062620.062620.06262062620.062620.062620.0626206262762627
74Inner MongoliaChina44.0935113.944828NaNBolivia-16.290200-63.58870000000017711...88478847884788478847884788478847884788471193009.01193256.01193418.011936501193815.01193908.01193970.0119406911941871194277
75JiangsuChina32.9711119.455015918334729NaNBosnia and Herzegovina43.91590017.679100000000...5075507550755075507550755075507550755075401575.0401636.0401636.0401636401636.0401636.0401636.0401636401729401729
76JiangxiChina27.6140115.72212718183672..................................................................3423342334233423342334233423342334233423
77JilinChina43.6661126.1923259NaNTuvalu-7.109500177.64930000000013446...40764407644076440764407644076440764407644076440764
78LiaoningChina41.2956122.6085234172127...35473547354735473547354735473547354735472805.02805.02805.028052805.02805.02805.0280528052805
79MacauChina22.1667113.5500260NaNUS40.000000-100.00000011222565...3514351435143514351435143514351435143514103443455.0103533872.0103589757.0103648690NaNNaN103655539.0103690910103755771103802702
80NingxiaChina37.2692106.1655112347261NaNUganda1.37333332.290275000000...1276127612761276127612761276127612761276170504.0170504.0170504.0170504170504.0170504.0170504.0170504170544170544
81QinghaiChina35.745295.9956262NaNUkraine48.37940031.165600000000116...7827827827827827827827827827825693846.05701249.05701333.057014745701602.05701743.05701855.0570195957118185711929
82ShaanxiChina35.1917108.8701263NaNUnited Arab Emirates23.42407653.84781800000035152235...73267326732673267326732673267326732673261051998.01052122.01052247.010523821052519.01052664.01052664.0105292610530681053213
83ShandongChina36.3427118.14982615274675264AnguillaUnited Kingdom18.220600-63.068600000000...58805880588058805880588058805880588058803904.03904.03904.039043904.03904.03904.0390439043904
84ShanghaiChina31.2020121.449191620334053265BermudaUnited Kingdom32.307800-64.750500000000...6704067040670406704067040670406704067040670406704018799.018814.018814.01881418814.018814.018814.0188141882818828
85ShanxiChina37.5777112.29221116913266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...71677167716771677167716771677167716771677305.07305.07305.073057305.07305.07305.0730573057305
86SichuanChina30.6171102.71035815284469267Cayman IslandsUnited Kingdom19.313300-81.254600000000...1456714567145671456714567145671456714567145671456731472.031472.031472.03147231472.031472.031472.0314723147231472
87TianjinChina39.3054117.3230448101423268Channel IslandsUnited Kingdom49.372300-2.364400000000...43924392439243924392439243924392439243920.00.00.000.00.00.0000
88TibetChina31.692788.0924269Falkland Islands (Malvinas)United Kingdom-51.796300-59.52360000000...16471647164716471647164716471647164716471930.01930.01930.019301930.01930.01930.0193019301930
89UnknownChinaNaNNaN270GibraltarUnited Kingdom36.140800-5.35360000000...152181615218161521816152181615218161521816152181615218161521816152181620423.020423.020423.02043320433.020433.020433.0204332043320433
90XinjiangChina41.112985.2401271GuernseyUnited Kingdom49.448196-2.58949000000022345...308930893089308930893089308930893089308934867.034929.034929.03492934929.034929.034929.0349293499134991
91YunnanChina24.9740101.4870125111626272Isle of ManUnited Kingdom54.236100-4.548100000000...974397439743974397439743974397439743974338008.038008.038008.03800838008.038008.038008.0380083800838008
92ZhejiangChina29.1832120.093410274362104128273JerseyUnited Kingdom49.213800-2.135800000000...66391.066391.066391.06639166391.066391.066391.0663916639166391
274MontserratUnited Kingdom16.742498-62.187366000000...1403.01403.01403.014031403.01403.01403.0140314031403
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...4.04.04.044.04.04.0444
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...2166.02166.02166.021662166.02166.02166.0216621662166
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...6551.06551.06551.065516551.06551.06551.0655765576561
278NaNUnited Kingdom55.378100-3.436000000000...24370150.024370150.024396530.02439653024396530.024396530.024396530.0243965302439653024425309
279NaNUruguay-32.522800-55.765800000000...1034303.01034303.01034303.010343031034303.01034303.01034303.0103430310343031034303
280NaNUzbekistan41.37749164.585262000000...250932.0251071.0251071.0251071251071.0251071.0251071.0251071251247251247
281NaNVanuatu-15.376700166.959200000000...12014.012014.012014.01201412014.012014.012014.0120141201412014
282NaNVenezuela6.423800-66.589700000000...551981.0551986.0551986.0552014552051.0552051.0552125.0552157552157552162
283NaNVietnam14.058324108.277199022222...11526917.011526926.011526937.01152695011526962.011526966.011526966.0115269861152699411526994
284NaNWest Bank and Gaza31.95220035.233200000000...703228.0703228.0703228.0703228703228.0703228.0703228.0703228703228703228
285NaNWinter Olympics 202239.904200116.407400000000...535.0535.0535.0535535.0535.0535.0535535535
286NaNYemen15.55272748.516388000000...11945.011945.011945.01194511945.011945.011945.0119451194511945
287NaNZambia-13.13389727.849332000000...343012.0343012.0343079.0343079343079.0343135.0343135.0343135343135343135
288NaNZimbabwe-19.01543829.154857000000...263921.0264127.0264127.0264127264127.0264127.0264127.0264127264276264276
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289 rows × 1147 columns

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\n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 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11945.0 11945.0 11945 11945 11945 \n", + "287 343079.0 343135.0 343135.0 343135 343135 343135 \n", + "288 264127.0 264127.0 264127.0 264127 264276 264276 \n", + "\n", + "[289 rows x 1147 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean_data = raw_data.copy()\n", + "columns_to_study = clean_data.iloc[:,4:].columns\n", + "\n", + "for coord in table_of_errors : \n", + " clean_data.iloc[coord[0],coord[1]] = np.nan\n", + "clean_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> il y a donc bien des valeurs possiblement incoherentes avec des jours ou le taux cummule de cas decroit par rapport a la veille. \n", + "Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", + "\n", + "Aux vues du faible nombre d'incoherence (362) par rapport au nombre de donnees total (288 lignes de donnees * 1142 comptage = 328896 donnees totales), on peut choisir de passer outre et de conserver la table ainsi\n", + "\n", + "\n", + "\n", + "## 1eres analyses realisees uniquement sur la France\n", + "\n", + "On commence par ne recuperer que la ligne de donnees correspondant a la France \n", + "\n", + "--> Country/Region = France ET Province/State = Nan" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", + "131 NaN France 46.2276 2.2137 0 0 2 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "131 3 3 3 ... 38579269.0 38583794.0 38587990.0 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 \\\n", + "131 38591184 38591184.0 38591184.0 38599330.0 38606393 38612201 \n", + "\n", + " 3/9/23 \n", + "131 38618509 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france = clean_data.loc[(raw_data['Country/Region'] == \"France\") & (raw_data['Province/State'].isnull())]\n", + "df_france" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour le plot, on transpose les donnees en ne conservant que les lignes des incidences cummulees - a partir de la colonne 5 " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_france_final = df_france.transpose()[5:]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour plus de clarte on change le nom de la colonne pour le nom du pays France" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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France
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" + ], + "text/plain": [ + " France\n", + "1/23/20 0\n", + "1/24/20 2\n", + "1/25/20 3\n", + "1/26/20 3\n", + "1/27/20 3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france_final.rename(columns={131: \"France\"}, inplace=True)\n", + "df_france_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On change les dates en un format interpretable par pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-01-23', '2020-01-24', '2020-01-25', '2020-01-26',\n", + " '2020-01-27', '2020-01-28', '2020-01-29', '2020-01-30',\n", + " '2020-01-31', '2020-02-01',\n", + " ...\n", + " '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',\n", + " '2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',\n", + " '2023-03-08', '2023-03-09'],\n", + " dtype='datetime64[ns]', length=1142, freq=None)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_dates = pd.to_datetime(df_france_final.index)\n", + "all_dates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On reinitialise ces dates formattees comme index de la table de donnees " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " France\n", + "2020-01-23 0\n", + "2020-01-24 2\n", + "2020-01-25 3\n", + "2020-01-26 3\n", + "2020-01-27 3" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_france_final.index = all_dates\n", + "df_france_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut ploter l'incidence en France " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", + "71 NaN Hong Kong 22.3 114.2 0 2 2 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "71 5 8 8 ... 2876106.0 2876106.0 2876106.0 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "71 2876106 2876106.0 2876106.0 2876106.0 2876106 2876106 2876106 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data= clean_data.copy()\n", + "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Country/Region'] = \"Hong Kong\"\n", + "new_data.loc[(new_data['Province/State'] == \"Hong Kong\"),'Province/State'] = np.nan\n", + "new_data.loc[(new_data['Country/Region'] == \"Hong Kong\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gestion particuliere de la Chine\n", + "La Chine apparait sous de multiples province que nous allons sommer en un unique pays.\n", + "\n", + "On commence par recuperer toutes les donnees de Chine\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
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62FujianChina26.0789117.98741510183559...17122.017122.017122.01712217122.017122.017122.0171221712217122
63GansuChina35.7518104.28610224714...1742.01742.01742.017421742.01742.01742.0174217421742
64GuangdongChina23.3417113.424426325378111151...103248.0103248.0103248.0103248103248.0103248.0103248.0103248103248103248
65GuangxiChina23.8298108.78812523233646...13371.013371.013371.01337113371.013371.013371.0133711337113371
66GuizhouChina26.8154106.8748133457...2534.02534.02534.025342534.02534.02534.0253425342534
67HainanChina19.1959109.7453458192233...10483.010483.010483.01048310483.010483.010483.0104831048310483
68HebeiChina39.5490116.130611281318...3292.03292.03292.032923292.03292.03292.0329232923292
69HeilongjiangChina47.8620127.761502491521...6603.06603.06603.066036603.06603.06603.0660366036603
70HenanChina37.8957114.90425593283128...9948.09948.09948.099489948.09948.09948.0994899489948
72HubeiChina30.9756112.270744444454976110581423...72131.072131.072131.07213172131.072131.072131.0721317213172131
73HunanChina27.6104111.708849244369100...7437.07437.07437.074377437.07437.07437.0743774377437
74Inner MongoliaChina44.0935113.94480017711...8847.08847.08847.088478847.08847.08847.0884788478847
75JiangsuChina32.9711119.4550159183347...5075.05075.05075.050755075.05075.05075.0507550755075
76JiangxiChina27.6140115.72212718183672...3423.03423.03423.034233423.03423.03423.0342334233423
77JilinChina43.6661126.1923013446...40764.040764.040764.04076440764.040764.040764.0407644076440764
78LiaoningChina41.2956122.6085234172127...3547.03547.03547.035473547.03547.03547.0354735473547
79MacauChina22.1667113.5500122256...3514.03514.03514.035143514.03514.03514.0351435143514
80NingxiaChina37.2692106.1655112347...1276.01276.01276.012761276.01276.01276.0127612761276
81QinghaiChina35.745295.9956000116...782.0782.0782.0782782.0782.0782.0782782782
82ShaanxiChina35.1917108.8701035152235...7326.07326.07326.073267326.07326.07326.0732673267326
83ShandongChina36.3427118.14982615274675...5880.05880.05880.058805880.05880.05880.0588058805880
84ShanghaiChina31.2020121.449191620334053...67040.067040.067040.06704067040.067040.067040.0670406704067040
85ShanxiChina37.5777112.29221116913...7167.07167.07167.071677167.07167.07167.0716771677167
86SichuanChina30.6171102.71035815284469...14567.014567.014567.01456714567.014567.014567.0145671456714567
87TianjinChina39.3054117.3230448101423...4392.04392.04392.043924392.04392.04392.0439243924392
88TibetChina31.692788.0924000000...1647.01647.01647.016471647.01647.01647.0164716471647
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92ZhejiangChina29.1832120.093410274362104128...11848.011848.011848.01184811848.011848.011848.0118481184811848
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33 rows × 1147 columns

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on la reformate en dataframe avec une tranposition. 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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", + "0 NaN China NaN NaN 548 641 918 1401 \n", + "\n", + " 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", + "0 2067 2869 ... 2.02742e+06 2.02742e+06 2.02742e+06 2027418 \n", + "\n", + " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 2.02742e+06 2.02742e+06 2.02742e+06 2027418 2027418 2027418 \n", + "\n", + "[1 rows x 1147 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_China_combined = df_china.sum()\n", + "df_China_combined[\"Province/State\"] = np.nan\n", + "df_China_combined[\"Lat\"] = np.nan\n", + "df_China_combined[\"Long\"] = np.nan\n", + "df_China_combined[\"Country/Region\"] = \"China\"\n", + "df_China_combined = pd.DataFrame(df_China_combined)\n", + "df_China_combined = df_China_combined.transpose()\n", + "df_China_combined" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On ajoute les donnees China \"total\" dans un nouveau dataframe pandas \"newSet\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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81QinghaiChina35.745295.9956000116...782782782782782782782782782782
82ShaanxiChina35.1917108.8701035152235...7326732673267326732673267326732673267326
83ShandongChina36.3427118.14982615274675...5880588058805880588058805880588058805880
84ShanghaiChina31.2020121.449191620334053...67040670406704067040670406704067040670406704067040
85ShanxiChina37.5777112.29221116913...7167716771677167716771677167716771677167
86SichuanChina30.6171102.71035815284469...14567145671456714567145671456714567145671456714567
87TianjinChina39.3054117.3230448101423...4392439243924392439243924392439243924392
88TibetChina31.692788.0924000000...1647164716471647164716471647164716471647
89UnknownChinaNaNNaN000000...1.52182e+061.52182e+061.52182e+0615218161.52182e+061.52182e+061.52182e+06152181615218161521816
90XinjiangChina41.112985.2401022345...3089308930893089308930893089308930893089
91YunnanChina24.9740101.4870125111626...9743974397439743974397439743974397439743
92ZhejiangChina29.1832120.093410274362104128...118481184811848118481184811848118481184811848118481184811848118481184811848118481184811848
0NaNChinaNaNNaN548641918140120672869...2.02742e+062.02742e+062.02742e+0620274182.02742e+062.02742e+062.02742e+06202741820274182027418
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China 41.2956 122.6085 2 3 4 \n", + "79 Macau China 22.1667 113.5500 1 2 2 \n", + "80 Ningxia China 37.2692 106.1655 1 1 2 \n", + "81 Qinghai China 35.7452 95.9956 0 0 0 \n", + "82 Shaanxi China 35.1917 108.8701 0 3 5 \n", + "83 Shandong China 36.3427 118.1498 2 6 15 \n", + "84 Shanghai China 31.2020 121.4491 9 16 20 \n", + "85 Shanxi China 37.5777 112.2922 1 1 1 \n", + "86 Sichuan China 30.6171 102.7103 5 8 15 \n", + "87 Tianjin China 39.3054 117.3230 4 4 8 \n", + "88 Tibet China 31.6927 88.0924 0 0 0 \n", + "89 Unknown China NaN NaN 0 0 0 \n", + "90 Xinjiang China 41.1129 85.2401 0 2 2 \n", + "91 Yunnan China 24.9740 101.4870 1 2 5 \n", + "92 Zhejiang China 29.1832 120.0934 10 27 43 \n", + "0 NaN China NaN NaN 548 641 918 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "59 39 60 70 ... 2275 2275 2275 \n", + "60 41 68 80 ... 40774 40774 40774 \n", + "61 57 75 110 ... 14715 14715 14715 \n", + "62 18 35 59 ... 17122 17122 17122 \n", + "63 4 7 14 ... 1742 1742 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1147 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newSet = pd.concat([new_data,df_China_combined])\n", + "newSet.loc[(newSet['Country/Region'] == \"China\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Recuperation des donnees pour les pays d'interet listes ci dessus\n", + "\n", + "On cree une liste avec les pays d'interet \"interest_countries\".\n", + "\n", + "On recupere par la suite un sous jeu de donnees avec uniquement ces pays et \"NA\" en province. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
24NaNBelgium50.8333004.469936000000...4.71766e+064.71766e+064.7278e+0647277954.7278e+064.7278e+064.7278e+06472779547277954739365
71NaNHong Kong22.300000114.200000022588...2.87611e+062.87611e+062.87611e+0628761062.87611e+062.87611e+062.87611e+06287610628761062876106
131NaNFrance46.2276002.213700002333...3.85793e+073.85838e+073.8588e+07385911843.85912e+073.85912e+073.85993e+07386063933861220138618509
135NaNGermany51.16569110.451526000001...3.81689e+073.819e+073.82026e+07382108503.82108e+073.82109e+073.82109e+07382316103824123138249060
150NaNIran32.42790853.688046000000...7.56791e+067.5689e+067.56926e+0675694837.56977e+067.57023e+067.57074e+06757135275719967572311
154NaNItaly41.87194012.567380000000...2.55769e+072.55769e+072.55769e+07256035102.56035e+072.56035e+072.56035e+07256035102560351025603510
156NaNJapan36.204824138.252924222244...3.32272e+073.32412e+073.32527e+07332632083.32736e+073.32824e+073.32866e+07332987993331060433320438
162NaNKorea, South35.907757127.766922112234...3.0526e+073.05336e+073.0544e+07305551023.05551e+073.05692e+073.05815e+07305942973060518730615522
200NaNNetherlands52.1326005.291300000000...8.59616e+068.59616e+068.59616e+0685980438.59804e+068.59804e+068.59804e+06859998185999818599981
218NaNPortugal39.399900-8.224500000000...5.56671e+065.56808e+065.56808e+0655680845.56808e+065.56808e+065.56808e+06556808455704735570473
241NaNSpain40.463667-3.749220000000...1.37633e+071.37633e+071.37633e+07137704291.37704e+071.37704e+071.37704e+07137704291377042913770429
260NaNUS40.000000-100.000000112255...1.03443e+081.03534e+081.0359e+08103648690NaNNaN1.03656e+08103690910103755771103802702
278NaNUnited Kingdom55.378100-3.436000000000...2.43702e+072.43702e+072.43965e+07243965302.43965e+072.43965e+072.43965e+07243965302439653024425309
0NaNChinaNaNNaN548641918140120672869...2.02742e+062.02742e+062.02742e+0620274182.02742e+062.02742e+062.02742e+06202741820274182027418
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14 rows × 1147 columns

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Country/RegionBelgiumHong KongFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUSUnited KingdomChina
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" + ], + "text/plain": [ + "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", + "1/23/20 0 2 0 0 0 0 2 1 \n", + "1/24/20 0 2 2 0 0 0 2 2 \n", + "1/25/20 0 5 3 0 0 0 2 2 \n", + "1/26/20 0 8 3 0 0 0 4 3 \n", + "1/27/20 0 8 3 1 0 0 4 4 \n", + "\n", + "Country/Region Netherlands Portugal Spain US United Kingdom China \n", + "1/23/20 0 0 0 1 0 641 \n", + "1/24/20 0 0 0 2 0 918 \n", + "1/25/20 0 0 0 2 0 1401 \n", + "1/26/20 0 0 0 5 0 2067 \n", + "1/27/20 0 0 0 5 0 2869 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_allCountries_final = df_allCountries.transpose()[5:]\n", + "df_allCountries_final.columns = df_allCountries[\"Country/Region\"]\n", + "df_allCountries_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On reformatte les dates " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country/RegionBelgiumHong KongFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUSUnited KingdomChina
2020-01-230200002100010641
2020-01-240220002200020918
2020-01-2505300022000201401
2020-01-2608300043000502067
2020-01-2708310044000502869
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" + ], + "text/plain": [ + "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", + "2020-01-23 0 2 0 0 0 0 2 1 \n", + "2020-01-24 0 2 2 0 0 0 2 2 \n", + "2020-01-25 0 5 3 0 0 0 2 2 \n", + "2020-01-26 0 8 3 0 0 0 4 3 \n", + "2020-01-27 0 8 3 1 0 0 4 4 \n", + "\n", + "Country/Region Netherlands Portugal Spain US United Kingdom China \n", + "2020-01-23 0 0 0 1 0 641 \n", + "2020-01-24 0 0 0 2 0 918 \n", + "2020-01-25 0 0 0 2 0 1401 \n", + "2020-01-26 0 0 0 5 0 2067 \n", + "2020-01-27 0 0 0 5 0 2869 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_dates = pd.to_datetime(df_allCountries_final.index)\n", + "df_allCountries_final.index = all_dates\n", + "df_allCountries_final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On plot le graph en format classique avec le nombre de cas en fonction des jours, en utilisant un code couleur pour les pays consideres. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "color = cm.rainbow(np.linspace(0, 1, len(interest_countries)))\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "df_allCountries_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On modifie l'echelle des y en log pour mieux voir les distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "plt.yscale(\"log\") \n", + "df_allCountries_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est donc les USA ayant eu le plus de cas recences, mais a normaliser par le nombre d'habitant global de chaque territoire et/ou du nombre de deces. \n", + "\n", + "## Question subsidiaire\n", + "\n", + "On recupere les donnees de deces en faisant une copie local au besoin. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "death_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n", + "death_file = \"time_series_covid19_deaths_global.csv\"\n", + "\n", + "if not os.path.isfile(death_file):\n", + " urlretrieve(death_url, death_file) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme precedemment, on se doit de regarder les donnees apres chargement." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(289, 1147)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "death_data = pd.read_csv(death_file)\n", + "death_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...7896789678967896789678967896789678967896
1NaNAlbania41.15330020.168300000000...3598359835983598359835983598359835983598
2NaNAlgeria28.0339001.659600000000...6881688168816881688168816881688168816881
3NaNAndorra42.5063001.521800000000...165165165165165165165165165165
4NaNAngola-11.20270017.873900000000...1933193319331933193319331933193319331933
5NaNAntarctica-71.94990023.347000000000...0000000000
6NaNAntigua and Barbuda17.060800-61.796400000000...146146146146146146146146146146
7NaNArgentina-38.416100-63.616700000000...130463130463130463130463130463130463130472130472130472130472
8NaNArmenia40.06910045.038200000000...8721872187218721872187218721872187278727
9Australian Capital TerritoryAustralia-35.473500149.012400000000...224224228228228228228228228228
10New South WalesAustralia-33.868800151.209300000000...6464646464936493649364936493649364936529
11Northern TerritoryAustralia-12.463400130.845600000000...90909090909090909091
12QueenslandAustralia-27.469800153.025100000000...2760276027832783278327832783278327832783
13South AustraliaAustralia-34.928500138.600700000000...1322132213221322132213221322132213221365
14TasmaniaAustralia-42.882100147.327200000000...252252252253253253253253253256
15VictoriaAustralia-37.813600144.963100000000...7317731773387338733873387338733873387370
16Western AustraliaAustralia-31.950500115.860500000000...944944952952952952952952952952
17NaNAustria47.51620014.550100000000...21887218912189921907219212192221923219412194921970
18NaNAzerbaijan40.14310047.576900000000...10119101191012210126101271012910129101351013810138
19NaNBahamas25.025885-78.035889000000...833833833833833833833833833833
20NaNBahrain26.02750050.550000000000...1548154915501552155215521552155315531553
21NaNBangladesh23.68500090.356300000000...29445294452944529445294452944529445294452944529445
22NaNBarbados13.193900-59.543200000000...575575575575575575575575575579
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25NaNBelize17.189900-88.497600000000...688688688688688688688688688688
26NaNBenin9.3077002.315800000000...163163163163163163163163163163
27NaNBhutan27.51420090.433600000000...21212121212121212121
28NaNBolivia-16.290200-63.588700000000...22365223652236522365223652236522365223652236522365
29NaNBosnia and Herzegovina43.91590017.679100000000...16278162791627916279162791627916279162791628016280
..................................................................
259NaNTuvalu-7.109500177.649300000000...0000000000
260NaNUS40.000000-100.000000000000...1119917112089711216581122165112217211221341122181112251611232461123836
261NaNUganda1.37333332.290275000000...3630363036303630363036303630363036303630
262NaNUkraine48.37940031.165600000000...119149119209119210119211119212119213119216119217119281119283
263NaNUnited Arab Emirates23.42407653.847818000000...2349234923492349234923492349234923492349
264AnguillaUnited Kingdom18.220600-63.068600000000...12121212121212121212
265BermudaUnited Kingdom32.307800-64.750500000000...160160160160160160160160160160
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...64646464646464646464
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...37373737373737373737
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0000000000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...0000000000
270GibraltarUnited Kingdom36.140800-5.353600000000...111111111111111111111111111111
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272Isle of ManUnited Kingdom54.236100-4.548100000000...116116116116116116116116116116
273JerseyUnited Kingdom49.213800-2.135800000000...161161161161161161161161161161
274MontserratUnited Kingdom16.742498-62.187366000000...8888888888
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...0000000000
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...0000000000
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...38383838383838383838
278NaNUnited Kingdom55.378100-3.436000000000...219948219948219948219948219948219948219948219948219948219948
279NaNUruguay-32.522800-55.765800000000...7617761776177617761776177617761776177617
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33 rows × 1147 columns

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" - ], - "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", - "59 Anhui China 31.8257 117.2264 1 9 \n", - "60 Beijing China 40.1824 116.4142 14 22 \n", - "61 Chongqing China 30.0572 107.8740 6 9 \n", - "62 Fujian China 26.0789 117.9874 1 5 \n", - "63 Gansu China 35.7518 104.2861 0 2 \n", - "64 Guangdong China 23.3417 113.4244 26 32 \n", - "65 Guangxi China 23.8298 108.7881 2 5 \n", - "66 Guizhou China 26.8154 106.8748 1 3 \n", - "67 Hainan China 19.1959 109.7453 4 5 \n", - "68 Hebei China 39.5490 116.1306 1 1 \n", - "69 Heilongjiang China 47.8620 127.7615 0 2 \n", - "70 Henan China 37.8957 114.9042 5 5 \n", - "72 Hubei China 30.9756 112.2707 444 444 \n", - "73 Hunan China 27.6104 111.7088 4 9 \n", - "74 Inner Mongolia China 44.0935 113.9448 0 0 \n", - "75 Jiangsu China 32.9711 119.4550 1 5 \n", - "76 Jiangxi China 27.6140 115.7221 2 7 \n", - "77 Jilin China 43.6661 126.1923 0 1 \n", - "78 Liaoning China 41.2956 122.6085 2 3 \n", - "79 Macau China 22.1667 113.5500 1 2 \n", - "80 Ningxia China 37.2692 106.1655 1 1 \n", - "81 Qinghai China 35.7452 95.9956 0 0 \n", - "82 Shaanxi China 35.1917 108.8701 0 3 \n", - "83 Shandong China 36.3427 118.1498 2 6 \n", - "84 Shanghai China 31.2020 121.4491 9 16 \n", - "85 Shanxi China 37.5777 112.2922 1 1 \n", - "86 Sichuan China 30.6171 102.7103 5 8 \n", - "87 Tianjin China 39.3054 117.3230 4 4 \n", - "88 Tibet China 31.6927 88.0924 0 0 \n", - "89 Unknown China NaN NaN 0 0 \n", - "90 Xinjiang China 41.1129 85.2401 0 2 \n", - "91 Yunnan China 24.9740 101.4870 1 2 \n", - "92 Zhejiang China 29.1832 120.0934 10 27 \n", + " \n", + " 280\n", + " NaN\n", + " Uzbekistan\n", + " 41.377491\n", + " 64.585262\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " 1637\n", + " \n", + " \n", + " 281\n", + " NaN\n", + " Vanuatu\n", + " -15.376700\n", + " 166.959200\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " 14\n", + " \n", + " \n", + " 282\n", + " NaN\n", + " Venezuela\n", + " 6.423800\n", + " -66.589700\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 5853\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " 5854\n", + " \n", + " \n", + " 283\n", + " NaN\n", + " Vietnam\n", + " 14.058324\n", + " 108.277199\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " 43186\n", + " \n", + " \n", + " 284\n", + " NaN\n", + " West Bank and Gaza\n", + " 31.952200\n", + " 35.233200\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " 5708\n", + " \n", + " \n", + " 285\n", + " NaN\n", + " Winter Olympics 2022\n", + " 39.904200\n", + " 116.407400\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 286\n", + " NaN\n", + " Yemen\n", + " 15.552727\n", + " 48.516388\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " 2159\n", + " \n", + " \n", + " 287\n", + " NaN\n", + " Zambia\n", + " -13.133897\n", + " 27.849332\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " 4057\n", + " \n", + " \n", + " 288\n", + " NaN\n", + " Zimbabwe\n", + " -19.015438\n", + " 29.154857\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 5663\n", + " 5668\n", + " 5668\n", + " 5668\n", + " 5668\n", + " 5668\n", + " 5668\n", + " 5668\n", + " 5671\n", + " 5671\n", + " \n", + " \n", + "\n", + "

289 rows × 1147 columns

\n", + "" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 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 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 0 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 0 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 27.953400 0 0 0 0 0 \n", + "24 50.833300 4.469936 0 0 0 0 0 \n", + "25 17.189900 -88.497600 0 0 0 0 0 \n", + "26 9.307700 2.315800 0 0 0 0 0 \n", + "27 27.514200 90.433600 0 0 0 0 0 \n", + "28 -16.290200 -63.588700 0 0 0 0 0 \n", + "29 43.915900 17.679100 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... \n", + "259 -7.109500 177.649300 0 0 0 0 0 \n", + "260 40.000000 -100.000000 0 0 0 0 0 \n", + "261 1.373333 32.290275 0 0 0 0 0 \n", + "262 48.379400 31.165600 0 0 0 0 0 \n", + "263 23.424076 53.847818 0 0 0 0 0 \n", + "264 18.220600 -63.068600 0 0 0 0 0 \n", + "265 32.307800 -64.750500 0 0 0 0 0 \n", + "266 18.420700 -64.640000 0 0 0 0 0 \n", + "267 19.313300 -81.254600 0 0 0 0 0 \n", + "268 49.372300 -2.364400 0 0 0 0 0 \n", + "269 -51.796300 -59.523600 0 0 0 0 0 \n", + "270 36.140800 -5.353600 0 0 0 0 0 \n", + "271 49.448196 -2.589490 0 0 0 0 0 \n", + "272 54.236100 -4.548100 0 0 0 0 0 \n", + "273 49.213800 -2.135800 0 0 0 0 0 \n", + "274 16.742498 -62.187366 0 0 0 0 0 \n", + "275 -24.376800 -128.324200 0 0 0 0 0 \n", + "276 -7.946700 -14.355900 0 0 0 0 0 \n", + "277 21.694000 -71.797900 0 0 0 0 0 \n", + "278 55.378100 -3.436000 0 0 0 0 0 \n", + "279 -32.522800 -55.765800 0 0 0 0 0 \n", + "280 41.377491 64.585262 0 0 0 0 0 \n", + "281 -15.376700 166.959200 0 0 0 0 0 \n", + "282 6.423800 -66.589700 0 0 0 0 0 \n", + "283 14.058324 108.277199 0 0 0 0 0 \n", + "284 31.952200 35.233200 0 0 0 0 0 \n", + "285 39.904200 116.407400 0 0 0 0 0 \n", + "286 15.552727 48.516388 0 0 0 0 0 \n", + "287 -13.133897 27.849332 0 0 0 0 0 \n", + "288 -19.015438 29.154857 0 0 0 0 0 \n", "\n", - 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"[33 rows x 1147 columns]" + "[289 rows x 1147 columns]" ] }, - "execution_count": 14, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_china = new_data.loc[(new_data['Country/Region'] == \"China\")]\n", - "df_china\n" + "death_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "On somme toutes les donnes et on reinitialise les province, lattitude, longitude a NA, le pays a China.\n", + "Le format est donc identique aux donnees precedentes et peuvent etre traitees de la meme maniere apres les memes verifications\n", + "* presence de donnees manquantes " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_study = death_data.iloc[:,4:].columns\n", "\n", - "On travaille sur une Serie pandas, on la reformate en dataframe avec une tranposition. " + "for i in raw_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (pd.isna(death_data.iloc[i,d+4]) or death_data.iloc[i,d+4]<0):\n", + " print(death_data.iloc[i,d+4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> pas de donnees manquantes\n", + "* incoherence avec un nombre de deces commule qui decroit ?" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Il y a 291 donnees superieurs a celle de la donnee suivante\n" + ] + } + ], + "source": [ + "flag = 0\n", + "table_of_errors = []\n", + "for i in death_data.index : \n", + " for d in range(len(columns_to_study[:-1])):\n", + " if (int(death_data.iloc[i,d+4]) > int(death_data.iloc[i,d+4 +1])):\n", + " data_problem = (death_data.iloc[i, 1:2], columns_to_study[d], columns_to_study[d+1])\n", + " table_of_errors.append((i,d+4))\n", + " table_of_errors.append((i,d+5))\n", + " flag = flag +1\n", + "print(\"Il y a %s donnees superieurs a celle de la donnee suivante\" % str(flag))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--> on a le meme type d'incoherence que precedemment, minoritaire comparee a la quantite de donnees, mais au'on choisit toutefois de supprimer en remettant ces valeurs aberrantes a na" + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -3469,801 +9198,1070 @@ " 3/8/23\n", " 3/9/23\n", " \n", - 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1 rows × 1147 columns

\n", - "" - ], - "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", - "0 NaN China NaN NaN 548 641 918 1401 \n", - "\n", - " 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 \\\n", - "0 2067 2869 ... 2027418 2027418 2027418 2027418 2027418 \n", - "\n", - " 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", - "0 2027418 2027418 2027418 2027418 2027418 \n", - "\n", - "[1 rows x 1147 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_China_combined = df_china.sum()\n", - "df_China_combined[\"Province/State\"] = np.nan\n", - "df_China_combined[\"Lat\"] = np.nan\n", - "df_China_combined[\"Long\"] = np.nan\n", - "df_China_combined[\"Country/Region\"] = \"China\"\n", - "df_China_combined = pd.DataFrame(df_China_combined)\n", - "df_China_combined = df_China_combined.transpose()\n", - "df_China_combined" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On ajoute les donnees China \"total\" dans un nouveau dataframe pandas \"newSet\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4271,23 +10269,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4295,307 +10293,143 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
11Northern TerritoryAustralia-12.463400130.845600000000...90.090909090.090.090909091
59AnhuiChina31.8257117.2264191539607012QueenslandAustralia-27.469800153.025100000000...22752275227522752275227522752275227522752760.02760278327832783.02783.02783278327832783
13South AustraliaAustralia-34.928500138.600700000000...1322.01322132213221322.01322.01322132213221365
14TasmaniaAustralia-42.882100147.327200000000...252.0252252253253.0253.0253253253256
15VictoriaAustralia-37.813600144.963100000000...7317.07317733873387338.07338.07338733873387370
60BeijingChina40.1824116.414214223641688016Western AustraliaAustralia-31.950500115.860500000000...40774407744077440774407744077440774407744077440774944.0944952952952.0952.0952952952952
61ChongqingChina30.0572107.87406927577511017NaNAustria47.51620014.550100000000...1471514715147151471514715147151471514715147151471521887.021891218992190721921.021922.021923219412194921970
62FujianChina26.0789117.9874151018355918NaNAzerbaijan40.14310047.576900000000...1712217122171221712217122171221712217122171221712210119.010119101221012610127.010129.010129101351013810138
63GansuChina35.7518104.286119NaNBahamas25.025885-78.035889000000224714...1742174217421742174217421742174217421742833.0833833833833.0833.0833833833833
64GuangdongChina23.3417113.42442632537811115120NaNBahrain26.02750050.550000000000...1032481032481032481032481032481032481032481032481032481032481548.01549155015521552.01552.01552155315531553
65GuangxiChina23.8298108.7881252323364621NaNBangladesh23.68500090.356300000000...1337113371133711337113371133711337113371133711337129445.029445294452944529445.029445.029445294452944529445
66GuizhouChina26.8154106.874813345722NaNBarbados13.193900-59.543200000000...2534253425342534253425342534253425342534575.0575575575575.0575.0575575575579
67HainanChina19.1959109.745345819223323NaNBelarus53.70980027.953400000000...104831048310483104831048310483104831048310483104837118.07118711871187118.07118.07118711871187118
68HebeiChina39.5490116.13061128131824NaNBelgium50.8333004.469936000000...329232923292329232923292329232923292329233717.033717337753377533775.033775.033775337753377533814
69HeilongjiangChina47.8620127.761525NaNBelize17.189900-88.4976000000002491521...6603660366036603660366036603660366036603688.0688688688688.0688.0688688688688
70HenanChina37.8957114.9042559328312826NaNBenin9.3077002.315800000000...9948994899489948994899489948994899489948163.0163163163163.0163.0163163163163
72HubeiChina30.9756112.27074444445497611058142327NaNBhutan27.51420090.433600000000...7213172131721317213172131721317213172131721317213121.021212121.021.021212121
73HunanChina27.6104111.70884924436910028NaNBolivia-16.290200-63.588700000000...743774377437743774377437743774377437743722365.022365223652236522365.022365.022365223652236522365
74Inner MongoliaChina44.0935113.944829NaNBosnia and Herzegovina43.91590017.67910000000017711...884788478847884788478847884788478847884716278.016279162791627916279.016279.016279162791628016280
75JiangsuChina32.9711119.4550159183347..................................................................5075507550755075507550755075507550755075
76JiangxiChina27.6140115.72212718183672259NaNTuvalu-7.109500177.649300000000...34233423342334233423342334233423342334230.00000.00.00000
77JilinChina43.6661126.1923260NaNUS40.000000-100.00000000000013446...407644076440764407644076440764407644076440764407641119917.0112089711216581122165NaNNaN1122181112251611232461123836
78LiaoningChina41.2956122.6085234172127261NaNUganda1.37333332.290275000000...35473547354735473547354735473547354735473630.03630363036303630.03630.03630363036303630
79MacauChina22.1667113.5500122256262NaNUkraine48.37940031.165600000000...3514351435143514351435143514351435143514119149.0119209119210119211119212.0119213.0119216119217119281119283
80NingxiaChina37.2692106.1655112347263NaNUnited Arab Emirates23.42407653.847818000000...12761276127612761276127612761276127612762349.02349234923492349.02349.02349234923492349
81QinghaiChina35.745295.9956264AnguillaUnited Kingdom18.220600-63.068600000000116...78278278278278278278278278278212.012121212.012.012121212
82ShaanxiChina35.1917108.8701265BermudaUnited Kingdom32.307800-64.75050000000035152235...7326732673267326732673267326732673267326160.0160160160160.0160.0160160160160
83ShandongChina36.3427118.14982615274675266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...588058805880588058805880588058805880588064.064646464.064.064646464
84ShanghaiChina31.2020121.449191620334053267Cayman IslandsUnited Kingdom19.313300-81.254600000000...6704067040670406704067040670406704067040670406704037.037373737.037.037373737
85ShanxiChina37.5777112.29221116913268Channel IslandsUnited Kingdom49.372300-2.364400000000...71677167716771677167716771677167716771670.00000.00.00000
86SichuanChina30.6171102.71035815284469269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...145671456714567145671456714567145671456714567145670.00000.00.00000
270GibraltarUnited Kingdom36.140800-5.353600000000...111.0111111111111.0111.0111111111111
87TianjinChina39.3054117.3230448101423271GuernseyUnited Kingdom49.448196-2.589490000000...439243924392439243924392439243924392439266.066666666.066.066666666
88TibetChina31.692788.0924272Isle of ManUnited Kingdom54.236100-4.54810000000...1647164716471647164716471647164716471647116.0116116116116.0116.0116116116116
89UnknownChinaNaNNaN273JerseyUnited Kingdom49.213800-2.13580000000...1521816152181615218161521816152181615218161521816152181615218161521816161.0161161161161.0161.0161161161161
90XinjiangChina41.112985.2401274MontserratUnited Kingdom16.742498-62.18736600000022345...30893089308930893089308930893089308930898.08888.08.08888
91YunnanChina24.9740101.4870125111626275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...97439743974397439743974397439743974397430.00000.00.00000
92ZhejiangChina29.1832120.093410274362104128276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...118481184811848118481184811848118481184811848118480.00000.00.00000
0NaNChinaNaNNaN548641918140120672869277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...202741820274182027418202741820274182027418202741820274182027418202741838.038383838.038.038383838
\n", - "

34 rows × 1147 columns

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" - ], - "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", - "59 Anhui China 31.8257 117.2264 1 9 15 \n", - "60 Beijing China 40.1824 116.4142 14 22 36 \n", - "61 Chongqing China 30.0572 107.8740 6 9 27 \n", - "62 Fujian China 26.0789 117.9874 1 5 10 \n", - "63 Gansu China 35.7518 104.2861 0 2 2 \n", - "64 Guangdong China 23.3417 113.4244 26 32 53 \n", - "65 Guangxi China 23.8298 108.7881 2 5 23 \n", - "66 Guizhou China 26.8154 106.8748 1 3 3 \n", - "67 Hainan China 19.1959 109.7453 4 5 8 \n", - "68 Hebei China 39.5490 116.1306 1 1 2 \n", - "69 Heilongjiang China 47.8620 127.7615 0 2 4 \n", - "70 Henan China 37.8957 114.9042 5 5 9 \n", - "72 Hubei China 30.9756 112.2707 444 444 549 \n", - "73 Hunan China 27.6104 111.7088 4 9 24 \n", - "74 Inner Mongolia China 44.0935 113.9448 0 0 1 \n", - "75 Jiangsu China 32.9711 119.4550 1 5 9 \n", - "76 Jiangxi China 27.6140 115.7221 2 7 18 \n", - "77 Jilin China 43.6661 126.1923 0 1 3 \n", - "78 Liaoning China 41.2956 122.6085 2 3 4 \n", - "79 Macau China 22.1667 113.5500 1 2 2 \n", - "80 Ningxia China 37.2692 106.1655 1 1 2 \n", - "81 Qinghai China 35.7452 95.9956 0 0 0 \n", - "82 Shaanxi China 35.1917 108.8701 0 3 5 \n", - "83 Shandong China 36.3427 118.1498 2 6 15 \n", - "84 Shanghai China 31.2020 121.4491 9 16 20 \n", - "85 Shanxi China 37.5777 112.2922 1 1 1 \n", - "86 Sichuan China 30.6171 102.7103 5 8 15 \n", - "87 Tianjin China 39.3054 117.3230 4 4 8 \n", - "88 Tibet China 31.6927 88.0924 0 0 0 \n", - "89 Unknown China NaN NaN 0 0 0 \n", - "90 Xinjiang China 41.1129 85.2401 0 2 2 \n", - "91 Yunnan China 24.9740 101.4870 1 2 5 \n", - "92 Zhejiang China 29.1832 120.0934 10 27 43 \n", - "0 NaN China NaN NaN 548 641 918 \n", - "\n", - " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", - "59 39 60 70 ... 2275 2275 2275 2275 \n", - "60 41 68 80 ... 40774 40774 40774 40774 \n", - "61 57 75 110 ... 14715 14715 14715 14715 \n", - "62 18 35 59 ... 17122 17122 17122 17122 \n", - "63 4 7 14 ... 1742 1742 1742 1742 \n", - 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"75 5075 5075 5075 5075 5075 5075 \n", - "76 3423 3423 3423 3423 3423 3423 \n", - "77 40764 40764 40764 40764 40764 40764 \n", - "78 3547 3547 3547 3547 3547 3547 \n", - "79 3514 3514 3514 3514 3514 3514 \n", - "80 1276 1276 1276 1276 1276 1276 \n", - "81 782 782 782 782 782 782 \n", - "82 7326 7326 7326 7326 7326 7326 \n", - "83 5880 5880 5880 5880 5880 5880 \n", - "84 67040 67040 67040 67040 67040 67040 \n", - "85 7167 7167 7167 7167 7167 7167 \n", - "86 14567 14567 14567 14567 14567 14567 \n", - "87 4392 4392 4392 4392 4392 4392 \n", - "88 1647 1647 1647 1647 1647 1647 \n", - "89 1521816 1521816 1521816 1521816 1521816 1521816 \n", - "90 3089 3089 3089 3089 3089 3089 \n", - "91 9743 9743 9743 9743 9743 9743 \n", - "92 11848 11848 11848 11848 11848 11848 \n", - "0 2027418 2027418 2027418 2027418 2027418 2027418 \n", - "\n", - "[34 rows x 1147 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "newSet = pd.concat([new_data,df_China_combined])\n", - "newSet.loc[(newSet['Country/Region'] == \"China\")]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Recuperation des donnees pour les pays d'interet listes ci dessus\n", - "\n", - "On cree une liste avec les pays d'interet \"interest_countries\".\n", - "\n", - "On recupere par la suite un sous jeu de donnees avec uniquement ces pays et \"NA\" en province. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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24279NaNBelgium50.8333004.469936Uruguay-32.522800-55.76580000000...47176554717655472779547277954727795472779547277954727795472779547393657617.07617761776177617.07617.07617761776177617
71280NaNHong Kong22.300000114.200000Uzbekistan41.37749164.58526200000022588...28761062876106287610628761062876106287610628761062876106287610628761061637.01637163716371637.01637.01637163716371637
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162NaNKorea, South35.907757127.766922112234...305260123053357330543981305551023055510230569215305814993059429730605187306155225708.05708570857085708.05708.05708570857085708
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14 rows × 1147 columns

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289 rows × 1147 columns

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" ], "text/plain": [ - " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", - "24 NaN Belgium 50.833300 4.469936 0 0 \n", - "71 NaN Hong Kong 22.300000 114.200000 0 2 \n", - "131 NaN France 46.227600 2.213700 0 0 \n", - "135 NaN Germany 51.165691 10.451526 0 0 \n", - "150 NaN Iran 32.427908 53.688046 0 0 \n", - "154 NaN Italy 41.871940 12.567380 0 0 \n", - "156 NaN Japan 36.204824 138.252924 2 2 \n", - "162 NaN Korea, South 35.907757 127.766922 1 1 \n", - "200 NaN Netherlands 52.132600 5.291300 0 0 \n", - "218 NaN Portugal 39.399900 -8.224500 0 0 \n", - "241 NaN Spain 40.463667 -3.749220 0 0 \n", - "260 NaN US 40.000000 -100.000000 1 1 \n", - "278 NaN United Kingdom 55.378100 -3.436000 0 0 \n", - "0 NaN China NaN NaN 548 641 \n", + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", "\n", - 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"[14 rows x 1147 columns]" + "[289 rows x 1147 columns]" ] }, - "execution_count": 17, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "interest_countries = [\"Belgium\", \"China\", \"France\", \"Germany\", \"Hong Kong\", \"Iran\", \"Italy\", \"Japan\", \"Korea, South\", \"Netherlands\", \"Portugal\", \"Spain\", \"United Kingdom\", \"US\"]\n", - "df_allCountries = newSet.loc[(newSet['Country/Region'].isin(interest_countries)) & (newSet['Province/State'].isnull()) ,]\n", - "df_allCountries\n" + "clean_death_data = death_data.copy()\n", + "columns_to_study = clean_death_data.iloc[:,4:].columns\n", + "\n", + "for coord in table_of_errors : \n", + " clean_death_data.iloc[coord[0],coord[1]] = np.nan\n", + "clean_death_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Analyse de l'évolution du nombre de cas cumulés au cours du temps\n", - "\n", - "On transforma la table pour etre plus comprehensible par matplotlib pour faire le graphique - globalement on realise une transposition en supprimant les data lattitude/longitude pour le moment et en renommant les colonnes avec le nom du pays correspondant.\n" + "On fait les memes manipulations de gestion de pays pour Hong Kong et la Chine aue precedemment" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -5044,482 +10971,397 @@ "\n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - 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Province/StateCountry/RegionBelgiumHong KongFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUSUnited KingdomChinaLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
1/23/2059AnhuiChina31.8257117.2264020000210...7777777777
60BeijingChina40.1824116.41420001064101...20202020202020202020
1/24/2061ChongqingChina30.0572107.87400220002200...11111111111111111111
62FujianChina26.0789117.98740002091800...2222222222
1/25/2063GansuChina35.7518104.286100053000...222222222000201401
1/26/20083064GuangdongChina23.3417113.42440043000502067...10101010101010101010
1/27/20083165GuangxiChina23.8298108.78810044000502869
\n", - "" - ], - "text/plain": [ - "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", - "1/23/20 0 2 0 0 0 0 2 1 \n", - "1/24/20 0 2 2 0 0 0 2 2 \n", - "1/25/20 0 5 3 0 0 0 2 2 \n", - "1/26/20 0 8 3 0 0 0 4 3 \n", - "1/27/20 0 8 3 1 0 0 4 4 \n", - "\n", - "Country/Region Netherlands Portugal Spain US United Kingdom China \n", - "1/23/20 0 0 0 1 0 641 \n", - "1/24/20 0 0 0 2 0 918 \n", - "1/25/20 0 0 0 2 0 1401 \n", - "1/26/20 0 0 0 5 0 2067 \n", - "1/27/20 0 0 0 5 0 2869 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_allCountries_final = df_allCountries.transpose()[5:]\n", - "df_allCountries_final.columns = df_allCountries[\"Country/Region\"]\n", - "df_allCountries_final.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On reformatte les dates " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2020-01-230200066GuizhouChina26.8154106.874802100010641
2020-01-240...22222220002200020918
2020-01-250530067HainanChina19.1959109.74530220002014011...6666666666
2020-01-2608368HebeiChina39.5490116.1306011111...7777777777
69HeilongjiangChina47.8620127.761500431111...18181818181818181818
70HenanChina37.8957114.904200050206711...23232323232323232323
2020-01-27083172HubeiChina30.9756112.2707171724405276...4515451545154515451545154515451545154515
73HunanChina27.6104111.70880044000502869...4444444444
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" - ], - "text/plain": [ - "Country/Region Belgium Hong Kong France Germany Iran Italy Japan Korea, South \\\n", - "2020-01-23 0 2 0 0 0 0 2 1 \n", - "2020-01-24 0 2 2 0 0 0 2 2 \n", - "2020-01-25 0 5 3 0 0 0 2 2 \n", - "2020-01-26 0 8 3 0 0 0 4 3 \n", - "2020-01-27 0 8 3 1 0 0 4 4 \n", - "\n", - "Country/Region Netherlands Portugal Spain US United Kingdom China \n", - "2020-01-23 0 0 0 1 0 641 \n", - "2020-01-24 0 0 0 2 0 918 \n", - "2020-01-25 0 0 0 2 0 1401 \n", - "2020-01-26 0 0 0 5 0 2067 \n", - "2020-01-27 0 0 0 5 0 2869 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_dates = pd.to_datetime(df_allCountries_final.index)\n", - "df_allCountries_final.index = all_dates\n", - "df_allCountries_final.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On plot le graph en format classique avec le nombre de cas en fonction des jours, en utilisant un code couleur pour les pays consideres. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "color = cm.rainbow(np.linspace(0, 1, len(interest_countries)))\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "df_allCountries_final.plot(ax=ax, color=color)\n", - "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On modifie l'echelle des y en log pour mieux voir les distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "plt.yscale(\"log\") \n", - "df_allCountries_final.plot(ax=ax, color=color)\n", - "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "C'est donc les USA ayant eu le plus de cas recences, mais a normaliser par le nombre d'habitant global de chaque territoire et/ou du nombre de deces. \n", - "\n", - "## Question subsidiaire\n", - "\n", - "On recupere les donnees de deces en faisant une copie local au besoin. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "death_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n", - "death_file = \"time_series_covid19_deaths_global.csv\"\n", - "\n", - "if not os.path.isfile(death_file):\n", - " urlretrieve(death_url, death_file) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comme precedemment, on se doit de regarder les donnees apres chargement." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(289, 1147)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "death_data = pd.read_csv(death_file)\n", - "death_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5527,23 +11369,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5551,23 +11393,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5575,23 +11417,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5599,23 +11441,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5623,47 +11465,167 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5671,23 +11633,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5695,23 +11657,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5719,71 +11681,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5791,23 +11705,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5815,23 +11729,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5839,23 +11753,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5863,23 +11777,248 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
74Inner MongoliaChina44.0935113.9448000000...1111111111
0NaNAfghanistan33.93911067.70995375JiangsuChina32.9711119.455000000...78967896789678967896789678967896789678960000000000
1NaNAlbania41.15330020.16830076JiangxiChina27.6140115.722100000...35983598359835983598359835983598359835982222222222
2NaNAlgeria28.0339001.65960077JilinChina43.6661126.192300000...68816881688168816881688168816881688168815555555555
3NaNAndorra42.5063001.52180078LiaoningChina41.2956122.608500000...1651651651651651651651651651652222222222
4NaNAngola-11.20270017.87390079MacauChina22.1667113.550000000...1933193319331933193319331933193319331933121121121121121121121121121121
5NaNAntarctica-71.94990023.34700080NingxiaChina37.2692106.1655000000...0000000000
81QinghaiChina35.745295.9956000000...0000000000
82ShaanxiChina35.1917108.8701000000...5555555555
83ShandongChina36.3427118.1498000000...10101010101010101010
84ShanghaiChina31.2020121.4491000011...595595595595595595595595595595
85ShanxiChina37.5777112.29220000000000...1111111111
6NaNAntigua and Barbuda17.060800-61.79640086SichuanChina30.6171102.710300000...14614614614614614614614614614612121212121212121212
7NaNArgentina-38.416100-63.61670087TianjinChina39.3054117.323000000...1304631304631304631304631304631304631304721304721304721304723333333333
8NaNArmenia40.06910045.03820088TibetChina31.692788.092400000...8721872187218721872187218721872187278727
9Australian Capital TerritoryAustralia-35.473500149.012400000000...224224228228228228228228228228
10New South WalesAustralia-33.868800151.209300000000...6464646464936493649364936493649364936529
11Northern TerritoryAustralia-12.463400130.84560089UnknownChinaNaNNaN00000...9090909090909090909182195821958219582195821958219582195821958219582195
12QueenslandAustralia-27.469800153.02510090XinjiangChina41.112985.240100000...27602760278327832783278327832783278327833333333333
13South AustraliaAustralia-34.928500138.60070091YunnanChina24.9740101.487000000...13221322132213221322132213221322132213654444444444
14TasmaniaAustralia-42.882100147.32720092ZhejiangChina29.1832120.093400000...2522522522532532532532532532561111111111
15VictoriaAustralia-37.813600144.9631000NaNChinaNaNNaN171826425682...87589875898758987589875898758987589875898758987589
\n", + "

34 rows × 1147 columns

\n", + "
" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n", + "59 Anhui China 31.8257 117.2264 0 0 0 \n", + "60 Beijing China 40.1824 116.4142 0 0 0 \n", + "61 Chongqing China 30.0572 107.8740 0 0 0 \n", + "62 Fujian China 26.0789 117.9874 0 0 0 \n", + "63 Gansu China 35.7518 104.2861 0 0 0 \n", + "64 Guangdong China 23.3417 113.4244 0 0 0 \n", + "65 Guangxi China 23.8298 108.7881 0 0 0 \n", + "66 Guizhou China 26.8154 106.8748 0 0 0 \n", + "67 Hainan China 19.1959 109.7453 0 0 0 \n", + "68 Hebei China 39.5490 116.1306 0 1 1 \n", + "69 Heilongjiang China 47.8620 127.7615 0 0 1 \n", + "70 Henan China 37.8957 114.9042 0 0 0 \n", + "72 Hubei China 30.9756 112.2707 17 17 24 \n", + "73 Hunan China 27.6104 111.7088 0 0 0 \n", + "74 Inner Mongolia China 44.0935 113.9448 0 0 0 \n", + "75 Jiangsu China 32.9711 119.4550 0 0 0 \n", + "76 Jiangxi China 27.6140 115.7221 0 0 0 \n", + "77 Jilin China 43.6661 126.1923 0 0 0 \n", + "78 Liaoning China 41.2956 122.6085 0 0 0 \n", + "79 Macau China 22.1667 113.5500 0 0 0 \n", + "80 Ningxia China 37.2692 106.1655 0 0 0 \n", + "81 Qinghai China 35.7452 95.9956 0 0 0 \n", + "82 Shaanxi China 35.1917 108.8701 0 0 0 \n", + "83 Shandong China 36.3427 118.1498 0 0 0 \n", + "84 Shanghai China 31.2020 121.4491 0 0 0 \n", + "85 Shanxi China 37.5777 112.2922 0 0 0 \n", + "86 Sichuan China 30.6171 102.7103 0 0 0 \n", + "87 Tianjin China 39.3054 117.3230 0 0 0 \n", + "88 Tibet China 31.6927 88.0924 0 0 0 \n", + "89 Unknown China NaN NaN 0 0 0 \n", + "90 Xinjiang China 41.1129 85.2401 0 0 0 \n", + "91 Yunnan China 24.9740 101.4870 0 0 0 \n", + "92 Zhejiang China 29.1832 120.0934 0 0 0 \n", + "0 NaN China NaN NaN 17 18 26 \n", + "\n", + " 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 3/5/23 \\\n", + "59 0 0 0 ... 7 7 7 7 7 7 \n", + "60 0 0 1 ... 20 20 20 20 20 20 \n", + "61 0 0 0 ... 11 11 11 11 11 11 \n", + "62 0 0 0 ... 2 2 2 2 2 2 \n", + "63 0 0 0 ... 2 2 2 2 2 2 \n", + "64 0 0 0 ... 10 10 10 10 10 10 \n", + "65 0 0 0 ... 2 2 2 2 2 2 \n", + "66 0 0 0 ... 2 2 2 2 2 2 \n", + "67 0 0 1 ... 6 6 6 6 6 6 \n", + "68 1 1 1 ... 7 7 7 7 7 7 \n", + "69 1 1 1 ... 18 18 18 18 18 18 \n", + "70 0 1 1 ... 23 23 23 23 23 23 \n", + "72 40 52 76 ... 4515 4515 4515 4515 4515 4515 \n", + "73 0 0 0 ... 4 4 4 4 4 4 \n", + "74 0 0 0 ... 1 1 1 1 1 1 \n", + "75 0 0 0 ... 0 0 0 0 0 0 \n", + "76 0 0 0 ... 2 2 2 2 2 2 \n", + "77 0 0 0 ... 5 5 5 5 5 5 \n", + "78 0 0 0 ... 2 2 2 2 2 2 \n", + "79 0 0 0 ... 121 121 121 121 121 121 \n", + "80 0 0 0 ... 0 0 0 0 0 0 \n", + "81 0 0 0 ... 0 0 0 0 0 0 \n", + "82 0 0 0 ... 5 5 5 5 5 5 \n", + "83 0 0 0 ... 10 10 10 10 10 10 \n", + "84 0 1 1 ... 595 595 595 595 595 595 \n", + "85 0 0 0 ... 1 1 1 1 1 1 \n", + "86 0 0 0 ... 12 12 12 12 12 12 \n", + "87 0 0 0 ... 3 3 3 3 3 3 \n", + "88 0 0 0 ... 0 0 0 0 0 0 \n", + "89 0 0 0 ... 82195 82195 82195 82195 82195 82195 \n", + "90 0 0 0 ... 3 3 3 3 3 3 \n", + "91 0 0 0 ... 4 4 4 4 4 4 \n", + "92 0 0 0 ... 1 1 1 1 1 1 \n", + "0 42 56 82 ... 87589 87589 87589 87589 87589 87589 \n", + "\n", + " 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "59 7 7 7 7 \n", + "60 20 20 20 20 \n", + "61 11 11 11 11 \n", + "62 2 2 2 2 \n", + "63 2 2 2 2 \n", + "64 10 10 10 10 \n", + "65 2 2 2 2 \n", + "66 2 2 2 2 \n", + "67 6 6 6 6 \n", + "68 7 7 7 7 \n", + "69 18 18 18 18 \n", + "70 23 23 23 23 \n", + "72 4515 4515 4515 4515 \n", + "73 4 4 4 4 \n", + "74 1 1 1 1 \n", + "75 0 0 0 0 \n", + "76 2 2 2 2 \n", + "77 5 5 5 5 \n", + "78 2 2 2 2 \n", + "79 121 121 121 121 \n", + "80 0 0 0 0 \n", + "81 0 0 0 0 \n", + "82 5 5 5 5 \n", + "83 10 10 10 10 \n", + "84 595 595 595 595 \n", + "85 1 1 1 1 \n", + "86 12 12 12 12 \n", + "87 3 3 3 3 \n", + "88 0 0 0 0 \n", + "89 82195 82195 82195 82195 \n", + "90 3 3 3 3 \n", + "91 4 4 4 4 \n", + "92 1 1 1 1 \n", + "0 87589 87589 87589 87589 \n", + "\n", + "[34 rows x 1147 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data_death= clean_death_data.copy()\n", + "# Hong Kong \n", + "new_data_death.loc[(new_data_death['Province/State'] == \"Hong Kong\"),'Country/Region'] = \"Hong Kong\"\n", + "new_data_death.loc[(new_data_death['Province/State'] == \"Hong Kong\"),'Province/State'] = np.nan\n", + "new_data_death.loc[(new_data_death['Country/Region'] == \"Hong Kong\")]\n", + "# China\n", + "df_china_death = new_data_death.loc[(new_data['Country/Region'] == \"China\")]\n", + "df_China_death_combined = df_china_death.sum()\n", + "df_China_death_combined[\"Province/State\"] = np.nan\n", + "df_China_death_combined[\"Lat\"] = np.nan\n", + "df_China_death_combined[\"Long\"] = np.nan\n", + "df_China_death_combined[\"Country/Region\"] = \"China\"\n", + "df_China_death_combined = pd.DataFrame(df_China_death_combined)\n", + "df_China_death_combined = df_China_death_combined.transpose()\n", + "# compilation of data\n", + "newSet_death = pd.concat([new_data_death,df_China_death_combined])\n", + "newSet_death.loc[(newSet['Country/Region'] == \"China\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On selectionne alors uniquement les pays d'interet comme precedemment\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5887,23 +12026,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5911,23 +12050,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5935,23 +12074,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5959,23 +12098,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5983,23 +12122,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6007,23 +12146,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6031,23 +12170,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6055,23 +12194,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6079,23 +12218,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6103,23 +12242,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6127,23 +12266,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6151,23 +12290,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6175,102 +12314,187 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
24NaNBelgium50.8333004.46993600000...731773177338733873387338733873387338737033717337173377533775337753377533775337753377533814
16Western AustraliaAustralia-31.950500115.86050071NaNHong Kong22.300000114.20000000000...94494495295295295295295295295213459134621346213463134641346513466134661346613467
17131NaNAustria47.51620014.550100France46.2276002.21370000000...21887218912189921907219212192221923219412194921970161340161365161386161407161407161407161450161474161501161512
18135NaNAzerbaijan40.14310047.576900Germany51.16569110.45152600000...10119101191012210126101271012910129101351013810138168086168175168296168397168397168397168397168709168808168935
19150NaNBahamas25.025885-78.035889Iran32.42790853.68804600000...833833833833833833833833833833144845144858144864144867144878144893144902144907144922144933
20154NaNBahrain26.02750050.550000Italy41.87194012.56738000000...1548154915501552155215521552155315531553188094188094188094188322188322188322188322188322188322188322
21156NaNBangladesh23.68500090.356300Japan36.204824138.25292400000...2944529445294452944529445294452944529445294452944572395724947258172648727297277972813728487291772997
22162NaNBarbados13.193900-59.543200Korea, South35.907757127.76692200000...57557557557557557557557557557933988340033401434020340203403434049340613408134093
23200NaNBelarus53.70980027.953400Netherlands52.1326005.29130000000...711871187118711871187118711871187118711822990229902299022990229902299022990229902299022990
24218NaNBelgium50.8333004.469936Portugal39.399900-8.22450000000...3371733717337753377533775337753377533775337753381426117261802618026180261802618026180261802626626266
25241NaNBelize17.189900-88.497600Spain40.463667-3.74922000000...688688688688688688688688688688119380119380119380119479119479119479119479119479119479119479
26260NaNBenin9.3077002.315800US40.000000-100.00000000000...1631631631631631631631631631631.11992e+06112089711216581122165NaNNaN1122181112251611232461123836
27278NaNBhutan27.51420090.433600United Kingdom55.378100-3.43600000000...21212121212121212121219948219948219948219948219948219948219948219948219948219948
280NaNBolivia-16.290200-63.588700ChinaNaNNaN171826425682...87589875898758987589875898758987589875898758987589
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14 rows × 1147 columns

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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "24 NaN Belgium 50.833300 4.469936 0 0 \n", + "71 NaN Hong Kong 22.300000 114.200000 0 0 \n", + "131 NaN France 46.227600 2.213700 0 0 \n", + "135 NaN Germany 51.165691 10.451526 0 0 \n", + "150 NaN Iran 32.427908 53.688046 0 0 \n", + "154 NaN Italy 41.871940 12.567380 0 0 \n", + "156 NaN Japan 36.204824 138.252924 0 0 \n", + "162 NaN Korea, South 35.907757 127.766922 0 0 \n", + "200 NaN Netherlands 52.132600 5.291300 0 0 \n", + "218 NaN Portugal 39.399900 -8.224500 0 0 \n", + "241 NaN Spain 40.463667 -3.749220 0 0 \n", + "260 NaN US 40.000000 -100.000000 0 0 \n", + "278 NaN United Kingdom 55.378100 -3.436000 0 0 \n", + "0 NaN China NaN NaN 17 18 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "24 0 0 0 0 ... 33717 33717 33775 \n", + "71 0 0 0 0 ... 13459 13462 13462 \n", + "131 0 0 0 0 ... 161340 161365 161386 \n", + "135 0 0 0 0 ... 168086 168175 168296 \n", + "150 0 0 0 0 ... 144845 144858 144864 \n", + "154 0 0 0 0 ... 188094 188094 188094 \n", + "156 0 0 0 0 ... 72395 72494 72581 \n", + "162 0 0 0 0 ... 33988 34003 34014 \n", + "200 0 0 0 0 ... 22990 22990 22990 \n", + "218 0 0 0 0 ... 26117 26180 26180 \n", + "241 0 0 0 0 ... 119380 119380 119380 \n", + "260 0 0 0 0 ... 1.11992e+06 1120897 1121658 \n", + "278 0 0 0 0 ... 219948 219948 219948 \n", + "0 26 42 56 82 ... 87589 87589 87589 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "24 33775 33775 33775 33775 33775 33775 33814 \n", + "71 13463 13464 13465 13466 13466 13466 13467 \n", + "131 161407 161407 161407 161450 161474 161501 161512 \n", + "135 168397 168397 168397 168397 168709 168808 168935 \n", + "150 144867 144878 144893 144902 144907 144922 144933 \n", + "154 188322 188322 188322 188322 188322 188322 188322 \n", + "156 72648 72729 72779 72813 72848 72917 72997 \n", + "162 34020 34020 34034 34049 34061 34081 34093 \n", + "200 22990 22990 22990 22990 22990 22990 22990 \n", + "218 26180 26180 26180 26180 26180 26266 26266 \n", + "241 119479 119479 119479 119479 119479 119479 119479 \n", + "260 1122165 NaN NaN 1122181 1122516 1123246 1123836 \n", + "278 219948 219948 219948 219948 219948 219948 219948 \n", + "0 87589 87589 87589 87589 87589 87589 87589 \n", + "\n", + "[14 rows x 1147 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interest_countries = [\"Belgium\", \"China\", \"France\", \"Germany\", \"Hong Kong\", \"Iran\", \"Italy\", \"Japan\", \"Korea, South\", \"Netherlands\", \"Portugal\", \"Spain\", \"United Kingdom\", \"US\"]\n", + "df_allCountries_death = newSet_death.loc[(newSet_death['Country/Region'].isin(interest_countries)) & (newSet_death['Province/State'].isnull()) ,]\n", + "df_allCountries_death" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true, + "hideOutput": true + }, + "source": [ + "On supprime les informations de lattitude/longitude et on reformatte les dates" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6521,140 +12653,99 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - 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Country/RegionBelgiumHong KongFranceGermanyIranItalyJapanKorea, SouthNetherlandsPortugalSpainUSUnited KingdomChina
2020-01-23000000...22365223652236522365223652236522365223652236522365
29NaNBosnia and Herzegovina43.91590017.679100000000...16278162791627916279162791627916279162791628016280
..................................................................018
259NaNTuvalu-7.109500177.6493002020-01-24000000...00000026
2020-01-25000
260NaNUS40.000000-100.000000000000...1119917112089711216581122165112217211221341122181112251611232461123836000042
261NaNUganda1.37333332.2902752020-01-26000000...3630363036303630363036303630363036303630
262NaNUkraine48.37940031.165600000000...119149119209119210119211119212119213119216119217119281119283056
263NaNUnited Arab Emirates23.42407653.8478182020-01-27000000...2349234923492349234923492349234923492349
264AnguillaUnited Kingdom18.220600-63.068600000000...12121212121212121212082
265BermudaUnited Kingdom32.307800-64.7505002020-01-28000000...160160160160160160160160160160
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...646464646464646464640131
267Cayman IslandsUnited Kingdom19.313300-81.2546002020-01-29000000...373737373737373737370000000133
268Channel IslandsUnited Kingdom49.372300-2.3644002020-01-300000000000...001171
2020-01-3100000000001213
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.5236002020-02-01000000...0000001259
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270GibraltarUnited Kingdom36.140800-5.3536002020-02-03000000...1111111111111111111111111111110000002425
271GuernseyUnited Kingdom49.448196-2.5894902020-02-040100000...66666666666666666666000002490
272Isle of ManUnited Kingdom54.236100-4.5481002020-02-050100000000002562
2020-02-06010000...116116116116116116116116116116
273JerseyUnited Kingdom49.213800-2.135800000000...1611611611611611611611611611612632
274MontserratUnited Kingdom16.742498-62.1873662020-02-070100000...8888888888
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000002717
2020-02-080...10000002804
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.3559002020-02-090100000...000002904
2020-02-10010000
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...3838383838383838383821011
278NaNUnited Kingdom55.378100-3.43600002020-02-11010000...219948219948219948219948219948219948219948219948219948219948
279NaNUruguay-32.522800-55.765800000000...761776177617761776177617761776177617761721111
280NaNUzbekistan41.37749164.5852622020-02-120100000...16371637163716371637163716371637163716370000021116
281NaNVanuatu-15.376700166.9592002020-02-13010000100...1414141414141414141400021368
282NaNVenezuela6.423800-66.5897002020-02-1401000010...5853585458545854585458545854585458545854000021520
283NaNVietnam14.058324108.2771992020-02-150110001000...431864318643186431864318643186431864318643186431860021662
284NaNWest Bank and Gaza31.95220035.2332002020-02-16011000100...570857085708570857085708570857085708570800021765
285NaNWinter Olympics 202239.904200116.4074002020-02-1701100010000...021863
2020-02-1801100020000022002
286NaNYemen15.55272748.5163882020-02-1902102020000...2159215921592159215921592159215921592159022114
287NaNZambia-13.13389727.8493322020-02-2002102021000...4057405740574057405740574057405740574057022236
288NaNZimbabwe-19.01543829.1548572020-02-2102104122000022236
.............................................5663566856685668566856685668566856715671
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289 rows × 1147 columns

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" - ], - "text/plain": [ - " Province/State Country/Region \\\n", - "0 NaN Afghanistan \n", - "1 NaN Albania \n", - "2 NaN Algeria \n", - "3 NaN Andorra \n", - "4 NaN Angola \n", - "5 NaN Antarctica \n", - "6 NaN Antigua and Barbuda \n", - "7 NaN Argentina \n", - "8 NaN Armenia \n", - "9 Australian Capital Territory Australia \n", - "10 New South Wales Australia \n", - "11 Northern Territory Australia \n", - "12 Queensland Australia \n", - "13 South Australia Australia \n", - "14 Tasmania Australia \n", - "15 Victoria Australia \n", - "16 Western Australia Australia \n", - "17 NaN Austria \n", - "18 NaN Azerbaijan \n", - "19 NaN Bahamas \n", - "20 NaN Bahrain \n", - "21 NaN Bangladesh \n", - "22 NaN Barbados \n", - "23 NaN Belarus \n", - "24 NaN Belgium \n", - "25 NaN Belize \n", - "26 NaN Benin \n", - "27 NaN Bhutan \n", - "28 NaN Bolivia \n", - "29 NaN Bosnia and Herzegovina \n", - ".. ... ... \n", - "259 NaN Tuvalu \n", - "260 NaN US \n", - "261 NaN Uganda \n", - "262 NaN Ukraine \n", - "263 NaN United Arab Emirates \n", - "264 Anguilla United Kingdom \n", - "265 Bermuda United Kingdom \n", - "266 British Virgin Islands United Kingdom \n", - "267 Cayman Islands United Kingdom \n", - "268 Channel Islands United Kingdom \n", - "269 Falkland Islands (Malvinas) United Kingdom \n", - "270 Gibraltar United Kingdom \n", - "271 Guernsey United Kingdom \n", - "272 Isle of Man United Kingdom \n", - "273 Jersey United Kingdom \n", - "274 Montserrat United Kingdom \n", - "275 Pitcairn Islands United Kingdom \n", - "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", - "277 Turks and Caicos Islands United Kingdom \n", - "278 NaN United Kingdom \n", - "279 NaN Uruguay \n", - "280 NaN Uzbekistan \n", - "281 NaN Vanuatu \n", - "282 NaN Venezuela \n", - "283 NaN Vietnam \n", - "284 NaN West Bank and Gaza \n", - "285 NaN Winter Olympics 2022 \n", - "286 NaN Yemen \n", - "287 NaN Zambia \n", - "288 NaN Zimbabwe \n", - "\n", - " Lat Long 1/22/20 1/23/20 1/24/20 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1142 rows × 14 columns

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" 3/6/23 3/7/23 3/8/23 3/9/23 \n", - "0 7896 7896 7896 7896 \n", - "1 3598 3598 3598 3598 \n", - "2 6881 6881 6881 6881 \n", - "3 165 165 165 165 \n", - "4 1933 1933 1933 1933 \n", - "5 0 0 0 0 \n", - "6 146 146 146 146 \n", - "7 130472 130472 130472 130472 \n", - "8 8721 8721 8727 8727 \n", - "9 228 228 228 228 \n", - "10 6493 6493 6493 6529 \n", - "11 90 90 90 91 \n", - "12 2783 2783 2783 2783 \n", - "13 1322 1322 1322 1365 \n", - "14 253 253 253 256 \n", - "15 7338 7338 7338 7370 \n", - "16 952 952 952 952 \n", - "17 21923 21941 21949 21970 \n", - "18 10129 10135 10138 10138 \n", - "19 833 833 833 833 \n", - "20 1552 1553 1553 1553 \n", - "21 29445 29445 29445 29445 \n", - "22 575 575 575 579 \n", - "23 7118 7118 7118 7118 \n", - "24 33775 33775 33775 33814 \n", - "25 688 688 688 688 \n", - "26 163 163 163 163 \n", - "27 21 21 21 21 \n", - "28 22365 22365 22365 22365 \n", - "29 16279 16279 16280 16280 \n", - ".. ... ... ... ... \n", - "259 0 0 0 0 \n", - "260 1122181 1122516 1123246 1123836 \n", - "261 3630 3630 3630 3630 \n", - "262 119216 119217 119281 119283 \n", - "263 2349 2349 2349 2349 \n", - "264 12 12 12 12 \n", - "265 160 160 160 160 \n", - "266 64 64 64 64 \n", - "267 37 37 37 37 \n", - "268 0 0 0 0 \n", - "269 0 0 0 0 \n", - "270 111 111 111 111 \n", - "271 66 66 66 66 \n", - "272 116 116 116 116 \n", - "273 161 161 161 161 \n", - "274 8 8 8 8 \n", - "275 0 0 0 0 \n", - "276 0 0 0 0 \n", - "277 38 38 38 38 \n", - "278 219948 219948 219948 219948 \n", - "279 7617 7617 7617 7617 \n", - "280 1637 1637 1637 1637 \n", - "281 14 14 14 14 \n", - "282 5854 5854 5854 5854 \n", - "283 43186 43186 43186 43186 \n", - "284 5708 5708 5708 5708 \n", - "285 0 0 0 0 \n", - "286 2159 2159 2159 2159 \n", - "287 4057 4057 4057 4057 \n", - "288 5668 5668 5671 5671 \n", + "Country/Region United Kingdom China \n", + "2020-01-23 0 18 \n", + "2020-01-24 0 26 \n", + "2020-01-25 0 42 \n", + "2020-01-26 0 56 \n", + "2020-01-27 0 82 \n", + "2020-01-28 0 131 \n", + "2020-01-29 0 133 \n", + "2020-01-30 1 171 \n", + "2020-01-31 1 213 \n", + "2020-02-01 1 259 \n", + "2020-02-02 2 361 \n", + "2020-02-03 2 425 \n", + "2020-02-04 2 490 \n", + "2020-02-05 2 562 \n", + "2020-02-06 2 632 \n", + "2020-02-07 2 717 \n", + "2020-02-08 2 804 \n", + "2020-02-09 2 904 \n", + "2020-02-10 2 1011 \n", + "2020-02-11 2 1111 \n", + "2020-02-12 2 1116 \n", + "2020-02-13 2 1368 \n", + "2020-02-14 2 1520 \n", + "2020-02-15 2 1662 \n", + "2020-02-16 2 1765 \n", + "2020-02-17 2 1863 \n", + "2020-02-18 2 2002 \n", + "2020-02-19 2 2114 \n", + "2020-02-20 2 2236 \n", + "2020-02-21 2 2236 \n", + "... ... ... \n", + "2023-02-08 219819 87589 \n", + "2023-02-09 219880 87589 \n", + "2023-02-10 219948 87589 \n", + "2023-02-11 219948 87589 \n", + "2023-02-12 219948 87589 \n", + "2023-02-13 219948 87589 \n", + "2023-02-14 219948 87589 \n", + "2023-02-15 219948 87589 \n", + "2023-02-16 219948 87589 \n", + "2023-02-17 219948 87589 \n", + "2023-02-18 219948 87589 \n", + "2023-02-19 219948 87589 \n", + "2023-02-20 219948 87589 \n", + "2023-02-21 219948 87589 \n", + "2023-02-22 219948 87589 \n", + "2023-02-23 219948 87589 \n", + "2023-02-24 219948 87589 \n", + "2023-02-25 219948 87589 \n", + "2023-02-26 219948 87589 \n", + "2023-02-27 219948 87589 \n", + "2023-02-28 219948 87589 \n", + "2023-03-01 219948 87589 \n", + "2023-03-02 219948 87589 \n", + "2023-03-03 219948 87589 \n", + "2023-03-04 219948 87589 \n", + "2023-03-05 219948 87589 \n", + "2023-03-06 219948 87589 \n", + "2023-03-07 219948 87589 \n", + "2023-03-08 219948 87589 \n", + "2023-03-09 219948 87589 \n", "\n", - "[289 rows x 1147 columns]" + "[1142 rows x 14 columns]" ] }, - "execution_count": 24, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "death_data" + "df_allCountries_death_final = df_allCountries_death.transpose()[5:]\n", + "df_allCountries_death_final.columns = df_allCountries_death[\"Country/Region\"]\n", + "\n", + "all_dates = pd.to_datetime(df_allCountries_death_final.index)\n", + "df_allCountries_death_final.index = all_dates\n", + "df_allCountries_death_final" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Le format est donc identique aux donnees precedentes et peuvent etre traitees de la meme maniere apres les memes verifications\n", - "* presence de donnees manquantes " + "On regarde alors la distribution des deces dans les pays concernes. Attention ici on est en effectif cummule et ces donnees ne sont pas normalisees par le taux de deces classiquement observe hors epidemie de covid ni par la population de ces territoires. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "columns_to_study = death_data.iloc[:,4:].columns\n", - "\n", - "for i in raw_data.index : \n", - " for d in range(len(columns_to_study[:-1])):\n", - " if (pd.isna(death_data.iloc[i,d+4]) or death_data.iloc[i,d+4]<0):\n", - " print(death_data.iloc[i,d+4])" - ] - }, - { - "cell_type": "markdown", + "execution_count": 34, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "--> pas de donnees manquantes\n", - "* incoherence avec un nombre de deces commule qui decroit ?" + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "df_allCountries_death_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "flag = 0\n", - "for i in death_data.index : \n", - " for d in range(len(columns_to_study[:-1])):\n", - " if (int(death_data.iloc[i,d+4]) > int(death_data.iloc[i,d+4 +1])):\n", - " data_problem = (death_data.iloc[i, 1:2], columns_to_study[d], columns_to_study[d+1])\n", - " flag = flag +1\n", - "print(\"Il y a %s donnees superieurs a celle de la donnee suivante\" % str(flag))" + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "plt.yscale(\"log\") \n", + "df_allCountries_death_final.plot(ax=ax, color=color)\n", + "ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "--> on a le meme type d'incoherence que precedemment, minoritaire comparee a la quantite de donnees, mais qu'il faudrait investiguer pour plus de fiabilite dans les donnees. Pour ce TP, je choisis de ne rien filtrer vu la faible proportion que cela represente. " + "On voit une fois encore aue ce sont les USA avec le plus grand effectif de deces, ce qui, compte tenu de sa population de environ 333 millions d'habitants n'est pas etonnant. \n", + "\n", + "Les epidemies se succedant (aujourd'hui 4 juillet 2024) malgre l'apparition des vaccins qui ont diminue le risque de mortalite, je vais m'arreter ici dans les analyses malgre une normalisation qui s'imposerait." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hideOutput": true + }, "outputs": [], "source": [] }