From be9ea82a5a719e018f6b275231c8779bca12bc31 Mon Sep 17 00:00:00 2001
From: 678362a9c2b5d46a6a161a762a6e18e0
 <678362a9c2b5d46a6a161a762a6e18e0@app-learninglab.inria.fr>
Date: Fri, 5 Mar 2021 16:28:53 +0000
Subject: [PATCH] data clean

---
 module3/exo3/exercice.ipynb | 258 +++++++++++++++++++++++++++++++++---
 1 file changed, 240 insertions(+), 18 deletions(-)

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 4c72dbb..148d8b9 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -12,7 +12,8 @@
     "### Prérequis\n",
     "\n",
     "Techniques de présentation graphique. Cet exercice peut être réalisé indifféremment en R ou en Python.\n",
-    "Sujet\n",
+    "\n",
+    "### Sujet\n",
     "\n",
     "Le but est ici de reproduire des graphes semblables à ceux du South China Morning Post (SCMP), sur la page The Coronavirus Pandemic et qui montrent pour différents pays le nombre cumulé (c'est-à-dire le nombre total de cas depuis le début de l'épidémie) de personnes atteintes de la maladie à coronavirus 2019.\n",
     "\n",
@@ -58,27 +59,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous pouvons maintenant télécharger les [données](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)."
+    "Nous pouvons maintenant télécharger les [données](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) si elles ne sont pas déjà téléchargés."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
-    "\n",
-    "data_file = \"data_covid.csv\"\n",
-    "\n",
-    "if not os.path.exists(data_file):\n",
-    "    urllib.request.urlretrieve(data_url, data_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1850,21 +1837,256 @@
        "[274 rows x 412 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# URL des données\n",
+    "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
+    "\n",
+    "# Nom du fichier csv\n",
+    "data_file = \"data_covid.csv\"\n",
+    "\n",
+    "# Téléchargement des données si elles ne sont pas déjà présentes dans le répertoire\n",
+    "if not os.path.exists(data_file):\n",
+    "    urllib.request.urlretrieve(data_url, data_file)\n",
+    "\n",
+    "# Affichage des données\n",
     "raw_data = pd.read_csv(data_file)\n",
     "raw_data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ces données sont organisés sur 274 lignes pour chaque pays et (pour le moment) 412 colonnes présentant la province, le pays, les latitudes et longitudes suivi du nombre de cas par jour du 22 Janvier 2020 au 4 Mars 2021 au moment de l'écriture de ce rapport.\n",
+    "\n",
+    "Filtrons les données en choisant les colonnes des dates la regex `\\d{1,2}\\/\\d{1,2}\\/\\d{2}` sélectionne les colonnes dont le nom commence par :\n",
+    "un ou deux chiffres suivi d'un \"/\", deux fois, puis se terminent par deux chiffres\n",
+    "c'est à dire les colonnes des dates (vous pouvez inverser la regex avec `[^\\d{1,2}\\/\\d{1,2}\\/\\d{2}]` pour constater que la commande fait bien l'inverse et nous rend des données avec des NaN)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Province/State</th>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th>Lat</th>\n",
+       "      <th>Long</th>\n",
+       "      <th>1/22/20</th>\n",
+       "      <th>1/23/20</th>\n",
+       "      <th>1/24/20</th>\n",
+       "      <th>1/25/20</th>\n",
+       "      <th>1/26/20</th>\n",
+       "      <th>1/27/20</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2/23/21</th>\n",
+       "      <th>2/24/21</th>\n",
+       "      <th>2/25/21</th>\n",
+       "      <th>2/26/21</th>\n",
+       "      <th>2/27/21</th>\n",
+       "      <th>2/28/21</th>\n",
+       "      <th>3/1/21</th>\n",
+       "      <th>3/2/21</th>\n",
+       "      <th>3/3/21</th>\n",
+       "      <th>3/4/21</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 412 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Province/State, Country/Region, Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20, 1/28/20, 1/29/20, 1/30/20, 1/31/20, 2/1/20, 2/2/20, 2/3/20, 2/4/20, 2/5/20, 2/6/20, 2/7/20, 2/8/20, 2/9/20, 2/10/20, 2/11/20, 2/12/20, 2/13/20, 2/14/20, 2/15/20, 2/16/20, 2/17/20, 2/18/20, 2/19/20, 2/20/20, 2/21/20, 2/22/20, 2/23/20, 2/24/20, 2/25/20, 2/26/20, 2/27/20, 2/28/20, 2/29/20, 3/1/20, 3/2/20, 3/3/20, 3/4/20, 3/5/20, 3/6/20, 3/7/20, 3/8/20, 3/9/20, 3/10/20, 3/11/20, 3/12/20, 3/13/20, 3/14/20, 3/15/20, 3/16/20, 3/17/20, 3/18/20, 3/19/20, 3/20/20, 3/21/20, 3/22/20, 3/23/20, 3/24/20, 3/25/20, 3/26/20, 3/27/20, 3/28/20, 3/29/20, 3/30/20, 3/31/20, 4/1/20, 4/2/20, 4/3/20, 4/4/20, 4/5/20, 4/6/20, 4/7/20, 4/8/20, 4/9/20, 4/10/20, 4/11/20, 4/12/20, 4/13/20, 4/14/20, 4/15/20, 4/16/20, 4/17/20, 4/18/20, 4/19/20, 4/20/20, 4/21/20, 4/22/20, 4/23/20, 4/24/20, 4/25/20, 4/26/20, ...]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 412 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.filter(regex=\"\\d{1,2}\\/\\d{1,2}\\/\\d{2}\").isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le tableau en sortie est bien vide, il ne manque donc aucune donnée importante.\n",
+    "\n",
+    "Listons les pays que nous allons analyser, ils sont tous représentés dans les données par le nom du pays dans la colonne `Country/Region` et `Nan` dans la colonne `Province/State` sauf dans le cas de la Chine ou nous allons devoir faire une somme de toutes les provinces d'un coté et de récupérer Hong-Knog de l'autre."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "countries = [ \n",
+    "        \"Belgium\",\n",
+    "        \"France\",\n",
+    "        \"Germany\",\n",
+    "        \"Iran\",\n",
+    "        \"Italy\",\n",
+    "        \"Japan\",\n",
+    "        \"Korea, South\",\n",
+    "        \"Netherlands\",\n",
+    "        \"Portugal\",\n",
+    "        \"Spain\",\n",
+    "        \"United Kingdom\",\n",
+    "        \"US\",\n",
+    "#         \"China\",\n",
+    "#         \"China, Hong-Kong\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enregistrons les lignes consernées dans un nouveau tableau :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[nan, 'France', 46.2276, 2.2137, 0, 0, 2, 3, 3, 3, 4, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 18, 38, 57, 100, 130, 191, 212, 285, 423, 613, 938, 1115, 1401, 1773, 2281, 2281, 3661, 4469, 4499, 6633, 7652, 9043, 10871, 12612, 14282, 16533, 19856, 22054, 24967, 28856, 32609, 37162, 39761, 44029, 51579, 56362, 58404, 63588, 46483, 47299, 49934, 46400, 50242, 54003, 55538, 56972, 107712, 110836, 128272, 131476, 144035, 145896, 149683, 150752, 153197, 155821, 157603, 155881, 158653, 160295, 160907, 164592, 167650, 166193, 167326, 167846, 168637, 168887, 169405, 170467, 174596, 175218, 175743, 176132, 176338, 176651, 177330, 177790, 178353, 178837, 179140, 179250, 179650, 180143, 180499, 180752, 181444, 181969, 181410, 181684, 181880, 182018, 185300, 185842, 187608, 187817, 187986, 187265, 184015, 188286, 188808, 189330, 189621, 189782, 190266, 190743, 191040, 191679, 192043, 192317, 192369, 192915, 193256, 193576, 194121, 194535, 194599, 194693, 195256, 195256, 195256, 196267, 196521, 196111, 196390, 196554, 197238, 197591, 197994, 197994, 197994, 198850, 199170, 199699, 200027, 200569, 200569, 200569, 201755, 201755, 202383, 202819, 203516, 203516, 203516, 205308, 205773, 206641, 207650, 208553, 208553, 208553, 210821, 211337, 212676, 213927, 215135, 215135, 215135, 218351, 219253, 220874, 222398, 224602, 224602, 224602, 229203, 230540, 232966, 235540, 238240, 241371, 244335, 244761, 246821, 250539, 255214, 259717, 263129, 267589, 269452, 272643, 272643, 283698, 290929, 296265, 301554, 304349, 309247, 316047, 322935, 331578, 340013, 346921, 351017, 357473, 365025, 374586, 383854, 394270, 400533, 406609, 414196, 423924, 433871, 446961, 460328, 470864, 476162, 485929, 497914, 513732, 529501, 543688, 554304, 558374, 566196, 578769, 591783, 603920, 620330, 632834, 637918, 648116, 666849, 684164, 704478, 731155, 747092, 755597, 768380, 790959, 820685, 845740, 877840, 907677, 920916, 941085, 967717, 1008483, 1050468, 1095708, 1102262, 1174279, 1207430, 1243855, 1290446, 1339640, 1374691, 1420870, 1525184, 1597139, 1549838, 1607147, 1667604, 1754202, 1792764, 1812919, 1834515, 1870380, 1902644, 1926314, 1958235, 1985427, 1994728, 2039938, 2068297, 2088784, 2111646, 2129287, 2142417, 2146869, 2155722, 2171967, 2184846, 2197283, 2209739, 2219502, 2223393, 2231344, 2245324, 2257894, 2269080, 2281889, 2292838, 2296249, 2309760, 2324300, 2337594, 2350923, 2364740, 2376228, 2379291, 2390419, 2407983, 2426063, 2441737, 2458839, 2471458, 2477255, 2488889, 2503693, 2525234, 2545358, 2548388, 2556621, 2559575, 2570767, 2597124, 2616902, 2636045, 2639404, 2651893, 2655915, 2676195, 2701215, 2722728, 2742349, 2762120, 2777921, 2781503, 2800770, 2824473, 2845701, 2866413, 2887532, 2903975, 2907711, 2931050, 2957547, 2979915, 3002910, 3026320, 3044358, 3048598, 3070458, 3097374, 3120634, 3143417, 3167274, 3186354, 3190701, 3213360, 3239256, 3238798, 3284085, 3303273, 3322988, 3327305, 3345558, 3370645, 3390952, 3410996, 3431805, 3447518, 3451894, 3471268, 3495775, 3517177, 3541282, 3562707, 3584326, 3588972, 3608271, 3639501, 3664050, 3689034, 3712474, 3732426, 3736390, 3759247, 3785326, 3810605]]\n",
+      "[[nan, 'Germany', 51.165690999999995, 10.451526, 0, 0, 0, 0, 0, 1, 4, 4, 4, 5, 8, 10, 12, 12, 12, 12, 13, 13, 14, 14, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 27, 46, 48, 79, 130, 159, 196, 262, 482, 670, 799, 1040, 1176, 1457, 1908, 2078, 3675, 4585, 5795, 7272, 9257, 12327, 15320, 19848, 22213, 24873, 29056, 32986, 37323, 43938, 50871, 57695, 62095, 66885, 71808, 77872, 84794, 91159, 96092, 100123, 103374, 107663, 113296, 118181, 122171, 124908, 127854, 130072, 131359, 134753, 137698, 141397, 143342, 145184, 147065, 148291, 150648, 153129, 154999, 156513, 157770, 158758, 159912, 161539, 163009, 164077, 164967, 165664, 166152, 167007, 168162, 169430, 170588, 171324, 171879, 172576, 173171, 174098, 174478, 175233, 175752, 176369, 176551, 177778, 178473, 179021, 179710, 179986, 180328, 180600, 181200, 181524, 182196, 182922, 183189, 183410, 183594, 183879, 184121, 184472, 184924, 185450, 185750, 186109, 186506, 186522, 186691, 187226, 187267, 187518, 187682, 188252, 188604, 189817, 190299, 190670, 191272, 191768, 192480, 192871, 193371, 194036, 194458, 194693, 195042, 195418, 195893, 196370, 196780, 197198, 197523, 198064, 198343, 198699, 199001, 199332, 199709, 199919, 200180, 200456, 200890, 201450, 202045, 202426, 202735, 203325, 203717, 204276, 204881, 205623, 206278, 206667, 207112, 207707, 208546, 209535, 210399, 211005, 211220, 212111, 212828, 214113, 215039, 216196, 216903, 217288, 218508, 219540, 220859, 222281, 223791, 224488, 225007, 226700, 228120, 229706, 231292, 233029, 233861, 234494, 236122, 237583, 239010, 240571, 242126, 242835, 243305, 244802, 246015, 247411, 248840, 250283, 251058, 251728, 253626, 254957, 256433, 258149, 259735, 260817, 261737, 263222, 265014, 266869, 269048, 271247, 272932, 273965, 275560, 277412, 279025, 281346, 283712, 285026, 286339, 288631, 290471, 292913, 295539, 298374, 300027, 301573, 304673, 307127, 311137, 315941, 320495, 323463, 326309, 332850, 337314, 344487, 352107, 359802, 364664, 368671, 377068, 385591, 397922, 403874, 417350, 427808, 437698, 450258, 463419, 479621, 498354, 517736, 531790, 544346, 569598, 577131, 608611, 631172, 653992, 668114, 682624, 689146, 715693, 738094, 762832, 785093, 799733, 802946, 817526, 843757, 867484, 891525, 914118, 927990, 932367, 946822, 963192, 995879, 1017830, 1038649, 1052494, 1055691, 1069912, 1094678, 1117953, 1141544, 1157514, 1183640, 1194550, 1200006, 1229269, 1254358, 1287092, 1314309, 1336101, 1350810, 1357261, 1391086, 1423830, 1454009, 1486839, 1508518, 1514962, 1534218, 1570371, 1604129, 1630596, 1632736, 1646240, 1658639, 1672643, 1692109, 1741153, 1760520, 1762637, 1773540, 1783896, 1796216, 1814565, 1841228, 1886561, 1905638, 1928462, 1929410, 1941116, 1968326, 1993892, 2015235, 2023828, 2038645, 2050129, 2059382, 2071615, 2100618, 2108895, 2125261, 2137691, 2147769, 2154656, 2164043, 2179679, 2194562, 2207393, 2224911, 2225659, 2232327, 2240017, 2252504, 2265536, 2276371, 2285003, 2291673, 2296323, 2302051, 2311297, 2321225, 2330422, 2336906, 2341744, 2346876, 2352766, 2362364, 2372209, 2381259, 2388421, 2394515, 2399499, 2405263, 2416037, 2427069, 2436506, 2444177, 2450295, 2455569, 2462061, 2472913, 2484306]]\n",
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+      "[[nan, 'US', 40.0, -100.0, 1, 1, 2, 2, 5, 5, 5, 6, 6, 8, 8, 8, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 16, 16, 16, 16, 16, 16, 17, 17, 25, 32, 55, 74, 107, 184, 237, 403, 519, 594, 782, 1147, 1586, 2219, 2978, 3212, 4679, 6512, 9169, 13663, 20030, 26025, 34898, 46136, 56755, 68837, 86693, 105383, 125013, 143912, 165987, 192301, 224544, 256779, 289066, 321482, 351359, 382740, 413507, 444699, 480640, 515055, 544185, 571440, 598370, 627151, 652591, 682626, 715656, 743588, 769684, 799512, 825429, 854288, 887858, 920185, 950580, 977081, 1000784, 1025361, 1051799, 1081019, 1115945, 1143295, 1167592, 1191677, 1216208, 1240767, 1268179, 1295017, 1320153, 1339020, 1358291, 1381238, 1401646, 1428464, 1453211, 1477370, 1495733, 1518123, 1539130, 1561827, 1587593, 1611249, 1632361, 1652428, 1671100, 1690750, 1709299, 1731621, 1756093, 1779726, 1798713, 1816148, 1837651, 1857495, 1879144, 1904544, 1925704, 1943620, 1961257, 1979639, 2000749, 2023881, 2048746, 2073954, 2092902, 2112721, 2136391, 2163449, 2191971, 2223531, 2255800, 2280946, 2313093, 2350156, 2386014, 2426329, 2472311, 2513651, 2554376, 2595655, 2642081, 2693890, 2750517, 2801874, 2847553, 2898322, 2941394, 3002036, 3062146, 3124633, 3192672, 3252687, 3311137, 3370026, 3438038, 3506145, 3581954, 3654203, 3716740, 3777211, 3839293, 3903780, 3974341, 4042769, 4116070, 4180969, 4235816, 4292554, 4359003, 4430816, 4498234, 4566931, 4623105, 4668652, 4714173, 4772945, 4827393, 4886727, 4945996, 5000107, 5045855, 5093475, 5141430, 5197413, 5248690, 5314012, 5360914, 5400103, 5436757, 5481755, 5529102, 5572890, 5621596, 5664641, 5698902, 5735502, 5775618, 5820908, 5866093, 5913046, 5955722, 5990118, 6025593, 6067260, 6108200, 6152285, 6202700, 6245682, 6276981, 6300449, 6327884, 6361638, 6397758, 6445407, 6486484, 6520750, 6555127, 6594554, 6633389, 6678579, 6727725, 6769847, 6808147, 6860061, 6899723, 6938731, 6985760, 7033944, 7078562, 7116078, 7149281, 7192498, 7231832, 7277418, 7332238, 7380797, 7416539, 7455962, 7501009, 7551949, 7610451, 7666716, 7721622, 7767500, 7809362, 7861469, 7921089, 7985864, 8054907, 8111563, 8160870, 8228585, 8290408, 8353581, 8429792, 8511514, 8594218, 8656238, 8723553, 8800136, 8879326, 8970125, 9069103, 9158771, 9263540, 9348641, 9475561, 9579953, 9709053, 9836805, 9964007, 10079085, 10199542, 10339769, 10486134, 10650509, 10830672, 10998189, 11134318, 11296818, 11460242, 11633088, 11824228, 12022158, 12201266, 12347905, 12521898, 12697001, 12879677, 12991818, 13199752, 13355018, 13495104, 13655220, 13842652, 14044625, 14267421, 14499637, 14714853, 14895691, 15089952, 15313819, 15535463, 15766130, 16004659, 16221125, 16408428, 16601499, 16810792, 17056440, 17295460, 17546621, 17738237, 17925587, 18123968, 18321157, 18550002, 18743916, 18841414, 19067574, 19222891, 19396787, 19595354, 19827770, 20061903, 20215297, 20515083, 20723157, 20906563, 21140217, 21394210, 21671278, 21963383, 22225720, 22438881, 22653070, 22879069, 23108826, 23343727, 23584671, 23785679, 23962788, 24105873, 24281531, 24464045, 24656940, 24846933, 25017235, 25148046, 25299108, 25445778, 25598384, 25767014, 25933090, 26075178, 26187035, 26321351, 26436155, 26557346, 26680589, 26814096, 26917986, 27007586, 27097493, 27192663, 27287431, 27392829, 27492273, 27579257, 27644213, 27698190, 27760660, 27830489, 27899756, 27979051, 28050458, 28107350, 28163046, 28234656, 28309085, 28386492, 28463190, 28527344, 28578548, 28637313, 28694071, 28759980, 28827144]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "for country in countries:\n",
+    "    new_row = raw_data[(raw_data['Country/Region']==country) & (raw_data['Province/State'].isnull())].values.tolist()\n",
+    "    print(new_row)\n",
+    "dataf = pd.DataFrame(data, columns=raw_data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Province/State</th>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th>Lat</th>\n",
+       "      <th>Long</th>\n",
+       "      <th>1/22/20</th>\n",
+       "      <th>1/23/20</th>\n",
+       "      <th>1/24/20</th>\n",
+       "      <th>1/25/20</th>\n",
+       "      <th>1/26/20</th>\n",
+       "      <th>1/27/20</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2/23/21</th>\n",
+       "      <th>2/24/21</th>\n",
+       "      <th>2/25/21</th>\n",
+       "      <th>2/26/21</th>\n",
+       "      <th>2/27/21</th>\n",
+       "      <th>2/28/21</th>\n",
+       "      <th>3/1/21</th>\n",
+       "      <th>3/2/21</th>\n",
+       "      <th>3/3/21</th>\n",
+       "      <th>3/4/21</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 412 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Province/State, Country/Region, Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20, 1/28/20, 1/29/20, 1/30/20, 1/31/20, 2/1/20, 2/2/20, 2/3/20, 2/4/20, 2/5/20, 2/6/20, 2/7/20, 2/8/20, 2/9/20, 2/10/20, 2/11/20, 2/12/20, 2/13/20, 2/14/20, 2/15/20, 2/16/20, 2/17/20, 2/18/20, 2/19/20, 2/20/20, 2/21/20, 2/22/20, 2/23/20, 2/24/20, 2/25/20, 2/26/20, 2/27/20, 2/28/20, 2/29/20, 3/1/20, 3/2/20, 3/3/20, 3/4/20, 3/5/20, 3/6/20, 3/7/20, 3/8/20, 3/9/20, 3/10/20, 3/11/20, 3/12/20, 3/13/20, 3/14/20, 3/15/20, 3/16/20, 3/17/20, 3/18/20, 3/19/20, 3/20/20, 3/21/20, 3/22/20, 3/23/20, 3/24/20, 3/25/20, 3/26/20, 3/27/20, 3/28/20, 3/29/20, 3/30/20, 3/31/20, 4/1/20, 4/2/20, 4/3/20, 4/4/20, 4/5/20, 4/6/20, 4/7/20, 4/8/20, 4/9/20, 4/10/20, 4/11/20, 4/12/20, 4/13/20, 4/14/20, 4/15/20, 4/16/20, 4/17/20, 4/18/20, 4/19/20, 4/20/20, 4/21/20, 4/22/20, 4/23/20, 4/24/20, 4/25/20, 4/26/20, ...]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 412 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": []
   }
  ],
-- 
2.18.1