diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..72e526f5abeff846cc7573b181d27fc77d33d582 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,5 +1,844 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Travail sur Covid-19"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chargement de l'url en question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = pd.read_csv(data_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                       Province/State          Country/Region        Lat  \\\n",
+      "0                                 NaN             Afghanistan  33.000000   \n",
+      "1                                 NaN                 Albania  41.153300   \n",
+      "2                                 NaN                 Algeria  28.033900   \n",
+      "3                                 NaN                 Andorra  42.506300   \n",
+      "4                                 NaN                  Angola -11.202700   \n",
+      "5                                 NaN     Antigua and Barbuda  17.060800   \n",
+      "6                                 NaN               Argentina -38.416100   \n",
+      "7                                 NaN                 Armenia  40.069100   \n",
+      "8        Australian Capital Territory               Australia -35.473500   \n",
+      "9                     New South Wales               Australia -33.868800   \n",
+      "10                 Northern Territory               Australia -12.463400   \n",
+      "11                         Queensland               Australia -28.016700   \n",
+      "12                    South Australia               Australia -34.928500   \n",
+      "13                           Tasmania               Australia -41.454500   \n",
+      "14                           Victoria               Australia -37.813600   \n",
+      "15                  Western Australia               Australia -31.950500   \n",
+      "16                                NaN                 Austria  47.516200   \n",
+      "17                                NaN              Azerbaijan  40.143100   \n",
+      "18                                NaN                 Bahamas  25.034300   \n",
+      "19                                NaN                 Bahrain  26.027500   \n",
+      "20                                NaN              Bangladesh  23.685000   \n",
+      "21                                NaN                Barbados  13.193900   \n",
+      "22                                NaN                 Belarus  53.709800   \n",
+      "23                                NaN                 Belgium  50.833300   \n",
+      "24                                NaN                   Benin   9.307700   \n",
+      "25                                NaN                  Bhutan  27.514200   \n",
+      "26                                NaN                 Bolivia -16.290200   \n",
+      "27                                NaN  Bosnia and Herzegovina  43.915900   \n",
+      "28                                NaN                  Brazil -14.235000   \n",
+      "29                                NaN                  Brunei   4.535300   \n",
+      "..                                ...                     ...        ...   \n",
+      "234                               NaN              Mozambique -18.665695   \n",
+      "235                               NaN                   Syria  34.802075   \n",
+      "236                               NaN             Timor-Leste  -8.874217   \n",
+      "237                               NaN                  Belize  13.193900   \n",
+      "238                         Recovered                  Canada   0.000000   \n",
+      "239                               NaN                    Laos  19.856270   \n",
+      "240                               NaN                   Libya  26.335100   \n",
+      "241                               NaN      West Bank and Gaza  31.952200   \n",
+      "242                               NaN           Guinea-Bissau  11.803700   \n",
+      "243                               NaN                    Mali  17.570692   \n",
+      "244                               NaN   Saint Kitts and Nevis  17.357822   \n",
+      "245             Northwest Territories                  Canada  64.825500   \n",
+      "246                             Yukon                  Canada  64.282300   \n",
+      "247                               NaN                  Kosovo  42.602636   \n",
+      "248                               NaN                   Burma  21.916200   \n",
+      "249                          Anguilla          United Kingdom  18.220600   \n",
+      "250            British Virgin Islands          United Kingdom  18.420700   \n",
+      "251          Turks and Caicos Islands          United Kingdom  21.694000   \n",
+      "252                               NaN              MS Zaandam   0.000000   \n",
+      "253                               NaN                Botswana -22.328500   \n",
+      "254                               NaN                 Burundi  -3.373100   \n",
+      "255                               NaN            Sierra Leone   8.460555   \n",
+      "256  Bonaire, Sint Eustatius and Saba             Netherlands  12.178400   \n",
+      "257                               NaN                  Malawi -13.254308   \n",
+      "258       Falkland Islands (Malvinas)          United Kingdom -51.796300   \n",
+      "259         Saint Pierre and Miquelon                  France  46.885200   \n",
+      "260                               NaN             South Sudan   6.877000   \n",
+      "261                               NaN          Western Sahara  24.215500   \n",
+      "262                               NaN   Sao Tome and Principe   0.186360   \n",
+      "263                               NaN                   Yemen  15.552727   \n",
+      "\n",
+      "           Long  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
+      "0     65.000000        0        0        0        0        0        0   \n",
+      "1     20.168300        0        0        0        0        0        0   \n",
+      "2      1.659600        0        0        0        0        0        0   \n",
+      "3      1.521800        0        0        0        0        0        0   \n",
+      "4     17.873900        0        0        0        0        0        0   \n",
+      "5    -61.796400        0        0        0        0        0        0   \n",
+      "6    -63.616700        0        0        0        0        0        0   \n",
+      "7     45.038200        0        0        0        0        0        0   \n",
+      "8    149.012400        0        0        0        0        0        0   \n",
+      "9    151.209300        0        0        0        0        3        4   \n",
+      "10   130.845600        0        0        0        0        0        0   \n",
+      "11   153.400000        0        0        0        0        0        0   \n",
+      "12   138.600700        0        0        0        0        0        0   \n",
+      "13   145.970700        0        0        0        0        0        0   \n",
+      "14   144.963100        0        0        0        0        1        1   \n",
+      "15   115.860500        0        0        0        0        0        0   \n",
+      "16    14.550100        0        0        0        0        0        0   \n",
+      "17    47.576900        0        0        0        0        0        0   \n",
+      "18   -77.396300        0        0        0        0        0        0   \n",
+      "19    50.550000        0        0        0        0        0        0   \n",
+      "20    90.356300        0        0        0        0        0        0   \n",
+      "21   -59.543200        0        0        0        0        0        0   \n",
+      "22    27.953400        0        0        0        0        0        0   \n",
+      "23     4.000000        0        0        0        0        0        0   \n",
+      "24     2.315800        0        0        0        0        0        0   \n",
+      "25    90.433600        0        0        0        0        0        0   \n",
+      "26   -63.588700        0        0        0        0        0        0   \n",
+      "27    17.679100        0        0        0        0        0        0   \n",
+      "28   -51.925300        0        0        0        0        0        0   \n",
+      "29   114.727700        0        0        0        0        0        0   \n",
+      "..          ...      ...      ...      ...      ...      ...      ...   \n",
+      "234   35.529562        0        0        0        0        0        0   \n",
+      "235   38.996815        0        0        0        0        0        0   \n",
+      "236  125.727539        0        0        0        0        0        0   \n",
+      "237  -59.543200        0        0        0        0        0        0   \n",
+      "238    0.000000        0        0        0        0        0        0   \n",
+      "239  102.495496        0        0        0        0        0        0   \n",
+      "240   17.228331        0        0        0        0        0        0   \n",
+      "241   35.233200        0        0        0        0        0        0   \n",
+      "242  -15.180400        0        0        0        0        0        0   \n",
+      "243   -3.996166        0        0        0        0        0        0   \n",
+      "244  -62.782998        0        0        0        0        0        0   \n",
+      "245 -124.845700        0        0        0        0        0        0   \n",
+      "246 -135.000000        0        0        0        0        0        0   \n",
+      "247   20.902977        0        0        0        0        0        0   \n",
+      "248   95.956000        0        0        0        0        0        0   \n",
+      "249  -63.068600        0        0        0        0        0        0   \n",
+      "250  -64.640000        0        0        0        0        0        0   \n",
+      "251  -71.797900        0        0        0        0        0        0   \n",
+      "252    0.000000        0        0        0        0        0        0   \n",
+      "253   24.684900        0        0        0        0        0        0   \n",
+      "254   29.918900        0        0        0        0        0        0   \n",
+      "255  -11.779889        0        0        0        0        0        0   \n",
+      "256  -68.238500        0        0        0        0        0        0   \n",
+      "257   34.301525        0        0        0        0        0        0   \n",
+      "258  -59.523600        0        0        0        0        0        0   \n",
+      "259  -56.315900        0        0        0        0        0        0   \n",
+      "260   31.307000        0        0        0        0        0        0   \n",
+      "261  -12.885800        0        0        0        0        0        0   \n",
+      "262    6.613081        0        0        0        0        0        0   \n",
+      "263   48.516388        0        0        0        0        0        0   \n",
+      "\n",
+      "      ...     4/13/20  4/14/20  4/15/20  4/16/20  4/17/20  4/18/20  4/19/20  \\\n",
+      "0     ...         665      714      784      840      906      933      996   \n",
+      "1     ...         467      475      494      518      539      548      562   \n",
+      "2     ...        1983     2070     2160     2268     2418     2534     2629   \n",
+      "3     ...         646      659      673      673      696      704      713   \n",
+      "4     ...          19       19       19       19       19       24       24   \n",
+      "5     ...          23       23       23       23       23       23       23   \n",
+      "6     ...        2208     2277     2443     2571     2669     2758     2839   \n",
+      "7     ...        1039     1067     1111     1159     1201     1248     1291   \n",
+      "8     ...         102      103      103      103      103      103      103   \n",
+      "9     ...        2863     2870     2886     2897     2926     2926     2926   \n",
+      "10    ...          28       28       28       28       28       28       28   \n",
+      "11    ...         987      998      999     1001     1007     1015     1015   \n",
+      "12    ...         429      433      433      433      435      435      435   \n",
+      "13    ...         144      165      165      169      180      180      180   \n",
+      "14    ...        1281     1291     1299     1299     1302     1319     1319   \n",
+      "15    ...         517      527      527      532      541      541      541   \n",
+      "16    ...       14041    14226    14336    14476    14595    14671    14749   \n",
+      "17    ...        1148     1197     1253     1283     1340     1373     1398   \n",
+      "18    ...          47       49       49       53       54       55       55   \n",
+      "19    ...        1361     1528     1671     1700     1740     1773     1881   \n",
+      "20    ...         803     1012     1231     1572     1838     2144     2456   \n",
+      "21    ...          72       72       73       75       75       75       75   \n",
+      "22    ...        2919     3281     3728     4204     4779     4779     4779   \n",
+      "23    ...       30589    31119    33573    34809    36138    37183    38496   \n",
+      "24    ...          35       35       35       35       35       35       35   \n",
+      "25    ...           5        5        5        5        5        5        5   \n",
+      "26    ...         330      354      397      441      465      493      520   \n",
+      "27    ...        1037     1083     1110     1167     1214     1268     1285   \n",
+      "28    ...       23430    25262    28320    30425    33682    36658    38654   \n",
+      "29    ...         136      136      136      136      136      137      138   \n",
+      "..    ...         ...      ...      ...      ...      ...      ...      ...   \n",
+      "234   ...          21       28       29       31       34       35       39   \n",
+      "235   ...          25       29       33       33       38       38       39   \n",
+      "236   ...           4        6        8       18       18       18       19   \n",
+      "237   ...          18       18       18       18       18       18       18   \n",
+      "238   ...           0        0        0        0        0        0        0   \n",
+      "239   ...          19       19       19       19       19       19       19   \n",
+      "240   ...          26       35       48       49       49       49       51   \n",
+      "241   ...         308      308      374      374      402      418      437   \n",
+      "242   ...          38       38       43       43       43       46       50   \n",
+      "243   ...         123      144      148      171      171      216      224   \n",
+      "244   ...          12       14       14       14       14       14       14   \n",
+      "245   ...           5        5        5        5        5        5        5   \n",
+      "246   ...           8        8        8        8        8        9        9   \n",
+      "247   ...         283      387      387      449      480      510      510   \n",
+      "248   ...          62       63       74       85       88       98      111   \n",
+      "249   ...           3        3        3        3        3        3        3   \n",
+      "250   ...           3        3        3        3        4        4        4   \n",
+      "251   ...          10       10       10       11       11       11       11   \n",
+      "252   ...           9        9        9        9        9        9        9   \n",
+      "253   ...          13       13       13       15       15       15       20   \n",
+      "254   ...           5        5        5        5        5        5        5   \n",
+      "255   ...          10       11       13       15       26       30       35   \n",
+      "256   ...           3        3        3        3        3        3        5   \n",
+      "257   ...          16       16       16       16       17       17       17   \n",
+      "258   ...           5       11       11       11       11       11       11   \n",
+      "259   ...           1        1        1        1        1        1        1   \n",
+      "260   ...           4        4        4        4        4        4        4   \n",
+      "261   ...           6        6        6        6        6        6        6   \n",
+      "262   ...           4        4        4        4        4        4        4   \n",
+      "263   ...           1        1        1        1        1        1        1   \n",
+      "\n",
+      "     4/20/20  4/21/20  4/22/20  \n",
+      "0       1026     1092     1176  \n",
+      "1        584      609      634  \n",
+      "2       2718     2811     2910  \n",
+      "3        717      717      723  \n",
+      "4         24       24       25  \n",
+      "5         23       23       24  \n",
+      "6       2941     3031     3144  \n",
+      "7       1339     1401     1473  \n",
+      "8        103      103      103  \n",
+      "9       2926     2926     2926  \n",
+      "10        28       28       28  \n",
+      "11      1015     1015     1015  \n",
+      "12       435      435      435  \n",
+      "13       180      180      180  \n",
+      "14      1319     1319     1319  \n",
+      "15       541      541      541  \n",
+      "16     14795    14873    14925  \n",
+      "17      1436     1480     1518  \n",
+      "18        60       65       65  \n",
+      "19      1907     1973     2027  \n",
+      "20      2948     3382     3772  \n",
+      "21        75       75       75  \n",
+      "22      6264     6723     7281  \n",
+      "23     39983    40956    41889  \n",
+      "24        54       54       54  \n",
+      "25         5        6        6  \n",
+      "26       564      598      609  \n",
+      "27      1309     1342     1368  \n",
+      "28     40743    43079    45757  \n",
+      "29       138      138      138  \n",
+      "..       ...      ...      ...  \n",
+      "234       39       39       41  \n",
+      "235       39       42       42  \n",
+      "236       22       23       23  \n",
+      "237       18       18       18  \n",
+      "238        0        0        0  \n",
+      "239       19       19       19  \n",
+      "240       51       51       59  \n",
+      "241      449      466      474  \n",
+      "242       50       50       50  \n",
+      "243      246      258      293  \n",
+      "244       15       15       15  \n",
+      "245        5        5        5  \n",
+      "246       11       11       11  \n",
+      "247      510      510      510  \n",
+      "248      119      121      123  \n",
+      "249        3        3        3  \n",
+      "250        5        5        5  \n",
+      "251       11       11       11  \n",
+      "252        9        9        9  \n",
+      "253       20       20       22  \n",
+      "254        5        5       11  \n",
+      "255       43       50       61  \n",
+      "256        5        5        5  \n",
+      "257       17       18       23  \n",
+      "258       11       11       11  \n",
+      "259        1        1        1  \n",
+      "260        4        4        4  \n",
+      "261        6        6        6  \n",
+      "262        4        4        4  \n",
+      "263        1        1        1  \n",
+      "\n",
+      "[264 rows x 96 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant que cela est fait, nous pouvons évaluer le fichier en récupérant d'abord les jours recensés que nous allons utilisé comme index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
+      "       '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n",
+      "       '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n",
+      "       '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n",
+      "       '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n",
+      "       '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n",
+      "       '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n",
+      "       '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
+      "       '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
+      "       '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20', '3/24/20',\n",
+      "       '3/25/20', '3/26/20', '3/27/20', '3/28/20', '3/29/20', '3/30/20',\n",
+      "       '3/31/20', '4/1/20', '4/2/20', '4/3/20', '4/4/20', '4/5/20', '4/6/20',\n",
+      "       '4/7/20', '4/8/20', '4/9/20', '4/10/20', '4/11/20', '4/12/20',\n",
+      "       '4/13/20', '4/14/20', '4/15/20', '4/16/20', '4/17/20', '4/18/20',\n",
+      "       '4/19/20', '4/20/20', '4/21/20', '4/22/20'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_index = data_file.columns[4:]\n",
+    "print(time_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0                 Afghanistan\n",
+      "1                     Albania\n",
+      "2                     Algeria\n",
+      "3                     Andorra\n",
+      "4                      Angola\n",
+      "5         Antigua and Barbuda\n",
+      "6                   Argentina\n",
+      "7                     Armenia\n",
+      "8                   Australia\n",
+      "9                   Australia\n",
+      "10                  Australia\n",
+      "11                  Australia\n",
+      "12                  Australia\n",
+      "13                  Australia\n",
+      "14                  Australia\n",
+      "15                  Australia\n",
+      "16                    Austria\n",
+      "17                 Azerbaijan\n",
+      "18                    Bahamas\n",
+      "19                    Bahrain\n",
+      "20                 Bangladesh\n",
+      "21                   Barbados\n",
+      "22                    Belarus\n",
+      "23                    Belgium\n",
+      "24                      Benin\n",
+      "25                     Bhutan\n",
+      "26                    Bolivia\n",
+      "27     Bosnia and Herzegovina\n",
+      "28                     Brazil\n",
+      "29                     Brunei\n",
+      "                ...          \n",
+      "234                Mozambique\n",
+      "235                     Syria\n",
+      "236               Timor-Leste\n",
+      "237                    Belize\n",
+      "238                    Canada\n",
+      "239                      Laos\n",
+      "240                     Libya\n",
+      "241        West Bank and Gaza\n",
+      "242             Guinea-Bissau\n",
+      "243                      Mali\n",
+      "244     Saint Kitts and Nevis\n",
+      "245                    Canada\n",
+      "246                    Canada\n",
+      "247                    Kosovo\n",
+      "248                     Burma\n",
+      "249            United Kingdom\n",
+      "250            United Kingdom\n",
+      "251            United Kingdom\n",
+      "252                MS Zaandam\n",
+      "253                  Botswana\n",
+      "254                   Burundi\n",
+      "255              Sierra Leone\n",
+      "256               Netherlands\n",
+      "257                    Malawi\n",
+      "258            United Kingdom\n",
+      "259                    France\n",
+      "260               South Sudan\n",
+      "261            Western Sahara\n",
+      "262     Sao Tome and Principe\n",
+      "263                     Yemen\n",
+      "Name: Country/Region, Length: 264, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data_file['Country/Region'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Belgium': (23,), 'China': (49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81), 'Hong Kong': (61,), 'France': (116,), 'Germany': (120,), 'Iran': (133,), 'Italy': (137,), 'Japan': (139,), 'Korea, South': (143,), 'Netherlands': (169,), 'Portugal': (184,), 'Spain': (201,), 'United Kingdom': (223,), 'US': (225,)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "to_study_country = {}\n",
+    "to_study_country['Belgium'] = ()\n",
+    "to_study_country['China'] = ()\n",
+    "to_study_country['Hong Kong'] = ()\n",
+    "to_study_country['France'] = ()\n",
+    "to_study_country['Germany'] = ()\n",
+    "to_study_country['Iran'] = ()\n",
+    "to_study_country['Italy'] = ()\n",
+    "to_study_country['Japan'] = ()\n",
+    "to_study_country['Korea, South'] = ()\n",
+    "to_study_country['Netherlands'] = ()\n",
+    "to_study_country['Portugal'] = ()\n",
+    "to_study_country['Spain'] = ()\n",
+    "to_study_country['United Kingdom'] = ()\n",
+    "to_study_country['US'] = ()\n",
+    "for i, country in enumerate(data_file['Country/Region']):\n",
+    "    if country == 'Belgium':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'China':\n",
+    "        if data_file['Province/State'][i] == 'Hong Kong':\n",
+    "            to_study_country['Hong Kong'] = to_study_country['Hong Kong'] + (i,)\n",
+    "        else:\n",
+    "            to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'France':\n",
+    "        if isinstance(data_file['Province/State'][i], str):\n",
+    "            pass\n",
+    "        else:\n",
+    "            to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Germany':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Iran':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Italy':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Japan':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Korea, South':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Netherlands':\n",
+    "        if isinstance(data_file['Province/State'][i], str):\n",
+    "            pass\n",
+    "        else:\n",
+    "            to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Portugal':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'Spain':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'United Kingdom':\n",
+    "        if isinstance(data_file['Province/State'][i], str):\n",
+    "            pass\n",
+    "        else:            \n",
+    "            to_study_country[country] = to_study_country[country] + (i,)\n",
+    "    if country == 'US':\n",
+    "        to_study_country[country] = to_study_country[country] + (i,)\n",
+    "        \n",
+    "print(to_study_country)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant que nous avons récupérer les indices de chaques lignes nous pouvons commencer à créer le dictionnaire qui servira de données au fichier pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_study_country['Belgium'] = data_file.iloc[to_study_country['Belgium'][0], 4:]\n",
+    "to_study_country['Hong Kong'] = data_file.iloc[to_study_country['Hong Kong'][0], 4:]\n",
+    "to_study_country['France'] = data_file.iloc[to_study_country['France'][0], 4:]\n",
+    "to_study_country['Germany'] = data_file.iloc[to_study_country['Germany'][0], 4:]\n",
+    "to_study_country['Iran'] = data_file.iloc[to_study_country['Iran'][0], 4:]\n",
+    "to_study_country['Italy'] = data_file.iloc[to_study_country['Italy'][0], 4:]\n",
+    "to_study_country['Japan'] = data_file.iloc[to_study_country['Japan'][0], 4:]\n",
+    "to_study_country['Korea, South'] = data_file.iloc[to_study_country['Korea, South'][0], 4:]\n",
+    "to_study_country['Netherlands'] = data_file.iloc[to_study_country['Netherlands'][0], 4:]\n",
+    "to_study_country['Portugal'] = data_file.iloc[to_study_country['Portugal'][0], 4:]\n",
+    "to_study_country['Spain'] = data_file.iloc[to_study_country['Spain'][0], 4:]\n",
+    "to_study_country['United Kingdom'] = data_file.iloc[to_study_country['United Kingdom'][0], 4:]\n",
+    "to_study_country['US'] = data_file.iloc[to_study_country['US'][0], 4:]\n",
+    "China_overall = data_file.iloc[to_study_country['China'][0], 4:]\n",
+    "for i, j in enumerate(to_study_country['China']):\n",
+    "    if i > 0:\n",
+    "        China_overall += data_file.iloc[j, 4:]\n",
+    "to_study_country['China'] = China_overall\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "study_data = pd.DataFrame(index = time_index, data = to_study_country)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
+      "       '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n",
+      "       '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n",
+      "       '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n",
+      "       '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n",
+      "       '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n",
+      "       '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n",
+      "       '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
+      "       '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
+      "       '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20', '3/24/20',\n",
+      "       '3/25/20', '3/26/20', '3/27/20', '3/28/20', '3/29/20', '3/30/20',\n",
+      "       '3/31/20', '4/1/20', '4/2/20', '4/3/20', '4/4/20', '4/5/20', '4/6/20',\n",
+      "       '4/7/20', '4/8/20', '4/9/20', '4/10/20', '4/11/20', '4/12/20',\n",
+      "       '4/13/20', '4/14/20', '4/15/20', '4/16/20', '4/17/20', '4/18/20',\n",
+      "       '4/19/20', '4/20/20', '4/21/20', '4/22/20'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axes = study_data.plot(x = time_index).axes\n",
+    "axes.xaxis.set_ticks(range(len(time_index)))\n",
+    "axes.xaxis.set_ticklabels(time_index)\n",
+    "print(time_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          Belgium     China    France   Germany  Hong Kong      Iran  \\\n",
+      "1/22/20  0.000000  0.000000  0.000000  0.000000   0.000000  0.000000   \n",
+      "1/23/20  0.693147  0.693147  0.693147  0.693147   0.693147  0.693147   \n",
+      "1/24/20  1.098612  1.098612  1.098612  1.098612   1.098612  1.098612   \n",
+      "1/25/20  1.386294  1.386294  1.386294  1.386294   1.386294  1.386294   \n",
+      "1/26/20  1.609438  1.609438  1.609438  1.609438   1.609438  1.609438   \n",
+      "1/27/20  1.791759  1.791759  1.791759  1.791759   1.791759  1.791759   \n",
+      "1/28/20  1.945910  1.945910  1.945910  1.945910   1.945910  1.945910   \n",
+      "1/29/20  2.079442  2.079442  2.079442  2.079442   2.079442  2.079442   \n",
+      "1/30/20  2.197225  2.197225  2.197225  2.197225   2.197225  2.197225   \n",
+      "1/31/20  2.302585  2.302585  2.302585  2.302585   2.302585  2.302585   \n",
+      "2/1/20   2.397895  2.397895  2.397895  2.397895   2.397895  2.397895   \n",
+      "2/2/20   2.484907  2.484907  2.484907  2.484907   2.484907  2.484907   \n",
+      "2/3/20   2.564949  2.564949  2.564949  2.564949   2.564949  2.564949   \n",
+      "2/4/20   2.639057  2.639057  2.639057  2.639057   2.639057  2.639057   \n",
+      "2/5/20   2.708050  2.708050  2.708050  2.708050   2.708050  2.708050   \n",
+      "2/6/20   2.772589  2.772589  2.772589  2.772589   2.772589  2.772589   \n",
+      "2/7/20   2.833213  2.833213  2.833213  2.833213   2.833213  2.833213   \n",
+      "2/8/20   2.890372  2.890372  2.890372  2.890372   2.890372  2.890372   \n",
+      "2/9/20   2.944439  2.944439  2.944439  2.944439   2.944439  2.944439   \n",
+      "2/10/20  2.995732  2.995732  2.995732  2.995732   2.995732  2.995732   \n",
+      "2/11/20  3.044522  3.044522  3.044522  3.044522   3.044522  3.044522   \n",
+      "2/12/20  3.091042  3.091042  3.091042  3.091042   3.091042  3.091042   \n",
+      "2/13/20  3.135494  3.135494  3.135494  3.135494   3.135494  3.135494   \n",
+      "2/14/20  3.178054  3.178054  3.178054  3.178054   3.178054  3.178054   \n",
+      "2/15/20  3.218876  3.218876  3.218876  3.218876   3.218876  3.218876   \n",
+      "2/16/20  3.258097  3.258097  3.258097  3.258097   3.258097  3.258097   \n",
+      "2/17/20  3.295837  3.295837  3.295837  3.295837   3.295837  3.295837   \n",
+      "2/18/20  3.332205  3.332205  3.332205  3.332205   3.332205  3.332205   \n",
+      "2/19/20  3.367296  3.367296  3.367296  3.367296   3.367296  3.367296   \n",
+      "2/20/20  3.401197  3.401197  3.401197  3.401197   3.401197  3.401197   \n",
+      "...           ...       ...       ...       ...        ...       ...   \n",
+      "3/24/20  4.143135  4.143135  4.143135  4.143135   4.143135  4.143135   \n",
+      "3/25/20  4.158883  4.158883  4.158883  4.158883   4.158883  4.158883   \n",
+      "3/26/20  4.174387  4.174387  4.174387  4.174387   4.174387  4.174387   \n",
+      "3/27/20  4.189655  4.189655  4.189655  4.189655   4.189655  4.189655   \n",
+      "3/28/20  4.204693  4.204693  4.204693  4.204693   4.204693  4.204693   \n",
+      "3/29/20  4.219508  4.219508  4.219508  4.219508   4.219508  4.219508   \n",
+      "3/30/20  4.234107  4.234107  4.234107  4.234107   4.234107  4.234107   \n",
+      "3/31/20  4.248495  4.248495  4.248495  4.248495   4.248495  4.248495   \n",
+      "4/1/20   4.262680  4.262680  4.262680  4.262680   4.262680  4.262680   \n",
+      "4/2/20   4.276666  4.276666  4.276666  4.276666   4.276666  4.276666   \n",
+      "4/3/20   4.290459  4.290459  4.290459  4.290459   4.290459  4.290459   \n",
+      "4/4/20   4.304065  4.304065  4.304065  4.304065   4.304065  4.304065   \n",
+      "4/5/20   4.317488  4.317488  4.317488  4.317488   4.317488  4.317488   \n",
+      "4/6/20   4.330733  4.330733  4.330733  4.330733   4.330733  4.330733   \n",
+      "4/7/20   4.343805  4.343805  4.343805  4.343805   4.343805  4.343805   \n",
+      "4/8/20   4.356709  4.356709  4.356709  4.356709   4.356709  4.356709   \n",
+      "4/9/20   4.369448  4.369448  4.369448  4.369448   4.369448  4.369448   \n",
+      "4/10/20  4.382027  4.382027  4.382027  4.382027   4.382027  4.382027   \n",
+      "4/11/20  4.394449  4.394449  4.394449  4.394449   4.394449  4.394449   \n",
+      "4/12/20  4.406719  4.406719  4.406719  4.406719   4.406719  4.406719   \n",
+      "4/13/20  4.418841  4.418841  4.418841  4.418841   4.418841  4.418841   \n",
+      "4/14/20  4.430817  4.430817  4.430817  4.430817   4.430817  4.430817   \n",
+      "4/15/20  4.442651  4.442651  4.442651  4.442651   4.442651  4.442651   \n",
+      "4/16/20  4.454347  4.454347  4.454347  4.454347   4.454347  4.454347   \n",
+      "4/17/20  4.465908  4.465908  4.465908  4.465908   4.465908  4.465908   \n",
+      "4/18/20  4.477337  4.477337  4.477337  4.477337   4.477337  4.477337   \n",
+      "4/19/20  4.488636  4.488636  4.488636  4.488636   4.488636  4.488636   \n",
+      "4/20/20  4.499810  4.499810  4.499810  4.499810   4.499810  4.499810   \n",
+      "4/21/20  4.510860  4.510860  4.510860  4.510860   4.510860  4.510860   \n",
+      "4/22/20  4.521789  4.521789  4.521789  4.521789   4.521789  4.521789   \n",
+      "\n",
+      "            Italy     Japan  Korea, South  Netherlands  Portugal     Spain  \\\n",
+      "1/22/20  0.000000  0.000000      0.000000     0.000000  0.000000  0.000000   \n",
+      "1/23/20  0.693147  0.693147      0.693147     0.693147  0.693147  0.693147   \n",
+      "1/24/20  1.098612  1.098612      1.098612     1.098612  1.098612  1.098612   \n",
+      "1/25/20  1.386294  1.386294      1.386294     1.386294  1.386294  1.386294   \n",
+      "1/26/20  1.609438  1.609438      1.609438     1.609438  1.609438  1.609438   \n",
+      "1/27/20  1.791759  1.791759      1.791759     1.791759  1.791759  1.791759   \n",
+      "1/28/20  1.945910  1.945910      1.945910     1.945910  1.945910  1.945910   \n",
+      "1/29/20  2.079442  2.079442      2.079442     2.079442  2.079442  2.079442   \n",
+      "1/30/20  2.197225  2.197225      2.197225     2.197225  2.197225  2.197225   \n",
+      "1/31/20  2.302585  2.302585      2.302585     2.302585  2.302585  2.302585   \n",
+      "2/1/20   2.397895  2.397895      2.397895     2.397895  2.397895  2.397895   \n",
+      "2/2/20   2.484907  2.484907      2.484907     2.484907  2.484907  2.484907   \n",
+      "2/3/20   2.564949  2.564949      2.564949     2.564949  2.564949  2.564949   \n",
+      "2/4/20   2.639057  2.639057      2.639057     2.639057  2.639057  2.639057   \n",
+      "2/5/20   2.708050  2.708050      2.708050     2.708050  2.708050  2.708050   \n",
+      "2/6/20   2.772589  2.772589      2.772589     2.772589  2.772589  2.772589   \n",
+      "2/7/20   2.833213  2.833213      2.833213     2.833213  2.833213  2.833213   \n",
+      "2/8/20   2.890372  2.890372      2.890372     2.890372  2.890372  2.890372   \n",
+      "2/9/20   2.944439  2.944439      2.944439     2.944439  2.944439  2.944439   \n",
+      "2/10/20  2.995732  2.995732      2.995732     2.995732  2.995732  2.995732   \n",
+      "2/11/20  3.044522  3.044522      3.044522     3.044522  3.044522  3.044522   \n",
+      "2/12/20  3.091042  3.091042      3.091042     3.091042  3.091042  3.091042   \n",
+      "2/13/20  3.135494  3.135494      3.135494     3.135494  3.135494  3.135494   \n",
+      "2/14/20  3.178054  3.178054      3.178054     3.178054  3.178054  3.178054   \n",
+      "2/15/20  3.218876  3.218876      3.218876     3.218876  3.218876  3.218876   \n",
+      "2/16/20  3.258097  3.258097      3.258097     3.258097  3.258097  3.258097   \n",
+      "2/17/20  3.295837  3.295837      3.295837     3.295837  3.295837  3.295837   \n",
+      "2/18/20  3.332205  3.332205      3.332205     3.332205  3.332205  3.332205   \n",
+      "2/19/20  3.367296  3.367296      3.367296     3.367296  3.367296  3.367296   \n",
+      "2/20/20  3.401197  3.401197      3.401197     3.401197  3.401197  3.401197   \n",
+      "...           ...       ...           ...          ...       ...       ...   \n",
+      "3/24/20  4.143135  4.143135      4.143135     4.143135  4.143135  4.143135   \n",
+      "3/25/20  4.158883  4.158883      4.158883     4.158883  4.158883  4.158883   \n",
+      "3/26/20  4.174387  4.174387      4.174387     4.174387  4.174387  4.174387   \n",
+      "3/27/20  4.189655  4.189655      4.189655     4.189655  4.189655  4.189655   \n",
+      "3/28/20  4.204693  4.204693      4.204693     4.204693  4.204693  4.204693   \n",
+      "3/29/20  4.219508  4.219508      4.219508     4.219508  4.219508  4.219508   \n",
+      "3/30/20  4.234107  4.234107      4.234107     4.234107  4.234107  4.234107   \n",
+      "3/31/20  4.248495  4.248495      4.248495     4.248495  4.248495  4.248495   \n",
+      "4/1/20   4.262680  4.262680      4.262680     4.262680  4.262680  4.262680   \n",
+      "4/2/20   4.276666  4.276666      4.276666     4.276666  4.276666  4.276666   \n",
+      "4/3/20   4.290459  4.290459      4.290459     4.290459  4.290459  4.290459   \n",
+      "4/4/20   4.304065  4.304065      4.304065     4.304065  4.304065  4.304065   \n",
+      "4/5/20   4.317488  4.317488      4.317488     4.317488  4.317488  4.317488   \n",
+      "4/6/20   4.330733  4.330733      4.330733     4.330733  4.330733  4.330733   \n",
+      "4/7/20   4.343805  4.343805      4.343805     4.343805  4.343805  4.343805   \n",
+      "4/8/20   4.356709  4.356709      4.356709     4.356709  4.356709  4.356709   \n",
+      "4/9/20   4.369448  4.369448      4.369448     4.369448  4.369448  4.369448   \n",
+      "4/10/20  4.382027  4.382027      4.382027     4.382027  4.382027  4.382027   \n",
+      "4/11/20  4.394449  4.394449      4.394449     4.394449  4.394449  4.394449   \n",
+      "4/12/20  4.406719  4.406719      4.406719     4.406719  4.406719  4.406719   \n",
+      "4/13/20  4.418841  4.418841      4.418841     4.418841  4.418841  4.418841   \n",
+      "4/14/20  4.430817  4.430817      4.430817     4.430817  4.430817  4.430817   \n",
+      "4/15/20  4.442651  4.442651      4.442651     4.442651  4.442651  4.442651   \n",
+      "4/16/20  4.454347  4.454347      4.454347     4.454347  4.454347  4.454347   \n",
+      "4/17/20  4.465908  4.465908      4.465908     4.465908  4.465908  4.465908   \n",
+      "4/18/20  4.477337  4.477337      4.477337     4.477337  4.477337  4.477337   \n",
+      "4/19/20  4.488636  4.488636      4.488636     4.488636  4.488636  4.488636   \n",
+      "4/20/20  4.499810  4.499810      4.499810     4.499810  4.499810  4.499810   \n",
+      "4/21/20  4.510860  4.510860      4.510860     4.510860  4.510860  4.510860   \n",
+      "4/22/20  4.521789  4.521789      4.521789     4.521789  4.521789  4.521789   \n",
+      "\n",
+      "               US  United Kingdom  \n",
+      "1/22/20  0.000000        0.000000  \n",
+      "1/23/20  0.693147        0.693147  \n",
+      "1/24/20  1.098612        1.098612  \n",
+      "1/25/20  1.386294        1.386294  \n",
+      "1/26/20  1.609438        1.609438  \n",
+      "1/27/20  1.791759        1.791759  \n",
+      "1/28/20  1.945910        1.945910  \n",
+      "1/29/20  2.079442        2.079442  \n",
+      "1/30/20  2.197225        2.197225  \n",
+      "1/31/20  2.302585        2.302585  \n",
+      "2/1/20   2.397895        2.397895  \n",
+      "2/2/20   2.484907        2.484907  \n",
+      "2/3/20   2.564949        2.564949  \n",
+      "2/4/20   2.639057        2.639057  \n",
+      "2/5/20   2.708050        2.708050  \n",
+      "2/6/20   2.772589        2.772589  \n",
+      "2/7/20   2.833213        2.833213  \n",
+      "2/8/20   2.890372        2.890372  \n",
+      "2/9/20   2.944439        2.944439  \n",
+      "2/10/20  2.995732        2.995732  \n",
+      "2/11/20  3.044522        3.044522  \n",
+      "2/12/20  3.091042        3.091042  \n",
+      "2/13/20  3.135494        3.135494  \n",
+      "2/14/20  3.178054        3.178054  \n",
+      "2/15/20  3.218876        3.218876  \n",
+      "2/16/20  3.258097        3.258097  \n",
+      "2/17/20  3.295837        3.295837  \n",
+      "2/18/20  3.332205        3.332205  \n",
+      "2/19/20  3.367296        3.367296  \n",
+      "2/20/20  3.401197        3.401197  \n",
+      "...           ...             ...  \n",
+      "3/24/20  4.143135        4.143135  \n",
+      "3/25/20  4.158883        4.158883  \n",
+      "3/26/20  4.174387        4.174387  \n",
+      "3/27/20  4.189655        4.189655  \n",
+      "3/28/20  4.204693        4.204693  \n",
+      "3/29/20  4.219508        4.219508  \n",
+      "3/30/20  4.234107        4.234107  \n",
+      "3/31/20  4.248495        4.248495  \n",
+      "4/1/20   4.262680        4.262680  \n",
+      "4/2/20   4.276666        4.276666  \n",
+      "4/3/20   4.290459        4.290459  \n",
+      "4/4/20   4.304065        4.304065  \n",
+      "4/5/20   4.317488        4.317488  \n",
+      "4/6/20   4.330733        4.330733  \n",
+      "4/7/20   4.343805        4.343805  \n",
+      "4/8/20   4.356709        4.356709  \n",
+      "4/9/20   4.369448        4.369448  \n",
+      "4/10/20  4.382027        4.382027  \n",
+      "4/11/20  4.394449        4.394449  \n",
+      "4/12/20  4.406719        4.406719  \n",
+      "4/13/20  4.418841        4.418841  \n",
+      "4/14/20  4.430817        4.430817  \n",
+      "4/15/20  4.442651        4.442651  \n",
+      "4/16/20  4.454347        4.454347  \n",
+      "4/17/20  4.465908        4.465908  \n",
+      "4/18/20  4.477337        4.477337  \n",
+      "4/19/20  4.488636        4.488636  \n",
+      "4/20/20  4.499810        4.499810  \n",
+      "4/21/20  4.510860        4.510860  \n",
+      "4/22/20  4.521789        4.521789  \n",
+      "\n",
+      "[92 rows x 14 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf6d1a7198>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_study_data = {}\n",
+    "for country in study_data.columns:\n",
+    "    list_for_country = list()\n",
+    "    for i in range(len(study_data[country])):\n",
+    "        list_for_country.append(math.log(i+1))\n",
+    "    log_study_data[country] = list_for_country\n",
+    "\n",
+    "log_study_data_df = pd.DataFrame(index = time_index, data = log_study_data)\n",
+    "print(log_study_data_df)\n",
+    "log_study_data_df.plot(x = time_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +855,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-