diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb
index d45f16ad9fcd60457f7eaaf5657f00177fdd3523..0f4a8df91fb2e23b8f0bd9baf5a915cdc423b74c 100644
--- a/module3/exo1/influenza-like-illness-analysis.ipynb
+++ b/module3/exo1/influenza-like-illness-analysis.ipynb
@@ -2479,8 +2479,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define the name of the local file\n",
-    "local_data_file = \"incidence-PAY-3.csv\""
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import os\n",
+    "import requests\n"
    ]
   },
   {
@@ -2488,13 +2492,45 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Define the URL of the remote CSV file\n",
+    "remote_data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
+    "\n",
+    "# Define the name of the local file\n",
+    "local_data_file = \"incidence-PAY-3.csv\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'local_data_file' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-f9172fedfd57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Read the data from the local file into a Pandas DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_data_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'local_data_file' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# Check if the local file exists\n",
     "if not os.path.isfile(local_data_file):\n",
     "    # If the local file does not exist, download the data from the remote URL\n",
     "    response = requests.get(remote_data_url)\n",
     "    with open(local_data_file, \"w\") as f:\n",
-    "        f.write(response.text)"
+    "        f.write(response.text)\n",
+    "\n",
+    "# Read the data from the local file into a Pandas DataFrame\n",
+    "raw_data = pd.read_csv(local_data_file, skiprows=1)\n",
+    "\n",
+    "# Remove rows with missing values\n",
+    "data = raw_data.dropna().copy()\n",
+    "\n"
    ]
   },
   {
@@ -2503,8 +2539,50 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Read the data from the local file into a Pandas DataFrame\n",
-    "raw_data = pd.read_csv(local_data_file, skiprows=1)"
+    "# Define a function to convert year and week integers to Pandas Period objects\n",
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "# Add a column to the DataFrame containing the Period objects\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]\n",
+    "\n",
+    "# Sort the data by the period column\n",
+    "sorted_data = data.set_index('period').sort_index()\n",
+    "\n",
+    "# Check for gaps in the data and print any that are found\n",
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)\n",
+    "\n",
+    "# Plot the incidence data over time\n",
+    "sorted_data['inc'].plot()\n",
+    "\n",
+    "# Plot the last 200 data points\n",
+    "sorted_data['inc'][-200:].plot()\n",
+    "\n",
+    "# Compute the total incidence for each year and plot the results\n",
+    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "                     for y in range(1985,\n",
+    "                                    sorted_data.index[-1].year)]\n",
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n",
+    "\n",
+    "yearly_incidence.plot(style='*')\n",
+    "yearly_incidence.sort_values()\n",
+    "yearly_incidence.hist(xrot=20)\n"
    ]
   }
  ],