diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..5188c74a5f6bdc99259baf510db808339109a849 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -1,25 +1,116 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import networkx as nx\n",
+    "from itertools import combinations\n",
+    "from random import random\n",
+    "\n",
+    "\n",
+    "def ER(n, p):\n",
+    "    V = set([v for v in range(n)])\n",
+    "    E = set()\n",
+    "    for combination in combinations(V, 2):\n",
+    "        a = random()\n",
+    "        if a < p:\n",
+    "            E.add(combination)\n",
+    "\n",
+    "    g = nx.Graph()\n",
+    "    g.add_nodes_from(V)\n",
+    "    g.add_edges_from(E)\n",
+    "\n",
+    "    return g\n",
+    "\n",
+    "\n",
+    "n = 10\n",
+    "p = 0.4\n",
+    "G = ER(n, p)\n",
+    "pos = nx.spring_layout(G)\n",
+    "nx.draw_networkx(G, pos)\n",
+    "plt.title(\"Random Graph Generation Example\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 5\n",
+      "[1] \"John\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 5\n",
+    "y = \"John\"\n",
+    "print(x)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ERROR",
+     "evalue": "Error in library(igraph): there is no package called ‘igraph’\n",
+     "output_type": "error",
+     "traceback": [
+      "Error in library(igraph): there is no package called ‘igraph’\nTraceback:\n",
+      "1. library(igraph)",
+      "2. stop(txt, domain = NA)"
+     ]
+    }
+   ],
+   "source": [
+    "library(igraph)\n",
+    "set.seed(1)\n",
+    "gs <- list()\n",
+    "for (x in seq_len(100L)) {\n",
+    "  gs[[x]] <- erdos.renyi.game(sample(1:100, 1), p.or.m = runif(1))\n",
+    "  E(gs[[x]])$weight <- sample(1:5, ecount(gs[[x]]), T)\n",
+    "}\n",
+    "plot(gs[[1]], edge.width = E(gs[[1]])$weight) # plot first graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..a98fb6402baf1b1759cc46e68498b73b4c0fc2fe 100644
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
@@ -1,5 +1,77 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min value element :  2.8\n",
+      "Max value element :  23.4\n",
+      "median= 14.5\n",
+      "standard deviation= 4.31\n",
+      "standard deviation= 4.33\n",
+      "Average of the list = 14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statistics\n",
+    "import numpy as np\n",
+    "\n",
+    "def Average(lst):\n",
+    "    return sum(lst) / len(lst)\n",
+    "\n",
+    "mylist = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "print (\"min value element : \", min(mylist))\n",
+    "print (\"Max value element : \", max(mylist))\n",
+    "print (\"median=\", statistics.median(mylist))\n",
+    "print (\"standard deviation=\", round(np.std(mylist),2))\n",
+    "print (\"standard deviation=\", round(statistics.stdev(mylist),2))\n",
+    "\n",
+    "average = Average(mylist)\n",
+    "print(\"Average of the list =\", round(average,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "mylist = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "an_list = [20,40,60,80,100]\n",
+    "plt.plot([20,40,60,80,100],[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
+    "#plt.plot([0.1, 0.2, 0.3, 0.4], [1, 2, 3, 4])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +88,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..7f2acbc27924ec4336449bb3371017436bf2c00e 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,129 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2021     772545\n",
+       "2014    1601698\n",
+       "1991    1663610\n",
+       "1995    1828304\n",
+       "2020    2017296\n",
+       "2022    2057596\n",
+       "2012    2183912\n",
+       "2003    2234514\n",
+       "2019    2254363\n",
+       "2006    2297262\n",
+       "2017    2322818\n",
+       "2001    2540826\n",
+       "1992    2590314\n",
+       "1993    2699482\n",
+       "2018    2701716\n",
+       "1988    2759663\n",
+       "2007    2786458\n",
+       "2011    2852504\n",
+       "2016    2859019\n",
+       "1987    2867464\n",
+       "2008    2984311\n",
+       "1998    3047298\n",
+       "2002    3115484\n",
+       "1994    3514133\n",
+       "1996    3540251\n",
+       "2009    3558474\n",
+       "2004    3572810\n",
+       "1997    3624129\n",
+       "2015    3647492\n",
+       "2000    3808190\n",
+       "2005    3831409\n",
+       "1999    3914003\n",
+       "2010    3992174\n",
+       "2013    4176872\n",
+       "1986    5050543\n",
+       "1990    5214494\n",
+       "1989    5461328\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "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": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "\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",
+    "\n",
+    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
+    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
+    "raw_data[raw_data.isnull().any(axis=1)]\n",
+    "data = raw_data.dropna().copy()\n",
+    "\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]\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",
+    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,sorted_data.index[-1].year)]\n",
+    "\n",
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_sept_week[:-1],\n",
+    "                        first_sept_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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +140,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/PN-Ex3.ipynb b/module3/exo3/PN-Ex3.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f0292372718fcecb80a32e30acf3b9afabbc0aa6
--- /dev/null
+++ b/module3/exo3/PN-Ex3.ipynb
@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CO2 concentration in the atmosphere since 1958"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = ['date','amOfCO2']\n",
+    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"\n",
+    "raw_data = pd.read_csv(data_url, skiprows=44, header=None, names=columns)\n",
+    "raw_data['date'] = pd.to_datetime(raw_data['date'])\n",
+    "raw_data;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "data_graph = data.set_index('date').sort_index()\n",
+    "data_graph['amOfCO2'].plot()\n",
+    "plt.title(\"Evolution of CO2 concentration (Hawaii)\")\n",
+    "plt.xlabel(\"Date\")\n",
+    "plt.ylabel(\"CO2 Concentration (ppm)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "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>max_amOfCO2</th>\n",
+       "      <th>min_amOfCO2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>419.66</td>\n",
+       "      <td>315.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>419.55</td>\n",
+       "      <td>316.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>419.00</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>420.57</td>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>421.36</td>\n",
+       "      <td>316.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>421.18</td>\n",
+       "      <td>317.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>419.89</td>\n",
+       "      <td>315.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>417.42</td>\n",
+       "      <td>314.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>415.85</td>\n",
+       "      <td>313.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>416.07</td>\n",
+       "      <td>313.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>417.73</td>\n",
+       "      <td>313.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>419.29</td>\n",
+       "      <td>314.41</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       max_amOfCO2  min_amOfCO2\n",
+       "month                          \n",
+       "1           419.66       315.24\n",
+       "2           419.55       316.61\n",
+       "3           419.00       316.19\n",
+       "4           420.57       316.48\n",
+       "5           421.36       316.95\n",
+       "6           421.18       317.76\n",
+       "7           419.89       315.46\n",
+       "8           417.42       314.14\n",
+       "9           415.85       313.33\n",
+       "10          416.07       313.04\n",
+       "11          417.73       313.05\n",
+       "12          419.29       314.41"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "month_max_min = pd.DataFrame({'max_amOfCO2': raw_data.groupby([raw_data['date'].dt.month])['amOfCO2'].max(),'min_amOfCO2': raw_data.groupby([raw_data['date'].dt.month])['amOfCO2'].min()})\n",
+    "month_max_min.index.names = ['month']\n",
+    "#month_max = raw_data.groupby([raw_data['date'].dt.month])['amOfCO2'].max()\n",
+    "month_max_min"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "#data_periodic_interval = month_max_min.set_index('month').sort_index()\n",
+    "#data_periodic_interval = month_max_min.set_index('month').sort_index()\n",
+    "plt.plot(month_max_min.index,month_max_min['max_amOfCO2'],label = \"Maximum  value\")\n",
+    "plt.plot(month_max_min.index,month_max_min['min_amOfCO2'],label=\"Minimum value\")\n",
+    "#data_periodic_interval['min_amOfCO2'].plot()\n",
+    "plt.title(\"Evolution interval of CO2 concentration in a year (1958-2023)\")\n",
+    "plt.xlabel(\"Month\")\n",
+    "plt.ylabel(\"CO2 Concentration (ppm)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
deleted file mode 100644
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000
--- a/module3/exo3/exercice.ipynb
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "cells": [],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-