From a1c6431b463ef421253703100a68834437fcf12b Mon Sep 17 00:00:00 2001
From: 35532fc8dd749578f83fdb9b0cfb5a8e
 <35532fc8dd749578f83fdb9b0cfb5a8e@app-learninglab.inria.fr>
Date: Sun, 5 Oct 2025 20:02:27 +0000
Subject: [PATCH] no commit message

---
 module3/exo3/CO2_concentration_fr_erol.ipynb | 1268 ++++++++++++++++++
 module3/exo3/exercice_fr.ipynb               |   25 -
 2 files changed, 1268 insertions(+), 25 deletions(-)
 create mode 100644 module3/exo3/CO2_concentration_fr_erol.ipynb
 delete mode 100644 module3/exo3/exercice_fr.ipynb

diff --git a/module3/exo3/CO2_concentration_fr_erol.ipynb b/module3/exo3/CO2_concentration_fr_erol.ipynb
new file mode 100644
index 0000000..616a06a
--- /dev/null
+++ b/module3/exo3/CO2_concentration_fr_erol.ipynb
@@ -0,0 +1,1268 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Concentration de CO2 dans l'atmosphère depuis 1958\n",
+    "\n",
+    "En 1958, Charles David Keeling  a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé \"Keeling Curve\" (voir [Wikipedia](https://en.wikipedia.org/wiki/Keeling_Curve) pour l'histoire et l'importance de ces données)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Récupération des données\n",
+    "Récupération des données sur le site de l'institut Scripps, le 05/10/2025."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline \n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = ['year','month','date_excel','date','CO2','seas_adjusted','fit','fit_seas_adjusted','CO2_filled','seas_adjusted_filled','Station']\n",
+    "raw_data = pd.read_csv(\"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\",skiprows=64,names=names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppression des lignes à valeurs manquantes :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>date_excel</th>\n",
+       "      <th>date</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>seas_adjusted</th>\n",
+       "      <th>fit</th>\n",
+       "      <th>fit_seas_adjusted</th>\n",
+       "      <th>CO2_filled</th>\n",
+       "      <th>seas_adjusted_filled</th>\n",
+       "      <th>Station</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21259</td>\n",
+       "      <td>1958.2027</td>\n",
+       "      <td>315.71</td>\n",
+       "      <td>314.43</td>\n",
+       "      <td>316.20</td>\n",
+       "      <td>314.91</td>\n",
+       "      <td>315.71</td>\n",
+       "      <td>314.43</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21290</td>\n",
+       "      <td>1958.2877</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.15</td>\n",
+       "      <td>317.31</td>\n",
+       "      <td>314.99</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.15</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>5</td>\n",
+       "      <td>21320</td>\n",
+       "      <td>1958.3699</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.68</td>\n",
+       "      <td>317.89</td>\n",
+       "      <td>315.07</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.68</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21351</td>\n",
+       "      <td>1958.4548</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>317.27</td>\n",
+       "      <td>315.14</td>\n",
+       "      <td>317.27</td>\n",
+       "      <td>315.14</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21381</td>\n",
+       "      <td>1958.5370</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>315.20</td>\n",
+       "      <td>315.85</td>\n",
+       "      <td>315.22</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>315.20</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>8</td>\n",
+       "      <td>21412</td>\n",
+       "      <td>1958.6219</td>\n",
+       "      <td>314.93</td>\n",
+       "      <td>316.23</td>\n",
+       "      <td>313.95</td>\n",
+       "      <td>315.29</td>\n",
+       "      <td>314.93</td>\n",
+       "      <td>316.23</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>9</td>\n",
+       "      <td>21443</td>\n",
+       "      <td>1958.7068</td>\n",
+       "      <td>313.21</td>\n",
+       "      <td>316.12</td>\n",
+       "      <td>312.42</td>\n",
+       "      <td>315.35</td>\n",
+       "      <td>313.21</td>\n",
+       "      <td>316.12</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>10</td>\n",
+       "      <td>21473</td>\n",
+       "      <td>1958.7890</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>312.41</td>\n",
+       "      <td>315.41</td>\n",
+       "      <td>312.41</td>\n",
+       "      <td>315.41</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21504</td>\n",
+       "      <td>1958.8740</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>315.21</td>\n",
+       "      <td>313.60</td>\n",
+       "      <td>315.46</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>315.21</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>12</td>\n",
+       "      <td>21534</td>\n",
+       "      <td>1958.9562</td>\n",
+       "      <td>314.67</td>\n",
+       "      <td>315.43</td>\n",
+       "      <td>314.77</td>\n",
+       "      <td>315.52</td>\n",
+       "      <td>314.67</td>\n",
+       "      <td>315.43</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21565</td>\n",
+       "      <td>1959.0411</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>315.52</td>\n",
+       "      <td>315.64</td>\n",
+       "      <td>315.57</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>315.52</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21596</td>\n",
+       "      <td>1959.1260</td>\n",
+       "      <td>316.49</td>\n",
+       "      <td>315.83</td>\n",
+       "      <td>316.30</td>\n",
+       "      <td>315.64</td>\n",
+       "      <td>316.49</td>\n",
+       "      <td>315.83</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21624</td>\n",
+       "      <td>1959.2027</td>\n",
+       "      <td>316.65</td>\n",
+       "      <td>315.37</td>\n",
+       "      <td>317.00</td>\n",
+       "      <td>315.70</td>\n",
+       "      <td>316.65</td>\n",
+       "      <td>315.37</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21655</td>\n",
+       "      <td>1959.2877</td>\n",
+       "      <td>317.72</td>\n",
+       "      <td>315.41</td>\n",
+       "      <td>318.10</td>\n",
+       "      <td>315.77</td>\n",
+       "      <td>317.72</td>\n",
+       "      <td>315.41</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>5</td>\n",
+       "      <td>21685</td>\n",
+       "      <td>1959.3699</td>\n",
+       "      <td>318.29</td>\n",
+       "      <td>315.46</td>\n",
+       "      <td>318.69</td>\n",
+       "      <td>315.85</td>\n",
+       "      <td>318.29</td>\n",
+       "      <td>315.46</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21716</td>\n",
+       "      <td>1959.4548</td>\n",
+       "      <td>318.15</td>\n",
+       "      <td>316.00</td>\n",
+       "      <td>318.08</td>\n",
+       "      <td>315.94</td>\n",
+       "      <td>318.15</td>\n",
+       "      <td>316.00</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21746</td>\n",
+       "      <td>1959.5370</td>\n",
+       "      <td>316.54</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>316.67</td>\n",
+       "      <td>316.03</td>\n",
+       "      <td>316.54</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>8</td>\n",
+       "      <td>21777</td>\n",
+       "      <td>1959.6219</td>\n",
+       "      <td>314.79</td>\n",
+       "      <td>316.10</td>\n",
+       "      <td>314.79</td>\n",
+       "      <td>316.13</td>\n",
+       "      <td>314.79</td>\n",
+       "      <td>316.10</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>9</td>\n",
+       "      <td>21808</td>\n",
+       "      <td>1959.7068</td>\n",
+       "      <td>313.84</td>\n",
+       "      <td>316.76</td>\n",
+       "      <td>313.28</td>\n",
+       "      <td>316.22</td>\n",
+       "      <td>313.84</td>\n",
+       "      <td>316.76</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>10</td>\n",
+       "      <td>21838</td>\n",
+       "      <td>1959.7890</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>316.35</td>\n",
+       "      <td>313.31</td>\n",
+       "      <td>316.31</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>316.35</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21869</td>\n",
+       "      <td>1959.8740</td>\n",
+       "      <td>314.81</td>\n",
+       "      <td>316.69</td>\n",
+       "      <td>314.53</td>\n",
+       "      <td>316.40</td>\n",
+       "      <td>314.81</td>\n",
+       "      <td>316.69</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>12</td>\n",
+       "      <td>21899</td>\n",
+       "      <td>1959.9562</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>316.35</td>\n",
+       "      <td>315.72</td>\n",
+       "      <td>316.48</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>316.35</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21930</td>\n",
+       "      <td>1960.0410</td>\n",
+       "      <td>316.43</td>\n",
+       "      <td>316.37</td>\n",
+       "      <td>316.63</td>\n",
+       "      <td>316.56</td>\n",
+       "      <td>316.43</td>\n",
+       "      <td>316.37</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21961</td>\n",
+       "      <td>1960.1257</td>\n",
+       "      <td>316.98</td>\n",
+       "      <td>316.33</td>\n",
+       "      <td>317.30</td>\n",
+       "      <td>316.64</td>\n",
+       "      <td>316.98</td>\n",
+       "      <td>316.33</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21990</td>\n",
+       "      <td>1960.2049</td>\n",
+       "      <td>317.58</td>\n",
+       "      <td>316.27</td>\n",
+       "      <td>318.04</td>\n",
+       "      <td>316.71</td>\n",
+       "      <td>317.58</td>\n",
+       "      <td>316.27</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>4</td>\n",
+       "      <td>22021</td>\n",
+       "      <td>1960.2896</td>\n",
+       "      <td>319.03</td>\n",
+       "      <td>316.69</td>\n",
+       "      <td>319.14</td>\n",
+       "      <td>316.79</td>\n",
+       "      <td>319.03</td>\n",
+       "      <td>316.69</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>5</td>\n",
+       "      <td>22051</td>\n",
+       "      <td>1960.3716</td>\n",
+       "      <td>320.03</td>\n",
+       "      <td>317.19</td>\n",
+       "      <td>319.70</td>\n",
+       "      <td>316.86</td>\n",
+       "      <td>320.03</td>\n",
+       "      <td>317.19</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>6</td>\n",
+       "      <td>22082</td>\n",
+       "      <td>1960.4563</td>\n",
+       "      <td>319.58</td>\n",
+       "      <td>317.44</td>\n",
+       "      <td>319.05</td>\n",
+       "      <td>316.92</td>\n",
+       "      <td>319.58</td>\n",
+       "      <td>317.44</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22112</td>\n",
+       "      <td>1960.5383</td>\n",
+       "      <td>318.18</td>\n",
+       "      <td>317.54</td>\n",
+       "      <td>317.59</td>\n",
+       "      <td>316.98</td>\n",
+       "      <td>318.18</td>\n",
+       "      <td>317.54</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>8</td>\n",
+       "      <td>22143</td>\n",
+       "      <td>1960.6230</td>\n",
+       "      <td>315.90</td>\n",
+       "      <td>317.24</td>\n",
+       "      <td>315.65</td>\n",
+       "      <td>317.02</td>\n",
+       "      <td>315.90</td>\n",
+       "      <td>317.24</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44972</td>\n",
+       "      <td>2023.1260</td>\n",
+       "      <td>420.33</td>\n",
+       "      <td>419.55</td>\n",
+       "      <td>420.40</td>\n",
+       "      <td>419.61</td>\n",
+       "      <td>420.33</td>\n",
+       "      <td>419.55</td>\n",
+       "      <td>MKO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45000</td>\n",
+       "      <td>2023.2027</td>\n",
+       "      <td>420.51</td>\n",
+       "      <td>418.97</td>\n",
+       "      <td>421.38</td>\n",
+       "      <td>419.83</td>\n",
+       "      <td>420.51</td>\n",
+       "      <td>418.97</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>783</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45031</td>\n",
+       "      <td>2023.2877</td>\n",
+       "      <td>422.73</td>\n",
+       "      <td>419.97</td>\n",
+       "      <td>422.88</td>\n",
+       "      <td>420.10</td>\n",
+       "      <td>422.73</td>\n",
+       "      <td>419.97</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>784</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>5</td>\n",
+       "      <td>45061</td>\n",
+       "      <td>2023.3699</td>\n",
+       "      <td>423.78</td>\n",
+       "      <td>420.39</td>\n",
+       "      <td>423.76</td>\n",
+       "      <td>420.37</td>\n",
+       "      <td>423.78</td>\n",
+       "      <td>420.39</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45092</td>\n",
+       "      <td>2023.4548</td>\n",
+       "      <td>423.39</td>\n",
+       "      <td>420.82</td>\n",
+       "      <td>423.22</td>\n",
+       "      <td>420.66</td>\n",
+       "      <td>423.39</td>\n",
+       "      <td>420.82</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>45122</td>\n",
+       "      <td>2023.5370</td>\n",
+       "      <td>421.62</td>\n",
+       "      <td>420.83</td>\n",
+       "      <td>421.72</td>\n",
+       "      <td>420.96</td>\n",
+       "      <td>421.62</td>\n",
+       "      <td>420.83</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>8</td>\n",
+       "      <td>45153</td>\n",
+       "      <td>2023.6219</td>\n",
+       "      <td>419.56</td>\n",
+       "      <td>421.12</td>\n",
+       "      <td>419.67</td>\n",
+       "      <td>421.27</td>\n",
+       "      <td>419.56</td>\n",
+       "      <td>421.12</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>9</td>\n",
+       "      <td>45184</td>\n",
+       "      <td>2023.7068</td>\n",
+       "      <td>418.06</td>\n",
+       "      <td>421.56</td>\n",
+       "      <td>418.07</td>\n",
+       "      <td>421.58</td>\n",
+       "      <td>418.06</td>\n",
+       "      <td>421.56</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10</td>\n",
+       "      <td>45214</td>\n",
+       "      <td>2023.7890</td>\n",
+       "      <td>418.41</td>\n",
+       "      <td>422.01</td>\n",
+       "      <td>418.30</td>\n",
+       "      <td>421.89</td>\n",
+       "      <td>418.41</td>\n",
+       "      <td>422.01</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>11</td>\n",
+       "      <td>45245</td>\n",
+       "      <td>2023.8740</td>\n",
+       "      <td>420.11</td>\n",
+       "      <td>422.37</td>\n",
+       "      <td>419.97</td>\n",
+       "      <td>422.20</td>\n",
+       "      <td>420.11</td>\n",
+       "      <td>422.37</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>12</td>\n",
+       "      <td>45275</td>\n",
+       "      <td>2023.9562</td>\n",
+       "      <td>421.65</td>\n",
+       "      <td>422.57</td>\n",
+       "      <td>421.60</td>\n",
+       "      <td>422.50</td>\n",
+       "      <td>421.65</td>\n",
+       "      <td>422.57</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45306</td>\n",
+       "      <td>2024.0410</td>\n",
+       "      <td>422.62</td>\n",
+       "      <td>422.55</td>\n",
+       "      <td>422.88</td>\n",
+       "      <td>422.80</td>\n",
+       "      <td>422.62</td>\n",
+       "      <td>422.55</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>2</td>\n",
+       "      <td>45337</td>\n",
+       "      <td>2024.1257</td>\n",
+       "      <td>424.34</td>\n",
+       "      <td>423.56</td>\n",
+       "      <td>423.89</td>\n",
+       "      <td>423.10</td>\n",
+       "      <td>424.34</td>\n",
+       "      <td>423.56</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>794</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45366</td>\n",
+       "      <td>2024.2049</td>\n",
+       "      <td>425.22</td>\n",
+       "      <td>423.65</td>\n",
+       "      <td>424.95</td>\n",
+       "      <td>423.37</td>\n",
+       "      <td>425.22</td>\n",
+       "      <td>423.65</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45397</td>\n",
+       "      <td>2024.2896</td>\n",
+       "      <td>426.30</td>\n",
+       "      <td>423.50</td>\n",
+       "      <td>426.47</td>\n",
+       "      <td>423.66</td>\n",
+       "      <td>426.30</td>\n",
+       "      <td>423.50</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5</td>\n",
+       "      <td>45427</td>\n",
+       "      <td>2024.3716</td>\n",
+       "      <td>426.70</td>\n",
+       "      <td>423.30</td>\n",
+       "      <td>427.33</td>\n",
+       "      <td>423.93</td>\n",
+       "      <td>426.70</td>\n",
+       "      <td>423.30</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45458</td>\n",
+       "      <td>2024.4563</td>\n",
+       "      <td>426.62</td>\n",
+       "      <td>424.07</td>\n",
+       "      <td>426.75</td>\n",
+       "      <td>424.21</td>\n",
+       "      <td>426.62</td>\n",
+       "      <td>424.07</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>7</td>\n",
+       "      <td>45488</td>\n",
+       "      <td>2024.5383</td>\n",
+       "      <td>425.40</td>\n",
+       "      <td>424.63</td>\n",
+       "      <td>425.22</td>\n",
+       "      <td>424.48</td>\n",
+       "      <td>425.40</td>\n",
+       "      <td>424.63</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>8</td>\n",
+       "      <td>45519</td>\n",
+       "      <td>2024.6230</td>\n",
+       "      <td>422.70</td>\n",
+       "      <td>424.30</td>\n",
+       "      <td>423.13</td>\n",
+       "      <td>424.76</td>\n",
+       "      <td>422.70</td>\n",
+       "      <td>424.30</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>9</td>\n",
+       "      <td>45550</td>\n",
+       "      <td>2024.7077</td>\n",
+       "      <td>421.60</td>\n",
+       "      <td>425.11</td>\n",
+       "      <td>421.50</td>\n",
+       "      <td>425.03</td>\n",
+       "      <td>421.60</td>\n",
+       "      <td>425.11</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>801</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>10</td>\n",
+       "      <td>45580</td>\n",
+       "      <td>2024.7896</td>\n",
+       "      <td>422.05</td>\n",
+       "      <td>425.66</td>\n",
+       "      <td>421.70</td>\n",
+       "      <td>425.29</td>\n",
+       "      <td>422.05</td>\n",
+       "      <td>425.66</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>11</td>\n",
+       "      <td>45611</td>\n",
+       "      <td>2024.8743</td>\n",
+       "      <td>423.61</td>\n",
+       "      <td>425.87</td>\n",
+       "      <td>423.31</td>\n",
+       "      <td>425.54</td>\n",
+       "      <td>423.61</td>\n",
+       "      <td>425.87</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>12</td>\n",
+       "      <td>45641</td>\n",
+       "      <td>2024.9563</td>\n",
+       "      <td>425.01</td>\n",
+       "      <td>425.93</td>\n",
+       "      <td>424.87</td>\n",
+       "      <td>425.76</td>\n",
+       "      <td>425.01</td>\n",
+       "      <td>425.93</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>804</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45672</td>\n",
+       "      <td>2025.0411</td>\n",
+       "      <td>426.42</td>\n",
+       "      <td>426.35</td>\n",
+       "      <td>426.07</td>\n",
+       "      <td>425.98</td>\n",
+       "      <td>426.42</td>\n",
+       "      <td>426.35</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>805</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>2</td>\n",
+       "      <td>45703</td>\n",
+       "      <td>2025.1260</td>\n",
+       "      <td>427.00</td>\n",
+       "      <td>426.21</td>\n",
+       "      <td>426.99</td>\n",
+       "      <td>426.19</td>\n",
+       "      <td>427.00</td>\n",
+       "      <td>426.21</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>806</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45731</td>\n",
+       "      <td>2025.2027</td>\n",
+       "      <td>427.73</td>\n",
+       "      <td>426.19</td>\n",
+       "      <td>427.92</td>\n",
+       "      <td>426.36</td>\n",
+       "      <td>427.73</td>\n",
+       "      <td>426.19</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>807</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45762</td>\n",
+       "      <td>2025.2877</td>\n",
+       "      <td>429.24</td>\n",
+       "      <td>426.47</td>\n",
+       "      <td>429.34</td>\n",
+       "      <td>426.55</td>\n",
+       "      <td>429.24</td>\n",
+       "      <td>426.47</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>5</td>\n",
+       "      <td>45792</td>\n",
+       "      <td>2025.3699</td>\n",
+       "      <td>430.21</td>\n",
+       "      <td>426.80</td>\n",
+       "      <td>430.13</td>\n",
+       "      <td>426.72</td>\n",
+       "      <td>430.21</td>\n",
+       "      <td>426.80</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45823</td>\n",
+       "      <td>2025.4548</td>\n",
+       "      <td>429.52</td>\n",
+       "      <td>426.93</td>\n",
+       "      <td>429.46</td>\n",
+       "      <td>426.90</td>\n",
+       "      <td>429.52</td>\n",
+       "      <td>426.93</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>7</td>\n",
+       "      <td>45853</td>\n",
+       "      <td>2025.5370</td>\n",
+       "      <td>427.56</td>\n",
+       "      <td>426.76</td>\n",
+       "      <td>427.83</td>\n",
+       "      <td>427.06</td>\n",
+       "      <td>427.56</td>\n",
+       "      <td>426.76</td>\n",
+       "      <td>MLO</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>809 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     year  month  date_excel       date     CO2  seas_adjusted     fit  \\\n",
+       "2    1958      3       21259  1958.2027  315.71         314.43  316.20   \n",
+       "3    1958      4       21290  1958.2877  317.45         315.15  317.31   \n",
+       "4    1958      5       21320  1958.3699  317.51         314.68  317.89   \n",
+       "5    1958      6       21351  1958.4548  -99.99         -99.99  317.27   \n",
+       "6    1958      7       21381  1958.5370  315.87         315.20  315.85   \n",
+       "7    1958      8       21412  1958.6219  314.93         316.23  313.95   \n",
+       "8    1958      9       21443  1958.7068  313.21         316.12  312.42   \n",
+       "9    1958     10       21473  1958.7890  -99.99         -99.99  312.41   \n",
+       "10   1958     11       21504  1958.8740  313.33         315.21  313.60   \n",
+       "11   1958     12       21534  1958.9562  314.67         315.43  314.77   \n",
+       "12   1959      1       21565  1959.0411  315.58         315.52  315.64   \n",
+       "13   1959      2       21596  1959.1260  316.49         315.83  316.30   \n",
+       "14   1959      3       21624  1959.2027  316.65         315.37  317.00   \n",
+       "15   1959      4       21655  1959.2877  317.72         315.41  318.10   \n",
+       "16   1959      5       21685  1959.3699  318.29         315.46  318.69   \n",
+       "17   1959      6       21716  1959.4548  318.15         316.00  318.08   \n",
+       "18   1959      7       21746  1959.5370  316.54         315.87  316.67   \n",
+       "19   1959      8       21777  1959.6219  314.79         316.10  314.79   \n",
+       "20   1959      9       21808  1959.7068  313.84         316.76  313.28   \n",
+       "21   1959     10       21838  1959.7890  313.33         316.35  313.31   \n",
+       "22   1959     11       21869  1959.8740  314.81         316.69  314.53   \n",
+       "23   1959     12       21899  1959.9562  315.58         316.35  315.72   \n",
+       "24   1960      1       21930  1960.0410  316.43         316.37  316.63   \n",
+       "25   1960      2       21961  1960.1257  316.98         316.33  317.30   \n",
+       "26   1960      3       21990  1960.2049  317.58         316.27  318.04   \n",
+       "27   1960      4       22021  1960.2896  319.03         316.69  319.14   \n",
+       "28   1960      5       22051  1960.3716  320.03         317.19  319.70   \n",
+       "29   1960      6       22082  1960.4563  319.58         317.44  319.05   \n",
+       "30   1960      7       22112  1960.5383  318.18         317.54  317.59   \n",
+       "31   1960      8       22143  1960.6230  315.90         317.24  315.65   \n",
+       "..    ...    ...         ...        ...     ...            ...     ...   \n",
+       "781  2023      2       44972  2023.1260  420.33         419.55  420.40   \n",
+       "782  2023      3       45000  2023.2027  420.51         418.97  421.38   \n",
+       "783  2023      4       45031  2023.2877  422.73         419.97  422.88   \n",
+       "784  2023      5       45061  2023.3699  423.78         420.39  423.76   \n",
+       "785  2023      6       45092  2023.4548  423.39         420.82  423.22   \n",
+       "786  2023      7       45122  2023.5370  421.62         420.83  421.72   \n",
+       "787  2023      8       45153  2023.6219  419.56         421.12  419.67   \n",
+       "788  2023      9       45184  2023.7068  418.06         421.56  418.07   \n",
+       "789  2023     10       45214  2023.7890  418.41         422.01  418.30   \n",
+       "790  2023     11       45245  2023.8740  420.11         422.37  419.97   \n",
+       "791  2023     12       45275  2023.9562  421.65         422.57  421.60   \n",
+       "792  2024      1       45306  2024.0410  422.62         422.55  422.88   \n",
+       "793  2024      2       45337  2024.1257  424.34         423.56  423.89   \n",
+       "794  2024      3       45366  2024.2049  425.22         423.65  424.95   \n",
+       "795  2024      4       45397  2024.2896  426.30         423.50  426.47   \n",
+       "796  2024      5       45427  2024.3716  426.70         423.30  427.33   \n",
+       "797  2024      6       45458  2024.4563  426.62         424.07  426.75   \n",
+       "798  2024      7       45488  2024.5383  425.40         424.63  425.22   \n",
+       "799  2024      8       45519  2024.6230  422.70         424.30  423.13   \n",
+       "800  2024      9       45550  2024.7077  421.60         425.11  421.50   \n",
+       "801  2024     10       45580  2024.7896  422.05         425.66  421.70   \n",
+       "802  2024     11       45611  2024.8743  423.61         425.87  423.31   \n",
+       "803  2024     12       45641  2024.9563  425.01         425.93  424.87   \n",
+       "804  2025      1       45672  2025.0411  426.42         426.35  426.07   \n",
+       "805  2025      2       45703  2025.1260  427.00         426.21  426.99   \n",
+       "806  2025      3       45731  2025.2027  427.73         426.19  427.92   \n",
+       "807  2025      4       45762  2025.2877  429.24         426.47  429.34   \n",
+       "808  2025      5       45792  2025.3699  430.21         426.80  430.13   \n",
+       "809  2025      6       45823  2025.4548  429.52         426.93  429.46   \n",
+       "810  2025      7       45853  2025.5370  427.56         426.76  427.83   \n",
+       "\n",
+       "     fit_seas_adjusted  CO2_filled  seas_adjusted_filled Station  \n",
+       "2               314.91      315.71                314.43     MLO  \n",
+       "3               314.99      317.45                315.15     MLO  \n",
+       "4               315.07      317.51                314.68     MLO  \n",
+       "5               315.14      317.27                315.14     MLO  \n",
+       "6               315.22      315.87                315.20     MLO  \n",
+       "7               315.29      314.93                316.23     MLO  \n",
+       "8               315.35      313.21                316.12     MLO  \n",
+       "9               315.41      312.41                315.41     MLO  \n",
+       "10              315.46      313.33                315.21     MLO  \n",
+       "11              315.52      314.67                315.43     MLO  \n",
+       "12              315.57      315.58                315.52     MLO  \n",
+       "13              315.64      316.49                315.83     MLO  \n",
+       "14              315.70      316.65                315.37     MLO  \n",
+       "15              315.77      317.72                315.41     MLO  \n",
+       "16              315.85      318.29                315.46     MLO  \n",
+       "17              315.94      318.15                316.00     MLO  \n",
+       "18              316.03      316.54                315.87     MLO  \n",
+       "19              316.13      314.79                316.10     MLO  \n",
+       "20              316.22      313.84                316.76     MLO  \n",
+       "21              316.31      313.33                316.35     MLO  \n",
+       "22              316.40      314.81                316.69     MLO  \n",
+       "23              316.48      315.58                316.35     MLO  \n",
+       "24              316.56      316.43                316.37     MLO  \n",
+       "25              316.64      316.98                316.33     MLO  \n",
+       "26              316.71      317.58                316.27     MLO  \n",
+       "27              316.79      319.03                316.69     MLO  \n",
+       "28              316.86      320.03                317.19     MLO  \n",
+       "29              316.92      319.58                317.44     MLO  \n",
+       "30              316.98      318.18                317.54     MLO  \n",
+       "31              317.02      315.90                317.24     MLO  \n",
+       "..                 ...         ...                   ...     ...  \n",
+       "781             419.61      420.33                419.55     MKO  \n",
+       "782             419.83      420.51                418.97     MLO  \n",
+       "783             420.10      422.73                419.97     MLO  \n",
+       "784             420.37      423.78                420.39     MLO  \n",
+       "785             420.66      423.39                420.82     MLO  \n",
+       "786             420.96      421.62                420.83     MLO  \n",
+       "787             421.27      419.56                421.12     MLO  \n",
+       "788             421.58      418.06                421.56     MLO  \n",
+       "789             421.89      418.41                422.01     MLO  \n",
+       "790             422.20      420.11                422.37     MLO  \n",
+       "791             422.50      421.65                422.57     MLO  \n",
+       "792             422.80      422.62                422.55     MLO  \n",
+       "793             423.10      424.34                423.56     MLO  \n",
+       "794             423.37      425.22                423.65     MLO  \n",
+       "795             423.66      426.30                423.50     MLO  \n",
+       "796             423.93      426.70                423.30     MLO  \n",
+       "797             424.21      426.62                424.07     MLO  \n",
+       "798             424.48      425.40                424.63     MLO  \n",
+       "799             424.76      422.70                424.30     MLO  \n",
+       "800             425.03      421.60                425.11     MLO  \n",
+       "801             425.29      422.05                425.66     MLO  \n",
+       "802             425.54      423.61                425.87     MLO  \n",
+       "803             425.76      425.01                425.93     MLO  \n",
+       "804             425.98      426.42                426.35     MLO  \n",
+       "805             426.19      427.00                426.21     MLO  \n",
+       "806             426.36      427.73                426.19     MLO  \n",
+       "807             426.55      429.24                426.47     MLO  \n",
+       "808             426.72      430.21                426.80     MLO  \n",
+       "809             426.90      429.52                426.93     MLO  \n",
+       "810             427.06      427.56                426.76     MLO  \n",
+       "\n",
+       "[809 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data[~((raw_data[\"CO2_filled\"] == -99.99) | (raw_data[\"seas_adjusted_filled\"] == -99.99))]\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse des données \n",
+    "La figure suivante présente l'évolution mensuelle de la concentration en CO$_2$ depuis 1958. L'évolution est caractérisée par une oscillation périodique saisonale superposée à une évolution systématique plus lente. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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": [
+    "plt.plot(data.date,data.CO2_filled,label='Evolution mensuelle')\n",
+    "plt.plot(data.date,data.seas_adjusted_filled,label='Evolution sans oscillation saisonale')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xlabel('année')\n",
+    "plt.ylabel('Concentration en CO$_2$ (ppm)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La figure suivante présente l'évolution de l'oscillation périodique saisonnale seule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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": [
+    "seas_osc_alone = data.CO2_filled-data.seas_adjusted_filled\n",
+    "plt.plot(data.date,seas_osc_alone)\n",
+    "\n",
+    "plt.xlabel('année')\n",
+    "plt.ylabel('Concentration en CO$_2$ (ppm)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On propose d'approcher l'évolution systématique de la concentration par un polynôme d'ordre 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L'évolution systématique de la concentration peut être modélisé par un polynôme d'ordre 2 sur l'année (a) :\n",
+      "0.013637384746552685*a^2 + -52.67898341320683*a + 51177.631186923776\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": [
+    "n = 2\n",
+    "\n",
+    "# Régression polynomiale\n",
+    "coeffs = np.polyfit(data.date, data.seas_adjusted_filled, n)  \n",
+    "poly = np.poly1d(coeffs)    \n",
+    "y_fit = poly(data.date)\n",
+    "\n",
+    "print(\"L'évolution systématique de la concentration peut être modélisé par un polynôme d'ordre 2 sur l'année (a) :\")\n",
+    "print(str(coeffs[0])+\"*a^2 + \"+str(coeffs[1])+\"*a + \"+str(coeffs[2]))\n",
+    "\n",
+    "plt.plot(data.date,data.seas_adjusted_filled,label='Données mesurées')\n",
+    "plt.plot(data.date,y_fit,label=\"Regression polynomiale d'ordre 2\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En extrapolant ce comportement jusqu'en 2100 on obtient une prédiction de l'évolution systématique pour les années à venir."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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": [
+    "plt.plot(data.date,data.seas_adjusted_filled,label='Données mesurées')\n",
+    "x_futur = np.linspace(np.max(data.date), 2100, 1000)\n",
+    "y_futur = poly(x_futur)\n",
+    "plt.plot(x_futur,y_futur,label=\"Prédiction\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_code_all_hidden": false,
+  "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_fr.ipynb b/module3/exo3/exercice_fr.ipynb
deleted file mode 100644
index 0bbbe37..0000000
--- a/module3/exo3/exercice_fr.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
-}
-
-- 
2.18.1