From 0a745837f9be1c08c6ed31d6b2577945489eed4a Mon Sep 17 00:00:00 2001
From: cb1e5ba91280d02583d0604166e71c1c
 <cb1e5ba91280d02583d0604166e71c1c@app-learninglab.inria.fr>
Date: Sat, 4 Apr 2020 09:14:01 +0000
Subject: [PATCH] no commit message

---
 module3/exo3/exercice.ipynb | 654 ++++++++++++++++++++++++++----------
 1 file changed, 480 insertions(+), 174 deletions(-)

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 354d43d..e249ac3 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,8 +19,8 @@
     "import matplotlib.pyplot as plt\n",
     "import datetime\n",
     "from scipy import interpolate\n",
-    "from IPython.core.interactiveshell import InteractiveShell\n",
-    "InteractiveShell.ast_node_interactivity = \"all\""
+    "#from IPython.core.interactiveshell import InteractiveShell\n",
+    "#InteractiveShell.ast_node_interactivity = \"all\""
    ]
   },
   {
@@ -32,17 +32,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Erreur lors du téléchargement : <urlopen error [Errno -2] Name or service not known>\n",
-      "Nous téléchargeons les dernières données enregistrer sur notre PC\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -64,7 +56,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Excel</th>\n",
@@ -80,7 +71,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
        "      <td>1958</td>\n",
        "      <td>1</td>\n",
        "      <td>21200</td>\n",
@@ -94,7 +84,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
        "      <td>1958</td>\n",
        "      <td>2</td>\n",
        "      <td>21231</td>\n",
@@ -108,7 +97,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2</td>\n",
        "      <td>1958</td>\n",
        "      <td>3</td>\n",
        "      <td>21259</td>\n",
@@ -122,7 +110,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3</td>\n",
        "      <td>1958</td>\n",
        "      <td>4</td>\n",
        "      <td>21290</td>\n",
@@ -136,7 +123,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4</td>\n",
        "      <td>1958</td>\n",
        "      <td>5</td>\n",
        "      <td>21320</td>\n",
@@ -153,22 +139,22 @@
        "</div>"
       ],
       "text/plain": [
-       "   Unnamed: 0                Excel                 [ppm]      [ppm]   \\\n",
-       "0           0  1958    1     21200   1958.0411    -99.99      -99.99   \n",
-       "1           1  1958    2     21231   1958.1260    -99.99      -99.99   \n",
-       "2           2  1958    3     21259   1958.2027    315.70      314.44   \n",
-       "3           3  1958    4     21290   1958.2877    317.45      315.16   \n",
-       "4           4  1958    5     21320   1958.3699    317.51      314.71   \n",
+       "                 Excel                 [ppm]      [ppm]         [ppm]  \\\n",
+       "0  1958    1     21200   1958.0411    -99.99      -99.99       -99.99   \n",
+       "1  1958    2     21231   1958.1260    -99.99      -99.99       -99.99   \n",
+       "2  1958    3     21259   1958.2027    315.70      314.44       316.19   \n",
+       "3  1958    4     21290   1958.2877    317.45      315.16       317.30   \n",
+       "4  1958    5     21320   1958.3699    317.51      314.71       317.86   \n",
        "\n",
-       "         [ppm]      [ppm]         [ppm]      [ppm].1  \n",
-       "0       -99.99     -99.99        -99.99       -99.99  \n",
-       "1       -99.99     -99.99        -99.99       -99.99  \n",
-       "2       316.19     314.91        315.70       314.44  \n",
-       "3       317.30     314.99        317.45       315.16  \n",
-       "4       317.86     315.06        317.51       314.71  "
+       "       [ppm]         [ppm]      [ppm].1  \n",
+       "0     -99.99        -99.99       -99.99  \n",
+       "1     -99.99        -99.99       -99.99  \n",
+       "2     314.91        315.70       314.44  \n",
+       "3     314.99        317.45       315.16  \n",
+       "4     315.06        317.51       314.71  "
       ]
      },
-     "execution_count": 72,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -193,7 +179,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Excel</th>\n",
@@ -209,7 +194,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>751</th>\n",
-       "      <td>751</td>\n",
        "      <td>2020</td>\n",
        "      <td>8</td>\n",
        "      <td>44058</td>\n",
@@ -223,7 +207,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>752</th>\n",
-       "      <td>752</td>\n",
        "      <td>2020</td>\n",
        "      <td>9</td>\n",
        "      <td>44089</td>\n",
@@ -237,7 +220,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>753</th>\n",
-       "      <td>753</td>\n",
        "      <td>2020</td>\n",
        "      <td>10</td>\n",
        "      <td>44119</td>\n",
@@ -251,7 +233,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>754</th>\n",
-       "      <td>754</td>\n",
        "      <td>2020</td>\n",
        "      <td>11</td>\n",
        "      <td>44150</td>\n",
@@ -265,7 +246,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>755</th>\n",
-       "      <td>755</td>\n",
        "      <td>2020</td>\n",
        "      <td>12</td>\n",
        "      <td>44180</td>\n",
@@ -282,29 +262,29 @@
        "</div>"
       ],
       "text/plain": [
-       "     Unnamed: 0                Excel                 [ppm]      [ppm]   \\\n",
-       "751         751  2020    8     44058   2020.6230    -99.99      -99.99   \n",
-       "752         752  2020    9     44089   2020.7077    -99.99      -99.99   \n",
-       "753         753  2020   10     44119   2020.7896    -99.99      -99.99   \n",
-       "754         754  2020   11     44150   2020.8743    -99.99      -99.99   \n",
-       "755         755  2020   12     44180   2020.9563    -99.99      -99.99   \n",
+       "                   Excel                 [ppm]      [ppm]         [ppm]  \\\n",
+       "751  2020    8     44058   2020.6230    -99.99      -99.99       -99.99   \n",
+       "752  2020    9     44089   2020.7077    -99.99      -99.99       -99.99   \n",
+       "753  2020   10     44119   2020.7896    -99.99      -99.99       -99.99   \n",
+       "754  2020   11     44150   2020.8743    -99.99      -99.99       -99.99   \n",
+       "755  2020   12     44180   2020.9563    -99.99      -99.99       -99.99   \n",
        "\n",
-       "           [ppm]      [ppm]         [ppm]      [ppm].1  \n",
-       "751       -99.99     -99.99        -99.99       -99.99  \n",
-       "752       -99.99     -99.99        -99.99       -99.99  \n",
-       "753       -99.99     -99.99        -99.99       -99.99  \n",
-       "754       -99.99     -99.99        -99.99       -99.99  \n",
-       "755       -99.99     -99.99        -99.99       -99.99  "
+       "         [ppm]         [ppm]      [ppm].1  \n",
+       "751     -99.99        -99.99       -99.99  \n",
+       "752     -99.99        -99.99       -99.99  \n",
+       "753     -99.99        -99.99       -99.99  \n",
+       "754     -99.99        -99.99       -99.99  \n",
+       "755     -99.99        -99.99       -99.99  "
       ]
      },
-     "execution_count": 72,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "try:\n",
-    "    raw_data = pd.read_csv(\"https://scrippsco.ucsd.edu/assets/data/atmospheric/stations/\"\n",
+    "    raw_data = pd.read_csv(\"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/\"\n",
     "                           \"in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\",skiprows=56)\n",
     "except OSError as err:\n",
     "    print(\"Erreur lors du téléchargement : {0}\".format(err))\n",
@@ -329,22 +309,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['Unnamed: 0', '    ', '   ', '   Excel', '          ', '   [ppm]',\n",
-       "       '    [ppm] ', '      [ppm]', '    [ppm]', '       [ppm]',\n",
-       "       '    [ppm].1'],\n",
+       "Index(['    ', '   ', '   Excel', '          ', '   [ppm]', '    [ppm] ',\n",
+       "       '      [ppm]', '    [ppm]', '       [ppm]', '    [ppm].1'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "raw_data.columns\n",
+    "raw_data.columns = ['Yr','Mn','Date 1','Date 2','s1','s2','s3','s4','s5','s6']\n",
+    "data = raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/html": [
@@ -376,13 +367,11 @@
        "      <th>s4</th>\n",
        "      <th>s5</th>\n",
        "      <th>s6</th>\n",
-       "      <th>s7</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
        "      <td>1958</td>\n",
        "      <td>1</td>\n",
        "      <td>21200</td>\n",
@@ -396,7 +385,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
        "      <td>1958</td>\n",
        "      <td>2</td>\n",
        "      <td>21231</td>\n",
@@ -410,7 +398,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2</td>\n",
        "      <td>1958</td>\n",
        "      <td>3</td>\n",
        "      <td>21259</td>\n",
@@ -424,7 +411,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3</td>\n",
        "      <td>1958</td>\n",
        "      <td>4</td>\n",
        "      <td>21290</td>\n",
@@ -438,7 +424,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4</td>\n",
        "      <td>1958</td>\n",
        "      <td>5</td>\n",
        "      <td>21320</td>\n",
@@ -455,30 +440,20 @@
        "</div>"
       ],
       "text/plain": [
-       "   Yr    Mn  Date 1  Date 2         s1      s2      s3      s4      s5  \\\n",
-       "0   0  1958       1   21200  1958.0411  -99.99  -99.99  -99.99  -99.99   \n",
-       "1   1  1958       2   21231  1958.1260  -99.99  -99.99  -99.99  -99.99   \n",
-       "2   2  1958       3   21259  1958.2027  315.70  314.44  316.19  314.91   \n",
-       "3   3  1958       4   21290  1958.2877  317.45  315.16  317.30  314.99   \n",
-       "4   4  1958       5   21320  1958.3699  317.51  314.71  317.86  315.06   \n",
-       "\n",
-       "       s6      s7  \n",
-       "0  -99.99  -99.99  \n",
-       "1  -99.99  -99.99  \n",
-       "2  315.70  314.44  \n",
-       "3  317.45  315.16  \n",
-       "4  317.51  314.71  "
+       "     Yr  Mn  Date 1     Date 2      s1      s2      s3      s4      s5      s6\n",
+       "0  1958   1   21200  1958.0411  -99.99  -99.99  -99.99  -99.99  -99.99  -99.99\n",
+       "1  1958   2   21231  1958.1260  -99.99  -99.99  -99.99  -99.99  -99.99  -99.99\n",
+       "2  1958   3   21259  1958.2027  315.70  314.44  316.19  314.91  315.70  314.44\n",
+       "3  1958   4   21290  1958.2877  317.45  315.16  317.30  314.99  317.45  315.16\n",
+       "4  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06  317.51  314.71"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "raw_data.columns \n",
-    "raw_data.columns = ['Yr','Mn','Date 1','Date 2','s1','s2','s3','s4','s5','s6','s7']\n",
-    "data = raw_data\n",
     "data.head(5)"
    ]
   },
@@ -491,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -507,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -541,13 +516,11 @@
        "      <th>s4</th>\n",
        "      <th>s5</th>\n",
        "      <th>s6</th>\n",
-       "      <th>s7</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
        "      <td>1958</td>\n",
        "      <td>1</td>\n",
        "      <td>21200</td>\n",
@@ -561,7 +534,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
        "      <td>1958</td>\n",
        "      <td>2</td>\n",
        "      <td>21231</td>\n",
@@ -575,7 +547,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>5</td>\n",
        "      <td>1958</td>\n",
        "      <td>6</td>\n",
        "      <td>21351</td>\n",
@@ -589,7 +560,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>9</td>\n",
        "      <td>1958</td>\n",
        "      <td>10</td>\n",
        "      <td>21473</td>\n",
@@ -603,7 +573,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
-       "      <td>73</td>\n",
        "      <td>1964</td>\n",
        "      <td>2</td>\n",
        "      <td>23422</td>\n",
@@ -617,7 +586,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>74</th>\n",
-       "      <td>74</td>\n",
        "      <td>1964</td>\n",
        "      <td>3</td>\n",
        "      <td>23451</td>\n",
@@ -631,7 +599,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
-       "      <td>75</td>\n",
        "      <td>1964</td>\n",
        "      <td>4</td>\n",
        "      <td>23482</td>\n",
@@ -645,7 +612,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>745</th>\n",
-       "      <td>745</td>\n",
        "      <td>2020</td>\n",
        "      <td>2</td>\n",
        "      <td>43876</td>\n",
@@ -659,7 +625,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>746</th>\n",
-       "      <td>746</td>\n",
        "      <td>2020</td>\n",
        "      <td>3</td>\n",
        "      <td>43905</td>\n",
@@ -673,7 +638,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>747</th>\n",
-       "      <td>747</td>\n",
        "      <td>2020</td>\n",
        "      <td>4</td>\n",
        "      <td>43936</td>\n",
@@ -687,7 +651,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>748</th>\n",
-       "      <td>748</td>\n",
        "      <td>2020</td>\n",
        "      <td>5</td>\n",
        "      <td>43966</td>\n",
@@ -701,7 +664,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>749</th>\n",
-       "      <td>749</td>\n",
        "      <td>2020</td>\n",
        "      <td>6</td>\n",
        "      <td>43997</td>\n",
@@ -715,7 +677,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>750</th>\n",
-       "      <td>750</td>\n",
        "      <td>2020</td>\n",
        "      <td>7</td>\n",
        "      <td>44027</td>\n",
@@ -729,7 +690,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>751</th>\n",
-       "      <td>751</td>\n",
        "      <td>2020</td>\n",
        "      <td>8</td>\n",
        "      <td>44058</td>\n",
@@ -743,7 +703,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>752</th>\n",
-       "      <td>752</td>\n",
        "      <td>2020</td>\n",
        "      <td>9</td>\n",
        "      <td>44089</td>\n",
@@ -757,7 +716,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>753</th>\n",
-       "      <td>753</td>\n",
        "      <td>2020</td>\n",
        "      <td>10</td>\n",
        "      <td>44119</td>\n",
@@ -771,7 +729,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>754</th>\n",
-       "      <td>754</td>\n",
        "      <td>2020</td>\n",
        "      <td>11</td>\n",
        "      <td>44150</td>\n",
@@ -785,7 +742,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>755</th>\n",
-       "      <td>755</td>\n",
        "      <td>2020</td>\n",
        "      <td>12</td>\n",
        "      <td>44180</td>\n",
@@ -802,48 +758,28 @@
        "</div>"
       ],
       "text/plain": [
-       "      Yr    Mn  Date 1  Date 2         s1  s2  s3      s4      s5      s6  \\\n",
-       "0      0  1958       1   21200  1958.0411 NaN NaN     NaN     NaN     NaN   \n",
-       "1      1  1958       2   21231  1958.1260 NaN NaN     NaN     NaN     NaN   \n",
-       "5      5  1958       6   21351  1958.4548 NaN NaN  317.24  315.14  317.24   \n",
-       "9      9  1958      10   21473  1958.7890 NaN NaN  312.44  315.40  312.44   \n",
-       "73    73  1964       2   23422  1964.1257 NaN NaN  320.01  319.36  320.01   \n",
-       "74    74  1964       3   23451  1964.2049 NaN NaN  320.74  319.41  320.74   \n",
-       "75    75  1964       4   23482  1964.2896 NaN NaN  321.83  319.45  321.83   \n",
-       "745  745  2020       2   43876  2020.1257 NaN NaN     NaN     NaN     NaN   \n",
-       "746  746  2020       3   43905  2020.2049 NaN NaN     NaN     NaN     NaN   \n",
-       "747  747  2020       4   43936  2020.2896 NaN NaN     NaN     NaN     NaN   \n",
-       "748  748  2020       5   43966  2020.3716 NaN NaN     NaN     NaN     NaN   \n",
-       "749  749  2020       6   43997  2020.4563 NaN NaN     NaN     NaN     NaN   \n",
-       "750  750  2020       7   44027  2020.5383 NaN NaN     NaN     NaN     NaN   \n",
-       "751  751  2020       8   44058  2020.6230 NaN NaN     NaN     NaN     NaN   \n",
-       "752  752  2020       9   44089  2020.7077 NaN NaN     NaN     NaN     NaN   \n",
-       "753  753  2020      10   44119  2020.7896 NaN NaN     NaN     NaN     NaN   \n",
-       "754  754  2020      11   44150  2020.8743 NaN NaN     NaN     NaN     NaN   \n",
-       "755  755  2020      12   44180  2020.9563 NaN NaN     NaN     NaN     NaN   \n",
-       "\n",
-       "         s7  \n",
-       "0       NaN  \n",
-       "1       NaN  \n",
-       "5    315.14  \n",
-       "9    315.40  \n",
-       "73   319.36  \n",
-       "74   319.41  \n",
-       "75   319.45  \n",
-       "745     NaN  \n",
-       "746     NaN  \n",
-       "747     NaN  \n",
-       "748     NaN  \n",
-       "749     NaN  \n",
-       "750     NaN  \n",
-       "751     NaN  \n",
-       "752     NaN  \n",
-       "753     NaN  \n",
-       "754     NaN  \n",
-       "755     NaN  "
+       "       Yr  Mn  Date 1     Date 2  s1  s2      s3      s4      s5      s6\n",
+       "0    1958   1   21200  1958.0411 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "1    1958   2   21231  1958.1260 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "5    1958   6   21351  1958.4548 NaN NaN  317.24  315.14  317.24  315.14\n",
+       "9    1958  10   21473  1958.7890 NaN NaN  312.44  315.40  312.44  315.40\n",
+       "73   1964   2   23422  1964.1257 NaN NaN  320.01  319.36  320.01  319.36\n",
+       "74   1964   3   23451  1964.2049 NaN NaN  320.74  319.41  320.74  319.41\n",
+       "75   1964   4   23482  1964.2896 NaN NaN  321.83  319.45  321.83  319.45\n",
+       "745  2020   2   43876  2020.1257 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "746  2020   3   43905  2020.2049 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "747  2020   4   43936  2020.2896 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "748  2020   5   43966  2020.3716 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "749  2020   6   43997  2020.4563 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "750  2020   7   44027  2020.5383 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "751  2020   8   44058  2020.6230 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "752  2020   9   44089  2020.7077 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "753  2020  10   44119  2020.7896 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "754  2020  11   44150  2020.8743 NaN NaN     NaN     NaN     NaN     NaN\n",
+       "755  2020  12   44180  2020.9563 NaN NaN     NaN     NaN     NaN     NaN"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -852,54 +788,424 @@
     "data[data.isnull().any(axis=1)]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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>Yr</th>\n",
+       "      <th>Mn</th>\n",
+       "      <th>Date 1</th>\n",
+       "      <th>Date 2</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21200</td>\n",
+       "      <td>1958.0411</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21231</td>\n",
+       "      <td>1958.1260</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\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>315.70</td>\n",
+       "      <td>314.44</td>\n",
+       "      <td>316.19</td>\n",
+       "      <td>314.91</td>\n",
+       "      <td>315.70</td>\n",
+       "      <td>314.44</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>315.16</td>\n",
+       "      <td>317.30</td>\n",
+       "      <td>314.99</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.16</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>314.71</td>\n",
+       "      <td>317.86</td>\n",
+       "      <td>315.06</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.71</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Yr  Mn  Date 1     Date 2      s1      s2      s3      s4      s5      s6\n",
+       "0  1958   1   21200  1958.0411     NaN     NaN     NaN     NaN     NaN     NaN\n",
+       "1  1958   2   21231  1958.1260     NaN     NaN     NaN     NaN     NaN     NaN\n",
+       "2  1958   3   21259  1958.2027  315.70  314.44  316.19  314.91  315.70  314.44\n",
+       "3  1958   4   21290  1958.2877  317.45  315.16  317.30  314.99  317.45  315.16\n",
+       "4  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06  317.51  314.71"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On ajoute un index ' période'  à la DataFrame, cet index représente la période de mesure. \n",
+    "On ajoute un index ' périod' à la DataFrame, cet index représente la période de mesure. \n",
     "Cette date est mise dans au format compréhensible par pandas. On visualise toutes les lignes qui seront supprimées."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 86,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Yr</th>\n",
+       "      <th>Mn</th>\n",
+       "      <th>Date 1</th>\n",
+       "      <th>Date 2</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>period</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1958-03-01</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21259</td>\n",
+       "      <td>1958.2027</td>\n",
+       "      <td>315.70</td>\n",
+       "      <td>314.44</td>\n",
+       "      <td>316.19</td>\n",
+       "      <td>314.91</td>\n",
+       "      <td>315.70</td>\n",
+       "      <td>314.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-04-01</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>315.16</td>\n",
+       "      <td>317.30</td>\n",
+       "      <td>314.99</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-05-01</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>314.71</td>\n",
+       "      <td>317.86</td>\n",
+       "      <td>315.06</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-07-01</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21381</td>\n",
+       "      <td>1958.5370</td>\n",
+       "      <td>315.86</td>\n",
+       "      <td>315.19</td>\n",
+       "      <td>315.86</td>\n",
+       "      <td>315.22</td>\n",
+       "      <td>315.86</td>\n",
+       "      <td>315.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958-08-01</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>316.19</td>\n",
+       "      <td>314.00</td>\n",
+       "      <td>315.29</td>\n",
+       "      <td>314.93</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Yr  Mn  Date 1     Date 2      s1      s2      s3      s4  \\\n",
+       "period                                                                    \n",
+       "1958-03-01  1958   3   21259  1958.2027  315.70  314.44  316.19  314.91   \n",
+       "1958-04-01  1958   4   21290  1958.2877  317.45  315.16  317.30  314.99   \n",
+       "1958-05-01  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06   \n",
+       "1958-07-01  1958   7   21381  1958.5370  315.86  315.19  315.86  315.22   \n",
+       "1958-08-01  1958   8   21412  1958.6219  314.93  316.19  314.00  315.29   \n",
+       "\n",
+       "                s5      s6  \n",
+       "period                      \n",
+       "1958-03-01  315.70  314.44  \n",
+       "1958-04-01  317.45  315.16  \n",
+       "1958-05-01  317.51  314.71  \n",
+       "1958-07-01  315.86  315.19  \n",
+       "1958-08-01  314.93  316.19  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['period'] = pd.Series([datetime.date(y,m,1) for y,m in zip(data['Yr'],data['Mn'])])\n",
+    "data1 = data.dropna().copy()\n",
+    "data1 = data1.set_index('period')   \n",
+    "data1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Représentation graphique de la concentration  de CO2 de 1958 à nos jours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1['s1'].plot(title = 'concentration de CO2',);\n",
+    "data1['s1'].plot(figsize=(15, 10),).grid(linestyle='--', linewidth=1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous allons approximé la concentration de CO2 avec une droite, puis faire la différence pour \n",
+    "n'obtenir que les variations de la concentration de CO2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'period'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'period'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-c15b3dbfe1f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Yr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Mn'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   3144\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3145\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3146\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3147\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3148\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'period'"
-     ]
+     "data": {
+      "text/plain": [
+       "[Text(0,0.5,'Concentration (ppm)'),\n",
+       " Text(0.5,0,'Période'),\n",
+       " Text(0.5,1,'Concentration CO2')]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe540f72400>]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe540eb9630>]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0,0.5,'Concentration (ppm)'),\n",
+       " Text(0.5,0,'Période'),\n",
+       " Text(0.5,1,'Variation de la concentration CO2')]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe540e93668>]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "data['period'] = [datetime.date(y,m,1) for y,m in zip(data['Yr'],data['Mn'])]\n",
-    "data = data.set_index('period')   \n",
-    "data = data.dropna().copy()\n",
-    "data"
+    "from scipy import stats\n",
+    "\n",
+    "slope, intercept, r_value, p_value, std_err = stats.linregress(data1['Date 2'], data1['s1'])\n",
+    "def predict(x):\n",
+    "    return slope*x+intercept\n",
+    "\n",
+    "data1['reg_lineaire'] = predict(data1['Date 2'])\n",
+    "\n",
+    "fig = plt.figure(figsize=(18, 5))\n",
+    "ax1 = fig.add_subplot(121)\n",
+    "ax2 = fig.add_subplot(122)\n",
+    "#plot(figsize=(8, 5))  .plot(figsize=(15, 10), grid=True).grid(linestyle='--', linewidth=1);\n",
+    "\n",
+    "ax1.set(title = 'Concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
+    "ax1.plot(data1['s1'])\n",
+    "#data1['s1'].plot()\n",
+    "ax1.plot(data1['reg_lineaire']) \n",
+    "data1['co2'] = data1['reg_lineaire']-data1['s1']\n",
+    "ax2.set(title = 'Variation de la concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
+    "ax2.plot(data1['co2']) "
    ]
   },
   {
-- 
2.18.1