diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index e062952a7c50ccaaac09709a478f88a820e7a814..90eb6021888b37e244fdd45e37f0e579b2411734 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 639,
+   "execution_count": 821,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 640,
+   "execution_count": 822,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -329,7 +329,7 @@
        "[756 rows x 10 columns]"
       ]
      },
-     "execution_count": 640,
+     "execution_count": 822,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 641,
+   "execution_count": 823,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -651,7 +651,7 @@
        "[756 rows x 10 columns]"
       ]
      },
-     "execution_count": 641,
+     "execution_count": 823,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 642,
+   "execution_count": 824,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 643,
+   "execution_count": 825,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -747,7 +747,7 @@
        "dtype: bool"
       ]
      },
-     "execution_count": 643,
+     "execution_count": 825,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -769,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 648,
+   "execution_count": 826,
    "metadata": {},
    "outputs": [
     {
@@ -913,222 +913,18 @@
        "1958-07-01  315.22  315.86  315.19  "
       ]
      },
-     "execution_count": 648,
+     "execution_count": 826,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df = data.copy()  \n",
-    "df = df.reset_index().copy()\n",
-    "d_i = datetime.date(df['Yr'][0], df['Mn'][0] , 1)\n",
-    "d_f = datetime.date(df['Yr'][-1:], df['Mn'][-1:] , 1)\n",
-    "per1 = pd.date_range(start = d_i,end = d_f, freq=pd.DateOffset(months=1))\n",
-    "df.index.drop\n",
-    "per1 = pd.date_range(start = d_i,end = d_f, freq=pd.DateOffset(months=1))\n",
-    "#df.reindex(per1)\n",
-    "df.index=per1 \n",
-    "df.index.name = 'period'\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 626,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-626-f472f6c102dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m                                 \u001b[0;31m# data.dropna().copy()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m##df = df.reset_index().copy()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0md_i\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[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Yr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\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;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0md_f\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[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Yr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\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;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#er1 = pd.date_range(start = d_i,end = d_f, freq=pd.DateOffset(months=1))\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/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    621\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\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[0m\n\u001b[0m\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\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_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2558\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2559\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2560\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2561\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2562\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\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_value\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\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.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
    "source": [
     "df = data.copy()                                 # data.dropna().copy()\n",
-    "##df = df.reset_index().copy()\n",
-    "d_i = datetime.date(df['Yr'][0], df['Mn'][0] , 1)\n",
-    "d_f = datetime.date(df['Yr'][-1:], df['Mn'][-1:] , 1)\n",
-    "#per1 = pd.date_range(start = d_i,end = d_f, freq=pd.DateOffset(months=1))\n",
-    "#type(per1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 621,
-   "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>index</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",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03-01</th>\n",
-       "      <td>2</td>\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>3</td>\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>4</td>\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>1958-06-01</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>6</td>\n",
-       "      <td>21351</td>\n",
-       "      <td>1958.4548</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>315.14</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>315.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-01</th>\n",
-       "      <td>6</td>\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",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            index    Yr  Mn  Date 1     Date 2      s1      s2      s3  \\\n",
-       "period                                                                   \n",
-       "1958-03-01      2  1958   3   21259  1958.2027  315.70  314.44  316.19   \n",
-       "1958-04-01      3  1958   4   21290  1958.2877  317.45  315.16  317.30   \n",
-       "1958-05-01      4  1958   5   21320  1958.3699  317.51  314.71  317.86   \n",
-       "1958-06-01      5  1958   6   21351  1958.4548     NaN     NaN  317.24   \n",
-       "1958-07-01      6  1958   7   21381  1958.5370  315.86  315.19  315.86   \n",
-       "\n",
-       "                s4      s5      s6  \n",
-       "period                              \n",
-       "1958-03-01  314.91  315.70  314.44  \n",
-       "1958-04-01  314.99  317.45  315.16  \n",
-       "1958-05-01  315.06  317.51  314.71  \n",
-       "1958-06-01  315.14  317.24  315.14  \n",
-       "1958-07-01  315.22  315.86  315.19  "
-      ]
-     },
-     "execution_count": 621,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#period = [datetime.date(y,m,1) for y,m in zip(df['Yr'],df['Mn'])]\n",
-    "#period = pd.Series(period,name = 'period')\n",
-    "#df = pd.concat([df,per1],axis=1)\n",
-    "#df = df.set_index('period') \n",
-    "df.index=per1 \n",
-    "df.index.name = 'period'\n",
-    "\n",
+    "df = df.reset_index().copy()\n",
+    "period = [datetime.date(y,m,1) for y,m in zip(df['Yr'],df['Mn'])]\n",
+    "period = pd.Series(period,name = 'period')\n",
+    "df = pd.concat([df,period],axis=1)\n",
+    "df = df.set_index('period') \n",
     "df.head()\n"
    ]
   },
@@ -1144,33 +940,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 647,
+   "execution_count": 857,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "NotImplementedError",
-     "evalue": "Prefix not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-647-83728289e629>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#df['s5'].plot(title = 'Concentration de CO2');\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m's5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   2501\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2502\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m                            **kwds)\n\u001b[0m\u001b[1;32m   2504\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   1925\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1926\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1727\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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   1730\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\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    253\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\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/plotting/_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_ts_plot\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[0m\u001b[1;32m    948\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeseries\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_maybe_convert_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_convert_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\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/plotting/_core.py\u001b[0m in \u001b[0;36m_is_ts_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_ts_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m         \u001b[0;31m# this is slightly deceptive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_index\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_dynamic_x\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    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/plotting/_core.py\u001b[0m in \u001b[0;36m_use_dynamic_x\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    942\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeseries\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/plotting/_timeseries.py\u001b[0m in \u001b[0;36m_use_dynamic_x\u001b[0;34m(ax, data)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDateOffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m         \u001b[0mfreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrule_code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m         \u001b[0mfreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrequencies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_alias\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\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/tseries/offsets.py\u001b[0m in \u001b[0;36mrule_code\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    500\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrule_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prefix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/tseries/offsets.py\u001b[0m in \u001b[0;36m_prefix\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    496\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prefix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Prefix not defined'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNotImplementedError\u001b[0m: Prefix not defined"
-     ]
-    },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 1 Axes>"
       ]
@@ -1182,8 +957,7 @@
     }
    ],
    "source": [
-    "#df['s5'].plot(title = 'Concentration de CO2');\n",
-    "df['s5'].plot(figsize=(15, 10),).grid(linestyle='--', linewidth=1);"
+    "df['s5'].plot(figsize=(15, 10),title = 'Concentration de CO2').grid(linestyle='--', linewidth=1);"
    ]
   },
   {
@@ -1199,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 598,
+   "execution_count": 828,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1257,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 483,
+   "execution_count": 829,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true,
@@ -1332,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 484,
+   "execution_count": 830,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1389,7 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 485,
+   "execution_count": 831,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1415,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 486,
+   "execution_count": 832,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true,
@@ -1460,7 +1234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 487,
+   "execution_count": 833,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true,
@@ -1496,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 488,
+   "execution_count": 834,
    "metadata": {},
    "outputs": [
     {
@@ -1505,7 +1279,7 @@
        "0.7829981215405544"
       ]
      },
-     "execution_count": 488,
+     "execution_count": 834,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1528,19 +1302,12 @@
     "ax1.grid(linestyle='--', linewidth=1)\n",
     "ax1.plot(x,yrapide,x,s5-ylent-yrapide);\n",
     "bruit  = (s5-ylent-yrapide).describe()\n",
-    "bruit['std']\n"
+    "bruit['std']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 489,
+   "execution_count": 835,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1572,7 +1339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 490,
+   "execution_count": 836,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1704,159 +1471,8 @@
        "      <td>316.69</td>\n",
        "      <td>317.70</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>318.74</td>\n",
-       "      <td>319.07</td>\n",
-       "      <td>319.86</td>\n",
-       "      <td>321.38</td>\n",
-       "      <td>322.25</td>\n",
-       "      <td>321.48</td>\n",
-       "      <td>319.74</td>\n",
-       "      <td>317.77</td>\n",
-       "      <td>316.21</td>\n",
-       "      <td>315.99</td>\n",
-       "      <td>317.07</td>\n",
-       "      <td>318.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>319.57</td>\n",
-       "      <td>320.01</td>\n",
-       "      <td>320.74</td>\n",
-       "      <td>321.83</td>\n",
-       "      <td>322.26</td>\n",
-       "      <td>321.89</td>\n",
-       "      <td>320.44</td>\n",
-       "      <td>318.69</td>\n",
-       "      <td>316.71</td>\n",
-       "      <td>316.87</td>\n",
-       "      <td>317.68</td>\n",
-       "      <td>318.71</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",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014</th>\n",
-       "      <td>397.93</td>\n",
-       "      <td>398.10</td>\n",
-       "      <td>399.47</td>\n",
-       "      <td>401.33</td>\n",
-       "      <td>401.88</td>\n",
-       "      <td>401.31</td>\n",
-       "      <td>399.07</td>\n",
-       "      <td>397.21</td>\n",
-       "      <td>395.40</td>\n",
-       "      <td>395.65</td>\n",
-       "      <td>397.23</td>\n",
-       "      <td>398.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015</th>\n",
-       "      <td>399.85</td>\n",
-       "      <td>400.31</td>\n",
-       "      <td>401.51</td>\n",
-       "      <td>403.45</td>\n",
-       "      <td>404.10</td>\n",
-       "      <td>402.88</td>\n",
-       "      <td>401.61</td>\n",
-       "      <td>399.00</td>\n",
-       "      <td>397.50</td>\n",
-       "      <td>398.28</td>\n",
-       "      <td>400.24</td>\n",
-       "      <td>401.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>402.65</td>\n",
-       "      <td>404.16</td>\n",
-       "      <td>404.85</td>\n",
-       "      <td>407.57</td>\n",
-       "      <td>407.66</td>\n",
-       "      <td>407.00</td>\n",
-       "      <td>404.50</td>\n",
-       "      <td>402.24</td>\n",
-       "      <td>401.01</td>\n",
-       "      <td>401.50</td>\n",
-       "      <td>403.64</td>\n",
-       "      <td>404.55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>406.07</td>\n",
-       "      <td>406.64</td>\n",
-       "      <td>407.06</td>\n",
-       "      <td>408.95</td>\n",
-       "      <td>409.91</td>\n",
-       "      <td>409.12</td>\n",
-       "      <td>407.20</td>\n",
-       "      <td>405.24</td>\n",
-       "      <td>403.27</td>\n",
-       "      <td>403.64</td>\n",
-       "      <td>405.17</td>\n",
-       "      <td>406.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>408.05</td>\n",
-       "      <td>408.34</td>\n",
-       "      <td>409.25</td>\n",
-       "      <td>410.30</td>\n",
-       "      <td>411.30</td>\n",
-       "      <td>410.88</td>\n",
-       "      <td>408.90</td>\n",
-       "      <td>407.10</td>\n",
-       "      <td>405.59</td>\n",
-       "      <td>405.99</td>\n",
-       "      <td>408.12</td>\n",
-       "      <td>409.23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>410.92</td>\n",
-       "      <td>411.66</td>\n",
-       "      <td>412.00</td>\n",
-       "      <td>413.52</td>\n",
-       "      <td>414.83</td>\n",
-       "      <td>413.96</td>\n",
-       "      <td>411.85</td>\n",
-       "      <td>410.08</td>\n",
-       "      <td>408.55</td>\n",
-       "      <td>408.43</td>\n",
-       "      <td>410.28</td>\n",
-       "      <td>411.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>413.37</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",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>63 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -1867,16 +1483,6 @@
        "1960  316.43  316.98  317.58  319.03  320.04  319.59  318.18  315.90  314.17   \n",
        "1961  316.89  317.70  318.54  319.48  320.58  319.77  318.57  316.79  314.99   \n",
        "1962  317.94  318.55  319.68  320.57  321.02  320.62  319.61  317.40  316.25   \n",
-       "1963  318.74  319.07  319.86  321.38  322.25  321.48  319.74  317.77  316.21   \n",
-       "1964  319.57  320.01  320.74  321.83  322.26  321.89  320.44  318.69  316.71   \n",
-       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
-       "2014  397.93  398.10  399.47  401.33  401.88  401.31  399.07  397.21  395.40   \n",
-       "2015  399.85  400.31  401.51  403.45  404.10  402.88  401.61  399.00  397.50   \n",
-       "2016  402.65  404.16  404.85  407.57  407.66  407.00  404.50  402.24  401.01   \n",
-       "2017  406.07  406.64  407.06  408.95  409.91  409.12  407.20  405.24  403.27   \n",
-       "2018  408.05  408.34  409.25  410.30  411.30  410.88  408.90  407.10  405.59   \n",
-       "2019  410.92  411.66  412.00  413.52  414.83  413.96  411.85  410.08  408.55   \n",
-       "2020  413.37     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
        "\n",
        "Mn        10      11      12  \n",
        "Yr                            \n",
@@ -1884,34 +1490,22 @@
        "1959  313.33  314.81  315.58  \n",
        "1960  313.83  315.00  316.19  \n",
        "1961  315.31  316.10  317.01  \n",
-       "1962  315.42  316.69  317.70  \n",
-       "1963  315.99  317.07  318.35  \n",
-       "1964  316.87  317.68  318.71  \n",
-       "...      ...     ...     ...  \n",
-       "2014  395.65  397.23  398.79  \n",
-       "2015  398.28  400.24  401.89  \n",
-       "2016  401.50  403.64  404.55  \n",
-       "2017  403.64  405.17  406.75  \n",
-       "2018  405.99  408.12  409.23  \n",
-       "2019  408.43  410.28  411.85  \n",
-       "2020     NaN     NaN     NaN  \n",
-       "\n",
-       "[63 rows x 12 columns]"
+       "1962  315.42  316.69  317.70  "
       ]
      },
-     "execution_count": 490,
+     "execution_count": 836,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df1 = pd.crosstab(df['Yr'],df['Mn'],values=df['s5'],aggfunc=pd.Series)\n",
-    "df1"
+    "df1.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 492,
+   "execution_count": 837,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true,
@@ -2066,7 +1660,7 @@
        "1962  0.7890  0.8740  0.9562  "
       ]
      },
-     "execution_count": 492,
+     "execution_count": 837,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,7 +1668,7 @@
    "source": [
     "m = df['Date 2'] - df['Yr']\n",
     "df2 = pd.crosstab(df['Yr'],df['Mn'],values= m,aggfunc=pd.Series)\n",
-    "df2.head(5)"
+    "df2.head()"
    ]
   },
   {
@@ -2099,7 +1693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 501,
+   "execution_count": 838,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -2182,7 +1776,7 @@
        "2020-01-01  413.37  413.33  "
       ]
      },
-     "execution_count": 501,
+     "execution_count": 838,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2200,7 +1794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 517,
+   "execution_count": 839,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true,
@@ -2228,7 +1822,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Data 2</th>\n",
+       "      <th>Date 2</th>\n",
        "      <th>Mn</th>\n",
        "      <th>Yr</th>\n",
        "    </tr>\n",
@@ -2264,93 +1858,20 @@
        "      <td>6</td>\n",
        "      <td>2020</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-07-01</th>\n",
-       "      <td>2020.5383</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-08-01</th>\n",
-       "      <td>2020.6230</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-06-01</th>\n",
-       "      <td>2025.4548</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01</th>\n",
-       "      <td>2025.5370</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-08-01</th>\n",
-       "      <td>2025.6219</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-09-01</th>\n",
-       "      <td>2025.7068</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-10-01</th>\n",
-       "      <td>2025.7890</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-11-01</th>\n",
-       "      <td>2025.8740</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-12-01</th>\n",
-       "      <td>2025.9562</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2025</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>71 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "               Data 2  Mn    Yr\n",
+       "               Date 2  Mn    Yr\n",
        "2020-02-01  2020.1257   2  2020\n",
        "2020-03-01  2020.2049   3  2020\n",
        "2020-04-01  2020.2896   4  2020\n",
        "2020-05-01  2020.3716   5  2020\n",
-       "2020-06-01  2020.4563   6  2020\n",
-       "2020-07-01  2020.5383   7  2020\n",
-       "2020-08-01  2020.6230   8  2020\n",
-       "...               ...  ..   ...\n",
-       "2025-06-01  2025.4548   6  2025\n",
-       "2025-07-01  2025.5370   7  2025\n",
-       "2025-08-01  2025.6219   8  2025\n",
-       "2025-09-01  2025.7068   9  2025\n",
-       "2025-10-01  2025.7890  10  2025\n",
-       "2025-11-01  2025.8740  11  2025\n",
-       "2025-12-01  2025.9562  12  2025\n",
-       "\n",
-       "[71 rows x 3 columns]"
+       "2020-06-01  2020.4563   6  2020"
       ]
      },
-     "execution_count": 517,
+     "execution_count": 839,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2362,20 +1883,22 @@
     "\n",
     "ymj = df[-1:].index[0].isoformat() \n",
     "per1 = pd.date_range(start = ymj,end =ymj_fin, freq=pd.DateOffset(months=1))\n",
-    "Yr = pd.Series([q.year for q in per1],index = per1 )\n",
-    "Mn = pd.Series([q.month for q in per1],index = per1)\n",
+    "period = []\n",
+    "[period.append(datetime.date(i.year,i.month,i.day)) for i in per1]\n",
+    "Yr = pd.Series([q.year for q in per1],index = period )\n",
+    "Mn = pd.Series([q.month for q in per1],index = period)\n",
     "    \n",
     "s = []         # oblication d'initial\n",
     "[s.extend(list(an_n)) if i%4!=0 else s.extend(list(an_b))  for i in np.arange(ymj_fin.year- year[0]+1)]\n",
-    "Data2 = pd.Series(s,index = per1)\n",
-    "df_inter = pd.DataFrame({'Yr' : Yr,'Mn' : Mn,'Data 2' : Data2 + Yr})\n",
+    "Data2 = pd.Series(s,index = period)\n",
+    "df_inter = pd.DataFrame({'Yr' : Yr,'Mn' : Mn,'Date 2' : Data2 + Yr})\n",
     "df_inter.drop(df_inter.index[0],inplace = True)\n",
-    "df_inter"
+    "df_inter.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 519,
+   "execution_count": 864,
    "metadata": {},
    "outputs": [
     {
@@ -2399,7 +1922,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Data 2</th>\n",
+       "      <th>Date 2</th>\n",
        "      <th>Mn</th>\n",
        "      <th>Yr</th>\n",
        "      <th>evolution</th>\n",
@@ -2418,8 +1941,8 @@
        "      <td>411.797771</td>\n",
        "      <td>-0.796055</td>\n",
        "      <td>-1.387941</td>\n",
-       "      <td>0.555845</td>\n",
-       "      <td>410.169621</td>\n",
+       "      <td>-1.359161</td>\n",
+       "      <td>411.030496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020-03-01</th>\n",
@@ -2429,8 +1952,8 @@
        "      <td>411.985952</td>\n",
        "      <td>-0.798681</td>\n",
        "      <td>-2.402639</td>\n",
-       "      <td>0.246584</td>\n",
-       "      <td>409.031216</td>\n",
+       "      <td>-0.081553</td>\n",
+       "      <td>413.508356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020-04-01</th>\n",
@@ -2440,8 +1963,8 @@
        "      <td>412.187381</td>\n",
        "      <td>-0.801317</td>\n",
        "      <td>-2.836106</td>\n",
-       "      <td>1.236244</td>\n",
-       "      <td>409.786203</td>\n",
+       "      <td>-0.214664</td>\n",
+       "      <td>414.007506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020-05-01</th>\n",
@@ -2451,8 +1974,8 @@
        "      <td>412.382567</td>\n",
        "      <td>-0.803696</td>\n",
        "      <td>-2.504793</td>\n",
-       "      <td>-0.732706</td>\n",
-       "      <td>408.341372</td>\n",
+       "      <td>-0.274924</td>\n",
+       "      <td>413.808739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020-06-01</th>\n",
@@ -2462,54 +1985,54 @@
        "      <td>412.584363</td>\n",
        "      <td>-0.805976</td>\n",
        "      <td>-1.479139</td>\n",
-       "      <td>0.489992</td>\n",
-       "      <td>410.789241</td>\n",
+       "      <td>0.841779</td>\n",
+       "      <td>414.099305</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               Data 2  Mn    Yr   evolution   s5_lent  s5_rapide     noise  \\\n",
-       "2020-02-01  2020.1257   2  2020  411.797771 -0.796055  -1.387941  0.555845   \n",
-       "2020-03-01  2020.2049   3  2020  411.985952 -0.798681  -2.402639  0.246584   \n",
-       "2020-04-01  2020.2896   4  2020  412.187381 -0.801317  -2.836106  1.236244   \n",
-       "2020-05-01  2020.3716   5  2020  412.382567 -0.803696  -2.504793 -0.732706   \n",
-       "2020-06-01  2020.4563   6  2020  412.584363 -0.805976  -1.479139  0.489992   \n",
+       "               Date 2  Mn    Yr   evolution   s5_lent  s5_rapide     noise  \\\n",
+       "2020-02-01  2020.1257   2  2020  411.797771 -0.796055  -1.387941 -1.359161   \n",
+       "2020-03-01  2020.2049   3  2020  411.985952 -0.798681  -2.402639 -0.081553   \n",
+       "2020-04-01  2020.2896   4  2020  412.187381 -0.801317  -2.836106 -0.214664   \n",
+       "2020-05-01  2020.3716   5  2020  412.382567 -0.803696  -2.504793 -0.274924   \n",
+       "2020-06-01  2020.4563   6  2020  412.584363 -0.805976  -1.479139  0.841779   \n",
        "\n",
        "              s5_inter  \n",
-       "2020-02-01  410.169621  \n",
-       "2020-03-01  409.031216  \n",
-       "2020-04-01  409.786203  \n",
-       "2020-05-01  408.341372  \n",
-       "2020-06-01  410.789241  "
+       "2020-02-01  411.030496  \n",
+       "2020-03-01  413.508356  \n",
+       "2020-04-01  414.007506  \n",
+       "2020-05-01  413.808739  \n",
+       "2020-06-01  414.099305  "
       ]
      },
-     "execution_count": 519,
+     "execution_count": 864,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ylent_inter = my_sin(df_inter['Data 2'],popt_l[0],popt_l[1],popt_l[2],popt_l[3])\n",
-    "yrapide_inter = my_sin(df_inter['Data 2'],popt_r[0],popt_r[1],popt_r[2],popt_r[3]) \n",
+    "ylent_inter = my_sin(df_inter['Date 2'],popt_l[0],popt_l[1],popt_l[2],popt_l[3])\n",
+    "yrapide_inter = my_sin(df_inter['Date 2'],popt_r[0],popt_r[1],popt_r[2],popt_r[3]) \n",
     "\n",
     "mu, sigma = 0, bruit['std'] \n",
     "# creating a noise\n",
-    "noise = np.random.normal(mu, sigma, len(df_inter['Data 2']))\n",
-    "evolution = fcube(df_inter['Data 2'])\n",
+    "noise = np.random.normal(mu, sigma, len(df_inter['Date 2']))\n",
+    "evolution = fcube(df_inter['Date 2'])\n",
     "\n",
     "df_inter['evolution'] = evolution\n",
     "df_inter['s5_lent'] = ylent_inter\n",
     "df_inter['s5_rapide'] = yrapide_inter\n",
     "df_inter['noise'] = noise\n",
-    "df_inter['s5_inter'] =  df_inter.s5_lent + df_inter.s5_rapide + df_inter.noise+df_inter.evolution\n",
+    "df_inter['s5_inter'] =  df_inter.s5_lent - df_inter.s5_rapide + df_inter.noise+df_inter.evolution\n",
     "df_inter.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 520,
+   "execution_count": 865,
    "metadata": {},
    "outputs": [
     {
@@ -2533,19 +2056,19 @@
     "ax2.grid(linestyle='--', linewidth=1)\n",
     "\n",
     "ax1.set(title = 'Interpolation concentration CO2 lente',xlabel='Période',ylabel='Concentration (ppm)')\n",
-    "ax1.plot(df_inter['Data 2'],df_inter.s5_lent);\n",
+    "ax1.plot(df_inter['Date 2'],df_inter.s5_lent);\n",
     "ax2.set(title = 'Interpolation concentration CO2 rapide',xlabel='Période',ylabel='Concentration (ppm)')\n",
-    "ax2.plot(df_inter['Data 2'],df_inter.s5_rapide);"
+    "ax2.plot(df_inter['Date 2'],df_inter.s5_rapide);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 521,
+   "execution_count": 868,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x360 with 1 Axes>"
       ]
@@ -2562,17 +2085,17 @@
     "ax1.grid(linestyle='--', linewidth=1)\n",
     "\n",
     "ax1.set(title = 'Interpolation concentration CO2 + bruit',xlabel='Période',ylabel='Concentration (ppm)')\n",
-    "ax1.plot(df_inter['Data 2'],df_inter.s5_lent + df_inter.s5_rapide + df_inter.noise);"
+    "ax1.plot(df_inter['Date 2'],df_inter.s5_lent - df_inter.s5_rapide + df_inter.noise);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 522,
+   "execution_count": 869,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x360 with 1 Axes>"
       ]
@@ -2589,846 +2112,68 @@
     "ax1.grid(linestyle='--', linewidth=1)\n",
     "\n",
     "ax1.set(title = 'Interpolation concentration CO2 + bruit',xlabel='Période',ylabel='Concentration (ppm)')\n",
-    "ax1.plot(df_inter['Data 2'],df_inter.s5_inter);"
+    "ax1.plot(df_inter['Date 2'],df_inter.s5_lent - df_inter.s5_rapide + df_inter.noise+df_inter.evolution);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 870,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_inter "
+    "dfb = pd.concat([df, df_inter])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 523,
+   "execution_count": 871,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1296x360 with 1 Axes>"
+       "Index([1958-03-01, 1958-04-01, 1958-05-01, 1958-06-01, 1958-07-01, 1958-08-01,\n",
+       "       1958-09-01, 1958-10-01, 1958-11-01, 1958-12-01,\n",
+       "       ...\n",
+       "       2025-03-01, 2025-04-01, 2025-05-01, 2025-06-01, 2025-07-01, 2025-08-01,\n",
+       "       2025-09-01, 2025-10-01, 2025-11-01, 2025-12-01],\n",
+       "      dtype='object', length=814)"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 871,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "fig = plt.figure(figsize=(18,5))\n",
-    "ax1 = fig.add_subplot(111)\n",
-    "ax1.grid(linestyle='--', linewidth=1)\n",
-    "\n",
-    "ax1.set(title = 'Interpolation concentration CO2 + bruit',xlabel='Période',ylabel='Concentration (ppm)')\n",
-    "ax1.plot(df_inter['Data 2'],df_inter.s5_lent + df_inter.s5_rapide + df_inter.noise+df_inter.evolution);"
+    "co2 = list(df['s5'])\n",
+    "co2.extend(list(df_inter['s5_inter']))\n",
+    "dfb.index"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 524,
+   "execution_count": 872,
    "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>index</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",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03-01</th>\n",
-       "      <td>2</td>\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>3</td>\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>4</td>\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>1958-06-01</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>6</td>\n",
-       "      <td>21351</td>\n",
-       "      <td>1958.4548</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>315.14</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>315.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-01</th>\n",
-       "      <td>6</td>\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>7</td>\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",
-       "    <tr>\n",
-       "      <th>1958-09-01</th>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>9</td>\n",
-       "      <td>21443</td>\n",
-       "      <td>1958.7068</td>\n",
-       "      <td>313.21</td>\n",
-       "      <td>316.08</td>\n",
-       "      <td>312.46</td>\n",
-       "      <td>315.35</td>\n",
-       "      <td>313.21</td>\n",
-       "      <td>316.08</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>2019-07-01</th>\n",
-       "      <td>738</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>7</td>\n",
-       "      <td>43661</td>\n",
-       "      <td>2019.5370</td>\n",
-       "      <td>411.85</td>\n",
-       "      <td>411.03</td>\n",
-       "      <td>412.36</td>\n",
-       "      <td>411.57</td>\n",
-       "      <td>411.85</td>\n",
-       "      <td>411.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-01</th>\n",
-       "      <td>739</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>8</td>\n",
-       "      <td>43692</td>\n",
-       "      <td>2019.6219</td>\n",
-       "      <td>410.08</td>\n",
-       "      <td>411.62</td>\n",
-       "      <td>410.23</td>\n",
-       "      <td>411.81</td>\n",
-       "      <td>410.08</td>\n",
-       "      <td>411.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-09-01</th>\n",
-       "      <td>740</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>9</td>\n",
-       "      <td>43723</td>\n",
-       "      <td>2019.7068</td>\n",
-       "      <td>408.55</td>\n",
-       "      <td>412.06</td>\n",
-       "      <td>408.52</td>\n",
-       "      <td>412.05</td>\n",
-       "      <td>408.55</td>\n",
-       "      <td>412.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-10-01</th>\n",
-       "      <td>741</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>10</td>\n",
-       "      <td>43753</td>\n",
-       "      <td>2019.7890</td>\n",
-       "      <td>408.43</td>\n",
-       "      <td>412.07</td>\n",
-       "      <td>408.67</td>\n",
-       "      <td>412.29</td>\n",
-       "      <td>408.43</td>\n",
-       "      <td>412.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-01</th>\n",
-       "      <td>742</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>11</td>\n",
-       "      <td>43784</td>\n",
-       "      <td>2019.8740</td>\n",
-       "      <td>410.28</td>\n",
-       "      <td>412.56</td>\n",
-       "      <td>410.29</td>\n",
-       "      <td>412.54</td>\n",
-       "      <td>410.28</td>\n",
-       "      <td>412.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-12-01</th>\n",
-       "      <td>743</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>12</td>\n",
-       "      <td>43814</td>\n",
-       "      <td>2019.9562</td>\n",
-       "      <td>411.85</td>\n",
-       "      <td>412.78</td>\n",
-       "      <td>411.88</td>\n",
-       "      <td>412.79</td>\n",
-       "      <td>411.85</td>\n",
-       "      <td>412.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-01</th>\n",
-       "      <td>744</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>1</td>\n",
-       "      <td>43845</td>\n",
-       "      <td>2020.0410</td>\n",
-       "      <td>413.37</td>\n",
-       "      <td>413.33</td>\n",
-       "      <td>413.11</td>\n",
-       "      <td>413.05</td>\n",
-       "      <td>413.37</td>\n",
-       "      <td>413.33</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>743 rows × 11 columns</p>\n",
-       "</div>"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "            index    Yr  Mn  Date 1     Date 2      s1      s2      s3  \\\n",
-       "period                                                                   \n",
-       "1958-03-01      2  1958   3   21259  1958.2027  315.70  314.44  316.19   \n",
-       "1958-04-01      3  1958   4   21290  1958.2877  317.45  315.16  317.30   \n",
-       "1958-05-01      4  1958   5   21320  1958.3699  317.51  314.71  317.86   \n",
-       "1958-06-01      5  1958   6   21351  1958.4548     NaN     NaN  317.24   \n",
-       "1958-07-01      6  1958   7   21381  1958.5370  315.86  315.19  315.86   \n",
-       "1958-08-01      7  1958   8   21412  1958.6219  314.93  316.19  314.00   \n",
-       "1958-09-01      8  1958   9   21443  1958.7068  313.21  316.08  312.46   \n",
-       "...           ...   ...  ..     ...        ...     ...     ...     ...   \n",
-       "2019-07-01    738  2019   7   43661  2019.5370  411.85  411.03  412.36   \n",
-       "2019-08-01    739  2019   8   43692  2019.6219  410.08  411.62  410.23   \n",
-       "2019-09-01    740  2019   9   43723  2019.7068  408.55  412.06  408.52   \n",
-       "2019-10-01    741  2019  10   43753  2019.7890  408.43  412.07  408.67   \n",
-       "2019-11-01    742  2019  11   43784  2019.8740  410.28  412.56  410.29   \n",
-       "2019-12-01    743  2019  12   43814  2019.9562  411.85  412.78  411.88   \n",
-       "2020-01-01    744  2020   1   43845  2020.0410  413.37  413.33  413.11   \n",
-       "\n",
-       "                s4      s5      s6  \n",
-       "period                              \n",
-       "1958-03-01  314.91  315.70  314.44  \n",
-       "1958-04-01  314.99  317.45  315.16  \n",
-       "1958-05-01  315.06  317.51  314.71  \n",
-       "1958-06-01  315.14  317.24  315.14  \n",
-       "1958-07-01  315.22  315.86  315.19  \n",
-       "1958-08-01  315.29  314.93  316.19  \n",
-       "1958-09-01  315.35  313.21  316.08  \n",
-       "...            ...     ...     ...  \n",
-       "2019-07-01  411.57  411.85  411.03  \n",
-       "2019-08-01  411.81  410.08  411.62  \n",
-       "2019-09-01  412.05  408.55  412.06  \n",
-       "2019-10-01  412.29  408.43  412.07  \n",
-       "2019-11-01  412.54  410.28  412.56  \n",
-       "2019-12-01  412.79  411.85  412.78  \n",
-       "2020-01-01  413.05  413.37  413.33  \n",
-       "\n",
-       "[743 rows x 11 columns]"
+       "<Figure size 1080x720 with 1 Axes>"
       ]
      },
-     "execution_count": 524,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 529,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        vertical-align: middle;\n",
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-       "\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>Data 2</th>\n",
-       "      <th>Date 1</th>\n",
-       "      <th>Date 2</th>\n",
-       "      <th>Mn</th>\n",
-       "      <th>Yr</th>\n",
-       "      <th>evolution</th>\n",
-       "      <th>index</th>\n",
-       "      <th>noise</th>\n",
-       "      <th>s1</th>\n",
-       "      <th>s2</th>\n",
-       "      <th>s3</th>\n",
-       "      <th>s4</th>\n",
-       "      <th>s5</th>\n",
-       "      <th>s5_inter</th>\n",
-       "      <th>s5_lent</th>\n",
-       "      <th>s5_rapide</th>\n",
-       "      <th>s6</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21259.0</td>\n",
-       "      <td>1958.2027</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</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>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>314.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21290.0</td>\n",
-       "      <td>1958.2877</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</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>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>315.16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21320.0</td>\n",
-       "      <td>1958.3699</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</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>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>314.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-06-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21351.0</td>\n",
-       "      <td>1958.4548</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>315.14</td>\n",
-       "      <td>317.24</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>315.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21381.0</td>\n",
-       "      <td>1958.5370</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NaN</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>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>315.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21412.0</td>\n",
-       "      <td>1958.6219</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>NaN</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>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>316.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-09-01</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>21443.0</td>\n",
-       "      <td>1958.7068</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>313.21</td>\n",
-       "      <td>316.08</td>\n",
-       "      <td>312.46</td>\n",
-       "      <td>315.35</td>\n",
-       "      <td>313.21</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>316.08</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-06-01 00:00:00</th>\n",
-       "      <td>2025.4548</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>424.823674</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.503622</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>423.236699</td>\n",
-       "      <td>-0.623649</td>\n",
-       "      <td>-1.466947</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 00:00:00</th>\n",
-       "      <td>2025.5370</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>425.030380</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.412459</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>424.755135</td>\n",
-       "      <td>-0.615896</td>\n",
-       "      <td>-0.071808</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-08-01 00:00:00</th>\n",
-       "      <td>2025.6219</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>425.244061</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.236941</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>425.786400</td>\n",
-       "      <td>-0.607756</td>\n",
-       "      <td>1.387035</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-09-01 00:00:00</th>\n",
-       "      <td>2025.7068</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>425.457929</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.677236</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>426.643957</td>\n",
-       "      <td>-0.599482</td>\n",
-       "      <td>2.462746</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-10-01 00:00:00</th>\n",
-       "      <td>2025.7890</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>425.665174</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.212393</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>426.717248</td>\n",
-       "      <td>-0.591346</td>\n",
-       "      <td>2.855813</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-11-01 00:00:00</th>\n",
-       "      <td>2025.8740</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>425.879664</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.257184</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>429.035257</td>\n",
-       "      <td>-0.582806</td>\n",
-       "      <td>2.481215</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-12-01 00:00:00</th>\n",
-       "      <td>2025.9562</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2025</td>\n",
-       "      <td>426.087267</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.183174</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>427.156922</td>\n",
-       "      <td>-0.574425</td>\n",
-       "      <td>1.460907</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>814 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        Data 2   Date 1     Date 2  Mn    Yr   evolution  \\\n",
-       "1958-03-01                 NaN  21259.0  1958.2027   3  1958         NaN   \n",
-       "1958-04-01                 NaN  21290.0  1958.2877   4  1958         NaN   \n",
-       "1958-05-01                 NaN  21320.0  1958.3699   5  1958         NaN   \n",
-       "1958-06-01                 NaN  21351.0  1958.4548   6  1958         NaN   \n",
-       "1958-07-01                 NaN  21381.0  1958.5370   7  1958         NaN   \n",
-       "1958-08-01                 NaN  21412.0  1958.6219   8  1958         NaN   \n",
-       "1958-09-01                 NaN  21443.0  1958.7068   9  1958         NaN   \n",
-       "...                        ...      ...        ...  ..   ...         ...   \n",
-       "2025-06-01 00:00:00  2025.4548      NaN        NaN   6  2025  424.823674   \n",
-       "2025-07-01 00:00:00  2025.5370      NaN        NaN   7  2025  425.030380   \n",
-       "2025-08-01 00:00:00  2025.6219      NaN        NaN   8  2025  425.244061   \n",
-       "2025-09-01 00:00:00  2025.7068      NaN        NaN   9  2025  425.457929   \n",
-       "2025-10-01 00:00:00  2025.7890      NaN        NaN  10  2025  425.665174   \n",
-       "2025-11-01 00:00:00  2025.8740      NaN        NaN  11  2025  425.879664   \n",
-       "2025-12-01 00:00:00  2025.9562      NaN        NaN  12  2025  426.087267   \n",
-       "\n",
-       "                     index     noise      s1      s2      s3      s4      s5  \\\n",
-       "1958-03-01             2.0       NaN  315.70  314.44  316.19  314.91  315.70   \n",
-       "1958-04-01             3.0       NaN  317.45  315.16  317.30  314.99  317.45   \n",
-       "1958-05-01             4.0       NaN  317.51  314.71  317.86  315.06  317.51   \n",
-       "1958-06-01             5.0       NaN     NaN     NaN  317.24  315.14  317.24   \n",
-       "1958-07-01             6.0       NaN  315.86  315.19  315.86  315.22  315.86   \n",
-       "1958-08-01             7.0       NaN  314.93  316.19  314.00  315.29  314.93   \n",
-       "1958-09-01             8.0       NaN  313.21  316.08  312.46  315.35  313.21   \n",
-       "...                    ...       ...     ...     ...     ...     ...     ...   \n",
-       "2025-06-01 00:00:00    NaN  0.503622     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-07-01 00:00:00    NaN  0.412459     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-08-01 00:00:00    NaN -0.236941     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-09-01 00:00:00    NaN -0.677236     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-10-01 00:00:00    NaN -1.212393     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-11-01 00:00:00    NaN  1.257184     NaN     NaN     NaN     NaN     NaN   \n",
-       "2025-12-01 00:00:00    NaN  0.183174     NaN     NaN     NaN     NaN     NaN   \n",
-       "\n",
-       "                       s5_inter   s5_lent  s5_rapide      s6  \n",
-       "1958-03-01                  NaN       NaN        NaN  314.44  \n",
-       "1958-04-01                  NaN       NaN        NaN  315.16  \n",
-       "1958-05-01                  NaN       NaN        NaN  314.71  \n",
-       "1958-06-01                  NaN       NaN        NaN  315.14  \n",
-       "1958-07-01                  NaN       NaN        NaN  315.19  \n",
-       "1958-08-01                  NaN       NaN        NaN  316.19  \n",
-       "1958-09-01                  NaN       NaN        NaN  316.08  \n",
-       "...                         ...       ...        ...     ...  \n",
-       "2025-06-01 00:00:00  423.236699 -0.623649  -1.466947     NaN  \n",
-       "2025-07-01 00:00:00  424.755135 -0.615896  -0.071808     NaN  \n",
-       "2025-08-01 00:00:00  425.786400 -0.607756   1.387035     NaN  \n",
-       "2025-09-01 00:00:00  426.643957 -0.599482   2.462746     NaN  \n",
-       "2025-10-01 00:00:00  426.717248 -0.591346   2.855813     NaN  \n",
-       "2025-11-01 00:00:00  429.035257 -0.582806   2.481215     NaN  \n",
-       "2025-12-01 00:00:00  427.156922 -0.574425   1.460907     NaN  \n",
-       "\n",
-       "[814 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 529,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = pd.concat([df,df_inter])\n",
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 531,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index([1958-03-01, 1958-04-01, 1958-05-01, 1958-06-01, 1958-07-01, 1958-08-01,\n",
-       "       1958-09-01, 1958-10-01, 1958-11-01, 1958-12-01,\n",
-       "       ...\n",
-       "       2019-04-01, 2019-05-01, 2019-06-01, 2019-07-01, 2019-08-01, 2019-09-01,\n",
-       "       2019-10-01, 2019-11-01, 2019-12-01, 2020-01-01],\n",
-       "      dtype='object', name='period', length=743)"
-      ]
-     },
-     "execution_count": 531,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "per1\n",
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 532,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',\n",
-       "               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',\n",
-       "               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',\n",
-       "               '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',\n",
-       "               '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01',\n",
-       "               '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01',\n",
-       "               '2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01',\n",
-       "               '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01',\n",
-       "               '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01',\n",
-       "               '2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01',\n",
-       "               '2023-05-01', '2023-06-01', '2023-07-01', '2023-08-01',\n",
-       "               '2023-09-01', '2023-10-01', '2023-11-01', '2023-12-01',\n",
-       "               '2024-01-01', '2024-02-01', '2024-03-01', '2024-04-01',\n",
-       "               '2024-05-01', '2024-06-01', '2024-07-01', '2024-08-01',\n",
-       "               '2024-09-01', '2024-10-01', '2024-11-01', '2024-12-01',\n",
-       "               '2025-01-01', '2025-02-01', '2025-03-01', '2025-04-01',\n",
-       "               '2025-05-01', '2025-06-01', '2025-07-01', '2025-08-01',\n",
-       "               '2025-09-01', '2025-10-01', '2025-11-01', '2025-12-01'],\n",
-       "              dtype='datetime64[ns]', freq='<DateOffset: kwds={'months': 1}>')"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "execution_count": 532,
-     "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "per1"
+    "id =  list(dfb.index)\n",
+    "co22 = pd.Series(co2, index = dfb.index)   \n",
+    "dfb['co2'] = co2\n",
+    "dfb['co2'].plot(figsize=(15, 10),title = 'Concentration + interlolarion (2020-2025) de CO2').grid(linestyle='--', linewidth=1);"
    ]
   },
   {