diff --git "a/module3/exo3/Travail_pratique_CO2_atmosph\303\251rique.ipynb" "b/module3/exo3/Travail_pratique_CO2_atmosph\303\251rique.ipynb"
index dcd9e1897773bbbb41be89f187d96b7023a8249d..c9496c0a0143fb85b664c8277bb5452e3577ebe2 100644
--- "a/module3/exo3/Travail_pratique_CO2_atmosph\303\251rique.ipynb"
+++ "b/module3/exo3/Travail_pratique_CO2_atmosph\303\251rique.ipynb"
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,9 @@
     "from datetime import date\n",
     "from datetime import datetime\n",
     "import numpy as np\n",
-    "from sklearn.linear_model import LinearRegression"
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n"
    ]
   },
   {
@@ -54,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,9 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "scrolled": true
+    "collapsed": true
    },
    "outputs": [
     {
@@ -111,153 +113,153 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1958-03-29</td>\n",
-       "      <td>316.19</td>\n",
+       "      <td>1959-06-06</td>\n",
+       "      <td>318.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1958-04-05</td>\n",
-       "      <td>317.31</td>\n",
+       "      <td>1959-06-13</td>\n",
+       "      <td>318.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1958-04-12</td>\n",
-       "      <td>317.69</td>\n",
+       "      <td>1959-06-20</td>\n",
+       "      <td>317.88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1958-04-19</td>\n",
-       "      <td>317.58</td>\n",
+       "      <td>1959-06-27</td>\n",
+       "      <td>317.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1958-04-26</td>\n",
-       "      <td>316.48</td>\n",
+       "      <td>1959-07-04</td>\n",
+       "      <td>316.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>1958-05-03</td>\n",
-       "      <td>316.95</td>\n",
+       "      <td>1959-07-11</td>\n",
+       "      <td>316.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1958-05-17</td>\n",
-       "      <td>317.56</td>\n",
+       "      <td>1959-07-18</td>\n",
+       "      <td>316.45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>1958-05-24</td>\n",
-       "      <td>317.99</td>\n",
+       "      <td>1959-07-25</td>\n",
+       "      <td>316.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>1958-07-05</td>\n",
-       "      <td>315.85</td>\n",
+       "      <td>1959-08-01</td>\n",
+       "      <td>315.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1958-07-12</td>\n",
-       "      <td>315.85</td>\n",
+       "      <td>1959-08-08</td>\n",
+       "      <td>314.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>1958-07-19</td>\n",
-       "      <td>315.46</td>\n",
+       "      <td>1959-08-22</td>\n",
+       "      <td>315.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>1958-07-26</td>\n",
-       "      <td>315.59</td>\n",
+       "      <td>1959-08-29</td>\n",
+       "      <td>314.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>1958-08-02</td>\n",
-       "      <td>315.64</td>\n",
+       "      <td>1959-09-05</td>\n",
+       "      <td>314.45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>1958-08-09</td>\n",
-       "      <td>315.10</td>\n",
+       "      <td>1959-09-12</td>\n",
+       "      <td>313.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>1958-08-16</td>\n",
-       "      <td>315.09</td>\n",
+       "      <td>1959-09-19</td>\n",
+       "      <td>313.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>1958-08-30</td>\n",
-       "      <td>314.14</td>\n",
+       "      <td>1959-09-26</td>\n",
+       "      <td>313.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>1958-09-06</td>\n",
-       "      <td>313.54</td>\n",
+       "      <td>1959-10-03</td>\n",
+       "      <td>313.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>1958-11-08</td>\n",
-       "      <td>313.05</td>\n",
+       "      <td>1959-10-10</td>\n",
+       "      <td>313.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>1958-11-15</td>\n",
-       "      <td>313.26</td>\n",
+       "      <td>1959-10-17</td>\n",
+       "      <td>313.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>1958-11-22</td>\n",
-       "      <td>313.57</td>\n",
+       "      <td>1959-10-24</td>\n",
+       "      <td>313.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>1958-11-29</td>\n",
-       "      <td>314.01</td>\n",
+       "      <td>1959-10-31</td>\n",
+       "      <td>314.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>1958-12-06</td>\n",
-       "      <td>314.56</td>\n",
+       "      <td>1959-11-07</td>\n",
+       "      <td>314.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>1958-12-13</td>\n",
-       "      <td>314.41</td>\n",
+       "      <td>1959-11-14</td>\n",
+       "      <td>314.88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>1958-12-20</td>\n",
-       "      <td>314.77</td>\n",
+       "      <td>1959-11-21</td>\n",
+       "      <td>315.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>1958-12-27</td>\n",
-       "      <td>315.21</td>\n",
+       "      <td>1959-11-28</td>\n",
+       "      <td>315.11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>1959-01-03</td>\n",
-       "      <td>315.24</td>\n",
+       "      <td>1959-12-05</td>\n",
+       "      <td>315.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>1959-01-10</td>\n",
-       "      <td>315.50</td>\n",
+       "      <td>1959-12-12</td>\n",
+       "      <td>315.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>1959-01-17</td>\n",
-       "      <td>315.69</td>\n",
+       "      <td>1959-12-19</td>\n",
+       "      <td>315.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>1959-01-24</td>\n",
-       "      <td>315.86</td>\n",
+       "      <td>1959-12-26</td>\n",
+       "      <td>315.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>1959-01-31</td>\n",
-       "      <td>315.42</td>\n",
+       "      <td>1960-01-02</td>\n",
+       "      <td>315.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -265,228 +267,228 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3178</th>\n",
-       "      <td>2020-07-11</td>\n",
-       "      <td>414.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3179</th>\n",
-       "      <td>2020-07-18</td>\n",
-       "      <td>414.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3180</th>\n",
+       "      <th>3136</th>\n",
        "      <td>2020-07-25</td>\n",
-       "      <td>413.63</td>\n",
+       "      <td>413.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3181</th>\n",
+       "      <th>3137</th>\n",
        "      <td>2020-08-01</td>\n",
-       "      <td>413.42</td>\n",
+       "      <td>413.35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3182</th>\n",
+       "      <th>3138</th>\n",
        "      <td>2020-08-08</td>\n",
-       "      <td>412.85</td>\n",
+       "      <td>412.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3183</th>\n",
+       "      <th>3139</th>\n",
        "      <td>2020-08-15</td>\n",
-       "      <td>412.75</td>\n",
+       "      <td>412.67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3184</th>\n",
+       "      <th>3140</th>\n",
        "      <td>2020-08-22</td>\n",
-       "      <td>412.25</td>\n",
+       "      <td>412.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3185</th>\n",
+       "      <th>3141</th>\n",
        "      <td>2020-08-29</td>\n",
-       "      <td>411.79</td>\n",
+       "      <td>411.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3186</th>\n",
+       "      <th>3142</th>\n",
        "      <td>2020-09-05</td>\n",
-       "      <td>411.55</td>\n",
+       "      <td>411.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3187</th>\n",
+       "      <th>3143</th>\n",
        "      <td>2020-09-12</td>\n",
-       "      <td>411.45</td>\n",
+       "      <td>411.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3188</th>\n",
+       "      <th>3144</th>\n",
        "      <td>2020-09-19</td>\n",
-       "      <td>411.17</td>\n",
+       "      <td>411.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3189</th>\n",
+       "      <th>3145</th>\n",
        "      <td>2020-09-26</td>\n",
-       "      <td>411.06</td>\n",
+       "      <td>410.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3190</th>\n",
+       "      <th>3146</th>\n",
        "      <td>2020-10-03</td>\n",
-       "      <td>411.08</td>\n",
+       "      <td>411.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3191</th>\n",
+       "      <th>3147</th>\n",
        "      <td>2020-10-10</td>\n",
-       "      <td>411.14</td>\n",
+       "      <td>411.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3192</th>\n",
+       "      <th>3148</th>\n",
        "      <td>2020-10-17</td>\n",
-       "      <td>411.16</td>\n",
+       "      <td>411.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3193</th>\n",
+       "      <th>3149</th>\n",
        "      <td>2020-10-24</td>\n",
-       "      <td>411.54</td>\n",
+       "      <td>411.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3194</th>\n",
+       "      <th>3150</th>\n",
        "      <td>2020-10-31</td>\n",
-       "      <td>411.92</td>\n",
+       "      <td>411.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3195</th>\n",
+       "      <th>3151</th>\n",
        "      <td>2020-11-07</td>\n",
-       "      <td>412.37</td>\n",
+       "      <td>412.30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3196</th>\n",
+       "      <th>3152</th>\n",
        "      <td>2020-11-14</td>\n",
-       "      <td>412.67</td>\n",
+       "      <td>412.61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3197</th>\n",
+       "      <th>3153</th>\n",
        "      <td>2020-11-21</td>\n",
-       "      <td>412.98</td>\n",
+       "      <td>412.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3198</th>\n",
+       "      <th>3154</th>\n",
        "      <td>2020-11-28</td>\n",
-       "      <td>414.32</td>\n",
+       "      <td>414.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3199</th>\n",
+       "      <th>3155</th>\n",
        "      <td>2020-12-05</td>\n",
-       "      <td>413.07</td>\n",
+       "      <td>413.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3200</th>\n",
+       "      <th>3156</th>\n",
        "      <td>2020-12-12</td>\n",
-       "      <td>413.67</td>\n",
+       "      <td>413.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3201</th>\n",
+       "      <th>3157</th>\n",
        "      <td>2020-12-19</td>\n",
-       "      <td>414.40</td>\n",
+       "      <td>414.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3202</th>\n",
+       "      <th>3158</th>\n",
        "      <td>2020-12-26</td>\n",
-       "      <td>414.71</td>\n",
+       "      <td>414.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3203</th>\n",
+       "      <th>3159</th>\n",
        "      <td>2021-01-02</td>\n",
-       "      <td>415.26</td>\n",
+       "      <td>415.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3204</th>\n",
+       "      <th>3160</th>\n",
        "      <td>2021-01-09</td>\n",
-       "      <td>414.90</td>\n",
+       "      <td>414.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3205</th>\n",
+       "      <th>3161</th>\n",
        "      <td>2021-01-16</td>\n",
-       "      <td>414.91</td>\n",
+       "      <td>414.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3206</th>\n",
+       "      <th>3162</th>\n",
        "      <td>2021-01-23</td>\n",
-       "      <td>415.53</td>\n",
+       "      <td>415.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3207</th>\n",
+       "      <th>3163</th>\n",
        "      <td>2021-01-30</td>\n",
-       "      <td>415.75</td>\n",
+       "      <td>415.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3164</th>\n",
+       "      <td>2021-02-06</td>\n",
+       "      <td>416.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3165</th>\n",
+       "      <td>2021-02-13</td>\n",
+       "      <td>416.45</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>3208 rows × 2 columns</p>\n",
+       "<p>3166 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "            date     CO2\n",
-       "0     1958-03-29  316.19\n",
-       "1     1958-04-05  317.31\n",
-       "2     1958-04-12  317.69\n",
-       "3     1958-04-19  317.58\n",
-       "4     1958-04-26  316.48\n",
-       "5     1958-05-03  316.95\n",
-       "6     1958-05-17  317.56\n",
-       "7     1958-05-24  317.99\n",
-       "8     1958-07-05  315.85\n",
-       "9     1958-07-12  315.85\n",
-       "10    1958-07-19  315.46\n",
-       "11    1958-07-26  315.59\n",
-       "12    1958-08-02  315.64\n",
-       "13    1958-08-09  315.10\n",
-       "14    1958-08-16  315.09\n",
-       "15    1958-08-30  314.14\n",
-       "16    1958-09-06  313.54\n",
-       "17    1958-11-08  313.05\n",
-       "18    1958-11-15  313.26\n",
-       "19    1958-11-22  313.57\n",
-       "20    1958-11-29  314.01\n",
-       "21    1958-12-06  314.56\n",
-       "22    1958-12-13  314.41\n",
-       "23    1958-12-20  314.77\n",
-       "24    1958-12-27  315.21\n",
-       "25    1959-01-03  315.24\n",
-       "26    1959-01-10  315.50\n",
-       "27    1959-01-17  315.69\n",
-       "28    1959-01-24  315.86\n",
-       "29    1959-01-31  315.42\n",
+       "0     1959-06-06  318.53\n",
+       "1     1959-06-13  318.14\n",
+       "2     1959-06-20  317.88\n",
+       "3     1959-06-27  317.76\n",
+       "4     1959-07-04  316.86\n",
+       "5     1959-07-11  316.83\n",
+       "6     1959-07-18  316.45\n",
+       "7     1959-07-25  316.16\n",
+       "8     1959-08-01  315.62\n",
+       "9     1959-08-08  314.91\n",
+       "10    1959-08-22  315.00\n",
+       "11    1959-08-29  314.15\n",
+       "12    1959-09-05  314.45\n",
+       "13    1959-09-12  313.93\n",
+       "14    1959-09-19  313.57\n",
+       "15    1959-09-26  313.54\n",
+       "16    1959-10-03  313.04\n",
+       "17    1959-10-10  313.15\n",
+       "18    1959-10-17  313.43\n",
+       "19    1959-10-24  313.46\n",
+       "20    1959-10-31  314.12\n",
+       "21    1959-11-07  314.42\n",
+       "22    1959-11-14  314.88\n",
+       "23    1959-11-21  315.20\n",
+       "24    1959-11-28  315.11\n",
+       "25    1959-12-05  315.06\n",
+       "26    1959-12-12  315.64\n",
+       "27    1959-12-19  315.86\n",
+       "28    1959-12-26  315.77\n",
+       "29    1960-01-02  315.72\n",
        "...          ...     ...\n",
-       "3178  2020-07-11  414.91\n",
-       "3179  2020-07-18  414.29\n",
-       "3180  2020-07-25  413.63\n",
-       "3181  2020-08-01  413.42\n",
-       "3182  2020-08-08  412.85\n",
-       "3183  2020-08-15  412.75\n",
-       "3184  2020-08-22  412.25\n",
-       "3185  2020-08-29  411.79\n",
-       "3186  2020-09-05  411.55\n",
-       "3187  2020-09-12  411.45\n",
-       "3188  2020-09-19  411.17\n",
-       "3189  2020-09-26  411.06\n",
-       "3190  2020-10-03  411.08\n",
-       "3191  2020-10-10  411.14\n",
-       "3192  2020-10-17  411.16\n",
-       "3193  2020-10-24  411.54\n",
-       "3194  2020-10-31  411.92\n",
-       "3195  2020-11-07  412.37\n",
-       "3196  2020-11-14  412.67\n",
-       "3197  2020-11-21  412.98\n",
-       "3198  2020-11-28  414.32\n",
-       "3199  2020-12-05  413.07\n",
-       "3200  2020-12-12  413.67\n",
-       "3201  2020-12-19  414.40\n",
-       "3202  2020-12-26  414.71\n",
-       "3203  2021-01-02  415.26\n",
-       "3204  2021-01-09  414.90\n",
-       "3205  2021-01-16  414.91\n",
-       "3206  2021-01-23  415.53\n",
-       "3207  2021-01-30  415.75\n",
+       "3136  2020-07-25  413.56\n",
+       "3137  2020-08-01  413.35\n",
+       "3138  2020-08-08  412.78\n",
+       "3139  2020-08-15  412.67\n",
+       "3140  2020-08-22  412.18\n",
+       "3141  2020-08-29  411.72\n",
+       "3142  2020-09-05  411.48\n",
+       "3143  2020-09-12  411.38\n",
+       "3144  2020-09-19  411.10\n",
+       "3145  2020-09-26  410.99\n",
+       "3146  2020-10-03  411.01\n",
+       "3147  2020-10-10  411.07\n",
+       "3148  2020-10-17  411.08\n",
+       "3149  2020-10-24  411.46\n",
+       "3150  2020-10-31  411.85\n",
+       "3151  2020-11-07  412.30\n",
+       "3152  2020-11-14  412.61\n",
+       "3153  2020-11-21  412.91\n",
+       "3154  2020-11-28  414.25\n",
+       "3155  2020-12-05  413.00\n",
+       "3156  2020-12-12  413.60\n",
+       "3157  2020-12-19  414.34\n",
+       "3158  2020-12-26  414.64\n",
+       "3159  2021-01-02  415.19\n",
+       "3160  2021-01-09  414.83\n",
+       "3161  2021-01-16  414.84\n",
+       "3162  2021-01-23  415.46\n",
+       "3163  2021-01-30  415.68\n",
+       "3164  2021-02-06  416.91\n",
+       "3165  2021-02-13  416.45\n",
        "\n",
-       "[3208 rows x 2 columns]"
+       "[3166 rows x 2 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +515,7 @@
     "\n",
     "## Si ce fichier existe, on prend les données qu'il contient\n",
     "if path.exists(local_file):\n",
-    "    raw_data = pd.read_csv(local_file, , skiprows=44, names=['date', 'CO2'])\n",
+    "    raw_data = pd.read_csv(local_file,skiprows=44, names=['date', 'CO2'])\n",
     "\n",
     "## Sinon, on le crée\n",
     "else:\n",
@@ -529,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -568,7 +570,7 @@
        "Index: []"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -586,1018 +588,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[datetime.datetime(1958, 3, 29, 0, 0),\n",
-       " datetime.datetime(1958, 4, 5, 0, 0),\n",
-       " datetime.datetime(1958, 4, 12, 0, 0),\n",
-       " datetime.datetime(1958, 4, 19, 0, 0),\n",
-       " datetime.datetime(1958, 4, 26, 0, 0),\n",
-       " datetime.datetime(1958, 5, 3, 0, 0),\n",
-       " datetime.datetime(1958, 5, 17, 0, 0),\n",
-       " datetime.datetime(1958, 5, 24, 0, 0),\n",
-       " datetime.datetime(1958, 7, 5, 0, 0),\n",
-       " datetime.datetime(1958, 7, 12, 0, 0),\n",
-       " datetime.datetime(1958, 7, 19, 0, 0),\n",
-       " datetime.datetime(1958, 7, 26, 0, 0),\n",
-       " datetime.datetime(1958, 8, 2, 0, 0),\n",
-       " datetime.datetime(1958, 8, 9, 0, 0),\n",
-       " datetime.datetime(1958, 8, 16, 0, 0),\n",
-       " datetime.datetime(1958, 8, 30, 0, 0),\n",
-       " datetime.datetime(1958, 9, 6, 0, 0),\n",
-       " datetime.datetime(1958, 11, 8, 0, 0),\n",
-       " datetime.datetime(1958, 11, 15, 0, 0),\n",
-       " datetime.datetime(1958, 11, 22, 0, 0),\n",
-       " datetime.datetime(1958, 11, 29, 0, 0),\n",
-       " datetime.datetime(1958, 12, 6, 0, 0),\n",
-       " datetime.datetime(1958, 12, 13, 0, 0),\n",
-       " datetime.datetime(1958, 12, 20, 0, 0),\n",
-       " datetime.datetime(1958, 12, 27, 0, 0),\n",
-       " datetime.datetime(1959, 1, 3, 0, 0),\n",
-       " datetime.datetime(1959, 1, 10, 0, 0),\n",
-       " datetime.datetime(1959, 1, 17, 0, 0),\n",
-       " datetime.datetime(1959, 1, 24, 0, 0),\n",
-       " datetime.datetime(1959, 1, 31, 0, 0),\n",
-       " datetime.datetime(1959, 2, 14, 0, 0),\n",
-       " datetime.datetime(1959, 2, 21, 0, 0),\n",
-       " datetime.datetime(1959, 2, 28, 0, 0),\n",
-       " datetime.datetime(1959, 3, 7, 0, 0),\n",
-       " datetime.datetime(1959, 3, 21, 0, 0),\n",
-       " datetime.datetime(1959, 3, 28, 0, 0),\n",
-       " datetime.datetime(1959, 4, 4, 0, 0),\n",
-       " datetime.datetime(1959, 4, 11, 0, 0),\n",
-       " datetime.datetime(1959, 4, 18, 0, 0),\n",
-       " datetime.datetime(1959, 4, 25, 0, 0),\n",
-       " datetime.datetime(1959, 5, 2, 0, 0),\n",
-       " datetime.datetime(1959, 5, 9, 0, 0),\n",
-       " datetime.datetime(1959, 5, 16, 0, 0),\n",
-       " datetime.datetime(1959, 5, 23, 0, 0),\n",
-       " datetime.datetime(1959, 6, 6, 0, 0),\n",
-       " datetime.datetime(1959, 6, 13, 0, 0),\n",
-       " datetime.datetime(1959, 6, 20, 0, 0),\n",
-       " datetime.datetime(1959, 6, 27, 0, 0),\n",
-       " datetime.datetime(1959, 7, 4, 0, 0),\n",
-       " datetime.datetime(1959, 7, 11, 0, 0),\n",
-       " datetime.datetime(1959, 7, 18, 0, 0),\n",
-       " datetime.datetime(1959, 7, 25, 0, 0),\n",
-       " datetime.datetime(1959, 8, 1, 0, 0),\n",
-       " datetime.datetime(1959, 8, 8, 0, 0),\n",
-       " datetime.datetime(1959, 8, 22, 0, 0),\n",
-       " datetime.datetime(1959, 8, 29, 0, 0),\n",
-       " datetime.datetime(1959, 9, 5, 0, 0),\n",
-       " datetime.datetime(1959, 9, 12, 0, 0),\n",
-       " datetime.datetime(1959, 9, 19, 0, 0),\n",
-       " datetime.datetime(1959, 9, 26, 0, 0),\n",
-       " datetime.datetime(1959, 10, 3, 0, 0),\n",
-       " datetime.datetime(1959, 10, 10, 0, 0),\n",
-       " datetime.datetime(1959, 10, 17, 0, 0),\n",
-       " datetime.datetime(1959, 10, 24, 0, 0),\n",
-       " datetime.datetime(1959, 10, 31, 0, 0),\n",
-       " datetime.datetime(1959, 11, 7, 0, 0),\n",
-       " datetime.datetime(1959, 11, 14, 0, 0),\n",
-       " datetime.datetime(1959, 11, 21, 0, 0),\n",
-       " datetime.datetime(1959, 11, 28, 0, 0),\n",
-       " datetime.datetime(1959, 12, 5, 0, 0),\n",
-       " datetime.datetime(1959, 12, 12, 0, 0),\n",
-       " datetime.datetime(1959, 12, 19, 0, 0),\n",
-       " datetime.datetime(1959, 12, 26, 0, 0),\n",
-       " datetime.datetime(1960, 1, 2, 0, 0),\n",
-       " datetime.datetime(1960, 1, 9, 0, 0),\n",
-       " datetime.datetime(1960, 1, 16, 0, 0),\n",
-       " datetime.datetime(1960, 1, 23, 0, 0),\n",
-       " datetime.datetime(1960, 1, 30, 0, 0),\n",
-       " datetime.datetime(1960, 2, 6, 0, 0),\n",
-       " datetime.datetime(1960, 2, 13, 0, 0),\n",
-       " datetime.datetime(1960, 2, 20, 0, 0),\n",
-       " datetime.datetime(1960, 2, 27, 0, 0),\n",
-       " datetime.datetime(1960, 3, 5, 0, 0),\n",
-       " datetime.datetime(1960, 3, 12, 0, 0),\n",
-       " datetime.datetime(1960, 3, 19, 0, 0),\n",
-       " datetime.datetime(1960, 3, 26, 0, 0),\n",
-       " datetime.datetime(1960, 4, 2, 0, 0),\n",
-       " datetime.datetime(1960, 4, 9, 0, 0),\n",
-       " datetime.datetime(1960, 4, 16, 0, 0),\n",
-       " datetime.datetime(1960, 4, 23, 0, 0),\n",
-       " datetime.datetime(1960, 4, 30, 0, 0),\n",
-       " datetime.datetime(1960, 5, 7, 0, 0),\n",
-       " datetime.datetime(1960, 5, 14, 0, 0),\n",
-       " datetime.datetime(1960, 5, 21, 0, 0),\n",
-       " datetime.datetime(1960, 5, 28, 0, 0),\n",
-       " datetime.datetime(1960, 6, 4, 0, 0),\n",
-       " datetime.datetime(1960, 6, 11, 0, 0),\n",
-       " datetime.datetime(1960, 6, 18, 0, 0),\n",
-       " datetime.datetime(1960, 6, 25, 0, 0),\n",
-       " datetime.datetime(1960, 7, 2, 0, 0),\n",
-       " datetime.datetime(1960, 7, 9, 0, 0),\n",
-       " datetime.datetime(1960, 7, 16, 0, 0),\n",
-       " datetime.datetime(1960, 7, 23, 0, 0),\n",
-       " datetime.datetime(1960, 7, 30, 0, 0),\n",
-       " datetime.datetime(1960, 8, 6, 0, 0),\n",
-       " datetime.datetime(1960, 8, 13, 0, 0),\n",
-       " datetime.datetime(1960, 8, 20, 0, 0),\n",
-       " datetime.datetime(1960, 8, 27, 0, 0),\n",
-       " datetime.datetime(1960, 9, 3, 0, 0),\n",
-       " datetime.datetime(1960, 9, 10, 0, 0),\n",
-       " datetime.datetime(1960, 9, 17, 0, 0),\n",
-       " datetime.datetime(1960, 9, 24, 0, 0),\n",
-       " datetime.datetime(1960, 10, 1, 0, 0),\n",
-       " datetime.datetime(1960, 10, 8, 0, 0),\n",
-       " datetime.datetime(1960, 10, 15, 0, 0),\n",
-       " datetime.datetime(1960, 10, 22, 0, 0),\n",
-       " datetime.datetime(1960, 10, 29, 0, 0),\n",
-       " datetime.datetime(1960, 11, 5, 0, 0),\n",
-       " datetime.datetime(1960, 11, 12, 0, 0),\n",
-       " datetime.datetime(1960, 11, 19, 0, 0),\n",
-       " datetime.datetime(1960, 11, 26, 0, 0),\n",
-       " datetime.datetime(1960, 12, 3, 0, 0),\n",
-       " datetime.datetime(1960, 12, 10, 0, 0),\n",
-       " datetime.datetime(1960, 12, 17, 0, 0),\n",
-       " datetime.datetime(1960, 12, 24, 0, 0),\n",
-       " datetime.datetime(1960, 12, 31, 0, 0),\n",
-       " datetime.datetime(1961, 1, 7, 0, 0),\n",
-       " datetime.datetime(1961, 1, 14, 0, 0),\n",
-       " datetime.datetime(1961, 1, 21, 0, 0),\n",
-       " datetime.datetime(1961, 1, 28, 0, 0),\n",
-       " datetime.datetime(1961, 2, 4, 0, 0),\n",
-       " datetime.datetime(1961, 2, 11, 0, 0),\n",
-       " datetime.datetime(1961, 2, 18, 0, 0),\n",
-       " datetime.datetime(1961, 2, 25, 0, 0),\n",
-       " datetime.datetime(1961, 3, 4, 0, 0),\n",
-       " datetime.datetime(1961, 3, 11, 0, 0),\n",
-       " datetime.datetime(1961, 3, 18, 0, 0),\n",
-       " datetime.datetime(1961, 3, 25, 0, 0),\n",
-       " datetime.datetime(1961, 4, 1, 0, 0),\n",
-       " datetime.datetime(1961, 4, 8, 0, 0),\n",
-       " datetime.datetime(1961, 4, 15, 0, 0),\n",
-       " datetime.datetime(1961, 4, 22, 0, 0),\n",
-       " datetime.datetime(1961, 4, 29, 0, 0),\n",
-       " datetime.datetime(1961, 5, 6, 0, 0),\n",
-       " datetime.datetime(1961, 5, 13, 0, 0),\n",
-       " datetime.datetime(1961, 5, 20, 0, 0),\n",
-       " datetime.datetime(1961, 5, 27, 0, 0),\n",
-       " datetime.datetime(1961, 6, 3, 0, 0),\n",
-       " datetime.datetime(1961, 6, 10, 0, 0),\n",
-       " datetime.datetime(1961, 6, 17, 0, 0),\n",
-       " datetime.datetime(1961, 6, 24, 0, 0),\n",
-       " datetime.datetime(1961, 7, 1, 0, 0),\n",
-       " datetime.datetime(1961, 7, 8, 0, 0),\n",
-       " datetime.datetime(1961, 7, 15, 0, 0),\n",
-       " datetime.datetime(1961, 7, 22, 0, 0),\n",
-       " datetime.datetime(1961, 7, 29, 0, 0),\n",
-       " datetime.datetime(1961, 8, 5, 0, 0),\n",
-       " datetime.datetime(1961, 8, 12, 0, 0),\n",
-       " datetime.datetime(1961, 8, 19, 0, 0),\n",
-       " datetime.datetime(1961, 8, 26, 0, 0),\n",
-       " datetime.datetime(1961, 9, 2, 0, 0),\n",
-       " datetime.datetime(1961, 9, 9, 0, 0),\n",
-       " datetime.datetime(1961, 9, 16, 0, 0),\n",
-       " datetime.datetime(1961, 9, 23, 0, 0),\n",
-       " datetime.datetime(1961, 9, 30, 0, 0),\n",
-       " datetime.datetime(1961, 10, 7, 0, 0),\n",
-       " datetime.datetime(1961, 10, 14, 0, 0),\n",
-       " datetime.datetime(1961, 10, 21, 0, 0),\n",
-       " datetime.datetime(1961, 10, 28, 0, 0),\n",
-       " datetime.datetime(1961, 11, 4, 0, 0),\n",
-       " datetime.datetime(1961, 11, 11, 0, 0),\n",
-       " datetime.datetime(1961, 11, 18, 0, 0),\n",
-       " datetime.datetime(1961, 11, 25, 0, 0),\n",
-       " datetime.datetime(1961, 12, 2, 0, 0),\n",
-       " datetime.datetime(1961, 12, 9, 0, 0),\n",
-       " datetime.datetime(1961, 12, 16, 0, 0),\n",
-       " datetime.datetime(1961, 12, 23, 0, 0),\n",
-       " datetime.datetime(1961, 12, 30, 0, 0),\n",
-       " datetime.datetime(1962, 1, 6, 0, 0),\n",
-       " datetime.datetime(1962, 1, 13, 0, 0),\n",
-       " datetime.datetime(1962, 1, 20, 0, 0),\n",
-       " datetime.datetime(1962, 1, 27, 0, 0),\n",
-       " datetime.datetime(1962, 2, 3, 0, 0),\n",
-       " datetime.datetime(1962, 2, 10, 0, 0),\n",
-       " datetime.datetime(1962, 2, 17, 0, 0),\n",
-       " datetime.datetime(1962, 2, 24, 0, 0),\n",
-       " datetime.datetime(1962, 3, 3, 0, 0),\n",
-       " datetime.datetime(1962, 3, 10, 0, 0),\n",
-       " datetime.datetime(1962, 3, 17, 0, 0),\n",
-       " datetime.datetime(1962, 3, 24, 0, 0),\n",
-       " datetime.datetime(1962, 3, 31, 0, 0),\n",
-       " datetime.datetime(1962, 4, 7, 0, 0),\n",
-       " datetime.datetime(1962, 4, 14, 0, 0),\n",
-       " datetime.datetime(1962, 4, 21, 0, 0),\n",
-       " datetime.datetime(1962, 4, 28, 0, 0),\n",
-       " datetime.datetime(1962, 5, 5, 0, 0),\n",
-       " datetime.datetime(1962, 5, 12, 0, 0),\n",
-       " datetime.datetime(1962, 5, 19, 0, 0),\n",
-       " datetime.datetime(1962, 5, 26, 0, 0),\n",
-       " datetime.datetime(1962, 6, 2, 0, 0),\n",
-       " datetime.datetime(1962, 6, 9, 0, 0),\n",
-       " datetime.datetime(1962, 6, 16, 0, 0),\n",
-       " datetime.datetime(1962, 6, 23, 0, 0),\n",
-       " datetime.datetime(1962, 6, 30, 0, 0),\n",
-       " datetime.datetime(1962, 7, 7, 0, 0),\n",
-       " datetime.datetime(1962, 7, 14, 0, 0),\n",
-       " datetime.datetime(1962, 7, 21, 0, 0),\n",
-       " datetime.datetime(1962, 7, 28, 0, 0),\n",
-       " datetime.datetime(1962, 8, 4, 0, 0),\n",
-       " datetime.datetime(1962, 8, 11, 0, 0),\n",
-       " datetime.datetime(1962, 8, 18, 0, 0),\n",
-       " datetime.datetime(1962, 9, 15, 0, 0),\n",
-       " datetime.datetime(1962, 9, 22, 0, 0),\n",
-       " datetime.datetime(1962, 9, 29, 0, 0),\n",
-       " datetime.datetime(1962, 10, 6, 0, 0),\n",
-       " datetime.datetime(1962, 10, 13, 0, 0),\n",
-       " datetime.datetime(1962, 10, 20, 0, 0),\n",
-       " datetime.datetime(1962, 10, 27, 0, 0),\n",
-       " datetime.datetime(1962, 11, 3, 0, 0),\n",
-       " datetime.datetime(1962, 11, 10, 0, 0),\n",
-       " datetime.datetime(1962, 11, 17, 0, 0),\n",
-       " datetime.datetime(1962, 11, 24, 0, 0),\n",
-       " datetime.datetime(1962, 12, 1, 0, 0),\n",
-       " datetime.datetime(1962, 12, 8, 0, 0),\n",
-       " datetime.datetime(1962, 12, 15, 0, 0),\n",
-       " datetime.datetime(1962, 12, 22, 0, 0),\n",
-       " datetime.datetime(1963, 1, 5, 0, 0),\n",
-       " datetime.datetime(1963, 1, 12, 0, 0),\n",
-       " datetime.datetime(1963, 1, 19, 0, 0),\n",
-       " datetime.datetime(1963, 1, 26, 0, 0),\n",
-       " datetime.datetime(1963, 2, 2, 0, 0),\n",
-       " datetime.datetime(1963, 2, 9, 0, 0),\n",
-       " datetime.datetime(1963, 2, 23, 0, 0),\n",
-       " datetime.datetime(1963, 3, 2, 0, 0),\n",
-       " datetime.datetime(1963, 3, 9, 0, 0),\n",
-       " datetime.datetime(1963, 3, 16, 0, 0),\n",
-       " datetime.datetime(1963, 3, 23, 0, 0),\n",
-       " datetime.datetime(1963, 3, 30, 0, 0),\n",
-       " datetime.datetime(1963, 4, 6, 0, 0),\n",
-       " datetime.datetime(1963, 4, 13, 0, 0),\n",
-       " datetime.datetime(1963, 4, 20, 0, 0),\n",
-       " datetime.datetime(1963, 4, 27, 0, 0),\n",
-       " datetime.datetime(1963, 5, 11, 0, 0),\n",
-       " datetime.datetime(1963, 5, 18, 0, 0),\n",
-       " datetime.datetime(1963, 5, 25, 0, 0),\n",
-       " datetime.datetime(1963, 6, 1, 0, 0),\n",
-       " datetime.datetime(1963, 6, 8, 0, 0),\n",
-       " datetime.datetime(1963, 6, 15, 0, 0),\n",
-       " datetime.datetime(1963, 6, 22, 0, 0),\n",
-       " datetime.datetime(1963, 6, 29, 0, 0),\n",
-       " datetime.datetime(1963, 7, 6, 0, 0),\n",
-       " datetime.datetime(1963, 7, 13, 0, 0),\n",
-       " datetime.datetime(1963, 7, 20, 0, 0),\n",
-       " datetime.datetime(1963, 7, 27, 0, 0),\n",
-       " datetime.datetime(1963, 8, 3, 0, 0),\n",
-       " datetime.datetime(1963, 8, 10, 0, 0),\n",
-       " datetime.datetime(1963, 8, 17, 0, 0),\n",
-       " datetime.datetime(1963, 8, 24, 0, 0),\n",
-       " datetime.datetime(1963, 8, 31, 0, 0),\n",
-       " datetime.datetime(1963, 9, 7, 0, 0),\n",
-       " datetime.datetime(1963, 9, 14, 0, 0),\n",
-       " datetime.datetime(1963, 9, 21, 0, 0),\n",
-       " datetime.datetime(1963, 9, 28, 0, 0),\n",
-       " datetime.datetime(1963, 10, 5, 0, 0),\n",
-       " datetime.datetime(1963, 10, 12, 0, 0),\n",
-       " datetime.datetime(1963, 10, 19, 0, 0),\n",
-       " datetime.datetime(1963, 10, 26, 0, 0),\n",
-       " datetime.datetime(1963, 11, 2, 0, 0),\n",
-       " datetime.datetime(1963, 11, 9, 0, 0),\n",
-       " datetime.datetime(1963, 11, 16, 0, 0),\n",
-       " datetime.datetime(1963, 11, 30, 0, 0),\n",
-       " datetime.datetime(1963, 12, 7, 0, 0),\n",
-       " datetime.datetime(1963, 12, 14, 0, 0),\n",
-       " datetime.datetime(1963, 12, 21, 0, 0),\n",
-       " datetime.datetime(1963, 12, 28, 0, 0),\n",
-       " datetime.datetime(1964, 1, 4, 0, 0),\n",
-       " datetime.datetime(1964, 1, 11, 0, 0),\n",
-       " datetime.datetime(1964, 1, 18, 0, 0),\n",
-       " datetime.datetime(1964, 5, 30, 0, 0),\n",
-       " datetime.datetime(1964, 6, 6, 0, 0),\n",
-       " datetime.datetime(1964, 6, 27, 0, 0),\n",
-       " datetime.datetime(1964, 7, 4, 0, 0),\n",
-       " datetime.datetime(1964, 7, 11, 0, 0),\n",
-       " datetime.datetime(1964, 7, 18, 0, 0),\n",
-       " datetime.datetime(1964, 7, 25, 0, 0),\n",
-       " datetime.datetime(1964, 8, 1, 0, 0),\n",
-       " datetime.datetime(1964, 8, 15, 0, 0),\n",
-       " datetime.datetime(1964, 8, 22, 0, 0),\n",
-       " datetime.datetime(1964, 8, 29, 0, 0),\n",
-       " datetime.datetime(1964, 9, 5, 0, 0),\n",
-       " datetime.datetime(1964, 9, 12, 0, 0),\n",
-       " datetime.datetime(1964, 9, 19, 0, 0),\n",
-       " datetime.datetime(1964, 9, 26, 0, 0),\n",
-       " datetime.datetime(1964, 10, 3, 0, 0),\n",
-       " datetime.datetime(1964, 10, 10, 0, 0),\n",
-       " datetime.datetime(1964, 10, 17, 0, 0),\n",
-       " datetime.datetime(1964, 10, 24, 0, 0),\n",
-       " datetime.datetime(1964, 10, 31, 0, 0),\n",
-       " datetime.datetime(1964, 11, 7, 0, 0),\n",
-       " datetime.datetime(1964, 11, 14, 0, 0),\n",
-       " datetime.datetime(1964, 11, 21, 0, 0),\n",
-       " datetime.datetime(1964, 11, 28, 0, 0),\n",
-       " datetime.datetime(1964, 12, 5, 0, 0),\n",
-       " datetime.datetime(1964, 12, 12, 0, 0),\n",
-       " datetime.datetime(1964, 12, 19, 0, 0),\n",
-       " datetime.datetime(1964, 12, 26, 0, 0),\n",
-       " datetime.datetime(1965, 1, 2, 0, 0),\n",
-       " datetime.datetime(1965, 1, 9, 0, 0),\n",
-       " datetime.datetime(1965, 1, 16, 0, 0),\n",
-       " datetime.datetime(1965, 1, 23, 0, 0),\n",
-       " datetime.datetime(1965, 1, 30, 0, 0),\n",
-       " datetime.datetime(1965, 2, 6, 0, 0),\n",
-       " datetime.datetime(1965, 2, 13, 0, 0),\n",
-       " datetime.datetime(1965, 2, 20, 0, 0),\n",
-       " datetime.datetime(1965, 2, 27, 0, 0),\n",
-       " datetime.datetime(1965, 3, 6, 0, 0),\n",
-       " datetime.datetime(1965, 3, 13, 0, 0),\n",
-       " datetime.datetime(1965, 3, 20, 0, 0),\n",
-       " datetime.datetime(1965, 3, 27, 0, 0),\n",
-       " datetime.datetime(1965, 4, 3, 0, 0),\n",
-       " datetime.datetime(1965, 4, 10, 0, 0),\n",
-       " datetime.datetime(1965, 4, 17, 0, 0),\n",
-       " datetime.datetime(1965, 4, 24, 0, 0),\n",
-       " datetime.datetime(1965, 5, 1, 0, 0),\n",
-       " datetime.datetime(1965, 5, 8, 0, 0),\n",
-       " datetime.datetime(1965, 5, 15, 0, 0),\n",
-       " datetime.datetime(1965, 5, 22, 0, 0),\n",
-       " datetime.datetime(1965, 5, 29, 0, 0),\n",
-       " datetime.datetime(1965, 6, 5, 0, 0),\n",
-       " datetime.datetime(1965, 6, 12, 0, 0),\n",
-       " datetime.datetime(1965, 6, 19, 0, 0),\n",
-       " datetime.datetime(1965, 6, 26, 0, 0),\n",
-       " datetime.datetime(1965, 7, 3, 0, 0),\n",
-       " datetime.datetime(1965, 7, 10, 0, 0),\n",
-       " datetime.datetime(1965, 7, 17, 0, 0),\n",
-       " datetime.datetime(1965, 7, 24, 0, 0),\n",
-       " datetime.datetime(1965, 7, 31, 0, 0),\n",
-       " datetime.datetime(1965, 8, 7, 0, 0),\n",
-       " datetime.datetime(1965, 8, 14, 0, 0),\n",
-       " datetime.datetime(1965, 8, 21, 0, 0),\n",
-       " datetime.datetime(1965, 8, 28, 0, 0),\n",
-       " datetime.datetime(1965, 9, 4, 0, 0),\n",
-       " datetime.datetime(1965, 9, 11, 0, 0),\n",
-       " datetime.datetime(1965, 9, 18, 0, 0),\n",
-       " datetime.datetime(1965, 9, 25, 0, 0),\n",
-       " datetime.datetime(1965, 10, 2, 0, 0),\n",
-       " datetime.datetime(1965, 10, 9, 0, 0),\n",
-       " datetime.datetime(1965, 10, 16, 0, 0),\n",
-       " datetime.datetime(1965, 10, 23, 0, 0),\n",
-       " datetime.datetime(1965, 10, 30, 0, 0),\n",
-       " datetime.datetime(1965, 11, 6, 0, 0),\n",
-       " datetime.datetime(1965, 11, 13, 0, 0),\n",
-       " datetime.datetime(1965, 11, 20, 0, 0),\n",
-       " datetime.datetime(1965, 11, 27, 0, 0),\n",
-       " datetime.datetime(1965, 12, 4, 0, 0),\n",
-       " datetime.datetime(1965, 12, 11, 0, 0),\n",
-       " datetime.datetime(1965, 12, 18, 0, 0),\n",
-       " datetime.datetime(1965, 12, 25, 0, 0),\n",
-       " datetime.datetime(1966, 1, 1, 0, 0),\n",
-       " datetime.datetime(1966, 1, 8, 0, 0),\n",
-       " datetime.datetime(1966, 1, 15, 0, 0),\n",
-       " datetime.datetime(1966, 1, 22, 0, 0),\n",
-       " datetime.datetime(1966, 1, 29, 0, 0),\n",
-       " datetime.datetime(1966, 2, 5, 0, 0),\n",
-       " datetime.datetime(1966, 2, 12, 0, 0),\n",
-       " datetime.datetime(1966, 2, 19, 0, 0),\n",
-       " datetime.datetime(1966, 2, 26, 0, 0),\n",
-       " datetime.datetime(1966, 3, 5, 0, 0),\n",
-       " datetime.datetime(1966, 3, 12, 0, 0),\n",
-       " datetime.datetime(1966, 3, 19, 0, 0),\n",
-       " datetime.datetime(1966, 3, 26, 0, 0),\n",
-       " datetime.datetime(1966, 4, 2, 0, 0),\n",
-       " datetime.datetime(1966, 4, 9, 0, 0),\n",
-       " datetime.datetime(1966, 4, 16, 0, 0),\n",
-       " datetime.datetime(1966, 4, 23, 0, 0),\n",
-       " datetime.datetime(1966, 4, 30, 0, 0),\n",
-       " datetime.datetime(1966, 5, 7, 0, 0),\n",
-       " datetime.datetime(1966, 5, 14, 0, 0),\n",
-       " datetime.datetime(1966, 5, 21, 0, 0),\n",
-       " datetime.datetime(1966, 5, 28, 0, 0),\n",
-       " datetime.datetime(1966, 6, 4, 0, 0),\n",
-       " datetime.datetime(1966, 6, 11, 0, 0),\n",
-       " datetime.datetime(1966, 6, 18, 0, 0),\n",
-       " datetime.datetime(1966, 6, 25, 0, 0),\n",
-       " datetime.datetime(1966, 7, 2, 0, 0),\n",
-       " datetime.datetime(1966, 7, 9, 0, 0),\n",
-       " datetime.datetime(1966, 8, 6, 0, 0),\n",
-       " datetime.datetime(1966, 8, 13, 0, 0),\n",
-       " datetime.datetime(1966, 8, 20, 0, 0),\n",
-       " datetime.datetime(1966, 8, 27, 0, 0),\n",
-       " datetime.datetime(1966, 9, 3, 0, 0),\n",
-       " datetime.datetime(1966, 9, 10, 0, 0),\n",
-       " datetime.datetime(1966, 9, 17, 0, 0),\n",
-       " datetime.datetime(1966, 9, 24, 0, 0),\n",
-       " datetime.datetime(1966, 10, 1, 0, 0),\n",
-       " datetime.datetime(1966, 10, 8, 0, 0),\n",
-       " datetime.datetime(1966, 10, 15, 0, 0),\n",
-       " datetime.datetime(1966, 10, 22, 0, 0),\n",
-       " datetime.datetime(1966, 10, 29, 0, 0),\n",
-       " datetime.datetime(1966, 11, 12, 0, 0),\n",
-       " datetime.datetime(1966, 11, 19, 0, 0),\n",
-       " datetime.datetime(1966, 11, 26, 0, 0),\n",
-       " datetime.datetime(1966, 12, 3, 0, 0),\n",
-       " datetime.datetime(1966, 12, 10, 0, 0),\n",
-       " datetime.datetime(1966, 12, 17, 0, 0),\n",
-       " datetime.datetime(1966, 12, 24, 0, 0),\n",
-       " datetime.datetime(1966, 12, 31, 0, 0),\n",
-       " datetime.datetime(1967, 1, 7, 0, 0),\n",
-       " datetime.datetime(1967, 1, 14, 0, 0),\n",
-       " datetime.datetime(1967, 2, 4, 0, 0),\n",
-       " datetime.datetime(1967, 2, 11, 0, 0),\n",
-       " datetime.datetime(1967, 2, 18, 0, 0),\n",
-       " datetime.datetime(1967, 2, 25, 0, 0),\n",
-       " datetime.datetime(1967, 3, 4, 0, 0),\n",
-       " datetime.datetime(1967, 3, 11, 0, 0),\n",
-       " datetime.datetime(1967, 3, 18, 0, 0),\n",
-       " datetime.datetime(1967, 3, 25, 0, 0),\n",
-       " datetime.datetime(1967, 4, 1, 0, 0),\n",
-       " datetime.datetime(1967, 4, 8, 0, 0),\n",
-       " datetime.datetime(1967, 4, 15, 0, 0),\n",
-       " datetime.datetime(1967, 4, 22, 0, 0),\n",
-       " datetime.datetime(1967, 4, 29, 0, 0),\n",
-       " datetime.datetime(1967, 5, 6, 0, 0),\n",
-       " datetime.datetime(1967, 5, 13, 0, 0),\n",
-       " datetime.datetime(1967, 5, 20, 0, 0),\n",
-       " datetime.datetime(1967, 5, 27, 0, 0),\n",
-       " datetime.datetime(1967, 6, 3, 0, 0),\n",
-       " datetime.datetime(1967, 6, 10, 0, 0),\n",
-       " datetime.datetime(1967, 6, 17, 0, 0),\n",
-       " datetime.datetime(1967, 6, 24, 0, 0),\n",
-       " datetime.datetime(1967, 7, 1, 0, 0),\n",
-       " datetime.datetime(1967, 7, 8, 0, 0),\n",
-       " datetime.datetime(1967, 7, 15, 0, 0),\n",
-       " datetime.datetime(1967, 7, 22, 0, 0),\n",
-       " datetime.datetime(1967, 7, 29, 0, 0),\n",
-       " datetime.datetime(1967, 8, 5, 0, 0),\n",
-       " datetime.datetime(1967, 8, 12, 0, 0),\n",
-       " datetime.datetime(1967, 8, 19, 0, 0),\n",
-       " datetime.datetime(1967, 8, 26, 0, 0),\n",
-       " datetime.datetime(1967, 9, 2, 0, 0),\n",
-       " datetime.datetime(1967, 9, 9, 0, 0),\n",
-       " datetime.datetime(1967, 9, 16, 0, 0),\n",
-       " datetime.datetime(1967, 9, 23, 0, 0),\n",
-       " datetime.datetime(1967, 9, 30, 0, 0),\n",
-       " datetime.datetime(1967, 10, 7, 0, 0),\n",
-       " datetime.datetime(1967, 10, 14, 0, 0),\n",
-       " datetime.datetime(1967, 10, 21, 0, 0),\n",
-       " datetime.datetime(1967, 10, 28, 0, 0),\n",
-       " datetime.datetime(1967, 11, 4, 0, 0),\n",
-       " datetime.datetime(1967, 11, 11, 0, 0),\n",
-       " datetime.datetime(1967, 11, 18, 0, 0),\n",
-       " datetime.datetime(1967, 11, 25, 0, 0),\n",
-       " datetime.datetime(1967, 12, 2, 0, 0),\n",
-       " datetime.datetime(1967, 12, 9, 0, 0),\n",
-       " datetime.datetime(1967, 12, 16, 0, 0),\n",
-       " datetime.datetime(1967, 12, 23, 0, 0),\n",
-       " datetime.datetime(1967, 12, 30, 0, 0),\n",
-       " datetime.datetime(1968, 1, 6, 0, 0),\n",
-       " datetime.datetime(1968, 1, 13, 0, 0),\n",
-       " datetime.datetime(1968, 1, 20, 0, 0),\n",
-       " datetime.datetime(1968, 1, 27, 0, 0),\n",
-       " datetime.datetime(1968, 2, 3, 0, 0),\n",
-       " datetime.datetime(1968, 2, 10, 0, 0),\n",
-       " datetime.datetime(1968, 2, 17, 0, 0),\n",
-       " datetime.datetime(1968, 2, 24, 0, 0),\n",
-       " datetime.datetime(1968, 3, 2, 0, 0),\n",
-       " datetime.datetime(1968, 3, 9, 0, 0),\n",
-       " datetime.datetime(1968, 3, 16, 0, 0),\n",
-       " datetime.datetime(1968, 3, 23, 0, 0),\n",
-       " datetime.datetime(1968, 3, 30, 0, 0),\n",
-       " datetime.datetime(1968, 4, 6, 0, 0),\n",
-       " datetime.datetime(1968, 4, 13, 0, 0),\n",
-       " datetime.datetime(1968, 4, 20, 0, 0),\n",
-       " datetime.datetime(1968, 4, 27, 0, 0),\n",
-       " datetime.datetime(1968, 5, 4, 0, 0),\n",
-       " datetime.datetime(1968, 5, 11, 0, 0),\n",
-       " datetime.datetime(1968, 5, 18, 0, 0),\n",
-       " datetime.datetime(1968, 5, 25, 0, 0),\n",
-       " datetime.datetime(1968, 6, 1, 0, 0),\n",
-       " datetime.datetime(1968, 6, 8, 0, 0),\n",
-       " datetime.datetime(1968, 6, 15, 0, 0),\n",
-       " datetime.datetime(1968, 6, 22, 0, 0),\n",
-       " datetime.datetime(1968, 6, 29, 0, 0),\n",
-       " datetime.datetime(1968, 7, 6, 0, 0),\n",
-       " datetime.datetime(1968, 7, 13, 0, 0),\n",
-       " datetime.datetime(1968, 7, 20, 0, 0),\n",
-       " datetime.datetime(1968, 7, 27, 0, 0),\n",
-       " datetime.datetime(1968, 8, 3, 0, 0),\n",
-       " datetime.datetime(1968, 8, 10, 0, 0),\n",
-       " datetime.datetime(1968, 8, 17, 0, 0),\n",
-       " datetime.datetime(1968, 8, 24, 0, 0),\n",
-       " datetime.datetime(1968, 8, 31, 0, 0),\n",
-       " datetime.datetime(1968, 9, 7, 0, 0),\n",
-       " datetime.datetime(1968, 9, 14, 0, 0),\n",
-       " datetime.datetime(1968, 9, 21, 0, 0),\n",
-       " datetime.datetime(1968, 9, 28, 0, 0),\n",
-       " datetime.datetime(1968, 10, 5, 0, 0),\n",
-       " datetime.datetime(1968, 10, 12, 0, 0),\n",
-       " datetime.datetime(1968, 10, 19, 0, 0),\n",
-       " datetime.datetime(1968, 10, 26, 0, 0),\n",
-       " datetime.datetime(1968, 11, 2, 0, 0),\n",
-       " datetime.datetime(1968, 11, 9, 0, 0),\n",
-       " datetime.datetime(1968, 11, 16, 0, 0),\n",
-       " datetime.datetime(1968, 11, 23, 0, 0),\n",
-       " datetime.datetime(1968, 11, 30, 0, 0),\n",
-       " datetime.datetime(1968, 12, 7, 0, 0),\n",
-       " datetime.datetime(1968, 12, 14, 0, 0),\n",
-       " datetime.datetime(1968, 12, 21, 0, 0),\n",
-       " datetime.datetime(1968, 12, 28, 0, 0),\n",
-       " datetime.datetime(1969, 1, 4, 0, 0),\n",
-       " datetime.datetime(1969, 1, 11, 0, 0),\n",
-       " datetime.datetime(1969, 1, 18, 0, 0),\n",
-       " datetime.datetime(1969, 1, 25, 0, 0),\n",
-       " datetime.datetime(1969, 2, 1, 0, 0),\n",
-       " datetime.datetime(1969, 2, 8, 0, 0),\n",
-       " datetime.datetime(1969, 2, 15, 0, 0),\n",
-       " datetime.datetime(1969, 2, 22, 0, 0),\n",
-       " datetime.datetime(1969, 3, 1, 0, 0),\n",
-       " datetime.datetime(1969, 3, 8, 0, 0),\n",
-       " datetime.datetime(1969, 3, 15, 0, 0),\n",
-       " datetime.datetime(1969, 3, 22, 0, 0),\n",
-       " datetime.datetime(1969, 3, 29, 0, 0),\n",
-       " datetime.datetime(1969, 4, 5, 0, 0),\n",
-       " datetime.datetime(1969, 4, 12, 0, 0),\n",
-       " datetime.datetime(1969, 4, 19, 0, 0),\n",
-       " datetime.datetime(1969, 4, 26, 0, 0),\n",
-       " datetime.datetime(1969, 5, 3, 0, 0),\n",
-       " datetime.datetime(1969, 5, 10, 0, 0),\n",
-       " datetime.datetime(1969, 5, 17, 0, 0),\n",
-       " datetime.datetime(1969, 5, 24, 0, 0),\n",
-       " datetime.datetime(1969, 5, 31, 0, 0),\n",
-       " datetime.datetime(1969, 6, 7, 0, 0),\n",
-       " datetime.datetime(1969, 6, 14, 0, 0),\n",
-       " datetime.datetime(1969, 6, 21, 0, 0),\n",
-       " datetime.datetime(1969, 6, 28, 0, 0),\n",
-       " datetime.datetime(1969, 7, 5, 0, 0),\n",
-       " datetime.datetime(1969, 7, 12, 0, 0),\n",
-       " datetime.datetime(1969, 7, 19, 0, 0),\n",
-       " datetime.datetime(1969, 7, 26, 0, 0),\n",
-       " datetime.datetime(1969, 8, 2, 0, 0),\n",
-       " datetime.datetime(1969, 8, 9, 0, 0),\n",
-       " datetime.datetime(1969, 8, 16, 0, 0),\n",
-       " datetime.datetime(1969, 8, 23, 0, 0),\n",
-       " datetime.datetime(1969, 8, 30, 0, 0),\n",
-       " datetime.datetime(1969, 9, 6, 0, 0),\n",
-       " datetime.datetime(1969, 9, 13, 0, 0),\n",
-       " datetime.datetime(1969, 9, 20, 0, 0),\n",
-       " datetime.datetime(1969, 9, 27, 0, 0),\n",
-       " datetime.datetime(1969, 10, 4, 0, 0),\n",
-       " datetime.datetime(1969, 10, 11, 0, 0),\n",
-       " datetime.datetime(1969, 10, 18, 0, 0),\n",
-       " datetime.datetime(1969, 10, 25, 0, 0),\n",
-       " datetime.datetime(1969, 11, 1, 0, 0),\n",
-       " datetime.datetime(1969, 11, 8, 0, 0),\n",
-       " datetime.datetime(1969, 11, 15, 0, 0),\n",
-       " datetime.datetime(1969, 11, 22, 0, 0),\n",
-       " datetime.datetime(1969, 11, 29, 0, 0),\n",
-       " datetime.datetime(1969, 12, 6, 0, 0),\n",
-       " datetime.datetime(1969, 12, 13, 0, 0),\n",
-       " datetime.datetime(1969, 12, 20, 0, 0),\n",
-       " datetime.datetime(1969, 12, 27, 0, 0),\n",
-       " datetime.datetime(1970, 1, 3, 0, 0),\n",
-       " datetime.datetime(1970, 1, 10, 0, 0),\n",
-       " datetime.datetime(1970, 1, 17, 0, 0),\n",
-       " datetime.datetime(1970, 1, 24, 0, 0),\n",
-       " datetime.datetime(1970, 1, 31, 0, 0),\n",
-       " datetime.datetime(1970, 2, 7, 0, 0),\n",
-       " datetime.datetime(1970, 2, 14, 0, 0),\n",
-       " datetime.datetime(1970, 2, 21, 0, 0),\n",
-       " datetime.datetime(1970, 2, 28, 0, 0),\n",
-       " datetime.datetime(1970, 3, 7, 0, 0),\n",
-       " datetime.datetime(1970, 3, 14, 0, 0),\n",
-       " datetime.datetime(1970, 3, 21, 0, 0),\n",
-       " datetime.datetime(1970, 3, 28, 0, 0),\n",
-       " datetime.datetime(1970, 4, 4, 0, 0),\n",
-       " datetime.datetime(1970, 4, 11, 0, 0),\n",
-       " datetime.datetime(1970, 4, 18, 0, 0),\n",
-       " datetime.datetime(1970, 4, 25, 0, 0),\n",
-       " datetime.datetime(1970, 5, 2, 0, 0),\n",
-       " datetime.datetime(1970, 5, 9, 0, 0),\n",
-       " datetime.datetime(1970, 5, 16, 0, 0),\n",
-       " datetime.datetime(1970, 5, 23, 0, 0),\n",
-       " datetime.datetime(1970, 5, 30, 0, 0),\n",
-       " datetime.datetime(1970, 6, 6, 0, 0),\n",
-       " datetime.datetime(1970, 6, 13, 0, 0),\n",
-       " datetime.datetime(1970, 6, 20, 0, 0),\n",
-       " datetime.datetime(1970, 6, 27, 0, 0),\n",
-       " datetime.datetime(1970, 7, 4, 0, 0),\n",
-       " datetime.datetime(1970, 7, 11, 0, 0),\n",
-       " datetime.datetime(1970, 7, 18, 0, 0),\n",
-       " datetime.datetime(1970, 7, 25, 0, 0),\n",
-       " datetime.datetime(1970, 8, 1, 0, 0),\n",
-       " datetime.datetime(1970, 8, 8, 0, 0),\n",
-       " datetime.datetime(1970, 8, 15, 0, 0),\n",
-       " datetime.datetime(1970, 8, 22, 0, 0),\n",
-       " datetime.datetime(1970, 8, 29, 0, 0),\n",
-       " datetime.datetime(1970, 9, 5, 0, 0),\n",
-       " datetime.datetime(1970, 9, 12, 0, 0),\n",
-       " datetime.datetime(1970, 9, 19, 0, 0),\n",
-       " datetime.datetime(1970, 9, 26, 0, 0),\n",
-       " datetime.datetime(1970, 10, 3, 0, 0),\n",
-       " datetime.datetime(1970, 10, 10, 0, 0),\n",
-       " datetime.datetime(1970, 10, 17, 0, 0),\n",
-       " datetime.datetime(1970, 10, 24, 0, 0),\n",
-       " datetime.datetime(1970, 10, 31, 0, 0),\n",
-       " datetime.datetime(1970, 11, 7, 0, 0),\n",
-       " datetime.datetime(1970, 11, 14, 0, 0),\n",
-       " datetime.datetime(1970, 11, 21, 0, 0),\n",
-       " datetime.datetime(1970, 11, 28, 0, 0),\n",
-       " datetime.datetime(1970, 12, 5, 0, 0),\n",
-       " datetime.datetime(1970, 12, 12, 0, 0),\n",
-       " datetime.datetime(1970, 12, 19, 0, 0),\n",
-       " datetime.datetime(1970, 12, 26, 0, 0),\n",
-       " datetime.datetime(1971, 1, 2, 0, 0),\n",
-       " datetime.datetime(1971, 1, 9, 0, 0),\n",
-       " datetime.datetime(1971, 1, 16, 0, 0),\n",
-       " datetime.datetime(1971, 1, 23, 0, 0),\n",
-       " datetime.datetime(1971, 1, 30, 0, 0),\n",
-       " datetime.datetime(1971, 2, 6, 0, 0),\n",
-       " datetime.datetime(1971, 2, 13, 0, 0),\n",
-       " datetime.datetime(1971, 2, 20, 0, 0),\n",
-       " datetime.datetime(1971, 2, 27, 0, 0),\n",
-       " datetime.datetime(1971, 3, 6, 0, 0),\n",
-       " datetime.datetime(1971, 3, 13, 0, 0),\n",
-       " datetime.datetime(1971, 3, 20, 0, 0),\n",
-       " datetime.datetime(1971, 3, 27, 0, 0),\n",
-       " datetime.datetime(1971, 4, 3, 0, 0),\n",
-       " datetime.datetime(1971, 4, 10, 0, 0),\n",
-       " datetime.datetime(1971, 4, 17, 0, 0),\n",
-       " datetime.datetime(1971, 4, 24, 0, 0),\n",
-       " datetime.datetime(1971, 5, 1, 0, 0),\n",
-       " datetime.datetime(1971, 5, 8, 0, 0),\n",
-       " datetime.datetime(1971, 5, 15, 0, 0),\n",
-       " datetime.datetime(1971, 5, 22, 0, 0),\n",
-       " datetime.datetime(1971, 5, 29, 0, 0),\n",
-       " datetime.datetime(1971, 6, 5, 0, 0),\n",
-       " datetime.datetime(1971, 6, 12, 0, 0),\n",
-       " datetime.datetime(1971, 6, 19, 0, 0),\n",
-       " datetime.datetime(1971, 6, 26, 0, 0),\n",
-       " datetime.datetime(1971, 7, 3, 0, 0),\n",
-       " datetime.datetime(1971, 7, 10, 0, 0),\n",
-       " datetime.datetime(1971, 7, 17, 0, 0),\n",
-       " datetime.datetime(1971, 7, 24, 0, 0),\n",
-       " datetime.datetime(1971, 7, 31, 0, 0),\n",
-       " datetime.datetime(1971, 8, 7, 0, 0),\n",
-       " datetime.datetime(1971, 8, 14, 0, 0),\n",
-       " datetime.datetime(1971, 8, 21, 0, 0),\n",
-       " datetime.datetime(1971, 8, 28, 0, 0),\n",
-       " datetime.datetime(1971, 9, 4, 0, 0),\n",
-       " datetime.datetime(1971, 9, 11, 0, 0),\n",
-       " datetime.datetime(1971, 9, 18, 0, 0),\n",
-       " datetime.datetime(1971, 9, 25, 0, 0),\n",
-       " datetime.datetime(1971, 10, 2, 0, 0),\n",
-       " datetime.datetime(1971, 10, 9, 0, 0),\n",
-       " datetime.datetime(1971, 10, 16, 0, 0),\n",
-       " datetime.datetime(1971, 10, 23, 0, 0),\n",
-       " datetime.datetime(1971, 10, 30, 0, 0),\n",
-       " datetime.datetime(1971, 11, 6, 0, 0),\n",
-       " datetime.datetime(1971, 11, 13, 0, 0),\n",
-       " datetime.datetime(1971, 11, 20, 0, 0),\n",
-       " datetime.datetime(1971, 11, 27, 0, 0),\n",
-       " datetime.datetime(1971, 12, 4, 0, 0),\n",
-       " datetime.datetime(1971, 12, 11, 0, 0),\n",
-       " datetime.datetime(1971, 12, 18, 0, 0),\n",
-       " datetime.datetime(1971, 12, 25, 0, 0),\n",
-       " datetime.datetime(1972, 1, 1, 0, 0),\n",
-       " datetime.datetime(1972, 1, 8, 0, 0),\n",
-       " datetime.datetime(1972, 1, 15, 0, 0),\n",
-       " datetime.datetime(1972, 1, 22, 0, 0),\n",
-       " datetime.datetime(1972, 1, 29, 0, 0),\n",
-       " datetime.datetime(1972, 2, 5, 0, 0),\n",
-       " datetime.datetime(1972, 2, 12, 0, 0),\n",
-       " datetime.datetime(1972, 2, 19, 0, 0),\n",
-       " datetime.datetime(1972, 2, 26, 0, 0),\n",
-       " datetime.datetime(1972, 3, 4, 0, 0),\n",
-       " datetime.datetime(1972, 3, 11, 0, 0),\n",
-       " datetime.datetime(1972, 3, 18, 0, 0),\n",
-       " datetime.datetime(1972, 3, 25, 0, 0),\n",
-       " datetime.datetime(1972, 4, 1, 0, 0),\n",
-       " datetime.datetime(1972, 4, 8, 0, 0),\n",
-       " datetime.datetime(1972, 4, 15, 0, 0),\n",
-       " datetime.datetime(1972, 4, 22, 0, 0),\n",
-       " datetime.datetime(1972, 4, 29, 0, 0),\n",
-       " datetime.datetime(1972, 5, 6, 0, 0),\n",
-       " datetime.datetime(1972, 5, 13, 0, 0),\n",
-       " datetime.datetime(1972, 5, 20, 0, 0),\n",
-       " datetime.datetime(1972, 5, 27, 0, 0),\n",
-       " datetime.datetime(1972, 6, 3, 0, 0),\n",
-       " datetime.datetime(1972, 6, 10, 0, 0),\n",
-       " datetime.datetime(1972, 6, 17, 0, 0),\n",
-       " datetime.datetime(1972, 6, 24, 0, 0),\n",
-       " datetime.datetime(1972, 7, 1, 0, 0),\n",
-       " datetime.datetime(1972, 7, 8, 0, 0),\n",
-       " datetime.datetime(1972, 7, 15, 0, 0),\n",
-       " datetime.datetime(1972, 7, 22, 0, 0),\n",
-       " datetime.datetime(1972, 7, 29, 0, 0),\n",
-       " datetime.datetime(1972, 8, 5, 0, 0),\n",
-       " datetime.datetime(1972, 8, 12, 0, 0),\n",
-       " datetime.datetime(1972, 8, 19, 0, 0),\n",
-       " datetime.datetime(1972, 8, 26, 0, 0),\n",
-       " datetime.datetime(1972, 9, 2, 0, 0),\n",
-       " datetime.datetime(1972, 9, 9, 0, 0),\n",
-       " datetime.datetime(1972, 9, 16, 0, 0),\n",
-       " datetime.datetime(1972, 9, 23, 0, 0),\n",
-       " datetime.datetime(1972, 9, 30, 0, 0),\n",
-       " datetime.datetime(1972, 10, 7, 0, 0),\n",
-       " datetime.datetime(1972, 10, 14, 0, 0),\n",
-       " datetime.datetime(1972, 10, 21, 0, 0),\n",
-       " datetime.datetime(1972, 10, 28, 0, 0),\n",
-       " datetime.datetime(1972, 11, 4, 0, 0),\n",
-       " datetime.datetime(1972, 11, 11, 0, 0),\n",
-       " datetime.datetime(1972, 11, 18, 0, 0),\n",
-       " datetime.datetime(1972, 11, 25, 0, 0),\n",
-       " datetime.datetime(1972, 12, 2, 0, 0),\n",
-       " datetime.datetime(1972, 12, 9, 0, 0),\n",
-       " datetime.datetime(1972, 12, 16, 0, 0),\n",
-       " datetime.datetime(1972, 12, 23, 0, 0),\n",
-       " datetime.datetime(1972, 12, 30, 0, 0),\n",
-       " datetime.datetime(1973, 1, 6, 0, 0),\n",
-       " datetime.datetime(1973, 1, 13, 0, 0),\n",
-       " datetime.datetime(1973, 1, 20, 0, 0),\n",
-       " datetime.datetime(1973, 1, 27, 0, 0),\n",
-       " datetime.datetime(1973, 2, 3, 0, 0),\n",
-       " datetime.datetime(1973, 2, 10, 0, 0),\n",
-       " datetime.datetime(1973, 2, 17, 0, 0),\n",
-       " datetime.datetime(1973, 2, 24, 0, 0),\n",
-       " datetime.datetime(1973, 3, 3, 0, 0),\n",
-       " datetime.datetime(1973, 3, 10, 0, 0),\n",
-       " datetime.datetime(1973, 3, 17, 0, 0),\n",
-       " datetime.datetime(1973, 3, 24, 0, 0),\n",
-       " datetime.datetime(1973, 3, 31, 0, 0),\n",
-       " datetime.datetime(1973, 4, 7, 0, 0),\n",
-       " datetime.datetime(1973, 4, 14, 0, 0),\n",
-       " datetime.datetime(1973, 4, 21, 0, 0),\n",
-       " datetime.datetime(1973, 4, 28, 0, 0),\n",
-       " datetime.datetime(1973, 5, 5, 0, 0),\n",
-       " datetime.datetime(1973, 5, 12, 0, 0),\n",
-       " datetime.datetime(1973, 5, 19, 0, 0),\n",
-       " datetime.datetime(1973, 5, 26, 0, 0),\n",
-       " datetime.datetime(1973, 6, 2, 0, 0),\n",
-       " datetime.datetime(1973, 6, 9, 0, 0),\n",
-       " datetime.datetime(1973, 6, 16, 0, 0),\n",
-       " datetime.datetime(1973, 6, 23, 0, 0),\n",
-       " datetime.datetime(1973, 6, 30, 0, 0),\n",
-       " datetime.datetime(1973, 7, 7, 0, 0),\n",
-       " datetime.datetime(1973, 7, 14, 0, 0),\n",
-       " datetime.datetime(1973, 7, 21, 0, 0),\n",
-       " datetime.datetime(1973, 7, 28, 0, 0),\n",
-       " datetime.datetime(1973, 8, 4, 0, 0),\n",
-       " datetime.datetime(1973, 8, 11, 0, 0),\n",
-       " datetime.datetime(1973, 8, 18, 0, 0),\n",
-       " datetime.datetime(1973, 8, 25, 0, 0),\n",
-       " datetime.datetime(1973, 9, 1, 0, 0),\n",
-       " datetime.datetime(1973, 9, 8, 0, 0),\n",
-       " datetime.datetime(1973, 9, 15, 0, 0),\n",
-       " datetime.datetime(1973, 9, 22, 0, 0),\n",
-       " datetime.datetime(1973, 9, 29, 0, 0),\n",
-       " datetime.datetime(1973, 10, 6, 0, 0),\n",
-       " datetime.datetime(1973, 10, 13, 0, 0),\n",
-       " datetime.datetime(1973, 10, 20, 0, 0),\n",
-       " datetime.datetime(1973, 10, 27, 0, 0),\n",
-       " datetime.datetime(1973, 11, 3, 0, 0),\n",
-       " datetime.datetime(1973, 11, 10, 0, 0),\n",
-       " datetime.datetime(1973, 11, 17, 0, 0),\n",
-       " datetime.datetime(1973, 11, 24, 0, 0),\n",
-       " datetime.datetime(1973, 12, 1, 0, 0),\n",
-       " datetime.datetime(1973, 12, 8, 0, 0),\n",
-       " datetime.datetime(1973, 12, 15, 0, 0),\n",
-       " datetime.datetime(1973, 12, 22, 0, 0),\n",
-       " datetime.datetime(1973, 12, 29, 0, 0),\n",
-       " datetime.datetime(1974, 1, 5, 0, 0),\n",
-       " datetime.datetime(1974, 1, 12, 0, 0),\n",
-       " datetime.datetime(1974, 1, 19, 0, 0),\n",
-       " datetime.datetime(1974, 1, 26, 0, 0),\n",
-       " datetime.datetime(1974, 2, 2, 0, 0),\n",
-       " datetime.datetime(1974, 2, 9, 0, 0),\n",
-       " datetime.datetime(1974, 2, 16, 0, 0),\n",
-       " datetime.datetime(1974, 2, 23, 0, 0),\n",
-       " datetime.datetime(1974, 3, 2, 0, 0),\n",
-       " datetime.datetime(1974, 3, 9, 0, 0),\n",
-       " datetime.datetime(1974, 3, 16, 0, 0),\n",
-       " datetime.datetime(1974, 3, 23, 0, 0),\n",
-       " datetime.datetime(1974, 3, 30, 0, 0),\n",
-       " datetime.datetime(1974, 4, 6, 0, 0),\n",
-       " datetime.datetime(1974, 4, 13, 0, 0),\n",
-       " datetime.datetime(1974, 4, 20, 0, 0),\n",
-       " datetime.datetime(1974, 4, 27, 0, 0),\n",
-       " datetime.datetime(1974, 5, 4, 0, 0),\n",
-       " datetime.datetime(1974, 5, 11, 0, 0),\n",
-       " datetime.datetime(1974, 5, 18, 0, 0),\n",
-       " datetime.datetime(1974, 5, 25, 0, 0),\n",
-       " datetime.datetime(1974, 6, 1, 0, 0),\n",
-       " datetime.datetime(1974, 6, 8, 0, 0),\n",
-       " datetime.datetime(1974, 6, 15, 0, 0),\n",
-       " datetime.datetime(1974, 6, 22, 0, 0),\n",
-       " datetime.datetime(1974, 6, 29, 0, 0),\n",
-       " datetime.datetime(1974, 7, 6, 0, 0),\n",
-       " datetime.datetime(1974, 7, 13, 0, 0),\n",
-       " datetime.datetime(1974, 7, 20, 0, 0),\n",
-       " datetime.datetime(1974, 7, 27, 0, 0),\n",
-       " datetime.datetime(1974, 8, 3, 0, 0),\n",
-       " datetime.datetime(1974, 8, 10, 0, 0),\n",
-       " datetime.datetime(1974, 8, 17, 0, 0),\n",
-       " datetime.datetime(1974, 8, 24, 0, 0),\n",
-       " datetime.datetime(1974, 8, 31, 0, 0),\n",
-       " datetime.datetime(1974, 9, 7, 0, 0),\n",
-       " datetime.datetime(1974, 9, 14, 0, 0),\n",
-       " datetime.datetime(1974, 9, 21, 0, 0),\n",
-       " datetime.datetime(1974, 9, 28, 0, 0),\n",
-       " datetime.datetime(1974, 10, 5, 0, 0),\n",
-       " datetime.datetime(1974, 10, 12, 0, 0),\n",
-       " datetime.datetime(1974, 10, 19, 0, 0),\n",
-       " datetime.datetime(1974, 10, 26, 0, 0),\n",
-       " datetime.datetime(1974, 11, 2, 0, 0),\n",
-       " datetime.datetime(1974, 11, 9, 0, 0),\n",
-       " datetime.datetime(1974, 11, 16, 0, 0),\n",
-       " datetime.datetime(1974, 11, 23, 0, 0),\n",
-       " datetime.datetime(1974, 11, 30, 0, 0),\n",
-       " datetime.datetime(1974, 12, 7, 0, 0),\n",
-       " datetime.datetime(1974, 12, 14, 0, 0),\n",
-       " datetime.datetime(1974, 12, 21, 0, 0),\n",
-       " datetime.datetime(1974, 12, 28, 0, 0),\n",
-       " datetime.datetime(1975, 1, 4, 0, 0),\n",
-       " datetime.datetime(1975, 1, 11, 0, 0),\n",
-       " datetime.datetime(1975, 1, 18, 0, 0),\n",
-       " datetime.datetime(1975, 1, 25, 0, 0),\n",
-       " datetime.datetime(1975, 2, 1, 0, 0),\n",
-       " datetime.datetime(1975, 2, 8, 0, 0),\n",
-       " datetime.datetime(1975, 2, 15, 0, 0),\n",
-       " datetime.datetime(1975, 2, 22, 0, 0),\n",
-       " datetime.datetime(1975, 3, 1, 0, 0),\n",
-       " datetime.datetime(1975, 3, 8, 0, 0),\n",
-       " datetime.datetime(1975, 3, 15, 0, 0),\n",
-       " datetime.datetime(1975, 3, 22, 0, 0),\n",
-       " datetime.datetime(1975, 3, 29, 0, 0),\n",
-       " datetime.datetime(1975, 4, 5, 0, 0),\n",
-       " datetime.datetime(1975, 4, 12, 0, 0),\n",
-       " datetime.datetime(1975, 4, 19, 0, 0),\n",
-       " datetime.datetime(1975, 4, 26, 0, 0),\n",
-       " datetime.datetime(1975, 5, 3, 0, 0),\n",
-       " datetime.datetime(1975, 5, 10, 0, 0),\n",
-       " datetime.datetime(1975, 5, 17, 0, 0),\n",
-       " datetime.datetime(1975, 5, 24, 0, 0),\n",
-       " datetime.datetime(1975, 5, 31, 0, 0),\n",
-       " datetime.datetime(1975, 6, 7, 0, 0),\n",
-       " datetime.datetime(1975, 6, 14, 0, 0),\n",
-       " datetime.datetime(1975, 6, 21, 0, 0),\n",
-       " datetime.datetime(1975, 6, 28, 0, 0),\n",
-       " datetime.datetime(1975, 7, 5, 0, 0),\n",
-       " datetime.datetime(1975, 7, 12, 0, 0),\n",
-       " datetime.datetime(1975, 7, 19, 0, 0),\n",
-       " datetime.datetime(1975, 7, 26, 0, 0),\n",
-       " datetime.datetime(1975, 8, 2, 0, 0),\n",
-       " datetime.datetime(1975, 8, 9, 0, 0),\n",
-       " datetime.datetime(1975, 8, 16, 0, 0),\n",
-       " datetime.datetime(1975, 8, 23, 0, 0),\n",
-       " datetime.datetime(1975, 8, 30, 0, 0),\n",
-       " datetime.datetime(1975, 9, 6, 0, 0),\n",
-       " datetime.datetime(1975, 9, 13, 0, 0),\n",
-       " datetime.datetime(1975, 9, 20, 0, 0),\n",
-       " datetime.datetime(1975, 9, 27, 0, 0),\n",
-       " datetime.datetime(1975, 10, 4, 0, 0),\n",
-       " datetime.datetime(1975, 10, 11, 0, 0),\n",
-       " datetime.datetime(1975, 10, 18, 0, 0),\n",
-       " datetime.datetime(1975, 10, 25, 0, 0),\n",
-       " datetime.datetime(1975, 11, 1, 0, 0),\n",
-       " datetime.datetime(1975, 11, 8, 0, 0),\n",
-       " datetime.datetime(1975, 11, 15, 0, 0),\n",
-       " datetime.datetime(1975, 11, 22, 0, 0),\n",
-       " datetime.datetime(1975, 11, 29, 0, 0),\n",
-       " datetime.datetime(1975, 12, 6, 0, 0),\n",
-       " datetime.datetime(1975, 12, 13, 0, 0),\n",
-       " datetime.datetime(1975, 12, 20, 0, 0),\n",
-       " datetime.datetime(1975, 12, 27, 0, 0),\n",
-       " datetime.datetime(1976, 1, 3, 0, 0),\n",
-       " datetime.datetime(1976, 1, 10, 0, 0),\n",
-       " datetime.datetime(1976, 1, 17, 0, 0),\n",
-       " datetime.datetime(1976, 1, 24, 0, 0),\n",
-       " datetime.datetime(1976, 1, 31, 0, 0),\n",
-       " datetime.datetime(1976, 2, 7, 0, 0),\n",
-       " datetime.datetime(1976, 2, 14, 0, 0),\n",
-       " datetime.datetime(1976, 2, 21, 0, 0),\n",
-       " datetime.datetime(1976, 2, 28, 0, 0),\n",
-       " datetime.datetime(1976, 3, 6, 0, 0),\n",
-       " datetime.datetime(1976, 3, 13, 0, 0),\n",
-       " datetime.datetime(1976, 3, 20, 0, 0),\n",
-       " datetime.datetime(1976, 3, 27, 0, 0),\n",
-       " datetime.datetime(1976, 4, 3, 0, 0),\n",
-       " datetime.datetime(1976, 4, 10, 0, 0),\n",
-       " datetime.datetime(1976, 4, 17, 0, 0),\n",
-       " datetime.datetime(1976, 4, 24, 0, 0),\n",
-       " datetime.datetime(1976, 5, 1, 0, 0),\n",
-       " datetime.datetime(1976, 5, 8, 0, 0),\n",
-       " datetime.datetime(1976, 5, 15, 0, 0),\n",
-       " datetime.datetime(1976, 5, 22, 0, 0),\n",
-       " datetime.datetime(1976, 5, 29, 0, 0),\n",
-       " datetime.datetime(1976, 6, 5, 0, 0),\n",
-       " datetime.datetime(1976, 6, 12, 0, 0),\n",
-       " datetime.datetime(1976, 6, 19, 0, 0),\n",
-       " datetime.datetime(1976, 7, 3, 0, 0),\n",
-       " datetime.datetime(1976, 7, 10, 0, 0),\n",
-       " datetime.datetime(1976, 7, 17, 0, 0),\n",
-       " datetime.datetime(1976, 7, 24, 0, 0),\n",
-       " datetime.datetime(1976, 7, 31, 0, 0),\n",
-       " datetime.datetime(1976, 8, 7, 0, 0),\n",
-       " datetime.datetime(1976, 8, 14, 0, 0),\n",
-       " datetime.datetime(1976, 8, 21, 0, 0),\n",
-       " datetime.datetime(1976, 8, 28, 0, 0),\n",
-       " datetime.datetime(1976, 9, 4, 0, 0),\n",
-       " datetime.datetime(1976, 9, 11, 0, 0),\n",
-       " datetime.datetime(1976, 9, 18, 0, 0),\n",
-       " datetime.datetime(1976, 9, 25, 0, 0),\n",
-       " datetime.datetime(1976, 10, 2, 0, 0),\n",
-       " datetime.datetime(1976, 10, 9, 0, 0),\n",
-       " datetime.datetime(1976, 10, 16, 0, 0),\n",
-       " datetime.datetime(1976, 10, 23, 0, 0),\n",
-       " datetime.datetime(1976, 10, 30, 0, 0),\n",
-       " datetime.datetime(1976, 11, 6, 0, 0),\n",
-       " datetime.datetime(1976, 11, 13, 0, 0),\n",
-       " datetime.datetime(1976, 11, 20, 0, 0),\n",
-       " datetime.datetime(1976, 11, 27, 0, 0),\n",
-       " datetime.datetime(1976, 12, 4, 0, 0),\n",
-       " datetime.datetime(1976, 12, 11, 0, 0),\n",
-       " datetime.datetime(1976, 12, 18, 0, 0),\n",
-       " datetime.datetime(1976, 12, 25, 0, 0),\n",
-       " datetime.datetime(1977, 1, 1, 0, 0),\n",
-       " datetime.datetime(1977, 1, 8, 0, 0),\n",
-       " datetime.datetime(1977, 1, 15, 0, 0),\n",
-       " datetime.datetime(1977, 1, 22, 0, 0),\n",
-       " datetime.datetime(1977, 1, 29, 0, 0),\n",
-       " datetime.datetime(1977, 2, 5, 0, 0),\n",
-       " datetime.datetime(1977, 2, 12, 0, 0),\n",
-       " datetime.datetime(1977, 2, 19, 0, 0),\n",
-       " datetime.datetime(1977, 2, 26, 0, 0),\n",
-       " datetime.datetime(1977, 3, 5, 0, 0),\n",
-       " datetime.datetime(1977, 3, 12, 0, 0),\n",
-       " datetime.datetime(1977, 3, 19, 0, 0),\n",
-       " datetime.datetime(1977, 3, 26, 0, 0),\n",
-       " datetime.datetime(1977, 4, 2, 0, 0),\n",
-       " datetime.datetime(1977, 4, 9, 0, 0),\n",
-       " datetime.datetime(1977, 4, 16, 0, 0),\n",
-       " datetime.datetime(1977, 4, 23, 0, 0),\n",
-       " datetime.datetime(1977, 4, 30, 0, 0),\n",
-       " datetime.datetime(1977, 5, 7, 0, 0),\n",
-       " datetime.datetime(1977, 5, 14, 0, 0),\n",
-       " datetime.datetime(1977, 5, 21, 0, 0),\n",
-       " datetime.datetime(1977, 5, 28, 0, 0),\n",
-       " datetime.datetime(1977, 6, 4, 0, 0),\n",
-       " datetime.datetime(1977, 6, 11, 0, 0),\n",
-       " datetime.datetime(1977, 6, 18, 0, 0),\n",
-       " datetime.datetime(1977, 6, 25, 0, 0),\n",
-       " datetime.datetime(1977, 7, 2, 0, 0),\n",
-       " datetime.datetime(1977, 7, 9, 0, 0),\n",
-       " datetime.datetime(1977, 7, 16, 0, 0),\n",
-       " datetime.datetime(1977, 7, 23, 0, 0),\n",
-       " datetime.datetime(1977, 7, 30, 0, 0),\n",
-       " datetime.datetime(1977, 8, 6, 0, 0),\n",
-       " datetime.datetime(1977, 8, 13, 0, 0),\n",
-       " datetime.datetime(1977, 8, 20, 0, 0),\n",
-       " datetime.datetime(1977, 8, 27, 0, 0),\n",
-       " datetime.datetime(1977, 9, 3, 0, 0),\n",
-       " datetime.datetime(1977, 9, 10, 0, 0),\n",
-       " datetime.datetime(1977, 9, 17, 0, 0),\n",
-       " datetime.datetime(1977, 9, 24, 0, 0),\n",
-       " datetime.datetime(1977, 10, 1, 0, 0),\n",
-       " datetime.datetime(1977, 10, 8, 0, 0),\n",
-       " datetime.datetime(1977, 10, 15, 0, 0),\n",
-       " datetime.datetime(1977, 10, 22, 0, 0),\n",
-       " datetime.datetime(1977, 10, 29, 0, 0),\n",
-       " datetime.datetime(1977, 11, 5, 0, 0),\n",
-       " datetime.datetime(1977, 11, 12, 0, 0),\n",
-       " datetime.datetime(1977, 11, 19, 0, 0),\n",
-       " datetime.datetime(1977, 11, 26, 0, 0),\n",
-       " datetime.datetime(1977, 12, 3, 0, 0),\n",
-       " datetime.datetime(1977, 12, 10, 0, 0),\n",
-       " datetime.datetime(1977, 12, 17, 0, 0),\n",
-       " datetime.datetime(1977, 12, 24, 0, 0),\n",
-       " datetime.datetime(1977, 12, 31, 0, 0),\n",
-       " datetime.datetime(1978, 1, 7, 0, 0),\n",
-       " datetime.datetime(1978, 1, 14, 0, 0),\n",
-       " datetime.datetime(1978, 1, 21, 0, 0),\n",
-       " datetime.datetime(1978, 1, 28, 0, 0),\n",
-       " datetime.datetime(1978, 2, 4, 0, 0),\n",
-       " datetime.datetime(1978, 2, 11, 0, 0),\n",
-       " datetime.datetime(1978, 2, 18, 0, 0),\n",
-       " datetime.datetime(1978, 2, 25, 0, 0),\n",
-       " datetime.datetime(1978, 3, 4, 0, 0),\n",
-       " datetime.datetime(1978, 3, 11, 0, 0),\n",
-       " datetime.datetime(1978, 3, 18, 0, 0),\n",
-       " datetime.datetime(1978, 3, 25, 0, 0),\n",
-       " datetime.datetime(1978, 4, 1, 0, 0),\n",
-       " datetime.datetime(1978, 4, 8, 0, 0),\n",
-       " datetime.datetime(1978, 4, 15, 0, 0),\n",
-       " datetime.datetime(1978, 4, 22, 0, 0),\n",
-       " datetime.datetime(1978, 4, 29, 0, 0),\n",
-       " datetime.datetime(1978, 5, 6, 0, 0),\n",
-       " datetime.datetime(1978, 5, 13, 0, 0),\n",
-       " datetime.datetime(1978, 5, 20, 0, 0),\n",
-       " datetime.datetime(1978, 5, 27, 0, 0),\n",
-       " datetime.datetime(1978, 6, 3, 0, 0),\n",
-       " ...]"
+       "43     1959-05-23\n",
+       "44     1959-06-06\n",
+       "45     1959-06-13\n",
+       "46     1959-06-20\n",
+       "47     1959-06-27\n",
+       "48     1959-07-04\n",
+       "49     1959-07-11\n",
+       "50     1959-07-18\n",
+       "51     1959-07-25\n",
+       "52     1959-08-01\n",
+       "53     1959-08-08\n",
+       "54     1959-08-22\n",
+       "55     1959-08-29\n",
+       "56     1959-09-05\n",
+       "57     1959-09-12\n",
+       "58     1959-09-19\n",
+       "59     1959-09-26\n",
+       "60     1959-10-03\n",
+       "61     1959-10-10\n",
+       "62     1959-10-17\n",
+       "63     1959-10-24\n",
+       "64     1959-10-31\n",
+       "65     1959-11-07\n",
+       "66     1959-11-14\n",
+       "67     1959-11-21\n",
+       "68     1959-11-28\n",
+       "69     1959-12-05\n",
+       "70     1959-12-12\n",
+       "71     1959-12-19\n",
+       "72     1959-12-26\n",
+       "          ...    \n",
+       "3178   2020-07-11\n",
+       "3179   2020-07-18\n",
+       "3180   2020-07-25\n",
+       "3181   2020-08-01\n",
+       "3182   2020-08-08\n",
+       "3183   2020-08-15\n",
+       "3184   2020-08-22\n",
+       "3185   2020-08-29\n",
+       "3186   2020-09-05\n",
+       "3187   2020-09-12\n",
+       "3188   2020-09-19\n",
+       "3189   2020-09-26\n",
+       "3190   2020-10-03\n",
+       "3191   2020-10-10\n",
+       "3192   2020-10-17\n",
+       "3193   2020-10-24\n",
+       "3194   2020-10-31\n",
+       "3195   2020-11-07\n",
+       "3196   2020-11-14\n",
+       "3197   2020-11-21\n",
+       "3198   2020-11-28\n",
+       "3199   2020-12-05\n",
+       "3200   2020-12-12\n",
+       "3201   2020-12-19\n",
+       "3202   2020-12-26\n",
+       "3203   2021-01-02\n",
+       "3204   2021-01-09\n",
+       "3205   2021-01-16\n",
+       "3206   2021-01-23\n",
+       "3207   2021-01-30\n",
+       "Name: period, Length: 3165, dtype: datetime64[ns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1618,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1634,22 +697,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a46073780>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f44f80f0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1673,18 +738,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a47aa6400>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f23dc5c0>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1714,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -1722,7 +785,7 @@
     {
      "data": {
       "text/plain": [
-       "[315.94541666666674,\n",
+       "[315.3783333333333,\n",
        " 316.8988679245283,\n",
        " 317.6340384615384,\n",
        " 318.5977083333333,\n",
@@ -1783,10 +846,10 @@
        " 406.57615384615383,\n",
        " 408.5823076923076,\n",
        " 411.48865384615385,\n",
-       " 381.6068421052631]"
+       " 414.02490196078423]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1815,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1825,22 +888,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a4420def0>"
+       "<matplotlib.legend.Legend at 0x7f55f1f8c898>"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1852,8 +915,11 @@
     }
    ],
    "source": [
-    "sorted_data['CO2'].plot()\n",
-    "yearly_mean.plot(style=':r')"
+    "sorted_data['CO2'].plot(style='-c', label = 'Atmospheric CO2 concentration')\n",
+    "yearly_mean.plot(style=':r', markersize = 5, label = 'Slow evolution component')\n",
+    "plt.title('Evolution of Atmospheric CO2 concentration')\n",
+    "plt.ylabel('Atmospheric CO2 concentration (ppm)')\n",
+    "plt.legend()"
    ]
   },
   {
@@ -1873,37 +939,207 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Expected 2D array, got 1D array instead:\narray=[datetime.datetime(1959, 1, 1, 0, 0) datetime.datetime(1960, 1, 1, 0, 0)\n datetime.datetime(1961, 1, 1, 0, 0) datetime.datetime(1962, 1, 1, 0, 0)\n datetime.datetime(1963, 1, 1, 0, 0) datetime.datetime(1964, 1, 1, 0, 0)\n datetime.datetime(1965, 1, 1, 0, 0) datetime.datetime(1966, 1, 1, 0, 0)\n datetime.datetime(1967, 1, 1, 0, 0) datetime.datetime(1968, 1, 1, 0, 0)\n datetime.datetime(1969, 1, 1, 0, 0) datetime.datetime(1970, 1, 1, 0, 0)\n datetime.datetime(1971, 1, 1, 0, 0) datetime.datetime(1972, 1, 1, 0, 0)\n datetime.datetime(1973, 1, 1, 0, 0) datetime.datetime(1974, 1, 1, 0, 0)\n datetime.datetime(1975, 1, 1, 0, 0) datetime.datetime(1976, 1, 1, 0, 0)\n datetime.datetime(1977, 1, 1, 0, 0) datetime.datetime(1978, 1, 1, 0, 0)\n datetime.datetime(1979, 1, 1, 0, 0) datetime.datetime(1980, 1, 1, 0, 0)\n datetime.datetime(1981, 1, 1, 0, 0) datetime.datetime(1982, 1, 1, 0, 0)\n datetime.datetime(1983, 1, 1, 0, 0) datetime.datetime(1984, 1, 1, 0, 0)\n datetime.datetime(1985, 1, 1, 0, 0) datetime.datetime(1986, 1, 1, 0, 0)\n datetime.datetime(1987, 1, 1, 0, 0) datetime.datetime(1988, 1, 1, 0, 0)\n datetime.datetime(1989, 1, 1, 0, 0) datetime.datetime(1990, 1, 1, 0, 0)\n datetime.datetime(1991, 1, 1, 0, 0) datetime.datetime(1992, 1, 1, 0, 0)\n datetime.datetime(1993, 1, 1, 0, 0) datetime.datetime(1994, 1, 1, 0, 0)\n datetime.datetime(1995, 1, 1, 0, 0) datetime.datetime(1996, 1, 1, 0, 0)\n datetime.datetime(1997, 1, 1, 0, 0) datetime.datetime(1998, 1, 1, 0, 0)\n datetime.datetime(1999, 1, 1, 0, 0) datetime.datetime(2000, 1, 1, 0, 0)\n datetime.datetime(2001, 1, 1, 0, 0) datetime.datetime(2002, 1, 1, 0, 0)\n datetime.datetime(2003, 1, 1, 0, 0) datetime.datetime(2004, 1, 1, 0, 0)\n datetime.datetime(2005, 1, 1, 0, 0) datetime.datetime(2006, 1, 1, 0, 0)\n datetime.datetime(2007, 1, 1, 0, 0) datetime.datetime(2008, 1, 1, 0, 0)\n datetime.datetime(2009, 1, 1, 0, 0) datetime.datetime(2010, 1, 1, 0, 0)\n datetime.datetime(2011, 1, 1, 0, 0) datetime.datetime(2012, 1, 1, 0, 0)\n datetime.datetime(2013, 1, 1, 0, 0) datetime.datetime(2014, 1, 1, 0, 0)\n datetime.datetime(2015, 1, 1, 0, 0) datetime.datetime(2016, 1, 1, 0, 0)\n datetime.datetime(2017, 1, 1, 0, 0) datetime.datetime(2018, 1, 1, 0, 0)\n datetime.datetime(2019, 1, 1, 0, 0) datetime.datetime(2020, 1, 1, 0, 0)].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-95-f10dd4d5b43d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#créer y et X\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodeleReg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myearly_mean\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/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 482\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\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[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    439\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    442\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0;31m# To ensure that array flags are maintained\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[datetime.datetime(1959, 1, 1, 0, 0) datetime.datetime(1960, 1, 1, 0, 0)\n datetime.datetime(1961, 1, 1, 0, 0) datetime.datetime(1962, 1, 1, 0, 0)\n datetime.datetime(1963, 1, 1, 0, 0) datetime.datetime(1964, 1, 1, 0, 0)\n datetime.datetime(1965, 1, 1, 0, 0) datetime.datetime(1966, 1, 1, 0, 0)\n datetime.datetime(1967, 1, 1, 0, 0) datetime.datetime(1968, 1, 1, 0, 0)\n datetime.datetime(1969, 1, 1, 0, 0) datetime.datetime(1970, 1, 1, 0, 0)\n datetime.datetime(1971, 1, 1, 0, 0) datetime.datetime(1972, 1, 1, 0, 0)\n datetime.datetime(1973, 1, 1, 0, 0) datetime.datetime(1974, 1, 1, 0, 0)\n datetime.datetime(1975, 1, 1, 0, 0) datetime.datetime(1976, 1, 1, 0, 0)\n datetime.datetime(1977, 1, 1, 0, 0) datetime.datetime(1978, 1, 1, 0, 0)\n datetime.datetime(1979, 1, 1, 0, 0) datetime.datetime(1980, 1, 1, 0, 0)\n datetime.datetime(1981, 1, 1, 0, 0) datetime.datetime(1982, 1, 1, 0, 0)\n datetime.datetime(1983, 1, 1, 0, 0) datetime.datetime(1984, 1, 1, 0, 0)\n datetime.datetime(1985, 1, 1, 0, 0) datetime.datetime(1986, 1, 1, 0, 0)\n datetime.datetime(1987, 1, 1, 0, 0) datetime.datetime(1988, 1, 1, 0, 0)\n datetime.datetime(1989, 1, 1, 0, 0) datetime.datetime(1990, 1, 1, 0, 0)\n datetime.datetime(1991, 1, 1, 0, 0) datetime.datetime(1992, 1, 1, 0, 0)\n datetime.datetime(1993, 1, 1, 0, 0) datetime.datetime(1994, 1, 1, 0, 0)\n datetime.datetime(1995, 1, 1, 0, 0) datetime.datetime(1996, 1, 1, 0, 0)\n datetime.datetime(1997, 1, 1, 0, 0) datetime.datetime(1998, 1, 1, 0, 0)\n datetime.datetime(1999, 1, 1, 0, 0) datetime.datetime(2000, 1, 1, 0, 0)\n datetime.datetime(2001, 1, 1, 0, 0) datetime.datetime(2002, 1, 1, 0, 0)\n datetime.datetime(2003, 1, 1, 0, 0) datetime.datetime(2004, 1, 1, 0, 0)\n datetime.datetime(2005, 1, 1, 0, 0) datetime.datetime(2006, 1, 1, 0, 0)\n datetime.datetime(2007, 1, 1, 0, 0) datetime.datetime(2008, 1, 1, 0, 0)\n datetime.datetime(2009, 1, 1, 0, 0) datetime.datetime(2010, 1, 1, 0, 0)\n datetime.datetime(2011, 1, 1, 0, 0) datetime.datetime(2012, 1, 1, 0, 0)\n datetime.datetime(2013, 1, 1, 0, 0) datetime.datetime(2014, 1, 1, 0, 0)\n datetime.datetime(2015, 1, 1, 0, 0) datetime.datetime(2016, 1, 1, 0, 0)\n datetime.datetime(2017, 1, 1, 0, 0) datetime.datetime(2018, 1, 1, 0, 0)\n datetime.datetime(2019, 1, 1, 0, 0) datetime.datetime(2020, 1, 1, 0, 0)].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f55f090f710>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "## Essai par regression linéaire.\n",
     "modeleReg=LinearRegression()\n",
     "\n",
     "#créer y et X\n",
-    "modeleReg.fit(year, yearly_mean)"
+    "modeleReg.fit(np.array([int(d.year) for d in year]).reshape(-1,1), np.array(col_moy))\n",
+    "\n",
+    "coeff_linreg = modeleReg.coef_[0]\n",
+    "ordalo_linreg = modeleReg.intercept_\n",
+    "\n",
+    "yearly_linreg = pd.Series(data=[int(d.year)*coeff_linreg + ordalo_linreg for d in year], index = year)\n",
+    "\n",
+    "yearly_mean.plot(style=':r', markersize = 5, label = 'Slow evolution component')\n",
+    "plt.title('Evolution of Atmospheric CO2 concentration')\n",
+    "plt.ylabel('Atmospheric CO2 concentration (ppm)')\n",
+    "yearly_linreg.plot(style='-k', label='Linear regression')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visiblement, la regression utilisée n'est pas d'un niveau suffisant pour reproduire le comportement de la courbe. Essayons une régression polynomiale."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([415.24208637, 417.67065788, 420.12521463, 422.6057566 ,\n",
+       "       425.11228379])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Essai par regression polynômiale.\n",
+    "degree=2\n",
+    "polyreg=make_pipeline(PolynomialFeatures(degree),LinearRegression())\n",
+    "polyreg.fit(np.array([int(d.year) for d in year]).reshape(-1,1), np.array(col_moy))\n",
+    "\n",
+    "yearly_sqreg = pd.Series(polyreg.predict(np.array([int(d.year) for d in year]).reshape(-1,1)), index = year)\n",
+    "\n",
+    "yearly_mean.plot(style=':r', markersize = 5, label = 'Slow evolution component')\n",
+    "plt.title('Evolution of Atmospheric CO2 concentration')\n",
+    "plt.ylabel('Atmospheric CO2 concentration (ppm)')\n",
+    "yearly_sqreg.plot(style='-k', label='Polynomial regression, degree 2')\n",
+    "\n",
+    "plt.legend()\n",
+    "\n",
+    "polyreg.predict(np.array([x for x in range(2021,2026)]).reshape(-1,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ce modèle polynomial de degré deux semble convenir pour la prédiction de l'évolution lente du taxu de CO2 atmosphérique. Une extrapolation a été effectuée jusqu'à l'année 2025, et on prévoit 425.11 ppm de CO2 dans l'atmosphère cette année là."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Caractérisation de l'évolution périodique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f0b4e978>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['CO2'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.240000000000009\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Prenons les données pour l'année 2002\n",
+    "\n",
+    "fixed_year = datetime.strptime(str(2002), '%Y')\n",
+    "data_2002 = []\n",
+    "dates_2002 = []\n",
+    "for n in range(43,43+ len(raw_data)):\n",
+    "    rdstr = raw_data['date'][n]\n",
+    "    if '2002' in rdstr:\n",
+    "        data_2002.append(raw_data.CO2[n])\n",
+    "        dates_2002.append(rdstr)\n",
+    "\n",
+    "osci_2002 = data_2002 - yearly_mean['2002-01-01']\n",
+    "#dates_2002 = [datetime.strptime(x, '%Y-%M-%d')for x in dates_2002]\n",
+    "\n",
+    "formated_2002 = pd.Series(np.array(osci_2002), index = dates_2002)\n",
+    "\n",
+    "formated_2002.plot()\n",
+    "plt.title(\"Oscillation annuelle\")\n",
+    "plt.ylabel(\"Variation de la concentration en CO2\\n par rapport à la moyenne annuelle\")\n",
+    "\n",
+    "ampli = np.max(np.array(osci_2002))-np.min(np.array(osci_2002))\n",
+    "print(ampli)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On remarque que les oscillations ont une périodicité annuelle. L'oscillation est sinusoidale de période T=1an, et son amplitude est d'environ 6.24 ppm sur l'année 2002."
+   ]
   }
  ],
  "metadata": {