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Commit d8804c4d authored by 93bddc7315f700347e10fb4afd2d4053's avatar 93bddc7315f700347e10fb4afd2d4053
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correction de dates mais toujours des pb de légende

parent 875e1c14
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Showing with 1145 additions and 133 deletions
module3/exo3/exercice.ipynb
View file @ d8804c4d
......@@ -41,9 +41,976 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Yr</th>\n",
" <th>Mn</th>\n",
" <th>Date</th>\n",
" <th>Date.1</th>\n",
" <th>CO2</th>\n",
" <th>seasonally</th>\n",
" <th>fit</th>\n",
" <th>seasonally.1</th>\n",
" <th>CO2.1</th>\n",
" <th>seasonally.2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>adjusted</td>\n",
" <td>NaN</td>\n",
" <td>adjusted fit</td>\n",
" <td>filled</td>\n",
" <td>adjusted filled</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Excel</td>\n",
" <td>NaN</td>\n",
" <td>[ppm]</td>\n",
" <td>[ppm]</td>\n",
" <td>[ppm]</td>\n",
" <td>[ppm]</td>\n",
" <td>[ppm]</td>\n",
" <td>[ppm]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958.0</td>\n",
" <td>1.0</td>\n",
" <td>21200</td>\n",
" <td>1958.0411</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1958.0</td>\n",
" <td>2.0</td>\n",
" <td>21231</td>\n",
" <td>1958.1260</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1958.0</td>\n",
" <td>3.0</td>\n",
" <td>21259</td>\n",
" <td>1958.2027</td>\n",
" <td>315.70</td>\n",
" <td>314.44</td>\n",
" <td>316.18</td>\n",
" <td>314.90</td>\n",
" <td>315.70</td>\n",
" <td>314.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1958.0</td>\n",
" <td>4.0</td>\n",
" <td>21290</td>\n",
" <td>1958.2877</td>\n",
" <td>317.46</td>\n",
" <td>315.16</td>\n",
" <td>317.29</td>\n",
" <td>314.98</td>\n",
" <td>317.46</td>\n",
" <td>315.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1958.0</td>\n",
" <td>5.0</td>\n",
" <td>21320</td>\n",
" <td>1958.3699</td>\n",
" <td>317.51</td>\n",
" <td>314.71</td>\n",
" <td>317.86</td>\n",
" <td>315.06</td>\n",
" <td>317.51</td>\n",
" <td>314.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1958.0</td>\n",
" <td>6.0</td>\n",
" <td>21351</td>\n",
" <td>1958.4548</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>317.24</td>\n",
" <td>315.14</td>\n",
" <td>317.24</td>\n",
" <td>315.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1958.0</td>\n",
" <td>7.0</td>\n",
" <td>21381</td>\n",
" <td>1958.5370</td>\n",
" <td>315.86</td>\n",
" <td>315.19</td>\n",
" <td>315.86</td>\n",
" <td>315.21</td>\n",
" <td>315.86</td>\n",
" <td>315.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1958.0</td>\n",
" <td>8.0</td>\n",
" <td>21412</td>\n",
" <td>1958.6219</td>\n",
" <td>314.93</td>\n",
" <td>316.19</td>\n",
" <td>313.99</td>\n",
" <td>315.28</td>\n",
" <td>314.93</td>\n",
" <td>316.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1958.0</td>\n",
" <td>9.0</td>\n",
" <td>21443</td>\n",
" <td>1958.7068</td>\n",
" <td>313.21</td>\n",
" <td>316.08</td>\n",
" <td>312.45</td>\n",
" <td>315.35</td>\n",
" <td>313.21</td>\n",
" <td>316.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1958.0</td>\n",
" <td>10.0</td>\n",
" <td>21473</td>\n",
" <td>1958.7890</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>312.43</td>\n",
" <td>315.40</td>\n",
" <td>312.43</td>\n",
" <td>315.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1958.0</td>\n",
" <td>11.0</td>\n",
" <td>21504</td>\n",
" <td>1958.8740</td>\n",
" <td>313.33</td>\n",
" <td>315.20</td>\n",
" <td>313.61</td>\n",
" <td>315.46</td>\n",
" <td>313.33</td>\n",
" <td>315.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1958.0</td>\n",
" <td>12.0</td>\n",
" <td>21534</td>\n",
" <td>1958.9562</td>\n",
" <td>314.67</td>\n",
" <td>315.43</td>\n",
" <td>314.76</td>\n",
" <td>315.51</td>\n",
" <td>314.67</td>\n",
" <td>315.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1959.0</td>\n",
" <td>1.0</td>\n",
" <td>21565</td>\n",
" <td>1959.0411</td>\n",
" <td>315.58</td>\n",
" <td>315.54</td>\n",
" <td>315.62</td>\n",
" <td>315.57</td>\n",
" <td>315.58</td>\n",
" <td>315.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1959.0</td>\n",
" <td>2.0</td>\n",
" <td>21596</td>\n",
" <td>1959.1260</td>\n",
" <td>316.49</td>\n",
" <td>315.86</td>\n",
" <td>316.27</td>\n",
" <td>315.63</td>\n",
" <td>316.49</td>\n",
" <td>315.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1959.0</td>\n",
" <td>3.0</td>\n",
" <td>21624</td>\n",
" <td>1959.2027</td>\n",
" <td>316.65</td>\n",
" <td>315.38</td>\n",
" <td>316.98</td>\n",
" <td>315.69</td>\n",
" <td>316.65</td>\n",
" <td>315.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1959.0</td>\n",
" <td>4.0</td>\n",
" <td>21655</td>\n",
" <td>1959.2877</td>\n",
" <td>317.72</td>\n",
" <td>315.42</td>\n",
" <td>318.09</td>\n",
" <td>315.77</td>\n",
" <td>317.72</td>\n",
" <td>315.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1959.0</td>\n",
" <td>5.0</td>\n",
" <td>21685</td>\n",
" <td>1959.3699</td>\n",
" <td>318.29</td>\n",
" <td>315.49</td>\n",
" <td>318.65</td>\n",
" <td>315.85</td>\n",
" <td>318.29</td>\n",
" <td>315.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1959.0</td>\n",
" <td>6.0</td>\n",
" <td>21716</td>\n",
" <td>1959.4548</td>\n",
" <td>318.15</td>\n",
" <td>316.03</td>\n",
" <td>318.04</td>\n",
" <td>315.94</td>\n",
" <td>318.15</td>\n",
" <td>316.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1959.0</td>\n",
" <td>7.0</td>\n",
" <td>21746</td>\n",
" <td>1959.5370</td>\n",
" <td>316.54</td>\n",
" <td>315.86</td>\n",
" <td>316.67</td>\n",
" <td>316.03</td>\n",
" <td>316.54</td>\n",
" <td>315.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1959.0</td>\n",
" <td>8.0</td>\n",
" <td>21777</td>\n",
" <td>1959.6219</td>\n",
" <td>314.80</td>\n",
" <td>316.06</td>\n",
" <td>314.82</td>\n",
" <td>316.12</td>\n",
" <td>314.80</td>\n",
" <td>316.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1959.0</td>\n",
" <td>9.0</td>\n",
" <td>21808</td>\n",
" <td>1959.7068</td>\n",
" <td>313.84</td>\n",
" <td>316.73</td>\n",
" <td>313.31</td>\n",
" <td>316.22</td>\n",
" <td>313.84</td>\n",
" <td>316.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1959.0</td>\n",
" <td>10.0</td>\n",
" <td>21838</td>\n",
" <td>1959.7890</td>\n",
" <td>313.33</td>\n",
" <td>316.33</td>\n",
" <td>313.32</td>\n",
" <td>316.30</td>\n",
" <td>313.33</td>\n",
" <td>316.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1959.0</td>\n",
" <td>11.0</td>\n",
" <td>21869</td>\n",
" <td>1959.8740</td>\n",
" <td>314.81</td>\n",
" <td>316.68</td>\n",
" <td>314.54</td>\n",
" <td>316.39</td>\n",
" <td>314.81</td>\n",
" <td>316.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1959.0</td>\n",
" <td>12.0</td>\n",
" <td>21899</td>\n",
" <td>1959.9562</td>\n",
" <td>315.58</td>\n",
" <td>316.35</td>\n",
" <td>315.72</td>\n",
" <td>316.47</td>\n",
" <td>315.58</td>\n",
" <td>316.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1960.0</td>\n",
" <td>1.0</td>\n",
" <td>21930</td>\n",
" <td>1960.0410</td>\n",
" <td>316.43</td>\n",
" <td>316.39</td>\n",
" <td>316.61</td>\n",
" <td>316.56</td>\n",
" <td>316.43</td>\n",
" <td>316.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1960.0</td>\n",
" <td>2.0</td>\n",
" <td>21961</td>\n",
" <td>1960.1257</td>\n",
" <td>316.98</td>\n",
" <td>316.35</td>\n",
" <td>317.27</td>\n",
" <td>316.64</td>\n",
" <td>316.98</td>\n",
" <td>316.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1960.0</td>\n",
" <td>3.0</td>\n",
" <td>21990</td>\n",
" <td>1960.2049</td>\n",
" <td>317.58</td>\n",
" <td>316.28</td>\n",
" <td>318.03</td>\n",
" <td>316.71</td>\n",
" <td>317.58</td>\n",
" <td>316.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1960.0</td>\n",
" <td>4.0</td>\n",
" <td>22021</td>\n",
" <td>1960.2896</td>\n",
" <td>319.03</td>\n",
" <td>316.70</td>\n",
" <td>319.14</td>\n",
" <td>316.79</td>\n",
" <td>319.03</td>\n",
" <td>316.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>728</th>\n",
" <td>2018.0</td>\n",
" <td>7.0</td>\n",
" <td>43296</td>\n",
" <td>2018.5370</td>\n",
" <td>408.90</td>\n",
" <td>408.08</td>\n",
" <td>409.44</td>\n",
" <td>408.65</td>\n",
" <td>408.90</td>\n",
" <td>408.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>729</th>\n",
" <td>2018.0</td>\n",
" <td>8.0</td>\n",
" <td>43327</td>\n",
" <td>2018.6219</td>\n",
" <td>407.10</td>\n",
" <td>408.63</td>\n",
" <td>407.34</td>\n",
" <td>408.91</td>\n",
" <td>407.10</td>\n",
" <td>408.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>730</th>\n",
" <td>2018.0</td>\n",
" <td>9.0</td>\n",
" <td>43358</td>\n",
" <td>2018.7068</td>\n",
" <td>405.59</td>\n",
" <td>409.08</td>\n",
" <td>405.67</td>\n",
" <td>409.19</td>\n",
" <td>405.59</td>\n",
" <td>409.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>731</th>\n",
" <td>2018.0</td>\n",
" <td>10.0</td>\n",
" <td>43388</td>\n",
" <td>2018.7890</td>\n",
" <td>405.99</td>\n",
" <td>409.61</td>\n",
" <td>405.85</td>\n",
" <td>409.45</td>\n",
" <td>405.99</td>\n",
" <td>409.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732</th>\n",
" <td>2018.0</td>\n",
" <td>11.0</td>\n",
" <td>43419</td>\n",
" <td>2018.8740</td>\n",
" <td>408.12</td>\n",
" <td>410.38</td>\n",
" <td>407.49</td>\n",
" <td>409.73</td>\n",
" <td>408.12</td>\n",
" <td>410.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>733</th>\n",
" <td>2018.0</td>\n",
" <td>12.0</td>\n",
" <td>43449</td>\n",
" <td>2018.9562</td>\n",
" <td>409.23</td>\n",
" <td>410.15</td>\n",
" <td>409.08</td>\n",
" <td>409.99</td>\n",
" <td>409.23</td>\n",
" <td>410.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>734</th>\n",
" <td>2019.0</td>\n",
" <td>1.0</td>\n",
" <td>43480</td>\n",
" <td>2019.0411</td>\n",
" <td>410.92</td>\n",
" <td>410.87</td>\n",
" <td>410.31</td>\n",
" <td>410.25</td>\n",
" <td>410.92</td>\n",
" <td>410.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>735</th>\n",
" <td>2019.0</td>\n",
" <td>2.0</td>\n",
" <td>43511</td>\n",
" <td>2019.1260</td>\n",
" <td>411.66</td>\n",
" <td>410.90</td>\n",
" <td>411.26</td>\n",
" <td>410.49</td>\n",
" <td>411.66</td>\n",
" <td>410.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>736</th>\n",
" <td>2019.0</td>\n",
" <td>3.0</td>\n",
" <td>43539</td>\n",
" <td>2019.2027</td>\n",
" <td>412.00</td>\n",
" <td>410.46</td>\n",
" <td>412.26</td>\n",
" <td>410.70</td>\n",
" <td>412.00</td>\n",
" <td>410.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>737</th>\n",
" <td>2019.0</td>\n",
" <td>4.0</td>\n",
" <td>43570</td>\n",
" <td>2019.2877</td>\n",
" <td>413.52</td>\n",
" <td>410.72</td>\n",
" <td>413.75</td>\n",
" <td>410.93</td>\n",
" <td>413.52</td>\n",
" <td>410.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>738</th>\n",
" <td>2019.0</td>\n",
" <td>5.0</td>\n",
" <td>43600</td>\n",
" <td>2019.3699</td>\n",
" <td>414.83</td>\n",
" <td>411.42</td>\n",
" <td>414.55</td>\n",
" <td>411.15</td>\n",
" <td>414.83</td>\n",
" <td>411.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>739</th>\n",
" <td>2019.0</td>\n",
" <td>6.0</td>\n",
" <td>43631</td>\n",
" <td>2019.4548</td>\n",
" <td>413.96</td>\n",
" <td>411.38</td>\n",
" <td>413.92</td>\n",
" <td>411.37</td>\n",
" <td>413.96</td>\n",
" <td>411.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>740</th>\n",
" <td>2019.0</td>\n",
" <td>7.0</td>\n",
" <td>43661</td>\n",
" <td>2019.5370</td>\n",
" <td>411.85</td>\n",
" <td>411.03</td>\n",
" <td>412.37</td>\n",
" <td>411.58</td>\n",
" <td>411.85</td>\n",
" <td>411.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>741</th>\n",
" <td>2019.0</td>\n",
" <td>8.0</td>\n",
" <td>43692</td>\n",
" <td>2019.6219</td>\n",
" <td>410.08</td>\n",
" <td>411.62</td>\n",
" <td>410.23</td>\n",
" <td>411.80</td>\n",
" <td>410.08</td>\n",
" <td>411.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>742</th>\n",
" <td>2019.0</td>\n",
" <td>9.0</td>\n",
" <td>43723</td>\n",
" <td>2019.7068</td>\n",
" <td>408.55</td>\n",
" <td>412.06</td>\n",
" <td>408.50</td>\n",
" <td>412.03</td>\n",
" <td>408.55</td>\n",
" <td>412.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>743</th>\n",
" <td>2019.0</td>\n",
" <td>10.0</td>\n",
" <td>43753</td>\n",
" <td>2019.7890</td>\n",
" <td>408.43</td>\n",
" <td>412.06</td>\n",
" <td>408.63</td>\n",
" <td>412.24</td>\n",
" <td>408.43</td>\n",
" <td>412.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>744</th>\n",
" <td>2019.0</td>\n",
" <td>11.0</td>\n",
" <td>43784</td>\n",
" <td>2019.8740</td>\n",
" <td>410.29</td>\n",
" <td>412.56</td>\n",
" <td>410.22</td>\n",
" <td>412.47</td>\n",
" <td>410.29</td>\n",
" <td>412.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>745</th>\n",
" <td>2019.0</td>\n",
" <td>12.0</td>\n",
" <td>43814</td>\n",
" <td>2019.9562</td>\n",
" <td>411.85</td>\n",
" <td>412.78</td>\n",
" <td>411.77</td>\n",
" <td>412.68</td>\n",
" <td>411.85</td>\n",
" <td>412.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>746</th>\n",
" <td>2020.0</td>\n",
" <td>1.0</td>\n",
" <td>43845</td>\n",
" <td>2020.0410</td>\n",
" <td>413.37</td>\n",
" <td>413.32</td>\n",
" <td>412.96</td>\n",
" <td>412.89</td>\n",
" <td>413.37</td>\n",
" <td>413.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>747</th>\n",
" <td>2020.0</td>\n",
" <td>2.0</td>\n",
" <td>43876</td>\n",
" <td>2020.1257</td>\n",
" <td>414.09</td>\n",
" <td>413.33</td>\n",
" <td>413.87</td>\n",
" <td>413.10</td>\n",
" <td>414.09</td>\n",
" <td>413.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>748</th>\n",
" <td>2020.0</td>\n",
" <td>3.0</td>\n",
" <td>43905</td>\n",
" <td>2020.2049</td>\n",
" <td>414.51</td>\n",
" <td>412.94</td>\n",
" <td>414.88</td>\n",
" <td>413.29</td>\n",
" <td>414.51</td>\n",
" <td>412.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749</th>\n",
" <td>2020.0</td>\n",
" <td>4.0</td>\n",
" <td>43936</td>\n",
" <td>2020.2896</td>\n",
" <td>416.18</td>\n",
" <td>413.35</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>416.18</td>\n",
" <td>413.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>750</th>\n",
" <td>2020.0</td>\n",
" <td>5.0</td>\n",
" <td>43966</td>\n",
" <td>2020.3716</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751</th>\n",
" <td>2020.0</td>\n",
" <td>6.0</td>\n",
" <td>43997</td>\n",
" <td>2020.4563</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>752</th>\n",
" <td>2020.0</td>\n",
" <td>7.0</td>\n",
" <td>44027</td>\n",
" <td>2020.5383</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>753</th>\n",
" <td>2020.0</td>\n",
" <td>8.0</td>\n",
" <td>44058</td>\n",
" <td>2020.6230</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754</th>\n",
" <td>2020.0</td>\n",
" <td>9.0</td>\n",
" <td>44089</td>\n",
" <td>2020.7077</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755</th>\n",
" <td>2020.0</td>\n",
" <td>10.0</td>\n",
" <td>44119</td>\n",
" <td>2020.7896</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>756</th>\n",
" <td>2020.0</td>\n",
" <td>11.0</td>\n",
" <td>44150</td>\n",
" <td>2020.8743</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>757</th>\n",
" <td>2020.0</td>\n",
" <td>12.0</td>\n",
" <td>44180</td>\n",
" <td>2020.9563</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>758 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Yr Mn Date Date.1 CO2 seasonally fit seasonally.1 \\\n",
"0 NaN NaN NaN NaN NaN adjusted NaN adjusted fit \n",
"1 NaN NaN Excel NaN [ppm] [ppm] [ppm] [ppm] \n",
"2 1958.0 1.0 21200 1958.0411 -99.99 -99.99 -99.99 -99.99 \n",
"3 1958.0 2.0 21231 1958.1260 -99.99 -99.99 -99.99 -99.99 \n",
"4 1958.0 3.0 21259 1958.2027 315.70 314.44 316.18 314.90 \n",
"5 1958.0 4.0 21290 1958.2877 317.46 315.16 317.29 314.98 \n",
"6 1958.0 5.0 21320 1958.3699 317.51 314.71 317.86 315.06 \n",
"7 1958.0 6.0 21351 1958.4548 -99.99 -99.99 317.24 315.14 \n",
"8 1958.0 7.0 21381 1958.5370 315.86 315.19 315.86 315.21 \n",
"9 1958.0 8.0 21412 1958.6219 314.93 316.19 313.99 315.28 \n",
"10 1958.0 9.0 21443 1958.7068 313.21 316.08 312.45 315.35 \n",
"11 1958.0 10.0 21473 1958.7890 -99.99 -99.99 312.43 315.40 \n",
"12 1958.0 11.0 21504 1958.8740 313.33 315.20 313.61 315.46 \n",
"13 1958.0 12.0 21534 1958.9562 314.67 315.43 314.76 315.51 \n",
"14 1959.0 1.0 21565 1959.0411 315.58 315.54 315.62 315.57 \n",
"15 1959.0 2.0 21596 1959.1260 316.49 315.86 316.27 315.63 \n",
"16 1959.0 3.0 21624 1959.2027 316.65 315.38 316.98 315.69 \n",
"17 1959.0 4.0 21655 1959.2877 317.72 315.42 318.09 315.77 \n",
"18 1959.0 5.0 21685 1959.3699 318.29 315.49 318.65 315.85 \n",
"19 1959.0 6.0 21716 1959.4548 318.15 316.03 318.04 315.94 \n",
"20 1959.0 7.0 21746 1959.5370 316.54 315.86 316.67 316.03 \n",
"21 1959.0 8.0 21777 1959.6219 314.80 316.06 314.82 316.12 \n",
"22 1959.0 9.0 21808 1959.7068 313.84 316.73 313.31 316.22 \n",
"23 1959.0 10.0 21838 1959.7890 313.33 316.33 313.32 316.30 \n",
"24 1959.0 11.0 21869 1959.8740 314.81 316.68 314.54 316.39 \n",
"25 1959.0 12.0 21899 1959.9562 315.58 316.35 315.72 316.47 \n",
"26 1960.0 1.0 21930 1960.0410 316.43 316.39 316.61 316.56 \n",
"27 1960.0 2.0 21961 1960.1257 316.98 316.35 317.27 316.64 \n",
"28 1960.0 3.0 21990 1960.2049 317.58 316.28 318.03 316.71 \n",
"29 1960.0 4.0 22021 1960.2896 319.03 316.70 319.14 316.79 \n",
".. ... ... ... ... ... ... ... ... \n",
"728 2018.0 7.0 43296 2018.5370 408.90 408.08 409.44 408.65 \n",
"729 2018.0 8.0 43327 2018.6219 407.10 408.63 407.34 408.91 \n",
"730 2018.0 9.0 43358 2018.7068 405.59 409.08 405.67 409.19 \n",
"731 2018.0 10.0 43388 2018.7890 405.99 409.61 405.85 409.45 \n",
"732 2018.0 11.0 43419 2018.8740 408.12 410.38 407.49 409.73 \n",
"733 2018.0 12.0 43449 2018.9562 409.23 410.15 409.08 409.99 \n",
"734 2019.0 1.0 43480 2019.0411 410.92 410.87 410.31 410.25 \n",
"735 2019.0 2.0 43511 2019.1260 411.66 410.90 411.26 410.49 \n",
"736 2019.0 3.0 43539 2019.2027 412.00 410.46 412.26 410.70 \n",
"737 2019.0 4.0 43570 2019.2877 413.52 410.72 413.75 410.93 \n",
"738 2019.0 5.0 43600 2019.3699 414.83 411.42 414.55 411.15 \n",
"739 2019.0 6.0 43631 2019.4548 413.96 411.38 413.92 411.37 \n",
"740 2019.0 7.0 43661 2019.5370 411.85 411.03 412.37 411.58 \n",
"741 2019.0 8.0 43692 2019.6219 410.08 411.62 410.23 411.80 \n",
"742 2019.0 9.0 43723 2019.7068 408.55 412.06 408.50 412.03 \n",
"743 2019.0 10.0 43753 2019.7890 408.43 412.06 408.63 412.24 \n",
"744 2019.0 11.0 43784 2019.8740 410.29 412.56 410.22 412.47 \n",
"745 2019.0 12.0 43814 2019.9562 411.85 412.78 411.77 412.68 \n",
"746 2020.0 1.0 43845 2020.0410 413.37 413.32 412.96 412.89 \n",
"747 2020.0 2.0 43876 2020.1257 414.09 413.33 413.87 413.10 \n",
"748 2020.0 3.0 43905 2020.2049 414.51 412.94 414.88 413.29 \n",
"749 2020.0 4.0 43936 2020.2896 416.18 413.35 -99.99 -99.99 \n",
"750 2020.0 5.0 43966 2020.3716 -99.99 -99.99 -99.99 -99.99 \n",
"751 2020.0 6.0 43997 2020.4563 -99.99 -99.99 -99.99 -99.99 \n",
"752 2020.0 7.0 44027 2020.5383 -99.99 -99.99 -99.99 -99.99 \n",
"753 2020.0 8.0 44058 2020.6230 -99.99 -99.99 -99.99 -99.99 \n",
"754 2020.0 9.0 44089 2020.7077 -99.99 -99.99 -99.99 -99.99 \n",
"755 2020.0 10.0 44119 2020.7896 -99.99 -99.99 -99.99 -99.99 \n",
"756 2020.0 11.0 44150 2020.8743 -99.99 -99.99 -99.99 -99.99 \n",
"757 2020.0 12.0 44180 2020.9563 -99.99 -99.99 -99.99 -99.99 \n",
"\n",
" CO2.1 seasonally.2 \n",
"0 filled adjusted filled \n",
"1 [ppm] [ppm] \n",
"2 -99.99 -99.99 \n",
"3 -99.99 -99.99 \n",
"4 315.70 314.44 \n",
"5 317.46 315.16 \n",
"6 317.51 314.71 \n",
"7 317.24 315.14 \n",
"8 315.86 315.19 \n",
"9 314.93 316.19 \n",
"10 313.21 316.08 \n",
"11 312.43 315.40 \n",
"12 313.33 315.20 \n",
"13 314.67 315.43 \n",
"14 315.58 315.54 \n",
"15 316.49 315.86 \n",
"16 316.65 315.38 \n",
"17 317.72 315.42 \n",
"18 318.29 315.49 \n",
"19 318.15 316.03 \n",
"20 316.54 315.86 \n",
"21 314.80 316.06 \n",
"22 313.84 316.73 \n",
"23 313.33 316.33 \n",
"24 314.81 316.68 \n",
"25 315.58 316.35 \n",
"26 316.43 316.39 \n",
"27 316.98 316.35 \n",
"28 317.58 316.28 \n",
"29 319.03 316.70 \n",
".. ... ... \n",
"728 408.90 408.08 \n",
"729 407.10 408.63 \n",
"730 405.59 409.08 \n",
"731 405.99 409.61 \n",
"732 408.12 410.38 \n",
"733 409.23 410.15 \n",
"734 410.92 410.87 \n",
"735 411.66 410.90 \n",
"736 412.00 410.46 \n",
"737 413.52 410.72 \n",
"738 414.83 411.42 \n",
"739 413.96 411.38 \n",
"740 411.85 411.03 \n",
"741 410.08 411.62 \n",
"742 408.55 412.06 \n",
"743 408.43 412.06 \n",
"744 410.29 412.56 \n",
"745 411.85 412.78 \n",
"746 413.37 413.32 \n",
"747 414.09 413.33 \n",
"748 414.51 412.94 \n",
"749 416.18 413.35 \n",
"750 -99.99 -99.99 \n",
"751 -99.99 -99.99 \n",
"752 -99.99 -99.99 \n",
"753 -99.99 -99.99 \n",
"754 -99.99 -99.99 \n",
"755 -99.99 -99.99 \n",
"756 -99.99 -99.99 \n",
"757 -99.99 -99.99 \n",
"\n",
"[758 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#raw_data"
"raw_data"
]
},
{
......@@ -66,7 +1033,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour ce jeu de données, les 4 premières colonnes sont des dates, et seule la colonne 5 contient des mesures brutes. Nous allons conserver uniquement les informations sur l'année, la date, et la valeur brute de la mesure."
"Pour ce jeu de données, les 4 premières colonnes sont des dates, et seule la colonne 5 contient des mesures brutes. Nous allons conserver uniquement les informations sur l'année, le mois, et la valeur brute de la mesure."
]
},
{
......@@ -75,7 +1042,7 @@
"metadata": {},
"outputs": [],
"source": [
"useful_data = data.iloc[0:758, [0,1,4]]\n",
"useful_data = data.iloc[0:len(data.index), [0,1,4]]\n",
"#useful_data"
]
},
......@@ -634,6 +1601,13 @@
"useful_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On souhaite maintenant convertir l'année et le mois en un format plus adapté à Pandas, et à l'utiliser comme index. Un méthode possible est présentée ici, en rassemblant les deux informations puis en appliquant une fonction pour une mise au format Pandas."
]
},
{
"cell_type": "code",
"execution_count": 10,
......@@ -696,123 +1670,123 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1958-01-13/1958-01-19</th>\n",
" <th>1958-03</th>\n",
" <td>315.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-01-20/1958-01-26</th>\n",
" <th>1958-04</th>\n",
" <td>317.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-01-27/1958-02-02</th>\n",
" <th>1958-05</th>\n",
" <td>317.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-02-10/1958-02-16</th>\n",
" <th>1958-07</th>\n",
" <td>315.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-02-17/1958-02-23</th>\n",
" <th>1958-08</th>\n",
" <td>314.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-02-24/1958-03-02</th>\n",
" <th>1958-09</th>\n",
" <td>313.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-03-10/1958-03-16</th>\n",
" <th>1958-11</th>\n",
" <td>313.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-03-17/1958-03-23</th>\n",
" <th>1958-12</th>\n",
" <td>314.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958-12-29/1959-01-04</th>\n",
" <th>1959-01</th>\n",
" <td>315.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-01-05/1959-01-11</th>\n",
" <th>1959-02</th>\n",
" <td>316.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-01-12/1959-01-18</th>\n",
" <th>1959-03</th>\n",
" <td>316.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-01-19/1959-01-25</th>\n",
" <th>1959-04</th>\n",
" <td>317.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-01-26/1959-02-01</th>\n",
" <th>1959-05</th>\n",
" <td>318.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-02-02/1959-02-08</th>\n",
" <th>1959-06</th>\n",
" <td>318.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-02-09/1959-02-15</th>\n",
" <th>1959-07</th>\n",
" <td>316.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-02-16/1959-02-22</th>\n",
" <th>1959-08</th>\n",
" <td>314.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-02-23/1959-03-01</th>\n",
" <th>1959-09</th>\n",
" <td>313.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-03-02/1959-03-08</th>\n",
" <th>1959-10</th>\n",
" <td>313.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-03-09/1959-03-15</th>\n",
" <th>1959-11</th>\n",
" <td>314.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959-03-16/1959-03-22</th>\n",
" <th>1959-12</th>\n",
" <td>315.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-01-04/1960-01-10</th>\n",
" <th>1960-01</th>\n",
" <td>316.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-01-11/1960-01-17</th>\n",
" <th>1960-02</th>\n",
" <td>316.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-01-18/1960-01-24</th>\n",
" <th>1960-03</th>\n",
" <td>317.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-01-25/1960-01-31</th>\n",
" <th>1960-04</th>\n",
" <td>319.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02-01/1960-02-07</th>\n",
" <th>1960-05</th>\n",
" <td>320.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02-08/1960-02-14</th>\n",
" <th>1960-06</th>\n",
" <td>319.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02-15/1960-02-21</th>\n",
" <th>1960-07</th>\n",
" <td>318.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02-22/1960-02-28</th>\n",
" <th>1960-08</th>\n",
" <td>315.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02-29/1960-03-06</th>\n",
" <th>1960-09</th>\n",
" <td>314.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-03-07/1960-03-13</th>\n",
" <th>1960-10</th>\n",
" <td>313.83</td>\n",
" </tr>\n",
" <tr>\n",
......@@ -820,123 +1794,123 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-13/2017-03-19</th>\n",
" <th>2017-11</th>\n",
" <td>405.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-20/2017-03-26</th>\n",
" <th>2017-12</th>\n",
" <td>406.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01/2018-01-07</th>\n",
" <th>2018-01</th>\n",
" <td>408.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-08/2018-01-14</th>\n",
" <th>2018-02</th>\n",
" <td>408.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-15/2018-01-21</th>\n",
" <th>2018-03</th>\n",
" <td>409.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-22/2018-01-28</th>\n",
" <th>2018-04</th>\n",
" <td>410.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-29/2018-02-04</th>\n",
" <th>2018-05</th>\n",
" <td>411.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-05/2018-02-11</th>\n",
" <th>2018-06</th>\n",
" <td>410.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-12/2018-02-18</th>\n",
" <th>2018-07</th>\n",
" <td>408.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-19/2018-02-25</th>\n",
" <th>2018-08</th>\n",
" <td>407.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-26/2018-03-04</th>\n",
" <th>2018-09</th>\n",
" <td>405.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-05/2018-03-11</th>\n",
" <th>2018-10</th>\n",
" <td>405.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-12/2018-03-18</th>\n",
" <th>2018-11</th>\n",
" <td>408.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-19/2018-03-25</th>\n",
" <th>2018-12</th>\n",
" <td>409.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-31/2019-01-06</th>\n",
" <th>2019-01</th>\n",
" <td>410.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-07/2019-01-13</th>\n",
" <th>2019-02</th>\n",
" <td>411.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-14/2019-01-20</th>\n",
" <th>2019-03</th>\n",
" <td>412.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-21/2019-01-27</th>\n",
" <th>2019-04</th>\n",
" <td>413.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-28/2019-02-03</th>\n",
" <th>2019-05</th>\n",
" <td>414.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-02-04/2019-02-10</th>\n",
" <th>2019-06</th>\n",
" <td>413.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-02-11/2019-02-17</th>\n",
" <th>2019-07</th>\n",
" <td>411.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-02-18/2019-02-24</th>\n",
" <th>2019-08</th>\n",
" <td>410.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-02-25/2019-03-03</th>\n",
" <th>2019-09</th>\n",
" <td>408.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-04/2019-03-10</th>\n",
" <th>2019-10</th>\n",
" <td>408.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-11/2019-03-17</th>\n",
" <th>2019-11</th>\n",
" <td>410.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-18/2019-03-24</th>\n",
" <th>2019-12</th>\n",
" <td>411.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-30/2020-01-05</th>\n",
" <th>2020-01</th>\n",
" <td>413.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-06/2020-01-12</th>\n",
" <th>2020-02</th>\n",
" <td>414.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-13/2020-01-19</th>\n",
" <th>2020-03</th>\n",
" <td>414.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-20/2020-01-26</th>\n",
" <th>2020-04</th>\n",
" <td>416.18</td>\n",
" </tr>\n",
" </tbody>\n",
......@@ -945,69 +1919,69 @@
"</div>"
],
"text/plain": [
" CO2\n",
"period \n",
"1958-01-13/1958-01-19 315.70\n",
"1958-01-20/1958-01-26 317.46\n",
"1958-01-27/1958-02-02 317.51\n",
"1958-02-10/1958-02-16 315.86\n",
"1958-02-17/1958-02-23 314.93\n",
"1958-02-24/1958-03-02 313.21\n",
"1958-03-10/1958-03-16 313.33\n",
"1958-03-17/1958-03-23 314.67\n",
"1958-12-29/1959-01-04 315.58\n",
"1959-01-05/1959-01-11 316.49\n",
"1959-01-12/1959-01-18 316.65\n",
"1959-01-19/1959-01-25 317.72\n",
"1959-01-26/1959-02-01 318.29\n",
"1959-02-02/1959-02-08 318.15\n",
"1959-02-09/1959-02-15 316.54\n",
"1959-02-16/1959-02-22 314.80\n",
"1959-02-23/1959-03-01 313.84\n",
"1959-03-02/1959-03-08 313.33\n",
"1959-03-09/1959-03-15 314.81\n",
"1959-03-16/1959-03-22 315.58\n",
"1960-01-04/1960-01-10 316.43\n",
"1960-01-11/1960-01-17 316.98\n",
"1960-01-18/1960-01-24 317.58\n",
"1960-01-25/1960-01-31 319.03\n",
"1960-02-01/1960-02-07 320.04\n",
"1960-02-08/1960-02-14 319.58\n",
"1960-02-15/1960-02-21 318.18\n",
"1960-02-22/1960-02-28 315.90\n",
"1960-02-29/1960-03-06 314.17\n",
"1960-03-07/1960-03-13 313.83\n",
"... ...\n",
"2017-03-13/2017-03-19 405.17\n",
"2017-03-20/2017-03-26 406.75\n",
"2018-01-01/2018-01-07 408.05\n",
"2018-01-08/2018-01-14 408.34\n",
"2018-01-15/2018-01-21 409.25\n",
"2018-01-22/2018-01-28 410.30\n",
"2018-01-29/2018-02-04 411.30\n",
"2018-02-05/2018-02-11 410.88\n",
"2018-02-12/2018-02-18 408.90\n",
"2018-02-19/2018-02-25 407.10\n",
"2018-02-26/2018-03-04 405.59\n",
"2018-03-05/2018-03-11 405.99\n",
"2018-03-12/2018-03-18 408.12\n",
"2018-03-19/2018-03-25 409.23\n",
"2018-12-31/2019-01-06 410.92\n",
"2019-01-07/2019-01-13 411.66\n",
"2019-01-14/2019-01-20 412.00\n",
"2019-01-21/2019-01-27 413.52\n",
"2019-01-28/2019-02-03 414.83\n",
"2019-02-04/2019-02-10 413.96\n",
"2019-02-11/2019-02-17 411.85\n",
"2019-02-18/2019-02-24 410.08\n",
"2019-02-25/2019-03-03 408.55\n",
"2019-03-04/2019-03-10 408.43\n",
"2019-03-11/2019-03-17 410.29\n",
"2019-03-18/2019-03-24 411.85\n",
"2019-12-30/2020-01-05 413.37\n",
"2020-01-06/2020-01-12 414.09\n",
"2020-01-13/2020-01-19 414.51\n",
"2020-01-20/2020-01-26 416.18\n",
" CO2\n",
"period \n",
"1958-03 315.70\n",
"1958-04 317.46\n",
"1958-05 317.51\n",
"1958-07 315.86\n",
"1958-08 314.93\n",
"1958-09 313.21\n",
"1958-11 313.33\n",
"1958-12 314.67\n",
"1959-01 315.58\n",
"1959-02 316.49\n",
"1959-03 316.65\n",
"1959-04 317.72\n",
"1959-05 318.29\n",
"1959-06 318.15\n",
"1959-07 316.54\n",
"1959-08 314.80\n",
"1959-09 313.84\n",
"1959-10 313.33\n",
"1959-11 314.81\n",
"1959-12 315.58\n",
"1960-01 316.43\n",
"1960-02 316.98\n",
"1960-03 317.58\n",
"1960-04 319.03\n",
"1960-05 320.04\n",
"1960-06 319.58\n",
"1960-07 318.18\n",
"1960-08 315.90\n",
"1960-09 314.17\n",
"1960-10 313.83\n",
"... ...\n",
"2017-11 405.17\n",
"2017-12 406.75\n",
"2018-01 408.05\n",
"2018-02 408.34\n",
"2018-03 409.25\n",
"2018-04 410.30\n",
"2018-05 411.30\n",
"2018-06 410.88\n",
"2018-07 408.90\n",
"2018-08 407.10\n",
"2018-09 405.59\n",
"2018-10 405.99\n",
"2018-11 408.12\n",
"2018-12 409.23\n",
"2019-01 410.92\n",
"2019-02 411.66\n",
"2019-03 412.00\n",
"2019-04 413.52\n",
"2019-05 414.83\n",
"2019-06 413.96\n",
"2019-07 411.85\n",
"2019-08 410.08\n",
"2019-09 408.55\n",
"2019-10 408.43\n",
"2019-11 410.29\n",
"2019-12 411.85\n",
"2020-01 413.37\n",
"2020-02 414.09\n",
"2020-03 414.51\n",
"2020-04 416.18\n",
"\n",
"[741 rows x 1 columns]"
]
......@@ -1018,11 +1992,10 @@
}
],
"source": [
"def convertIntoPeriod(anneeEtSemaine):\n",
" y = (int)(anneeEtSemaine/100)\n",
" w = (int)(anneeEtSemaine%100)\n",
" per = isoweek.Week(y,w)\n",
" return pd.Period(per.day(0), 'W')\n",
"def convertIntoPeriod(anneeEtMois):\n",
" y = (int)(anneeEtMois/100)\n",
" m = (int)(anneeEtMois%100)\n",
" return pd.Period(pd.Timestamp(y,m,1), 'M')\n",
"useful_data['period'] = [convertIntoPeriod(date) for date in useful_data['period']]\n",
"useful_data.set_index('period')"
]
......@@ -1035,7 +2008,7 @@
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f385044a278>"
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce2c0686a0>"
]
},
"execution_count": 14,
......@@ -1059,6 +2032,45 @@
"useful_data['CO2'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce25e19e10>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"useful_data['CO2'][-60:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On voit de prime abord une augmentation globale, et des oscillations assez régulières avec des minima locaux les mois de Septembre / Octobre et des maxima locaux les mois de Mai et Juin."
]
},
{
"cell_type": "code",
"execution_count": null,
......
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