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module3/exo1/analyse-syndrome-grippal.ipynb
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......@@ -1209,9 +1209,1038 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>inc</th>\n",
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" <th>inc100</th>\n",
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" <td>154</td>\n",
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" <td>206669</td>\n",
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" <td>314</td>\n",
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" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>6</th>\n",
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" <td>3</td>\n",
" <td>187957</td>\n",
" <td>177445.0</td>\n",
" <td>198469.0</td>\n",
" <td>285</td>\n",
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" <td>301.0</td>\n",
" <td>FR</td>\n",
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" <th>7</th>\n",
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" <td>202004</td>\n",
" <td>3</td>\n",
" <td>122331</td>\n",
" <td>113492.0</td>\n",
" <td>131170.0</td>\n",
" <td>186</td>\n",
" <td>173.0</td>\n",
" <td>199.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>202003</td>\n",
" <td>3</td>\n",
" <td>78413</td>\n",
" <td>71330.0</td>\n",
" <td>85496.0</td>\n",
" <td>119</td>\n",
" <td>108.0</td>\n",
" <td>130.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>9</th>\n",
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" <td>53614</td>\n",
" <td>47654.0</td>\n",
" <td>59574.0</td>\n",
" <td>81</td>\n",
" <td>72.0</td>\n",
" <td>90.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>202001</td>\n",
" <td>3</td>\n",
" <td>36850</td>\n",
" <td>31608.0</td>\n",
" <td>42092.0</td>\n",
" <td>56</td>\n",
" <td>48.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>201952</td>\n",
" <td>3</td>\n",
" <td>28135</td>\n",
" <td>23220.0</td>\n",
" <td>33050.0</td>\n",
" <td>43</td>\n",
" <td>36.0</td>\n",
" <td>50.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>12</th>\n",
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" <td>201951</td>\n",
" <td>3</td>\n",
" <td>29786</td>\n",
" <td>25042.0</td>\n",
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" <td>45</td>\n",
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" <td>52.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>201950</td>\n",
" <td>3</td>\n",
" <td>34223</td>\n",
" <td>29156.0</td>\n",
" <td>39290.0</td>\n",
" <td>52</td>\n",
" <td>44.0</td>\n",
" <td>60.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>201949</td>\n",
" <td>3</td>\n",
" <td>25662</td>\n",
" <td>21414.0</td>\n",
" <td>29910.0</td>\n",
" <td>39</td>\n",
" <td>33.0</td>\n",
" <td>45.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>201948</td>\n",
" <td>3</td>\n",
" <td>22367</td>\n",
" <td>18055.0</td>\n",
" <td>26679.0</td>\n",
" <td>34</td>\n",
" <td>27.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>201947</td>\n",
" <td>3</td>\n",
" <td>18669</td>\n",
" <td>14759.0</td>\n",
" <td>22579.0</td>\n",
" <td>28</td>\n",
" <td>22.0</td>\n",
" <td>34.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>201946</td>\n",
" <td>3</td>\n",
" <td>16030</td>\n",
" <td>12567.0</td>\n",
" <td>19493.0</td>\n",
" <td>24</td>\n",
" <td>19.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>201945</td>\n",
" <td>3</td>\n",
" <td>10138</td>\n",
" <td>7160.0</td>\n",
" <td>13116.0</td>\n",
" <td>15</td>\n",
" <td>10.0</td>\n",
" <td>20.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>201944</td>\n",
" <td>3</td>\n",
" <td>7822</td>\n",
" <td>5010.0</td>\n",
" <td>10634.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20</td>\n",
" <td>201943</td>\n",
" <td>3</td>\n",
" <td>9487</td>\n",
" <td>6448.0</td>\n",
" <td>12526.0</td>\n",
" <td>14</td>\n",
" <td>9.0</td>\n",
" <td>19.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>21</td>\n",
" <td>201942</td>\n",
" <td>3</td>\n",
" <td>7747</td>\n",
" <td>5243.0</td>\n",
" <td>10251.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22</td>\n",
" <td>201941</td>\n",
" <td>3</td>\n",
" <td>7122</td>\n",
" <td>4720.0</td>\n",
" <td>9524.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>23</td>\n",
" <td>201940</td>\n",
" <td>3</td>\n",
" <td>8505</td>\n",
" <td>5784.0</td>\n",
" <td>11226.0</td>\n",
" <td>13</td>\n",
" <td>9.0</td>\n",
" <td>17.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>24</td>\n",
" <td>201939</td>\n",
" <td>3</td>\n",
" <td>7091</td>\n",
" <td>4462.0</td>\n",
" <td>9720.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25</td>\n",
" <td>201938</td>\n",
" <td>3</td>\n",
" <td>4897</td>\n",
" <td>2891.0</td>\n",
" <td>6903.0</td>\n",
" <td>7</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>26</td>\n",
" <td>201937</td>\n",
" <td>3</td>\n",
" <td>3172</td>\n",
" <td>1367.0</td>\n",
" <td>4977.0</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27</td>\n",
" <td>201936</td>\n",
" <td>3</td>\n",
" <td>2295</td>\n",
" <td>728.0</td>\n",
" <td>3862.0</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>201935</td>\n",
" <td>3</td>\n",
" <td>1010</td>\n",
" <td>2.0</td>\n",
" <td>2018.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>201934</td>\n",
" <td>3</td>\n",
" <td>1672</td>\n",
" <td>279.0</td>\n",
" <td>3065.0</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1816</th>\n",
" <td>1816</td>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1817</th>\n",
" <td>1817</td>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1818</th>\n",
" <td>1818</td>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1819</th>\n",
" <td>1819</td>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1820</th>\n",
" <td>1820</td>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1821</th>\n",
" <td>1821</td>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1822</th>\n",
" <td>1822</td>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1823</th>\n",
" <td>1823</td>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1824</th>\n",
" <td>1824</td>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1825</th>\n",
" <td>1825</td>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>1826</td>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1827</th>\n",
" <td>1827</td>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td>1828</td>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td>1829</td>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1830</th>\n",
" <td>1830</td>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td>1831</td>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1832</th>\n",
" <td>1832</td>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td>1833</td>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1834</th>\n",
" <td>1834</td>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1835</th>\n",
" <td>1835</td>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1836</th>\n",
" <td>1836</td>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1837</th>\n",
" <td>1837</td>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td>1838</td>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td>1839</td>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1840</th>\n",
" <td>1840</td>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1841</th>\n",
" <td>1841</td>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1842</th>\n",
" <td>1842</td>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1843</th>\n",
" <td>1843</td>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1844</th>\n",
" <td>1844</td>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1845</th>\n",
" <td>1845</td>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1845 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n",
"0 0 202011 3 101704 93652.0 109756.0 154 \n",
"1 1 202010 3 104977 96650.0 113304.0 159 \n",
"2 2 202009 3 110696 102066.0 119326.0 168 \n",
"3 3 202008 3 143753 133984.0 153522.0 218 \n",
"4 4 202007 3 183610 172812.0 194408.0 279 \n",
"5 5 202006 3 206669 195481.0 217857.0 314 \n",
"6 6 202005 3 187957 177445.0 198469.0 285 \n",
"7 7 202004 3 122331 113492.0 131170.0 186 \n",
"8 8 202003 3 78413 71330.0 85496.0 119 \n",
"9 9 202002 3 53614 47654.0 59574.0 81 \n",
"10 10 202001 3 36850 31608.0 42092.0 56 \n",
"11 11 201952 3 28135 23220.0 33050.0 43 \n",
"12 12 201951 3 29786 25042.0 34530.0 45 \n",
"13 13 201950 3 34223 29156.0 39290.0 52 \n",
"14 14 201949 3 25662 21414.0 29910.0 39 \n",
"15 15 201948 3 22367 18055.0 26679.0 34 \n",
"16 16 201947 3 18669 14759.0 22579.0 28 \n",
"17 17 201946 3 16030 12567.0 19493.0 24 \n",
"18 18 201945 3 10138 7160.0 13116.0 15 \n",
"19 19 201944 3 7822 5010.0 10634.0 12 \n",
"20 20 201943 3 9487 6448.0 12526.0 14 \n",
"21 21 201942 3 7747 5243.0 10251.0 12 \n",
"22 22 201941 3 7122 4720.0 9524.0 11 \n",
"23 23 201940 3 8505 5784.0 11226.0 13 \n",
"24 24 201939 3 7091 4462.0 9720.0 11 \n",
"25 25 201938 3 4897 2891.0 6903.0 7 \n",
"26 26 201937 3 3172 1367.0 4977.0 5 \n",
"27 27 201936 3 2295 728.0 3862.0 3 \n",
"28 28 201935 3 1010 2.0 2018.0 2 \n",
"29 29 201934 3 1672 279.0 3065.0 3 \n",
"... ... ... ... ... ... ... ... \n",
"1816 1816 198521 3 26096 19621.0 32571.0 47 \n",
"1817 1817 198520 3 27896 20885.0 34907.0 51 \n",
"1818 1818 198519 3 43154 32821.0 53487.0 78 \n",
"1819 1819 198518 3 40555 29935.0 51175.0 74 \n",
"1820 1820 198517 3 34053 24366.0 43740.0 62 \n",
"1821 1821 198516 3 50362 36451.0 64273.0 91 \n",
"1822 1822 198515 3 63881 45538.0 82224.0 116 \n",
"1823 1823 198514 3 134545 114400.0 154690.0 244 \n",
"1824 1824 198513 3 197206 176080.0 218332.0 357 \n",
"1825 1825 198512 3 245240 223304.0 267176.0 445 \n",
"1826 1826 198511 3 276205 252399.0 300011.0 501 \n",
"1827 1827 198510 3 353231 326279.0 380183.0 640 \n",
"1828 1828 198509 3 369895 341109.0 398681.0 670 \n",
"1829 1829 198508 3 389886 359529.0 420243.0 707 \n",
"1830 1830 198507 3 471852 432599.0 511105.0 855 \n",
"1831 1831 198506 3 565825 518011.0 613639.0 1026 \n",
"1832 1832 198505 3 637302 592795.0 681809.0 1155 \n",
"1833 1833 198504 3 424937 390794.0 459080.0 770 \n",
"1834 1834 198503 3 213901 174689.0 253113.0 388 \n",
"1835 1835 198502 3 97586 80949.0 114223.0 177 \n",
"1836 1836 198501 3 85489 65918.0 105060.0 155 \n",
"1837 1837 198452 3 84830 60602.0 109058.0 154 \n",
"1838 1838 198451 3 101726 80242.0 123210.0 185 \n",
"1839 1839 198450 3 123680 101401.0 145959.0 225 \n",
"1840 1840 198449 3 101073 81684.0 120462.0 184 \n",
"1841 1841 198448 3 78620 60634.0 96606.0 143 \n",
"1842 1842 198447 3 72029 54274.0 89784.0 131 \n",
"1843 1843 198446 3 87330 67686.0 106974.0 159 \n",
"1844 1844 198445 3 135223 101414.0 169032.0 246 \n",
"1845 1845 198444 3 68422 20056.0 116788.0 125 \n",
"\n",
" inc100_low inc100_up geo_insee geo_name \n",
"0 142.0 166.0 FR France \n",
"1 146.0 172.0 FR France \n",
"2 155.0 181.0 FR France \n",
"3 203.0 233.0 FR France \n",
"4 263.0 295.0 FR France \n",
"5 297.0 331.0 FR France \n",
"6 269.0 301.0 FR France \n",
"7 173.0 199.0 FR France \n",
"8 108.0 130.0 FR France \n",
"9 72.0 90.0 FR France \n",
"10 48.0 64.0 FR France \n",
"11 36.0 50.0 FR France \n",
"12 38.0 52.0 FR France \n",
"13 44.0 60.0 FR France \n",
"14 33.0 45.0 FR France \n",
"15 27.0 41.0 FR France \n",
"16 22.0 34.0 FR France \n",
"17 19.0 29.0 FR France \n",
"18 10.0 20.0 FR France \n",
"19 8.0 16.0 FR France \n",
"20 9.0 19.0 FR France \n",
"21 8.0 16.0 FR France \n",
"22 7.0 15.0 FR France \n",
"23 9.0 17.0 FR France \n",
"24 7.0 15.0 FR France \n",
"25 4.0 10.0 FR France \n",
"26 2.0 8.0 FR France \n",
"27 1.0 5.0 FR France \n",
"28 0.0 4.0 FR France \n",
"29 1.0 5.0 FR France \n",
"... ... ... ... ... \n",
"1816 35.0 59.0 FR France \n",
"1817 38.0 64.0 FR France \n",
"1818 59.0 97.0 FR France \n",
"1819 55.0 93.0 FR France \n",
"1820 44.0 80.0 FR France \n",
"1821 66.0 116.0 FR France \n",
"1822 83.0 149.0 FR France \n",
"1823 207.0 281.0 FR France \n",
"1824 319.0 395.0 FR France \n",
"1825 405.0 485.0 FR France \n",
"1826 458.0 544.0 FR France \n",
"1827 591.0 689.0 FR France \n",
"1828 618.0 722.0 FR France \n",
"1829 652.0 762.0 FR France \n",
"1830 784.0 926.0 FR France \n",
"1831 939.0 1113.0 FR France \n",
"1832 1074.0 1236.0 FR France \n",
"1833 708.0 832.0 FR France \n",
"1834 317.0 459.0 FR France \n",
"1835 147.0 207.0 FR France \n",
"1836 120.0 190.0 FR France \n",
"1837 110.0 198.0 FR France \n",
"1838 146.0 224.0 FR France \n",
"1839 184.0 266.0 FR France \n",
"1840 149.0 219.0 FR France \n",
"1841 110.0 176.0 FR France \n",
"1842 99.0 163.0 FR France \n",
"1843 123.0 195.0 FR France \n",
"1844 184.0 308.0 FR France \n",
"1845 37.0 213.0 FR France \n",
"\n",
"[1845 rows x 11 columns]"
]
},
"execution_count": 20,
"metadata": {},
"outputs": [],
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -1237,7 +2266,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
......@@ -1267,10 +2296,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
......@@ -1294,9 +2321,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 23,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
......@@ -1314,9 +2349,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 24,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0131ce87f0>"
]
},
"execution_count": 24,
"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['inc'].plot()"
]
......@@ -1455,10 +2513,29 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'first_august_week' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-717b963901fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfirst_august_week\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'first_august_week' is not defined"
]
}
],
"source": [
"first_august_week"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
......
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