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Commit 049e522a authored by 720d0853c18a70320e63c05851c89be9's avatar 720d0853c18a70320e63c05851c89be9
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create local copy and read it

parent 7493e377
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module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 049e522a
......@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -28,10 +28,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
......@@ -61,12 +59,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data"
"raw_data\n",
"file = '/home/jovyan/work/module3/exo1/incidence-PAY-3.csv'\n",
"try:\n",
" local_data = pd.read_csv(file)\n",
"except FileNotFoundError:\n",
" raw_data.to_csv('incidence-PAY-3.csv')\n",
"rax_data = pd.read_csv('incidence-PAY-3.csv')"
]
},
{
......@@ -78,9 +82,73 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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" <th>inc</th>\n",
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"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1656 198919 3 0 NaN NaN 0 NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1656 FR France "
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
......@@ -94,9 +162,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
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" <tr>\n",
" <th>3</th>\n",
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" <td>17320</td>\n",
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" <th>4</th>\n",
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" <td>39.0</td>\n",
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" <th>5</th>\n",
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" <td>16428</td>\n",
" <td>12285.0</td>\n",
" <td>20571.0</td>\n",
" <td>25</td>\n",
" <td>19.0</td>\n",
" <td>31.0</td>\n",
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" <th>7</th>\n",
" <td>202051</td>\n",
" <td>3</td>\n",
" <td>21619</td>\n",
" <td>17370.0</td>\n",
" <td>25868.0</td>\n",
" <td>33</td>\n",
" <td>27.0</td>\n",
" <td>39.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202050</td>\n",
" <td>3</td>\n",
" <td>16845</td>\n",
" <td>13220.0</td>\n",
" <td>20470.0</td>\n",
" <td>26</td>\n",
" <td>20.0</td>\n",
" <td>32.0</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>202049</td>\n",
" <td>3</td>\n",
" <td>12939</td>\n",
" <td>9923.0</td>\n",
" <td>15955.0</td>\n",
" <td>20</td>\n",
" <td>15.0</td>\n",
" <td>25.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>202048</td>\n",
" <td>3</td>\n",
" <td>13804</td>\n",
" <td>10641.0</td>\n",
" <td>16967.0</td>\n",
" <td>21</td>\n",
" <td>16.0</td>\n",
" <td>26.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>202047</td>\n",
" <td>3</td>\n",
" <td>19085</td>\n",
" <td>15285.0</td>\n",
" <td>22885.0</td>\n",
" <td>29</td>\n",
" <td>23.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>202046</td>\n",
" <td>3</td>\n",
" <td>24801</td>\n",
" <td>20503.0</td>\n",
" <td>29099.0</td>\n",
" <td>38</td>\n",
" <td>31.0</td>\n",
" <td>45.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>13</th>\n",
" <td>202045</td>\n",
" <td>3</td>\n",
" <td>42516</td>\n",
" <td>36857.0</td>\n",
" <td>48175.0</td>\n",
" <td>65</td>\n",
" <td>56.0</td>\n",
" <td>74.0</td>\n",
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" <td>France</td>\n",
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" <th>14</th>\n",
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" <td>44567</td>\n",
" <td>38521.0</td>\n",
" <td>50613.0</td>\n",
" <td>68</td>\n",
" <td>59.0</td>\n",
" <td>77.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>202043</td>\n",
" <td>3</td>\n",
" <td>43737</td>\n",
" <td>37523.0</td>\n",
" <td>49951.0</td>\n",
" <td>66</td>\n",
" <td>57.0</td>\n",
" <td>75.0</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>202042</td>\n",
" <td>3</td>\n",
" <td>35145</td>\n",
" <td>29812.0</td>\n",
" <td>40478.0</td>\n",
" <td>53</td>\n",
" <td>45.0</td>\n",
" <td>61.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202041</td>\n",
" <td>3</td>\n",
" <td>27877</td>\n",
" <td>23206.0</td>\n",
" <td>32548.0</td>\n",
" <td>42</td>\n",
" <td>35.0</td>\n",
" <td>49.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202040</td>\n",
" <td>3</td>\n",
" <td>20443</td>\n",
" <td>16381.0</td>\n",
" <td>24505.0</td>\n",
" <td>31</td>\n",
" <td>25.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202039</td>\n",
" <td>3</td>\n",
" <td>19810</td>\n",
" <td>15900.0</td>\n",
" <td>23720.0</td>\n",
" <td>30</td>\n",
" <td>24.0</td>\n",
" <td>36.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202038</td>\n",
" <td>3</td>\n",
" <td>25562</td>\n",
" <td>21142.0</td>\n",
" <td>29982.0</td>\n",
" <td>39</td>\n",
" <td>32.0</td>\n",
" <td>46.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>202037</td>\n",
" <td>3</td>\n",
" <td>18485</td>\n",
" <td>14649.0</td>\n",
" <td>22321.0</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>22</th>\n",
" <td>202036</td>\n",
" <td>3</td>\n",
" <td>10390</td>\n",
" <td>7646.0</td>\n",
" <td>13134.0</td>\n",
" <td>16</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>202035</td>\n",
" <td>3</td>\n",
" <td>9918</td>\n",
" <td>6842.0</td>\n",
" <td>12994.0</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>24</th>\n",
" <td>202034</td>\n",
" <td>3</td>\n",
" <td>6084</td>\n",
" <td>3090.0</td>\n",
" <td>9078.0</td>\n",
" <td>9</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202033</td>\n",
" <td>3</td>\n",
" <td>6106</td>\n",
" <td>3411.0</td>\n",
" <td>8801.0</td>\n",
" <td>9</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202032</td>\n",
" <td>3</td>\n",
" <td>5918</td>\n",
" <td>3330.0</td>\n",
" <td>8506.0</td>\n",
" <td>9</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202031</td>\n",
" <td>3</td>\n",
" <td>4351</td>\n",
" <td>2269.0</td>\n",
" <td>6433.0</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>28</th>\n",
" <td>202030</td>\n",
" <td>3</td>\n",
" <td>8179</td>\n",
" <td>5442.0</td>\n",
" <td>10916.0</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>29</th>\n",
" <td>202029</td>\n",
" <td>3</td>\n",
" <td>8687</td>\n",
" <td>5860.0</td>\n",
" <td>11514.0</td>\n",
" <td>13</td>\n",
" <td>9.0</td>\n",
" <td>17.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <tr>\n",
" <th>1863</th>\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>1864</th>\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>1865</th>\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>1866</th>\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>1867</th>\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>1868</th>\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>1869</th>\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>1870</th>\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>1871</th>\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>1872</th>\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>1873</th>\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>1874</th>\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>1875</th>\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>1876</th>\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>1877</th>\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>1878</th>\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>1879</th>\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>1880</th>\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>1881</th>\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>1882</th>\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>1883</th>\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>1884</th>\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>1885</th>\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>1886</th>\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>1887</th>\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>1888</th>\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>1889</th>\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>1890</th>\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>1891</th>\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>1892</th>\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>1892 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202105 3 22491 18436.0 26546.0 34 28.0 \n",
"1 202104 3 25804 21491.0 30117.0 39 32.0 \n",
"2 202103 3 21810 17894.0 25726.0 33 27.0 \n",
"3 202102 3 17320 13906.0 20734.0 26 21.0 \n",
"4 202101 3 21799 17778.0 25820.0 33 27.0 \n",
"5 202053 3 21220 16498.0 25942.0 32 25.0 \n",
"6 202052 3 16428 12285.0 20571.0 25 19.0 \n",
"7 202051 3 21619 17370.0 25868.0 33 27.0 \n",
"8 202050 3 16845 13220.0 20470.0 26 20.0 \n",
"9 202049 3 12939 9923.0 15955.0 20 15.0 \n",
"10 202048 3 13804 10641.0 16967.0 21 16.0 \n",
"11 202047 3 19085 15285.0 22885.0 29 23.0 \n",
"12 202046 3 24801 20503.0 29099.0 38 31.0 \n",
"13 202045 3 42516 36857.0 48175.0 65 56.0 \n",
"14 202044 3 44567 38521.0 50613.0 68 59.0 \n",
"15 202043 3 43737 37523.0 49951.0 66 57.0 \n",
"16 202042 3 35145 29812.0 40478.0 53 45.0 \n",
"17 202041 3 27877 23206.0 32548.0 42 35.0 \n",
"18 202040 3 20443 16381.0 24505.0 31 25.0 \n",
"19 202039 3 19810 15900.0 23720.0 30 24.0 \n",
"20 202038 3 25562 21142.0 29982.0 39 32.0 \n",
"21 202037 3 18485 14649.0 22321.0 28 22.0 \n",
"22 202036 3 10390 7646.0 13134.0 16 12.0 \n",
"23 202035 3 9918 6842.0 12994.0 15 10.0 \n",
"24 202034 3 6084 3090.0 9078.0 9 4.0 \n",
"25 202033 3 6106 3411.0 8801.0 9 5.0 \n",
"26 202032 3 5918 3330.0 8506.0 9 5.0 \n",
"27 202031 3 4351 2269.0 6433.0 7 4.0 \n",
"28 202030 3 8179 5442.0 10916.0 12 8.0 \n",
"29 202029 3 8687 5860.0 11514.0 13 9.0 \n",
"... ... ... ... ... ... ... ... \n",
"1863 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"1864 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"1865 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"1866 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"1867 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"1868 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"1869 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"1870 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"1871 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"1872 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"1873 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"1874 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"1875 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"1876 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"1877 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"1878 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"1879 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"1880 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"1881 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"1882 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"1883 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"1884 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"1885 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"1886 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"1887 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"1888 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1889 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1890 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1891 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1892 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 40.0 FR France \n",
"1 46.0 FR France \n",
"2 39.0 FR France \n",
"3 31.0 FR France \n",
"4 39.0 FR France \n",
"5 39.0 FR France \n",
"6 31.0 FR France \n",
"7 39.0 FR France \n",
"8 32.0 FR France \n",
"9 25.0 FR France \n",
"10 26.0 FR France \n",
"11 35.0 FR France \n",
"12 45.0 FR France \n",
"13 74.0 FR France \n",
"14 77.0 FR France \n",
"15 75.0 FR France \n",
"16 61.0 FR France \n",
"17 49.0 FR France \n",
"18 37.0 FR France \n",
"19 36.0 FR France \n",
"20 46.0 FR France \n",
"21 34.0 FR France \n",
"22 20.0 FR France \n",
"23 20.0 FR France \n",
"24 14.0 FR France \n",
"25 13.0 FR France \n",
"26 13.0 FR France \n",
"27 10.0 FR France \n",
"28 16.0 FR France \n",
"29 17.0 FR France \n",
"... ... ... ... \n",
"1863 59.0 FR France \n",
"1864 64.0 FR France \n",
"1865 97.0 FR France \n",
"1866 93.0 FR France \n",
"1867 80.0 FR France \n",
"1868 116.0 FR France \n",
"1869 149.0 FR France \n",
"1870 281.0 FR France \n",
"1871 395.0 FR France \n",
"1872 485.0 FR France \n",
"1873 544.0 FR France \n",
"1874 689.0 FR France \n",
"1875 722.0 FR France \n",
"1876 762.0 FR France \n",
"1877 926.0 FR France \n",
"1878 1113.0 FR France \n",
"1879 1236.0 FR France \n",
"1880 832.0 FR France \n",
"1881 459.0 FR France \n",
"1882 207.0 FR France \n",
"1883 190.0 FR France \n",
"1884 198.0 FR France \n",
"1885 224.0 FR France \n",
"1886 266.0 FR France \n",
"1887 219.0 FR France \n",
"1888 176.0 FR France \n",
"1889 163.0 FR France \n",
"1890 195.0 FR France \n",
"1891 308.0 FR France \n",
"1892 213.0 FR France \n",
"\n",
"[1892 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -122,7 +1157,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -152,10 +1187,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
......@@ -179,9 +1212,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"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",
......@@ -199,9 +1240,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbd8f661ba8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qI2PDIQVKNd+9KmNDvMYXaoXgY7cvxZxvPVHtYtQVpa6UH5FUL2y4vvbyKmU/rqDyR/l8BwdTyGQExrY25B1rkitYB1NVsnMiJ6BTIScy1XTKD0uzXEOi/ueqyzZVbPONwwYWKEVQrSivQqjq5ooROE9y203r84pyFn76DYuQyQps/s5lecdqQUNRDNeBD2VIljFe//KEKQJ+7UVQrQ5b2MLG8pWjkphkU5SP5zdBaExaXSTsYF5O0iGjQUQVi6rq8nBpf0xhsEApgrrQUGqgiIeDUz4es7pI2JDdcqAmMGGfuRZCqqtfAqYasEApgvrYy6u+nfKBY2KFHi8hQ6xKfedL1u9H/3BxoccxKqwMVQ3IoOqXgakeLFCKoB46Sx0UsSQqJTDjUqCUoqFs6+zHJ36+DF998PXiMpCDdNhgkFJk32vbuvHmzoPFZyA53Nsfo4ed8kVQNQ2lzhY2lrMMlXoFOQ2leMeEcuiv3d1b1PVK4wsb5VfKtjvzfrwEALQBCmFQQRT1MOliooc1lCLwEyj3LtuC17d3l+W+9bKXVxQr5IOetVLPl5DhSukS1h7FpFAqdlPRmC3U/M/LmZuKuk2kVFue/PzZjfjQT5ZEklct7ItXL7CGUgBEVkfxEyhf//0qAMXP8KKimoNKlP2PDNKpUo+nwl9LMXnZPpAiK6YefSjVHoO/9cjqyPISgj/JEBbWUAqg1IGhVAq7bQ3Mqkqop0oubAxzn1LMnPECBYKXWEhHtxrzamGXhMPJ5HU4PUu5YYFSAHbHrvKShDDtu5p9QHj+loJpYhiVGSJokFe3KSUIoPRZe2F+iWq+e6VRmoqwdncv0iG3kImCsPuf+VELJsR6gQVKAVC1NZQCzq2FTlDWdSgR5RO0WLBSCyj9UBOZoOtV+6xVDWXVjh5ccstfcftfN1asPF39wyXnwRpKeFigFEBOQ6luAwszW66qI7EC947qFkH5FLqo0O8exQ5Mha7tqNXJxKod1pb2G/f1lf3+4+S+bFsO9JecF8uT8LBAKYAg52jZB/ECBreqOuXtv+UrRFR5Bw7SEdxG3aNogaJMXkFRXp77VQXbvJdfhpRslGo7m3LSJPdgi2LyxxpKeFigFECQU77c61PqZaW8XYY62Hol6JVFoaGoPIqNFLNNXhVYhxIVuhLk1tOU//4581/pebFACQ8LlAKgAFt2pXwrNe+Utx3ZpeQhfPOIqpMH5ROFtlVqSe3BMdCHYv2tpnZqa0maQuRCbytXwCiEay2YEOsFFigFEA/Y16nsGkoB2Vc3yqv0Wb2dlyGTSvlQyvkMYSlUUNTCjFqvocgIsAoWL4ouWQsaX73AAqUAYgFRNJXakiXMbLk2BpXqlyGIoMEiinosNYuwTvmwvpZy4hdpVsnFgVSgmdAP1lDCwwKlAILCN8vdkQuZ+VezD9jli2R2WFh6oQQNFvajlORDKf5aJ2GzqYXJhK6wlfWhWH+jqIuaqM86gQVKAVBAlFfYDyAVSyHtupqdIAp5EnRtxXwoEQ5IxWYV+roa2PYk5mOesxd41p0PhQVKWFigFEDQFhgVc8pHdlJ5KaUzB61Qj+rxggVK6fcrtVnYZQi9DqV6Lz/MbsMV0VAiNP+xPAkPC5QCyK1D0R8vv8nL+8NMVTUU4f4bRV756dE8X7BTPkoNpbzvpCbWoUh8nfIVLAebvCpLyQKFiOJE9AoR/Un+exwRLSKidfLvWMe51xHReiJaS0SXONLPJKKV8titJG1LRNRIRPfL9GVENNNxzQJ5j3VEtKDU5whDTqDoJUe5TV6FUBNRXpHkVVh6oQQNFtkIhWOlqGrYsJ8WXwWTXDTrUErPY6QQhYbyZQDOvaKvBbBYCDEbwGL5bxDRCQDmAzgRwKUAfkJEcXnNbQCuBjBb/nepTL8KQJcQ4hgANwO4SeY1DsD1AM4CMBfA9U7BVS7k58Wr55QPMAO5ylIDI2BJ25UEfg+l+LydhHXKlyLCSn0XYTWb3CaU1X/3vk75CugoUdZFtbdaqidKEihENA3AZQB+7kieB+Bu+ftuAJc70u8TQgwJITYBWA9gLhFNBtAmhFgqrLd/j+caldeDAC6S2sslABYJITqFEF0AFiEnhMpGbqW8/ni1No3UUdWSFCD4ArMqs8kraLCopYVxYYtSC+NfLUxogKjWoZSex0ihVA3lFgD/CsA5N58khNgFAPLvRJk+FcA2x3nbZdpU+dub7rpGCJEG0ANgvE9eZSUesGK5lM/EhqGQsOEb/vhm1WeqkfhQyuyUD7uwsbTNIUvUUOy/te+UV+iKUIgPsFSi9CfVQn3WC0ULFCL6AIC9QogVYS/RpAmf9GKvcd+U6GoiWk5Ey/ft2xeqoCaCFkuV/TMPBUQc7egeQO9QuqzFMRGJ7yRgII/O5BWgoUTwNNkC3lsUVPeLjeYor6DtdMpBvQsUIQRuf2YDdvUMVK0MhVCKhnIOgA8S0WYA9wG4kIj+F8AeacaC/LtXnr8dwHTH9dMA7JTp0zTprmuIKAGgHUCnT155CCHuEELMEULM6ejoKO5JJUG7DVdqpXytYw8cZeyIldptOBphUKoPxf3XRDW2NjGhK0Kh4c+lYH/oq86d8lsO9OPbj63BP/wy7Ly9uhQtUIQQ1wkhpgkhZsJytj8phPgkgIcBqKirBQAekr8fBjBfRm7NguV8f1GaxXqJ6GzpH7nSc43K6wp5DwHgcQAXE9FY6Yy/WKaVlViAyavcMxnb9FELI0aZCXrCqKyLQfeJYhAsdUAqVHhWs3nkVsPnF6LSmpp1zyj8eNWrULVD9aHB6lgbCiVRhjy/A+ABIroKwFYAHwEAIcQbRPQAgDcBpAFcI4TIyGs+D+AuAM0AHpP/AcAvAPySiNbD0kzmy7w6ieibAF6S590ghOgsw7O4CDJ5Fbs9eaGEvUuxWyct23gAE0Y34uiOUUVdH8ViwFxeJh9KNHUddi8vKmEjqmxEI2l4p3z52mEmK7C3dxCT25u1x/0+dxzFpwDCkvOhlJ4XGx7CE4lAEUI8DeBp+fsAgIsM590I4EZN+nIAJ2nSByEFkubYQgALiy1zKVRvt+HKtOyP3fECAGDzdy4r6vqcJlV6Waq+l1cUM9xSrw+ZQZT7V5m46c9rcMdfN+Klr78HHaMbfcqQf63w/K0E9e5DqTd4pXwRGNehVGrrlTpp35GEDUdQDj/C+lAiuUeJu+2GLUo528fi1XsAAD0D/t9q93PKV5L638srmrZTKVigFIBqV0Npw0p50wKViO8fllLMNKVQia1XItscMsAXE+n29UVmVaiJs5wDoC0bDW3Lb3sVZfqriGCxdwYvPat6mcDVAixQCkDNuPsM4bgVc8qX9S6lE8XWK2HXh5RK+K1Xir9hVGUNvzlkNPfT521lHjMJFJ8V6pV0yvM6lOrAAqUAVLvqG85oj1csbLhOGnhpxVRCyeSUj4awm0OW8mptZ3SR14cNcvBbAxIVKiAlHqD9Vn1hox02HIVAKTmLkqkTixcLlEJQ7WqgSgKl0L5RrXBHEeHIUfatV0JGeZUySFfak1TO9x5kIvTTDKrRHiP1gVWBWhBmhcACpQBUhzBtsVK5TwDXB6VtVyL/mo4Xn7WLSuw2HNmAFJBNlKGyJsIKWN+w4UpsDum5ZylUcx1KvZnbWKAUgHq1pvUm5d4cspC9vKzzq0sUAsWUSXQaStDxCDSUEsta6OXlHIRU3sa27mN2U3Vdya881Ps6lHrbfYMFSiGoDmHoTPX28suFiGAmWqnt64MGexHwzsPdo+hL3fmEVFEiGUSNa61kWQwPlduiPp9KTrYPl+3rlfCtVsRmobBAKQBbQzGEB1fKh+I3sDTEc6+02tpyfZi8Ao5no3DKW3+LHdwKvaqcay9yZl/9df5RXpUzeXnvWVoeERSk6HvX1ySVBUoB5DqT/iXXwstPxh0zmSoVR3j+FkOQ76K+woajKWxQNtGGyprSi/eh5DYNLaloBRHN91Cq169r6RtLYWCBUgCBPpSIpjJZuV+SsRw+tzGtD6gkUSxszK1lKV14pzJZXHPvy3hrT69vPn6z6ig0lGIpdECLwkdhql/1LKa27hcYEDb8OQrUAsu611Dkzavfq8PBAqUAREBnimpzyFufXIe5Ny7Gzm73NxDCzPCE63d1Zzcl3T/g0kLGiVU7evDIyl346oOv++ajHwSjc8oXm0NYjS/KdShGgZINboOA/t1XY2COYoJfTctDvfllWaAUgOokJsERlfPu6bXWh8B29Zi1FBPCNeOOpDiFl6GQL4EZ85B/TSavAjJXW+U0JvKbe7CG4v5bDFG9htDRfZGYefTpmQABmxNq+cfC7jb8tz9Zgu89viZcQQ3Ym1RG0CdrYR1KDRgeQsECpQByGophHUpEDU/5QbyzkzAz1VqYz0QzoAXM6gu4h79A8c8yNwgW/1D2FvhFXh/21lHuNmw2eUk/YlCUl8/CxqDyvbK1Gz9+akPIkvpT7m/Kv7ipE6d843H09KdKv5GGWvDLFgILlAJQ77YUH8r2rn5sOdDne47yg6Q9oTQ534T5PiJggKwkpdw/WEMJz1DK2tmgMRHPO+bssL5rJ0oSKNbfUt9H+G/Kl3gjnzyyQWHDPt9DUUmVHCTLvZfXrYvX4eBgGq9t7y443xVbunD5j5dgMKXfeQNgk9eIoJTvoZx701M4/3tP+56TkBpKMT6ZavtNrDLIvxGs3YjGKW+d25DI1xGCTIT2rLoER3fpCxvDXV9qlJcIMRuxNRRT2LCPMzx3bfnbaJjJV1j8imsL0CLy/c+HVuHVbd1Yv/eQz72r358LgQVKAQSFDUfVURIx67V4zQphhIVrTChjYxRC4LGVu7TPHEU0TzFhqSbSUhrEYxqTV8jt66NY2Fjq6wjvQynuRs5XGWTyKm7rFfOx3HXhyr63dxCPrtxlzgeqnKGy8y1HuQb1MCZKFiiHMerVhhEopQzmiZgyeRWjoeh/R80jK3fh8/e+jNv/arZ1lxY27P1hOB4C1SnjGidGJmDgiGIvr1K1xrBX+znEQ93Hqa0Zzsltn+J/k2I1lLBlX7DwJfzTvS/jkPFTEtbfYv2aLt9aiDyK6e/2t2N8LlWaINVJ4DALlAII9KG4Bqfi72MK/4xqphsFBw5ZX+zb1a2LRBOO/xdJgJZTSAdWnTIWK9zkFWZ1987uAXz09qXo7td/xbDkdSEFanzFzmrDaCi6c0PfWwSf4zxm2tUbALZ39gMIXmRcfF2E68v2NvlF3CMWwlzGPpTDGDWoGBuxI72cDcF3xuuaWZWtCI7OYL5JST4UeyNMfR6FZL1iSxcA/Tc8gqK8hMg/z8ttT2/Ai5s68dCrO7XHSzVbhNZQ7PsVex9/4erENPP3G8jDDPLOfrNhn9m3YGOcccg/RWtrud9+5bVbVDH3CbFuSLV/Dhs+DLE1FINH0qm5lDKImCJlwuRYMae8j3klCh9KkDYW9jkPHBrCr1/cCgCIazSU8CvlfQaVgI0II3sjIdtU0XuGFWDmCVpJ7+dD8ZtshR3I7U/8Bgm2IqVrWA1FHdI908BwBj/760bjeBELaDcAb71yWOPXeAB34w1qB36NyDQZCWPycgfqlK8x5tYb+JShhNsH11+4fNQaFEBv8nKbefKvD+ND8dth17o22GwWhqCrS12HIgLqwn2uSXNUAticv1/ezgE0TJRj2mBPVFeWey8vtSZNV+e3LH4LNz66Go8YggdiFMaHwgLlsKUwH0pxNmjPHQtI1RwrY1sMsz14KQNo0Gdzw86mnVpJIsCHortZOA3Ff2AoPbqrsAxMbUsIgT+8ssPom3CZvALenSlsOLfmpnSTl99gSgHnROtDCS6vrhhbD1h+HtPlYUyUdaagsEApDOvthtnLK0hVNc2sAOdMU39/3xJWqAX6za4K/RCYDlvJMZq8cvg6TR2/dRtnukxemvotZC8v0xlRTTKDi+Bvk391Wze+cv+r+I+HVmmPB2lr7nP9B3K95hpcl872G2Z2boqEzGnzxQoUx2+foAq/hZ69g1YEWsogfcNMylhDOYwJ0lCcJq/AsEqfRqpCBE2NyV8r0P+OGj+nfCHRaHt7B3H6DX/Bmt0H9XmEWNjoa5N3/NZpKM73oDV5ZfPPK5S5YN7tAAAgAElEQVSwe1iZCHuZGqAyhkFWPZ9pIZ074i2o/frfQ3c850Mx5xtaQ6GgPuK+Z6GEXYeiJoa6CaQSJOadmYPDvNmHchjjfLW6RuLsKEEzCz8NRWEKG/bDbQoKPt8/Lz+TQ3BnCGPyWrx6L7r6U7jzuc15V1tlMJUt9ztsGGopTnnfuiizUz7sbFtFsaUML6W10dp6JmjthvOeJvzMaoD+mYMi97z5+vlQVJ2bzil1l+iwdaFkt99mmKYy2hMAv3U5rKEcvgSp465Zc+AMz3zM1NCiiJ4qBN+2HGLPpjB9OSgAwUzuBL+Bx1mHus+oBrhQcjNuX7Oaf0xnGLPLUDqDW554S7uvU1hflDrPZGKxNWyTilCAQCkubFhe67uw0dnHgiddZh+K+2+hhNVQctv56yaYwreMOaFofs46kycsUAohSENxNoxSNBSTQMkWKFFKjSoKs0jN1+RVwL28+QRd6yxa2DBUPw3E+9t7TbiwYf3xMLPM+17chlueWIfbnvbZeSAgD3WbVFrftvwcyFZ6uEEUMAtJv6i4MHXpFijm+ysRbo7yCtaG/Agb8Zb2ERpKewkyefm1XyW4+ZvyhyHORqZryIWYvPw0GNMGe8GDbLTTGb9nSNszM3M5QmkohsE4yNTkLFrYGa/frNm6mfn6MI5/kwAP81bUfbo0q+3DvlZ1XpCGYvQ7aMpjIih0Xr99vco7XL7RaCjF9YnwUV4qbFiTR4CGoraWS/lssVTNzw8XAwuUAgg0ebmc8v55hYne8I4LQTN/b9srpi2G/UCXEqjagUOTlwl7P6O8ckCbnruH/7vQHfOzc5vulZtxl6ChhNDYmpOWf8Nvu5Gg6lRlHDYMUEEfxwqzl5fCVOX2REN7jf8AC7j7jb8PxX9Hbr/1MGFw+1CC25euTtUxUxljAYEFQcdqERYoBSCQc+zqTV6OQa7IGR4A4yrg3Mw/3EzYrwTLN3fidc03HFymJL/olox54LB3ZS7BiaKuNA9+ud/+JhT/84Js5YWsZzBZJcII1kRc7jAdMmLN77hJQ/Eb/Kx0R15BPkDDcf8B1v9awN1mwk269OeoWX/RizzhbBfm88JMOILKwD4UAEQ0nYieIqLVRPQGEX1Zpo8jokVEtE7+Heu45joiWk9Ea4noEkf6mUS0Uh67leT0g4gaieh+mb6MiGY6rlkg77GOiBYU+xwFIXKhp4FO+SCTV4hFW17be+CAUkDnueKnS/HBHy3Jz8PxO8yCLj+nfCGdIV+78p9hOpP9ZrLBmz+ay+BMK6VjFyJXtQI65H3U+xo2+FCCzHeF7OVl8gv5tYsw26FkQvohbR+KQRtTgQfFWozCTljssGFNlaurTGUMo6GMpCivNIB/EUK8DcDZAK4hohMAXAtgsRBiNoDF8t+Qx+YDOBHApQB+QkTqE3q3AbgawGz536Uy/SoAXUKIYwDcDOAmmdc4ANcDOAvAXADXOwVXuRAAGuRMUjeIuc0rQbOSwmdfQZ/FzdNQiuhNrhl7CB+K/5bvIWb16r6m9SYBJo2gcgZvTx+dhmIiTDCFr881ZH0G+VBUPZnr1HFukEAxHM+1C901/gLNeb33twmjhhKgjQHAqf/1F3z99yu1x8Ls5SWEwLbOAeN9cj4U/4WNfs8Zpt0t3XAAT67ZE3heJShaoAghdgkhXpa/ewGsBjAVwDwAd8vT7gZwufw9D8B9QoghIcQmAOsBzCWiyQDahBBLhdVj7vFco/J6EMBFUnu5BMAiIUSnEKILwCLkhFDZyAphf01Rvw4lvIbiNwiatq/3i6DxSy+EbMhBxW8GWOoaAOe1YUxeYW3yQU55v1l1KXVbyKV6n1S4HFRZjSavAFNk0K4BurxMeeiut8NoQ2i+3t9egkJugxYVAkDPQAr3LtuqPSZc7UKfx4AjxFsbNhxQ30Hbx/hd6+TjP3sBn71reeB5lSASH4o0RZ0OYBmASUKIXYAldABMlKdNBbDNcdl2mTZV/vamu64RQqQB9AAY75OXrmxXE9FyIlq+b9++4h5QYgkUs627EIHi63A05KH+ZRxkvaG3RQyCYZ3dOeerbgAMvj6IXAy//rjzvmFneFqzhPDPJ0y0UKB5KMSLiMSyIfMwOeVzq/71x10adkBQiWmQ9ZtohFkk6ixDsRpKJisK2q1BR5gor0ODuQWifuNBkFPeuC4II8vkBQAgolEAfgvgK0KIg36natKET3qx17gThbhDCDFHCDGno6PDp3jBCBFg8orIh6LLDwi2QUehoYSZmQG58mtDHu1BOPh+dny951y/PZK85Qw72AetQ9GZJgrRtkwLHMO8F3Vv3alh36utoZjWoQRoW0F1GmafLVsAa6Mg/a/1HvMfTM1RXk4NrZw7Lx90CBTdObaWZBDwSTmWDBnel9+9a5WSBAoRJWEJk3uFEL+TyXukGQvy716Zvh3AdMfl0wDslOnTNOmua4goAaAdQKdPXmVFCCAZ1uQV0JDDqPN5HUqEy7sUXKYkX2eknH1pZldhZqIKkzM62DzjKItPHH/YT/wCQRqK8RY2xnUoIepBCWa/U4OyUYeDfCimOg0004YYZNM+gtF+pyEFShgNRSd0ovguUZgJi3MLG7+wYVN9NySs4bffJ1R8xIQNS1/GLwCsFkL8j+PQwwAWyN8LADzkSJ8vI7dmwXK+vyjNYr1EdLbM80rPNSqvKwA8Kf0sjwO4mIjGSmf8xTKtrDhNXvqFjf6zq2xIgZMzebnTc1+MDFfeYvqSs2P4DdRKkOg0FJVFKX0hMCIphEnCe8zPUQzon7cQ4Wgqq+378vFL2NFRmmNhq9GO8jIJlIABPWyAAmDWHmyzmo+T2q8qXaH3IXaT0GooaaeGYr6XH+493vTn9A6mtOcr/FbRA7kNVgc02+3k7h2+/dUCiRKuPQfApwCsJKJXZdrXAHwHwANEdBWArQA+AgBCiDeI6AEAb8KKELtGCKFq8vMA7gLQDOAx+R9gCaxfEtF6WJrJfJlXJxF9E8BL8rwbhBCdJTxLKLJC2Gqqrq0HhQ0XHGPvNXkFxPF7k4vZeiXsCmHVWXSDV9CnknX383aYQiKS/Lde8X8eZ5JWQ/GJWvIStPOtHyatwrpeCRv/jNR9TOXwC+kFggfRMG0jt+BVc/8ArRPwTLpCTJx0zzqY9neWe++jw3nUdO6hAJNXkA9FzV8ODqS0x61zVPs7zAWKEOI5mPf2u8hwzY0AbtSkLwdwkiZ9EFIgaY4tBLAwbHmjICtyJi+dhuKc4eo6TVinvckOHTRQR/GFRmfWfltC2J1FJ1DUjDzUrF4/Mw/qSC7NwteEkl8uVz4BM+IwHVodMW8TEzwzzw32fsLRfL3zeLEfnXLWVZDw1d1CCOEbMv7yli7f+wMFaCia8xXO3QZMTSMVEHUQJmzYuauz7pnUJMGszVnpG/bpPyfgvHcok6sQVd/zi1fKh0R1kKRflJcQ9kp6XXsNL1D0Hd82JYWcCRczqXEOBJv29xnPUx1ZJ3QK6QSmWahKL3XfqUL28tKZvNIO30aQgDRGpBmEpus+flqW712d54UTGGGEdJB50M9nYJUlH+XE9hvLC/0EsE7oOE1IRk3KZ7IEhDOpOu+t65NBGorK18+HktOQw9RF9bUYFighUe9TrZQ3mUf8NJiwJi/b1u01A6kZqMnkZcwxPM5ife4ec2x7zoeiGx0K6QR6E0mQqSmsr6eQQTJo9wPT44iQ78VPIPltZePNx4TdPgI1FP317uv8ha92AC3Cr5WXR8b/fSj8fCjOTwAYv64aKFCcv4Pz0N0myIei0n0FinpnIYSFn0WhUrBACYl6sSoyw9ShVFixn1MSCLcYL/8e/g3U2/CLaV5hbbW5sGGzyauQvZi8Z+ZCXE2Do/O3z+w+aBB0pOk+TBUmYiio04dbIW42j4TVNIO2qwl6t0EmraAoRpeG4nOrMNGD3vy8+G39PpTKtUnTrYI+cJcNqAvvvX2jvII0FMMHz6xr/cvgJMiMVwlYoIREvVA/DSWTFWhIxOXv/DzCdpZcNI473TZ5BcyET57aLs8vXKSEFSj+Ji8lDILzMU2qgkJcnZmH3204/zxnUIHOfOLaQTpgYDFqKGFmuz71qctHR5CGUsgnFYL8TUECx3vcK2yC/E1hygvoNY0h+U6TcTJrKAF5hwtAcJzj9XcKEUKgWH/7faK8giLznJjWH1USFighUfZpXx9KVqDBZ52Ks9GFWd2dv/VKkIZi/Q3yy/lHRflf681D65SXfwszebnPzTnD9dc508MIZ1N5nBsp6ganMGYctR7BrMHofztR9Rjm09Dae4SYrASvM/F/Vr8BND9/93GvJmush5B9xO/zuUpDaUrEje/EL6rOm2/QO9Od434O/92f+4eCBUoha5mqCQuUkKj3mQzYHDKZMJu8wvtQ9OcIx3FtA7MFiv4bIwr/7bLDNUrVIXXbfARpUk5Un/SeaW+DHmAucJ6rI8iM41ylrJ0kOJ5P9zj7eofw+1d2+JY1jIM3t1DU51n81rGEuEeghhNgsgrS9vw0cO8AbhZ64b966r2nQmmdTQ1mgRLkQ0m76iKEhuKdEIUQ8M51QwcH9aHDJn+qjiAhWQlYoIRgW2c/nlht7eYZtFK+IexeXyGclvnfQ3GeYy5vUOBgGGEWRC6CxU9DCc7H7oiecwPNe47ksN+U1w0MTg1F50MJGqi3dfU77qUvg/OqoAHOtCgxiDADWNCg5LzMb7ND77l2mo92cfI3/gIAOKKtSV5vEijO3z4ain1Ofn2pd9qcjIfShPTlCN6+xa8/hzFvO/P961v6fQYL8qHUgEApZWHjiOH9P3gWvdKs4bs5pBBokj6UoLBKvxh70+zcOytXIcoKNYMN+oKgn2rsvOe5x0wwnmfb/DV220L2vwoyzxS7qtt7LEb6TjmcyYLIqivtOpSAtRnuVdmGsoZwVpu2sgnt6A7QHrznBOehOZ7xv4dzEDV9k6UxadbgrTI4TJDFaigOgWL2ofgPvqmACC7nvWOU/26c79G4sDErMLopgd7BtLmdy3Q2eR1G9DqiMGwNxRDFpaLAAjUUn/asZqthfQMKlRQj0w5Z+eUw5WG6hzcP/61Xwg8IXnNOIRsZhtG4ErGYwYeSQYv8/K7Wh5I128kBr1PfoE0Zyu0k50PxznTDzTrDmFODo7wcwku3/bxTiAe0b5NAyU249GVQ1RmjgEmXUAJY/04BoCkZCxXyq9XGChDQDYlYXn2E0lCywMTRjQDcG026zgn0m+bSa0FDYYFSILmFi/oZmhIo+o0GC9NQ/ExeugamUoJMXr7bZTtu4mdnVo03lc1qwpWVhhVQEDiiubzamKEOvPfQXevKR16fMET8DKWzaG5IGPNxpWluE2Zn2zDalElAB9n67XxDDWC59L0HB/OOu30CujI6hGfAIGwy3SkNJciH0pCIhTJl6kJlbR9KMm5sP6mAiYDbh6IvgzrHmqx4joVYT5MVAqMarbY3aFiLEhSc4mwvLFDqkERM3yGEENjeNZBb2Kid7ep/ezGtSXAJJK2GolRw6ZQP6Ag6nPfwi2vPbRWiq4v8vEyoe3jzCN56Jb8s+vOsY8l4THvecDqLlgapoRgEigoV15sx8+/lxTURMA1wBpNXGOewKidgadDG2b8jfbdWoDjXbwS1X38NJkhDCXJ0NybiAZ8KFsb7qCiv5oa4cVITFE0WzoeSRSJG0pzqPidIYAFWfbXIyYxpg0i7L4Uw3bHJqw4xmbwefs3aPX/J+gMA9LOFtCuCxTxY2ytsPe3DZY7y6dBKizI1r7BOed/dhn06ZNCeUU5SaaHNI6ooL3XINON1CRTNO8uI3Fc6/fZr8iuHq6yGOlXtIc/k5ch/b++Q9lpVTsAsOAF3XR7SmFhSARFtzvarG8jDmLyCNRQrPUhDya3b0WsoRNa3i0xt0Ln4UZdHKsBfpMoQjxHiMco7J0z4szKRN8RjxtXydj8walqsodQ1ibh+pfw+T2fXvdxswAxPoQbyIKe8F3XY66z3EmZn26Dz/MwbOQ3FtxgAcoOUyeRlyiPMrN86Tw5Q8ZhWYAxn/DWUbDYXKq71objCjvVlcOZr0vrUwOCtc+e19yzdor8B3AOx8XsnjnSdzd7lV/D5hC8QQqA4TYGO9Ea18DfIL+EjGJ15qgmJk+F0Fg3xGOIx88LG4UxuANdbE5wCRV+GTEZIDSVfoITRcLLC6qtNyZhruxj3OWpyaRBqjnr+xM+X6QtaQVigFIgyfwSFYOrUz3TICBZThJPQnKO7LhbzN3n5hdK6NBSfMjoHPm+kl7oq3P5Dcmbuqa+gjzGF+Xqg81ijj4bS2mj2oaSzWVug6Mw0Q+ngjQid9WAyS+QWippnun6o6xOxfAex9xzAXe7cvZxCIP96Z1mGdNqcan/k1gB0i/yMbVP4vy+FMhHqfDVD6SwaEjHENJqDwh0urtNQ/M1/QE5DIaK8yYTzPZs0/UxWIEaWaa5/2N8pb6qKsO2jUrBAKZCkQUPxtjmtKh4ixBRw+BW85wTMylWHjgd45f0iUJx7lvk57zOuQUJfzjAmr+G0XnBkA/Jwnt7VN2zM32Xy0kYEZdGc1Gsoajv2pG3yys9/KMQ7dWkoBlOQai/egcXvHThRY2JDnIyTHWf70312Nh1g5nHOov00lJaGhDH67fgj2vLSXHlkcu3Pb0Ki6kUnUIYzWTQm4lJz0F8/FLBDgnMLfD+BkIjHEI/lCx11jZ+WlBUCMSK0NCQwkNK/56BPYdeCmcsJC5QQOE1IowyzWecsh0g/EDgbfxgNpVSTl2lltd93W9Q9GuOxUN9DAfIHlzCbIdplsX0HnjwCbMcqfWxLEls6+7XnOM9LxmPaENwhhw8lLzDAMes3lUU9+/jWhkC/ABC8DcfBwbRrkAitoajnTMSMe2U589UJBJdTXnMPNcjGyCCQZFmbG+JaDeDr738bjupoBRAsfP00lEw2990Vk1O+MRFDXOMst88JECjKpzG2JYlBwyRAaSgxyhca6pmbfEyQWSGkySuOAYOGEqSpq7JPH9cMoLj9+6KEBUoInC4Je/BxvLfv/nkNvvvntQCsBpiMx7Rbkrjs7X4huWm9huLeSyn/ujynvFFNNjuSVQNt9FkUBlgdMm7YKFP9K5RT3rD+IihcUmU9qa0JPT5fvFOCyeiUz2TRbHDK5xzdZqe8GphGNSVCLSgc1tj8rXvn0p0aV9iwYWWz99trzilQdALB7ZQ3D7JjWhrstR5O1PO3NMTdW9pkcvWoIhBNMSnWrN1/Zu8ytxombg2JmHagVwwFmLxU1NXY1gajf0NFeVmTFX0/avLpR5bJi9DSEPeJ8goyeVllb5XRYrr3WklYoITA2VjOPmo8ALf28JOnN9i///fvz0IyRnoNxdnJjCGmAp391oCSN+vJ+F9vm7wCnPJ+2kVulqmf0Sv6htMY25LMK5d6BiCcD0Vn8hJChPiYmAAR0Nac9BUoytbenIxrB+d9vUNIxmNIxCg/Wk0+Vs6Hkp//kBwIGhNmJ3IYDcWZfsApUEIubFT1qMx3uvYxnM6iSUZZ6Xwo+w/lAkt0z6o0lDHNSb2Gk8mVwSlw1ICtzEOAv4aSiMWQiJnbn8uEqO1nGTTEY0jEySiQgzYFHRjOIEZAW1PSKFCUhpKMU57pTfX/pmTc+BxZYfk7m5PxwCgvwLz2DYDtByx2656oYIESAtX2Lzt5Mia2WStbnR024RjAZ01oRTIR0zb0r9z/qv3bNPj0DKQwKO2peaGIAYulwq5Dcc5EvQODyrcpEfc1efUNpdHenNTmkdNQjJc7ypIf5RVmlXJWWAs425uTvt/kznXs/AF/o/z06j1Lt2gXPqYdA6GpLEMZy7ySiIUTKCabdzorbGHQWYSGknI8J6B/98OZ3EK6IY3N/pYn1tm/dY+i/DvtLUntwKXqp7khrvWhqIgowDyhymYdobiGsdHph9IJtmHplG9Oxl3fl3fiFKi6dzKQyqA5GUdTMqatK/VcSkPx+sbURKYxEfPVxuLSKT9gXNjoPt+LinJr9XmvlYQFSgGcMKUt1yEcPS7mECiNiTgSsVjeRoOpTDZwZ1sAuP7hN4znpDJZ2/zmt5txkIbiWk/gN7MyDH7D6SxSGYExLQ0yP4+5KsD/oSuLSfvyWylPRGhr8tdQ7CivZDxv4HCGzlozYr2G0uDnlE9Zg1dLg3mW6Z5Rm01earKys3tAe60fKcd7A/TtazhtOasb4rHAmazO/6a+22HUUGynvOVDURMcJRQT8ZjD5GXWUOIxsrQLU4i1q/3qzZiNiRiafAZql4aiKUv/cAbNDQk0+QilVCaLRDyGhkR+faq+0+ijoSiTV3PSbPJKB1glVNnGqMkdayi1z/nHdgAA/vH8o7VbrzgbZzxGaIhT3ozF+c2DhkTMuPBrs/yO+7jWhrzZqYpeAfzDhoOc8oMp8wxPda6mZL5QVPTJvc1UI/Z2Wud32INIaUxezv5nXOks1wCMbkpoF+nZ+TvMMCan+w/mn6a12YfRUO57aSt6B9NobUzY9eLFuSbBrKFkcdSEVhABa3f3Op7TOj+34ae/CcdvnUcqk0UyTmhMmGfdCt2rHxjOgAgY3ZTU2upVn1C7/NobXhagoWR8HN2553AI6AANZSidNQpXhW7iNDCcRnODlYefUGqIWwsTve9VldHPFJrNCsRi0odiuIeznv3MkMpaMOTzsa5KwAIlBP/vQyfjma++25o5ycHaL1xPZ/Lqk+aC73zoZAyns/j5c5vyrusbSuO17T1oa0rgtOlj8mZG6YzAGOm30A1edpRXgMnLORvyzmhsk5ePhqKeZfq4FgDAJikE7XKawp41qPu7Qpkd1/mtEG6IxzCqMYG+4bTPJoA5U5D3najOd0RbExIxyv9mh8gNhIB+MFfCeVRTwrWJqDufnEDwM3m1NibQMarRrl+VDgDnzbYmNaYZqEpXAQY6DcASKDE0JmNaH8q0sc04dfoYAGanvJ8ZKOd/k/Z8ORiqd5CIU26vO79QXNnPTNqZMyJK9xxDDoECQOsDcfYBndY4kMqgJemvoaj7WCYv/WTFT6CkswJxUlFewaY5XV9Qz6HGBdZQ6oCpY5px5Hgr3JGI5CDm1jgA4N8vexsAawDyzu6VAFAdHsjvtO/9n2cAWPZQ3SwylcmiQ+5O2qlZe+FcLe38txe/9QRO00lW6AemPqltnTDZWlNwaMhtcrJX+hdg8nI5rkNsuGiZGwgtjVY5Bw0zbvUemhL5GorqjE3JuDUoGIRro2GdipPRPhpKyrHWxc/klZCz1T6HNqvuOarRut4UxaPybfJ592rm3hCPafMZTGXQ2qD22sq/R/9wBi0NcYxuSqJX80EopYmp3ZttgeLQUBoT5qAAdW5M+lBM7bffMSvXvXelOai+pjMn9Q46hXZ+HpbJKy5XsZvq3LpPUmfyyub6kandDGesIAm/KC/nvXX1ofpyTkNhgVJ3jGp0m1lUx/nsObMAQOuk6+q3OuBY6XcA4BJKALCzx9qw76pzZ2lnRoOpDI6dNBrxGGHVjp68cqkZdZM9eOkbV/9QftntPLKePDSdTW0zM03Gvg8Mex2S1r9N6yFc52r28lJlSsYJA8MZXH3Pcpz/vadc16Wzlv1aOZkPGQbzdMYK7UzE8014qrM2JeNoaYznvQ91fLS8h5+DfDCVwZ6DQ9jdk7/p4qGhtP3eTQte9xwcRDwWQ3NDwrW4UbUz5a8yRRypa+xV/7ooL1tDiWsFiiUwrOt1gvyVrV1WWZqT6BvOaASw9bdFCj/Vfm0fSiyWEyiGgW8olUFjQj9IK5TAHd/aoBVMwx4NRTf7d/Zf3XsdlE75xkTcWOdOAZ0/McuZjo2hy6lcOdNZoV9T49JQ8vPwmrxYQ6lDmhvieHad9YU1ZyNQzvnRTYm8T3p29lmD8LjWBnz3w6cAcK83+MydL9q/k/FYnoZyzb0vo284gyPamjBjXAs2eMxMQgj8QX6KVkX66BrXyu09+MYf37T/ndcRssEzXbVT7ZT2ZjQkYuhPeVd3O7SNAKeyMu84tRJV7omjm3BwMI2/vLkHWw64Fy8qk5eKvzdpB+mstbljQhPKrWaFzcl43iQByHXmUSFCMp+WX9zzCr7hdBYrd/Rg/CgZwKAZvG5/ZgOG0lkMpNKY0t6EbZ05p/y+XquuZ0jzomkg/vJ9VgShGlh0vqeUXJ/RmIjlrSMRQmAglUFbs154DqUzWLO7F/sPDdsbPC7f3OU6Rwm1SfKrjEoLUBOMeJxsbc+kaR0cTKG9OWlFaBnMQI+u3AUA6BjdqBcWQ2k0J+O2hqITCL1DKd9+orSxpmTcWOdqvUtDIt9cajvlE3oNRQiBoXQGjYk4RjWZ27BTW9UJzwGPhuLsz8s2HsB9L27Vlr1csEApgk37+7CzZxAb9h3Cv/zmNQDA+046wj4+fVwLVu/qdQ3Gj67cDQCYMKrRHlzUeoOe/hSeWpv7BGiMrFmzsyM8IjvRwcEUxrTkh8ouXr3X3jxQOWZ15pVn17s/Nerdk0lpL23Nao1Jfh5v7LS0o0ltTdqO7+xAvb4O8yy2dw3kXaM6hTLv6UhLk5eakfcZVhoPpqxOm5DbujtNeIO2yUv6YjwdesjhHwHyHcBf+/1K+/cjXzoPADB31jjXOTc/8RYA4MAh6117NxEFgIVLLH/awYE0xrU2uCYje3uHkIgRpo+1BIrp2+MK09qgfb1DeGFjJwaGM2hI5Ju8htJZCGG1TyB/CxinsH1j50EAwFcffM11jjJFTW63BIpa16IG/ZZk3NZQTCaegwNpjG5K+EY+/fIFq51PHduMgVTGpQUfODSEvb1DOHbSaLtt6OqsdzCNcS16IS+EwMZ9fZg2thlNSUtT0k2sNu/vx6S2Rq251J6YJfVbyKSz1mr/xoRZy05lsujsG7Y1ZF1fci42BdxC52N3vIBrf7fSqL2XAxYoJdA3lJEAXSAAABr6SURBVMYf5bb17z95sp0+58hx6BlIYVePNVhu3t9nb28/tjWJsa3Wy1cayoE+9yATk7Zm3Szu0pOOwOimZN5usb0OP8ZowwAI5Gsk3hmeWlSpvv2tmxU9sXoPzj1mApob4lr7bzqTtTuBX0jvJ+XuqKMaExh0DAxKiE1qMwuUlHTeqs7onMkplm08gHuWbsHopoS9ODGtESiNyThaGxN5HU8dV/fwCtdfLbNmf1+6aDamjmnG8UeMtk2Fiq0OzWpyexNW7z6Y/ywOn5M3oGNv7xA6RjfadbH/kNl3BgAT5XvzPsttcvHtyh09Wv+cagfjZNvs97xTZ35/e/pUALlFvt5zTp1mOfZX77Ki1ZS/ZXRTEtPGNiPmiWRzcnAwhbampDEMO5MVtlA6akIrssKtYahteI6e2Iop7ZZJdkd3vhmydzDtCHvPN08PpDKYNaHVNpt5+8FwOotDQ2lMaW+WAsWj0cn6a2lIIJ0VeabfXNuL2f3VKzBWbLE0QOUT1QmUO+VkROWhM5t1atpMuWCBUgQqEkb5RQC3b2TmeGs2qQaT636Xm8k2JuIYLzutcqx/57E1rvzjRLadW4icbfWLFx6Ddx49QYbKugfqA45GM8YwS3WWSfF/f5ObZXb1DdtbyEwb25yXLwB8/fcrsa1zAKdObwcAOZPMtx9PkNpFd7++Mb+4qRPLNnUCAGZPGoVURtgDUsph8jIxnLb8Aa3SXq8zF3zsjhcAANu7BrTReUoItTTEMdojUHb1DODJtXsBOAS0YQfapMy7tTGRN7NXmt6sCa2YNaE1LyIOyA2IU8Y059njH1yxHbt6Bm1tTafhrNubG5x1g9NQOmMP6h84ZTIaE/G8AVIJkLEtSRC5w9yd+d3+qTPtiLOpY5pd53T3D6MpGcOMcS2IkfVvIQT+6d6X7bKNbkpi4ugm/On1nXnPoe7T1py0TE3pbN7s/ufPbsRQOotjJ42yo8kGHT48pbm3NzdgbKtV9z2eNjiYymDT/j67jXuFgfKDTWprsicI3onXxv3WotiWxoR2HYoShup9eJWUHrucSYxussrpnQR0y/HlmguOkXXj7vODqYw9wcgFO+QHuXQPsECpab53heUDeXVrt52mZnZALpxWzZbUrHTeaVMAwNZQVmztwuvbu/GXN/e48o8RuRqIakhKELU1JfJmK3scX+Ab0ywFlmYw33zAGtAuPH4iALhmgf/229ft37oBbDCVwb1yVj5NmmA27u+ztTTAMin1DaftwcakoXz09qX274/NmW6VVwpYpfEc0e4WKE7TRU+/ZWsPcsorlJDtknXy1Nq9+MOrO9AmtRevhnLFbUtx+zMbAegFijNEeFtXv/2sS9YfsJ/5u39eg19LG/atHz/dKFCOmmBFEP7bpce7Bidlhx/dmLCF66I3d7uuve/Frbj0lmcBAO8+rsMenFSbOTiYwtu/9QR+s2I7AOCWj52GJs1WH2rAbG5IYMKoRuztdc/qVd2Mbkogbm8X4m2DQziirQmxGGHi6Cas3d2L3qG0PZiqNrX74CA27OvThqUfHMhpKEC+aewpKeS3dw3knO6Oc5wDdZusC682/6fXLfOxMk+6/HfpLJ7fsB+Ape2pdtPtaceX/3iJfb7OKd8/bG3/0mLvseV+Dmc5c23Y63e12qraUPPggPs59h7M9c0Gj0BRmgsA3LVkM6647flQWyGVCguUIpg9cRTGtTbY9nEAmCG1EsCaaSbjhK2OXXDPmz0Bt3zsNADWAJGIEX61bCs++KMlmP92a0C94Dhr5heLkR2+ubtn0BYearAY1ZjAgb5h1yx5t2xcLQ1xnDTV0h6+/vtV2CIFiBACn77zRby8tRufPHsGFn767Xjb5DaXzd/ZMXUCxfk8p0ktzcvKHT0QAjhS1scezadmnQ376+9/m71CvFM+k/IFzZIDrWKzYzDu6h/GuNYGu06eXLPXda4yRwGWwFLP878vbIUQAp+58yVs2t9na1KjmiwfiqpT5ydylWnEKXC6+3Kd/4Hl1mCtZrwvb+nCnUs2ufZ4G9fagBOmtKG7P4X1ew/Z6Wt2H8Sa3b1Y8I4j0TG6Eck42TNmNfu89v3H2w7mx9/Yg22O93CtQ/v93hWn5mkoz7613x5Qx7YkkYjHMGtCCzbtzw3om/b34T0yZL05Gcf0sc22b0uhtAw1+FkC2D1I7jk4aJvczjxyLN7a0+vSdLzmwF2eiLj/+uMb6B1Ko605oQ35XbGlEy9stLTan37yTDQ3uP0xz6/fbwcnHNHehEYZgeX1oSit/PxjO5CIETbsy72PXy3bgm89shqAZXId32q1D6em/sSbe+wIwJjcT653MOXSCrZ19qOtOWmHe3tNsj0OTUppsV197nIu32I961ETRgHI1zRWSV/mbz//Dkce1jnqGQDgd6/swPItXVhjMDNGSV0LFCK6lIjWEtF6Irq2gvfF5847yv73Fy44xu5ogPwKWyKO257egAOHhtDdn8K5x0wAydVtRO5FW/EYYWxL0m4U8Rhw0dsmAbCc8TmBkuvMmazArOsexcrtPRBCYE/PIM6YMQbPX3uhy5mtoqP29g7haen4VyaLKe1NeHFTp90IlV39ux8+xXbOOreG3yAHwv/+yKl4m1yD8uEzprnqZp6cuXWMbsS41gYsXu0e6H/81Hoc9bVHAQDXXHA0Pveuo9AxyhqEHnp1J17d1m1rPDPHt+LUae04WQpINbsXQmDd3kNobUzYvoXfv7IDq3dZmuCungHbYX7+sR246Yrc8/z0mQ34jRQAADBBDhijGhNIZYQ9w7vguIn2OTPlGqSHX9uJu5ZsghDC1nQA4GdXzgFgaSExAu5Zuhn/5YikU8w50hLe7/mfZ7B49R7c/swGW7tQ65zUJ3wzWYGXZZjuJI/p73P3LM/LGwAmjGqw24gaRJ2axvy5MwAAsyeOxlA6aw/ov3s5Vx9HtDVh2tgWW+sCgAde2mbPllXbam3M11CWbepEh6znmRNasK1rAL9Zvg0A7MkUANz2iTMAwBW5t+fgIO5cstmuAyV8nALpw7fltNp3HduBca3uSc/X/7DKPj6qMQEiayeF25/ZaLfLB1fknvWojlF4+8xxeNERreYMjukY7QigkQEGmazA3zvqf/7bZ6BjdCOyIucLfXlrFx5ZuQtnHTXO1lCcJtkXNh7A3/3M8h+2NyftIAbnljsAsGFfH6aOacbUsVY0pTfS8ZWtXWhIxHDy1DFoa0piUluj7XeZ3J5vLj5m4qi8tKipW4FCRHEAPwbwPgAnAPg4EZ1Qqft//t1H278njGrIO65MIu/+/tMAgMkee7Nzv617l23FUR2jcNIUa+CcMa4F08e1oK0pge89vhZ/86PnAADjZWdVCwoB4G9+9Bx+9OR6vLi5E1PHttizaWWWW7GlC8+u2+eahb1HCqszjhwLAJh/xwvo7h/Gnt5BfOCUyfjo26ejtTGBtqYEbl28zh6cXtrchUSM8L6TcxFtyl9005/XuBZbfuacWbjo+IlYsaULu3sGbae7c/D6wgWzAeS+5XDX85ux0+FAPaK9CQ994Vzc/w9nI0bA69utGdms6yyBtGl/H4jINpl9WoZev+IwRSohrQQKAPyrw7T3nhMswaEG4q7+Ybyw8QCeWJ0zQ6oor6fX7sM3/vgm7lm6xRYo11xwNN57glWfbU1JnDilHUs3HoCTt8+06nmiQ9BfdfdyfNvhO3vnMZaDW5lxfrl0M1bKtUZK4zxxivXe1+zuxYd+sgSvbcs958UnTAIR2ZFLP392E7JZYfv5Ljp+Iq4611onpcw4z623TDsvbe608zl5Wjumj2vGzu5B/PWtfchmBe57ydL2po5pxmTp6G5vTmLjPkvAbz3Qj5v+bD3LdimIZo5vRSYr8N+LLC3eOeFSzvzHVu1Cd/8wnl+/344cBCzz29Ed1uCnZulOLeP6vznBLg8A7OjuRyqT1ZoT1Xt9bVs3evpTtnby8bnTEY8RZk5oxaZ9h+yItGdk+PeV7zgSjYm4LUB3yMH+x0+tt/P+1efOQntL0haiSrB96CfPO+rLGthVZBoAfPHXr9i/p45pRlMyjtFNCdcu00+8uQevbevG+046AvEYYdb4VnszUwBYvesgfvbsJjTKvcQA4N3HTsSyTZ24Z+lm7OoZxGWnTLbb/evfuNg+r5wkgk+pWeYCWC+E2AgARHQfgHkA8qeGZeL//e3J+NrvV+JE2eGd3H/12fjYHS/Y2sXcme5w0r/887sw70dLbDPKyVPbcdW5s3DOMRNwghw4/vb0qbjb8R1xtRbhvSdMwjUXHI0fP2WZVFSnVZ0HAD4yZzq++uDr+MFiawfZi6TP5Nl/vcAWZledOwvfe3wt1u7pxWk3LAJgOW0VX730ePzHH1bhlG/8xU5ra0rYsy4AGCeF6W1Pb7Ajie789NvR3pzE2ya34TcrtuPsby/Oq5+HrjnHNmuMaWnAu4/rwNNr9+Fnz1p+i9f+82K0y4GvpSGBc46ZgF88twlv7sxFSalZ75ffMxv3L9+GPQeHMPPaR1z3+dx51iA6XiP077v6bHtwU2aFd3z7Sdc5DYmY7btSODfwvOLM6a5jBwdTrtXNHzpjKq7/wIkAcsLNy/1Xn21/yfBMKeSda4WUL+kP15yD2V9/DADw8tZue9Z9y8dOw+Uy8krtO7a1s9/WBAHgF59+u/1bmQmv+91K7JfhxADw5YssAT91TAsyWYErF+bWRo1uTGDJtRfa/z5pajt+tWxrXn1/+0PWRObc2RNc6Rccn9P4xrY24JRp7bh32VbbJ6eYOb4Ff3fWDDvK8P888Br+zwPu8ORPv3MmgJyJ8Z/vfw3/fH/unE3ffr/9+5iJo7BZzuxPvSHXjv/1kuMBAB2jGnBwMI0533rCPtbSEMcN804CkPNbfuuR1RAC+OGTVn96/toLMUUKtCljrLJedutzrnJ+9pxZtm/1F89tQltTEotW77YFz3GTRttt/NBQGnc9vxmrdvTgvNkdtjn9XXIfwaM6WvHYqt347YrtGNuaxFV3W1rSpY7lCnNnjcP9y7fhPx+y2uc7jx6Pmz96mr17cyWoWw0FwFQA2xz/3i7TKsbfnTUDL//He/F2j7AAgLOOGo+PnGmZg2ZPHJXnYD66YxQelWsXZk1oxVfeMxuxGNnCBAC+8cETcdyk0QCAq991lD1bIiJ89ZLj8dy/XYDjjxhtn3/FmW7zkzMKZ/GavWhrSrjSmpJx/OmL57rSjp2Uy+8Tc2fYGojiy+851vXv9580GV7UIH3ZKfnHAMv5fKrHB/N/Lz4OAPDqtm6846jx9gI7xeWnWa9Wzf5/+skz7OCHKWOa7cHQyaZvvx+nyBDWxkQcX7poNi45cZJ9/CyH/+hkw6TgrW+9D03JOFbfcClOn5HvN1KDmuLz5x/t+vc3PniiPWjEY4SvvMddzstPm4KzHOG3czxtyanVJOMxPPbl81waKgBbQ1JcdrK+3hVvm5x7x2oy8q+XHmeX7TyPMABg+7kUV77jyLxzvjnvRLv9Tm5vxh+uOQdnzBiDV/7jvXkD2s8XzMm7/sLjJ+Lpr16AKWOaEYsRvvE3boPD1DHNWHrdhbbpuCkZz8v3kS+dax8HciHMTl782kV2YIxT0AHWO3r8K++y/01Edlu98dHVSGUE5p02xRYmgGUxOPcYd509/IVzcER7ExoSMXziLMvUePMTb2HVDmtC9I/nH43f/dM77fPVe16+pQs3P/EW2poS+OHHT7ffxTzZ/v/lN6/hs3cthxDAv7z3WNwkF0mrZ1HBPBcc14EPnzENDYlYxYQJAFC1PxlZLET0EQCXCCH+Xv77UwDmCiG+6DnvagBXA8CMGTPO3LJlS15e5WIoncFDr+zEWUeNs23kXvqH064Zf6GkMlk8uWYvLjx+or3WQtE7mMI9S7egpSGOB1dsxw3zTrJnwF5+tWwrpo9rdvl6AMtfobbZzgiRdw/F3t5BDKezmNLe7NrOHwCeXLMHv315BzpGNeKLFx6Dca0NrnsodnYP4KFXd+Ljc6fbpjsnm/b34Xcvb0dTMu7a+RmwHP3r9h7Cm7t6sLN7EH9/3ix7gacTIQQeWbkL5x3TYQ/0ikG57ceKLV2YPXF03nF1/Rd+9Qr2HRrCD+afZpuAnAwMZ3D9w6vwrmM78IFTpuQdz2QFnnlrL3Z0DWDe6VPtaCRF/3AaWw7047Vt3TjzyLGY7RDyTnZ0DyCTEa6AEJV/KpPFc+v2Y1tXPz4+d0aeQxwAXt/ejQ/+aAkuOK4Dt39qjssk0t0/jDd3HsR3/rwGzck4vv2hk3FUh9sGv3l/Hx5ZuQvnHDMB6UwWp88YW9DgdWgoje1d/di8vx9v7OzBly+abWtYgPzYXN8wlmw4ACEELjt5sus4YPk29hwcQlYITB/XYq8YVwymMnh67V40JuPY1zuEOUeOzXuOTFZg0/5DOKK9Gft7hzBzQn5fPTSUxlt7evHYyl34+NwZeXmoez3y+i6cOLXN1jgBy6e38LlNSMZjOP/YDhzRbu124ewDPQMpfP/xtXjXsR1IZbI4aUq7670KIfDoyt1Ys/sgskLg6I5R+NvTp+b1IyGEtYtExOYtIlohhMifBXjPq2OB8g4A3xBCXCL/fR0ACCG+bbpmzpw5YvlyvUOTYRiG0RNWoNSzyeslALOJaBYRNQCYD+DhKpeJYRhmxFK3TnkhRJqIvgDgcQBxAAuFEG8EXMYwDMOUiboVKAAghHgUwKOBJzIMwzBlp55NXgzDMEwNwQKFYRiGiQQWKAzDMEwksEBhGIZhIoEFCsMwDBMJdbuwsRiIqBfAWp9T2gH0+ByfASDoI81BeQQdjyIPLme0eXA5o82DyxltHpUo53FCCP2WDU6EECPmPwDLA47fEXB8X4h7BOXhezyKPLicXE4uJ5cz4nv4jp3qPzZ5ufljwPHugONh8gg6HkUeXM5o8+ByRpsHlzPaPCpVzkBGmslruQixH025rq8UXM5o4XJGC5czWipRzrD3GGkayh1Vvr5ScDmjhcsZLVzOaKlEOUPdY0RpKAzDMEz5GGkaCsMwDFMmRrxAIaKFRLSXiFY50k4loqVEtJKI/khEbTI9SUR3y/TV6hss8tjTRLSWiF6V/03U3a9C5Wwgojtl+mtE9G7HNWfK9PVEdCvpvnRVG+UsW30S0XQiekq+wzeI6MsyfRwRLSKidfLvWMc118k6W0tElzjSy1afEZezZuqTiMbL8w8R0Y88edVMfQaUs5bq871EtELW2woiutCRV1n7ex5hQsEO5/8AvAvAGQBWOdJeAnC+/P1ZAN+Uv/8OwH3ydwuAzQBmyn8/DWBOjZTzGgB3yt8TAawAEJP/fhHAOwAQgMcAvK9Gy1m2+gQwGcAZ8vdoAG8BOAHAdwFcK9OvBXCT/H0CgNcANAKYBWADgHi56zPictZSfbYCOBfAPwL4kSevWqpPv3LWUn2eDmCK/H0SgB2VqE/dfyNeQxFC/BVApyf5OAB/lb8XAfiwOh1AKxElADQDGAZwsAbLeQKAxfK6vbDCCucQ0WQAbUKIpcJqbfcAuLzWyhlleQxl3CWEeFn+7gWwGsBUAPMA3C1Puxu5upkHayIxJITYBGA9gLnlrs+oyhlVeaIqpxCiTwjxHIBBZz61Vp+mcpabIsr5ihBip0x/A0ATETVWor97GfECxcAqAB+Uvz8CYLr8/SCAPgC7YK1M/b4Qwjl43inV3/8ou2rpX87XAMwjogQRzQJwpjw2FcB2x/XbZVqtlVNR9vokopmwZnjLAEwSQuwCrE4NS2sCrDra5rhM1VvF6rPEcipqpT5N1Fp9BlGL9flhAK8IIYZQhf7OAkXPZwFcQ0QrYKmcwzJ9LoAMgCmwTAr/QkRHyWOfEEKcDOA8+d+nqljOhbAaz3IAtwB4HkAaltrrpRJhfoWWE6hAfRLRKAC/BfAVIYSfpmmqt4rUZwTlBGqrPo1ZaNKqWZ9+1Fx9EtGJAG4C8A8qSXNaWfs7CxQNQog1QoiLhRBnAvg1LFs0YPlQ/iyESEkTzRJIE40QYof82wvgV6iMqUFbTiFEWgjxz0KI04QQ8wCMAbAO1uA9zZHFNAA7vfnWQDnLXp9ElITVWe8VQvxOJu+RZgJlftkr07fDrTmpeit7fUZUzlqrTxO1Vp9Gaq0+iWgagN8DuFIIocarivd3FigaVMQGEcUA/DuAn8pDWwFcSBatAM4GsEaabCbIa5IAPgDLzFOVchJRiywfiOi9ANJCiDelmtxLRGdLFf1KAA/VWjnLXZ/y2X8BYLUQ4n8chx4GsED+XoBc3TwMYL60S88CMBvAi+Wuz6jKWYP1qaUG69OUT03VJxGNAfAIgOuEEEvUyVXp71F7+evtP1gz5l0AUrAk+lUAvgwrsuItAN9BbgHoKAC/geX4ehPAV0UuGmQFgNflsR9ARtdUqZwzYe2qvBrAEwCOdOQzB1bj3wDgR+qaWipnuesTVuSOkPm/Kv97P4DxsIIE1sm/4xzXfF3W2Vo4ImXKWZ9RlbNG63MzrOCNQ7KdnFCj9ZlXzlqrT1iTtD7Hua8CmFiJ/u79j1fKMwzDMJHAJi+GYRgmEligMAzDMJHAAoVhGIaJBBYoDMMwTCSwQGEYhmEigQUKw9QIRPSPRHRlAefPJMeuzgxTbRLVLgDDMNZiOSHET4PPZJjahQUKw0SE3Mjvz7A28jsd1kLOKwG8DcD/wFoYux/Ap4UQu4joaVj7l50D4GEiGg3gkBDi+0R0GqwdBVpgLUr7rBCii4jOhLUHWj+A5yr3dAwTDJu8GCZajgNwhxDiFFifNrgGwA8BXCGsvcwWArjRcf4YIcT5Qoj/9uRzD4B/k/msBHC9TL8TwJeEEO8o50MwTDGwhsIw0bJN5PZT+l8AX4P10aNFcofzOKytaRT3ezMgonZYguYZmXQ3gN9o0n8J4H3RPwLDFAcLFIaJFu9eRr0A3vDRKPoKyJs0+TNMzcAmL4aJlhlEpITHxwG8AKBDpRFRUn63wogQogdAFxGdJ5M+BeAZIUQ3gB4iOlemfyL64jNM8bCGwjDRshrAAiK6HdausD8E8DiAW6XJKgHrY2JvBOSzAMBPiagFwEYAn5HpnwGwkIj6Zb4MUzPwbsMMExEyyutPQoiTqlwUhqkKbPJiGIZhIoE1FIZhGCYSWENhGIZh/n97dSwAAAAAMMjfehI7S6KFUABYCAWAhVAAWAgFgIVQAFgEs24sEjTX87QAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'].plot()"
]
......@@ -215,9 +1279,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbd8d5200f0>"
]
},
"execution_count": 10,
"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'][-200:].plot()"
]
......@@ -364,7 +1451,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/incidence-PAY-3.csv 0 → 100644
View file @ 049e522a
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