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Commit 487a241b authored by b8da4ddbfed996242cdcb845f51d28fe's avatar b8da4ddbfed996242cdcb845f51d28fe
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update analyse-syndrome-grippal and csv file

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module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 487a241b
......@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -26,17 +26,6 @@
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -56,16 +45,1053 @@
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
"Nous vérifions si le fichier csv contenant les données est déjà téléchargé. Si tel est le cas, nous chargeons simplement les données en mémoire. Sinon, nous téléchargeons le fichier csv avant de charger les données en mémoire depuis celui-ci. La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`. Nous précisons aussi l'encodage des données et nous ne chargeons que les 11 premières colonnes du tableau, qui sont celles présentées ci-dessus."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>inc_up</th>\n",
" <th>inc100</th>\n",
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" <th>inc100_up</th>\n",
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" <th>5</th>\n",
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" <tr>\n",
" <th>6</th>\n",
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" <td>202524</td>\n",
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" <td>18577.0</td>\n",
" <td>27731.0</td>\n",
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" <td>28.0</td>\n",
" <td>42.0</td>\n",
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" <th>7</th>\n",
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" <td>202523</td>\n",
" <td>3</td>\n",
" <td>24391</td>\n",
" <td>19307.0</td>\n",
" <td>29475.0</td>\n",
" <td>36</td>\n",
" <td>28.0</td>\n",
" <td>44.0</td>\n",
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" <td>France</td>\n",
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" <tr>\n",
" <th>8</th>\n",
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" <th>9</th>\n",
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" <td>28849.0</td>\n",
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" <td>27.0</td>\n",
" <td>43.0</td>\n",
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" <th>10</th>\n",
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" <td>202520</td>\n",
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" <td>20265</td>\n",
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" <td>24716.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>202519</td>\n",
" <td>3</td>\n",
" <td>16264</td>\n",
" <td>12394.0</td>\n",
" <td>20134.0</td>\n",
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" <td>18.0</td>\n",
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" <th>12</th>\n",
" <td>13</td>\n",
" <td>202518</td>\n",
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" <th>13</th>\n",
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" <td>17291.0</td>\n",
" <td>27009.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>14</th>\n",
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" <td>202516</td>\n",
" <td>3</td>\n",
" <td>28564</td>\n",
" <td>22550.0</td>\n",
" <td>34578.0</td>\n",
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" <td>52.0</td>\n",
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" <td>France</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>202515</td>\n",
" <td>3</td>\n",
" <td>35721</td>\n",
" <td>29592.0</td>\n",
" <td>41850.0</td>\n",
" <td>53</td>\n",
" <td>44.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>16</th>\n",
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" <td>202514</td>\n",
" <td>3</td>\n",
" <td>37579</td>\n",
" <td>31232.0</td>\n",
" <td>43926.0</td>\n",
" <td>56</td>\n",
" <td>47.0</td>\n",
" <td>65.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>202513</td>\n",
" <td>3</td>\n",
" <td>39673</td>\n",
" <td>33686.0</td>\n",
" <td>45660.0</td>\n",
" <td>59</td>\n",
" <td>50.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>202512</td>\n",
" <td>3</td>\n",
" <td>52543</td>\n",
" <td>45627.0</td>\n",
" <td>59459.0</td>\n",
" <td>78</td>\n",
" <td>68.0</td>\n",
" <td>88.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>202511</td>\n",
" <td>3</td>\n",
" <td>59469</td>\n",
" <td>52154.0</td>\n",
" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>202510</td>\n",
" <td>3</td>\n",
" <td>60334</td>\n",
" <td>53048.0</td>\n",
" <td>67620.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
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" <td>94068.0</td>\n",
" <td>126</td>\n",
" <td>112.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
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" <td>293.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>202505</td>\n",
" <td>3</td>\n",
" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>350043</td>\n",
" <td>332885.0</td>\n",
" <td>367201.0</td>\n",
" <td>522</td>\n",
" <td>496.0</td>\n",
" <td>548.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>202503</td>\n",
" <td>3</td>\n",
" <td>252772</td>\n",
" <td>238917.0</td>\n",
" <td>266627.0</td>\n",
" <td>377</td>\n",
" <td>356.0</td>\n",
" <td>398.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
" <td>242991.0</td>\n",
" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</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",
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" <tr>\n",
" <th>2096</th>\n",
" <td>2097</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",
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" <tr>\n",
" <th>2097</th>\n",
" <td>2098</td>\n",
" <td>198520</td>\n",
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" <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>2098</th>\n",
" <td>2099</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>2099</th>\n",
" <td>2100</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>2100</th>\n",
" <td>2101</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>2101</th>\n",
" <td>2102</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>2102</th>\n",
" <td>2103</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>2103</th>\n",
" <td>2104</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>2104</th>\n",
" <td>2105</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>2105</th>\n",
" <td>2106</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>2106</th>\n",
" <td>2107</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>2107</th>\n",
" <td>2108</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>2108</th>\n",
" <td>2109</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>2109</th>\n",
" <td>2110</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>2110</th>\n",
" <td>2111</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>2111</th>\n",
" <td>2112</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>2112</th>\n",
" <td>2113</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>2113</th>\n",
" <td>2114</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>2114</th>\n",
" <td>2115</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>2115</th>\n",
" <td>2116</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>2116</th>\n",
" <td>2117</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>2117</th>\n",
" <td>2118</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>2118</th>\n",
" <td>2119</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>2119</th>\n",
" <td>2120</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>2120</th>\n",
" <td>2121</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>2121</th>\n",
" <td>2122</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>2122</th>\n",
" <td>2123</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>2123</th>\n",
" <td>2124</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>2124</th>\n",
" <td>2125</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>2125</th>\n",
" <td>2126</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>2126 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202530 3 21219 14914.0 27524.0 32 23.0 \n",
"1 2 202529 3 18648 13750.0 23546.0 28 21.0 \n",
"2 3 202528 3 23285 18131.0 28439.0 35 27.0 \n",
"3 4 202527 3 21453 17129.0 25777.0 32 26.0 \n",
"4 5 202526 3 21945 17422.0 26468.0 33 26.0 \n",
"5 6 202525 3 23323 18546.0 28100.0 35 28.0 \n",
"6 7 202524 3 23154 18577.0 27731.0 35 28.0 \n",
"7 8 202523 3 24391 19307.0 29475.0 36 28.0 \n",
"8 9 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"9 10 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"10 11 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"11 12 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"12 13 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"13 14 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"14 15 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"15 16 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"16 17 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"17 18 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"18 19 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"19 20 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"20 21 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"21 22 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"22 23 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"23 24 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"24 25 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"25 26 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"26 27 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"27 28 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"28 29 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"29 30 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"2096 2097 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2097 2098 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2098 2099 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2099 2100 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2100 2101 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2101 2102 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2102 2103 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2103 2104 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2104 2105 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2105 2106 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2106 2107 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2107 2108 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2108 2109 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2109 2110 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2110 2111 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2111 2112 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2112 2113 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2113 2114 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2114 2115 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2115 2116 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2116 2117 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2117 2118 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2118 2119 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2119 2120 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2120 2121 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2121 2122 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2122 2123 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2123 2124 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2124 2125 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2125 2126 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 41.0 FR France \n",
"1 35.0 FR France \n",
"2 43.0 FR France \n",
"3 38.0 FR France \n",
"4 40.0 FR France \n",
"5 42.0 FR France \n",
"6 42.0 FR France \n",
"7 44.0 FR France \n",
"8 35.0 FR France \n",
"9 43.0 FR France \n",
"10 37.0 FR France \n",
"11 30.0 FR France \n",
"12 33.0 FR France \n",
"13 40.0 FR France \n",
"14 52.0 FR France \n",
"15 62.0 FR France \n",
"16 65.0 FR France \n",
"17 68.0 FR France \n",
"18 88.0 FR France \n",
"19 100.0 FR France \n",
"20 101.0 FR France \n",
"21 140.0 FR France \n",
"22 220.0 FR France \n",
"23 331.0 FR France \n",
"24 431.0 FR France \n",
"25 523.0 FR France \n",
"26 548.0 FR France \n",
"27 398.0 FR France \n",
"28 405.0 FR France \n",
"29 370.0 FR France \n",
"... ... ... ... \n",
"2096 59.0 FR France \n",
"2097 64.0 FR France \n",
"2098 97.0 FR France \n",
"2099 93.0 FR France \n",
"2100 80.0 FR France \n",
"2101 116.0 FR France \n",
"2102 149.0 FR France \n",
"2103 281.0 FR France \n",
"2104 395.0 FR France \n",
"2105 485.0 FR France \n",
"2106 544.0 FR France \n",
"2107 689.0 FR France \n",
"2108 722.0 FR France \n",
"2109 762.0 FR France \n",
"2110 926.0 FR France \n",
"2111 1113.0 FR France \n",
"2112 1236.0 FR France \n",
"2113 832.0 FR France \n",
"2114 459.0 FR France \n",
"2115 207.0 FR France \n",
"2116 190.0 FR France \n",
"2117 198.0 FR France \n",
"2118 224.0 FR France \n",
"2119 266.0 FR France \n",
"2120 219.0 FR France \n",
"2121 176.0 FR France \n",
"2122 163.0 FR France \n",
"2123 195.0 FR France \n",
"2124 308.0 FR France \n",
"2125 213.0 FR France \n",
"\n",
"[2126 rows x 11 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"try:\n",
" data_path = \"incidence-PAY-3.csv\"\n",
" raw_data = pd.read_csv(data_path, encoding = 'iso-8859-1', skiprows=1, usecols=[i for i in range(11)])\n",
"except:\n",
" data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
" tmp_data = pd.read_csv(data_url, encoding = 'iso-8859-1')\n",
" tmp_data.to_csv(\"incidence-PAY-3.csv\")\n",
" data_path = \"incidence-PAY-3.csv\"\n",
" raw_data = pd.read_csv(data_path, encoding = 'iso-8859-1', skiprows=1, usecols=[i for i in range(11)])\n",
"raw_data"
]
},
......@@ -78,9 +1104,75 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1889</th>\n",
" <td>1890</td>\n",
" <td>198919</td>\n",
" <td>3</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"1889 1890 198919 3 - NaN NaN - NaN \n",
"\n",
" inc100_up geo_insee geo_name \n",
"1889 NaN FR France "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
......@@ -94,9 +1186,1038 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>202530</td>\n",
" <td>3</td>\n",
" <td>21219</td>\n",
" <td>14914.0</td>\n",
" <td>27524.0</td>\n",
" <td>32</td>\n",
" <td>23.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>202529</td>\n",
" <td>3</td>\n",
" <td>18648</td>\n",
" <td>13750.0</td>\n",
" <td>23546.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>202528</td>\n",
" <td>3</td>\n",
" <td>23285</td>\n",
" <td>18131.0</td>\n",
" <td>28439.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>202527</td>\n",
" <td>3</td>\n",
" <td>21453</td>\n",
" <td>17129.0</td>\n",
" <td>25777.0</td>\n",
" <td>32</td>\n",
" <td>26.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>202526</td>\n",
" <td>3</td>\n",
" <td>21945</td>\n",
" <td>17422.0</td>\n",
" <td>26468.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>202525</td>\n",
" <td>3</td>\n",
" <td>23323</td>\n",
" <td>18546.0</td>\n",
" <td>28100.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>202524</td>\n",
" <td>3</td>\n",
" <td>23154</td>\n",
" <td>18577.0</td>\n",
" <td>27731.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>202523</td>\n",
" <td>3</td>\n",
" <td>24391</td>\n",
" <td>19307.0</td>\n",
" <td>29475.0</td>\n",
" <td>36</td>\n",
" <td>28.0</td>\n",
" <td>44.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>202522</td>\n",
" <td>3</td>\n",
" <td>18755</td>\n",
" <td>14333.0</td>\n",
" <td>23177.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>202521</td>\n",
" <td>3</td>\n",
" <td>23760</td>\n",
" <td>18671.0</td>\n",
" <td>28849.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>202520</td>\n",
" <td>3</td>\n",
" <td>20265</td>\n",
" <td>15814.0</td>\n",
" <td>24716.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>202519</td>\n",
" <td>3</td>\n",
" <td>16264</td>\n",
" <td>12394.0</td>\n",
" <td>20134.0</td>\n",
" <td>24</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>202518</td>\n",
" <td>3</td>\n",
" <td>18115</td>\n",
" <td>13975.0</td>\n",
" <td>22255.0</td>\n",
" <td>27</td>\n",
" <td>21.0</td>\n",
" <td>33.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>202517</td>\n",
" <td>3</td>\n",
" <td>22150</td>\n",
" <td>17291.0</td>\n",
" <td>27009.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>202516</td>\n",
" <td>3</td>\n",
" <td>28564</td>\n",
" <td>22550.0</td>\n",
" <td>34578.0</td>\n",
" <td>43</td>\n",
" <td>34.0</td>\n",
" <td>52.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>202515</td>\n",
" <td>3</td>\n",
" <td>35721</td>\n",
" <td>29592.0</td>\n",
" <td>41850.0</td>\n",
" <td>53</td>\n",
" <td>44.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>202514</td>\n",
" <td>3</td>\n",
" <td>37579</td>\n",
" <td>31232.0</td>\n",
" <td>43926.0</td>\n",
" <td>56</td>\n",
" <td>47.0</td>\n",
" <td>65.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>202513</td>\n",
" <td>3</td>\n",
" <td>39673</td>\n",
" <td>33686.0</td>\n",
" <td>45660.0</td>\n",
" <td>59</td>\n",
" <td>50.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>202512</td>\n",
" <td>3</td>\n",
" <td>52543</td>\n",
" <td>45627.0</td>\n",
" <td>59459.0</td>\n",
" <td>78</td>\n",
" <td>68.0</td>\n",
" <td>88.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>202511</td>\n",
" <td>3</td>\n",
" <td>59469</td>\n",
" <td>52154.0</td>\n",
" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>202510</td>\n",
" <td>3</td>\n",
" <td>60334</td>\n",
" <td>53048.0</td>\n",
" <td>67620.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>202509</td>\n",
" <td>3</td>\n",
" <td>84531</td>\n",
" <td>74994.0</td>\n",
" <td>94068.0</td>\n",
" <td>126</td>\n",
" <td>112.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
" <td>312</td>\n",
" <td>293.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>202505</td>\n",
" <td>3</td>\n",
" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>350043</td>\n",
" <td>332885.0</td>\n",
" <td>367201.0</td>\n",
" <td>522</td>\n",
" <td>496.0</td>\n",
" <td>548.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>202503</td>\n",
" <td>3</td>\n",
" <td>252772</td>\n",
" <td>238917.0</td>\n",
" <td>266627.0</td>\n",
" <td>377</td>\n",
" <td>356.0</td>\n",
" <td>398.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
" <td>242991.0</td>\n",
" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</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>2096</th>\n",
" <td>2097</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>2097</th>\n",
" <td>2098</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>2098</th>\n",
" <td>2099</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>2099</th>\n",
" <td>2100</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>2100</th>\n",
" <td>2101</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>2101</th>\n",
" <td>2102</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>2102</th>\n",
" <td>2103</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>2103</th>\n",
" <td>2104</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>2104</th>\n",
" <td>2105</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>2105</th>\n",
" <td>2106</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>2106</th>\n",
" <td>2107</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>2107</th>\n",
" <td>2108</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>2108</th>\n",
" <td>2109</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>2109</th>\n",
" <td>2110</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>2110</th>\n",
" <td>2111</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>2111</th>\n",
" <td>2112</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>2112</th>\n",
" <td>2113</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>2113</th>\n",
" <td>2114</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>2114</th>\n",
" <td>2115</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>2115</th>\n",
" <td>2116</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>2116</th>\n",
" <td>2117</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>2117</th>\n",
" <td>2118</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>2118</th>\n",
" <td>2119</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>2119</th>\n",
" <td>2120</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>2120</th>\n",
" <td>2121</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>2121</th>\n",
" <td>2122</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>2122</th>\n",
" <td>2123</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>2123</th>\n",
" <td>2124</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>2124</th>\n",
" <td>2125</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>2125</th>\n",
" <td>2126</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>2125 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202530 3 21219 14914.0 27524.0 32 23.0 \n",
"1 2 202529 3 18648 13750.0 23546.0 28 21.0 \n",
"2 3 202528 3 23285 18131.0 28439.0 35 27.0 \n",
"3 4 202527 3 21453 17129.0 25777.0 32 26.0 \n",
"4 5 202526 3 21945 17422.0 26468.0 33 26.0 \n",
"5 6 202525 3 23323 18546.0 28100.0 35 28.0 \n",
"6 7 202524 3 23154 18577.0 27731.0 35 28.0 \n",
"7 8 202523 3 24391 19307.0 29475.0 36 28.0 \n",
"8 9 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"9 10 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"10 11 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"11 12 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"12 13 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"13 14 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"14 15 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"15 16 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"16 17 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"17 18 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"18 19 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"19 20 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"20 21 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"21 22 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"22 23 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"23 24 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"24 25 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"25 26 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"26 27 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"27 28 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"28 29 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"29 30 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"2096 2097 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2097 2098 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2098 2099 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2099 2100 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2100 2101 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2101 2102 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2102 2103 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2103 2104 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2104 2105 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2105 2106 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2106 2107 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2107 2108 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2108 2109 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2109 2110 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2110 2111 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2111 2112 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2112 2113 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2113 2114 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2114 2115 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2115 2116 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2116 2117 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2117 2118 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2118 2119 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2119 2120 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2120 2121 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2121 2122 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2122 2123 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2123 2124 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2124 2125 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2125 2126 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 41.0 FR France \n",
"1 35.0 FR France \n",
"2 43.0 FR France \n",
"3 38.0 FR France \n",
"4 40.0 FR France \n",
"5 42.0 FR France \n",
"6 42.0 FR France \n",
"7 44.0 FR France \n",
"8 35.0 FR France \n",
"9 43.0 FR France \n",
"10 37.0 FR France \n",
"11 30.0 FR France \n",
"12 33.0 FR France \n",
"13 40.0 FR France \n",
"14 52.0 FR France \n",
"15 62.0 FR France \n",
"16 65.0 FR France \n",
"17 68.0 FR France \n",
"18 88.0 FR France \n",
"19 100.0 FR France \n",
"20 101.0 FR France \n",
"21 140.0 FR France \n",
"22 220.0 FR France \n",
"23 331.0 FR France \n",
"24 431.0 FR France \n",
"25 523.0 FR France \n",
"26 548.0 FR France \n",
"27 398.0 FR France \n",
"28 405.0 FR France \n",
"29 370.0 FR France \n",
"... ... ... ... \n",
"2096 59.0 FR France \n",
"2097 64.0 FR France \n",
"2098 97.0 FR France \n",
"2099 93.0 FR France \n",
"2100 80.0 FR France \n",
"2101 116.0 FR France \n",
"2102 149.0 FR France \n",
"2103 281.0 FR France \n",
"2104 395.0 FR France \n",
"2105 485.0 FR France \n",
"2106 544.0 FR France \n",
"2107 689.0 FR France \n",
"2108 722.0 FR France \n",
"2109 762.0 FR France \n",
"2110 926.0 FR France \n",
"2111 1113.0 FR France \n",
"2112 1236.0 FR France \n",
"2113 832.0 FR France \n",
"2114 459.0 FR France \n",
"2115 207.0 FR France \n",
"2116 190.0 FR France \n",
"2117 198.0 FR France \n",
"2118 224.0 FR France \n",
"2119 266.0 FR France \n",
"2120 219.0 FR France \n",
"2121 176.0 FR France \n",
"2122 163.0 FR France \n",
"2123 195.0 FR France \n",
"2124 308.0 FR France \n",
"2125 213.0 FR France \n",
"\n",
"[2125 rows x 11 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -122,9 +2243,81 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0 2025-07-21/2025-07-27\n",
"1 2025-07-14/2025-07-20\n",
"2 2025-07-07/2025-07-13\n",
"3 2025-06-30/2025-07-06\n",
"4 2025-06-23/2025-06-29\n",
"5 2025-06-16/2025-06-22\n",
"6 2025-06-09/2025-06-15\n",
"7 2025-06-02/2025-06-08\n",
"8 2025-05-26/2025-06-01\n",
"9 2025-05-19/2025-05-25\n",
"10 2025-05-12/2025-05-18\n",
"11 2025-05-05/2025-05-11\n",
"12 2025-04-28/2025-05-04\n",
"13 2025-04-21/2025-04-27\n",
"14 2025-04-14/2025-04-20\n",
"15 2025-04-07/2025-04-13\n",
"16 2025-03-31/2025-04-06\n",
"17 2025-03-24/2025-03-30\n",
"18 2025-03-17/2025-03-23\n",
"19 2025-03-10/2025-03-16\n",
"20 2025-03-03/2025-03-09\n",
"21 2025-02-24/2025-03-02\n",
"22 2025-02-17/2025-02-23\n",
"23 2025-02-10/2025-02-16\n",
"24 2025-02-03/2025-02-09\n",
"25 2025-01-27/2025-02-02\n",
"26 2025-01-20/2025-01-26\n",
"27 2025-01-13/2025-01-19\n",
"28 2025-01-06/2025-01-12\n",
"29 2024-12-30/2025-01-05\n",
" ... \n",
"2096 1985-05-20/1985-05-26\n",
"2097 1985-05-13/1985-05-19\n",
"2098 1985-05-06/1985-05-12\n",
"2099 1985-04-29/1985-05-05\n",
"2100 1985-04-22/1985-04-28\n",
"2101 1985-04-15/1985-04-21\n",
"2102 1985-04-08/1985-04-14\n",
"2103 1985-04-01/1985-04-07\n",
"2104 1985-03-25/1985-03-31\n",
"2105 1985-03-18/1985-03-24\n",
"2106 1985-03-11/1985-03-17\n",
"2107 1985-03-04/1985-03-10\n",
"2108 1985-02-25/1985-03-03\n",
"2109 1985-02-18/1985-02-24\n",
"2110 1985-02-11/1985-02-17\n",
"2111 1985-02-04/1985-02-10\n",
"2112 1985-01-28/1985-02-03\n",
"2113 1985-01-21/1985-01-27\n",
"2114 1985-01-14/1985-01-20\n",
"2115 1985-01-07/1985-01-13\n",
"2116 1984-12-31/1985-01-06\n",
"2117 1984-12-24/1984-12-30\n",
"2118 1984-12-17/1984-12-23\n",
"2119 1984-12-10/1984-12-16\n",
"2120 1984-12-03/1984-12-09\n",
"2121 1984-11-26/1984-12-02\n",
"2122 1984-11-19/1984-11-25\n",
"2123 1984-11-12/1984-11-18\n",
"2124 1984-11-05/1984-11-11\n",
"2125 1984-10-29/1984-11-04\n",
"Name: period, Length: 2125, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
......@@ -133,7 +2326,8 @@
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]"
"data['period'] = [convert_week(yw) for yw in data['week']]\n",
"data['period']"
]
},
{
......@@ -152,13 +2346,85 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"period\n",
"1984-10-29/1984-11-04 68422\n",
"1984-11-05/1984-11-11 135223\n",
"1984-11-12/1984-11-18 87330\n",
"1984-11-19/1984-11-25 72029\n",
"1984-11-26/1984-12-02 78620\n",
"1984-12-03/1984-12-09 101073\n",
"1984-12-10/1984-12-16 123680\n",
"1984-12-17/1984-12-23 101726\n",
"1984-12-24/1984-12-30 84830\n",
"1984-12-31/1985-01-06 85489\n",
"1985-01-07/1985-01-13 97586\n",
"1985-01-14/1985-01-20 213901\n",
"1985-01-21/1985-01-27 424937\n",
"1985-01-28/1985-02-03 637302\n",
"1985-02-04/1985-02-10 565825\n",
"1985-02-11/1985-02-17 471852\n",
"1985-02-18/1985-02-24 389886\n",
"1985-02-25/1985-03-03 369895\n",
"1985-03-04/1985-03-10 353231\n",
"1985-03-11/1985-03-17 276205\n",
"1985-03-18/1985-03-24 245240\n",
"1985-03-25/1985-03-31 197206\n",
"1985-04-01/1985-04-07 134545\n",
"1985-04-08/1985-04-14 63881\n",
"1985-04-15/1985-04-21 50362\n",
"1985-04-22/1985-04-28 34053\n",
"1985-04-29/1985-05-05 40555\n",
"1985-05-06/1985-05-12 43154\n",
"1985-05-13/1985-05-19 27896\n",
"1985-05-20/1985-05-26 26096\n",
" ... \n",
"2024-12-30/2025-01-05 231549\n",
"2025-01-06/2025-01-12 257247\n",
"2025-01-13/2025-01-19 252772\n",
"2025-01-20/2025-01-26 350043\n",
"2025-01-27/2025-02-02 334395\n",
"2025-02-03/2025-02-09 273519\n",
"2025-02-10/2025-02-16 208952\n",
"2025-02-17/2025-02-23 136020\n",
"2025-02-24/2025-03-02 84531\n",
"2025-03-03/2025-03-09 60334\n",
"2025-03-10/2025-03-16 59469\n",
"2025-03-17/2025-03-23 52543\n",
"2025-03-24/2025-03-30 39673\n",
"2025-03-31/2025-04-06 37579\n",
"2025-04-07/2025-04-13 35721\n",
"2025-04-14/2025-04-20 28564\n",
"2025-04-21/2025-04-27 22150\n",
"2025-04-28/2025-05-04 18115\n",
"2025-05-05/2025-05-11 16264\n",
"2025-05-12/2025-05-18 20265\n",
"2025-05-19/2025-05-25 23760\n",
"2025-05-26/2025-06-01 18755\n",
"2025-06-02/2025-06-08 24391\n",
"2025-06-09/2025-06-15 23154\n",
"2025-06-16/2025-06-22 23323\n",
"2025-06-23/2025-06-29 21945\n",
"2025-06-30/2025-07-06 21453\n",
"2025-07-07/2025-07-13 23285\n",
"2025-07-14/2025-07-20 18648\n",
"2025-07-21/2025-07-27 21219\n",
"Freq: W-SUN, Name: inc, Length: 2125, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_data = data.set_index('period').sort_index()"
"sorted_data = data.set_index('period').sort_index()\n",
"sorted_data['inc']"
]
},
{
......@@ -179,9 +2445,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"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 +2473,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-0966cd984262>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'].plot()"
]
......@@ -215,9 +2506,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-495b7092a92e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'][-200:].plot()"
]
......@@ -364,7 +2672,7 @@
"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 @ 487a241b
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