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khinsen/influenza-like-illness-analysis-jupyter.ipynb

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Incidence of influenza-like illness in France
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influenza-like-illness-analysis-jupyter.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence of influenza-like illness in France"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
"\n",
"| Column name | Description |\n",
"|--------------|---------------------------------------------------------------------------------------------------------------------------|\n",
"| `week` | ISO8601 Yearweek number as numeric (year times 100 + week nubmer) |\n",
"| `indicator` | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n",
"| `inc` | Estimated incidence value for the time step, in the geographic level |\n",
"| `inc_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n",
"| `inc_up` | Upper bound of the estimated incidence 95% Confidence Interval |\n",
"| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n",
"| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n",
"| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n",
"| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n",
"| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n",
"\n",
"The first line of the CSV file is a comment, which we ignore with `skip=1`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></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",
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" <th>0</th>\n",
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" <td>50263.0</td>\n",
" <td>66525.0</td>\n",
" <td>89</td>\n",
" <td>77.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>201909</td>\n",
" <td>3</td>\n",
" <td>89391</td>\n",
" <td>80479.0</td>\n",
" <td>98303.0</td>\n",
" <td>136</td>\n",
" <td>122.0</td>\n",
" <td>150.0</td>\n",
" <td>FR</td>\n",
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" <th>2</th>\n",
" <td>201908</td>\n",
" <td>3</td>\n",
" <td>172604</td>\n",
" <td>160024.0</td>\n",
" <td>185184.0</td>\n",
" <td>262</td>\n",
" <td>243.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>201907</td>\n",
" <td>3</td>\n",
" <td>307338</td>\n",
" <td>291220.0</td>\n",
" <td>323456.0</td>\n",
" <td>467</td>\n",
" <td>443.0</td>\n",
" <td>491.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>201906</td>\n",
" <td>3</td>\n",
" <td>394286</td>\n",
" <td>376782.0</td>\n",
" <td>411790.0</td>\n",
" <td>599</td>\n",
" <td>572.0</td>\n",
" <td>626.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>201905</td>\n",
" <td>3</td>\n",
" <td>355785</td>\n",
" <td>339295.0</td>\n",
" <td>372275.0</td>\n",
" <td>540</td>\n",
" <td>515.0</td>\n",
" <td>565.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>201904</td>\n",
" <td>3</td>\n",
" <td>241090</td>\n",
" <td>227261.0</td>\n",
" <td>254919.0</td>\n",
" <td>366</td>\n",
" <td>345.0</td>\n",
" <td>387.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>201903</td>\n",
" <td>3</td>\n",
" <td>147063</td>\n",
" <td>135890.0</td>\n",
" <td>158236.0</td>\n",
" <td>223</td>\n",
" <td>206.0</td>\n",
" <td>240.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>201902</td>\n",
" <td>3</td>\n",
" <td>75548</td>\n",
" <td>67632.0</td>\n",
" <td>83464.0</td>\n",
" <td>115</td>\n",
" <td>103.0</td>\n",
" <td>127.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>201901</td>\n",
" <td>3</td>\n",
" <td>50295</td>\n",
" <td>43525.0</td>\n",
" <td>57065.0</td>\n",
" <td>76</td>\n",
" <td>66.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>201852</td>\n",
" <td>3</td>\n",
" <td>37903</td>\n",
" <td>31375.0</td>\n",
" <td>44431.0</td>\n",
" <td>58</td>\n",
" <td>48.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>201851</td>\n",
" <td>3</td>\n",
" <td>39259</td>\n",
" <td>32977.0</td>\n",
" <td>45541.0</td>\n",
" <td>60</td>\n",
" <td>50.0</td>\n",
" <td>70.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>201850</td>\n",
" <td>3</td>\n",
" <td>27781</td>\n",
" <td>22638.0</td>\n",
" <td>32924.0</td>\n",
" <td>42</td>\n",
" <td>34.0</td>\n",
" <td>50.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>201849</td>\n",
" <td>3</td>\n",
" <td>19738</td>\n",
" <td>15481.0</td>\n",
" <td>23995.0</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>14</th>\n",
" <td>201848</td>\n",
" <td>3</td>\n",
" <td>19501</td>\n",
" <td>15275.0</td>\n",
" <td>23727.0</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>15</th>\n",
" <td>201847</td>\n",
" <td>3</td>\n",
" <td>15949</td>\n",
" <td>12105.0</td>\n",
" <td>19793.0</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>16</th>\n",
" <td>201846</td>\n",
" <td>3</td>\n",
" <td>11278</td>\n",
" <td>7957.0</td>\n",
" <td>14599.0</td>\n",
" <td>17</td>\n",
" <td>12.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>201845</td>\n",
" <td>3</td>\n",
" <td>11065</td>\n",
" <td>7791.0</td>\n",
" <td>14339.0</td>\n",
" <td>17</td>\n",
" <td>12.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>201844</td>\n",
" <td>3</td>\n",
" <td>6586</td>\n",
" <td>3875.0</td>\n",
" <td>9297.0</td>\n",
" <td>10</td>\n",
" <td>6.0</td>\n",
" <td>14.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>201843</td>\n",
" <td>3</td>\n",
" <td>6550</td>\n",
" <td>3988.0</td>\n",
" <td>9112.0</td>\n",
" <td>10</td>\n",
" <td>6.0</td>\n",
" <td>14.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>201842</td>\n",
" <td>3</td>\n",
" <td>7787</td>\n",
" <td>5129.0</td>\n",
" <td>10445.0</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>21</th>\n",
" <td>201841</td>\n",
" <td>3</td>\n",
" <td>8048</td>\n",
" <td>5098.0</td>\n",
" <td>10998.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>201840</td>\n",
" <td>3</td>\n",
" <td>7409</td>\n",
" <td>4717.0</td>\n",
" <td>10101.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>201839</td>\n",
" <td>3</td>\n",
" <td>7174</td>\n",
" <td>4235.0</td>\n",
" <td>10113.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>201838</td>\n",
" <td>3</td>\n",
" <td>7349</td>\n",
" <td>4399.0</td>\n",
" <td>10299.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>201837</td>\n",
" <td>3</td>\n",
" <td>4915</td>\n",
" <td>2386.0</td>\n",
" <td>7444.0</td>\n",
" <td>7</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>201836</td>\n",
" <td>3</td>\n",
" <td>3215</td>\n",
" <td>1349.0</td>\n",
" <td>5081.0</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>201835</td>\n",
" <td>3</td>\n",
" <td>1506</td>\n",
" <td>239.0</td>\n",
" <td>2773.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>201834</td>\n",
" <td>3</td>\n",
" <td>1368</td>\n",
" <td>116.0</td>\n",
" <td>2620.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>201833</td>\n",
" <td>3</td>\n",
" <td>1962</td>\n",
" <td>5.0</td>\n",
" <td>3919.0</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>6.0</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",
" </tr>\n",
" <tr>\n",
" <th>1763</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>1764</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>1765</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>1766</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>1767</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>1768</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>1769</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>1770</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>1771</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>1772</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>1773</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>1774</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>1775</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>1776</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>1777</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>1778</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>1779</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>1780</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>1781</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>1782</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>1783</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>1784</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>1785</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>1786</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>1787</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>1788</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>1789</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>1790</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>1791</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>1792</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>1793 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 201910 3 58394 50263.0 66525.0 89 77.0 \n",
"1 201909 3 89391 80479.0 98303.0 136 122.0 \n",
"2 201908 3 172604 160024.0 185184.0 262 243.0 \n",
"3 201907 3 307338 291220.0 323456.0 467 443.0 \n",
"4 201906 3 394286 376782.0 411790.0 599 572.0 \n",
"5 201905 3 355785 339295.0 372275.0 540 515.0 \n",
"6 201904 3 241090 227261.0 254919.0 366 345.0 \n",
"7 201903 3 147063 135890.0 158236.0 223 206.0 \n",
"8 201902 3 75548 67632.0 83464.0 115 103.0 \n",
"9 201901 3 50295 43525.0 57065.0 76 66.0 \n",
"10 201852 3 37903 31375.0 44431.0 58 48.0 \n",
"11 201851 3 39259 32977.0 45541.0 60 50.0 \n",
"12 201850 3 27781 22638.0 32924.0 42 34.0 \n",
"13 201849 3 19738 15481.0 23995.0 30 24.0 \n",
"14 201848 3 19501 15275.0 23727.0 30 24.0 \n",
"15 201847 3 15949 12105.0 19793.0 24 18.0 \n",
"16 201846 3 11278 7957.0 14599.0 17 12.0 \n",
"17 201845 3 11065 7791.0 14339.0 17 12.0 \n",
"18 201844 3 6586 3875.0 9297.0 10 6.0 \n",
"19 201843 3 6550 3988.0 9112.0 10 6.0 \n",
"20 201842 3 7787 5129.0 10445.0 12 8.0 \n",
"21 201841 3 8048 5098.0 10998.0 12 8.0 \n",
"22 201840 3 7409 4717.0 10101.0 11 7.0 \n",
"23 201839 3 7174 4235.0 10113.0 11 7.0 \n",
"24 201838 3 7349 4399.0 10299.0 11 7.0 \n",
"25 201837 3 4915 2386.0 7444.0 7 3.0 \n",
"26 201836 3 3215 1349.0 5081.0 5 2.0 \n",
"27 201835 3 1506 239.0 2773.0 2 0.0 \n",
"28 201834 3 1368 116.0 2620.0 2 0.0 \n",
"29 201833 3 1962 5.0 3919.0 3 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"1763 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"1764 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"1765 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"1766 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"1767 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"1768 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"1769 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"1770 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"1771 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"1772 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"1773 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"1774 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"1775 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"1776 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"1777 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"1778 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"1779 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"1780 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"1781 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"1782 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"1783 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"1784 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"1785 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"1786 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"1787 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"1788 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1789 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1790 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1791 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1792 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 101.0 FR France \n",
"1 150.0 FR France \n",
"2 281.0 FR France \n",
"3 491.0 FR France \n",
"4 626.0 FR France \n",
"5 565.0 FR France \n",
"6 387.0 FR France \n",
"7 240.0 FR France \n",
"8 127.0 FR France \n",
"9 86.0 FR France \n",
"10 68.0 FR France \n",
"11 70.0 FR France \n",
"12 50.0 FR France \n",
"13 36.0 FR France \n",
"14 36.0 FR France \n",
"15 30.0 FR France \n",
"16 22.0 FR France \n",
"17 22.0 FR France \n",
"18 14.0 FR France \n",
"19 14.0 FR France \n",
"20 16.0 FR France \n",
"21 16.0 FR France \n",
"22 15.0 FR France \n",
"23 15.0 FR France \n",
"24 15.0 FR France \n",
"25 11.0 FR France \n",
"26 8.0 FR France \n",
"27 4.0 FR France \n",
"28 4.0 FR France \n",
"29 6.0 FR France \n",
"... ... ... ... \n",
"1763 59.0 FR France \n",
"1764 64.0 FR France \n",
"1765 97.0 FR France \n",
"1766 93.0 FR France \n",
"1767 80.0 FR France \n",
"1768 116.0 FR France \n",
"1769 149.0 FR France \n",
"1770 281.0 FR France \n",
"1771 395.0 FR France \n",
"1772 485.0 FR France \n",
"1773 544.0 FR France \n",
"1774 689.0 FR France \n",
"1775 722.0 FR France \n",
"1776 762.0 FR France \n",
"1777 926.0 FR France \n",
"1778 1113.0 FR France \n",
"1779 1236.0 FR France \n",
"1780 832.0 FR France \n",
"1781 459.0 FR France \n",
"1782 207.0 FR France \n",
"1783 190.0 FR France \n",
"1784 198.0 FR France \n",
"1785 224.0 FR France \n",
"1786 266.0 FR France \n",
"1787 219.0 FR France \n",
"1788 176.0 FR France \n",
"1789 163.0 FR France \n",
"1790 195.0 FR France \n",
"1791 308.0 FR France \n",
"1792 213.0 FR France \n",
"\n",
"[1793 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Are there missing data points? Yes, week 19 of year 1989 does not have any observed values."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>1556</th>\n",
" <td>198919</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</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": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1556 198919 3 0 NaN NaN 0 NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1556 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We delete this point, which does not have big consequence for our rather simple analysis."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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>201910</td>\n",
" <td>3</td>\n",
" <td>58394</td>\n",
" <td>50263.0</td>\n",
" <td>66525.0</td>\n",
" <td>89</td>\n",
" <td>77.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>201909</td>\n",
" <td>3</td>\n",
" <td>89391</td>\n",
" <td>80479.0</td>\n",
" <td>98303.0</td>\n",
" <td>136</td>\n",
" <td>122.0</td>\n",
" <td>150.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201908</td>\n",
" <td>3</td>\n",
" <td>172604</td>\n",
" <td>160024.0</td>\n",
" <td>185184.0</td>\n",
" <td>262</td>\n",
" <td>243.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201907</td>\n",
" <td>3</td>\n",
" <td>307338</td>\n",
" <td>291220.0</td>\n",
" <td>323456.0</td>\n",
" <td>467</td>\n",
" <td>443.0</td>\n",
" <td>491.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>201906</td>\n",
" <td>3</td>\n",
" <td>394286</td>\n",
" <td>376782.0</td>\n",
" <td>411790.0</td>\n",
" <td>599</td>\n",
" <td>572.0</td>\n",
" <td>626.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>201905</td>\n",
" <td>3</td>\n",
" <td>355785</td>\n",
" <td>339295.0</td>\n",
" <td>372275.0</td>\n",
" <td>540</td>\n",
" <td>515.0</td>\n",
" <td>565.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>201904</td>\n",
" <td>3</td>\n",
" <td>241090</td>\n",
" <td>227261.0</td>\n",
" <td>254919.0</td>\n",
" <td>366</td>\n",
" <td>345.0</td>\n",
" <td>387.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>201903</td>\n",
" <td>3</td>\n",
" <td>147063</td>\n",
" <td>135890.0</td>\n",
" <td>158236.0</td>\n",
" <td>223</td>\n",
" <td>206.0</td>\n",
" <td>240.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>201902</td>\n",
" <td>3</td>\n",
" <td>75548</td>\n",
" <td>67632.0</td>\n",
" <td>83464.0</td>\n",
" <td>115</td>\n",
" <td>103.0</td>\n",
" <td>127.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>201901</td>\n",
" <td>3</td>\n",
" <td>50295</td>\n",
" <td>43525.0</td>\n",
" <td>57065.0</td>\n",
" <td>76</td>\n",
" <td>66.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>201852</td>\n",
" <td>3</td>\n",
" <td>37903</td>\n",
" <td>31375.0</td>\n",
" <td>44431.0</td>\n",
" <td>58</td>\n",
" <td>48.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>201851</td>\n",
" <td>3</td>\n",
" <td>39259</td>\n",
" <td>32977.0</td>\n",
" <td>45541.0</td>\n",
" <td>60</td>\n",
" <td>50.0</td>\n",
" <td>70.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>201850</td>\n",
" <td>3</td>\n",
" <td>27781</td>\n",
" <td>22638.0</td>\n",
" <td>32924.0</td>\n",
" <td>42</td>\n",
" <td>34.0</td>\n",
" <td>50.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>201849</td>\n",
" <td>3</td>\n",
" <td>19738</td>\n",
" <td>15481.0</td>\n",
" <td>23995.0</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>14</th>\n",
" <td>201848</td>\n",
" <td>3</td>\n",
" <td>19501</td>\n",
" <td>15275.0</td>\n",
" <td>23727.0</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>15</th>\n",
" <td>201847</td>\n",
" <td>3</td>\n",
" <td>15949</td>\n",
" <td>12105.0</td>\n",
" <td>19793.0</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>16</th>\n",
" <td>201846</td>\n",
" <td>3</td>\n",
" <td>11278</td>\n",
" <td>7957.0</td>\n",
" <td>14599.0</td>\n",
" <td>17</td>\n",
" <td>12.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>201845</td>\n",
" <td>3</td>\n",
" <td>11065</td>\n",
" <td>7791.0</td>\n",
" <td>14339.0</td>\n",
" <td>17</td>\n",
" <td>12.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>201844</td>\n",
" <td>3</td>\n",
" <td>6586</td>\n",
" <td>3875.0</td>\n",
" <td>9297.0</td>\n",
" <td>10</td>\n",
" <td>6.0</td>\n",
" <td>14.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>201843</td>\n",
" <td>3</td>\n",
" <td>6550</td>\n",
" <td>3988.0</td>\n",
" <td>9112.0</td>\n",
" <td>10</td>\n",
" <td>6.0</td>\n",
" <td>14.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>201842</td>\n",
" <td>3</td>\n",
" <td>7787</td>\n",
" <td>5129.0</td>\n",
" <td>10445.0</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>21</th>\n",
" <td>201841</td>\n",
" <td>3</td>\n",
" <td>8048</td>\n",
" <td>5098.0</td>\n",
" <td>10998.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>201840</td>\n",
" <td>3</td>\n",
" <td>7409</td>\n",
" <td>4717.0</td>\n",
" <td>10101.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>201839</td>\n",
" <td>3</td>\n",
" <td>7174</td>\n",
" <td>4235.0</td>\n",
" <td>10113.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>201838</td>\n",
" <td>3</td>\n",
" <td>7349</td>\n",
" <td>4399.0</td>\n",
" <td>10299.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>201837</td>\n",
" <td>3</td>\n",
" <td>4915</td>\n",
" <td>2386.0</td>\n",
" <td>7444.0</td>\n",
" <td>7</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>201836</td>\n",
" <td>3</td>\n",
" <td>3215</td>\n",
" <td>1349.0</td>\n",
" <td>5081.0</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>201835</td>\n",
" <td>3</td>\n",
" <td>1506</td>\n",
" <td>239.0</td>\n",
" <td>2773.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>201834</td>\n",
" <td>3</td>\n",
" <td>1368</td>\n",
" <td>116.0</td>\n",
" <td>2620.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>201833</td>\n",
" <td>3</td>\n",
" <td>1962</td>\n",
" <td>5.0</td>\n",
" <td>3919.0</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>6.0</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",
" </tr>\n",
" <tr>\n",
" <th>1763</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>1764</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>1765</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>1766</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>1767</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>1768</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>1769</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>1770</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>1771</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>1772</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>1773</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>1774</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>1775</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>1776</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>1777</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>1778</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>1779</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>1780</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>1781</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>1782</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>1783</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>1784</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>1785</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>1786</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>1787</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>1788</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>1789</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>1790</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>1791</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>1792</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>1792 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 201910 3 58394 50263.0 66525.0 89 77.0 \n",
"1 201909 3 89391 80479.0 98303.0 136 122.0 \n",
"2 201908 3 172604 160024.0 185184.0 262 243.0 \n",
"3 201907 3 307338 291220.0 323456.0 467 443.0 \n",
"4 201906 3 394286 376782.0 411790.0 599 572.0 \n",
"5 201905 3 355785 339295.0 372275.0 540 515.0 \n",
"6 201904 3 241090 227261.0 254919.0 366 345.0 \n",
"7 201903 3 147063 135890.0 158236.0 223 206.0 \n",
"8 201902 3 75548 67632.0 83464.0 115 103.0 \n",
"9 201901 3 50295 43525.0 57065.0 76 66.0 \n",
"10 201852 3 37903 31375.0 44431.0 58 48.0 \n",
"11 201851 3 39259 32977.0 45541.0 60 50.0 \n",
"12 201850 3 27781 22638.0 32924.0 42 34.0 \n",
"13 201849 3 19738 15481.0 23995.0 30 24.0 \n",
"14 201848 3 19501 15275.0 23727.0 30 24.0 \n",
"15 201847 3 15949 12105.0 19793.0 24 18.0 \n",
"16 201846 3 11278 7957.0 14599.0 17 12.0 \n",
"17 201845 3 11065 7791.0 14339.0 17 12.0 \n",
"18 201844 3 6586 3875.0 9297.0 10 6.0 \n",
"19 201843 3 6550 3988.0 9112.0 10 6.0 \n",
"20 201842 3 7787 5129.0 10445.0 12 8.0 \n",
"21 201841 3 8048 5098.0 10998.0 12 8.0 \n",
"22 201840 3 7409 4717.0 10101.0 11 7.0 \n",
"23 201839 3 7174 4235.0 10113.0 11 7.0 \n",
"24 201838 3 7349 4399.0 10299.0 11 7.0 \n",
"25 201837 3 4915 2386.0 7444.0 7 3.0 \n",
"26 201836 3 3215 1349.0 5081.0 5 2.0 \n",
"27 201835 3 1506 239.0 2773.0 2 0.0 \n",
"28 201834 3 1368 116.0 2620.0 2 0.0 \n",
"29 201833 3 1962 5.0 3919.0 3 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"1763 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"1764 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"1765 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"1766 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"1767 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"1768 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"1769 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"1770 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"1771 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"1772 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"1773 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"1774 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"1775 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"1776 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"1777 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"1778 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"1779 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"1780 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"1781 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"1782 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"1783 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"1784 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"1785 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"1786 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"1787 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"1788 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1789 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1790 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1791 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1792 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 101.0 FR France \n",
"1 150.0 FR France \n",
"2 281.0 FR France \n",
"3 491.0 FR France \n",
"4 626.0 FR France \n",
"5 565.0 FR France \n",
"6 387.0 FR France \n",
"7 240.0 FR France \n",
"8 127.0 FR France \n",
"9 86.0 FR France \n",
"10 68.0 FR France \n",
"11 70.0 FR France \n",
"12 50.0 FR France \n",
"13 36.0 FR France \n",
"14 36.0 FR France \n",
"15 30.0 FR France \n",
"16 22.0 FR France \n",
"17 22.0 FR France \n",
"18 14.0 FR France \n",
"19 14.0 FR France \n",
"20 16.0 FR France \n",
"21 16.0 FR France \n",
"22 15.0 FR France \n",
"23 15.0 FR France \n",
"24 15.0 FR France \n",
"25 11.0 FR France \n",
"26 8.0 FR France \n",
"27 4.0 FR France \n",
"28 4.0 FR France \n",
"29 6.0 FR France \n",
"... ... ... ... \n",
"1763 59.0 FR France \n",
"1764 64.0 FR France \n",
"1765 97.0 FR France \n",
"1766 93.0 FR France \n",
"1767 80.0 FR France \n",
"1768 116.0 FR France \n",
"1769 149.0 FR France \n",
"1770 281.0 FR France \n",
"1771 395.0 FR France \n",
"1772 485.0 FR France \n",
"1773 544.0 FR France \n",
"1774 689.0 FR France \n",
"1775 722.0 FR France \n",
"1776 762.0 FR France \n",
"1777 926.0 FR France \n",
"1778 1113.0 FR France \n",
"1779 1236.0 FR France \n",
"1780 832.0 FR France \n",
"1781 459.0 FR France \n",
"1782 207.0 FR France \n",
"1783 190.0 FR France \n",
"1784 198.0 FR France \n",
"1785 224.0 FR France \n",
"1786 266.0 FR France \n",
"1787 219.0 FR France \n",
"1788 176.0 FR France \n",
"1789 163.0 FR France \n",
"1790 195.0 FR France \n",
"1791 308.0 FR France \n",
"1792 213.0 FR France \n",
"\n",
"[1792 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our dataset uses an uncommon encoding; the week number is attached\n",
"to the year number, leaving the impression of a six-digit integer.\n",
"That is how Pandas interprets it.\n",
"\n",
"A second problem is that Pandas does not know about week numbers.\n",
"It needs to be given the dates of the beginning and end of the week.\n",
"We use the library `isoweek` for that.\n",
"\n",
"Since the conversion is a bit lengthy, we write a small Python \n",
"function for doing it. Then we apply it to all points in our dataset. \n",
"The results go into a new column 'period'."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" 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']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two more small changes to make.\n",
"\n",
"First, we define the observation periods as the new index of\n",
"our dataset. That turns it into a time series, which will be\n",
"convenient later on.\n",
"\n",
"Second, we sort the points chronologically."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We check the consistency of the data. Between the end of a period and\n",
"the beginning of the next one, the difference should be zero, or very small.\n",
"We tolerate an error of one second.\n",
"\n",
"This is OK except for one pair of consecutive periods between which\n",
"a whole week is missing.\n",
"\n",
"We recognize the dates: it's the week without observations that we\n",
"have deleted earlier!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"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",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A first look at the data!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110f4b9b0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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v1lSSShhNJYK32Decx+BoefiyUhSUbbuSPmkqSZEsCmnCfkQ/ebQdd6/Z4Xks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\nATTAOcE6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110f30f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A zoom on the last few years shows more clearly that the peaks are situated in winter."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119a9fc88>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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J1np4VZUtrpE5niCMZ2lUFSDeaKJRRiSSSnt/ZOqWRtBPNO6k/pUqA25MIzNL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119aadb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'][-200:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Study of the annual incidence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the peaks of the epidemic happen in winter, near the transition\n",
"between calendar years, we define the reference period for the annual\n",
"incidence from August 1st of year $N$ to August 1st of year $N+1$. We\n",
"label this period as year $N+1$ because the peak is always located in\n",
"year $N+1$. The very low incidence in summer ensures that the arbitrariness\n",
"of the choice of reference period has no impact on our conclusions.\n",
"\n",
"Our task is a bit complicated by the fact that a year does not have an\n",
"integer number of weeks. Therefore we modify our reference period a bit:\n",
"instead of August 1st, we use the first day of the week containing August 1st.\n",
"\n",
"A final detail: the dataset starts in October 1984, the first peak is thus\n",
"incomplete, We start the analysis with the first full peak."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Starting from this list of weeks that contain August 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n",
"\n",
"We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here are the annual incidences."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119ab44e0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eWuZQ7gcWHCN9RUTMztdTAJJmUWzfe3GW+ZakUzL/KuCzFHvMX1Sq82bgQERc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\nBxQzM6sLBxQzM6uL/w8T9rXtfaQJQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119aad898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A sorted list makes it easier to find the highest values (at the end)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\n",
"2012 2175217\n",
"2003 2234584\n",
"2006 2307352\n",
"2017 2321583\n",
"2001 2529279\n",
"1992 2574578\n",
"1993 2703886\n",
"2018 2705325\n",
"1988 2765617\n",
"2007 2780164\n",
"1987 2855570\n",
"2016 2856393\n",
"2011 2857040\n",
"2008 2973918\n",
"1998 3034904\n",
"2002 3125418\n",
"2009 3444020\n",
"1994 3514763\n",
"1996 3539413\n",
"2004 3567744\n",
"1997 3620066\n",
"2015 3654892\n",
"2000 3826372\n",
"2005 3835025\n",
"1999 3908112\n",
"2010 4111392\n",
"2013 4182691\n",
"1986 5115251\n",
"1990 5235827\n",
"1989 5466192\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population,\n",
"but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119f774e0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x119a7e390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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