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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The incidence of chickenpox in France"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data on the incidence of chickenpox-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. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: isoweek in /opt/conda/lib/python3.6/site-packages (1.3.3)\r\n"
]
}
],
"source": [
"!pip install isoweek"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek \n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" \n",
"filename = \"inc-7-PAY-ds3.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Download -> if there is not a local file already"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(filename):\n",
" raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1' , skiprows= 1 )\n",
"else:\n",
" raw_data = pd.read_csv(filename)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Remove rows with missing values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>1883</th>\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": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1883 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1883 FR France "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull(). any (axis= 1 )] "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>202524</td>\n",
" <td>3</td>\n",
" <td>22816</td>\n",
" <td>17621.0</td>\n",
" <td>28011.0</td>\n",
" <td>34</td>\n",
" <td>26.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202523</td>\n",
" <td>3</td>\n",
" <td>24564</td>\n",
" <td>19382.0</td>\n",
" <td>29746.0</td>\n",
" <td>37</td>\n",
" <td>29.0</td>\n",
" <td>45.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\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>3</th>\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>4</th>\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>5</th>\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>6</th>\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>7</th>\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>8</th>\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>9</th>\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>10</th>\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>11</th>\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>12</th>\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>13</th>\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>14</th>\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>15</th>\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>16</th>\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>17</th>\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>18</th>\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>19</th>\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>20</th>\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>21</th>\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>22</th>\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>23</th>\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>24</th>\n",
" <td>202452</td>\n",
" <td>3</td>\n",
" <td>201726</td>\n",
" <td>185870.0</td>\n",
" <td>217582.0</td>\n",
" <td>302</td>\n",
" <td>278.0</td>\n",
" <td>326.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202451</td>\n",
" <td>3</td>\n",
" <td>201697</td>\n",
" <td>187843.0</td>\n",
" <td>215551.0</td>\n",
" <td>302</td>\n",
" <td>281.0</td>\n",
" <td>323.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202450</td>\n",
" <td>3</td>\n",
" <td>136694</td>\n",
" <td>126369.0</td>\n",
" <td>147019.0</td>\n",
" <td>205</td>\n",
" <td>190.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202449</td>\n",
" <td>3</td>\n",
" <td>108487</td>\n",
" <td>99037.0</td>\n",
" <td>117937.0</td>\n",
" <td>163</td>\n",
" <td>149.0</td>\n",
" <td>177.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202448</td>\n",
" <td>3</td>\n",
" <td>87381</td>\n",
" <td>78687.0</td>\n",
" <td>96075.0</td>\n",
" <td>131</td>\n",
" <td>118.0</td>\n",
" <td>144.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202447</td>\n",
" <td>3</td>\n",
" <td>76286</td>\n",
" <td>67626.0</td>\n",
" <td>84946.0</td>\n",
" <td>114</td>\n",
" <td>101.0</td>\n",
" <td>127.0</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>2090</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>2091</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>2092</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>2093</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>2094</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>2095</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>2096</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>2097</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>2098</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>2099</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>2100</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>2101</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>2102</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>2103</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>2104</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>2105</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>2106</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>2107</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>2108</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>2109</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>2110</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>2111</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>2112</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>2113</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>2114</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>2115</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>2116</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>2117</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>2118</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>2119</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>2119 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202524 3 22816 17621.0 28011.0 34 26.0 \n",
"1 202523 3 24564 19382.0 29746.0 37 29.0 \n",
"2 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"3 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"4 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"5 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"6 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"7 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"8 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"9 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"10 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"11 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"12 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"13 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"14 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"15 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"16 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"17 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"18 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"19 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"20 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"21 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"22 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"23 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"24 202452 3 201726 185870.0 217582.0 302 278.0 \n",
"25 202451 3 201697 187843.0 215551.0 302 281.0 \n",
"26 202450 3 136694 126369.0 147019.0 205 190.0 \n",
"27 202449 3 108487 99037.0 117937.0 163 149.0 \n",
"28 202448 3 87381 78687.0 96075.0 131 118.0 \n",
"29 202447 3 76286 67626.0 84946.0 114 101.0 \n",
"... ... ... ... ... ... ... ... \n",
"2090 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2091 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2092 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2093 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2094 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2095 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2096 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2097 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2098 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2099 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2100 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2101 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2102 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2103 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2104 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2105 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2106 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2107 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2108 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2109 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2110 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2111 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2112 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2113 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2114 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2115 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2116 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2117 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2118 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2119 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 42.0 FR France \n",
"1 45.0 FR France \n",
"2 35.0 FR France \n",
"3 43.0 FR France \n",
"4 37.0 FR France \n",
"5 30.0 FR France \n",
"6 33.0 FR France \n",
"7 40.0 FR France \n",
"8 52.0 FR France \n",
"9 62.0 FR France \n",
"10 65.0 FR France \n",
"11 68.0 FR France \n",
"12 88.0 FR France \n",
"13 100.0 FR France \n",
"14 101.0 FR France \n",
"15 140.0 FR France \n",
"16 220.0 FR France \n",
"17 331.0 FR France \n",
"18 431.0 FR France \n",
"19 523.0 FR France \n",
"20 548.0 FR France \n",
"21 398.0 FR France \n",
"22 405.0 FR France \n",
"23 370.0 FR France \n",
"24 326.0 FR France \n",
"25 323.0 FR France \n",
"26 220.0 FR France \n",
"27 177.0 FR France \n",
"28 144.0 FR France \n",
"29 127.0 FR France \n",
"... ... ... ... \n",
"2090 59.0 FR France \n",
"2091 64.0 FR France \n",
"2092 97.0 FR France \n",
"2093 93.0 FR France \n",
"2094 80.0 FR France \n",
"2095 116.0 FR France \n",
"2096 149.0 FR France \n",
"2097 281.0 FR France \n",
"2098 395.0 FR France \n",
"2099 485.0 FR France \n",
"2100 544.0 FR France \n",
"2101 689.0 FR France \n",
"2102 722.0 FR France \n",
"2103 762.0 FR France \n",
"2104 926.0 FR France \n",
"2105 1113.0 FR France \n",
"2106 1236.0 FR France \n",
"2107 832.0 FR France \n",
"2108 459.0 FR France \n",
"2109 207.0 FR France \n",
"2110 190.0 FR France \n",
"2111 198.0 FR France \n",
"2112 224.0 FR France \n",
"2113 266.0 FR France \n",
"2114 219.0 FR France \n",
"2115 176.0 FR France \n",
"2116 163.0 FR France \n",
"2117 195.0 FR France \n",
"2118 308.0 FR France \n",
"2119 213.0 FR France \n",
"\n",
"[2119 rows x 10 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Convert 'week' to period "
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": [
"4. Set 'period' as index and sort the dataset"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index( 'period' ).sort_index() \n",
"# Ensure the 'inc' column is numeric\n",
"sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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": [
"5. Choose September 1st as the beginning of each annual period"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Period('1985-08-26/1985-09-01', 'W-SUN'),\n",
" Period('1986-09-01/1986-09-07', 'W-SUN'),\n",
" Period('1987-08-31/1987-09-06', 'W-SUN'),\n",
" Period('1988-08-29/1988-09-04', 'W-SUN'),\n",
" Period('1989-08-28/1989-09-03', 'W-SUN'),\n",
" Period('1990-08-27/1990-09-02', 'W-SUN'),\n",
" Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
" Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
" Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
" Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
" Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
" Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
" Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
" Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
" Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
" Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
" Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
" Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
" Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
" Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
" Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
" Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
" Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
" Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
" Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
" Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
" Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
" Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
" Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
" Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
" Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
" Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
" Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
" Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
" Period('2019-08-26/2019-09-01', 'W-SUN'),\n",
" Period('2020-08-31/2020-09-06', 'W-SUN'),\n",
" Period('2021-08-30/2021-09-05', 'W-SUN'),\n",
" Period('2022-08-29/2022-09-04', 'W-SUN'),\n",
" Period('2023-08-28/2023-09-03', 'W-SUN'),\n",
" Period('2024-08-26/2024-09-01', 'W-SUN')]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_sept_week = [pd.Period(pd.Timestamp(y, 9 , 1 ), 'W' )\n",
" for y in range ( 1985 ,\n",
" sorted_data.index[- 1 ].year)] \n",
"first_sept_week"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. Collect the incidence per year information"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2021 772545\n",
"2014 1601698\n",
"1991 1663610\n",
"1995 1828304\n",
"2020 2017296\n",
"2022 2057596\n",
"2012 2183912\n",
"2003 2234514\n",
"2019 2254363\n",
"2006 2297262\n",
"2017 2322818\n",
"2001 2540826\n",
"1992 2590314\n",
"1993 2699482\n",
"2018 2701716\n",
"1988 2759663\n",
"2007 2786458\n",
"2011 2852504\n",
"2016 2859019\n",
"1987 2867464\n",
"2023 2908672\n",
"2008 2984311\n",
"1998 3047298\n",
"2002 3115484\n",
"1994 3514133\n",
"1996 3540251\n",
"2009 3558474\n",
"2004 3572810\n",
"1997 3624129\n",
"2015 3647492\n",
"2024 3691245\n",
"2000 3808190\n",
"2005 3831409\n",
"1999 3914003\n",
"2010 3992174\n",
"2013 4176872\n",
"1986 5050543\n",
"1990 5214494\n",
"1989 5461328\n",
"dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip (first_sept_week[:- 1 ],first_sept_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) \n",
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.hist()\n",
"plt.title(\"Distribution of Yearly Incidence\")\n",
"plt.xlabel(\"Incidence\")\n",
"plt.ylabel(\"Count\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Strongest epidemic year: 1989\n",
"Weakest epidemic year: 2021\n"
]
}
],
"source": [
"strongest = yearly_incidence.idxmax()\n",
"weakest = yearly_incidence.idxmin()\n",
"print(f\"Strongest epidemic year: {strongest}\")\n",
"print(f\"Weakest epidemic year: {weakest}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module3/exo2/exercice.ipynb
View file @ fe9d1c7a
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