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incidence varicelle

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module3/exo2/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse de l'incidence de la varicelle"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
]
},
{
"cell_type": "code",
"execution_count": 43,
"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>202239</td>\n",
" <td>7</td>\n",
" <td>1345</td>\n",
" <td>111</td>\n",
" <td>2579</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202238</td>\n",
" <td>7</td>\n",
" <td>1781</td>\n",
" <td>421</td>\n",
" <td>3141</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202237</td>\n",
" <td>7</td>\n",
" <td>1731</td>\n",
" <td>498</td>\n",
" <td>2964</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202236</td>\n",
" <td>7</td>\n",
" <td>1069</td>\n",
" <td>178</td>\n",
" <td>1960</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202235</td>\n",
" <td>7</td>\n",
" <td>1581</td>\n",
" <td>400</td>\n",
" <td>2762</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202234</td>\n",
" <td>7</td>\n",
" <td>2266</td>\n",
" <td>788</td>\n",
" <td>3744</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202233</td>\n",
" <td>7</td>\n",
" <td>7340</td>\n",
" <td>0</td>\n",
" <td>17399</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202232</td>\n",
" <td>7</td>\n",
" <td>7801</td>\n",
" <td>4086</td>\n",
" <td>11516</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202231</td>\n",
" <td>7</td>\n",
" <td>6896</td>\n",
" <td>4170</td>\n",
" <td>9622</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202230</td>\n",
" <td>7</td>\n",
" <td>9039</td>\n",
" <td>5770</td>\n",
" <td>12308</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" <td>19</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202229</td>\n",
" <td>7</td>\n",
" <td>14851</td>\n",
" <td>10060</td>\n",
" <td>19642</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202228</td>\n",
" <td>7</td>\n",
" <td>15471</td>\n",
" <td>11028</td>\n",
" <td>19914</td>\n",
" <td>23</td>\n",
" <td>16</td>\n",
" <td>30</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202227</td>\n",
" <td>7</td>\n",
" <td>21191</td>\n",
" <td>16198</td>\n",
" <td>26184</td>\n",
" <td>32</td>\n",
" <td>24</td>\n",
" <td>40</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202226</td>\n",
" <td>7</td>\n",
" <td>16854</td>\n",
" <td>12806</td>\n",
" <td>20902</td>\n",
" <td>25</td>\n",
" <td>19</td>\n",
" <td>31</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202225</td>\n",
" <td>7</td>\n",
" <td>22246</td>\n",
" <td>18011</td>\n",
" <td>26481</td>\n",
" <td>34</td>\n",
" <td>28</td>\n",
" <td>40</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202224</td>\n",
" <td>7</td>\n",
" <td>22458</td>\n",
" <td>18105</td>\n",
" <td>26811</td>\n",
" <td>34</td>\n",
" <td>27</td>\n",
" <td>41</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202223</td>\n",
" <td>7</td>\n",
" <td>18772</td>\n",
" <td>14875</td>\n",
" <td>22669</td>\n",
" <td>28</td>\n",
" <td>22</td>\n",
" <td>34</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202222</td>\n",
" <td>7</td>\n",
" <td>18916</td>\n",
" <td>14941</td>\n",
" <td>22891</td>\n",
" <td>29</td>\n",
" <td>23</td>\n",
" <td>35</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202221</td>\n",
" <td>7</td>\n",
" <td>20310</td>\n",
" <td>16307</td>\n",
" <td>24313</td>\n",
" <td>31</td>\n",
" <td>25</td>\n",
" <td>37</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202220</td>\n",
" <td>7</td>\n",
" <td>23585</td>\n",
" <td>19004</td>\n",
" <td>28166</td>\n",
" <td>36</td>\n",
" <td>29</td>\n",
" <td>43</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202219</td>\n",
" <td>7</td>\n",
" <td>18593</td>\n",
" <td>14181</td>\n",
" <td>23005</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>35</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202218</td>\n",
" <td>7</td>\n",
" <td>17851</td>\n",
" <td>13963</td>\n",
" <td>21739</td>\n",
" <td>27</td>\n",
" <td>21</td>\n",
" <td>33</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202217</td>\n",
" <td>7</td>\n",
" <td>20314</td>\n",
" <td>16001</td>\n",
" <td>24627</td>\n",
" <td>31</td>\n",
" <td>24</td>\n",
" <td>38</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202216</td>\n",
" <td>7</td>\n",
" <td>19660</td>\n",
" <td>14860</td>\n",
" <td>24460</td>\n",
" <td>30</td>\n",
" <td>23</td>\n",
" <td>37</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202215</td>\n",
" <td>7</td>\n",
" <td>17799</td>\n",
" <td>13715</td>\n",
" <td>21883</td>\n",
" <td>27</td>\n",
" <td>21</td>\n",
" <td>33</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202214</td>\n",
" <td>7</td>\n",
" <td>17005</td>\n",
" <td>13162</td>\n",
" <td>20848</td>\n",
" <td>26</td>\n",
" <td>20</td>\n",
" <td>32</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202213</td>\n",
" <td>7</td>\n",
" <td>15448</td>\n",
" <td>11659</td>\n",
" <td>19237</td>\n",
" <td>23</td>\n",
" <td>17</td>\n",
" <td>29</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202212</td>\n",
" <td>7</td>\n",
" <td>14702</td>\n",
" <td>10794</td>\n",
" <td>18610</td>\n",
" <td>22</td>\n",
" <td>16</td>\n",
" <td>28</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202211</td>\n",
" <td>7</td>\n",
" <td>11729</td>\n",
" <td>8347</td>\n",
" <td>15111</td>\n",
" <td>18</td>\n",
" <td>13</td>\n",
" <td>23</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202210</td>\n",
" <td>7</td>\n",
" <td>13314</td>\n",
" <td>10036</td>\n",
" <td>16592</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>25</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>1631</th>\n",
" <td>199126</td>\n",
" <td>7</td>\n",
" <td>17608</td>\n",
" <td>11304</td>\n",
" <td>23912</td>\n",
" <td>31</td>\n",
" <td>20</td>\n",
" <td>42</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1632</th>\n",
" <td>199125</td>\n",
" <td>7</td>\n",
" <td>16169</td>\n",
" <td>10700</td>\n",
" <td>21638</td>\n",
" <td>28</td>\n",
" <td>18</td>\n",
" <td>38</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1633</th>\n",
" <td>199124</td>\n",
" <td>7</td>\n",
" <td>16171</td>\n",
" <td>10071</td>\n",
" <td>22271</td>\n",
" <td>28</td>\n",
" <td>17</td>\n",
" <td>39</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1634</th>\n",
" <td>199123</td>\n",
" <td>7</td>\n",
" <td>11947</td>\n",
" <td>7671</td>\n",
" <td>16223</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>29</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1635</th>\n",
" <td>199122</td>\n",
" <td>7</td>\n",
" <td>15452</td>\n",
" <td>9953</td>\n",
" <td>20951</td>\n",
" <td>27</td>\n",
" <td>17</td>\n",
" <td>37</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1636</th>\n",
" <td>199121</td>\n",
" <td>7</td>\n",
" <td>14903</td>\n",
" <td>8975</td>\n",
" <td>20831</td>\n",
" <td>26</td>\n",
" <td>16</td>\n",
" <td>36</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1637</th>\n",
" <td>199120</td>\n",
" <td>7</td>\n",
" <td>19053</td>\n",
" <td>12742</td>\n",
" <td>25364</td>\n",
" <td>34</td>\n",
" <td>23</td>\n",
" <td>45</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>199119</td>\n",
" <td>7</td>\n",
" <td>16739</td>\n",
" <td>11246</td>\n",
" <td>22232</td>\n",
" <td>29</td>\n",
" <td>19</td>\n",
" <td>39</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1639</th>\n",
" <td>199118</td>\n",
" <td>7</td>\n",
" <td>21385</td>\n",
" <td>13882</td>\n",
" <td>28888</td>\n",
" <td>38</td>\n",
" <td>25</td>\n",
" <td>51</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1640</th>\n",
" <td>199117</td>\n",
" <td>7</td>\n",
" <td>13462</td>\n",
" <td>8877</td>\n",
" <td>18047</td>\n",
" <td>24</td>\n",
" <td>16</td>\n",
" <td>32</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1641</th>\n",
" <td>199116</td>\n",
" <td>7</td>\n",
" <td>14857</td>\n",
" <td>10068</td>\n",
" <td>19646</td>\n",
" <td>26</td>\n",
" <td>18</td>\n",
" <td>34</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1642</th>\n",
" <td>199115</td>\n",
" <td>7</td>\n",
" <td>13975</td>\n",
" <td>9781</td>\n",
" <td>18169</td>\n",
" <td>25</td>\n",
" <td>18</td>\n",
" <td>32</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1643</th>\n",
" <td>199114</td>\n",
" <td>7</td>\n",
" <td>12265</td>\n",
" <td>7684</td>\n",
" <td>16846</td>\n",
" <td>22</td>\n",
" <td>14</td>\n",
" <td>30</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1644</th>\n",
" <td>199113</td>\n",
" <td>7</td>\n",
" <td>9567</td>\n",
" <td>6041</td>\n",
" <td>13093</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1645</th>\n",
" <td>199112</td>\n",
" <td>7</td>\n",
" <td>10864</td>\n",
" <td>7331</td>\n",
" <td>14397</td>\n",
" <td>19</td>\n",
" <td>13</td>\n",
" <td>25</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1646</th>\n",
" <td>199111</td>\n",
" <td>7</td>\n",
" <td>15574</td>\n",
" <td>11184</td>\n",
" <td>19964</td>\n",
" <td>27</td>\n",
" <td>19</td>\n",
" <td>35</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1647</th>\n",
" <td>199110</td>\n",
" <td>7</td>\n",
" <td>16643</td>\n",
" <td>11372</td>\n",
" <td>21914</td>\n",
" <td>29</td>\n",
" <td>20</td>\n",
" <td>38</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1648</th>\n",
" <td>199109</td>\n",
" <td>7</td>\n",
" <td>13741</td>\n",
" <td>8780</td>\n",
" <td>18702</td>\n",
" <td>24</td>\n",
" <td>15</td>\n",
" <td>33</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1649</th>\n",
" <td>199108</td>\n",
" <td>7</td>\n",
" <td>13289</td>\n",
" <td>8813</td>\n",
" <td>17765</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>31</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1650</th>\n",
" <td>199107</td>\n",
" <td>7</td>\n",
" <td>12337</td>\n",
" <td>8077</td>\n",
" <td>16597</td>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1651</th>\n",
" <td>199106</td>\n",
" <td>7</td>\n",
" <td>10877</td>\n",
" <td>7013</td>\n",
" <td>14741</td>\n",
" <td>19</td>\n",
" <td>12</td>\n",
" <td>26</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1652</th>\n",
" <td>199105</td>\n",
" <td>7</td>\n",
" <td>10442</td>\n",
" <td>6544</td>\n",
" <td>14340</td>\n",
" <td>18</td>\n",
" <td>11</td>\n",
" <td>25</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1653</th>\n",
" <td>199104</td>\n",
" <td>7</td>\n",
" <td>7913</td>\n",
" <td>4563</td>\n",
" <td>11263</td>\n",
" <td>14</td>\n",
" <td>8</td>\n",
" <td>20</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1654</th>\n",
" <td>199103</td>\n",
" <td>7</td>\n",
" <td>15387</td>\n",
" <td>10484</td>\n",
" <td>20290</td>\n",
" <td>27</td>\n",
" <td>18</td>\n",
" <td>36</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1655</th>\n",
" <td>199102</td>\n",
" <td>7</td>\n",
" <td>16277</td>\n",
" <td>11046</td>\n",
" <td>21508</td>\n",
" <td>29</td>\n",
" <td>20</td>\n",
" <td>38</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1656</th>\n",
" <td>199101</td>\n",
" <td>7</td>\n",
" <td>15565</td>\n",
" <td>10271</td>\n",
" <td>20859</td>\n",
" <td>27</td>\n",
" <td>18</td>\n",
" <td>36</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1657</th>\n",
" <td>199052</td>\n",
" <td>7</td>\n",
" <td>19375</td>\n",
" <td>13295</td>\n",
" <td>25455</td>\n",
" <td>34</td>\n",
" <td>23</td>\n",
" <td>45</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1658</th>\n",
" <td>199051</td>\n",
" <td>7</td>\n",
" <td>19080</td>\n",
" <td>13807</td>\n",
" <td>24353</td>\n",
" <td>34</td>\n",
" <td>25</td>\n",
" <td>43</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1659</th>\n",
" <td>199050</td>\n",
" <td>7</td>\n",
" <td>11079</td>\n",
" <td>6660</td>\n",
" <td>15498</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>28</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1660</th>\n",
" <td>199049</td>\n",
" <td>7</td>\n",
" <td>1143</td>\n",
" <td>0</td>\n",
" <td>2610</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1661 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202239 7 1345 111 2579 2 0 \n",
"1 202238 7 1781 421 3141 3 1 \n",
"2 202237 7 1731 498 2964 3 1 \n",
"3 202236 7 1069 178 1960 2 1 \n",
"4 202235 7 1581 400 2762 2 0 \n",
"5 202234 7 2266 788 3744 3 1 \n",
"6 202233 7 7340 0 17399 11 0 \n",
"7 202232 7 7801 4086 11516 12 6 \n",
"8 202231 7 6896 4170 9622 10 6 \n",
"9 202230 7 9039 5770 12308 14 9 \n",
"10 202229 7 14851 10060 19642 22 15 \n",
"11 202228 7 15471 11028 19914 23 16 \n",
"12 202227 7 21191 16198 26184 32 24 \n",
"13 202226 7 16854 12806 20902 25 19 \n",
"14 202225 7 22246 18011 26481 34 28 \n",
"15 202224 7 22458 18105 26811 34 27 \n",
"16 202223 7 18772 14875 22669 28 22 \n",
"17 202222 7 18916 14941 22891 29 23 \n",
"18 202221 7 20310 16307 24313 31 25 \n",
"19 202220 7 23585 19004 28166 36 29 \n",
"20 202219 7 18593 14181 23005 28 21 \n",
"21 202218 7 17851 13963 21739 27 21 \n",
"22 202217 7 20314 16001 24627 31 24 \n",
"23 202216 7 19660 14860 24460 30 23 \n",
"24 202215 7 17799 13715 21883 27 21 \n",
"25 202214 7 17005 13162 20848 26 20 \n",
"26 202213 7 15448 11659 19237 23 17 \n",
"27 202212 7 14702 10794 18610 22 16 \n",
"28 202211 7 11729 8347 15111 18 13 \n",
"29 202210 7 13314 10036 16592 20 15 \n",
"... ... ... ... ... ... ... ... \n",
"1631 199126 7 17608 11304 23912 31 20 \n",
"1632 199125 7 16169 10700 21638 28 18 \n",
"1633 199124 7 16171 10071 22271 28 17 \n",
"1634 199123 7 11947 7671 16223 21 13 \n",
"1635 199122 7 15452 9953 20951 27 17 \n",
"1636 199121 7 14903 8975 20831 26 16 \n",
"1637 199120 7 19053 12742 25364 34 23 \n",
"1638 199119 7 16739 11246 22232 29 19 \n",
"1639 199118 7 21385 13882 28888 38 25 \n",
"1640 199117 7 13462 8877 18047 24 16 \n",
"1641 199116 7 14857 10068 19646 26 18 \n",
"1642 199115 7 13975 9781 18169 25 18 \n",
"1643 199114 7 12265 7684 16846 22 14 \n",
"1644 199113 7 9567 6041 13093 17 11 \n",
"1645 199112 7 10864 7331 14397 19 13 \n",
"1646 199111 7 15574 11184 19964 27 19 \n",
"1647 199110 7 16643 11372 21914 29 20 \n",
"1648 199109 7 13741 8780 18702 24 15 \n",
"1649 199108 7 13289 8813 17765 23 15 \n",
"1650 199107 7 12337 8077 16597 22 15 \n",
"1651 199106 7 10877 7013 14741 19 12 \n",
"1652 199105 7 10442 6544 14340 18 11 \n",
"1653 199104 7 7913 4563 11263 14 8 \n",
"1654 199103 7 15387 10484 20290 27 18 \n",
"1655 199102 7 16277 11046 21508 29 20 \n",
"1656 199101 7 15565 10271 20859 27 18 \n",
"1657 199052 7 19375 13295 25455 34 23 \n",
"1658 199051 7 19080 13807 24353 34 25 \n",
"1659 199050 7 11079 6660 15498 20 12 \n",
"1660 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 4 FR France \n",
"1 5 FR France \n",
"2 5 FR France \n",
"3 3 FR France \n",
"4 4 FR France \n",
"5 5 FR France \n",
"6 26 FR France \n",
"7 18 FR France \n",
"8 14 FR France \n",
"9 19 FR France \n",
"10 29 FR France \n",
"11 30 FR France \n",
"12 40 FR France \n",
"13 31 FR France \n",
"14 40 FR France \n",
"15 41 FR France \n",
"16 34 FR France \n",
"17 35 FR France \n",
"18 37 FR France \n",
"19 43 FR France \n",
"20 35 FR France \n",
"21 33 FR France \n",
"22 38 FR France \n",
"23 37 FR France \n",
"24 33 FR France \n",
"25 32 FR France \n",
"26 29 FR France \n",
"27 28 FR France \n",
"28 23 FR France \n",
"29 25 FR France \n",
"... ... ... ... \n",
"1631 42 FR France \n",
"1632 38 FR France \n",
"1633 39 FR France \n",
"1634 29 FR France \n",
"1635 37 FR France \n",
"1636 36 FR France \n",
"1637 45 FR France \n",
"1638 39 FR France \n",
"1639 51 FR France \n",
"1640 32 FR France \n",
"1641 34 FR France \n",
"1642 32 FR France \n",
"1643 30 FR France \n",
"1644 23 FR France \n",
"1645 25 FR France \n",
"1646 35 FR France \n",
"1647 38 FR France \n",
"1648 33 FR France \n",
"1649 31 FR France \n",
"1650 29 FR France \n",
"1651 26 FR France \n",
"1652 25 FR France \n",
"1653 20 FR France \n",
"1654 36 FR France \n",
"1655 38 FR France \n",
"1656 36 FR France \n",
"1657 45 FR France \n",
"1658 43 FR France \n",
"1659 28 FR France \n",
"1660 5 FR France \n",
"\n",
"[1661 rows x 10 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
"Index: []"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"data = raw_data.copy()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"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": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f509e280c18>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1991,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_september_week[:-1],\n",
" first_september_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",
" \n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f509e2e8be0>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2020 221186\n",
"2021 376290\n",
"2002 516689\n",
"2018 542312\n",
"2017 551041\n",
"1996 564901\n",
"2019 584066\n",
"2015 604382\n",
"2000 617597\n",
"2001 619041\n",
"2012 624573\n",
"2005 628464\n",
"2006 632833\n",
"2011 642368\n",
"1993 643387\n",
"1995 652478\n",
"1994 661409\n",
"1998 677775\n",
"1997 683434\n",
"2014 685769\n",
"2013 698332\n",
"2007 717352\n",
"2008 749478\n",
"1999 756456\n",
"2003 758363\n",
"2004 777388\n",
"2016 782114\n",
"2010 829911\n",
"1992 832939\n",
"2009 842373\n",
"dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +1270,9 @@ ...@@ -16,10 +1270,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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