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a18376894a788ea9b9787b8ab4d7a164
mooc-rr
Commits
9386230e
Commit
9386230e
authored
Jan 04, 2023
by
a18376894a788ea9b9787b8ab4d7a164
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exercice.ipynb
module3/exo2/exercice.ipynb
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module3/exo2/exercice.ipynb
View file @
9386230e
{
"cells": [],
"cells": [
{
"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": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>202251</td>\n",
" <td>7</td>\n",
" <td>6372</td>\n",
" <td>3911</td>\n",
" <td>8833</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>1</th>\n",
" <td>202250</td>\n",
" <td>7</td>\n",
" <td>6590</td>\n",
" <td>3100</td>\n",
" <td>10080</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202249</td>\n",
" <td>7</td>\n",
" <td>5095</td>\n",
" <td>3212</td>\n",
" <td>6978</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202248</td>\n",
" <td>7</td>\n",
" <td>4985</td>\n",
" <td>3043</td>\n",
" <td>6927</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202247</td>\n",
" <td>7</td>\n",
" <td>6087</td>\n",
" <td>3733</td>\n",
" <td>8441</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202246</td>\n",
" <td>7</td>\n",
" <td>3033</td>\n",
" <td>1392</td>\n",
" <td>4674</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202245</td>\n",
" <td>7</td>\n",
" <td>3827</td>\n",
" <td>1720</td>\n",
" <td>5934</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202244</td>\n",
" <td>7</td>\n",
" <td>4271</td>\n",
" <td>2231</td>\n",
" <td>6311</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202243</td>\n",
" <td>7</td>\n",
" <td>5863</td>\n",
" <td>3302</td>\n",
" <td>8424</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202242</td>\n",
" <td>7</td>\n",
" <td>3770</td>\n",
" <td>1950</td>\n",
" <td>5590</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202241</td>\n",
" <td>7</td>\n",
" <td>4177</td>\n",
" <td>2219</td>\n",
" <td>6135</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202240</td>\n",
" <td>7</td>\n",
" <td>4883</td>\n",
" <td>1472</td>\n",
" <td>8294</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202239</td>\n",
" <td>7</td>\n",
" <td>2041</td>\n",
" <td>331</td>\n",
" <td>3751</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202238</td>\n",
" <td>7</td>\n",
" <td>1771</td>\n",
" <td>419</td>\n",
" <td>3123</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>14</th>\n",
" <td>202237</td>\n",
" <td>7</td>\n",
" <td>1725</td>\n",
" <td>499</td>\n",
" <td>2951</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>15</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>16</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>17</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>18</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>19</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>20</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>21</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>22</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>23</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>24</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>25</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>26</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>27</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>28</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>29</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>...</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>1643</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>1644</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>1645</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>1646</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>1647</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>1648</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>1649</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>1650</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>1651</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>1652</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>1653</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>1654</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>1655</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>1656</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>1657</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>1658</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>1659</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>1660</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>1661</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>1662</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>1663</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>1664</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>1665</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>1666</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>1667</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>1668</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>1669</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>1670</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>1671</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>1672</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>1673 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202251 7 6372 3911 8833 10 6 \n",
"1 202250 7 6590 3100 10080 10 5 \n",
"2 202249 7 5095 3212 6978 8 5 \n",
"3 202248 7 4985 3043 6927 8 5 \n",
"4 202247 7 6087 3733 8441 9 5 \n",
"5 202246 7 3033 1392 4674 5 3 \n",
"6 202245 7 3827 1720 5934 6 3 \n",
"7 202244 7 4271 2231 6311 6 3 \n",
"8 202243 7 5863 3302 8424 9 5 \n",
"9 202242 7 3770 1950 5590 6 3 \n",
"10 202241 7 4177 2219 6135 6 3 \n",
"11 202240 7 4883 1472 8294 7 2 \n",
"12 202239 7 2041 331 3751 3 0 \n",
"13 202238 7 1771 419 3123 3 1 \n",
"14 202237 7 1725 499 2951 3 1 \n",
"15 202236 7 1069 178 1960 2 1 \n",
"16 202235 7 1581 400 2762 2 0 \n",
"17 202234 7 2266 788 3744 3 1 \n",
"18 202233 7 7340 0 17399 11 0 \n",
"19 202232 7 7801 4086 11516 12 6 \n",
"20 202231 7 6896 4170 9622 10 6 \n",
"21 202230 7 9039 5770 12308 14 9 \n",
"22 202229 7 14851 10060 19642 22 15 \n",
"23 202228 7 15471 11028 19914 23 16 \n",
"24 202227 7 21191 16198 26184 32 24 \n",
"25 202226 7 16854 12806 20902 25 19 \n",
"26 202225 7 22246 18011 26481 34 28 \n",
"27 202224 7 22458 18105 26811 34 27 \n",
"28 202223 7 18772 14875 22669 28 22 \n",
"29 202222 7 18916 14941 22891 29 23 \n",
"... ... ... ... ... ... ... ... \n",
"1643 199126 7 17608 11304 23912 31 20 \n",
"1644 199125 7 16169 10700 21638 28 18 \n",
"1645 199124 7 16171 10071 22271 28 17 \n",
"1646 199123 7 11947 7671 16223 21 13 \n",
"1647 199122 7 15452 9953 20951 27 17 \n",
"1648 199121 7 14903 8975 20831 26 16 \n",
"1649 199120 7 19053 12742 25364 34 23 \n",
"1650 199119 7 16739 11246 22232 29 19 \n",
"1651 199118 7 21385 13882 28888 38 25 \n",
"1652 199117 7 13462 8877 18047 24 16 \n",
"1653 199116 7 14857 10068 19646 26 18 \n",
"1654 199115 7 13975 9781 18169 25 18 \n",
"1655 199114 7 12265 7684 16846 22 14 \n",
"1656 199113 7 9567 6041 13093 17 11 \n",
"1657 199112 7 10864 7331 14397 19 13 \n",
"1658 199111 7 15574 11184 19964 27 19 \n",
"1659 199110 7 16643 11372 21914 29 20 \n",
"1660 199109 7 13741 8780 18702 24 15 \n",
"1661 199108 7 13289 8813 17765 23 15 \n",
"1662 199107 7 12337 8077 16597 22 15 \n",
"1663 199106 7 10877 7013 14741 19 12 \n",
"1664 199105 7 10442 6544 14340 18 11 \n",
"1665 199104 7 7913 4563 11263 14 8 \n",
"1666 199103 7 15387 10484 20290 27 18 \n",
"1667 199102 7 16277 11046 21508 29 20 \n",
"1668 199101 7 15565 10271 20859 27 18 \n",
"1669 199052 7 19375 13295 25455 34 23 \n",
"1670 199051 7 19080 13807 24353 34 25 \n",
"1671 199050 7 11079 6660 15498 20 12 \n",
"1672 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 14 FR France \n",
"1 15 FR France \n",
"2 11 FR France \n",
"3 11 FR France \n",
"4 13 FR France \n",
"5 7 FR France \n",
"6 9 FR France \n",
"7 9 FR France \n",
"8 13 FR France \n",
"9 9 FR France \n",
"10 9 FR France \n",
"11 12 FR France \n",
"12 6 FR France \n",
"13 5 FR France \n",
"14 5 FR France \n",
"15 3 FR France \n",
"16 4 FR France \n",
"17 5 FR France \n",
"18 26 FR France \n",
"19 18 FR France \n",
"20 14 FR France \n",
"21 19 FR France \n",
"22 29 FR France \n",
"23 30 FR France \n",
"24 40 FR France \n",
"25 31 FR France \n",
"26 40 FR France \n",
"27 41 FR France \n",
"28 34 FR France \n",
"29 35 FR France \n",
"... ... ... ... \n",
"1643 42 FR France \n",
"1644 38 FR France \n",
"1645 39 FR France \n",
"1646 29 FR France \n",
"1647 37 FR France \n",
"1648 36 FR France \n",
"1649 45 FR France \n",
"1650 39 FR France \n",
"1651 51 FR France \n",
"1652 32 FR France \n",
"1653 34 FR France \n",
"1654 32 FR France \n",
"1655 30 FR France \n",
"1656 23 FR France \n",
"1657 25 FR France \n",
"1658 35 FR France \n",
"1659 38 FR France \n",
"1660 33 FR France \n",
"1661 31 FR France \n",
"1662 29 FR France \n",
"1663 26 FR France \n",
"1664 25 FR France \n",
"1665 20 FR France \n",
"1666 36 FR France \n",
"1667 38 FR France \n",
"1668 36 FR France \n",
"1669 45 FR France \n",
"1670 43 FR France \n",
"1671 28 FR France \n",
"1672 5 FR France \n",
"\n",
"[1673 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"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",
"raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = raw_data.set_index('period').sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 18,
"metadata": {},
"outputs": [],
"source": [
" first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1990,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f14c2725d30>"
]
},
"execution_count": 22,
"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": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2020 221186\n",
"2021 376290\n",
"2002 516689\n",
"2018 542312\n",
"2017 551041\n",
"1991 553090\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": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
...
...
@@ -16,10 +1228,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.
3
"
"version": "3.6.
4
"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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