{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome Varicelle"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek\n",
"import os.path\n",
"from os import path\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On télécharge le fichier si celui ci n'est pas present."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"file do not exist , downloading...\n",
"saved\n"
]
}
],
"source": [
"if not path.exists(\"incidence-PAY-7_downloaded.csv\"):\n",
" print(\"file do not exist , downloading...\")\n",
" #req = requests.get(csv_url)\n",
"\n",
" #url_content = req.content\n",
" #csv_file = open('incidence-PAY-3_downloaded.csv', 'wb')\n",
" #csv_file.write(url_content)\n",
" #csv_file.close()\n",
" pd.read_csv(data_url,encoding='latin-1').to_csv(\"incidence-PAY-7_downloaded.csv\")\n",
" print(\"saved\")\n",
" \n",
"else:\n",
" print(\"file already exist\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
"\n",
"| Nom de colonne | Libellé de colonne |\n",
"|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
"| week | Semaine calendaire (ISO 8601) |\n",
"| indicator | Code de l'indicateur de surveillance |\n",
"| inc | Estimation de l'incidence de consultations en nombre de cas |\n",
"| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n",
"| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n",
"| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
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1538
\n",
"
199123
\n",
"
7
\n",
"
11947
\n",
"
7671
\n",
"
16223
\n",
"
21
\n",
"
13
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1538
\n",
"
1539
\n",
"
199122
\n",
"
7
\n",
"
15452
\n",
"
9953
\n",
"
20951
\n",
"
27
\n",
"
17
\n",
"
37
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1539
\n",
"
1540
\n",
"
199121
\n",
"
7
\n",
"
14903
\n",
"
8975
\n",
"
20831
\n",
"
26
\n",
"
16
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1540
\n",
"
1541
\n",
"
199120
\n",
"
7
\n",
"
19053
\n",
"
12742
\n",
"
25364
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1541
\n",
"
1542
\n",
"
199119
\n",
"
7
\n",
"
16739
\n",
"
11246
\n",
"
22232
\n",
"
29
\n",
"
19
\n",
"
39
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1542
\n",
"
1543
\n",
"
199118
\n",
"
7
\n",
"
21385
\n",
"
13882
\n",
"
28888
\n",
"
38
\n",
"
25
\n",
"
51
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1543
\n",
"
1544
\n",
"
199117
\n",
"
7
\n",
"
13462
\n",
"
8877
\n",
"
18047
\n",
"
24
\n",
"
16
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1544
\n",
"
1545
\n",
"
199116
\n",
"
7
\n",
"
14857
\n",
"
10068
\n",
"
19646
\n",
"
26
\n",
"
18
\n",
"
34
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1545
\n",
"
1546
\n",
"
199115
\n",
"
7
\n",
"
13975
\n",
"
9781
\n",
"
18169
\n",
"
25
\n",
"
18
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1546
\n",
"
1547
\n",
"
199114
\n",
"
7
\n",
"
12265
\n",
"
7684
\n",
"
16846
\n",
"
22
\n",
"
14
\n",
"
30
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1547
\n",
"
1548
\n",
"
199113
\n",
"
7
\n",
"
9567
\n",
"
6041
\n",
"
13093
\n",
"
17
\n",
"
11
\n",
"
23
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1548
\n",
"
1549
\n",
"
199112
\n",
"
7
\n",
"
10864
\n",
"
7331
\n",
"
14397
\n",
"
19
\n",
"
13
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1549
\n",
"
1550
\n",
"
199111
\n",
"
7
\n",
"
15574
\n",
"
11184
\n",
"
19964
\n",
"
27
\n",
"
19
\n",
"
35
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1550
\n",
"
1551
\n",
"
199110
\n",
"
7
\n",
"
16643
\n",
"
11372
\n",
"
21914
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1551
\n",
"
1552
\n",
"
199109
\n",
"
7
\n",
"
13741
\n",
"
8780
\n",
"
18702
\n",
"
24
\n",
"
15
\n",
"
33
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1552
\n",
"
1553
\n",
"
199108
\n",
"
7
\n",
"
13289
\n",
"
8813
\n",
"
17765
\n",
"
23
\n",
"
15
\n",
"
31
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1553
\n",
"
1554
\n",
"
199107
\n",
"
7
\n",
"
12337
\n",
"
8077
\n",
"
16597
\n",
"
22
\n",
"
15
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1554
\n",
"
1555
\n",
"
199106
\n",
"
7
\n",
"
10877
\n",
"
7013
\n",
"
14741
\n",
"
19
\n",
"
12
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1555
\n",
"
1556
\n",
"
199105
\n",
"
7
\n",
"
10442
\n",
"
6544
\n",
"
14340
\n",
"
18
\n",
"
11
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1556
\n",
"
1557
\n",
"
199104
\n",
"
7
\n",
"
7913
\n",
"
4563
\n",
"
11263
\n",
"
14
\n",
"
8
\n",
"
20
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1557
\n",
"
1558
\n",
"
199103
\n",
"
7
\n",
"
15387
\n",
"
10484
\n",
"
20290
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1558
\n",
"
1559
\n",
"
199102
\n",
"
7
\n",
"
16277
\n",
"
11046
\n",
"
21508
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1559
\n",
"
1560
\n",
"
199101
\n",
"
7
\n",
"
15565
\n",
"
10271
\n",
"
20859
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1560
\n",
"
1561
\n",
"
199052
\n",
"
7
\n",
"
19375
\n",
"
13295
\n",
"
25455
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1561
\n",
"
1562
\n",
"
199051
\n",
"
7
\n",
"
19080
\n",
"
13807
\n",
"
24353
\n",
"
34
\n",
"
25
\n",
"
43
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1562
\n",
"
1563
\n",
"
199050
\n",
"
7
\n",
"
11079
\n",
"
6660
\n",
"
15498
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
"
\n",
"
1563
\n",
"
1564
\n",
"
199049
\n",
"
7
\n",
"
1143
\n",
"
0
\n",
"
2610
\n",
"
2
\n",
"
0
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
\n",
" \n",
"
\n",
"
1564 rows × 15 columns
\n",
"
"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202047 7 4983 2943 7023 8 5 \n",
"1 2 202046 7 3752 1963 5541 6 3 \n",
"2 3 202045 7 3696 2016 5376 6 3 \n",
"3 4 202044 7 4391 2375 6407 7 4 \n",
"4 5 202043 7 4376 2505 6247 7 4 \n",
"5 6 202042 7 4000 1979 6021 6 3 \n",
"6 7 202041 7 3961 2099 5823 6 3 \n",
"7 8 202040 7 2078 675 3481 3 1 \n",
"8 9 202039 7 1049 237 1861 2 1 \n",
"9 10 202038 7 2253 782 3724 3 1 \n",
"10 11 202037 7 1584 405 2763 2 0 \n",
"11 12 202036 7 919 100 1738 1 0 \n",
"12 13 202035 7 828 0 1694 1 0 \n",
"13 14 202034 7 2272 371 4173 3 0 \n",
"14 15 202033 7 1284 177 2391 2 0 \n",
"15 16 202032 7 2650 689 4611 4 1 \n",
"16 17 202031 7 1303 100 2506 2 0 \n",
"17 18 202030 7 1385 75 2695 2 0 \n",
"18 19 202029 7 841 10 1672 1 0 \n",
"19 20 202028 7 728 0 1515 1 0 \n",
"20 21 202027 7 986 149 1823 1 0 \n",
"21 22 202026 7 694 0 1454 1 0 \n",
"22 23 202025 7 228 0 597 0 0 \n",
"23 24 202024 7 388 0 959 1 0 \n",
"24 25 202023 7 558 1 1115 1 0 \n",
"25 26 202022 7 277 0 633 0 0 \n",
"26 27 202021 7 602 36 1168 1 0 \n",
"27 28 202020 7 824 20 1628 1 0 \n",
"28 29 202019 7 310 0 753 0 0 \n",
"29 30 202018 7 849 98 1600 1 0 \n",
"... ... ... ... ... ... ... ... ... \n",
"1534 1535 199126 7 17608 11304 23912 31 20 \n",
"1535 1536 199125 7 16169 10700 21638 28 18 \n",
"1536 1537 199124 7 16171 10071 22271 28 17 \n",
"1537 1538 199123 7 11947 7671 16223 21 13 \n",
"1538 1539 199122 7 15452 9953 20951 27 17 \n",
"1539 1540 199121 7 14903 8975 20831 26 16 \n",
"1540 1541 199120 7 19053 12742 25364 34 23 \n",
"1541 1542 199119 7 16739 11246 22232 29 19 \n",
"1542 1543 199118 7 21385 13882 28888 38 25 \n",
"1543 1544 199117 7 13462 8877 18047 24 16 \n",
"1544 1545 199116 7 14857 10068 19646 26 18 \n",
"1545 1546 199115 7 13975 9781 18169 25 18 \n",
"1546 1547 199114 7 12265 7684 16846 22 14 \n",
"1547 1548 199113 7 9567 6041 13093 17 11 \n",
"1548 1549 199112 7 10864 7331 14397 19 13 \n",
"1549 1550 199111 7 15574 11184 19964 27 19 \n",
"1550 1551 199110 7 16643 11372 21914 29 20 \n",
"1551 1552 199109 7 13741 8780 18702 24 15 \n",
"1552 1553 199108 7 13289 8813 17765 23 15 \n",
"1553 1554 199107 7 12337 8077 16597 22 15 \n",
"1554 1555 199106 7 10877 7013 14741 19 12 \n",
"1555 1556 199105 7 10442 6544 14340 18 11 \n",
"1556 1557 199104 7 7913 4563 11263 14 8 \n",
"1557 1558 199103 7 15387 10484 20290 27 18 \n",
"1558 1559 199102 7 16277 11046 21508 29 20 \n",
"1559 1560 199101 7 15565 10271 20859 27 18 \n",
"1560 1561 199052 7 19375 13295 25455 34 23 \n",
"1561 1562 199051 7 19080 13807 24353 34 25 \n",
"1562 1563 199050 7 11079 6660 15498 20 12 \n",
"1563 1564 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name Unnamed: 11 Unnamed: 12 Unnamed: 13 \\\n",
"0 11 FR France NaN NaN NaN \n",
"1 9 FR France NaN NaN NaN \n",
"2 9 FR France NaN NaN NaN \n",
"3 10 FR France NaN NaN NaN \n",
"4 10 FR France NaN NaN NaN \n",
"5 9 FR France NaN NaN NaN \n",
"6 9 FR France NaN NaN NaN \n",
"7 5 FR France NaN NaN NaN \n",
"8 3 FR France NaN NaN NaN \n",
"9 5 FR France NaN NaN NaN \n",
"10 4 FR France NaN NaN NaN \n",
"11 2 FR France NaN NaN NaN \n",
"12 2 FR France NaN NaN NaN \n",
"13 6 FR France NaN NaN NaN \n",
"14 4 FR France NaN NaN NaN \n",
"15 7 FR France NaN NaN NaN \n",
"16 4 FR France NaN NaN NaN \n",
"17 4 FR France NaN NaN NaN \n",
"18 2 FR France NaN NaN NaN \n",
"19 2 FR France NaN NaN NaN \n",
"20 2 FR France NaN NaN NaN \n",
"21 2 FR France NaN NaN NaN \n",
"22 1 FR France NaN NaN NaN \n",
"23 2 FR France NaN NaN NaN \n",
"24 2 FR France NaN NaN NaN \n",
"25 1 FR France NaN NaN NaN \n",
"26 2 FR France NaN NaN NaN \n",
"27 2 FR France NaN NaN NaN \n",
"28 1 FR France NaN NaN NaN \n",
"29 2 FR France NaN NaN NaN \n",
"... ... ... ... ... ... ... \n",
"1534 42 FR France NaN NaN NaN \n",
"1535 38 FR France NaN NaN NaN \n",
"1536 39 FR France NaN NaN NaN \n",
"1537 29 FR France NaN NaN NaN \n",
"1538 37 FR France NaN NaN NaN \n",
"1539 36 FR France NaN NaN NaN \n",
"1540 45 FR France NaN NaN NaN \n",
"1541 39 FR France NaN NaN NaN \n",
"1542 51 FR France NaN NaN NaN \n",
"1543 32 FR France NaN NaN NaN \n",
"1544 34 FR France NaN NaN NaN \n",
"1545 32 FR France NaN NaN NaN \n",
"1546 30 FR France NaN NaN NaN \n",
"1547 23 FR France NaN NaN NaN \n",
"1548 25 FR France NaN NaN NaN \n",
"1549 35 FR France NaN NaN NaN \n",
"1550 38 FR France NaN NaN NaN \n",
"1551 33 FR France NaN NaN NaN \n",
"1552 31 FR France NaN NaN NaN \n",
"1553 29 FR France NaN NaN NaN \n",
"1554 26 FR France NaN NaN NaN \n",
"1555 25 FR France NaN NaN NaN \n",
"1556 20 FR France NaN NaN NaN \n",
"1557 36 FR France NaN NaN NaN \n",
"1558 38 FR France NaN NaN NaN \n",
"1559 36 FR France NaN NaN NaN \n",
"1560 45 FR France NaN NaN NaN \n",
"1561 43 FR France NaN NaN NaN \n",
"1562 28 FR France NaN NaN NaN \n",
"1563 5 FR France NaN NaN NaN \n",
"\n",
" Unnamed: 14 \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"... ... \n",
"1534 NaN \n",
"1535 NaN \n",
"1536 NaN \n",
"1537 NaN \n",
"1538 NaN \n",
"1539 NaN \n",
"1540 NaN \n",
"1541 NaN \n",
"1542 NaN \n",
"1543 NaN \n",
"1544 NaN \n",
"1545 NaN \n",
"1546 NaN \n",
"1547 NaN \n",
"1548 NaN \n",
"1549 NaN \n",
"1550 NaN \n",
"1551 NaN \n",
"1552 NaN \n",
"1553 NaN \n",
"1554 NaN \n",
"1555 NaN \n",
"1556 NaN \n",
"1557 NaN \n",
"1558 NaN \n",
"1559 NaN \n",
"1560 NaN \n",
"1561 NaN \n",
"1562 NaN \n",
"1563 NaN \n",
"\n",
"[1564 rows x 15 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(\"incidence-PAY-7_downloaded.csv\", skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On enlève les columns inutiles"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
0
\n",
"
week
\n",
"
indicator
\n",
"
inc
\n",
"
inc_low
\n",
"
inc_up
\n",
"
inc100
\n",
"
inc100_low
\n",
"
inc100_up
\n",
"
geo_insee
\n",
"
geo_name
\n",
"
\n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [0, week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
"Index: []"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data.dropna(axis=1,how='all', inplace=True)\n",
"\n",
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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7
\n",
"
13289
\n",
"
8813
\n",
"
17765
\n",
"
23
\n",
"
15
\n",
"
31
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1553
\n",
"
1554
\n",
"
199107
\n",
"
7
\n",
"
12337
\n",
"
8077
\n",
"
16597
\n",
"
22
\n",
"
15
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1554
\n",
"
1555
\n",
"
199106
\n",
"
7
\n",
"
10877
\n",
"
7013
\n",
"
14741
\n",
"
19
\n",
"
12
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1555
\n",
"
1556
\n",
"
199105
\n",
"
7
\n",
"
10442
\n",
"
6544
\n",
"
14340
\n",
"
18
\n",
"
11
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1556
\n",
"
1557
\n",
"
199104
\n",
"
7
\n",
"
7913
\n",
"
4563
\n",
"
11263
\n",
"
14
\n",
"
8
\n",
"
20
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1557
\n",
"
1558
\n",
"
199103
\n",
"
7
\n",
"
15387
\n",
"
10484
\n",
"
20290
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1558
\n",
"
1559
\n",
"
199102
\n",
"
7
\n",
"
16277
\n",
"
11046
\n",
"
21508
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1559
\n",
"
1560
\n",
"
199101
\n",
"
7
\n",
"
15565
\n",
"
10271
\n",
"
20859
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1560
\n",
"
1561
\n",
"
199052
\n",
"
7
\n",
"
19375
\n",
"
13295
\n",
"
25455
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1561
\n",
"
1562
\n",
"
199051
\n",
"
7
\n",
"
19080
\n",
"
13807
\n",
"
24353
\n",
"
34
\n",
"
25
\n",
"
43
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1562
\n",
"
1563
\n",
"
199050
\n",
"
7
\n",
"
11079
\n",
"
6660
\n",
"
15498
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1563
\n",
"
1564
\n",
"
199049
\n",
"
7
\n",
"
1143
\n",
"
0
\n",
"
2610
\n",
"
2
\n",
"
0
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
1564 rows × 11 columns
\n",
"
"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202047 7 4983 2943 7023 8 5 \n",
"1 2 202046 7 3752 1963 5541 6 3 \n",
"2 3 202045 7 3696 2016 5376 6 3 \n",
"3 4 202044 7 4391 2375 6407 7 4 \n",
"4 5 202043 7 4376 2505 6247 7 4 \n",
"5 6 202042 7 4000 1979 6021 6 3 \n",
"6 7 202041 7 3961 2099 5823 6 3 \n",
"7 8 202040 7 2078 675 3481 3 1 \n",
"8 9 202039 7 1049 237 1861 2 1 \n",
"9 10 202038 7 2253 782 3724 3 1 \n",
"10 11 202037 7 1584 405 2763 2 0 \n",
"11 12 202036 7 919 100 1738 1 0 \n",
"12 13 202035 7 828 0 1694 1 0 \n",
"13 14 202034 7 2272 371 4173 3 0 \n",
"14 15 202033 7 1284 177 2391 2 0 \n",
"15 16 202032 7 2650 689 4611 4 1 \n",
"16 17 202031 7 1303 100 2506 2 0 \n",
"17 18 202030 7 1385 75 2695 2 0 \n",
"18 19 202029 7 841 10 1672 1 0 \n",
"19 20 202028 7 728 0 1515 1 0 \n",
"20 21 202027 7 986 149 1823 1 0 \n",
"21 22 202026 7 694 0 1454 1 0 \n",
"22 23 202025 7 228 0 597 0 0 \n",
"23 24 202024 7 388 0 959 1 0 \n",
"24 25 202023 7 558 1 1115 1 0 \n",
"25 26 202022 7 277 0 633 0 0 \n",
"26 27 202021 7 602 36 1168 1 0 \n",
"27 28 202020 7 824 20 1628 1 0 \n",
"28 29 202019 7 310 0 753 0 0 \n",
"29 30 202018 7 849 98 1600 1 0 \n",
"... ... ... ... ... ... ... ... ... \n",
"1534 1535 199126 7 17608 11304 23912 31 20 \n",
"1535 1536 199125 7 16169 10700 21638 28 18 \n",
"1536 1537 199124 7 16171 10071 22271 28 17 \n",
"1537 1538 199123 7 11947 7671 16223 21 13 \n",
"1538 1539 199122 7 15452 9953 20951 27 17 \n",
"1539 1540 199121 7 14903 8975 20831 26 16 \n",
"1540 1541 199120 7 19053 12742 25364 34 23 \n",
"1541 1542 199119 7 16739 11246 22232 29 19 \n",
"1542 1543 199118 7 21385 13882 28888 38 25 \n",
"1543 1544 199117 7 13462 8877 18047 24 16 \n",
"1544 1545 199116 7 14857 10068 19646 26 18 \n",
"1545 1546 199115 7 13975 9781 18169 25 18 \n",
"1546 1547 199114 7 12265 7684 16846 22 14 \n",
"1547 1548 199113 7 9567 6041 13093 17 11 \n",
"1548 1549 199112 7 10864 7331 14397 19 13 \n",
"1549 1550 199111 7 15574 11184 19964 27 19 \n",
"1550 1551 199110 7 16643 11372 21914 29 20 \n",
"1551 1552 199109 7 13741 8780 18702 24 15 \n",
"1552 1553 199108 7 13289 8813 17765 23 15 \n",
"1553 1554 199107 7 12337 8077 16597 22 15 \n",
"1554 1555 199106 7 10877 7013 14741 19 12 \n",
"1555 1556 199105 7 10442 6544 14340 18 11 \n",
"1556 1557 199104 7 7913 4563 11263 14 8 \n",
"1557 1558 199103 7 15387 10484 20290 27 18 \n",
"1558 1559 199102 7 16277 11046 21508 29 20 \n",
"1559 1560 199101 7 15565 10271 20859 27 18 \n",
"1560 1561 199052 7 19375 13295 25455 34 23 \n",
"1561 1562 199051 7 19080 13807 24353 34 25 \n",
"1562 1563 199050 7 11079 6660 15498 20 12 \n",
"1563 1564 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 11 FR France \n",
"1 9 FR France \n",
"2 9 FR France \n",
"3 10 FR France \n",
"4 10 FR France \n",
"5 9 FR France \n",
"6 9 FR France \n",
"7 5 FR France \n",
"8 3 FR France \n",
"9 5 FR France \n",
"10 4 FR France \n",
"11 2 FR France \n",
"12 2 FR France \n",
"13 6 FR France \n",
"14 4 FR France \n",
"15 7 FR France \n",
"16 4 FR France \n",
"17 4 FR France \n",
"18 2 FR France \n",
"19 2 FR France \n",
"20 2 FR France \n",
"21 2 FR France \n",
"22 1 FR France \n",
"23 2 FR France \n",
"24 2 FR France \n",
"25 1 FR France \n",
"26 2 FR France \n",
"27 2 FR France \n",
"28 1 FR France \n",
"29 2 FR France \n",
"... ... ... ... \n",
"1534 42 FR France \n",
"1535 38 FR France \n",
"1536 39 FR France \n",
"1537 29 FR France \n",
"1538 37 FR France \n",
"1539 36 FR France \n",
"1540 45 FR France \n",
"1541 39 FR France \n",
"1542 51 FR France \n",
"1543 32 FR France \n",
"1544 34 FR France \n",
"1545 32 FR France \n",
"1546 30 FR France \n",
"1547 23 FR France \n",
"1548 25 FR France \n",
"1549 35 FR France \n",
"1550 38 FR France \n",
"1551 33 FR France \n",
"1552 31 FR France \n",
"1553 29 FR France \n",
"1554 26 FR France \n",
"1555 25 FR France \n",
"1556 20 FR France \n",
"1557 36 FR France \n",
"1558 38 FR France \n",
"1559 36 FR France \n",
"1560 45 FR France \n",
"1561 43 FR France \n",
"1562 28 FR France \n",
"1563 5 FR France \n",
"\n",
"[1564 rows x 11 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nos données utilisent une convention inhabituelle: le numéro de\n",
"semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
"de nombre entier. C'est comme ça que Pandas les interprète.\n",
" \n",
"Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
"semaine. Il faut lui fournir les dates de début et de fin de\n",
"semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
"\n",
"Comme la conversion des semaines est devenu assez complexe, nous\n",
"écrivons une petite fonction Python pour cela. Ensuite, nous\n",
"l'appliquons à tous les points de nos donnés. Les résultats vont\n",
"dans une nouvelle colonne 'period'."
]
},
{
"cell_type": "code",
"execution_count": 34,
"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": [
"Il restent deux petites modifications à faire.\n",
"\n",
"Premièrement, nous définissons les périodes d'observation\n",
"comme nouvel index de notre jeux de données. Ceci en fait\n",
"une suite chronologique, ce qui sera pratique par la suite.\n",
"\n",
"Deuxièmement, nous trions les points par période, dans\n",
"le sens chronologique."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
"le début de la période qui suit, la différence temporelle doit être\n",
"zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
"d'une seconde.\n",
"\n",
"Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
"entre lesquelles il manque une semaine.\n",
"\n",
"Nous reconnaissons ces dates: c'est la semaine sans observations\n",
"que nous avions supprimées !"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": "markdown",
"metadata": {},
"source": [
"Un premier regard sur les données !"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'][-200:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etude de l'incidence annuelle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
"entre deux années civiles, nous définissons la période de référence\n",
"entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
"1er août de l'année $N+1$.\n",
"\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
"pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
"de référence: à la place du 1er août de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
"\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
]
},
{
"cell_type": "code",
"execution_count": 41,
"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": [
"Voici les incidences annuelles."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\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": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
" française, sont assez rares: il y en eu trois au cours des 35 dernières années."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}