{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence de la varicelle (basé sur l'analyse-syndrome-grippal-jupyter)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=bj5wh\""
]
},
{
"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`.\n",
"ESTA CELDA NO REPRESENTA LOS DATOS DE LA VARICELA SI DE LA GRIPE"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\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",
"
0
\n",
"
202402
\n",
"
7
\n",
"
8021
\n",
"
5132
\n",
"
10910
\n",
"
12
\n",
"
8
\n",
"
16
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1
\n",
"
202401
\n",
"
7
\n",
"
13467
\n",
"
9285
\n",
"
17649
\n",
"
20
\n",
"
14
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2
\n",
"
202352
\n",
"
7
\n",
"
11636
\n",
"
7354
\n",
"
15918
\n",
"
18
\n",
"
12
\n",
"
24
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
3
\n",
"
202351
\n",
"
7
\n",
"
6912
\n",
"
4227
\n",
"
9597
\n",
"
10
\n",
"
6
\n",
"
14
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
4
\n",
"
202350
\n",
"
7
\n",
"
8799
\n",
"
6215
\n",
"
11383
\n",
"
13
\n",
"
9
\n",
"
17
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"0 202402 7 8021 5132 10910 12 8 16 \n",
"1 202401 7 13467 9285 17649 20 14 26 \n",
"2 202352 7 11636 7354 15918 18 12 24 \n",
"3 202351 7 6912 4227 9597 10 6 14 \n",
"4 202350 7 8799 6215 11383 13 9 17 \n",
"\n",
" geo_insee geo_name \n",
"0 FR France \n",
"1 FR France \n",
"2 FR France \n",
"3 FR France \n",
"4 FR France "
]
},
"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\n",
"raw_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\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: [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": "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": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\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",
"
0
\n",
"
202402
\n",
"
7
\n",
"
8021
\n",
"
5132
\n",
"
10910
\n",
"
12
\n",
"
8
\n",
"
16
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1
\n",
"
202401
\n",
"
7
\n",
"
13467
\n",
"
9285
\n",
"
17649
\n",
"
20
\n",
"
14
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2
\n",
"
202352
\n",
"
7
\n",
"
11636
\n",
"
7354
\n",
"
15918
\n",
"
18
\n",
"
12
\n",
"
24
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
3
\n",
"
202351
\n",
"
7
\n",
"
6912
\n",
"
4227
\n",
"
9597
\n",
"
10
\n",
"
6
\n",
"
14
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
4
\n",
"
202350
\n",
"
7
\n",
"
8799
\n",
"
6215
\n",
"
11383
\n",
"
13
\n",
"
9
\n",
"
17
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
5
\n",
"
202349
\n",
"
7
\n",
"
7817
\n",
"
5362
\n",
"
10272
\n",
"
12
\n",
"
8
\n",
"
16
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
6
\n",
"
202348
\n",
"
7
\n",
"
7351
\n",
"
4749
\n",
"
9953
\n",
"
11
\n",
"
7
\n",
"
15
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
7
\n",
"
202347
\n",
"
7
\n",
"
6537
\n",
"
4277
\n",
"
8797
\n",
"
10
\n",
"
7
\n",
"
13
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
8
\n",
"
202346
\n",
"
7
\n",
"
5223
\n",
"
2968
\n",
"
7478
\n",
"
8
\n",
"
5
\n",
"
11
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
9
\n",
"
202345
\n",
"
7
\n",
"
5007
\n",
"
2675
\n",
"
7339
\n",
"
8
\n",
"
4
\n",
"
12
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
10
\n",
"
202344
\n",
"
7
\n",
"
3688
\n",
"
1664
\n",
"
5712
\n",
"
6
\n",
"
3
\n",
"
9
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
11
\n",
"
202343
\n",
"
7
\n",
"
3891
\n",
"
1675
\n",
"
6107
\n",
"
6
\n",
"
3
\n",
"
9
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
12
\n",
"
202342
\n",
"
7
\n",
"
3968
\n",
"
1212
\n",
"
6724
\n",
"
6
\n",
"
2
\n",
"
10
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
13
\n",
"
202341
\n",
"
7
\n",
"
3356
\n",
"
1764
\n",
"
4948
\n",
"
5
\n",
"
3
\n",
"
7
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
14
\n",
"
202340
\n",
"
7
\n",
"
2845
\n",
"
1410
\n",
"
4280
\n",
"
4
\n",
"
2
\n",
"
6
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
15
\n",
"
202339
\n",
"
7
\n",
"
1739
\n",
"
629
\n",
"
2849
\n",
"
3
\n",
"
1
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
16
\n",
"
202338
\n",
"
7
\n",
"
1663
\n",
"
274
\n",
"
3052
\n",
"
3
\n",
"
1
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
17
\n",
"
202337
\n",
"
7
\n",
"
1122
\n",
"
223
\n",
"
2021
\n",
"
2
\n",
"
1
\n",
"
3
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
18
\n",
"
202336
\n",
"
7
\n",
"
726
\n",
"
10
\n",
"
1442
\n",
"
1
\n",
"
0
\n",
"
2
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
19
\n",
"
202335
\n",
"
7
\n",
"
961
\n",
"
96
\n",
"
1826
\n",
"
1
\n",
"
0
\n",
"
2
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
20
\n",
"
202334
\n",
"
7
\n",
"
1168
\n",
"
9
\n",
"
2327
\n",
"
2
\n",
"
0
\n",
"
4
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
21
\n",
"
202333
\n",
"
7
\n",
"
3308
\n",
"
1184
\n",
"
5432
\n",
"
5
\n",
"
2
\n",
"
8
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
22
\n",
"
202332
\n",
"
7
\n",
"
7996
\n",
"
1120
\n",
"
14872
\n",
"
12
\n",
"
2
\n",
"
22
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
23
\n",
"
202331
\n",
"
7
\n",
"
3318
\n",
"
1398
\n",
"
5238
\n",
"
5
\n",
"
2
\n",
"
8
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
24
\n",
"
202330
\n",
"
7
\n",
"
5821
\n",
"
3269
\n",
"
8373
\n",
"
9
\n",
"
5
\n",
"
13
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
25
\n",
"
202329
\n",
"
7
\n",
"
13558
\n",
"
8297
\n",
"
18819
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
26
\n",
"
202328
\n",
"
7
\n",
"
6700
\n",
"
4043
\n",
"
9357
\n",
"
10
\n",
"
6
\n",
"
14
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
27
\n",
"
202327
\n",
"
7
\n",
"
7253
\n",
"
4599
\n",
"
9907
\n",
"
11
\n",
"
7
\n",
"
15
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
28
\n",
"
202326
\n",
"
7
\n",
"
9192
\n",
"
6223
\n",
"
12161
\n",
"
14
\n",
"
10
\n",
"
18
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
29
\n",
"
202325
\n",
"
7
\n",
"
11498
\n",
"
8257
\n",
"
14739
\n",
"
17
\n",
"
12
\n",
"
22
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
1698
\n",
"
199126
\n",
"
7
\n",
"
17608
\n",
"
11304
\n",
"
23912
\n",
"
31
\n",
"
20
\n",
"
42
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1699
\n",
"
199125
\n",
"
7
\n",
"
16169
\n",
"
10700
\n",
"
21638
\n",
"
28
\n",
"
18
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1700
\n",
"
199124
\n",
"
7
\n",
"
16171
\n",
"
10071
\n",
"
22271
\n",
"
28
\n",
"
17
\n",
"
39
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1701
\n",
"
199123
\n",
"
7
\n",
"
11947
\n",
"
7671
\n",
"
16223
\n",
"
21
\n",
"
13
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1702
\n",
"
199122
\n",
"
7
\n",
"
15452
\n",
"
9953
\n",
"
20951
\n",
"
27
\n",
"
17
\n",
"
37
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1703
\n",
"
199121
\n",
"
7
\n",
"
14903
\n",
"
8975
\n",
"
20831
\n",
"
26
\n",
"
16
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1704
\n",
"
199120
\n",
"
7
\n",
"
19053
\n",
"
12742
\n",
"
25364
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1705
\n",
"
199119
\n",
"
7
\n",
"
16739
\n",
"
11246
\n",
"
22232
\n",
"
29
\n",
"
19
\n",
"
39
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1706
\n",
"
199118
\n",
"
7
\n",
"
21385
\n",
"
13882
\n",
"
28888
\n",
"
38
\n",
"
25
\n",
"
51
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1707
\n",
"
199117
\n",
"
7
\n",
"
13462
\n",
"
8877
\n",
"
18047
\n",
"
24
\n",
"
16
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1708
\n",
"
199116
\n",
"
7
\n",
"
14857
\n",
"
10068
\n",
"
19646
\n",
"
26
\n",
"
18
\n",
"
34
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1709
\n",
"
199115
\n",
"
7
\n",
"
13975
\n",
"
9781
\n",
"
18169
\n",
"
25
\n",
"
18
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1710
\n",
"
199114
\n",
"
7
\n",
"
12265
\n",
"
7684
\n",
"
16846
\n",
"
22
\n",
"
14
\n",
"
30
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1711
\n",
"
199113
\n",
"
7
\n",
"
9567
\n",
"
6041
\n",
"
13093
\n",
"
17
\n",
"
11
\n",
"
23
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1712
\n",
"
199112
\n",
"
7
\n",
"
10864
\n",
"
7331
\n",
"
14397
\n",
"
19
\n",
"
13
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1713
\n",
"
199111
\n",
"
7
\n",
"
15574
\n",
"
11184
\n",
"
19964
\n",
"
27
\n",
"
19
\n",
"
35
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1714
\n",
"
199110
\n",
"
7
\n",
"
16643
\n",
"
11372
\n",
"
21914
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1715
\n",
"
199109
\n",
"
7
\n",
"
13741
\n",
"
8780
\n",
"
18702
\n",
"
24
\n",
"
15
\n",
"
33
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1716
\n",
"
199108
\n",
"
7
\n",
"
13289
\n",
"
8813
\n",
"
17765
\n",
"
23
\n",
"
15
\n",
"
31
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1717
\n",
"
199107
\n",
"
7
\n",
"
12337
\n",
"
8077
\n",
"
16597
\n",
"
22
\n",
"
15
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1718
\n",
"
199106
\n",
"
7
\n",
"
10877
\n",
"
7013
\n",
"
14741
\n",
"
19
\n",
"
12
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1719
\n",
"
199105
\n",
"
7
\n",
"
10442
\n",
"
6544
\n",
"
14340
\n",
"
18
\n",
"
11
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1720
\n",
"
199104
\n",
"
7
\n",
"
7913
\n",
"
4563
\n",
"
11263
\n",
"
14
\n",
"
8
\n",
"
20
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1721
\n",
"
199103
\n",
"
7
\n",
"
15387
\n",
"
10484
\n",
"
20290
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1722
\n",
"
199102
\n",
"
7
\n",
"
16277
\n",
"
11046
\n",
"
21508
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1723
\n",
"
199101
\n",
"
7
\n",
"
15565
\n",
"
10271
\n",
"
20859
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1724
\n",
"
199052
\n",
"
7
\n",
"
19375
\n",
"
13295
\n",
"
25455
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1725
\n",
"
199051
\n",
"
7
\n",
"
19080
\n",
"
13807
\n",
"
24353
\n",
"
34
\n",
"
25
\n",
"
43
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1726
\n",
"
199050
\n",
"
7
\n",
"
11079
\n",
"
6660
\n",
"
15498
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1727
\n",
"
199049
\n",
"
7
\n",
"
1143
\n",
"
0
\n",
"
2610
\n",
"
2
\n",
"
0
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
1728 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202402 7 8021 5132 10910 12 8 \n",
"1 202401 7 13467 9285 17649 20 14 \n",
"2 202352 7 11636 7354 15918 18 12 \n",
"3 202351 7 6912 4227 9597 10 6 \n",
"4 202350 7 8799 6215 11383 13 9 \n",
"5 202349 7 7817 5362 10272 12 8 \n",
"6 202348 7 7351 4749 9953 11 7 \n",
"7 202347 7 6537 4277 8797 10 7 \n",
"8 202346 7 5223 2968 7478 8 5 \n",
"9 202345 7 5007 2675 7339 8 4 \n",
"10 202344 7 3688 1664 5712 6 3 \n",
"11 202343 7 3891 1675 6107 6 3 \n",
"12 202342 7 3968 1212 6724 6 2 \n",
"13 202341 7 3356 1764 4948 5 3 \n",
"14 202340 7 2845 1410 4280 4 2 \n",
"15 202339 7 1739 629 2849 3 1 \n",
"16 202338 7 1663 274 3052 3 1 \n",
"17 202337 7 1122 223 2021 2 1 \n",
"18 202336 7 726 10 1442 1 0 \n",
"19 202335 7 961 96 1826 1 0 \n",
"20 202334 7 1168 9 2327 2 0 \n",
"21 202333 7 3308 1184 5432 5 2 \n",
"22 202332 7 7996 1120 14872 12 2 \n",
"23 202331 7 3318 1398 5238 5 2 \n",
"24 202330 7 5821 3269 8373 9 5 \n",
"25 202329 7 13558 8297 18819 20 12 \n",
"26 202328 7 6700 4043 9357 10 6 \n",
"27 202327 7 7253 4599 9907 11 7 \n",
"28 202326 7 9192 6223 12161 14 10 \n",
"29 202325 7 11498 8257 14739 17 12 \n",
"... ... ... ... ... ... ... ... \n",
"1698 199126 7 17608 11304 23912 31 20 \n",
"1699 199125 7 16169 10700 21638 28 18 \n",
"1700 199124 7 16171 10071 22271 28 17 \n",
"1701 199123 7 11947 7671 16223 21 13 \n",
"1702 199122 7 15452 9953 20951 27 17 \n",
"1703 199121 7 14903 8975 20831 26 16 \n",
"1704 199120 7 19053 12742 25364 34 23 \n",
"1705 199119 7 16739 11246 22232 29 19 \n",
"1706 199118 7 21385 13882 28888 38 25 \n",
"1707 199117 7 13462 8877 18047 24 16 \n",
"1708 199116 7 14857 10068 19646 26 18 \n",
"1709 199115 7 13975 9781 18169 25 18 \n",
"1710 199114 7 12265 7684 16846 22 14 \n",
"1711 199113 7 9567 6041 13093 17 11 \n",
"1712 199112 7 10864 7331 14397 19 13 \n",
"1713 199111 7 15574 11184 19964 27 19 \n",
"1714 199110 7 16643 11372 21914 29 20 \n",
"1715 199109 7 13741 8780 18702 24 15 \n",
"1716 199108 7 13289 8813 17765 23 15 \n",
"1717 199107 7 12337 8077 16597 22 15 \n",
"1718 199106 7 10877 7013 14741 19 12 \n",
"1719 199105 7 10442 6544 14340 18 11 \n",
"1720 199104 7 7913 4563 11263 14 8 \n",
"1721 199103 7 15387 10484 20290 27 18 \n",
"1722 199102 7 16277 11046 21508 29 20 \n",
"1723 199101 7 15565 10271 20859 27 18 \n",
"1724 199052 7 19375 13295 25455 34 23 \n",
"1725 199051 7 19080 13807 24353 34 25 \n",
"1726 199050 7 11079 6660 15498 20 12 \n",
"1727 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 16 FR France \n",
"1 26 FR France \n",
"2 24 FR France \n",
"3 14 FR France \n",
"4 17 FR France \n",
"5 16 FR France \n",
"6 15 FR France \n",
"7 13 FR France \n",
"8 11 FR France \n",
"9 12 FR France \n",
"10 9 FR France \n",
"11 9 FR France \n",
"12 10 FR France \n",
"13 7 FR France \n",
"14 6 FR France \n",
"15 5 FR France \n",
"16 5 FR France \n",
"17 3 FR France \n",
"18 2 FR France \n",
"19 2 FR France \n",
"20 4 FR France \n",
"21 8 FR France \n",
"22 22 FR France \n",
"23 8 FR France \n",
"24 13 FR France \n",
"25 28 FR France \n",
"26 14 FR France \n",
"27 15 FR France \n",
"28 18 FR France \n",
"29 22 FR France \n",
"... ... ... ... \n",
"1698 42 FR France \n",
"1699 38 FR France \n",
"1700 39 FR France \n",
"1701 29 FR France \n",
"1702 37 FR France \n",
"1703 36 FR France \n",
"1704 45 FR France \n",
"1705 39 FR France \n",
"1706 51 FR France \n",
"1707 32 FR France \n",
"1708 34 FR France \n",
"1709 32 FR France \n",
"1710 30 FR France \n",
"1711 23 FR France \n",
"1712 25 FR France \n",
"1713 35 FR France \n",
"1714 38 FR France \n",
"1715 33 FR France \n",
"1716 31 FR France \n",
"1717 29 FR France \n",
"1718 26 FR France \n",
"1719 25 FR France \n",
"1720 20 FR France \n",
"1721 36 FR France \n",
"1722 38 FR France \n",
"1723 36 FR France \n",
"1724 45 FR France \n",
"1725 43 FR France \n",
"1726 28 FR France \n",
"1727 5 FR France \n",
"\n",
"[1728 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": 6,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\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",
"
period
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
1990-12-03/1990-12-09
\n",
"
199049
\n",
"
7
\n",
"
1143
\n",
"
0
\n",
"
2610
\n",
"
2
\n",
"
0
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1990-12-10/1990-12-16
\n",
"
199050
\n",
"
7
\n",
"
11079
\n",
"
6660
\n",
"
15498
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1990-12-17/1990-12-23
\n",
"
199051
\n",
"
7
\n",
"
19080
\n",
"
13807
\n",
"
24353
\n",
"
34
\n",
"
25
\n",
"
43
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1990-12-24/1990-12-30
\n",
"
199052
\n",
"
7
\n",
"
19375
\n",
"
13295
\n",
"
25455
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1990-12-31/1991-01-06
\n",
"
199101
\n",
"
7
\n",
"
15565
\n",
"
10271
\n",
"
20859
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-01-07/1991-01-13
\n",
"
199102
\n",
"
7
\n",
"
16277
\n",
"
11046
\n",
"
21508
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-01-14/1991-01-20
\n",
"
199103
\n",
"
7
\n",
"
15387
\n",
"
10484
\n",
"
20290
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-01-21/1991-01-27
\n",
"
199104
\n",
"
7
\n",
"
7913
\n",
"
4563
\n",
"
11263
\n",
"
14
\n",
"
8
\n",
"
20
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-01-28/1991-02-03
\n",
"
199105
\n",
"
7
\n",
"
10442
\n",
"
6544
\n",
"
14340
\n",
"
18
\n",
"
11
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-02-04/1991-02-10
\n",
"
199106
\n",
"
7
\n",
"
10877
\n",
"
7013
\n",
"
14741
\n",
"
19
\n",
"
12
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-02-11/1991-02-17
\n",
"
199107
\n",
"
7
\n",
"
12337
\n",
"
8077
\n",
"
16597
\n",
"
22
\n",
"
15
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-02-18/1991-02-24
\n",
"
199108
\n",
"
7
\n",
"
13289
\n",
"
8813
\n",
"
17765
\n",
"
23
\n",
"
15
\n",
"
31
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-02-25/1991-03-03
\n",
"
199109
\n",
"
7
\n",
"
13741
\n",
"
8780
\n",
"
18702
\n",
"
24
\n",
"
15
\n",
"
33
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-03-04/1991-03-10
\n",
"
199110
\n",
"
7
\n",
"
16643
\n",
"
11372
\n",
"
21914
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-03-11/1991-03-17
\n",
"
199111
\n",
"
7
\n",
"
15574
\n",
"
11184
\n",
"
19964
\n",
"
27
\n",
"
19
\n",
"
35
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-03-18/1991-03-24
\n",
"
199112
\n",
"
7
\n",
"
10864
\n",
"
7331
\n",
"
14397
\n",
"
19
\n",
"
13
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-03-25/1991-03-31
\n",
"
199113
\n",
"
7
\n",
"
9567
\n",
"
6041
\n",
"
13093
\n",
"
17
\n",
"
11
\n",
"
23
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-04-01/1991-04-07
\n",
"
199114
\n",
"
7
\n",
"
12265
\n",
"
7684
\n",
"
16846
\n",
"
22
\n",
"
14
\n",
"
30
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-04-08/1991-04-14
\n",
"
199115
\n",
"
7
\n",
"
13975
\n",
"
9781
\n",
"
18169
\n",
"
25
\n",
"
18
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-04-15/1991-04-21
\n",
"
199116
\n",
"
7
\n",
"
14857
\n",
"
10068
\n",
"
19646
\n",
"
26
\n",
"
18
\n",
"
34
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-04-22/1991-04-28
\n",
"
199117
\n",
"
7
\n",
"
13462
\n",
"
8877
\n",
"
18047
\n",
"
24
\n",
"
16
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-04-29/1991-05-05
\n",
"
199118
\n",
"
7
\n",
"
21385
\n",
"
13882
\n",
"
28888
\n",
"
38
\n",
"
25
\n",
"
51
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-05-06/1991-05-12
\n",
"
199119
\n",
"
7
\n",
"
16739
\n",
"
11246
\n",
"
22232
\n",
"
29
\n",
"
19
\n",
"
39
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-05-13/1991-05-19
\n",
"
199120
\n",
"
7
\n",
"
19053
\n",
"
12742
\n",
"
25364
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-05-20/1991-05-26
\n",
"
199121
\n",
"
7
\n",
"
14903
\n",
"
8975
\n",
"
20831
\n",
"
26
\n",
"
16
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-05-27/1991-06-02
\n",
"
199122
\n",
"
7
\n",
"
15452
\n",
"
9953
\n",
"
20951
\n",
"
27
\n",
"
17
\n",
"
37
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-06-03/1991-06-09
\n",
"
199123
\n",
"
7
\n",
"
11947
\n",
"
7671
\n",
"
16223
\n",
"
21
\n",
"
13
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-06-10/1991-06-16
\n",
"
199124
\n",
"
7
\n",
"
16171
\n",
"
10071
\n",
"
22271
\n",
"
28
\n",
"
17
\n",
"
39
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-06-17/1991-06-23
\n",
"
199125
\n",
"
7
\n",
"
16169
\n",
"
10700
\n",
"
21638
\n",
"
28
\n",
"
18
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1991-06-24/1991-06-30
\n",
"
199126
\n",
"
7
\n",
"
17608
\n",
"
11304
\n",
"
23912
\n",
"
31
\n",
"
20
\n",
"
42
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
2023-06-19/2023-06-25
\n",
"
202325
\n",
"
7
\n",
"
11498
\n",
"
8257
\n",
"
14739
\n",
"
17
\n",
"
12
\n",
"
22
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-06-26/2023-07-02
\n",
"
202326
\n",
"
7
\n",
"
9192
\n",
"
6223
\n",
"
12161
\n",
"
14
\n",
"
10
\n",
"
18
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-07-03/2023-07-09
\n",
"
202327
\n",
"
7
\n",
"
7253
\n",
"
4599
\n",
"
9907
\n",
"
11
\n",
"
7
\n",
"
15
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-07-10/2023-07-16
\n",
"
202328
\n",
"
7
\n",
"
6700
\n",
"
4043
\n",
"
9357
\n",
"
10
\n",
"
6
\n",
"
14
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-07-17/2023-07-23
\n",
"
202329
\n",
"
7
\n",
"
13558
\n",
"
8297
\n",
"
18819
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-07-24/2023-07-30
\n",
"
202330
\n",
"
7
\n",
"
5821
\n",
"
3269
\n",
"
8373
\n",
"
9
\n",
"
5
\n",
"
13
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-07-31/2023-08-06
\n",
"
202331
\n",
"
7
\n",
"
3318
\n",
"
1398
\n",
"
5238
\n",
"
5
\n",
"
2
\n",
"
8
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-08-07/2023-08-13
\n",
"
202332
\n",
"
7
\n",
"
7996
\n",
"
1120
\n",
"
14872
\n",
"
12
\n",
"
2
\n",
"
22
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-08-14/2023-08-20
\n",
"
202333
\n",
"
7
\n",
"
3308
\n",
"
1184
\n",
"
5432
\n",
"
5
\n",
"
2
\n",
"
8
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-08-21/2023-08-27
\n",
"
202334
\n",
"
7
\n",
"
1168
\n",
"
9
\n",
"
2327
\n",
"
2
\n",
"
0
\n",
"
4
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-08-28/2023-09-03
\n",
"
202335
\n",
"
7
\n",
"
961
\n",
"
96
\n",
"
1826
\n",
"
1
\n",
"
0
\n",
"
2
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-09-04/2023-09-10
\n",
"
202336
\n",
"
7
\n",
"
726
\n",
"
10
\n",
"
1442
\n",
"
1
\n",
"
0
\n",
"
2
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-09-11/2023-09-17
\n",
"
202337
\n",
"
7
\n",
"
1122
\n",
"
223
\n",
"
2021
\n",
"
2
\n",
"
1
\n",
"
3
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-09-18/2023-09-24
\n",
"
202338
\n",
"
7
\n",
"
1663
\n",
"
274
\n",
"
3052
\n",
"
3
\n",
"
1
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-09-25/2023-10-01
\n",
"
202339
\n",
"
7
\n",
"
1739
\n",
"
629
\n",
"
2849
\n",
"
3
\n",
"
1
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-10-02/2023-10-08
\n",
"
202340
\n",
"
7
\n",
"
2845
\n",
"
1410
\n",
"
4280
\n",
"
4
\n",
"
2
\n",
"
6
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-10-09/2023-10-15
\n",
"
202341
\n",
"
7
\n",
"
3356
\n",
"
1764
\n",
"
4948
\n",
"
5
\n",
"
3
\n",
"
7
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-10-16/2023-10-22
\n",
"
202342
\n",
"
7
\n",
"
3968
\n",
"
1212
\n",
"
6724
\n",
"
6
\n",
"
2
\n",
"
10
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-10-23/2023-10-29
\n",
"
202343
\n",
"
7
\n",
"
3891
\n",
"
1675
\n",
"
6107
\n",
"
6
\n",
"
3
\n",
"
9
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-10-30/2023-11-05
\n",
"
202344
\n",
"
7
\n",
"
3688
\n",
"
1664
\n",
"
5712
\n",
"
6
\n",
"
3
\n",
"
9
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-11-06/2023-11-12
\n",
"
202345
\n",
"
7
\n",
"
5007
\n",
"
2675
\n",
"
7339
\n",
"
8
\n",
"
4
\n",
"
12
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-11-13/2023-11-19
\n",
"
202346
\n",
"
7
\n",
"
5223
\n",
"
2968
\n",
"
7478
\n",
"
8
\n",
"
5
\n",
"
11
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-11-20/2023-11-26
\n",
"
202347
\n",
"
7
\n",
"
6537
\n",
"
4277
\n",
"
8797
\n",
"
10
\n",
"
7
\n",
"
13
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-11-27/2023-12-03
\n",
"
202348
\n",
"
7
\n",
"
7351
\n",
"
4749
\n",
"
9953
\n",
"
11
\n",
"
7
\n",
"
15
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-12-04/2023-12-10
\n",
"
202349
\n",
"
7
\n",
"
7817
\n",
"
5362
\n",
"
10272
\n",
"
12
\n",
"
8
\n",
"
16
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-12-11/2023-12-17
\n",
"
202350
\n",
"
7
\n",
"
8799
\n",
"
6215
\n",
"
11383
\n",
"
13
\n",
"
9
\n",
"
17
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-12-18/2023-12-24
\n",
"
202351
\n",
"
7
\n",
"
6912
\n",
"
4227
\n",
"
9597
\n",
"
10
\n",
"
6
\n",
"
14
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2023-12-25/2023-12-31
\n",
"
202352
\n",
"
7
\n",
"
11636
\n",
"
7354
\n",
"
15918
\n",
"
18
\n",
"
12
\n",
"
24
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2024-01-01/2024-01-07
\n",
"
202401
\n",
"
7
\n",
"
13467
\n",
"
9285
\n",
"
17649
\n",
"
20
\n",
"
14
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2024-01-08/2024-01-14
\n",
"
202402
\n",
"
7
\n",
"
8021
\n",
"
5132
\n",
"
10910
\n",
"
12
\n",
"
8
\n",
"
16
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
1728 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 \\\n",
"period \n",
"1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n",
"1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n",
"1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n",
"1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n",
"1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n",
"1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n",
"1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n",
"1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n",
"1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n",
"1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n",
"1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n",
"1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n",
"1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n",
"1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n",
"1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n",
"1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n",
"1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n",
"1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n",
"1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n",
"1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n",
"1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n",
"1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n",
"1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n",
"1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n",
"1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n",
"1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n",
"1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n",
"1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n",
"1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n",
"1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n",
"... ... ... ... ... ... ... \n",
"2023-06-19/2023-06-25 202325 7 11498 8257 14739 17 \n",
"2023-06-26/2023-07-02 202326 7 9192 6223 12161 14 \n",
"2023-07-03/2023-07-09 202327 7 7253 4599 9907 11 \n",
"2023-07-10/2023-07-16 202328 7 6700 4043 9357 10 \n",
"2023-07-17/2023-07-23 202329 7 13558 8297 18819 20 \n",
"2023-07-24/2023-07-30 202330 7 5821 3269 8373 9 \n",
"2023-07-31/2023-08-06 202331 7 3318 1398 5238 5 \n",
"2023-08-07/2023-08-13 202332 7 7996 1120 14872 12 \n",
"2023-08-14/2023-08-20 202333 7 3308 1184 5432 5 \n",
"2023-08-21/2023-08-27 202334 7 1168 9 2327 2 \n",
"2023-08-28/2023-09-03 202335 7 961 96 1826 1 \n",
"2023-09-04/2023-09-10 202336 7 726 10 1442 1 \n",
"2023-09-11/2023-09-17 202337 7 1122 223 2021 2 \n",
"2023-09-18/2023-09-24 202338 7 1663 274 3052 3 \n",
"2023-09-25/2023-10-01 202339 7 1739 629 2849 3 \n",
"2023-10-02/2023-10-08 202340 7 2845 1410 4280 4 \n",
"2023-10-09/2023-10-15 202341 7 3356 1764 4948 5 \n",
"2023-10-16/2023-10-22 202342 7 3968 1212 6724 6 \n",
"2023-10-23/2023-10-29 202343 7 3891 1675 6107 6 \n",
"2023-10-30/2023-11-05 202344 7 3688 1664 5712 6 \n",
"2023-11-06/2023-11-12 202345 7 5007 2675 7339 8 \n",
"2023-11-13/2023-11-19 202346 7 5223 2968 7478 8 \n",
"2023-11-20/2023-11-26 202347 7 6537 4277 8797 10 \n",
"2023-11-27/2023-12-03 202348 7 7351 4749 9953 11 \n",
"2023-12-04/2023-12-10 202349 7 7817 5362 10272 12 \n",
"2023-12-11/2023-12-17 202350 7 8799 6215 11383 13 \n",
"2023-12-18/2023-12-24 202351 7 6912 4227 9597 10 \n",
"2023-12-25/2023-12-31 202352 7 11636 7354 15918 18 \n",
"2024-01-01/2024-01-07 202401 7 13467 9285 17649 20 \n",
"2024-01-08/2024-01-14 202402 7 8021 5132 10910 12 \n",
"\n",
" inc100_low inc100_up geo_insee geo_name \n",
"period \n",
"1990-12-03/1990-12-09 0 5 FR France \n",
"1990-12-10/1990-12-16 12 28 FR France \n",
"1990-12-17/1990-12-23 25 43 FR France \n",
"1990-12-24/1990-12-30 23 45 FR France \n",
"1990-12-31/1991-01-06 18 36 FR France \n",
"1991-01-07/1991-01-13 20 38 FR France \n",
"1991-01-14/1991-01-20 18 36 FR France \n",
"1991-01-21/1991-01-27 8 20 FR France \n",
"1991-01-28/1991-02-03 11 25 FR France \n",
"1991-02-04/1991-02-10 12 26 FR France \n",
"1991-02-11/1991-02-17 15 29 FR France \n",
"1991-02-18/1991-02-24 15 31 FR France \n",
"1991-02-25/1991-03-03 15 33 FR France \n",
"1991-03-04/1991-03-10 20 38 FR France \n",
"1991-03-11/1991-03-17 19 35 FR France \n",
"1991-03-18/1991-03-24 13 25 FR France \n",
"1991-03-25/1991-03-31 11 23 FR France \n",
"1991-04-01/1991-04-07 14 30 FR France \n",
"1991-04-08/1991-04-14 18 32 FR France \n",
"1991-04-15/1991-04-21 18 34 FR France \n",
"1991-04-22/1991-04-28 16 32 FR France \n",
"1991-04-29/1991-05-05 25 51 FR France \n",
"1991-05-06/1991-05-12 19 39 FR France \n",
"1991-05-13/1991-05-19 23 45 FR France \n",
"1991-05-20/1991-05-26 16 36 FR France \n",
"1991-05-27/1991-06-02 17 37 FR France \n",
"1991-06-03/1991-06-09 13 29 FR France \n",
"1991-06-10/1991-06-16 17 39 FR France \n",
"1991-06-17/1991-06-23 18 38 FR France \n",
"1991-06-24/1991-06-30 20 42 FR France \n",
"... ... ... ... ... \n",
"2023-06-19/2023-06-25 12 22 FR France \n",
"2023-06-26/2023-07-02 10 18 FR France \n",
"2023-07-03/2023-07-09 7 15 FR France \n",
"2023-07-10/2023-07-16 6 14 FR France \n",
"2023-07-17/2023-07-23 12 28 FR France \n",
"2023-07-24/2023-07-30 5 13 FR France \n",
"2023-07-31/2023-08-06 2 8 FR France \n",
"2023-08-07/2023-08-13 2 22 FR France \n",
"2023-08-14/2023-08-20 2 8 FR France \n",
"2023-08-21/2023-08-27 0 4 FR France \n",
"2023-08-28/2023-09-03 0 2 FR France \n",
"2023-09-04/2023-09-10 0 2 FR France \n",
"2023-09-11/2023-09-17 1 3 FR France \n",
"2023-09-18/2023-09-24 1 5 FR France \n",
"2023-09-25/2023-10-01 1 5 FR France \n",
"2023-10-02/2023-10-08 2 6 FR France \n",
"2023-10-09/2023-10-15 3 7 FR France \n",
"2023-10-16/2023-10-22 2 10 FR France \n",
"2023-10-23/2023-10-29 3 9 FR France \n",
"2023-10-30/2023-11-05 3 9 FR France \n",
"2023-11-06/2023-11-12 4 12 FR France \n",
"2023-11-13/2023-11-19 5 11 FR France \n",
"2023-11-20/2023-11-26 7 13 FR France \n",
"2023-11-27/2023-12-03 7 15 FR France \n",
"2023-12-04/2023-12-10 8 16 FR France \n",
"2023-12-11/2023-12-17 9 17 FR France \n",
"2023-12-18/2023-12-24 6 14 FR France \n",
"2023-12-25/2023-12-31 12 24 FR France \n",
"2024-01-01/2024-01-07 14 26 FR France \n",
"2024-01-08/2024-01-14 8 16 FR France \n",
"\n",
"[1728 rows x 10 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_data = data.set_index('period').sort_index()\n",
"sorted_data"
]
},
{
"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": 8,
"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": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"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 $N+1$. Le 1er septembre comme début de chaque période annuelle dans le cas de la véricelle.\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 (varicelle) 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 (1991), ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1991,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août (1er septembre), 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": 12,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici les incidences annuelles."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2020 221186\n",
"2023 366227\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",
"2022 641397\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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"À partir du relevé de résultats annuel triés, nous obtenons que l’incidence la plus forte s’est produite en 2009 et l’incidence la plus faible en 2020.\n",
"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": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEuVJREFUeJzt3X2wXHV9x/H3l0Q0cCGigUsbtKFIqZSolGvxYUrvBXVQqFa0VUQLHW3s+ERtOg51QLT1Ia3iaB1oJyqCol4V7MiD9WHUq+IDkoBjwIhQQSQoikr0UgQC3/7xO5feXBOSu2dzd89v3q+ZTM6e3T37/d6z+9mzv3PObmQmkqTu223QBUiS+sNAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEga6JFVi8UI+2LJly3LFihVbzbvzzjvZc889F7KMvrOH4WAPw8Ee+m/9+vW3Z+a+O7rdggb6ihUrWLdu3VbzpqamGB8fX8gy+s4ehoM9DAd76L+I+OHO3M4hF0mqhIEuSZUw0CWpEga6JFXCQJekSuww0CPi3Ij4aURcM2veIyLi8xFxffP/Pru2TEnSjuzMFvp5wLFz5p0GfCEzDwa+0FyWJA3QDgM9M78C/GLO7OcA5zfT5wN/0ee6JEnzFDvzm6IRsQK4NDMPay7fkZkPn3X9LzNzm8MuEbEKWAUwOjp6xOTk5FbXT09PMzIy0mv9Q8EehkNXe9iwafMD06NL4La7FuZxVy5fukuW29X1MNuw9TAxMbE+M8d2dLtdfqZoZq4F1gKMjY3l3LOvhu2MrF7Yw3Doag+nnHbZA9OrV27hrA0LcwL3TSeN75LldnU9zNbVHno9yuW2iPgdgOb/n/avJElSL3oN9IuBk5vpk4FP9accSVKvduawxY8C3wAOiYhbIuKlwBrg6RFxPfD05rIkaYB2OFiXmSdu56pj+lyLJKkFzxSVpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEga6JFXCQJekShjoklQJA12SKmGgS1IlDHRJqoSBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVaJVoEfEayPi2oi4JiI+GhEP61dhkqT56TnQI2I58BpgLDMPAxYBL+xXYZKk+Wk75LIYWBIRi4E9gFvblyRJ6kXPgZ6Zm4B3ADcDPwY2Z+bn+lWYJGl+IjN7u2PEPsBFwAuAO4BPABdm5gVzbrcKWAUwOjp6xOTk5FbLmZ6eZmRkpKcahoU9DIeu9rBh0+YHpkeXwG13Lczjrly+dJcst6vrYbZh62FiYmJ9Zo7t6HaLWzzG04AbM/NnABHxSeApwFaBnplrgbUAY2NjOT4+vtVCpqammDuva+xhOHS1h1NOu+yB6dUrt3DWhjYvy51300nju2S5XV0Ps3W1hzZj6DcDT4qIPSIigGOAjf0pS5I0X23G0K8ALgSuAjY0y1rbp7okSfPU6rNdZp4JnNmnWiRJLXimqCRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEga6JFXCQJekShjoklQJA12SKmGgS1IlDHRJqoSBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVaJVoEfEwyPiwoj4XkRsjIgn96swSdL8LG55/3cDn8nM50fE7sAefahJktSDngM9IvYGjgJOAcjMe4B7+lOWJGm+IjN7u2PEE4C1wHeBxwPrgVMz8845t1sFrAIYHR09YnJycqvlTE9PMzIy0lMNw8IehkNXe9iwafMD06NL4La7BlhMH+xMDyuXL12YYno0bM+liYmJ9Zk5tqPbtQn0MeCbwFMz84qIeDfwq8w8Y3v3GRsby3Xr1m01b2pqivHx8Z5qGBb2MBy62sOK0y57YHr1yi2ctaHtSOhg7UwPN605boGq6c2wPZciYqcCvc1O0VuAWzLziubyhcAft1ieJKmFngM9M38C/CgiDmlmHUMZfpEkDUDbz3avBj7cHOHyA+Bv2pckSepFq0DPzG8DOxzXkSTtep4pKkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEga6JFXCQJekShjoklQJA12SKmGgS1IlDHRJqoSBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SapE60CPiEURcXVEXNqPgiRJvenHFvqpwMY+LEeS1EKrQI+IA4DjgPf1pxxJUq8iM3u/c8SFwNuAvYB/zMzjt3GbVcAqgNHR0SMmJye3un56epqRkZGeaxgG9jAc2vSwYdPmPlfTm9ElcNtdg66iHXvYtpXLl/Z834mJifWZObaj2y3u9QEi4njgp5m5PiLGt3e7zFwLrAUYGxvL8fGtbzo1NcXceV1jD8OhTQ+nnHZZf4vp0eqVWzhrQ88vy6FgD9t200njfV3etrQZcnkq8OyIuAmYBI6OiAv6UpUkad56DvTM/KfMPCAzVwAvBL6YmS/uW2WSpHnxOHRJqkRfBokycwqY6seyJEm9cQtdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEga6JFXCQJekShjoklQJA12SKmGgS1IlDHRJqoSBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIq0XOgR8SjIuJLEbExIq6NiFP7WZgkaX4Wt7jvFmB1Zl4VEXsB6yPi85n53T7VJkmah5630DPzx5l5VTP9a2AjsLxfhUmS5icys/1CIlYAXwEOy8xfzbluFbAKYHR09IjJycmt7js9Pc3IyEjrGgZpV/WwYdPmvi9ze0aXwG13lemVy5cu2OP2U5v1sJB/6wczez10lT1sW5vX1cTExPrMHNvR7VoHekSMAF8G3pKZn3yw246NjeW6deu2mjc1NcX4+HirGgZtV/Ww4rTL+r7M7Vm9cgtnbSgjcDetOW7BHref2qyHhfxbP5jZ66Gr7GHb2ryuImKnAr3VUS4R8RDgIuDDOwpzSdKu1eYolwDeD2zMzHf2ryRJUi/abKE/FXgJcHREfLv596w+1SVJmqeeB4ky83Ig+liLJKkFzxSVpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEp354b9B/uZjV39js4varOfVK7dwypD8Nqg0CG6hS1IlDHRJqoSBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJQx0SaqEgS5JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIq0SrQI+LYiLguIm6IiNP6VZQkaf56DvSIWAScDTwTOBQ4MSIO7VdhkqT5abOF/ifADZn5g8y8B5gEntOfsiRJ8xWZ2dsdI54PHJuZL2suvwQ4MjNfNed2q4BVzcVDgOvmLGoZcHtPRQwPexgO9jAc7KH/fi8z993Rjdr8SHRsY95vvTtk5lpg7XYXErEuM8da1DFw9jAc7GE42MPgtBlyuQV41KzLBwC3titHktSrNoF+JXBwRBwYEbsDLwQu7k9ZkqT56nnIJTO3RMSrgM8Ci4BzM/PaHha13eGYDrGH4WAPw8EeBqTnnaKSpOHimaKSVAkDXZIqYaBLUiU6GegRcXREHDjoOtroeg9dr39GDX3Yw+ANS/2d2inafFfMJHAHcD9wZmZ+ebBVzU/Xe+h6/TNq6MMeBm/Y6h/qLfSIOCAi9p416wXARZl5FOWPeGJEPHkw1e2crvcwn/ojYltnDw+Frq8HsIdhMOz1D2WgR8RjI+LTwOXAP0fEzJd+/QbYo5n+OOW7Fp40jEHS9R56qT+H8ONe19cD2MMw6Er9QxPoEbHnrItPAG7JzBXAF4F3NPN/AdwdEXtl5i+A7wOjwIoFLHW7ut7Dg9T/JX67/pGm/usZkvpndH09gD0wBD10sf6BBnpE7BMR50XElcCaiNi3eWd7HPC1ZqvvYuCOiDiO8sfaC1jZLOJ6yrei3TOI+qH7PTxI/Stn1f+pbdT/uGYR3x9k/TO6vh7AHppFDOvroRP1D3oL/ShgC/Asyrc3vh7Ym1LX/rM+wp8PvAj4FvBryo9qkJnfAI4GfrWwZW+l6z1sr/5FdKP+GV1fD2APw9BDt+vPzF3+jxIOLwe+TPlu9GXN/I8Dr2mmDwTWNNc/kTJWtai5bgT4WbOc5cBG4FXAB4BzgD3soe76a+rDHgbfQ9fr396/hdpCPx54NvAm4MnAvzXzPw88pZn+EfBV4JmZeSXl3XECIDOngSuAJ2bmJuAllHGqnwCnZ+b/2kP19dfUhz0Mvoeu179NbX7gYiszRzlExBMpH0W+ClyWmXcDfwD8IDO/GBE3Am+PiGcA64HnRsSyzLw9Iq4H7oyIRwPvAV4cEftRvmv955SPN2TmOmBdv2qvpYeu119bH00v9uDrYcH0ZQt91h/uKOBcyqE8TwPe1tzkfuD7EbEkM2+kfDx5HGXs6VbKsZwA91E+wuwGXET5CsuTgCOAtZl5fz/q3U4Pi5oe/ozysalTPTR1ZUSM09F10PSxd9f7iIhHNv8/BTivoz3sFxGPjIgxynhxp3qIiN27/HruWa9jNZRjL/8O+Ajwt8BDgL8HXtlcvw/wHeBwyh9nDbCiue54yh9mWTO9AVhK2ZnwaWD3WY+zW7/HmWYte0/gZZQVtZqy86NrPewFXEb5PnqA13ap/lnPpZOBL1BO0uhUH/z/Gdd/SRlnnaL8fm7Xnkt7AqdQhh02A8d1qQdKBr0CuAT4D+AxwKldqb8f/3raQo+I/YFLgXHgQ5SdBidQxqK2AGTmL4FPAa9pniD7AY9tFvEV4M+AezLzUuD9wIXA2ZStgXtnHit33Tv4npQAORp4L/AM4HmUnR/3d6GHxhLgocBBEbEMOIiyRdGJ+iPiIcC1wPOBt2fm85qrDp/1+EPdR2ZmRCwF/gp4V2aOZ+Z1lK24TvQQEY+hDEkcA5wObAJupuwY7Mrz6ZWU1/O7KL9vfEJz+b6O1N9ej++ES4AjZ10+hbJT4WTgW7Pm/y5wazP9Ssqpsfs0978EePSs2y5b6Hcz4OGzpl9HWdEndayHk4G3A2cALwX+HLiyK/U3j/tJ4KQ5814AXNGVPihbhv/STM9ssZ/QlR4oof3QWZfPpbzJPqdDPVwC/HUz/VLg1c3zqDOv57b/eh1D/w3wreaAeygfY47MzPMpW4r7A2TmrcC1EXFkZp4N3AB8jPID05dn5s0zC8zM23uspWeZeUdE7B0R51GGXJZRVupBETE6zD3M+tvvBvwPZSv36My8BDhw2Ouf41zgzIg4KyKmIuINwDcpv1m7X1PbsPdxO/CnEXESsD4izqdsGf5h88lpqHvIzPuy7Cyc2QcQlC+cuoSyHrrwmv5v4OSI+ATwRuDxwDVN/ft2oP72Wr4jzmyJnA+c2kx/CPjXZvoRwPto3vUoY1yHAQ8b9DvZnD5eQflouZYypv514A2UJ/VQ9wB8grJ1tZRyTO3plCfxGR1bB5+lnMTxKMoL7FTgGx1aDwc39f47ZYvvRcA7Kccqv47yxjvUPczp52rghGb6gq68ppvX77mUjbM3AmcC1zXPrU6tg17+tTrKJTMzIg4A9qe8O9L8EYmISylj1IuyedfLzHsz85rM/E2bx+23zDwny57ucyjjnv9J+Qh2MUPcQ0SMULYM3wt8hrKX/kjgRGCfiLiEIa5/judm5lsz80fAWymHlb2HDqyHxs2U070XZxmrvbSZdyllp28XeiAiZjLhcspORYC3lKs68Xw6FJjKsnX9AcrGzkfpzvOolX4ch344ZY/wjRHxMspHl9dTxq6+l5lX9eExFsrPKTsVT8/MD0bEi4FrM/PqAde1PVsoYXEvZcv8PuD1mbkB+IcO1P+A3PpEjDsogX5GZn6kC31k5t0RsQZ4czNrN8pBAmsy8+ou9ABlh19EzHx74A3NvI3A65rhpO8Oaw8RsYiSP0cBHwTupBzk8IrMvLEr66CN1j9wERFfA34fuIly/OabMvM77UtbGM3RCcdQPiIfShl2OTsz733QOw6h5uSHE4DJzPzJoOuZj4h4KHAs5Yy7P6IcdnZOZm4ZaGHzFBFvobweDgc+R/nBg18Otqr5i4jrgDdk5sdmzjMZdE07IyIOoryG76Gsh/8C3pzlzM7qtQr05pCzM4EbgQuy2anSJRGxmPKdDndTeujcR69my+T+rrzoticiXk45ZPRDXVwPMyLiEOCHXexh1kmCT6DsaN/StedVs2FzMPD1zLxr0PUspE79BJ0kafsG/fW5kqQ+MdAlqRIGuiRVwkCXpEoY6JJUCQNdkiphoEtSJf4Pm1ZPCOJC1cIAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}