{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome grippal"
]
},
{
"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\n",
"import os"
]
},
{
"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."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"- Si le fichier n'existe pas en local, téléchargez les données et déposez-les dans un fichier local!\n",
"\n",
"- Lisez le fichier CSV local."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data from /home/jovyan/work/module3/exo1/local_sentiweb_incidence_pay_3.csv\n"
]
}
],
"source": [
"data_local = \"/home/jovyan/work/module3/exo1/local_sentiweb_incidence_pay_3.csv\"\n",
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" \n",
"\n",
"if os.path.isfile(data_local):\n",
" print(f\"Loading data from {data_local}\")\n",
" raw_data = pd.read_csv(data_local)\n",
"else:\n",
" print(f\"File not found. Downloading from {data_url}\")\n",
" raw_data = pd.read_csv(data_url, skiprows=1)\n",
" raw_data.to_csv(data_local, index=False) # Save it locally for future use\n",
" print(f\"Data saved to {data_local}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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\n",
"
France
\n",
"
\n",
"
\n",
"
2064
\n",
"
198504
\n",
"
3
\n",
"
424937
\n",
"
390794.0
\n",
"
459080.0
\n",
"
770
\n",
"
708.0
\n",
"
832.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2065
\n",
"
198503
\n",
"
3
\n",
"
213901
\n",
"
174689.0
\n",
"
253113.0
\n",
"
388
\n",
"
317.0
\n",
"
459.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2066
\n",
"
198502
\n",
"
3
\n",
"
97586
\n",
"
80949.0
\n",
"
114223.0
\n",
"
177
\n",
"
147.0
\n",
"
207.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2067
\n",
"
198501
\n",
"
3
\n",
"
85489
\n",
"
65918.0
\n",
"
105060.0
\n",
"
155
\n",
"
120.0
\n",
"
190.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2068
\n",
"
198452
\n",
"
3
\n",
"
84830
\n",
"
60602.0
\n",
"
109058.0
\n",
"
154
\n",
"
110.0
\n",
"
198.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2069
\n",
"
198451
\n",
"
3
\n",
"
101726
\n",
"
80242.0
\n",
"
123210.0
\n",
"
185
\n",
"
146.0
\n",
"
224.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2070
\n",
"
198450
\n",
"
3
\n",
"
123680
\n",
"
101401.0
\n",
"
145959.0
\n",
"
225
\n",
"
184.0
\n",
"
266.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2071
\n",
"
198449
\n",
"
3
\n",
"
101073
\n",
"
81684.0
\n",
"
120462.0
\n",
"
184
\n",
"
149.0
\n",
"
219.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2072
\n",
"
198448
\n",
"
3
\n",
"
78620
\n",
"
60634.0
\n",
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96606.0
\n",
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143
\n",
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110.0
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176.0
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FR
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France
\n",
"
\n",
"
\n",
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2073
\n",
"
198447
\n",
"
3
\n",
"
72029
\n",
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54274.0
\n",
"
89784.0
\n",
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131
\n",
"
99.0
\n",
"
163.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2074
\n",
"
198446
\n",
"
3
\n",
"
87330
\n",
"
67686.0
\n",
"
106974.0
\n",
"
159
\n",
"
123.0
\n",
"
195.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2075
\n",
"
198445
\n",
"
3
\n",
"
135223
\n",
"
101414.0
\n",
"
169032.0
\n",
"
246
\n",
"
184.0
\n",
"
308.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2076
\n",
"
198444
\n",
"
3
\n",
"
68422
\n",
"
20056.0
\n",
"
116788.0
\n",
"
125
\n",
"
37.0
\n",
"
213.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
2077 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202433 3 28278 18414.0 38142.0 42 27.0 \n",
"1 202432 3 23427 17604.0 29250.0 35 26.0 \n",
"2 202431 3 26035 20267.0 31803.0 39 30.0 \n",
"3 202430 3 36393 28593.0 44193.0 55 43.0 \n",
"4 202429 3 39560 32592.0 46528.0 59 49.0 \n",
"5 202428 3 54342 45781.0 62903.0 81 68.0 \n",
"6 202427 3 47364 40234.0 54494.0 71 60.0 \n",
"7 202426 3 44219 36956.0 51482.0 66 55.0 \n",
"8 202425 3 47204 40300.0 54108.0 71 61.0 \n",
"9 202424 3 41110 34671.0 47549.0 62 52.0 \n",
"10 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"11 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"12 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"13 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"14 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"15 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"16 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"17 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"18 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"19 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"20 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"21 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"22 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"23 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"24 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"25 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"26 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"27 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"28 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"29 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"... ... ... ... ... ... ... ... \n",
"2047 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2048 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2049 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2050 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2051 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2052 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2053 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2054 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2055 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2056 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2057 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2058 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2059 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2060 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2061 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2062 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2063 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2064 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2065 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2066 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2067 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2068 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2069 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2070 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2071 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2072 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2073 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2074 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2075 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2076 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 57.0 FR France \n",
"1 44.0 FR France \n",
"2 48.0 FR France \n",
"3 67.0 FR France \n",
"4 69.0 FR France \n",
"5 94.0 FR France \n",
"6 82.0 FR France \n",
"7 77.0 FR France \n",
"8 81.0 FR France \n",
"9 72.0 FR France \n",
"10 62.0 FR France \n",
"11 59.0 FR France \n",
"12 40.0 FR France \n",
"13 36.0 FR France \n",
"14 29.0 FR France \n",
"15 41.0 FR France \n",
"16 49.0 FR France \n",
"17 51.0 FR France \n",
"18 53.0 FR France \n",
"19 56.0 FR France \n",
"20 61.0 FR France \n",
"21 70.0 FR France \n",
"22 85.0 FR France \n",
"23 101.0 FR France \n",
"24 119.0 FR France \n",
"25 172.0 FR France \n",
"26 224.0 FR France \n",
"27 303.0 FR France \n",
"28 343.0 FR France \n",
"29 339.0 FR France \n",
"... ... ... ... \n",
"2047 59.0 FR France \n",
"2048 64.0 FR France \n",
"2049 97.0 FR France \n",
"2050 93.0 FR France \n",
"2051 80.0 FR France \n",
"2052 116.0 FR France \n",
"2053 149.0 FR France \n",
"2054 281.0 FR France \n",
"2055 395.0 FR France \n",
"2056 485.0 FR France \n",
"2057 544.0 FR France \n",
"2058 689.0 FR France \n",
"2059 722.0 FR France \n",
"2060 762.0 FR France \n",
"2061 926.0 FR France \n",
"2062 1113.0 FR France \n",
"2063 1236.0 FR France \n",
"2064 832.0 FR France \n",
"2065 459.0 FR France \n",
"2066 207.0 FR France \n",
"2067 190.0 FR France \n",
"2068 198.0 FR France \n",
"2069 224.0 FR France \n",
"2070 266.0 FR France \n",
"2071 219.0 FR France \n",
"2072 176.0 FR France \n",
"2073 163.0 FR France \n",
"2074 195.0 FR France \n",
"2075 308.0 FR France \n",
"2076 213.0 FR France \n",
"\n",
"[2077 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
]
},
{
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"#raw_data = pd.read_csv(data_url, skiprows=1)\n",
"#raw_data"
]
},
{
"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": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
"
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" \n",
"
\n",
"
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"
week
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indicator
\n",
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inc
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inc_low
\n",
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inc_up
\n",
"
inc100
\n",
"
inc100_low
\n",
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inc100_up
\n",
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geo_insee
\n",
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geo_name
\n",
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" \n",
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1840
\n",
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198919
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" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1840 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1840 FR France "
]
},
"execution_count": 6,
"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": 7,
"metadata": {},
"outputs": [
{
"data": {
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62
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36.0
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France
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14
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"
202419
\n",
"
3
\n",
"
15375
\n",
"
11274.0
\n",
"
19476.0
\n",
"
23
\n",
"
17.0
\n",
"
29.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
15
\n",
"
202418
\n",
"
3
\n",
"
22409
\n",
"
17653.0
\n",
"
27165.0
\n",
"
34
\n",
"
27.0
\n",
"
41.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
16
\n",
"
202417
\n",
"
3
\n",
"
27042
\n",
"
21410.0
\n",
"
32674.0
\n",
"
41
\n",
"
33.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
17
\n",
"
202416
\n",
"
3
\n",
"
28882
\n",
"
23305.0
\n",
"
34459.0
\n",
"
43
\n",
"
35.0
\n",
"
51.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
18
\n",
"
202415
\n",
"
3
\n",
"
30229
\n",
"
24648.0
\n",
"
35810.0
\n",
"
45
\n",
"
37.0
\n",
"
53.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
19
\n",
"
202414
\n",
"
3
\n",
"
31813
\n",
"
26529.0
\n",
"
37097.0
\n",
"
48
\n",
"
40.0
\n",
"
56.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
20
\n",
"
202413
\n",
"
3
\n",
"
35090
\n",
"
29607.0
\n",
"
40573.0
\n",
"
53
\n",
"
45.0
\n",
"
61.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
21
\n",
"
202412
\n",
"
3
\n",
"
40639
\n",
"
34582.0
\n",
"
46696.0
\n",
"
61
\n",
"
52.0
\n",
"
70.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
22
\n",
"
202411
\n",
"
3
\n",
"
50268
\n",
"
43331.0
\n",
"
57205.0
\n",
"
75
\n",
"
65.0
\n",
"
85.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
23
\n",
"
202410
\n",
"
3
\n",
"
60107
\n",
"
52623.0
\n",
"
67591.0
\n",
"
90
\n",
"
79.0
\n",
"
101.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
24
\n",
"
202409
\n",
"
3
\n",
"
71121
\n",
"
62920.0
\n",
"
79322.0
\n",
"
107
\n",
"
95.0
\n",
"
119.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
25
\n",
"
202408
\n",
"
3
\n",
"
104566
\n",
"
94520.0
\n",
"
114612.0
\n",
"
157
\n",
"
142.0
\n",
"
172.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
26
\n",
"
202407
\n",
"
3
\n",
"
138078
\n",
"
127050.0
\n",
"
149106.0
\n",
"
207
\n",
"
190.0
\n",
"
224.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
27
\n",
"
202406
\n",
"
3
\n",
"
190062
\n",
"
177955.0
\n",
"
202169.0
\n",
"
285
\n",
"
267.0
\n",
"
303.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
28
\n",
"
202405
\n",
"
3
\n",
"
216237
\n",
"
203595.0
\n",
"
228879.0
\n",
"
324
\n",
"
305.0
\n",
"
343.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
29
\n",
"
202404
\n",
"
3
\n",
"
213196
\n",
"
200547.0
\n",
"
225845.0
\n",
"
320
\n",
"
301.0
\n",
"
339.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
2047
\n",
"
198521
\n",
"
3
\n",
"
26096
\n",
"
19621.0
\n",
"
32571.0
\n",
"
47
\n",
"
35.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2048
\n",
"
198520
\n",
"
3
\n",
"
27896
\n",
"
20885.0
\n",
"
34907.0
\n",
"
51
\n",
"
38.0
\n",
"
64.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2049
\n",
"
198519
\n",
"
3
\n",
"
43154
\n",
"
32821.0
\n",
"
53487.0
\n",
"
78
\n",
"
59.0
\n",
"
97.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2050
\n",
"
198518
\n",
"
3
\n",
"
40555
\n",
"
29935.0
\n",
"
51175.0
\n",
"
74
\n",
"
55.0
\n",
"
93.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2051
\n",
"
198517
\n",
"
3
\n",
"
34053
\n",
"
24366.0
\n",
"
43740.0
\n",
"
62
\n",
"
44.0
\n",
"
80.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2052
\n",
"
198516
\n",
"
3
\n",
"
50362
\n",
"
36451.0
\n",
"
64273.0
\n",
"
91
\n",
"
66.0
\n",
"
116.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2053
\n",
"
198515
\n",
"
3
\n",
"
63881
\n",
"
45538.0
\n",
"
82224.0
\n",
"
116
\n",
"
83.0
\n",
"
149.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2054
\n",
"
198514
\n",
"
3
\n",
"
134545
\n",
"
114400.0
\n",
"
154690.0
\n",
"
244
\n",
"
207.0
\n",
"
281.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2055
\n",
"
198513
\n",
"
3
\n",
"
197206
\n",
"
176080.0
\n",
"
218332.0
\n",
"
357
\n",
"
319.0
\n",
"
395.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2056
\n",
"
198512
\n",
"
3
\n",
"
245240
\n",
"
223304.0
\n",
"
267176.0
\n",
"
445
\n",
"
405.0
\n",
"
485.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2057
\n",
"
198511
\n",
"
3
\n",
"
276205
\n",
"
252399.0
\n",
"
300011.0
\n",
"
501
\n",
"
458.0
\n",
"
544.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2058
\n",
"
198510
\n",
"
3
\n",
"
353231
\n",
"
326279.0
\n",
"
380183.0
\n",
"
640
\n",
"
591.0
\n",
"
689.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2059
\n",
"
198509
\n",
"
3
\n",
"
369895
\n",
"
341109.0
\n",
"
398681.0
\n",
"
670
\n",
"
618.0
\n",
"
722.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2060
\n",
"
198508
\n",
"
3
\n",
"
389886
\n",
"
359529.0
\n",
"
420243.0
\n",
"
707
\n",
"
652.0
\n",
"
762.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2061
\n",
"
198507
\n",
"
3
\n",
"
471852
\n",
"
432599.0
\n",
"
511105.0
\n",
"
855
\n",
"
784.0
\n",
"
926.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2062
\n",
"
198506
\n",
"
3
\n",
"
565825
\n",
"
518011.0
\n",
"
613639.0
\n",
"
1026
\n",
"
939.0
\n",
"
1113.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2063
\n",
"
198505
\n",
"
3
\n",
"
637302
\n",
"
592795.0
\n",
"
681809.0
\n",
"
1155
\n",
"
1074.0
\n",
"
1236.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2064
\n",
"
198504
\n",
"
3
\n",
"
424937
\n",
"
390794.0
\n",
"
459080.0
\n",
"
770
\n",
"
708.0
\n",
"
832.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2065
\n",
"
198503
\n",
"
3
\n",
"
213901
\n",
"
174689.0
\n",
"
253113.0
\n",
"
388
\n",
"
317.0
\n",
"
459.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2066
\n",
"
198502
\n",
"
3
\n",
"
97586
\n",
"
80949.0
\n",
"
114223.0
\n",
"
177
\n",
"
147.0
\n",
"
207.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2067
\n",
"
198501
\n",
"
3
\n",
"
85489
\n",
"
65918.0
\n",
"
105060.0
\n",
"
155
\n",
"
120.0
\n",
"
190.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2068
\n",
"
198452
\n",
"
3
\n",
"
84830
\n",
"
60602.0
\n",
"
109058.0
\n",
"
154
\n",
"
110.0
\n",
"
198.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2069
\n",
"
198451
\n",
"
3
\n",
"
101726
\n",
"
80242.0
\n",
"
123210.0
\n",
"
185
\n",
"
146.0
\n",
"
224.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2070
\n",
"
198450
\n",
"
3
\n",
"
123680
\n",
"
101401.0
\n",
"
145959.0
\n",
"
225
\n",
"
184.0
\n",
"
266.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2071
\n",
"
198449
\n",
"
3
\n",
"
101073
\n",
"
81684.0
\n",
"
120462.0
\n",
"
184
\n",
"
149.0
\n",
"
219.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2072
\n",
"
198448
\n",
"
3
\n",
"
78620
\n",
"
60634.0
\n",
"
96606.0
\n",
"
143
\n",
"
110.0
\n",
"
176.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2073
\n",
"
198447
\n",
"
3
\n",
"
72029
\n",
"
54274.0
\n",
"
89784.0
\n",
"
131
\n",
"
99.0
\n",
"
163.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2074
\n",
"
198446
\n",
"
3
\n",
"
87330
\n",
"
67686.0
\n",
"
106974.0
\n",
"
159
\n",
"
123.0
\n",
"
195.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2075
\n",
"
198445
\n",
"
3
\n",
"
135223
\n",
"
101414.0
\n",
"
169032.0
\n",
"
246
\n",
"
184.0
\n",
"
308.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2076
\n",
"
198444
\n",
"
3
\n",
"
68422
\n",
"
20056.0
\n",
"
116788.0
\n",
"
125
\n",
"
37.0
\n",
"
213.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
2076 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202433 3 28278 18414.0 38142.0 42 27.0 \n",
"1 202432 3 23427 17604.0 29250.0 35 26.0 \n",
"2 202431 3 26035 20267.0 31803.0 39 30.0 \n",
"3 202430 3 36393 28593.0 44193.0 55 43.0 \n",
"4 202429 3 39560 32592.0 46528.0 59 49.0 \n",
"5 202428 3 54342 45781.0 62903.0 81 68.0 \n",
"6 202427 3 47364 40234.0 54494.0 71 60.0 \n",
"7 202426 3 44219 36956.0 51482.0 66 55.0 \n",
"8 202425 3 47204 40300.0 54108.0 71 61.0 \n",
"9 202424 3 41110 34671.0 47549.0 62 52.0 \n",
"10 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"11 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"12 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"13 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"14 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"15 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"16 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"17 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"18 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"19 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"20 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"21 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"22 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"23 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"24 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"25 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"26 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"27 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"28 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"29 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"... ... ... ... ... ... ... ... \n",
"2047 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2048 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2049 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2050 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2051 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2052 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2053 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2054 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2055 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2056 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2057 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2058 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2059 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2060 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2061 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2062 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2063 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2064 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2065 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2066 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2067 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2068 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2069 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2070 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2071 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2072 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2073 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2074 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2075 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2076 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 57.0 FR France \n",
"1 44.0 FR France \n",
"2 48.0 FR France \n",
"3 67.0 FR France \n",
"4 69.0 FR France \n",
"5 94.0 FR France \n",
"6 82.0 FR France \n",
"7 77.0 FR France \n",
"8 81.0 FR France \n",
"9 72.0 FR France \n",
"10 62.0 FR France \n",
"11 59.0 FR France \n",
"12 40.0 FR France \n",
"13 36.0 FR France \n",
"14 29.0 FR France \n",
"15 41.0 FR France \n",
"16 49.0 FR France \n",
"17 51.0 FR France \n",
"18 53.0 FR France \n",
"19 56.0 FR France \n",
"20 61.0 FR France \n",
"21 70.0 FR France \n",
"22 85.0 FR France \n",
"23 101.0 FR France \n",
"24 119.0 FR France \n",
"25 172.0 FR France \n",
"26 224.0 FR France \n",
"27 303.0 FR France \n",
"28 343.0 FR France \n",
"29 339.0 FR France \n",
"... ... ... ... \n",
"2047 59.0 FR France \n",
"2048 64.0 FR France \n",
"2049 97.0 FR France \n",
"2050 93.0 FR France \n",
"2051 80.0 FR France \n",
"2052 116.0 FR France \n",
"2053 149.0 FR France \n",
"2054 281.0 FR France \n",
"2055 395.0 FR France \n",
"2056 485.0 FR France \n",
"2057 544.0 FR France \n",
"2058 689.0 FR France \n",
"2059 722.0 FR France \n",
"2060 762.0 FR France \n",
"2061 926.0 FR France \n",
"2062 1113.0 FR France \n",
"2063 1236.0 FR France \n",
"2064 832.0 FR France \n",
"2065 459.0 FR France \n",
"2066 207.0 FR France \n",
"2067 190.0 FR France \n",
"2068 198.0 FR France \n",
"2069 224.0 FR France \n",
"2070 266.0 FR France \n",
"2071 219.0 FR France \n",
"2072 176.0 FR France \n",
"2073 163.0 FR France \n",
"2074 195.0 FR France \n",
"2075 308.0 FR France \n",
"2076 213.0 FR France \n",
"\n",
"[2076 rows x 10 columns]"
]
},
"execution_count": 7,
"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": 8,
"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": 9,
"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": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un premier regard sur les données !"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"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\"] = sorted_data[\"inc\"].astype(int)\n",
"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": 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": [
"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": 14,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 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": 15,
"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": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2021 743449\n",
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\n",
"2020 2010315\n",
"2022 2060304\n",
"2012 2175217\n",
"2003 2234584\n",
"2019 2254386\n",
"2006 2307352\n",
"2017 2321583\n",
"2001 2529279\n",
"1992 2574578\n",
"1993 2703886\n",
"2018 2705325\n",
"1988 2765617\n",
"2007 2780164\n",
"1987 2855570\n",
"2016 2856393\n",
"2011 2857040\n",
"2023 2873501\n",
"2008 2973918\n",
"1998 3034904\n",
"2002 3125418\n",
"2009 3444020\n",
"1994 3514763\n",
"1996 3539413\n",
"2004 3567744\n",
"1997 3620066\n",
"2015 3654892\n",
"2000 3826372\n",
"2005 3835025\n",
"1999 3908112\n",
"2010 4111392\n",
"2013 4182691\n",
"1986 5115251\n",
"1990 5235827\n",
"1989 5466192\n",
"dtype: int64"
]
},
"execution_count": 17,
"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": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"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.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
}