{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence de la varicelle"
]
},
{
"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 pathlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données de l'incidence de la varicelle 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 1990 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": "markdown",
"metadata": {},
"source": [
"Pour plus de pérennité, les données brutes téléchargées sont gardées sur le dépôt afin de pouvoir les réutiliser sans avoir à les télécharger à chaque fois. Cela nécessite de ne les télécharger et les ajouter au dépôt si elles n'existent pas déjà dessus."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cached_file = \"cached_raw_data.csv\"\n",
"if pathlib.Path(cached_file).is_file():\n",
" raw_data = pd.read_csv(cached_file)\n",
"else:\n",
" raw_data = pd.read_csv(data_url, skiprows=1)\n",
" raw_data.to_csv(cached_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le jeu de données est complet, il n'y a pas de points manquants."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
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},
{
"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": [
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"
14857
\n",
"
10068
\n",
"
19646
\n",
"
26
\n",
"
18
\n",
"
34
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1525
\n",
"
1525
\n",
"
199115
\n",
"
7
\n",
"
13975
\n",
"
9781
\n",
"
18169
\n",
"
25
\n",
"
18
\n",
"
32
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1526
\n",
"
1526
\n",
"
199114
\n",
"
7
\n",
"
12265
\n",
"
7684
\n",
"
16846
\n",
"
22
\n",
"
14
\n",
"
30
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1527
\n",
"
1527
\n",
"
199113
\n",
"
7
\n",
"
9567
\n",
"
6041
\n",
"
13093
\n",
"
17
\n",
"
11
\n",
"
23
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1528
\n",
"
1528
\n",
"
199112
\n",
"
7
\n",
"
10864
\n",
"
7331
\n",
"
14397
\n",
"
19
\n",
"
13
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1529
\n",
"
1529
\n",
"
199111
\n",
"
7
\n",
"
15574
\n",
"
11184
\n",
"
19964
\n",
"
27
\n",
"
19
\n",
"
35
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1530
\n",
"
1530
\n",
"
199110
\n",
"
7
\n",
"
16643
\n",
"
11372
\n",
"
21914
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1531
\n",
"
1531
\n",
"
199109
\n",
"
7
\n",
"
13741
\n",
"
8780
\n",
"
18702
\n",
"
24
\n",
"
15
\n",
"
33
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1532
\n",
"
1532
\n",
"
199108
\n",
"
7
\n",
"
13289
\n",
"
8813
\n",
"
17765
\n",
"
23
\n",
"
15
\n",
"
31
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1533
\n",
"
1533
\n",
"
199107
\n",
"
7
\n",
"
12337
\n",
"
8077
\n",
"
16597
\n",
"
22
\n",
"
15
\n",
"
29
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1534
\n",
"
1534
\n",
"
199106
\n",
"
7
\n",
"
10877
\n",
"
7013
\n",
"
14741
\n",
"
19
\n",
"
12
\n",
"
26
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1535
\n",
"
1535
\n",
"
199105
\n",
"
7
\n",
"
10442
\n",
"
6544
\n",
"
14340
\n",
"
18
\n",
"
11
\n",
"
25
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1536
\n",
"
1536
\n",
"
199104
\n",
"
7
\n",
"
7913
\n",
"
4563
\n",
"
11263
\n",
"
14
\n",
"
8
\n",
"
20
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1537
\n",
"
1537
\n",
"
199103
\n",
"
7
\n",
"
15387
\n",
"
10484
\n",
"
20290
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1538
\n",
"
1538
\n",
"
199102
\n",
"
7
\n",
"
16277
\n",
"
11046
\n",
"
21508
\n",
"
29
\n",
"
20
\n",
"
38
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1539
\n",
"
1539
\n",
"
199101
\n",
"
7
\n",
"
15565
\n",
"
10271
\n",
"
20859
\n",
"
27
\n",
"
18
\n",
"
36
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1540
\n",
"
1540
\n",
"
199052
\n",
"
7
\n",
"
19375
\n",
"
13295
\n",
"
25455
\n",
"
34
\n",
"
23
\n",
"
45
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1541
\n",
"
1541
\n",
"
199051
\n",
"
7
\n",
"
19080
\n",
"
13807
\n",
"
24353
\n",
"
34
\n",
"
25
\n",
"
43
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1542
\n",
"
1542
\n",
"
199050
\n",
"
7
\n",
"
11079
\n",
"
6660
\n",
"
15498
\n",
"
20
\n",
"
12
\n",
"
28
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1543
\n",
"
1543
\n",
"
199049
\n",
"
7
\n",
"
1143
\n",
"
0
\n",
"
2610
\n",
"
2
\n",
"
0
\n",
"
5
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
1544 rows × 11 columns
\n",
"
"
],
"text/plain": [
" Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n",
"0 0 202027 7 1483 221 2745 2 \n",
"1 1 202026 7 707 0 1481 1 \n",
"2 2 202025 7 228 0 597 0 \n",
"3 3 202024 7 388 0 959 1 \n",
"4 4 202023 7 558 1 1115 1 \n",
"5 5 202022 7 277 0 633 0 \n",
"6 6 202021 7 602 36 1168 1 \n",
"7 7 202020 7 824 20 1628 1 \n",
"8 8 202019 7 310 0 753 0 \n",
"9 9 202018 7 849 98 1600 1 \n",
"10 10 202017 7 272 0 658 0 \n",
"11 11 202016 7 758 78 1438 1 \n",
"12 12 202015 7 1918 675 3161 3 \n",
"13 13 202014 7 3879 2227 5531 6 \n",
"14 14 202013 7 7326 5236 9416 11 \n",
"15 15 202012 7 8123 5790 10456 12 \n",
"16 16 202011 7 10198 7568 12828 15 \n",
"17 17 202010 7 9011 6691 11331 14 \n",
"18 18 202009 7 13631 10544 16718 21 \n",
"19 19 202008 7 10424 7708 13140 16 \n",
"20 20 202007 7 8959 6574 11344 14 \n",
"21 21 202006 7 9264 6925 11603 14 \n",
"22 22 202005 7 8505 6314 10696 13 \n",
"23 23 202004 7 7991 5831 10151 12 \n",
"24 24 202003 7 5968 4100 7836 9 \n",
"25 25 202002 7 6534 4530 8538 10 \n",
"26 26 202001 7 9835 7019 12651 15 \n",
"27 27 201952 7 7941 5246 10636 12 \n",
"28 28 201951 7 5823 3675 7971 9 \n",
"29 29 201950 7 6424 4276 8572 10 \n",
"... ... ... ... ... ... ... ... \n",
"1514 1514 199126 7 17608 11304 23912 31 \n",
"1515 1515 199125 7 16169 10700 21638 28 \n",
"1516 1516 199124 7 16171 10071 22271 28 \n",
"1517 1517 199123 7 11947 7671 16223 21 \n",
"1518 1518 199122 7 15452 9953 20951 27 \n",
"1519 1519 199121 7 14903 8975 20831 26 \n",
"1520 1520 199120 7 19053 12742 25364 34 \n",
"1521 1521 199119 7 16739 11246 22232 29 \n",
"1522 1522 199118 7 21385 13882 28888 38 \n",
"1523 1523 199117 7 13462 8877 18047 24 \n",
"1524 1524 199116 7 14857 10068 19646 26 \n",
"1525 1525 199115 7 13975 9781 18169 25 \n",
"1526 1526 199114 7 12265 7684 16846 22 \n",
"1527 1527 199113 7 9567 6041 13093 17 \n",
"1528 1528 199112 7 10864 7331 14397 19 \n",
"1529 1529 199111 7 15574 11184 19964 27 \n",
"1530 1530 199110 7 16643 11372 21914 29 \n",
"1531 1531 199109 7 13741 8780 18702 24 \n",
"1532 1532 199108 7 13289 8813 17765 23 \n",
"1533 1533 199107 7 12337 8077 16597 22 \n",
"1534 1534 199106 7 10877 7013 14741 19 \n",
"1535 1535 199105 7 10442 6544 14340 18 \n",
"1536 1536 199104 7 7913 4563 11263 14 \n",
"1537 1537 199103 7 15387 10484 20290 27 \n",
"1538 1538 199102 7 16277 11046 21508 29 \n",
"1539 1539 199101 7 15565 10271 20859 27 \n",
"1540 1540 199052 7 19375 13295 25455 34 \n",
"1541 1541 199051 7 19080 13807 24353 34 \n",
"1542 1542 199050 7 11079 6660 15498 20 \n",
"1543 1543 199049 7 1143 0 2610 2 \n",
"\n",
" inc100_low inc100_up geo_insee geo_name \n",
"0 0 4 FR France \n",
"1 0 2 FR France \n",
"2 0 1 FR France \n",
"3 0 2 FR France \n",
"4 0 2 FR France \n",
"5 0 1 FR France \n",
"6 0 2 FR France \n",
"7 0 2 FR France \n",
"8 0 1 FR France \n",
"9 0 2 FR France \n",
"10 0 1 FR France \n",
"11 0 2 FR France \n",
"12 1 5 FR France \n",
"13 3 9 FR France \n",
"14 8 14 FR France \n",
"15 8 16 FR France \n",
"16 11 19 FR France \n",
"17 10 18 FR France \n",
"18 16 26 FR France \n",
"19 12 20 FR France \n",
"20 10 18 FR France \n",
"21 10 18 FR France \n",
"22 10 16 FR France \n",
"23 9 15 FR France \n",
"24 6 12 FR France \n",
"25 7 13 FR France \n",
"26 11 19 FR France \n",
"27 8 16 FR France \n",
"28 6 12 FR France \n",
"29 7 13 FR France \n",
"... ... ... ... ... \n",
"1514 20 42 FR France \n",
"1515 18 38 FR France \n",
"1516 17 39 FR France \n",
"1517 13 29 FR France \n",
"1518 17 37 FR France \n",
"1519 16 36 FR France \n",
"1520 23 45 FR France \n",
"1521 19 39 FR France \n",
"1522 25 51 FR France \n",
"1523 16 32 FR France \n",
"1524 18 34 FR France \n",
"1525 18 32 FR France \n",
"1526 14 30 FR France \n",
"1527 11 23 FR France \n",
"1528 13 25 FR France \n",
"1529 19 35 FR France \n",
"1530 20 38 FR France \n",
"1531 15 33 FR France \n",
"1532 15 31 FR France \n",
"1533 15 29 FR France \n",
"1534 12 26 FR France \n",
"1535 11 25 FR France \n",
"1536 8 20 FR France \n",
"1537 18 36 FR France \n",
"1538 20 38 FR France \n",
"1539 18 36 FR France \n",
"1540 23 45 FR France \n",
"1541 25 43 FR France \n",
"1542 12 28 FR France \n",
"1543 0 5 FR France \n",
"\n",
"[1544 rows x 11 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.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):\n",
" year = year_and_week // 100\n",
" week = year_and_week % 100\n",
" return pd.Period(isoweek.Week(year, week).day(0), 'W')\n",
"\n",
"data['period'] = list(map(convert_week, 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": 7,
"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."
]
},
{
"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 au printemps. Le creux des incidences se trouve en automne."
]
},
{
"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'][-150:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etude de l'incidence annuelle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour des réunions techniques liées à la validation automatiques des résultats du MOOC,nous définissons la période de référence entre deux minima de l'incidence, du $1^{er}$ septembre de l'année $N$ au $1^{er}$ septembre de l'année $N+1$.\n",
"\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du $1^{er}$ septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le $1^{er}$ septembre.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an decembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"starting_year = 1991\n",
"first_septembre_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(starting_year,\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": 12,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_septembre_week[:-1],\n",
" first_septembre_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": [
"2002 516689\n",
"2018 542312\n",
"2017 551041\n",
"1996 564901\n",
"2019 584066\n",
"2015 604382\n",
"2000 617597\n",
"2001 619041\n",
"2012 624573\n",
"2005 628464\n",
"2006 632833\n",
"2011 642368\n",
"1993 643387\n",
"1995 652478\n",
"1994 661409\n",
"1998 677775\n",
"1997 683434\n",
"2014 685769\n",
"2013 698332\n",
"2007 717352\n",
"2008 749478\n",
"1999 756456\n",
"2003 758363\n",
"2004 777388\n",
"2016 782114\n",
"2010 829911\n",
"1992 832939\n",
"2009 842373\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre que les épidémies les plus fortes ne touchent qu'environ 1% de la population et sont deux fois moins fréquente que les épidémies les plus fréquente."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"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)"
]
}
],
"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
}