diff --git a/module3/exo2/exercice_fr.ipynb b/module3/exo2/exercice_fr.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..e86a0587efe35965c00690260b6edb12e12ad6be 100644 --- a/module3/exo2/exercice_fr.ipynb +++ b/module3/exo2/exercice_fr.ipynb @@ -1,5 +1,2462 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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. 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": 3, + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Afin d'éviter une éventuelle modification du fichier sur le serveur du réseau sentinelle, nous faisons une copie locale du jeu de données. Ainsi les données ne sont téléchargées que si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Si le fichier csv n'existe pas dans le repertoire local je l'importe de l'url:\n", + "import urllib.request\n", + "import os\n", + "data_file = \"incidence-PAY-7.csv\"\n", + "if not os.path.isfile(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020202779991501848213FRFrance
1202026769401454102FRFrance
220202572280597001FRFrance
320202473880959102FRFrance
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1020201772720658001FRFrance
112020167758781438102FRFrance
12202015719186753161315FRFrance
132020147387922275531639FRFrance
14202013773265236941611814FRFrance
152020127812357901045612816FRFrance
16202011710198756812828151119FRFrance
1720201079011669111331141018FRFrance
182020097136311054416718211626FRFrance
19202008710424770813140161220FRFrance
2020200778959657411344141018FRFrance
2120200679264692511603141018FRFrance
2220200578505631410696131016FRFrance
232020047799158311015112915FRFrance
2420200375968410078369612FRFrance
25202002765344530853810713FRFrance
2620200179835701912651151119FRFrance
272019527794152461063612816FRFrance
2820195175823367579719612FRFrance
29201950764244276857210713FRFrance
.................................
15141991267176081130423912312042FRFrance
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1519199121714903897520831261636FRFrance
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15221991187213851388228888382551FRFrance
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15431990497114302610205FRFrance
\n", + "

1544 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202027 7 999 150 1848 2 1 \n", + "1 202026 7 694 0 1454 1 0 \n", + "2 202025 7 228 0 597 0 0 \n", + "3 202024 7 388 0 959 1 0 \n", + "4 202023 7 558 1 1115 1 0 \n", + "5 202022 7 277 0 633 0 0 \n", + "6 202021 7 602 36 1168 1 0 \n", + "7 202020 7 824 20 1628 1 0 \n", + "8 202019 7 310 0 753 0 0 \n", + "9 202018 7 849 98 1600 1 0 \n", + "10 202017 7 272 0 658 0 0 \n", + "11 202016 7 758 78 1438 1 0 \n", + "12 202015 7 1918 675 3161 3 1 \n", + "13 202014 7 3879 2227 5531 6 3 \n", + "14 202013 7 7326 5236 9416 11 8 \n", + "15 202012 7 8123 5790 10456 12 8 \n", + "16 202011 7 10198 7568 12828 15 11 \n", + "17 202010 7 9011 6691 11331 14 10 \n", + "18 202009 7 13631 10544 16718 21 16 \n", + "19 202008 7 10424 7708 13140 16 12 \n", + "20 202007 7 8959 6574 11344 14 10 \n", + "21 202006 7 9264 6925 11603 14 10 \n", + "22 202005 7 8505 6314 10696 13 10 \n", + "23 202004 7 7991 5831 10151 12 9 \n", + "24 202003 7 5968 4100 7836 9 6 \n", + "25 202002 7 6534 4530 8538 10 7 \n", + "26 202001 7 9835 7019 12651 15 11 \n", + "27 201952 7 7941 5246 10636 12 8 \n", + "28 201951 7 5823 3675 7971 9 6 \n", + "29 201950 7 6424 4276 8572 10 7 \n", + "... ... ... ... ... ... ... ... \n", + "1514 199126 7 17608 11304 23912 31 20 \n", + "1515 199125 7 16169 10700 21638 28 18 \n", + "1516 199124 7 16171 10071 22271 28 17 \n", + "1517 199123 7 11947 7671 16223 21 13 \n", + "1518 199122 7 15452 9953 20951 27 17 \n", + "1519 199121 7 14903 8975 20831 26 16 \n", + "1520 199120 7 19053 12742 25364 34 23 \n", + "1521 199119 7 16739 11246 22232 29 19 \n", + "1522 199118 7 21385 13882 28888 38 25 \n", + "1523 199117 7 13462 8877 18047 24 16 \n", + "1524 199116 7 14857 10068 19646 26 18 \n", + "1525 199115 7 13975 9781 18169 25 18 \n", + "1526 199114 7 12265 7684 16846 22 14 \n", + "1527 199113 7 9567 6041 13093 17 11 \n", + "1528 199112 7 10864 7331 14397 19 13 \n", + "1529 199111 7 15574 11184 19964 27 19 \n", + "1530 199110 7 16643 11372 21914 29 20 \n", + "1531 199109 7 13741 8780 18702 24 15 \n", + "1532 199108 7 13289 8813 17765 23 15 \n", + "1533 199107 7 12337 8077 16597 22 15 \n", + "1534 199106 7 10877 7013 14741 19 12 \n", + "1535 199105 7 10442 6544 14340 18 11 \n", + "1536 199104 7 7913 4563 11263 14 8 \n", + "1537 199103 7 15387 10484 20290 27 18 \n", + "1538 199102 7 16277 11046 21508 29 20 \n", + "1539 199101 7 15565 10271 20859 27 18 \n", + "1540 199052 7 19375 13295 25455 34 23 \n", + "1541 199051 7 19080 13807 24353 34 25 \n", + "1542 199050 7 11079 6660 15498 20 12 \n", + "1543 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 3 FR France \n", + "1 2 FR France \n", + "2 1 FR France \n", + "3 2 FR France \n", + "4 2 FR France \n", + "5 1 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 1 FR France \n", + "9 2 FR France \n", + "10 1 FR France \n", + "11 2 FR France \n", + "12 5 FR France \n", + "13 9 FR France \n", + "14 14 FR France \n", + "15 16 FR France \n", + "16 19 FR France \n", + "17 18 FR France \n", + "18 26 FR France \n", + "19 20 FR France \n", + "20 18 FR France \n", + "21 18 FR France \n", + "22 16 FR France \n", + "23 15 FR France \n", + "24 12 FR France \n", + "25 13 FR France \n", + "26 19 FR France \n", + "27 16 FR France \n", + "28 12 FR France \n", + "29 13 FR France \n", + "... ... ... ... \n", + "1514 42 FR France \n", + "1515 38 FR France \n", + "1516 39 FR France \n", + "1517 29 FR France \n", + "1518 37 FR France \n", + "1519 36 FR France \n", + "1520 45 FR France \n", + "1521 39 FR France \n", + "1522 51 FR France \n", + "1523 32 FR France \n", + "1524 34 FR France \n", + "1525 32 FR France \n", + "1526 30 FR France \n", + "1527 23 FR France \n", + "1528 25 FR France \n", + "1529 35 FR France \n", + "1530 38 FR France \n", + "1531 33 FR France \n", + "1532 31 FR France \n", + "1533 29 FR France \n", + "1534 26 FR France \n", + "1535 25 FR France \n", + "1536 20 FR France \n", + "1537 36 FR France \n", + "1538 38 FR France \n", + "1539 36 FR France \n", + "1540 45 FR France \n", + "1541 43 FR France \n", + "1542 28 FR France \n", + "1543 5 FR France \n", + "\n", + "[1544 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, 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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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il n'y a aucun point manquant. Nous faisons une copie des données pour ne pas toucher au jeu de données initiales." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1544 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202027 7 999 150 1848 2 1 \n", + "1 202026 7 694 0 1454 1 0 \n", + "2 202025 7 228 0 597 0 0 \n", + "3 202024 7 388 0 959 1 0 \n", + "4 202023 7 558 1 1115 1 0 \n", + "5 202022 7 277 0 633 0 0 \n", + "6 202021 7 602 36 1168 1 0 \n", + "7 202020 7 824 20 1628 1 0 \n", + "8 202019 7 310 0 753 0 0 \n", + "9 202018 7 849 98 1600 1 0 \n", + "10 202017 7 272 0 658 0 0 \n", + "11 202016 7 758 78 1438 1 0 \n", + "12 202015 7 1918 675 3161 3 1 \n", + "13 202014 7 3879 2227 5531 6 3 \n", + "14 202013 7 7326 5236 9416 11 8 \n", + "15 202012 7 8123 5790 10456 12 8 \n", + "16 202011 7 10198 7568 12828 15 11 \n", + "17 202010 7 9011 6691 11331 14 10 \n", + "18 202009 7 13631 10544 16718 21 16 \n", + "19 202008 7 10424 7708 13140 16 12 \n", + "20 202007 7 8959 6574 11344 14 10 \n", + "21 202006 7 9264 6925 11603 14 10 \n", + "22 202005 7 8505 6314 10696 13 10 \n", + "23 202004 7 7991 5831 10151 12 9 \n", + "24 202003 7 5968 4100 7836 9 6 \n", + "25 202002 7 6534 4530 8538 10 7 \n", + "26 202001 7 9835 7019 12651 15 11 \n", + "27 201952 7 7941 5246 10636 12 8 \n", + "28 201951 7 5823 3675 7971 9 6 \n", + "29 201950 7 6424 4276 8572 10 7 \n", + "... ... ... ... ... ... ... ... \n", + "1514 199126 7 17608 11304 23912 31 20 \n", + "1515 199125 7 16169 10700 21638 28 18 \n", + "1516 199124 7 16171 10071 22271 28 17 \n", + "1517 199123 7 11947 7671 16223 21 13 \n", + "1518 199122 7 15452 9953 20951 27 17 \n", + "1519 199121 7 14903 8975 20831 26 16 \n", + "1520 199120 7 19053 12742 25364 34 23 \n", + "1521 199119 7 16739 11246 22232 29 19 \n", + "1522 199118 7 21385 13882 28888 38 25 \n", + "1523 199117 7 13462 8877 18047 24 16 \n", + "1524 199116 7 14857 10068 19646 26 18 \n", + "1525 199115 7 13975 9781 18169 25 18 \n", + "1526 199114 7 12265 7684 16846 22 14 \n", + "1527 199113 7 9567 6041 13093 17 11 \n", + "1528 199112 7 10864 7331 14397 19 13 \n", + "1529 199111 7 15574 11184 19964 27 19 \n", + "1530 199110 7 16643 11372 21914 29 20 \n", + "1531 199109 7 13741 8780 18702 24 15 \n", + "1532 199108 7 13289 8813 17765 23 15 \n", + "1533 199107 7 12337 8077 16597 22 15 \n", + "1534 199106 7 10877 7013 14741 19 12 \n", + "1535 199105 7 10442 6544 14340 18 11 \n", + "1536 199104 7 7913 4563 11263 14 8 \n", + "1537 199103 7 15387 10484 20290 27 18 \n", + "1538 199102 7 16277 11046 21508 29 20 \n", + "1539 199101 7 15565 10271 20859 27 18 \n", + "1540 199052 7 19375 13295 25455 34 23 \n", + "1541 199051 7 19080 13807 24353 34 25 \n", + "1542 199050 7 11079 6660 15498 20 12 \n", + "1543 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 3 FR France \n", + "1 2 FR France \n", + "2 1 FR France \n", + "3 2 FR France \n", + "4 2 FR France \n", + "5 1 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 1 FR France \n", + "9 2 FR France \n", + "10 1 FR France \n", + "11 2 FR France \n", + "12 5 FR France \n", + "13 9 FR France \n", + "14 14 FR France \n", + "15 16 FR France \n", + "16 19 FR France \n", + "17 18 FR France \n", + "18 26 FR France \n", + "19 20 FR France \n", + "20 18 FR France \n", + "21 18 FR France \n", + "22 16 FR France \n", + "23 15 FR France \n", + "24 12 FR France \n", + "25 13 FR France \n", + "26 19 FR France \n", + "27 16 FR France \n", + "28 12 FR France \n", + "29 13 FR France \n", + "... ... ... ... \n", + "1514 42 FR France \n", + "1515 38 FR France \n", + "1516 39 FR France \n", + "1517 29 FR France \n", + "1518 37 FR France \n", + "1519 36 FR France \n", + "1520 45 FR France \n", + "1521 39 FR France \n", + "1522 51 FR France \n", + "1523 32 FR France \n", + "1524 34 FR France \n", + "1525 32 FR France \n", + "1526 30 FR France \n", + "1527 23 FR France \n", + "1528 25 FR France \n", + "1529 35 FR France \n", + "1530 38 FR France \n", + "1531 33 FR France \n", + "1532 31 FR France \n", + "1533 29 FR France \n", + "1534 26 FR France \n", + "1535 25 FR France \n", + "1536 20 FR France \n", + "1537 36 FR France \n", + "1538 38 FR France \n", + "1539 36 FR France \n", + "1540 45 FR France \n", + "1541 43 FR France \n", + "1542 28 FR France \n", + "1543 5 FR France \n", + "\n", + "[1544 rows x 10 columns]" + ] + }, + "execution_count": 11, + "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": 12, + "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": 15, + "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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 au mois de septembre." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "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 couvre la majorité de l'année, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année 𝑁 au 1er septembre de l'année 𝑁+1\n", + "\n", + ".\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 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de la varicelle est très faible en septembre, cette modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an novembre 1990, ce qui rend la première année incomplète et se termine début juillet 2020 ce qui rend aussi la dernière année incomplète. Nous commençons donc l'analyse en 1991 et la terminons en 2019." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991, 2019)]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1],\n", + " first_sept_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": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\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": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies sont assez similaires, elles touchent entre 0,8 et 1,3% de la population française, et se répartissent plutôt de manière homogènes au cours des 30 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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