diff --git a/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b73d997cd74244ee908ccbe5c1c4cdb6bc8bbbcf --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb @@ -0,0 +1,2527 @@ +{ + "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" + ] + }, + { + "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 = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", + "#data_url3 = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\"\n", + "data_url = \"https://app-learninglab.inria.fr/moocrr/gitlab/dfa5b61add2096b8c3911f0d73f434f3/mooc-rr/blob/master/module3/exo1/inc-3-PAY.csv\"\n", + "data_url = \"module3/exo1/incidence-PAY-3.csv\"\n", + "data_url = \"https://app-learninglab.inria.fr/moocrr/gitlab/dfa5b61add2096b8c3911f0d73f434f3/mooc-rr/blob/master/module3/exo1/inc-3-PAY-mod.csv\"\n", + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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2146 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 3 190419 176434.0 204404.0 284 263.0 \n", + "1 202549 3 133235 122213.0 144257.0 199 183.0 \n", + "2 202548 3 82609 73827.0 91391.0 123 110.0 \n", + "3 202547 3 58860 51442.0 66278.0 88 77.0 \n", + "4 202546 3 40657 34407.0 46907.0 61 52.0 \n", + "5 202545 3 47645 40423.0 54867.0 71 60.0 \n", + "6 202544 3 44837 38385.0 51289.0 67 57.0 \n", + "7 202543 3 55400 47640.0 63160.0 83 71.0 \n", + "8 202542 3 75239 66515.0 83963.0 112 99.0 \n", + "9 202541 3 86860 77648.0 96072.0 130 116.0 \n", + "10 202540 3 79169 71180.0 87158.0 118 106.0 \n", + "11 202539 3 72930 64872.0 80988.0 109 97.0 \n", + "12 202538 3 61435 54131.0 68739.0 92 81.0 \n", + "13 202537 3 46373 39689.0 53057.0 69 59.0 \n", + "14 202536 3 25581 20702.0 30460.0 38 31.0 \n", + "15 202535 3 22717 17480.0 27954.0 34 26.0 \n", + "16 202534 3 21429 16177.0 26681.0 32 24.0 \n", + "17 202533 3 16766 12022.0 21510.0 25 18.0 \n", + "18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2123 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2124 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2125 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2126 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2127 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2128 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2129 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2130 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2131 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2132 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2133 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2134 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2135 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2136 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2137 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2138 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2139 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2140 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2146 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# je ne comprend par pourquoi j'ai une erreur =\n", + "# ParserError: Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n", + "# quand j'essaye d'utiliser les données qui sont dans le même dossier\n", + "# que celui où se trouve ce notebook, alors qu'avec le lien url ça fonctionne\n", + "# je n'y arrive pas même avec un fichier modifié (sans la ligne qui pause problème)\n", + "\n", + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', 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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
19091989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1909 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1909 FR France " + ] + }, + "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": [ + "
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21281985093369895341109.0398681.0670618.0722.0FRFrance
21291985083389886359529.0420243.0707652.0762.0FRFrance
21301985073471852432599.0511105.0855784.0926.0FRFrance
21311985063565825518011.0613639.01026939.01113.0FRFrance
21321985053637302592795.0681809.011551074.01236.0FRFrance
21331985043424937390794.0459080.0770708.0832.0FRFrance
21341985033213901174689.0253113.0388317.0459.0FRFrance
213519850239758680949.0114223.0177147.0207.0FRFrance
213619850138548965918.0105060.0155120.0190.0FRFrance
213719845238483060602.0109058.0154110.0198.0FRFrance
2138198451310172680242.0123210.0185146.0224.0FRFrance
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214519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2145 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 3 190419 176434.0 204404.0 284 263.0 \n", + "1 202549 3 133235 122213.0 144257.0 199 183.0 \n", + "2 202548 3 82609 73827.0 91391.0 123 110.0 \n", + "3 202547 3 58860 51442.0 66278.0 88 77.0 \n", + "4 202546 3 40657 34407.0 46907.0 61 52.0 \n", + "5 202545 3 47645 40423.0 54867.0 71 60.0 \n", + "6 202544 3 44837 38385.0 51289.0 67 57.0 \n", + "7 202543 3 55400 47640.0 63160.0 83 71.0 \n", + "8 202542 3 75239 66515.0 83963.0 112 99.0 \n", + "9 202541 3 86860 77648.0 96072.0 130 116.0 \n", + "10 202540 3 79169 71180.0 87158.0 118 106.0 \n", + "11 202539 3 72930 64872.0 80988.0 109 97.0 \n", + "12 202538 3 61435 54131.0 68739.0 92 81.0 \n", + "13 202537 3 46373 39689.0 53057.0 69 59.0 \n", + "14 202536 3 25581 20702.0 30460.0 38 31.0 \n", + "15 202535 3 22717 17480.0 27954.0 34 26.0 \n", + "16 202534 3 21429 16177.0 26681.0 32 24.0 \n", + "17 202533 3 16766 12022.0 21510.0 25 18.0 \n", + "18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2123 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2124 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2125 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2126 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2127 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2128 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2129 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2130 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2131 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2132 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2133 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2134 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2135 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2136 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2137 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2138 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2139 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2140 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2145 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": 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.\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": [ + { + "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 !\n", + "\n", + "Toute la colonne 'inc' est représentée par des chaînes de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989" + ] + }, + { + "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'] = 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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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M+ENGTEOLRlr51Y4Wrvy/z/PL7SeyvSs5S7dlaZRPJho6eyqTaNHQxGAFwi1LQ8c0Us/WIz38wy93A3CsZzjLe5O7dA36KHLaKHXZEy6nA+GZJZnOfZoCYiQQorzIoS2NNPLotuOUuh04bDY6zIJ8mvF0DfqpLZ24hIhFsdPOsHZPZQwtGpoYvIEQc8vdo6Kh52mkHI83wLzyIkrddto9WjQmwpjYl9g1BTp7KtNo95QmhtFAuOES0JZG6hnyhSh125lXUUTHgC/bu5OzdA36qZsk3RYM95Q/GCYUVhnYK40WDU0MI4HYQLjO7kk9Hl+QsiInc8vdkX4RmvF0DfomzZyC0aKFuv5UZphUNERkoYg8IyL7RGSPiHzeHK8RkU0ictD8Wx21zldFpFlEDojIlVHja0TkDfOzO8V0VoqIW0R+bo5vEZGmqHU2mt9xUEQ2pvLgNePxBsIUOW16RngaGfQGKHc7mFtRxIA3qIO4cQiHFT1DfmqTsDSKtGhklGQsjSDwJaXUGcAFwC0isgL4CrBZKbUc2Gz+G/OzDcBK4CrgLhGx0h/uBm4Clpuvq8zxG4FepdQpwB3At8xt1QC3AucD64Bbo8VJk3pGAiGKXHbcTp09lS6i3VMAHTquMY6+kQChsErK0igyr1WvvlYzwqSioZRqU0rtMN97gH1AA3AN8IC52APAteb7a4BHlFI+pdQRoBlYJyL1QIVS6hWllAIeHLOOta3HgMtMK+RKYJNSqkcp1QtsYlRoNCkmHFb4g7G1p7RopJ5BX5AytzPS97pdxzViuHPzQS7/7nPA5BP7IKp7n7bYMsKUYhqm2+gcYAswTynVBoawAHPNxRqA41GrtZhjDeb7seMx6yilgkA/UJtgW5o04DXjF0W6YGHaCIcVQ/4gZUWOiKWh4xqxvNTchdMufPqSJbzz1LpJl9fuqcySdMqtiJQBvwS+oJQaSJA7He8DlWB8uutE79tNGG4vFi1aNNF+aSbBelIrdtqx2QSX3aZTblPMcCCEUlDmtjOvXItGPLyBEGfUV/BPV69IavmiSHFNLRqZIClLQ0ScGILxU6XUr8zhdtPlhPm3wxxvARZGrd4ItJrjjXHGY9YREQdQCfQk2FYMSql7lFJrlVJr6+omfzLRxMfyCVs+YpfDppswpZhBr1HuosztpKLYgdtho8Oj3VPRDJulbJKlOOKe0tdqJkgme0qAe4F9SqnvRn30BGBlM20EHo8a32BmRC3BCHhvNV1YHhG5wNzmDWPWsbb1YeBPZtzjaWC9iFSbAfD15pgmDViWhvXk5nLY8If001sqGfSZolHkQESYV1GkLY0xjARCFE9SOiSaSCBcu6cyQjLuqYuBTwJviMhOc+wfgW8Cj4rIjcAx4DoApdQeEXkU2IuReXWLUsr637wZuB8oBp4yX2CI0kMi0oxhYWwwt9UjIrcBr5nLfUMp1TPNY9VMgvWjs57cXHabjmmkmIhouI1zPK9Cz9UYy8gULY1ITEO7pzLCpKKhlHqR+LEFgMsmWOd24PY449uAVXHGvZiiE+ez+4D7JttPzcyxRMP6EbqdWjRSTbR7CmBueRH72gayuUs5hzXBNFmKdfYUAM/s7yAYVlyxYl5av0fPCNdEsHoSWK4Bl92mU25TjGVplJqWRl25W8c0olBKMRIIUTIF95Rbz9MA4N4Xj3D3s81p/x4tGlPkse0t7G7py/ZupAWvGfQuckTFNAr8h5hqLNEoNy2NimIng74gYV03CTDmBSkFRVOKaZjZUwUe0xj2Bylxpb8GrRaNKXLr429y/0tHs70baWHU0jAuC7dDp9ymmkFvADAC4TAa2xjSTYSAURdTyVRiGg49TwOMrLOpWGjTRYvGFBjyBRnyh+gcnJ3uBOtH546yNHTKbWoZMm+Klnuq1G2Ix5CvsG94FsNjXKTJ4LQLdpsUfMvXIX8wcj2lEy0aU6DT9D3P1nLW3rExDYcdn7Y0UorHG8Rlt0WEucz8kVtuq0InMsF0Cm4WEaHIYYu4VwuVYZ+2NHIOK2A5Wy2N6BnhYLqndEwjpQz5ghHXFECpyxEZ14y/BpOlyGkvePeUtjRyEMvS6Bnyz8qbaSQQHj25T+e+p5RBXzDimoJo95QWDYiKq01DNArZPRUKK7yBsLY0co3oEtbdQ7PP2hgJhHDZbdhtxrQct065TTkebzAyRwO0e2osw2ZCwFRiGmDMCi/k+Jt13kp19lRuEZ1PPxvjGt5AKJLzDnpyXzoY8gUpj3IhlOrsqRjGViVIlkJ3T0Wyztza0sgpOqNEo3MWTsjyjpmJq6vcpp6x7qlRS6Nwb3jRDFs3vylaGsVOe0GXEYlk5WlLI7fo8PioKzeawszGYPjYQnE65Tb1DJr9wS10TCOWsVUJkqXIaS/oMiLW9TPV8zYdtGhMgY4BL2fUV5jvZ59oeAOhyEQpsKrcatFIJUbXvtFzXOKyI6JFw2I05XbqMY1CTrkd1pZGbtI16KOhqoiaUhedg7OvMulIIBxTvsHtsBMKK0K6xEXKGPQGIy4pMOYYlLocOhBuMqOU24J2TxnXj45p5BDBUJjuIT915UXUlblnsaUxekm4HLrlayoJhY1ifNHZU2AEw7WlYTASCOGwCU771G5NRU473gJ2Tw37tKWRc3QP+VEK5pa7mVvhnpUxDe/YmIb5w9VtNFPD2Aq3FqVuhy4jYjLsn1oDJosip62gq9xaKbd6nkYOYVkWdeXuWWtpjPhjYxpW+q22NFLDaAOm2KfBcrd2T1mMzeBLluICT7mNxDT0jPDcwZrYN7fcTV25YWkYHWlnD2NLXIxaGlo0UkH/sFHhtqpkrHvKod1TJtOt1GrN05htv8lkGdKWRu5hzcuYW1FEXbkbfzDMwMjs+qEPeINURKWDWjENLRqpoXfYD0B1iStmvFRbGhFGAqFIGZupUOS0E1YUbLbfsC+E3Sa4Hem/pWvRSJIe8wdfU+KiptQVMzYbCIbCDPqCVBSPWhpWJVbtnkoNPUPmNVQaKxplboeeEW4yMk1Lw7pZFmra7ZA/SInTjshEnblThxaNJBn0BnHYhCKnbVZOyPKYvaujLQ3rh1ioT2+pJmJplI61NOw6EG4ydoJpsljrFGr3vmFfKCPptpCEaIjIfSLSISJvRo19TUROiMhO8/W+qM++KiLNInJARK6MGl8jIm+Yn90ppiSKiFtEfm6ObxGRpqh1NorIQfO1MVUHPR08XsPfLyKRQOZsEo0Bs6NcZfF40SjkAGMqsSyNquLxMQ3tnjIY9k8vEG4lcBRqpdshfzAj6baQnKVxP3BVnPE7lFKrzdfvAURkBbABWGmuc5eIWFfA3cBNwHLzZW3zRqBXKXUKcAfwLXNbNcCtwPnAOuBWEame8hGmiEFfkHIzSByxNGaRS8GKz1RE3dAqzYBtnxnA1cyM3iE/lcVOHGPmIJS5HPiDYe0GxEr7nvrNz4qDFKp7asSfQ5aGUup5oCfJ7V0DPKKU8imljgDNwDoRqQcqlFKvKCO94UHg2qh1HjDfPwZcZlohVwKblFI9SqleYBPxxSsjeLwBys1JWVYZiNlUZK5/xBCGiqjsKcv33juLYjfZpGc4MC6eAbr+VDTD/iDFzql7za2+9oVqFQ/5g5TkkKUxEZ8Vkd2m+8qyABqA41HLtJhjDeb7seMx6yilgkA/UJtgW1nBck/B7PyRW+6paEvDyvKx3Cqa5OjweHmpuWtc+mfvkJ/qMem2oHtqRGMEwqdhaTgsS6MwRWPYH6I0A+m2ANOVpruB2wBl/v0O8FdAvNC9SjDONNeJQURuwnB9sWjRokT7PW083iALqoqAWSoaI+NjGkVOO6UuuxaNJOj0+Pj6b/ew/6SH5o5BAB776wtZ21QTWaZnyB+5hqKZje7O6eINhKeVcut2FnhMwxdkYU1JRr5rWpaGUqpdKRVSSoWBH2HEHMCwBhZGLdoItJrjjXHGY9YREQdQieEOm2hb8fbnHqXUWqXU2rq6uukc0qQY1UlNS8M1+54M41kaYGT69GrRmJTn3+rkyd1tNFYXc8OFiwE4ORBb1LJ32D9ujgZENWKaRdfTdAiGwvhD02tZWlzgMY1MWhrTEg0zRmHxAcDKrHoC2GBmRC3BCHhvVUq1AR4RucCMV9wAPB61jpUZ9WHgT2bc42lgvYhUm+6v9eZYVvB4A5Sb6ah2M/V2Nv3I+0cC2G0y7sKrKXXNqvko6aKtfwSAH3xiDX/7nuVArFtPKUXPkD9uTEM3YjKYbn9wMGpPQeHWSRvyZS6mMem3iMjPgHcBc0SkBSOj6V0ishrDXXQU+AyAUmqPiDwK7AWCwC1KKet/8WaMTKxi4CnzBXAv8JCINGNYGBvMbfWIyG3Aa+Zy31BKJRuQTylKqZiYBhg/9Nn0Ix8YCVJhphRHU12iLY1kaO33UlPqoshpj/RYjxaNkUAIXzA8bo4GzE5353SYbi8NGM2eKtRGTNMtvzIdJhUNpdT1cYbvTbD87cDtcca3AavijHuB6ybY1n3AfZPtY7rxBcMEwyqScguzr17QgDcwzjUFhqVxqHMwC3uUX7T1jVBfacQrnHYblcXOGLGNzAaP457SgXCDmVgao+6pwhMNv3l/ykSxQtAzwpPC8veXR7fpdDki5YhnA/0jgZjZ4Bba0kiOtn4v9ZXFkX/XlLrojjpvvUPGNRTP0piNk0Wnw3AKLI1CLI+eybLooEUjKQbNEhvl7rHuqdnzIx8YCcTUnbKoKXUy5A8V5BPcVGjtG4nJjKouccbMb4nULisdL8zWE6J1nRUq0+0PDqPVCwrRPTWUwVavoEUjKay6TLHuqdlVL2jAG4xJt7WoKXUDelZ4IoZ8QQa8wTGWhpueodFz1meKRlUc95TLYaPUZae3wM+xJZrTufnZzAqvhdjyddiXuVavoEUjKeI1z5l1MY0J3FPWk3H30OxrOpUqrMypaEujptRJT9Q5SxTTAKgpcxX8zPuuwdFGZ9Oh1O2ItD0tJLSlkYN44sQ0Zpt7qn8kfiDcmlfQO1TYT8GJaO0z5mNEWxrG/JZAZFZ475Afm4yfB2NRU+Iq+EmUVs+a6YtGYfZatyyN6bj1poMWjSSI756aPZaG10wHje+emn29Q1KNZWlY2VMAtaUu/KFw5CmwZ9hPVYkrko47lupSbWl0enwUm1UIpkOpy4Fnlvwmp4InjicknWjRSIK4ouGyM+QPEQ7nf3vJ0V4a4y86K9tHZ1BNTGufFxGYXxkdCDfFdtA4byd6R5hTFt81BYal0T1Y2Oe4c9BHXbl72o2EymbRg9xUGNKikXtYN9XSMTENgOFZkFU0UQkRGO39UOiuk0S09Y8wt9yNM6rkebSF5g2EeOVwNxcurZ1wGzXa0qDT45u2awpml/U/FSKiEeehLx1o0UiCQV+AYqc95qYwm2bxjpZFHy8aDruNqhKnFo0EjJ2jAVFl5Yf8vNTchTcQ5vIV8ybcRnWpi+ECT23u9PioK5u+aMy2OGOyaPdUDuLxBmNcUzC7ZvFaFW4TBmkL/Ck4ES29IzRUxReN7iE/m/a2U+Z2cP6SxJYGFLZF1zFjS2N2pcEny5AviN1MOc4EWjSSwOMLjjP9Iu6pWXCRDpjut8o4k/vAeAruKXB/+0R4vAGOdg9x+vzymHErFtQ96OOP+zq49LQ6XAl+1IXeu8QXDNE/EpiRaJS5nbPC8p8qg16jAvd0Y0FTRYtGEhiWxti+zlb3vvy/SLvN/Ph4ZbvBiGtYLixNLHtaB1AKVjVWxoyXux047cLv32ija9DHFWdM7JoCqC0r7C6JXeZDycxEw86gPziu+dVsZ9AXyphrCrRoJMWgNxBTQgRmV72gkwNeXHZb3LLdYGSNeXxaNOLx5ol+AM5siBUNEaG6xMWuln7mVbi5atX8hNspdEvDmqMxd4aBcKVGa1gVCkNRvX4ygRaNJIgX05hN3dba+73MrZg41bGi2MnASP4fZzp440Q/9ZVFzIkTwLVE+G/edcqk3egKPaYx04l9MLuSU6bCoC8Y8XxkAi0aSeDxjlfy2RQIPzngZX7F+DakFhVFTjzeQMGZ/cnwxol+Vo2xMizmVRQxr8LNR89bGPfzaCqLndikcOfDpEI0ZtNvcioYopE5SyNz35RhkTn4AAAgAElEQVTHDPrixTRmz1NN+4CPFQsqJvy8vMhBWBk1bjJpBuc6Hm+AI11DfGB1Q9zP/+3aVQRCyfW8ttuEqgLOUrNEo7Y0FZZGYbmnBn3BuL3n04W2NCbBHwwz6AtSVRIrGiVOKxCe3xeoUoqT/ZNYGmYq7oAOhscwURDcYmFNCUvrypLeXnWJs2BrfHUOeqkucSbMMJuM2ZScMhWGfMGMFSsELRqTYpW0Hts8x2YTSlz5XyDN4wsyEggxr2LiJzwrnuMp8H4PY7E6Go5Nt50uRuOmwqwmPNPZ4ADlbuPhptBEI9PuKS0ak2B1X6udoLdzvotGe79RoXXeJDENGC03ojFoH/AhwoxmMUdTY1bGLURmOrEPRi2NfP9NTgWlFEO+8Yk66USLxiRYgcl46ahlbkekimm+cnLAEI1k3FMeLRoxdHq81Ja6cNhT8zOqKS3cmMaJOLPqp0ohBsJHAiHCityyNETkPhHpEJE3o8ZqRGSTiBw0/1ZHffZVEWkWkQMicmXU+BoRecP87E4x8ztFxC0iPzfHt4hIU9Q6G83vOCgiG1N10FOhO4FozIb6/SdNSyO6QutYrKcYnXYbS8eAj7ry1AUgrX7shZal5g2E6PD4aKwumdF2ZlNySrJY3Q5zbZ7G/cBVY8a+AmxWSi0HNpv/RkRWABuAleY6d4mIlTpyN3ATsNx8Wdu8EehVSp0C3AF8y9xWDXArcD6wDrg1WpwyRe9wAtFw5X+BtPYB7Z6aLh0e34wmo42lptRFMKwiZV0KhdY+ox9JY/XMLI0Slx2RAhONDBcrhCREQyn1PNAzZvga4AHz/QPAtVHjjyilfEqpI0AzsE5E6oEKpdQryniMenDMOta2HgMuM62QK4FNSqkepVQvsInx4pV2rB4HVXGK+c2G+v0nB7xUlTgTpoXqQHh8OjzehAkEU6WmQHuXtPRaojEzS0NEzAe5/HYZTwUrvTin3FMTME8p1QZg/p1rjjcAx6OWazHHGsz3Y8dj1lFKBYF+oDbBtjJK77CfqhJnXL/1rAiED/gSxjMAipx2XA6bTrmNIhRWdA36mZtK91SBdkkcFY2ZWRpguIwHC6jkjVXeJ6csjSkSrw6FSjA+3XViv1TkJhHZJiLbOjs7k9rRZOke8lMzQSG/Unf+P9W0D3iZO4logOGiiuc2aesf4V8ffzOSfloo9Az5CYUVc1NpaYzp9lcotPQO47BJQhdpshjWf37/JqeCdaz5IBrtpssJ82+HOd4CRNdMaARazfHGOOMx64iIA6jEcIdNtK1xKKXuUUqtVUqtraurm+Yhxad3yD9hIb+yWRIIn5/Eja+iyDEuprHtaA/v/d4LPPjK29z97KF07WJO0uExYkGpjmlAYVoaC6qKJ+yfPhUKrRGTdf/Jh9pTTwBWNtNG4PGo8Q1mRtQSjID3VtOF5RGRC8x4xQ1j1rG29WHgT2bc42lgvYhUmwHw9eZYRukZ8o+b2GdR4nIwEggRytM+4eGwonsoORdLebFzXEzjJy8fxS7Cu06r4w9vniyornMdkVpJqXNPFW5MY5iFNTN3TcHscBlPhUjXvlyapyEiPwNeAU4TkRYRuRH4JnCFiBwErjD/jVJqD/AosBf4A3CLUsq6k9wM/BgjOH4IeMocvxeoFZFm4IuYmVhKqR7gNuA18/UNcyyj9Az5407sg6jy6Hla6bZ32HCxzCmLf3zRVBQ5xsU0jvcMs2JBBZ96x1IGfUGe2d8xwdqzj86BmZfyHkuJy4gd5Vul2yNdQ9zz/CEe3nJsWhl2Lb0jNFbNLAhuUVqglkYm3VOTfpNS6voJPrpsguVvB26PM74NWBVn3AtcN8G27gPum2wf04VSit7hiS2N6LzweP21c53OweSfliuKnJwwUyMtjvcMs+rMei5cVsucMjeP72zlvWfWp2Vfcw3LPTXTWczRiIjRWjfPROPuZ5t5dJuR5/J29xBffd8ZSa87OkcjNZZGobmnBr1BbALFSRTFTBV6RngCBrxBAiE1oaUxWrYgP90yUylHXVHsiHFPDfqC9A4HWFhdgt0mXH3mfJ450EEgFE7b/uYS7QM+KosTpypPh+pSV9517+se9HP6/HKuWDGPx7a34A8mfw1YDyKNKXNP5X+ccSpYdacy1eoVtGgkxPItT9QGNd+793WZlkYy7qnyImeMe+p4zzBAxBd9zqJqfMFwwWRRdXi8KXVNWdSW5p+l0TPsZ06Zm4+dv4juIT+b9rYnva51HTWk0D2Vrw9xFs0dHn7w3KGkKgNkumsfaNFISKSEyAQ31XwvWzAlS6PIgS8Yxhc0fpAR0TAnZFn9OPacGEjHruYcHR5fStNtLarzUDT6hgNUlTh55/I6GqqKeXjr20mvu/N4HyJwWooqBZe7HfhD4SlZO7nGHX88yDef2j/OHRyPQS0auUWkWOEklka++lA7PT6KnLakLrrRooXGsR43J2QtrDFEY+mcUtwOG3taC0Q0BnwpndhnUVPizDvR6B32U13iwm4Trl+3kJeau9mb5HXw2tEeTp9fQWWcigvToTzPS94M+YJs3mdYatvf7p10+UyXRQctGgnpSVCsEPK/T3jXoJ+68ol7g0czWrTQ+DEe7xmm1GWn2mxO5bDbOL2+gr1t/enb4Ryhd8jPib4Rls9LvsFSslSXusxYWn48KYfCiv6RQOQ6+OQFTZS67Nz93OTzdgKhMDve7uP8JTUp2x+rrItVUy3f+OO+drwB4/8+GdHweDNbFh20aCSkJ0GxQojuFJafPtROj485SfaCsLLDLEvDyK0viRGclQsq2Ns6MOurtL5+3Pgxn7so9fUzraSLvuH8eFIeGAmg1GgJlMoSJ5+4cDG/293Kka6hhOvuaR1gJBDivKbUicb8SiPGZlVvzjd+u6uV+RVFXLi0NinRaB/wpsXiTYQWjQT0DvlxOWyUuOJnyOR7ILzT40u6gdBYs/94z8i4AnMrF1Qw4A1GagnNVna83YfdJpw1QZvXmRCpP5UnLirrwSo6WeRT71iKTYRfbDs+0WoAvHbEmHZ13pLUiW+9WeK/NQ9Fo2vQx3NvdfJnZ9Vz3pIa9rUNJLy3BENh2ge8Ge0PDlo0EtI3bJjdE7lvip35XYq5czD5bmmLaw2BePOEYUkcjzOLd+UC4ya6p9VwUf369Ra2HO5O4R7nBjuO9XJGfTklaejLHKk/lSeiYbVDrioZjUnUlbtZ2VA56ZPyliM9NNWWpPRJeU6ZG7tNONmffw8uP9tyjEBIsWHdItYsriasjESBiegc9BFWiXvhpAMtGgnoHfZTVTxxOupoKeb8E41AKEyvmSqZDPMqiljVUMEf97XTNehn2B+KZE5ZnD6/HLtN2NNqPCF98dFdfPSeV/naE3sYyfMOhxahsGLX8T7WpME1BaOWRr7M1bDa045NS1+zqJpdLX0JYzN7Wvs5J8Xn0W4T5pW7acszSyMQCvM/W97mkuVzOGVuGecsqkIkcVyjtc84xgWVqZnjkixaNBJgpRImIl8nE/UM+VFqajOaLz9jHjuO9fIDM8i5tin2B1/ktLOsrpQ9rQPsP+lBKTh/SQ33v3yUq+98gQMnPSk9hlSjlJo0HnPgpIchf4hzF6dHNKyYRneeWBq9cdxTAGsWV+MNhCfMovIGQpwc8EYs2FQyv7Io72Ia/7unnfYBHxsvbAKMGGJDVTGHE8x7ajOtKW1p5BB9I/4kRCM/+4RPZY6GxeVnzEMpuPfFI1x6ah1nNVaNW2ZFfQV7WvvZ12bcLL7zkbN5+FPn0zcS4Pbf70vNzqeJ720+yPo7no+cm3hY7oJzFqZHNKpK8qtooRWwry6N/Z2cu9i4NiZ6Uj7RN4JSsKgm9aJRX1Wcf6Kx9yR15W7effrcyNiimhKOmfOh4mEdo7Y0coje4cCEs8Et8rV7X2dkNnjyorFyQUUk0Pj5y5dPsEwl7QM+XjzYRUWRg4aqYi46ZQ4fPW8hLzV35ezNUCnFo68d52DHIJ9+cNuEFXvf7hnCZbelrFbSWFwOG3Xl7kkzj3KFnmE/DpuMm+tTX1lMQ1Ux24/FFw3rZpgOS6O+ooi2fm9eZfFtO9rLeU3VMeXhDdGYODbT2uelxGWnolin3OYESin6hwORJ7+JKHXlqWh4pl6lVUT460uX8RcXNU2YbrrSnBm+eX87Z9RXRJIIrj6znlBY8fSekzPc8/Swt22A1n4vV59Zz87jffx0y7G4y53oHWFBVRG2FPR+mIjzmqrZeiTjBZ2nRd+wn6oSV9xkkXMXV7P9aHzRGC1Dkx731EggxMBIfvwuT/Z7OdE3wprFsanHC2tK6Br0TXh/OTkwwvzKoozWnQItGhMy7A/hD4WTck/l4zyNjoHpVWndeFETX3v/ygk/t8qJBEIq8h4MMVlcW8Lv3mibxt6mnz/u7UAEvvb+lSydU8rLzV1xlzvRN0JDmqwMi/OaajjRN5JUGYls0zs0OrFvLGc2VHBywEt/nDknx7qHKXLakk75ngr1prumbSD3zx/AtreNB4S1Y+JklhV2vDe+i6q1z5tx1xRo0ZiQ0QBfYtHI1+59J/pGmFPmSnmV1qoSFw1VxoV8Rv2oaIgIV59Zz8uHuiNpmrnE5v3trF5YRV25m/OX1rL1SE/c5lonekfS/kNdZ86Qfi0PrI1ErQOs7LqWvvE3vWM9wywaMzk0VViB4XzJoNp2tJdipz3mIQtG4z3HuuOLRlv/SMaD4KBFY0KsAF9lgpRbyN9OYSf6vCyoSs/Nz3JRraiP/RGcv7SWUFjlXBZVp8fH7pZ+Lj9jHgAXLqvF4wtG5ptY+IJG74d0Wxqnz6+g3O1gSx6IhjWXKR7W5M94kz0t0UgHVtwtX4Lh297u4eyFlTjtsbfjiGjECYYHQmE6PD4WaNHIHSJZIUm5p/JPNFr70vfEvG5JDZXFznG1mZbVlQJwqDO3grxvnjDEwSpncYH5pP/qmImJ1k2oIU1ia2G3CWubqnntaO6LRo9ZrDAeVrLAWNFQSnGsZzgt8QwwXK42gaPduXWdxWPYH2Rfm4e1i8eXUqksdlJe5IjEf6Lp8PhQysgUyzRaNCYg4p6awPS2KHUZJcODeVJgDowfbWvfSNosjb+8eAnP/59343bEur4WVBZT7LTT3JFbPTf2munBp9cb5bnnVhSxrK6UVw7FisYJ8+aXbtEAWLekluaOwYTpv9lGKRUJhMejqsRJqctOyxiffPeQMTk0XZaG025jcW0pP3zuMNd8/yU8OVzx9sBJD6GwYlXD+JI0IsKimhLejiMaJ7M0RwO0aExIn1nNtWqSks352L2vbzjAsD+UNjeL3SZUxrHQbDZhaV1pzjVq2ts6wKKakpiWve84ZQ7PH+ziG7/dy7BZxbjFDEyn2z0FcPEptQC8fCh+QD4XGPKHCITUhNa4iNBYXTLO0rDcLekSDYCff+YC/v6q09h1vI8ndrWm7XtmiuWqPX2CfiITzdVoyeADzFi0aExAnzmfIN7NL5pIT408Ko9uZeU0ZLjQGcCyurKctDRWjglCfvGK0/jI2kbue+kI9zx/GDBceiKj2TnpZOWCSqpLnDz/Vu6KxmSdLcFwUY0VjcOmezKdojG3vIibL13G6fPLI/3Lc5H9Jz0UO+0TnotFtSW09IwQHpOUcaRrCJH0nsOJmJFoiMhREXlDRHaKyDZzrEZENonIQfNvddTyXxWRZhE5ICJXRo2vMbfTLCJ3iplSISJuEfm5Ob5FRJpmsr9ToW8kQInLPs7FMharp8agN39Eo9UUjXS5pxJxytwyTvSN5EwtqkFfkKPdQ+OC9pUlTv7jg2exckFFZM7Eid4R5pa7cTnS/6xltwkXnzKHFw52ZmSS2pce3cUvt0/t5mplJyVykSysKRnnnnrxYCc1pS6W1qW+H0k0IsJ1axey63hfziVfWBw46eHUeWUTzvtZNqcMfyjM4TGTPY92DbGgsjjl2Y/JkIqr/91KqdVKqbXmv78CbFZKLQc2m/9GRFYAG4CVwFXAXSJiHfHdwE3AcvN1lTl+I9CrlDoFuAP4Vgr2Nyl6EwT4opkfKcWcHznhEG1pZF40lpk3isNduWFtHDg5gFKMS3e0OHdRNbuO9xEKK06kMQ4Uj0uWz6HD4+Ngmi2zN0/088sdLfxm54kprWfVPkp0Thqri/F4g/Sb7t5QWPHcW51cempdzOzndHHt6gU47ZOXac8GSikOtHs4fX78aw/gvAmSMo50D7NkTmla928i0vHIdA3wgPn+AeDaqPFHlFI+pdQRoBlYJyL1QIVS6hVlPFI9OGYda1uPAZdJOhK745BMsUKAplrjP+7tPCn7AIal4XbYJmwulU6WzTXOV664qKyCehOKxuIqhvwhDpz0GBP7Miga71heB8Dzb3Wm9XseMy2M/VN8Gj8RsVgntjRGM6gMa2Pn8V56hwMxNZbSSW2Zm/OaanIyfblz0EfPkD9hf/Sm2hLmVxTFiIZSiiOdgzTNybxrCmYuGgr4XxHZLiI3mWPzlFJtAOZf6+poAKLlvsUcazDfjx2PWUcpFQT6gdqxOyEiN4nINhHZ1tmZmh+YkRUyuWjMKXNR5nZwdIIJONlCKcVDrxzl5eauce6N1j4vDVXFGS8/AIbI2iR30m73tA5QXeJkfkX8G59VLuXxXSdo6R1haQaf7hqqillWV8oLB9MX1/AFQ/xm5wlcdhudHh/dg8lna7X2jVBV4kzYV2TsXI0/7e/AbhMuNQUxE5zVWMX+kwP4grnhErWYLAgOhovtgqU1vHq4J/I77h0OMOANRh5YM81MReNipdS5wHuBW0TknQmWjXeHUgnGE60TO6DUPUqptUqptXV1qbkY+5KoOwXGf+ri2pKcKzD3yqFu/uXxPXzsx1u49vsvxdwMMu1miabIaWdxbSn72+KXzM4029/u5azGqgkFdFFNCXPKXNzz/GGcduHjFyzO6P5dsryOLUe6JyygOB12HOvln379BiP+EI/vbKVvOMDGi4zjmorvP5kyFmPnajyzv5M1i6onTTBJJWc3VhIIKfa35VZcwzrXiSwNgAuW1tI16OP23+3j3d9+lq1HDKtjaV0eioZSqtX82wH8GlgHtJsuJ8y/HebiLcDCqNUbgVZzvDHOeMw6IuIAKoGM2JlGA6bkLuymOaU5N5Ho4a3HqCx28u8fOJP9Jz184t6tkfIdmXazjGXNYmPi2tiMkEzTPWjEC85fOnGPahHhnEXVKGXMP5k3gUWSLi5ZPgdvIMyOJPpFJ8ttT+7lp1uOsfEnW/nXx9/k3EVVfPqSpQDsm5JoTP7wUVnspKLIQXOHh/7hAHvbBnjH8jkz2v+pctZCo0z77paJu+Blg4Ptg8wpc1E7Sf2tC5cZzpUfv3iEI11DfHfTWwD5Z2mISKmIlFvvgfXAm8ATwEZzsY3A4+b7J4ANZkbUEoyA91bTheURkQvMeMUNY9axtvVh4E8qA6kk4bCif2TysugWS2pLaekdSdilLJN0D/p4es9JPnhuAx87fxE/3riWt9o9/OiFw3R4vHR6fFl7SgHjyal3OMBbHdl98rNmXJ+/ZJzHM4YrzphHQ1Uxf/3OZZnYrRguWFqL0y48nyIX1c7jfbx+rI8Lltaw9UgPtaVufvjJtcytKGJOmXtKFmBr38ikadsiwnlNhntl+zGzJ3jTxCKdDhZUFlFb6mJXS//kC2eQZEupLKop4Yz6Ct55ah3nLqrirfZB7DZJ24z6yZhJIfZ5wK9Ns94BPKyU+oOIvAY8KiI3AseA6wCUUntE5FFgLxAEblFKWTb3zcD9QDHwlPkCuBd4SESaMSyMDTPY36Q50TdCWEF9kvMYFteWEAorWnpHspbRYKGU4p4XDhMIKa5ftwgwXBznLqrixYNdkUyNC5YmvlGmk/OtjJBD3QkzR9LNq4d7KHLaODPObNxoPnLeQq5b25iVGFCp28G5i6p54WAnX3nv6TPe3k9eOkKZ28GPbljLK4e6OaO+IlLp+Iz68qSD4R6v4VdPpozFhctq2by/gyd3t+GwCasXjm/elU5EhLMaK3kjx0SjpW84qWZeIsLjt1yMy2Hj0W3H2XGsj8bq4nG1qjLFtL9VKXVYKXW2+VqplLrdHO9WSl2mlFpu/u2JWud2pdQypdRpSqmnosa3KaVWmZ991rImlFJepdR1SqlTlFLrlFKHZ3KwyXLQfAI+dV5iX6OFJRRHsxzXCIcVX3x0Fz987jB/fvaCmP2/aNkcdp/o5+k9JylzO8ZNZsskC2tKaKwu5tXD2c1o2XKkhzWLq5Oad5ENwbC4ZPkc9rQORFJcp8vxnmF+t7uN69Y2Ul7kZP3K+TFPq6fPL+etdk9SJXGsORrJxMasB5THd7ayqqGSYlfm5xac1VjFwQ5PztSJC4bCtPZ5WViTnJvYukavPrOeEpc9qw+nekZ4HN5qN9JBT52bnGg0mf+B2Q6G7zjWy69fP8FnLl3K9z66Ouazdyyfg1LwuzfaOK+pGkeWnlIsLlhay5Yj3VmLa/QPB9h/coB1TdmzuJLlmtUN2G3CfS8emdF2vv9MMzYRbnrn0rifn72wCl8wzEOvvj3ptqZSVeCM+goqihyEworzmtLTJncyLlpWS1jBJ368Zdxkw2xwcsBLKKwi5eOTpdTt4K6Pn8uX15+Wpj2bHC0acXir3cO8CnfSGR61pS7K3Q7enmYw3BsIpaQe01bTR/+Zdy4bN8P07MYqSlx2lMqua8rCimscaM9OXOOpN9tQCi45NbNB2emwsKaE95+9gJ9uOTbtdrnHuof5xfYWPnb+ognLoLxvVT3rV8zjtif3Tjo3pK0veUvDbhPWmXGjsd3pMsX5S2v5/sfO5VDHIO/73gtZ7yB53Gzj2jhF0QB412lz4xY4zBRaNOJwsH0wadcUGK6LpXWlkWqpU+U//3CA933vhRn35XjtSA+nzC2LO2nP5bBFmvvkgmhYBfleOJjeiWsT8fDWY5w2r5xzMuxfny43v2sZw/4QP3x+6h7at7uHuOmhbThsws3vmjiYb7MJd3x0NQtrSvj+M80Jt9naN4LdJswtTy7u9+7T6yhy2rJmaQBcfVY9T37uHSyuLeUzD21nWxZLz1vd+JJ1T+USWjSiCIcV4bCiuWOQ5Um6pizeeWod29/undLkKIARf4hfbD+OLxiO9HWYDqGwYtvbvQkzUz50biOrF1ZlNZ5hUV9ZzKnzyrJSkG93Sx+7W/r5+AWLshqrmAqnzivnw2sa+eHzh/jT/vak1xvyBfngXS9zcsDLj25YO2nKcKnbwZUr5/P6sb6Ec0OOdg8xv6Io6VIg15+3iBf/4T2Tppemm8W1pfz8MxdQ7nbwyGvZKy3S0juCLUPFL1ONFg2Ttv4RPnDXS/xi+3FGAiFOnTe1YmpXrpxPWMHmfR2TLxzFk7tb8ZjFDnfPILvjwEkPHm8w4ZPcn5+9gN/ccnHW4xkW71xex9ajPRkvXvg/r75NsdPOtec0TL5wDnHbNatYUV/B53+2k1+/3pJUIcPXj/XRPeTnux85m3eemtzE1wuX1uIPhdk+wdyQ/uEAm/d1RKzFZLDZhDlZFgyLEpeD951Zz1NvtEXK3mealp5h5lcUZaT4ZarJvz1OExVFTjy+IP/46zcBWD4F9xQYLU4bqoqn7Ct9eOsxltWV0lBVzM4ZTD6y5hxkOgd+Jrzz1Dr8wTCvHumefOEU8Xb3EL/acYLr1jbG9M/IB4pddn50w1qW1pXydz/fxQfvfpkXJ5m/seNYLyKwdgrXxXlLarDbZFwTKotHtxkPVhsvaprK7ucUH1rTyJA/xB/ezE5so6V3hMYszbOYKVo0TErdDv77+nMi5vbYVqWTISKsXzmPF5q7kk7r29c2wOvH+rh+3SJWL6xi1/Hpi8bLh7qoryyKlG3IB9YtqcHtsPHwlmNxW1qmgzs2vYXDLnz23adk5PtSzYKqYn79NxfzrQ+dycl+L5+4dwtfeOT1SBXZsWx/u5dT55ZPSSDL3A7OaqyM2wCqw+PlwVePsq6phpULsheMnSlrF1ezsKY4Uqwx0xzvHc6r32o0WjSiWLmgkv/80Fl8dO3CaT2Frl8xH38wzMvNyfnpH95yDJfDxofXNHL2wkpaekemHBMB6B8J8MyBTq5aNT9vfPRg1KG6ft0iNu1t593ffpadMxDNZGju8PD4rlb+8uIlzM1wOZBUYrMJHz1vEc/+n3fxd5efym93t/HJe7cQGpO+HA4rXj/Wy7mLpx7sv2hZLbtb+hmIapX677/fx7rbN3O8Z2TCtN18wWYTNpy3iJcPdWe8k6QvGOLkgHfK6ba5ghaNMVx7TgPf+vBZ01r33MVVuB02Xjk8ubtl2B/kN6+f4Ooz66kqcXF2o1UfZ+pxjd+/0YY/GOYDeeajB/ja+1ea/cRt/GzLsbR+1y93nMAmwo3vWJLW78kUboedz1++nO9cdza7W/rHNVE61DnIgDcYqdQ7Fa5YMZ+QUnz+Z68TCIU52e/lvhePcOXKefz+c5dw+Yp5qTqMrPGRtQtx2oWfvhr/uusa9KGUQinF/S8dYdPe9pTMK3p4yzGUgmVz09uEKl1o0Ughboed85pqJvQFR/Pr10/g8QX52PlGqY9VDZXYBH65o2XKF+avXz/BsrrSScth5CqLakt475n1/C6NgclwWPHEzlYuWT4nZwKyqeKa1Qs4d1EV//n0ATxRlsGOY0Yg+9zFUxeN1QuruP3aM3nmQCdfeGQnP3npCGGl+OerV0zYeyTfqCt3c9Wqen6x/fi46+7l5i7O//fNfG/zQV493MPXfruXTz+4jXP/bRNn3vo0n3rgNXYd78MXDHHHpre48o7nIx0xJ8IbCPHD5w7x9d/uZf2Kebxv1fx0Hl7a0KKRYi5cVsv+k56EbqZj3cN886n9nLOoirXmD7rU7eBv37OcJ3e38Q+/3J10i8/jPcNsPdLDB85pyCvX1Fg+vKaRQV8wbZOudlnDMY4AAAyfSURBVBzr5UTfCNesXpCW7WcTEeFf/mwFXYO+iI/+jZZ+7nr2EHPKXNPuAfKx8xfxz1efwe/eaOOHzx/m8jPmZa1IXrq44cLFeLxB7n1hdLZ9x4CXzz2yk1BYcc/zh/mvp/czp8zNd647m6tWzufPVy9g65Eervn+S6z416f53uaDHOzw8M2n9k/4PS29w7z728/yH0/t592n1XHn9efkTBbjVJlJwUJNHKyJc1uO9PC+M+vHfd4/EuCWh3cgwJ0bzom50X/h8uUEQmHuevYQ157TwMWnTD5b+XGzRec1q/PPNRXNuqYaFtYU86Pnj/DeVfXjeh+HwmpG7UF/s/MERU4bV6zIz6e7yThnUTWrGip4bHsLF58yhw/d/TI1pS7u+viaGT1MfOqSpThswn8+fYDPXJrfcYx4nNdUw5+dVc9//6mZK1fNZ/ncMr782G4GfQF+8Ik13PLwDnYc6+PvrzqND61p5ENrjC4O/3Dl6Wza186BkwOsbarhzRP9/PefmllWV4YvGOLMhkq2vd3L4ztb+fZ1Z/HY9hZ6h/389FPnc9Gy2rx+wJNMNK3PJGvXrlXbtm3L2vcHQmFWf/1/WdVQySXL57DxoibKzaB6c8cgn35wGy29w9z18TVcEccv7A2EeMe3nmHFggoe/Kt1Cb9LKcXl332O2lI3j/71hWk5nkzy9J6T/PX/bGf9inl85yOrjY6IXUN86Re76B3y88TfvoMy99Sfc473DHP5d5/j6rPq+e5HVk++Qp5y/0tH+Npv93LqvDJO9nvZ/KV3RSrYzpRgKJy3T8aT0T3o4/LvPkdNqYv3nWkIyDeuWckNFzbxz795g9/tbuO5v393wuSYYX+Qy77zHG39XkRAKbAJzClz4/EGGQmE+Pxly/m7K07N4JFNDRHZrpRaO+lyWjRSz2cf3sGTu9sA+Nx7TuGL60/jeM8wH7r7ZcJK8YNPrEmYN//9Z5r5r6cP8NTnL+GM+on9x7tb+nj//3uJ//jgmZEy6PnOvS8e4bYn9+K0C7Wlbjo8XkrdDgZ9QT5+/iL+7dozk97W4c5BPN4gdz3bzPNvdbH5S5dmrWNhJugd8rPu3/9IIKT456vP4FOXzD7LIF28fKiLz/3sdboG/Zy/pIafffoCbDYhGAoz5AslVYeue9DHSCBEXbmbN0/0U1Pqxu2wce33X8Imwp++fGnC1rjZRotGFgmHFcOBEF96dCevHOrmV39zMTc9tI0uj4/Hbr5o0rpW/cMBLvrmZuZWFPGtD50VqRk1lq89sYeHtxzjtX+6PKPtM9PNa0d72Lyvg65BHw1VxWxYt5Afv3CEe188wgN/tY5LJ5nZrJTi/pePcvvv9hE0kwq+vP5UPvue5ZnY/azydz/fyZsn+vnd5y7Jy9nG2aRr0MeDLx9lw7pFKX246PT4CIbDOV8yRItGDmBZAi67DadduP+v1iU9Y/vVw918+Re7aOkd4dxFVXzlvWfEiEf7gJdL/+sZrlo5n/+74Zx0HULOMOIP8YG7XqKld4RHP3PhhBk8g74gX/3VG/x2VyuXnzGP969ewIneEf7qHU24HZnv45BpgqEwIaUK4lg1qUWLRo5w4/2vsfN4H/f/5TrObJxaSuyQL8jDW45x/8tHCYTCPPPld1Fq+vS/8svd/HJHC5u/+C4W1c6ujJaJaOsf4YN3vcygN8j15y/ihgsXx5SW3n9ygL/5nx0c7R7iS+tP4+ZLx5eI12g08dGikSN4AyHCSs3Il7njWC8fvOtlbnn3Mr68/jQe39nKFx/dyV9ctIR//fMVKdzb3OdI1xDf/t8D/OHNkyilOK+phhKXnWF/iF0tfVQUObnz+nNyovy7RpNPaNGYZXzhkdd5cncb8yqKONFnuKx+8hfrZlUsYyq09o3w0Ktv88qhbkJhRbHLzuKaEv7+qtNTljGk0RQSyYpG7obyNTH885+toKrERfeQnxvfsYSNFzXNaN5CvrOgqph/uOr0bO+GRlNw5IVoiMhVwPcAO/BjpdQ3s7xLGWdOmZuvvX9ltndDo9EUODmfkyciduD7wHuBFcD1IlJYjnyNRqPJEXJeNIB1QLNS6rBSyg88AlyT5X3SaDSagiQfRKMBiG7m22KORRCRm0Rkm4hs6+zszOjOaTQaTSGRD6IRL9obk/KllLpHKbVWKbW2ri65PsgajUajmTr5IBotwMKofzcCrVnaF41Goylo8kE0XgOWi8gSEXEBG4AnsrxPGo1GU5DkfMqtUiooIp8FnsZIub1PKbUny7ul0Wg0BUnOiwaAUur3wO+zvR8ajUZT6My6MiIi4gEOjBmeA3Sl8Gsqgf4C2Z4+d6khlecx14851dvT1+DMSOb8zQFKlVKTZxIppWbVC9iWzNgMv+OeQtmePne5dx5z/ZjTsD19Dab5/E3lHOdDIDwX+W2BbS+V5Pqx5vK5s8j1Y871c5jrx5vT5282uqe2qTGVGuONaZJDn7vUoM/j9NHnbmYkc/6mco5no6VxT5JjmuTQ5y416PM4ffS5mxnJnL+kz/GsszQ0Go1Gkz5mo6Wh0Wg0mjSRl6IhIgtF5BkR2Scie0Tk8+Z4jYhsEpGD5t9qc/wKEdkuIm+Yf99jjpeIyO9EZL+5nVnfpyNV58787A8issvczg/MMvYFQSrPY9Q2nxCRNzN9LJkmxdfgsyJyQER2mq+52TquTJHi8+cSkXtE5C3zPvihSXcglaldmXoB9cC55vty4C2MXhv/CXzFHP8K8C3z/TnAAvP9KuCE+b4EeLf53gW8ALw328eXD+fO/HeF+VeAXwIbsn18+XgezbEPAg8Db2b72PLp3AHPAmuzfUx5fP6+Dvyb+d4GzJn0+7N9AlJ0Eh8HrsCY1FcfdWIPxFlWgG7AHeez7wGfzvbx5Nu5A5wYaYIfzfbx5ON5BMqAF80f/qwXjRSfu4ITjRSfv+MYk/qS/r68dE9FIyJNGEq6BZinlGoDMP/GM1U/BLyulPKN2U4V8OfA5nTuby6RinMnIk8DHYAHeCzNu5yTpOA83gZ8BxhO+87mGCn6/f7EdE39i4jEa6Uwa5nJ+TPveQC3icgOEfmFiMyb9EuzrZIzVNgyYDvwQfPffWM+7x3z75XAIWDZmHEH8BTwhWwfU76dO/OzIgz31BXZPq58O4/AauC35vsmCsjSSMU1CDSYf8uB/wVuyPZx5cv5wygdooAPmf/+IvDQpN+b7QOfwQlzYlS+/WLU2ITmGUYfjreAi+Ns6z7gzmwfUz6eu6hlNgL/L9vHlm/nEbgZoz/MUYzeMX7g2WwfWz6cuzjb/ItCuQb/f3v3EyplFQZg/Hlz3JShWBoG6t2JKFG0Cm8kuFJauhFRU1wIQriTQnBTO7ukLrwFJVobaZUUBBKkoLgUw24ounHhJvBfun1bnHNpGNT7zTDXb4b7/GCYmTOHM+ccDvNyzsy835DWXgBPgFfq89XAjbneeyyPp+oW9DtgJjOnul46T/nwot7/XOsvA34FPsvMyz1tfUFJEHZovvs9CoY1dxGxJCJW1ccdYBvw9/yPYDQMax4z81Rmvp2ZE8AkcDMzN8//CNozxDXYiYg36+PFwMfAQvj12bDWXlK+i9xci7YAf83ZgbYj5oBRdpKyrboOXKu3bcAblO8kbtX75bX+EUpEvdZ1W0mJvgnMdJXvb3t8YzJ3b1EukHUduAGcBDptj2/c5rGnzQkWwPHUENfga5Tjmdk1eBxY1Pb4xmX+6mtrgUu1rd+BNXO9v/8IlyQ1NpbHU5Kkdhg0JEmNGTQkSY0ZNCRJjRk0JEmNGTSklywiDkTE7j7qTyyE7LcaD522OyAtJBHRyczptvshDcqgIfWpJon7jZIk7j1KeobdwHpgipIT6B/gk8y8FxF/AFeATcD5iHgd+Dczj0XEu8A0JU3/bWBfZt6PiPcp6W2eUjLgSiPB4ylpMOuAbzPzHeARcJDyr/jtmTn7gf9lV/1lmflRZn7V085Z4HBt50/gaC0/DXyamR/M5yCkfrnTkAZzN//P4/Mj8DnlAjcXanbuRcC9rvrnehuIiKWUYHKxFp0BfnpG+Q/A1uEPQeqfQUMaTG/+nceUDKHP2xk86aPteEb70kjweEoazJqImA0QO4CrwIrZsohYHBEbXtRAZj4E7kfEh7VoF3AxMx8ADyNispbvHH73pcG405AGMwPsiYhvKFlFT1Kub3CiHi91gK8p2VdfZA8wHRGvAneAvbV8L/B9RDyt7UojwSy3Up/qr6d+ycyNLXdFeuk8npIkNeZOQ5LUmDsNSVJjBg1JUmMGDUlSYwYNSVJjBg1JUmMGDUlSY/8Bz8ds8rPP+ckAAAAASUVORK5CYII=\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": 11, + "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": 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", 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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": 14, + "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", + "2024 3670417\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": 14, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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 +} diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..ef448331225e5cc92baaffc7aebc5b889a61249a 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,15 +28,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ + "#data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", + "#data_url3 = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\"\n", + "data_url = \"https://app-learninglab.inria.fr/moocrr/gitlab/dfa5b61add2096b8c3911f0d73f434f3/mooc-rr/blob/master/module3/exo1/inc-3-PAY.csv\"\n", + "data_url = \"module3/exo1/incidence-PAY-3.csv\"\n", + "data_url = \"https://app-learninglab.inria.fr/moocrr/gitlab/dfa5b61add2096b8c3911f0d73f434f3/mooc-rr/blob/master/module3/exo1/inc-3-PAY-mod.csv\"\n", "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -61,11 +69,985 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025503190419176434.0204404.0284263.0305.0FRFrance
12025493133235122213.0144257.0199183.0215.0FRFrance
220254838260973827.091391.0123110.0136.0FRFrance
320254735886051442.066278.08877.099.0FRFrance
420254634065734407.046907.06152.070.0FRFrance
520254534764540423.054867.07160.082.0FRFrance
620254434483738385.051289.06757.077.0FRFrance
720254335540047640.063160.08371.095.0FRFrance
820254237523966515.083963.011299.0125.0FRFrance
920254138686077648.096072.0130116.0144.0FRFrance
1020254037916971180.087158.0118106.0130.0FRFrance
1120253937293064872.080988.010997.0121.0FRFrance
1220253836143554131.068739.09281.0103.0FRFrance
1320253734637339689.053057.06959.079.0FRFrance
1420253632558120702.030460.03831.045.0FRFrance
1520253532271717480.027954.03426.042.0FRFrance
1620253432142916177.026681.03224.040.0FRFrance
1720253331676612022.021510.02518.032.0FRFrance
1820253231990014303.025497.03022.038.0FRFrance
1920253131847012625.024315.02819.037.0FRFrance
2020253031916614283.024049.02922.036.0FRFrance
2120252931867313815.023531.02821.035.0FRFrance
2220252832328518131.028439.03527.043.0FRFrance
2320252732145317129.025777.03226.038.0FRFrance
2420252632194517422.026468.03326.040.0FRFrance
2520252532332318546.028100.03528.042.0FRFrance
2620252432315418577.027731.03528.042.0FRFrance
2720252332439119307.029475.03628.044.0FRFrance
2820252231875514333.023177.02821.035.0FRFrance
2920252132376018671.028849.03527.043.0FRFrance
.................................
211619852132609619621.032571.04735.059.0FRFrance
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211819851934315432821.053487.07859.097.0FRFrance
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21231985143134545114400.0154690.0244207.0281.0FRFrance
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21261985113276205252399.0300011.0501458.0544.0FRFrance
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2146 rows × 10 columns

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"18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 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81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2146 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "\n", + "# je ne comprend par pourquoi j'ai une erreur =\n", + "# ParserError: Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n", + "# quand j'essaye d'utiliser les données qui sont dans le même dossier\n", + "# que celui où se trouve ce notebook, alors qu'avec le lien url ça fonctionne\n", + "# je n'y arrive pas même avec un fichier modifié (sans la ligne qui pause problème)\n", + "\n", + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1060,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2145 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 3 190419 176434.0 204404.0 284 263.0 \n", + "1 202549 3 133235 122213.0 144257.0 199 183.0 \n", + "2 202548 3 82609 73827.0 91391.0 123 110.0 \n", + "3 202547 3 58860 51442.0 66278.0 88 77.0 \n", + "4 202546 3 40657 34407.0 46907.0 61 52.0 \n", + "5 202545 3 47645 40423.0 54867.0 71 60.0 \n", + "6 202544 3 44837 38385.0 51289.0 67 57.0 \n", + "7 202543 3 55400 47640.0 63160.0 83 71.0 \n", + "8 202542 3 75239 66515.0 83963.0 112 99.0 \n", + "9 202541 3 86860 77648.0 96072.0 130 116.0 \n", + "10 202540 3 79169 71180.0 87158.0 118 106.0 \n", + "11 202539 3 72930 64872.0 80988.0 109 97.0 \n", + "12 202538 3 61435 54131.0 68739.0 92 81.0 \n", + "13 202537 3 46373 39689.0 53057.0 69 59.0 \n", + "14 202536 3 25581 20702.0 30460.0 38 31.0 \n", + "15 202535 3 22717 17480.0 27954.0 34 26.0 \n", + "16 202534 3 21429 16177.0 26681.0 32 24.0 \n", + "17 202533 3 16766 12022.0 21510.0 25 18.0 \n", + "18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2123 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2124 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2125 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2126 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2127 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2128 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2129 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2130 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2131 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2132 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2133 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2134 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2135 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2136 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2137 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2138 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2139 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2140 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2145 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2135,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2165,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2190,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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", @@ -194,15 +2213,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Un premier regard sur les données !" + "Un premier regard sur les données !\n", + "\n", + "Toute la colonne 'inc' est représentée par des chaînes de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "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'] = sorted_data['inc'].astype(int)\n", "sorted_data['inc'].plot()" ] }, @@ -215,9 +2260,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2470,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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