{ "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" ] }, { "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 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": [ "Afin d'éviter de télécharger les données depuis l'URL donnée précédemment à chaque exécution et pour nous prémunir d'une modification ou disparition des données on les récupère en local. C'est ce fichier local dont le nom est dans la variable `data_file` que l'on utilisera. Les données sont téléchargés uniquement si le fichier local manque." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Les données sont déjà présentes en local.\n" ] } ], "source": [ "data_file = \"incidence_varicelle.csv\"\n", "\n", "import os\n", "if not os.access(data_file, os.R_OK):\n", " import urllib.request\n", " print(\"Les données n'existent pas en local, on les télécharges.\")\n", " urllib.request.urlretrieve(data_url, data_file)\n", " if os.access(data_file, os.R_OK):\n", " print(\"Fichier récupéré.\")\n", " else:\n", " raise Exception(\"Le fichier n'a pas pu être récupéré !\")\n", "else:\n", " print(\"Les données sont déjà présentes en local.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le format de données est le même que pour le syndrome grippal." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020202272800638001FRFrance
12020217602361168102FRFrance
22020207824201628102FRFrance
320201973100753001FRFrance
42020187849981600102FRFrance
520201772720658001FRFrance
62020167758781438102FRFrance
7202015719186753161315FRFrance
82020147387922275531639FRFrance
9202013773265236941611814FRFrance
102020127812357901045612816FRFrance
11202011710198756812828151119FRFrance
1220201079011669111331141018FRFrance
132020097136311054416718211626FRFrance
14202008710424770813140161220FRFrance
1520200778959657411344141018FRFrance
1620200679264692511603141018FRFrance
1720200578505631410696131016FRFrance
182020047799158311015112915FRFrance
1920200375968410078369612FRFrance
20202002765344530853810713FRFrance
2120200179835701912651151119FRFrance
222019527794152461063612816FRFrance
2320195175823367579719612FRFrance
24201950764244276857210713FRFrance
25201949766214540870210713FRFrance
2620194875542338377018511FRFrance
272019477753650581001411715FRFrance
282019467263813163960426FRFrance
2920194574492261563697410FRFrance
.................................
15091991267176081130423912312042FRFrance
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1513199122715452995320951271737FRFrance
1514199121714903897520831261636FRFrance
15151991207190531274225364342345FRFrance
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15171991187213851388228888382551FRFrance
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15381990497114302610205FRFrance
\n", "

1539 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202022 7 280 0 638 0 0 \n", "1 202021 7 602 36 1168 1 0 \n", "2 202020 7 824 20 1628 1 0 \n", "3 202019 7 310 0 753 0 0 \n", "4 202018 7 849 98 1600 1 0 \n", "5 202017 7 272 0 658 0 0 \n", "6 202016 7 758 78 1438 1 0 \n", "7 202015 7 1918 675 3161 3 1 \n", "8 202014 7 3879 2227 5531 6 3 \n", "9 202013 7 7326 5236 9416 11 8 \n", "10 202012 7 8123 5790 10456 12 8 \n", "11 202011 7 10198 7568 12828 15 11 \n", "12 202010 7 9011 6691 11331 14 10 \n", "13 202009 7 13631 10544 16718 21 16 \n", "14 202008 7 10424 7708 13140 16 12 \n", "15 202007 7 8959 6574 11344 14 10 \n", "16 202006 7 9264 6925 11603 14 10 \n", "17 202005 7 8505 6314 10696 13 10 \n", "18 202004 7 7991 5831 10151 12 9 \n", "19 202003 7 5968 4100 7836 9 6 \n", "20 202002 7 6534 4530 8538 10 7 \n", "21 202001 7 9835 7019 12651 15 11 \n", "22 201952 7 7941 5246 10636 12 8 \n", "23 201951 7 5823 3675 7971 9 6 \n", "24 201950 7 6424 4276 8572 10 7 \n", "25 201949 7 6621 4540 8702 10 7 \n", "26 201948 7 5542 3383 7701 8 5 \n", "27 201947 7 7536 5058 10014 11 7 \n", "28 201946 7 2638 1316 3960 4 2 \n", "29 201945 7 4492 2615 6369 7 4 \n", "... ... ... ... ... ... ... ... \n", "1509 199126 7 17608 11304 23912 31 20 \n", "1510 199125 7 16169 10700 21638 28 18 \n", "1511 199124 7 16171 10071 22271 28 17 \n", "1512 199123 7 11947 7671 16223 21 13 \n", "1513 199122 7 15452 9953 20951 27 17 \n", "1514 199121 7 14903 8975 20831 26 16 \n", "1515 199120 7 19053 12742 25364 34 23 \n", "1516 199119 7 16739 11246 22232 29 19 \n", "1517 199118 7 21385 13882 28888 38 25 \n", "1518 199117 7 13462 8877 18047 24 16 \n", "1519 199116 7 14857 10068 19646 26 18 \n", "1520 199115 7 13975 9781 18169 25 18 \n", "1521 199114 7 12265 7684 16846 22 14 \n", "1522 199113 7 9567 6041 13093 17 11 \n", "1523 199112 7 10864 7331 14397 19 13 \n", "1524 199111 7 15574 11184 19964 27 19 \n", "1525 199110 7 16643 11372 21914 29 20 \n", "1526 199109 7 13741 8780 18702 24 15 \n", "1527 199108 7 13289 8813 17765 23 15 \n", "1528 199107 7 12337 8077 16597 22 15 \n", "1529 199106 7 10877 7013 14741 19 12 \n", "1530 199105 7 10442 6544 14340 18 11 \n", "1531 199104 7 7913 4563 11263 14 8 \n", "1532 199103 7 15387 10484 20290 27 18 \n", "1533 199102 7 16277 11046 21508 29 20 \n", "1534 199101 7 15565 10271 20859 27 18 \n", "1535 199052 7 19375 13295 25455 34 23 \n", "1536 199051 7 19080 13807 24353 34 25 \n", "1537 199050 7 11079 6660 15498 20 12 \n", "1538 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 1 FR France \n", "1 2 FR France \n", "2 2 FR France \n", "3 1 FR France \n", "4 2 FR France \n", "5 1 FR France \n", "6 2 FR France \n", "7 5 FR France \n", "8 9 FR France \n", "9 14 FR France \n", "10 16 FR France \n", "11 19 FR France \n", "12 18 FR France \n", "13 26 FR France \n", "14 20 FR France \n", "15 18 FR France \n", "16 18 FR France \n", "17 16 FR France \n", "18 15 FR France \n", "19 12 FR France \n", "20 13 FR France \n", "21 19 FR France \n", "22 16 FR France \n", "23 12 FR France \n", "24 13 FR France \n", "25 13 FR France \n", "26 11 FR France \n", "27 15 FR France \n", "28 6 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1509 42 FR France \n", "1510 38 FR France \n", "1511 39 FR France \n", "1512 29 FR France \n", "1513 37 FR France \n", "1514 36 FR France \n", "1515 45 FR France \n", "1516 39 FR France \n", "1517 51 FR France \n", "1518 32 FR France \n", "1519 34 FR France \n", "1520 32 FR France \n", "1521 30 FR France \n", "1522 23 FR France \n", "1523 25 FR France \n", "1524 35 FR France \n", "1525 38 FR France \n", "1526 33 FR France \n", "1527 31 FR France \n", "1528 29 FR France \n", "1529 26 FR France \n", "1530 25 FR France \n", "1531 20 FR France \n", "1532 36 FR France \n", "1533 38 FR France \n", "1534 36 FR France \n", "1535 45 FR France \n", "1536 43 FR France \n", "1537 28 FR France \n", "1538 5 FR France \n", "\n", "[1539 rows x 10 columns]" ] }, "execution_count": 4, "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 ?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visiblement non, pas de point manquant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les semaines sont numérotés de la même façon que pour le syndrome grippal on va donc les décoder suivant la même procédure." ] }, { "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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']] # ici on utilise raw_data car on a pas eu à retoucher aux données.\n", "\n", "# Tri par ordre chronologique\n", "sorted_data = raw_data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme pour le syndrone gripal 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", "Il ne devrait y avoir aucun trou car nous avons normalement toutes les semaines." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Aucun problème trouvé.\n" ] } ], "source": [ "periods = sorted_data.index\n", "il_y_a_un_probleme = False\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)\n", " il_y_a_un_probleme = True\n", "\n", "if not il_y_a_un_probleme:\n", " print(\"Aucun problème trouvé.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "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." ] }, { "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de la variabilité moyenne sur un an\n", "\n", "contrairement à la grippe il n'y a pas de période large où l'on peut dire qu'il n'y a rien. Il y a bien des chutes importantes vers le 3e trimestre mais il faudra être plus précis pour découper les données en années." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFlBJREFUeJzt3X+MndWd3/H3p5CldLNkCQwR9Y+aFCdaQLuObFmW0l2xy3bxZqOYVNAaqcFVqJwgUiVqqhbSSkl3ZSlpm1DRNqycBQFpFrAgBKuFNhTSZlcisEPCxvwIzSSwYWILOyFNXGVDZefbP+6Z9jLP9cx45po7c+f9kq7uc7/Pc+6cI4S/9/x4npOqQpKkfn9l1BWQJC0/JgdJUofJQZLUYXKQJHWYHCRJHSYHSVKHyUGS1GFykCR1mBwkSR2nj7oCi3XuuefWhg0bRl0NSVpRnnzyyR9U1cR8163Y5LBhwwYmJydHXQ1JWlGS/MVCrnNYSZLUYXKQJHWYHCRJHSYHSVKHyUGS1GFykCR1mBwkSR0mB0lSh8lBktSxYu+QlqTVYMMN/7kTe/GTv3fK/649B0lSx7zJIcm6JF9J8lySZ5J8uMXfnOThJN9u72f3lbkxyVSS55Nc3hffnORAO3dzkrT4GUnuafHHk2wYflMlSQu1kJ7DMeCjVfUrwDbg+iQXATcAj1TVRuCR9pl2bidwMbAd+GyS09p33QLsBja21/YWvxb4UVVdCNwEfGoIbZMkLdK8yaGqDlXV19vxUeA5YA2wA7ijXXYHcEU73gHcXVWvVtULwBSwNcn5wFlV9VhVFXDnrDIz33UvcNlMr0KS9Po7qTmHNtzzDuBx4C1VdQh6CQQ4r122Bnipr9h0i61px7PjrylTVceAHwPnnEzdJEnDs+DkkOSNwH3AR6rqJ3NdOiBWc8TnKjO7DruTTCaZPHLkyHxVliQt0oKSQ5I30EsMX6iqL7bwy22oiPZ+uMWngXV9xdcCB1t87YD4a8okOR14E/DK7HpU1d6q2lJVWyYm5t3ISJK0SAtZrRTgVuC5qvpM36n9wK52vAt4oC++s61AuoDexPMTbejpaJJt7TuvmVVm5ruuBB5t8xKSpBFYyE1w7wTeBxxI8lSLfQz4JLAvybXA94CrAKrqmST7gGfprXS6vqqOt3LXAbcDZwIPtRf0ks/nk0zR6zHsXGK7pJMy+0ajmZuMThSXxt28yaGq/pTBcwIAl52gzB5gz4D4JHDJgPjPaMlFkjR63iEtSeowOUiSOkwOkqQOk4MkqcPkIEnqMDlIkjpMDpKkDneCk07SqHbmkl5P9hwkSR32HLQo/nqWxpvJQRoSE6bGicNKkqQOk4MkqcPkIEnqMDlIkjpMDpKkjoVsE3pbksNJnu6L3ZPkqfZ6cWaHuCQbkvxl37k/7CuzOcmBJFNJbm5bhdK2E72nxR9PsmH4zZQknYyF9BxuB7b3B6rq71XVpqraBNwHfLHv9HdmzlXVB/vitwC76e0pvbHvO68FflRVFwI3AZ9aVEskSUMzb3Koqq/S29e5o/36/7vAXXN9R5LzgbOq6rGqKuBO4Ip2egdwRzu+F7hsplchSRqNpd4E9+vAy1X17b7YBUm+AfwE+BdV9SfAGmC675rpFqO9vwRQVceS/Bg4B/jBEuumk3CiG7iGdWPX7O/x5jBpeVtqcria1/YaDgHrq+qHSTYDX0pyMTCoJ1Dtfa5zr5FkN72hKdavX7/oSkuS5rbo1UpJTgf+DnDPTKyqXq2qH7bjJ4HvAG+j11NY21d8LXCwHU8D6/q+802cYBirqvZW1Zaq2jIxMbHYqkuS5rGUnsNvA9+qqv83XJRkAnilqo4neSu9iefvVtUrSY4m2QY8DlwD/LtWbD+wC3gMuBJ4tM1LSGPBZy5pJVrIUta76P3D/fYk00mubad20p2I/g3gm0n+nN7k8geraqYXcB3wR8AUvR7FQy1+K3BOkingHwM3LKE9kqQhmLfnUFVXnyD+DwbE7qO3tHXQ9ZPAJQPiPwOumq8e0jD4K15aGO+QliR1uJ+Dhsolq9J4sOcgSeqw56CROZXj/84tSEtjclimHJ6RNEqrMjn4q1KS5uacgySpY1X2HLS82bOTRs+egySpw57DCnKqH6u93J1okt7Je2n47DlIkjrsObxORvHrfqE9DX9pS5rN5DBi/kMtaTlyWEmS1GFykCR1mBwkSR0L2QnutiSHkzzdF/tEku8neaq93tV37sYkU0meT3J5X3xzkgPt3M1J0uJnJLmnxR9PsmG4TZQknayF9BxuB7YPiN9UVZva60GAJBfR2z704lbms0lOa9ffAuymt6/0xr7vvBb4UVVdCNwEfGqRbZEkDcm8yaGqvgq8Mt91zQ7g7qp6tapeoLdf9NYk5wNnVdVjVVXAncAVfWXuaMf3ApfN9CokSaOxlDmHDyX5Zht2OrvF1gAv9V0z3WJr2vHs+GvKVNUx4MfAOUuolyRpiRZ7n8MtwB8A1d4/DbwfGPSLv+aIM8+510iym97QFOvXrz+5GkvLjPe4aDlbVM+hql6uquNV9XPgc8DWdmoaWNd36VrgYIuvHRB/TZkkpwNv4gTDWFW1t6q2VNWWiYmJxVRdkrQAi0oObQ5hxnuBmZVM+4GdbQXSBfQmnp+oqkPA0STb2nzCNcADfWV2teMrgUfbvIQkaUTmHVZKchdwKXBukmng48ClSTbRG/55EfgAQFU9k2Qf8CxwDLi+qo63r7qO3sqnM4GH2gvgVuDzSabo9Rh2DqNhkqTFmzc5VNXVA8K3znH9HmDPgPgkcMmA+M+Aq+arhyTp9eMd0pKkDpODJKnD5CBJ6nA/B0laBpbbdr/2HCRJHfYclsA7XCWNK3sOkqQOew6S9DpbCaMO9hwkSR32HIZsua04kKTFsOcgSeowOUiSOhxWkpYRhyW1XNhzkCR12HPo4682Seqx5yBJ6jA5SJI65k0OSW5LcjjJ032xf53kW0m+meT+JL/c4huS/GWSp9rrD/vKbE5yIMlUkpvbXtK0/abvafHHk2wYfjMlSSdjIT2H24Hts2IPA5dU1a8C/xO4se/cd6pqU3t9sC9+C7Ab2NheM995LfCjqroQuAn41Em3QpI0VAvZQ/qrs3/NV9WX+z5+Dbhyru9Icj5wVlU91j7fCVwBPATsAD7RLr0X+PdJUlW1sCaceivhOSiSNEzDmHN4P71/5GdckOQbSf5Hkl9vsTXAdN810y02c+4lgKo6BvwYOGfQH0qyO8lkkskjR44MoeqSpEGWtJQ1yT8HjgFfaKFDwPqq+mGSzcCXklwMZEDxmZ7BXOdeG6zaC+wF2LJly7LpWUjSICt51GHRySHJLuDdwGUzQ0BV9Srwajt+Msl3gLfR6yms7Su+FjjYjqeBdcB0ktOBNwGvLLZekqSlW9SwUpLtwD8D3lNVP+2LTyQ5rR2/ld7E83er6hBwNMm2tkrpGuCBVmw/sKsdXwk8upzmGyRpNZq355DkLuBS4Nwk08DH6a1OOgN4uK1I/VpbmfQbwO8nOQYcBz5YVTO9gOvorXw6k94cxcw8xa3A55NM0esx7BxKyyRJi7aQ1UpXDwjfeoJr7wPuO8G5SeCSAfGfAVfNVw9J0uvHO6QlSR0mB0lSh8lBktRhcpAkdZgcJEkdJgdJUofJQZLUYXKQJHWYHCRJHSYHSVLHkh7ZLUnqPpobVtbjuQex5yBJ6jA5SJI6TA6SpA7nHKQVYBzHtLW82XOQJHXMmxyS3JbkcJKn+2JvTvJwkm+397P7zt2YZCrJ80ku74tvTnKgnbu5bRdKkjOS3NPijyfZMNwmSpJO1kJ6DrcD22fFbgAeqaqNwCPtM0kuorfN58WtzGdn9pQGbgF209tXemPfd14L/KiqLgRuAj612MZIkoZj3uRQVV+lt7dzvx3AHe34DuCKvvjdVfVqVb0ATAFbk5wPnFVVj1VVAXfOKjPzXfcCl830KiRJo7HYOYe3VNUhgPZ+XouvAV7qu266xda049nx15SpqmPAj4FzBv3RJLuTTCaZPHLkyCKrLkmaz7AnpAf94q854nOV6Qar9lbVlqraMjExscgqSpLms9jk8HIbKqK9H27xaWBd33VrgYMtvnZA/DVlkpwOvInuMJYk6XW02OSwH9jVjncBD/TFd7YVSBfQm3h+og09HU2yrc0nXDOrzMx3XQk82uYlJEkjMu9NcEnuAi4Fzk0yDXwc+CSwL8m1wPeAqwCq6pkk+4BngWPA9VV1vH3VdfRWPp0JPNReALcCn08yRa/HsHMoLZMkLdq8yaGqrj7BqctOcP0eYM+A+CRwyYD4z2jJRZK0PPj4DEma5USPK1lNjzHx8RmSpA6TgySpw+QgSeowOUiSOkwOkqQOk4MkqcOlrJJWhdW0DHUYTA6SxopJYDgcVpIkdZgcJEkdJgdJUodzDtIK5vi6ThWTg6QVycR4ajmsJEnqMDlIkjoWnRySvD3JU32vnyT5SJJPJPl+X/xdfWVuTDKV5Pkkl/fFNyc50M7d3LYSlSSNyKKTQ1U9X1WbqmoTsBn4KXB/O33TzLmqehAgyUX0tgC9GNgOfDbJae36W4Dd9Pac3tjOS5JGZFjDSpcB36mqv5jjmh3A3VX1alW9AEwBW5OcD5xVVY9VVQF3AlcMqV6SpEUYVnLYCdzV9/lDSb6Z5LYkZ7fYGuClvmumW2xNO54d70iyO8lkkskjR44MqeqSpNmWvJQ1yS8A7wFubKFbgD8Aqr1/Gng/MGgeoeaId4NVe4G9AFu2bBl4jaTxM3vZqktWT71h9Bx+F/h6Vb0MUFUvV9Xxqvo58Dlga7tuGljXV24tcLDF1w6IS5JGZBjJ4Wr6hpTaHMKM9wJPt+P9wM4kZyS5gN7E8xNVdQg4mmRbW6V0DfDAEOolSVqkJQ0rJflrwN8GPtAX/ldJNtEbGnpx5lxVPZNkH/AscAy4vqqOtzLXAbcDZwIPtZckaUSWlByq6qfAObNi75vj+j3AngHxSeCSpdRFkjQ83iEtSerwwXuSVjVXQg1mz0GS1GHPQdKy4a/45cOegySpw+QgSeowOUiSOkwOkqQOJ6SlMeTErpbKnoMkqcPkIEnqcFhJ0utu9rAXOPS13NhzkCR1mBwkSR0mB0lSh8lBktSxpOSQ5MUkB5I8lWSyxd6c5OEk327vZ/ddf2OSqSTPJ7m8L765fc9UkpvbdqGSpBEZRs/hN6tqU1VtaZ9vAB6pqo3AI+0zSS4CdgIXA9uBzyY5rZW5BdhNb1/pje28JGlETsWw0g7gjnZ8B3BFX/zuqnq1ql4ApoCtSc4Hzqqqx6qqgDv7ykiSRmCpyaGALyd5MsnuFntLVR0CaO/ntfga4KW+stMttqYdz45LkkZkqTfBvbOqDiY5D3g4ybfmuHbQPELNEe9+QS8B7QZYv379ydZVWvV85pIWakk9h6o62N4PA/cDW4GX21AR7f1wu3waWNdXfC1wsMXXDogP+nt7q2pLVW2ZmJhYStUlSXNYdHJI8otJfmnmGPgd4GlgP7CrXbYLeKAd7wd2JjkjyQX0Jp6faENPR5Nsa6uUrukrI0kagaUMK70FuL+tOj0d+OOq+i9J/gzYl+Ra4HvAVQBV9UySfcCzwDHg+qo63r7rOuB24EzgofaSJI3IopNDVX0X+LUB8R8Cl52gzB5gz4D4JHDJYusiSRou75CWJHWYHCRJHSYHSVKHyUGS1GFykCR1mBwkSR0mB0lSx1KfrSRJJzT7WU7g85xWCnsOkqQOk4MkqcPkIEnqMDlIkjpMDpKkDlcrSaucK4o0iMlB0lC4Bel4cVhJktSxlG1C1yX5SpLnkjyT5MMt/okk30/yVHu9q6/MjUmmkjyf5PK++OYkB9q5m9t2oZKkEVnKsNIx4KNV9fW2l/STSR5u526qqn/Tf3GSi4CdwMXAXwf+W5K3ta1CbwF2A18DHgS241ahkjQyS9km9BBwqB0fTfIcsGaOIjuAu6vqVeCFJFPA1iQvAmdV1WMASe4ErsDkII3UiSaqnVtYHYYy55BkA/AO4PEW+lCSbya5LcnZLbYGeKmv2HSLrWnHs+OSpBFZcnJI8kbgPuAjVfUTekNEfxPYRK9n8emZSwcUrznig/7W7iSTSSaPHDmy1KpLkk5gSckhyRvoJYYvVNUXAarq5ao6XlU/Bz4HbG2XTwPr+oqvBQ62+NoB8Y6q2ltVW6pqy8TExFKqLkmaw1JWKwW4FXiuqj7TFz+/77L3Ak+34/3AziRnJLkA2Ag80eYujibZ1r7zGuCBxdZLkrR0S1mt9E7gfcCBJE+12MeAq5Nsojc09CLwAYCqeibJPuBZeiudrm8rlQCuA24HzqQ3Ee1ktCSN0FJWK/0pg+cLHpyjzB5gz4D4JHDJYusiSRou75CWJHWYHCRJHSYHSVKHyUGS1GFykCR1mBwkSR0mB0lSh8lBktRhcpAkdZgcJEkdJgdJUofJQZLUYXKQJHWYHCRJHSYHSVKHyUGS1LFskkOS7UmeTzKV5IZR10eSVrNlkRySnAb8B+B3gYvobTV60WhrJUmr17JIDsBWYKqqvltV/we4G9gx4jpJ0qq1XJLDGuClvs/TLSZJGoFU1ajrQJKrgMur6h+2z+8DtlbVP5p13W5gd/v4duD5k/gz5wI/GEJ1l7PV0EawneNkNbQRllc7/0ZVTcx30emvR00WYBpY1/d5LXBw9kVVtRfYu5g/kGSyqrYsrnorw2poI9jOcbIa2ggrs53LZVjpz4CNSS5I8gvATmD/iOskSavWsug5VNWxJB8C/itwGnBbVT0z4mpJ0qq1LJIDQFU9CDx4Cv/EooajVpjV0EawneNkNbQRVmA7l8WEtCRpeVkucw6SpGVk7JPDuD6WI8ltSQ4nebov9uYkDyf5dns/e5R1XKok65J8JclzSZ5J8uEWH7d2/tUkTyT589bOf9niY9VO6D0NIck3kvyn9nkc2/hikgNJnkoy2WIrrp1jnRzG/LEctwPbZ8VuAB6pqo3AI+3zSnYM+GhV/QqwDbi+/fcbt3a+CvxWVf0asAnYnmQb49dOgA8Dz/V9Hsc2AvxmVW3qW7664to51smBMX4sR1V9FXhlVngHcEc7vgO44nWt1JBV1aGq+no7PkrvH5U1jF87q6r+d/v4hvYqxqydSdYCvwf8UV94rNo4hxXXznFPDqvtsRxvqapD0PuHFThvxPUZmiQbgHcAjzOG7WzDLU8Bh4GHq2oc2/lvgX8K/LwvNm5thF5i/3KSJ9tTHWAFtnPZLGU9RTIg5vKsFSbJG4H7gI9U1U+SQf9ZV7aqOg5sSvLLwP1JLhl1nYYpybuBw1X1ZJJLR12fU+ydVXUwyXnAw0m+NeoKLca49xwW9FiOMfJykvMB2vvhEddnyZK8gV5i+EJVfbGFx66dM6rqfwH/nd580ji1853Ae5K8SG9497eS/EfGq40AVNXB9n4YuJ/e8PaKa+e4J4fV9liO/cCudrwLeGCEdVmy9LoItwLPVdVn+k6NWzsnWo+BJGcCvw18izFqZ1XdWFVrq2oDvf8PH62qv88YtREgyS8m+aWZY+B3gKdZge0c+5vgkryL3ljnzGM59oy4SkOR5C7gUnpPe3wZ+DjwJWAfsB74HnBVVc2etF4xkvwt4E+AA/z/ceqP0Zt3GKd2/iq9ScrT6P1g21dVv5/kHMaonTPasNI/qap3j1sbk7yVXm8BesP2f1xVe1ZiO8c+OUiSTt64DytJkhbB5CBJ6jA5SJI6TA6SpA6TgySpw+QgSeowOUiSOkwOkqSO/ws+i2B05MyCTQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import statistics\n", "\n", "weeks_t = dict()\n", "for i in range(len(raw_data[\"week\"])): # sorted_data cause des répétitions et suppressions de lignes très bizares, de troutes façon on a pas besoin d'avoir un tri donc on ne trie pas.\n", " num_week = int(str(raw_data[\"week\"][i])[-2:])\n", " num_inc = int(raw_data[\"inc\"][i])\n", " if not num_week in weeks_t:\n", " weeks_t[num_week] = []\n", " weeks_t[num_week].append(num_inc)\n", "\n", "weeks = dict()\n", "for week in weeks_t:\n", " weeks[week] = statistics.mean(weeks_t[week])\n", "\n", "plt.bar(list(weeks.keys()), list(weeks.values()), align='center')\n", "plt.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On essaye de voir où ce trouve le minimum" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le minimum est de 2631.9 durant la semaine 36.\n", "Semaine 35 : 3182.0\n", "Semaine 36 : 2631.9\n", "Semaine 37 : 2655.5\n" ] } ], "source": [ "val_min = min(weeks.values())\n", "week_min = None\n", "for week in weeks:\n", " if val_min == weeks[week]:\n", " week_min = week\n", "\n", "print(\"Le minimum est de %.1f durant la semaine %i.\" % (val_min, week_min))\n", "\n", "for delta in range(-1, 2):\n", " print(\"Semaine %d : %.1f\" % ((week_min+delta), weeks[week_min+delta]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On va donc couper l'année entre la semaine 36 et 37." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle (coupe après la semaine 36)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L'année 2019 n'a que 38 semaines avec des données.\n", "2018 : 584834 cas\n", "2017 : 538421 cas\n", "2016 : 553188 cas\n", "2015 : 781437 cas\n", "2014 : 605469 cas\n", "2013 : 681567 cas\n", "2012 : 697854 cas\n", "2011 : 622706 cas\n", "2010 : 644204 cas\n", "2009 : 835706 cas\n", "2008 : 841233 cas\n", "2007 : 747248 cas\n", "2006 : 720969 cas\n", "2005 : 627774 cas\n", "2004 : 629836 cas\n", "2003 : 778914 cas\n", "2002 : 758094 cas\n", "2001 : 517623 cas\n", "2000 : 614635 cas\n", "1999 : 621746 cas\n", "1998 : 753288 cas\n", "1997 : 681363 cas\n", "1996 : 679000 cas\n", "1995 : 567254 cas\n", "1994 : 651659 cas\n", "1993 : 661527 cas\n", "1992 : 646135 cas\n", "1991 : 831882 cas\n", "L'année 1990 n'a que 40 semaines avec des données.\n" ] } ], "source": [ "cas_par_annee_t = dict()\n", "for i in range(len(raw_data[\"week\"])): # Usage de raw_data particulièrement critique ici\n", " num_year = int(str(raw_data[\"week\"][i])[:4])\n", " num_week = int(str(raw_data[\"week\"][i])[-2:])\n", " num_inc = int(raw_data[\"inc\"][i])\n", "\n", " # Fin de l'année précédente ?\n", " if num_week <= week_min:\n", " num_year = num_year - 1\n", "\n", " #print(\"%d-%d => %d : %d\" % (int(str(raw_data[\"week\"][i])[:4]), num_week, num_year, num_inc))\n", "\n", " # Rajoute aux cas\n", " if not num_year in cas_par_annee_t:\n", " cas_par_annee_t[num_year] = []\n", " cas_par_annee_t[num_year].append(num_inc)\n", "\n", "# Sommes par année\n", "cas_par_annee = dict()\n", "for year in cas_par_annee_t:\n", " nb_sem = len(cas_par_annee_t[year])\n", " #print(cas_par_annee_t[year])\n", " if nb_sem >= 51:\n", " somme = sum(cas_par_annee_t[year])\n", " print(\"%d : %d cas\" % (year, somme))\n", " cas_par_annee[year] = somme\n", " else:\n", " print(\"L'année %d n'a que %d semaines avec des données.\" % (year, nb_sem))" ] }, { "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": [ "plt.bar(list(cas_par_annee.keys()), list(cas_par_annee.values()), align='center')\n", "plt.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calcul des extrèmes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le minimum est de 517623 durant l'année' 2001-2002.\n", "Le maximum est de 841233 durant l'année' 2008-2009.\n" ] } ], "source": [ "val_annee_min = min(cas_par_annee.values())\n", "val_annee_max = max(cas_par_annee.values())\n", "annee_min = None\n", "annee_max = None\n", "for annee in cas_par_annee:\n", " if val_annee_min == cas_par_annee[annee]:\n", " annee_min = annee\n", " if val_annee_max == cas_par_annee[annee]:\n", " annee_max = annee\n", "\n", "print(\"Le minimum est de %d durant l'année' %i-%i.\" % (val_annee_min, annee_min, annee_min+1))\n", "print(\"Le maximum est de %d durant l'année' %i-%i.\" % (val_annee_max, annee_max, annee_max+1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sauf que voila ... en fait il fallait couper au 1er septembre. Bon, du coup je vais pouvoir reprendre le code préexistant que le syndrôme gripal. En plus c'est mieux vu ma maitrise de ces libs ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle (coupe au 1er septembre)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On reprend donc le code du syndrome grippal mais cette fois il y a un bug, l'assertion ne passe pas car l'année 1990 à des données pour 38 semaines. Je l'ai donc exclue de l'analyse." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]\n", "\n", "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1], first_sept_week[1:]):\n", " if week1.year != 1990: # Année 1990 exclue car données partielles.\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2, \"L'année %d à des données pour %d semaines !\" % (week1.year, len(one_year))\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": [ "On regarde les incidences :" ] }, { "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": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut maintenant faire une liste des incidence par nombre de cas par année :" ] }, { "cell_type": "code", "execution_count": 20, "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": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit l'année avec le minimum et le maximum." ] } ], "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": 2 }