{ "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 de la varicelle 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 1991 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(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." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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132021077135611031516807211626FRFrance
14202106713401981016992201525FRFrance
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272020467375219635541639FRFrance
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2920204474391237564077410FRFrance
.................................
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\n", "

1590 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202120 7 8003 4590 11416 12 7 \n", "1 202119 7 6654 4370 8938 10 7 \n", "2 202118 7 3912 2110 5714 6 3 \n", "3 202117 7 4686 2878 6494 7 4 \n", "4 202116 7 4780 2891 6669 7 4 \n", "5 202115 7 11215 7627 14803 17 12 \n", "6 202114 7 11197 7994 14400 17 12 \n", "7 202113 7 9714 6289 13139 15 10 \n", "8 202112 7 11520 8415 14625 17 12 \n", "9 202111 7 9386 6678 12094 14 10 \n", "10 202110 7 9056 6452 11660 14 10 \n", "11 202109 7 10988 7938 14038 17 12 \n", "12 202108 7 11281 8361 14201 17 13 \n", "13 202107 7 13561 10315 16807 21 16 \n", "14 202106 7 13401 9810 16992 20 15 \n", "15 202105 7 12210 8988 15432 18 13 \n", "16 202104 7 12026 8826 15226 18 13 \n", "17 202103 7 8913 6375 11451 13 9 \n", "18 202102 7 7795 5430 10160 12 8 \n", "19 202101 7 10525 7750 13300 16 12 \n", "20 202053 7 11978 8406 15550 18 13 \n", "21 202052 7 12012 8285 15739 18 12 \n", "22 202051 7 10564 7574 13554 16 11 \n", "23 202050 7 7063 4744 9382 11 7 \n", "24 202049 7 5026 3145 6907 8 5 \n", "25 202048 7 6683 4312 9054 10 6 \n", "26 202047 7 4999 2963 7035 8 5 \n", "27 202046 7 3752 1963 5541 6 3 \n", "28 202045 7 3696 2016 5376 6 3 \n", "29 202044 7 4391 2375 6407 7 4 \n", "... ... ... ... ... ... ... ... \n", "1560 199126 7 17608 11304 23912 31 20 \n", "1561 199125 7 16169 10700 21638 28 18 \n", "1562 199124 7 16171 10071 22271 28 17 \n", "1563 199123 7 11947 7671 16223 21 13 \n", "1564 199122 7 15452 9953 20951 27 17 \n", "1565 199121 7 14903 8975 20831 26 16 \n", "1566 199120 7 19053 12742 25364 34 23 \n", "1567 199119 7 16739 11246 22232 29 19 \n", "1568 199118 7 21385 13882 28888 38 25 \n", "1569 199117 7 13462 8877 18047 24 16 \n", "1570 199116 7 14857 10068 19646 26 18 \n", "1571 199115 7 13975 9781 18169 25 18 \n", "1572 199114 7 12265 7684 16846 22 14 \n", "1573 199113 7 9567 6041 13093 17 11 \n", "1574 199112 7 10864 7331 14397 19 13 \n", "1575 199111 7 15574 11184 19964 27 19 \n", "1576 199110 7 16643 11372 21914 29 20 \n", "1577 199109 7 13741 8780 18702 24 15 \n", "1578 199108 7 13289 8813 17765 23 15 \n", "1579 199107 7 12337 8077 16597 22 15 \n", "1580 199106 7 10877 7013 14741 19 12 \n", "1581 199105 7 10442 6544 14340 18 11 \n", "1582 199104 7 7913 4563 11263 14 8 \n", "1583 199103 7 15387 10484 20290 27 18 \n", "1584 199102 7 16277 11046 21508 29 20 \n", "1585 199101 7 15565 10271 20859 27 18 \n", "1586 199052 7 19375 13295 25455 34 23 \n", "1587 199051 7 19080 13807 24353 34 25 \n", "1588 199050 7 11079 6660 15498 20 12 \n", "1589 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 13 FR France \n", "2 9 FR France \n", "3 10 FR France \n", "4 10 FR France \n", "5 22 FR France \n", "6 22 FR France \n", "7 20 FR France \n", "8 22 FR France \n", "9 18 FR France \n", "10 18 FR France \n", "11 22 FR France \n", "12 21 FR France \n", "13 26 FR France \n", "14 25 FR France \n", "15 23 FR France \n", "16 23 FR France \n", "17 17 FR France \n", "18 16 FR France \n", "19 20 FR France \n", "20 23 FR France \n", "21 24 FR France \n", "22 21 FR France \n", "23 15 FR France \n", "24 11 FR France \n", "25 14 FR France \n", "26 11 FR France \n", "27 9 FR France \n", "28 9 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1560 42 FR France \n", "1561 38 FR France \n", "1562 39 FR France \n", "1563 29 FR France \n", "1564 37 FR France \n", "1565 36 FR France \n", "1566 45 FR France \n", "1567 39 FR France \n", "1568 51 FR France \n", "1569 32 FR France \n", "1570 34 FR France \n", "1571 32 FR France \n", "1572 30 FR France \n", "1573 23 FR France \n", "1574 25 FR France \n", "1575 35 FR France \n", "1576 38 FR France \n", "1577 33 FR France \n", "1578 31 FR France \n", "1579 29 FR France \n", "1580 26 FR France \n", "1581 25 FR France \n", "1582 20 FR France \n", "1583 36 FR France \n", "1584 38 FR France \n", "1585 36 FR France \n", "1586 45 FR France \n", "1587 43 FR France \n", "1588 28 FR France \n", "1589 5 FR France \n", "\n", "[1590 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 ? Non. Nous renommons alors notre \"raw-data\" en \"data\", vu que ces données semblent exploitables. " ] }, { "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": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data = raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n" ] }, { "cell_type": "code", "execution_count": 7, "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 comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans le sens chronologique.\n" ] }, { "cell_type": "code", "execution_count": 8, "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." ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur quelques années récentes (en évitant la période du covid) montre mieux la situation. Le creux des incidences se trouve en septembre." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gnvGYqqJ8eoajxzSmdriNpKG8gM7BsZSPn1USS3PPhMV6vGs4KT+jpXeUzZ97Ys7ECyX7UNGIQsfAGIGgmVSjYVNT4gSga4EZVJ44LA1Hbg6N5QUxWRr2zI8ty+a2NKJN4usf9c5oaSwrKyBoiDm2omQmpyNF41xyROOZI+cY8QZ49si5pFxfSR8qGlGw01sbZ3BPAQvOoJpwT80d0wBYUVnEmd65Yxp2b6xZ3VPF+Yz7gwyPT67u9gWCjHgDlBXkRz1P52osDk6fH6UoP5eaEicnEiAaI+PTuwT87kSodc0rZ/sWfH0ls1DRiIJtvq+qKpr2XI3bBbDgYLjHKu5zOmL7L1hZEVuL9IPtA6ysLJzRWgCoLAoJ3/kpLqrBcDV4dCFrsES0fUBFI5tp7hlhVVURF9SWcOLcwubD7Dp1nov+/imOd01cJxA0vHTqPACvtwyoO3ORoaIRhTPnR3A6cqizBCKS6mLL0lioe8oXoCAvl5wcien4lZWFDI756Zuj1fn+toFZXVMQck8B04Lh4RYiUeo0AJaVqqWxGDh9PiQa62qKOX5uOKqbMlZOdo/gCxh+8mpbeO1A2wCDY35uaKrB4wtwJIGDy5T0M6doiMhyEXlWRA6LyEER+QtrvUJEfiUix61/yyPO+bSInBCRoyJyU8T6DhHZbz33NRERa90pIj+y1neJyKrEv9TYae4ZZVVlUdQ39HxHDuWFeQlxT8USz7DZvqIMgO/+rnnGYzoGPLT0esJFfTNRVRxyP80oGjNYKQX5uVQW5WvabRbjCwRp7fOwujIkGqPeAO0LaIvTZ7WdefS19rD4/O5kyDX10WvXAfCquqgWFbFYGn7gE8aYJuBy4KMisgn4FPC0MWY98LT1PdZztwObgZuBr4uI/e74DeBuYL31dbO1fhfQZ4xZB3wV+FICXtu8CX0Smx4Et0lErUbfqJdCZ+yisWNlBbdub+Drz52M2mXXGMPnHzmI05HDm7fUzXqtqmLb0phstcwlGmDXamggPFtp6R0lEDSsqipifU0xwILiGvY909bvCccvXjhxngtqi7loeRnVJU5eOdu/8I0rGcOcomGM6TDGvGI9HgIOAw3ALcD91mH3A++0Ht8C/NAYM26MaQZOAJeKSD3gNsa8aEIfSR6Yco59rR8D19tWSKoJBA1nz4csjZmoXmBV+OCYj2ePdHPl2qq4zvvc2zZRVpjHX/143zQ/8S/3d/LUoS4+fuMFUWMxkVQUzc/SgJCLqk1z+7OW01aB6OqqQtbXlgBMikfES99IKNvO6cjhkdfaGfMFePl0L1euq0JEuHhFmQbDFxlxxTQst9F2YBdQa4zpgJCwADXWYQ1AS8RprdZag/V46vqkc4wxfmAAqIzy8+8WkT0isqe7uzuercdMx4AHbyA46xtvTYmLcwtIO/3Zq214fAHec9nKuM4rL8rn796xhf1tA3z7+Qk3Vd+Il88/eoCtDaV86KrVc14nLzfkYptJNGaq04CJqvCF+MGV9NHcExL8VZVFVBTlU1mUvyBLo2/UR32pixuaanlsXwcvn+5l3B8MfyC6eEU5Z86PzlhMqmQfseV7AiJSDDwMfMwYMziLIRDtCTPL+mznTF4w5l7gXoCdO3cm5V3rdMQf1UzUuJ10D40TDJqYA9k2xhgefOksWxtK2do4e+whGm/ZWsebNtXylV8d41jnENtXlPHCyfP0j/p44IOX4ciN7XNAZbFzWvbUwOjclkZ9qYsxX9BqbBg9NVfJXE73jFDicoStzbVWMHy+9I96KSvM4+3blvHY/g7++alj5OYIl62pAODilaFQ56tn+7lxU+3CX4CSdmJ6hxGRPEKC8aAx5ifWcpflcsL6167iaQWWR5zeCLRb641R1iedIyIOoBRISylpc9h8n83ScOIPmnAQMB72nunjaNcQ77lsxbz2JyLcc+tWbmyq5TfHe/jbRw7y+IFOPnLNWjYtm7kKfCpVxflRLY2CvFycjpljLfVWBlVHAmaKKKnn9PkRVlcVYX/oW19TzIkFZFD1jXopL8znmg3VlLgcvN7Sz4WNpeG+Z1sbSnHkiAbDFxGxZE8JcB9w2BjzlYinHgXutB7fCTwSsX67lRG1mlDAe7flwhoSkcuta75vyjn2td4NPGPS5P840zOCKy8nXMQXjZqS+ddq/OeusxQ7Hbx927J577G6xMm/v+diXv7s9Tz/19fywAcv5S+uXx/XNSqLnVED4bNZGQB1paHXrlXh2Ulzz8gkK3p9TTEDHh/d83Qf2RanKy+XmzeHEjAiY3WuvFw2L3NrXGMREYulcSXwR8B1IvKa9fUW4IvAjSJyHLjR+h5jzEHgIeAQ8ATwUWNMwLrWR4BvEwqOnwQet9bvAypF5ATwl1iZWOng9PmRGdNtbWrcdlV4fH9ofSNefrG/g1u3N1DkjNkzOCMiQmN5IVdfUB2zW8qmunh608JYRKPeFg21NLKOcX+A9n7PpHidHQw/MY8eVMYY+kd9lFt1PbftXE6OwHVNNZOO276iXIv8FhFzvnMZY54neswB4PoZzrkHuCfK+h5gS5T1MeC2ufaSCpp7RlhfUzLrMeFWInF+2n74lVa8/iB/OE/XVCKpKs5naMzPmC8QnhnSO+KNOoApkuoSJzmi7qlspKV3lKAJZU7ZrLPTbruHecO6+LL5hsb9+IMmfM9curqCvX9zI+VFk2NdF68s53svnOZI59CcNURK5qMV4REEgoaWXg8rZ6nRgPm7p5462MWWBjdN9bHHHpJFpVWrYU/wM8ZwonuYNdXFs56Xl5tDVbGTTm0lknU0R0nyqClxUuJyzKvbbf9IKHEiMiFiqmAAbF8eKkzdZw0HU7IbFY0I2vtD6barZ8mcglBldInLEZelEQwaDnUMsn15+dwHp4CJAr+Q8J0f8dI/6gsXfM1GfalLLY0sxO5uG5nkISKsrynm2DxqNexEkPI5sugaygrIz82JqeGmkvmoaERgFz7NVRwH8Y99be3zMDzujyvDKZlMbSVif9JcXzu3aNSVujSmkYU0nx+hrDBvWqr0hroSjnYNxZ1BNSEas7s0c3KExvICWnvVOl0MqGhEEO2T2EzUlMTXSuSQ1fojE1xTML2ViN3tdK54DoTSblU0sosxX4DDHYNR64+a6t30j/rizoizi0FjqddZXlEYHjmgZDcqGhE094xSkJc7a7qtTY3bGVfTwkMdg+QIbKid+005FUx1Tx0/N0yJy0Gte+7XXlfqYmjcz9CYL6l7VBbOsa4h/vZnB7jsfz/Nq2f7uWLttEYL4Q8y0XqazYbdcXkuSwNgeUUBLdp+ZlGw8LzPRcSZ8yOsrCwklrZXNSVOzg2Gpt/FcvzhjkFWVxVREEdn22RSkJ9LUX4uPUOhP/xjXUOsrymO6bXYabddg2PhIi4lM/mj+3bRP+rj5i113LZjeVTR2FgX+iBzuGOI6zbGXrXdF0MHAZsVFYX0j/oYHPPh1nsmq1FLI4Jmq1o2FmpKXIz7gwyO+THG8O3fngqPWo3GofZBNs0x5yLVVBY7OT8SsjROnBuOyTUFhOeMaDA8szHGcH7Yy11XreZfb9/OVeuryI1Sf1TiymN5RUHYhRor/aNeSlyOmGqE7NHJLeqiynpUNCz8gSAtvaMxBcEhosBvcIyvP3eSf3jsMP/w2KGoxw54fLT1e2iqzwzXlI3dSqR3xEvPsDemIDhMtBLRuEZmM+4P4g+amKzBpjp3/O6pUd+cmVM2yytUNBYLKhoW7f1j+AKGVZWz12jY2LUaD+46yz89eZSKonx2NfdGfSO1/xg3ZUgQ3Kaq2EnPkDfc5XRdDOm2MCGYKhqZzaAVcyp2ze2Fbqp3c7pnBI83MOexNqG+U7G5miZEQzOosh0VDYu9Z0P9EWfrbhuJ/cb5vRdOc2FjKQ9+6DKMgV/sa592bKaKhu2eOm5nTsUYpHflhSb4dWj/qYxmeCw0h94do2gEDRyNo14jnk7HpQV5lBbkaQbVIkBFAzjVPcznfnaQTfVutq+IrfjOzrCqKnbyf/9oB031bi5sLOWR16aLxqH2QaqK86mOISsrlVQX59M74uVo5xBF+bksK50+E30mtFYj8xmyRKM4hj5nm+bIoNrd3MvHf/QaweBELUc8lgaEMqhUNLKfJS8aQ2M+7v7+XvIcOdz7vh3kO2L7lZS48vj0mzdy/wcvCfv437FtGfvbBjjZPbklw+HOQZrq3TFlJqWSqhInQRN6Q1gXY+aUjVaFZz7D47GLRmN5AcVOx4yi8chrbfz01TbaI9rH9I/EN1NlRUWhpt0uApa0aASDhk889DrNPSP82x9up7E8tniGzR+/cS2bIzKi3nbhMkTg0QhrwxcIcqxzOONcUwCVRSHL50jnEOtizJyyqXW7tP9UhmNbGrEEwnNyhI11JTOKxtHOkNuq2SqA9QWCDI37Yw6EQyiDqrXXM8laUbKPJS0aTx3q4qlDXXz6zRt5Q5zzuqNRV+ri8tWV/Pz19nBLhlPdI3gDwYypBI/EbiUCsbUPiaS+1EXfqI8xX+yBUyW12MWXJTHENCAU1zjSMb2diDFmmmj0j9rV4PG4pwrxBoLzmkOjZA5LWjRaLVP5tp3L5zgydm65aBmnekY40Bb6xHaoI1S7kSk9pyKpioixxNKoMJI6TbvNeGz3VDyiMTTup7VvsgXZ1u9hyLrWqW5bNEJFofGIxgorg0rjGtnNkhaNQY8PEShJwEAkmzdvqScvV/jMT/fz0Mst7G7uJd+Rw5oY6z9SSVXRhGhcEGd7E7sqXOMamYvtnop14JddRzS1yM+2MvJzczhlWxpW36m43FNaq7EoWNqiMean2OmYdUpfvJQW5vH3t2yh3+Plrx7exw92t7ChtiTuyXqpwF3gID83B1deDg1lBXGdOzH2VeMamcrwuB9XXg55Md57G+pKEJmeQXXEEo2r1ldxykrymOg7FbtoNJQVIKKWRrazpHtPDcYw3nQ+3HHpCm6/ZDkH2gZ58mAnF68sS/jPSAQiQmVxPpXF+XELp91KpHNA/dOZytCYP67eYIX5DlZXFkUVjYayArY2lPLs0XOM+QLzimnkO3Kod7smZVA98OJpSgvyeMvW+pjFTUkvS1o0BjzJa54mImxtLGVrY2b1m5rKNRtq4qrPsClyOnC7HJpBlcEMjfnidr02LXPz6pm+SY04j3YO0lRfwprqIowJWQrhWRpRJvXNxvKKwrB7atep83zukYMAfOnxI3zwqtW857KVGdPUU4nOkpb2wTEf7oIlrZv8n3dt5c+vXz+vc+tLCzSmkcEMj/tjDoLbXL2+ivaBsXBcY9wf4GT3CBvqSlhTFUqWONU9TN+oD0eOUBTnG7w9V8MYwz8+eZRad6g4dnlFIf/w2GG+/NTRuK6npJ6lLRoef1LcU0uFulJX3IN7lNQxNOaPqe9UJDc01ZIj8OTBLgBOnhshEDRsqHOzqioUyD7VM0L/qJeywvy4C1ZXVBTSNTjO4wc62Xumj/9x/Xpu2lzHj/74CrY1lsbVxkRJD0taNJLpnloKaFV4ZjNsJXrEQ2Wxk0tWVfDkgU4AjnZZEyfrSihx5VFT4qS5e4T+UV9cLURslleEEi4+98gBVlUW8gcR6e51pS669ENIxrOkRSPknlLRmC91pS56hscZ92uBXyYSck/Ff3/fvKWOo11DNPeMcKRjiPzcnPDIgNVVRZzqGbH6TsUXz4CJWo2eYS8fv/GCScHvOrf2M8sGlqxo+AJBRr0BdU8tgM3LSjEG9p7pS/dWlCgMjvnitjQA3rS5DoAnD3ZypHOItTXF4Tf3NdXFNPeELI14Mqds7GFMTfVu3n7hsknP1Za6GBzzx9WeXUk9S1Y0Bq3ipFjaRivRuWJtJXm5wn8f6073VpQpGGMYHvfP6/5uKCvgwsZSnjjQyZHOQZrqJgo/11QV0TvipaVvdF6WRnWJkw9dtZovvmvrtDTvcBq3uqgymjlFQ0S+IyLnRORAxNoXRKRNRF6zvt4S8dynReSEiBwVkZsi1neIyH7rua+JFUETEaeI/Mha3yUiqxL7EqMzaM8aUEtj3hQ7HexcWcF/H1XRyDRGvAGMiW0AUzRu2lzHay39dA2OsyFSNKpDbqpRb4Cyovj/dkRQ47aMAAAgAElEQVSEv3nbJrYtn167NFH7E5tofPd3zXzyv16Pew/KwojF0vgecHOU9a8aYy6yvn4JICKbgNuBzdY5XxcROyfvG8DdwHrry77mXUCfMWYd8FXgS/N8LXFhWxrqnloY12yo5kjnkPqiM4zh8CyN+d3fN1kuKmCSaKyOaIdTVhC/pTEbNZZoxBIMf+JAJ3/380P8cn9HQvegzM2comGM+Q3QG+P1bgF+aIwZN8Y0AyeAS0WkHnAbY140oRaaDwDvjDjnfuvxj4HrJd48vnkwYLunVDQWxBs3VAPwG3VRZRTD4/F1uJ3Kuppi1lpWRWSH5uUVhTgst9J8sqdmY6I1zeyicbhjkL986DVEQhaPPxBM6D6U2VlITOPPRGSf5b6yx901AC0Rx7Raaw3W46nrk84xxviBAaAy2g8UkbtFZI+I7OnuXtiblD0/WVNuF8aG2hLq3C6eO3Yu3VtRIrDdr/N1T0GoHc7GupLwlEqAvNyccAZUPAOYYqHY6aDY6ZjVaj0/PM6H7t9DicvBH1+9Fpjo5qukhvmKxjeAtcBFQAfwZWs9moVgZlmf7Zzpi8bca4zZaYzZWV1dHd+OpzDoCd1o6p5aGCLCGy+o5rfHe/QTXwoZ9fr5wHd3c+Jc9GK4eOaDz8SHfm8NT3zs6mkFfLaLKtGWBkCt2zmre+qfnjxKz/A433rfznB8xe7mq6SGeYmGMabLGBMwxgSBbwGXWk+1ApHDKRqBdmu9Mcr6pHNExAGUErs7bN5MuKc0e2qhvHFDNUNjfl5t6U/3VpYMhzuGePZod7hyeypDC4xpzIb9Zh1v36lYmKvLwOutA7xhbSUXNpaFBVFFI7XMSzSsGIXNrYCdWfUocLuVEbWaUMB7tzGmAxgSkcuteMX7gEcizrnTevxu4BkzdXRYEhgcC/XOKcjT5mgL5cp1VeTmiGZRpRB7gNih9ujjWe2YxkLcUzNx0fJyCvJyqXXH3+hyLmrdLrpmcE8FgoaT3cOst2a/2IWL9oRCJTXMeUeJyA+Aa4AqEWkFPg9cIyIXEXIjnQb+GMAYc1BEHgIOAX7go8YYu1LnI4QysQqAx60vgPuA74vICUIWxu2JeGFzYbdFT0HMfdFTWpDHxSvK+O9j3Xzypg3p3s6SoK0/1F14ppneE/PBEy8ab9lax1Xrq5Li2q1zuzg3NE4waKbVcZztHcXrD4anTJaopZEW5ryjjDF3RFm+b5bj7wHuibK+B9gSZX0MuG2ufSSawTG/Zk4lkDdeUM0/P3WM/a0DGd8OfjHQZo1kbT4/wqjXT2H+5D/l8NS+/MSLhogkLRZYV+rCHzT0jIxTUzLZkjluNTO0LQ272n1oXC2NVLJkK8JDzQo1npEobr90BQ1lBdx1/8u09+uMjWRjz/E2ZmKyXiTD46FmhbkJnEqZCmyXV1eU4V7Hz4WmBq4LWxoh4RpWSyOlLFnRGPRos8JEUlXs5DvvvwSPN8AHvvtyOKVZSQ5t/R62NITqJ6K5qIbm2Xcq3czWSuR4V2iCoP26bPfUoIpGSlm6oqEdbhPOhroSvvHeHZzsHuajD75CIJj0fIYliTGG1r5RLl1VSYnLETUYPjwe/yyNTGC2Ar/j54bDVgaAKy+X/NwcjWmkmKUrGjpLIylctb6KT715I7893jNjZo+yMHpHvIz5giyvKKCp3j2DpRH/1L5MoKrYSW6OTMugCgQNJ84Nh4PgNsUuh2ZPpZglKRrGGJ3al0S2NoQC4eqiSg52PKOhrIBN9W6OdA4RnGLVDc1jAFMmkJsjVBdPL/Br7Rtl3B/kgtqSSeslLodWhKeYJSka4/4g3kBQC/uSRJH1ZqV/zMnBTrdtLC9kU72bUW+AM72jk46Zz3zwTKE2SoHfsS4rCF472dIocTnUPZVilqRohKvB1T2VFOxPuCMqGknBLuxrKC8INxOc6qIaGvNRkoRq8FRQWzLd0jhutUuZ6p4qceapeyrFLEnR0LboyaVIRSOptPV5KHE6KC3IY31tMbk5Mi1+NDyWnYFwsFqJTIlpnOgapr7UNW18bbFaGilnaYrGmLZFTybFYfeUju1MBm39HhrKC4BQBtHa6qJJlkYgaBjxBrLXPeWePvb12LmhSZlTNuqeSj1LUjQGdNRrUnHl5ZAjE/2PlMTS2ueh0RINCM27OBQhGnYsKRsD4TC9ViNoZU5NDYJDyMWs7qnUsiRFQ9uiJxcRocjpYEQtjYRjjKGtz0NjeWF4bVO9m46BMfpGvMCEaGSrpRGu1bBcVK19HsZ8wWnxDJjInkpBj1PFYmmKhrqnkk6xU1Mhk8Ggx8/QuJ+GssmWBkwEw4fDzQqz8/6unTL2NRwEr50uGsVOB0ETmomupIalKRqaPZV0ip0ODYQngdb+UOZUpHtq07KQaBxoHwAmWoVnrXtqSlX4RM+p6e4p7T+VepakaAx4fBTk5ZLvWJIvPyUUqaWRFMKFfRGiUVXspLG8gFfPhoZgDWW5e8oe+3qsc4inD3fx60Nd1LqdUd3JE+3RNa6RKrLzrloggx6/FvYlGbU0kkNbRDV4JDtWlvPiyfMYY5I6SyNV1Je6+Mmrbfzk1TYA/ujylVGP06aFqSd776oFMDimfaeSTZEzl+6h6e2tlYXR1u+hIC+XiimjVnesLOeR19pp6/eEXTXJGPWaKv7XO7dwrGuIzcvcbKhzz+hqU0sj9SxJ0RiwpvYpyUPdU8mhtW+UhvKCaRMnL15RDsDeM33hVOdstjQuX1PJ5Wsq5zwuHNPQey1lLEmnvrZFTz7FTgcjXv1DTjRt/ZNrNGw21pVQmJ/LK2f6GBrzkyNQmJ+bhh2mFh35mnqWpmh4/FrYl2SKrJiG5s8nltY+z7R4BoAjN4dtjWXsPdsX7nA71RpZjNiWxlJxTxlj+OX+jmm9uVLJkhQNdU8ln2KnA1/AMO4Ppnsri4bhcT/9o75JhX2R7FhZzuGOIc4NjWVtjUa8FOblIrJ0LI32gTH+9MFXeOpQV9r2sOREIxg0DKl7KukUWa4RzaBKHJHdbaOxY2U5gaDhxZPns7ZGI15ycoRi59LpP3XEKuDcVD+9ZiVVLDnRGPH6CRot7Es2xdbvV1uJJI7nj/cAcKE15Goq21eUAdA36svqIHi8hPpPLQ3RsKv+o/XhShVLTjQGtC16Sih2hiwNzWpJHL/Y18GWBjerqoqiPl9WmM/a6tBz2doWfT6ULKGRr4c7h1heUZBW9+OSEw27WaEW9yWX8EwNzaBKCC29o7zW0s9bty6b9bgdK0Opt0slpgEsKffU4Y5Bmurcad3D0hONMe07lQp05GtiefxABwBv3Vo/63G2aCyVmAYsnTnhHm+A0z0jbKzPcNEQke+IyDkRORCxViEivxKR49a/5RHPfVpETojIURG5KWJ9h4jst577mlj5gCLiFJEfWeu7RGRVYl/iZMLNCtU9lVR05GtieWx/J1sbSllRGT1zysYu8ltKMY2SJTJT4/i5IYIGmurSF8+A2CyN7wE3T1n7FPC0MWY98LT1PSKyCbgd2Gyd83URsSuMvgHcDay3vuxr3gX0GWPWAV8FvjTfFxMLGtNIDTryNXG09I7yeks/b71wdisDYG11MddvrOHyNRUp2FlmsFSm99lB8KZMtzSMMb8Beqcs3wLcbz2+H3hnxPoPjTHjxphm4ARwqYjUA25jzIsmVO31wJRz7Gv9GLhekliVZDc2U/dUcinOX3ojX5882Mnu5ql/Kgvnl/tjc01BKAX1vvdfwnUbaxO+j0xlqcwJP9wxREFeLisqZrc2k818Yxq1xpgOAOvfGmu9AWiJOK7VWmuwHk9dn3SOMcYPDABRm86IyN0iskdE9nR3d89r42uqirjlomVLKrskHRTZ2VNL4I8ZoH/Uy8d++BpfeuJIwq/92P4OtjWWsjzNbxaZituVhzcQZNy/uD+gHOkcZENdCTk56a30T3QgPNqrMbOsz3bO9EVj7jXG7DTG7Kyurp7XBq/dWMO/3r6d3DT/4hc7jtwcnI6cJZM99cOXW/D4AhxqH8QfSFwV/OmeEfa1DsTkmlqqLIX+U8YYDncMpd01BfMXjS7L5YT17zlrvRVYHnFcI9BurTdGWZ90jog4gFKmu8OULGSpjHz1B4I88MJpXHk5eHwBTnaPJOza//TkUfIdObx92+yptkuZpSAanYNjDHh8NKWxEtxmvqLxKHCn9fhO4JGI9dutjKjVhALeuy0X1pCIXG7FK9435Rz7Wu8GnjHa5W5RUOzKnEFMxhh6hpMz3+PJg120D4zx8RsuAOD11v6EXPfZo+d4bH8Hf37tOupLo7cOUSbmhizmDKojHaE56RvTXKMBsaXc/gB4EdggIq0ichfwReBGETkO3Gh9jzHmIPAQcAh4AvioMcZ2NH4E+Dah4PhJ4HFr/T6gUkROAH+JlYmlZD9F+ZkjGg+/0saVX3yG/lFvwq/9nd81s7KykLuuWk2x08H+1oG4r/F///sk1335OV44EWoVMuYL8LlHDrCmuoi737gm0VteVNiWxmKOnx2yMqc2ZoClMWc02BhzxwxPXT/D8fcA90RZ3wNsibI+Btw21z6U7COT3FO/OdbNuD9Ia5+HssL8uU+Ikddb+tl7po/PvW0TjtwctjS42dc2s2jsbu7lN8e6ufMNq6gucWKM4ctPHePfnj1BsdPBe+7bxZ9ftx5fIEhLr4f//PBlOB2Lfy7GQlgKI1+PdA7RUFaQEVmfmkKkJI0iZy49w4n/ZD8f9pwOhck6B8bYMkPDv/nw3d81U+x0cNvOUMjuwsYyvvfCabz+IPmO6Yb8Pzx2iH2tA3znd83cddVqhsb8fO+F09x+yXI++9YmvvDoIb729HEA3rW9gTesrUrYXhcr7iUwU+Nwx2BGBMFBRUNJIkVOB2fOj6Z7G7T1e2gfCA2t6Uzg8JqW3lF+vq+DO69YFe71tLWhFK8/yLGuoWnidKxriH2tA9x11Wq6Bsf4/585AcAHr1zN376tCRHhy3+wjcvXVPDo6+185q1NCdvrYsbuPrCYAuGDYz7+/ueHKMrPpb6sgOaeEd68pS7d2wJUNJQkkinuKdvKABI68ez//uYkuSJ8+OrV4bULG0NCsb9tYJpoPLy3FUeO8JFr1lJV7ORP3jhAc88Ib7uwftKUvdt2Lue2nctRYsOuucqEey1RvHCihx/vbaUwP5dRbygsbLe+TzcqGkrSsEe+ppuXT/dS7HTgystJmGh0DY7x0MutvHtn46TMphUVhbhdDva1DnDHpRPH+wNBfvpqG9dsqKaq2AnAlobShLrKlip5uTkU5OUuKveUnba9+7M3EDSGgVFf1Nnw6UBFQ0kaRU4HI94AwaBJaxXrntN9bF9RRv+oj87BxKTd3vubUwSM4SNvXDtpXUS4sLGM/W2T026fP9HDuaFxfv/iRpTEs9j6TzX3jFBT4gy73jIhAG6z5FqjK6nDHsSUzqrwAY+Po11DXLKqglq3i66BhVsa54fHeXDXGW65aFnU1h5bG0s52jnEmG+ircXDr7RRVpjHdU01045XFs5i6z91qnuYNdXRh22lGxUNJWnYRVfpHPn6ytk+jIGdK8upK3XSNbRw0bjv+WbG/UH+9Jp1UZ+/sKEUX8BwtDNUkDXg8fHkwU7esW2Zps8miRJXHkMZ4ApNFKd6RlhdVZzubURFRUNJGkUZMPJ1z+lecnOEi1aUUed20T/qm2QBxMvgmI/vv3iGt2ypZ11N9D/qC5eHApb72gbwBYL8x0tn8PqD6ppKIu5FNPK1b8RL/6gvPLo309CYhpI0MmEQ057TfWxZ5qYw30Gt2wWEgtgrK+f3B/mj3S0Mjfv5kymxjEiWlbqoLMrnm8+d5J+eOMLgmJ9ty8vCmVVK4il2OuhIgOsxEzjVMwyg7ill6ZHuQUxef5DXWvrZsTI0kMgWjc55vrn4AkG+87tmrlhTydZZBEBEuL6phnF/gDdtruOb772YH3z4MpI4JmbJU7KILA07cypT3VNqaShJozjNc8IPtA8w7g9yyarQCNS6UsvSGJpfBtVj+zroGBjjnlundcOZxj++exvGGBWKFFHiymPQ42fMF8CVl91xo+aeEfJyheUZkmI7FbU0lKQRtjTSlD1lT9HbYYlG2D01D0vDGMO3fnuKdTXFXHNBbBlQKhipY0uDG48vwLX//Bw/2H0WXwJnmqSaU93DrKgoxJGbmW/PmbkrZVEwEQhPT/bUrw910VTvpqYkJBZul4OCvNx5tRJ58eR5DrYP8uHfW532yWnKdG7d3siDH7qMWreLT/9kP+/899/h9WencJzqztzMKVDRUJJI2D2Vhvz57qFx9p7t46bNE7OyRYRatzNm0Wjv97DndC/PHOniX359nKrifG65qGHuE5W0cOW6Kn76p2/gr2/eyMH2QZp7EjcMa6GMev209M7dhy0QNJw5P5qxmVOgMQ0liRTk5ZIj6QmE//pwF8bATZsnN3mrdbs4F4NonBsc4+p/fBZ/cGIe2F/dvCHr/eWLHRHh6guq+NITcLJ7mA116Z8/AfC1p0/w/RdP89Jnrg83t4xGW58HbyCYsZlToKKhJBERoSg/PU0LnzzYyfKKAjZOedOoK3Xxytm+Oc9/5Wwf/qDhnlu3sHlZKWUFeaysnF79rWQeq6tCb7gnzw2neScTvHK2jxFvgMf3d/IHl8zcjPKklW6r7illyZKOka9DYz5eOHGemzbVTQtG17lddA2OM9dE4ddaBsjLFX7/4kYuWl7GqqoiDWxnCYX5DhrKCjjZnRmiYYzhcHto8t7Dr7TOemyzlW6byZaGioaSVEJNC1MrGs8e7cYbCHJTlPkDNW4XXn+QvtHZc/r3tfazsc6t7qgsZU11UbjeId209HoYGvezqrKQXc29s8Y2TvUMU+JyUFmUuOmSiUZFQ0kqRU5HyrOnnjrYSWVRPhevKJ/2XF1EVfhMBIOG/a0DbFuuFdzZytrqYk51D89pUaaCg+2h8b9/dfNGAH72atuMx57qHmFNdXFGW7UqGkpSKXbmptQ9Ne4P8NzRbm7cVEtulNTYutLQLIvZMqhO9YwwNO7nwsbMGHqjxM/ammJGvAG6EtQKfyEcbB8kN0e4bmMNl6+p4Cevts0oZs09I6ytylzXFKhoKEmmKD+1MY0XTpxneNw/LWvKJpYCv32toVkYFy1X0chW7JTVTIhrHGwfYG11Ea68XN51cSPNPSO8crZ/2nGjXj8dA2PhQH6moqKhJJVUj3z9we6zlDgdXLG2MurzdqHfbJ9AX2/ppzA/l7XVmZvBoszOOuv/LhNE41DHIJuXhVydb95Shysvh59ECYifCgfBM/u+U9FQkkoqR76+fLqXpw51cffVa2YMYOc7cqgsyp/VPfV6a2i+dzT3lpIdVJc4KXE60p522zM8TtfgOJuXuYFQj6ybNtfx89fbGZzSYPG5o+cAMqa2ZCZUNJSkUpQiS8MYwz88dphat5MP/d6aWY+tdbtmDIR7/UEOtQ+qayrLERHW1BSnPYPqoJVqu8kSDYC7rlrN0Lifrzx1LLx2bnCMbzx3kjdtqp1xTkumsCDREJHTIrJfRF4TkT3WWoWI/EpEjlv/lkcc/2kROSEiR0Xkpoj1HdZ1TojI1ySTUweUuCh25uILGMb9yc2g+sW+Dl5v6eeTb9pAQf7sabJ1pa4Z26Mf7RzCGwjq7ItFwNrqorS7p+zMqc31E/fThY1lvPeylTzw4mkOtIWe/+enjuINBPnMW5rSsc24SISlca0x5iJjzE7r+08BTxtj1gNPW98jIpuA24HNwM3A10XE/uv+BnA3sN76ujkB+1IygIlBTMkTjXF/gC89cYSmejfvimE6Xq3bybkZxr6+ZgXBt2nmVNaztrqYjoGxtE6OPNQ+SENZAaWFk1uHfPKmDVQUOfnsT/ezv3WA/9rbyp1XrGJVhgfBITnuqVuA+63H9wPvjFj/oTFm3BjTDJwALhWResBtjHnRhPLQHog4R8lyUjGI6T9eOktrn4fPvqUppjhErdtFz7A3ahfUfS39VBTl05ihswyU2LETGZrT6KI61D4YjmdEUlqQx9++rYnXWwd433d2UVaQx59fvz4NO4yfhYqGAZ4Skb0icre1VmuM6QCw/rWHDzQALRHntlprDdbjqevTEJG7RWSPiOzp7u5e4NaVVJCKQUzPHT1HU72bq9ZXxXT8bAV+r7f2c2FjaUYXVymxsa4mNWm3X3z8CF956ui09ZFxP83nR8KZU1N5x7ZlXLWuir5RH3954wWUFszcyDCTWKhoXGmMuRh4M/BREbl6lmOj/RWaWdanLxpzrzFmpzFmZ3V1dfy7VVJOKiyNwx1DUT/NzcRFK0KupycOdE5aHx73c/zcsLqmFgkrKorIzZGkikYwaPjB7rM8/Mr0Ku/DHYMYw4z3pojw5T/Yxuffvok7Ll2RtD0mmgWJhjGm3fr3HPBT4FKgy3I5Yf17zjq8FYhs79gItFvrjVHWlUVAhdVD58z5uWcJzIfzw+P0DI9P62Y7Gxvr3Fy6qoIHXjpNIKL1+S/3dWAMXLq6IhlbVVJMviOHFRWF4fqHZHCye5gBj4+2fg99I95Jz9mZU5sbZv5AU+t28YErV2fslL5ozHunIlIkIiX2Y+BNwAHgUeBO67A7gUesx48Ct4uIU0RWEwp477ZcWEMicrmVNfW+iHOULGdTvZuGsgJ+vi85nwOOdg4BISGIhzvfsIqWXg/PHAl9phn3B/iXXx9jW2Mpb5ihMFDJPpKdQbXnzESb/UMdg5OeO9A2QEVRftgdulhYiLzVAs+LyOvAbuAxY8wTwBeBG0XkOHCj9T3GmIPAQ8Ah4Ango8YYO6XmI8C3CQXHTwKPL2BfSgaRkyO8fdsyfnu8h/PDie8DdNgSjXgLot60uZb6Uhf3v3AagP/cdZb2gTH+500bNZ6xiFhbXcypnpFJFmUi2XumLxy3s9Nrw8+d7WP78rJFdz/NWzSMMaeMMdusr83GmHus9fPGmOuNMeutf3sjzrnHGLPWGLPBGPN4xPoeY8wW67k/M5nQmlJJGLdctIxA0PDL/R0Jv/bRzkGqivOpLnHGdV5ebg7vvXwlz5/oYV9rP//+7AmuWFPJlevUylhMrKspxusPcqRzcO6D58HeM31csbaS+lJX2B0FIbfpqe4Rdq5afK7O7HGkKVlLU72bDbUl/Oy1xLuojnQOzbvtwu2XLCffkcMHv/cyPcNePnnThkX3qXCpc0NTLU5HDv/x0tlpz/kC01Ou46FneJzmnhF2rCxn8zL3JNHYa7mtdq6a3p4/21HRUFLCOy5axt4zfbMOoImXQNBwrGuIDbXxxTNsKoudvGPbMnqGvVy/sYYdKxffH/hSp7won1u3N/DTV1vpH50IVO86dZ5tf/cU9z3fPO9rv2ILw8pyNi0r5WT3MKPWwLG9Z/rIz81ha8Pi6yygoqGkhHdsWwbAo68nzto42zvKmC/Ixvr5N3i7++o1rKkuCg/IURYf779yFWO+ID/YHSoT8wWC/M3PDjDmC/C/fnEoHNeKF1sYtjSUsmWZG2NC6d8Qap65tbF0UU5+VNFQUsLyikJ2riznkddmHkATL0esbJV40m2nckFtCc984pqM7yyqzJ+NdW6uWFPJ9188jT8Q5DvPN3P83DBff8/F3NBUy+cfPciDu87Efd09Z/rY0hAaCbzZsigOtQ8w5gtwoG2QnYvUclXRUFLGLRct41jXMP99LDHV/Ec6hxCB9TX6hq/MzgeuXEX7wBjfe+E0//Lr49zQVMvNW+r59/ds59oN1Xz2pwd44WRPzNcb9wfY3zoQDnQvK3VRVpjHwfZB9rcN4A0EF2UQHFQ0lBTy9m3LaCwv4P3ffZlP/2TfJB/zfDjaOcSqyqI5u9oqyvVNtSyvKOAfHjuMwfD5t28CwOnI5Rvv3UGJ08Ejr8buOj1gCYM9h15EwsHwl0+HEkYXa4xMRUNJGWWF+Tz5sav58O+t5qE9rVz/5f+elts+ldM9I3QPRa/vONI5uCDXlLJ0yM0R7rxiFQB/ft16llcUhp9z5eXyxg3VPH3kHMEY6zn2nA4FwSOFYfOyUo52DvHSqV7WVBeFuyEsNlQ0lJRS5HTw2bdu4ud/dhXj/mDUVEgbfyDIu7/5Im/92m85c35yK4hRr58zvaMai1Bi5r2Xr+TLt23j7qunD+m6oamWnuFxXm+dPrs7GnvP9LGysnBSfdDmZW68gSDPH+/mkpWL0zUFKhpKmti0zM3VF1TxzJGuGQPju0/30jM8zvkRL3/4rV10DHjCzx3vGsaYhQXBlaWFKy+X39/RSF6UPk/XbKgmN0d4+vC5KGdO5sz5EZ471s2V6yZ3VbYbEwYN7FiE9Rk2KhpK2rhuYy1dg+OTiqIieeJAJ668HP7zQ5cx6PHxnm/tCg9Psit84+05pSjRKCvMZ+fKcn59uGvOY//u54fIyxH+Ysr8i9VVxRRYKbaLNXMKVDSUNHLthmpEiPqHGgwanjjQyRsvqOayNZV85wOX0D7g4covPsPvf+MFvv/SGQryclkR4ZtWlIVwQ1MtRzqHaO2buQD114e6eObIOT52wwXUTmlEmJsjNNWXUFmUz+osmMA3X1Q0lLRRWexk+/KycKfZSF5t6ePc0Dhv3lIPwCWrKvjJR67krqvWEDSGwx1DXLyyjJwYJvUpSizcsKkWIOyi6h3x8v7v7uYLjx6kvd/DmC/AF35+kPU1xbz/ylVRr/HJmzZwz61bFnU7Gke6N6Asba5vquWfnjxK1+DYpE9uj+/vJD83h+uaasJrm5a52WT5jUe9/phGuypKrKyuKmJNdRG/PtzF27ct4w+/9RKnekZ4/ngPD+46Q1O9m9Y+Dz/48OVR4yIAb1gb2/TIbEYtDSWtXG+JwrMR1oYxhscPdHLV+ircrugjMAvzHTgdWp+hJJYbmmp56dR57rj3JZp7RvjOnZfw3P+8hjZO1+AAAAn+SURBVDsuXcGRziHetb2BK5b4vBUVDSWtbKgtoaGsgKcjRONA2yBt/R5u3lKXxp0pS5EbmmrxBQynz49w352XcNX6KhrLC/n7W7bwyt/eyD+++8J0bzHtqHtKSSsiwnUba/jx3lbGfAFcebk8fqCD3BzhxqbadG9PWWLsWFnO3Vev4bqNNVy+ZrJFYQ9bWurob0FJO9c31fD9l87wif96nTFvgN3NvVyxppLyRVpRq2QuuTnCZ97SlO5tZDQqGkrauXxNJTUlTp4+3MWqyiKuWl/Fh6NU7SqKkn5UNJS048rL5YVPXUeOiKbQKkqGo6KhZASOGVIYFUXJLPQvVVEURYkZFQ1FURQlZlQ0FEVRlJhR0VAURVFiRkVDURRFiRkVDUVRFCVmVDQURVGUmJGZRm1mOiIyBByd47AVwMxDqOemFBhI4vHx7i/e6y/0/IX8/uazV93f/H/WfM7R/S3snEzfXySRe11pjKme53VCbaiz8QvYE8Mx3Qv8Gfcm+fi49hfv9VO9v4XuVfen+9P9JfacROx16tdid0/1L/D8nyf5+Hj3F+/1F3r+Qn5/89mr7m/+P2s+5+j+FnZOpu8vkoW+F4bJZvfUHmPMzoUek050fwtD97cwdH8LI9P3F0ki95rNlsa9CTomnej+Fobub2Ho/hZGpu8vkoTtNWstDUVRFCX1ZLOloSiKoqQYFQ1FURQlZhaFaIjIcLr3MBsicquIGBHZmO69zMZcv0cReU5EUhr4E5FGEXlERI6LyEkR+VcRmXEOrIh8TEQKU7zHjL3/9N5bGNlw/035+Um/FxeFaGQBdwDPA7fHc5KI5CZnO9mBiAjwE+Bnxpj1wAVAMXDPLKd9DEjbH20GovfePNH7LzqLRjREpFhEnhaRV0Rkv4jcYq2vEpHDIvItETkoIk+JSEEq9wVcCdyF9YcrIteIyG9E5KcickhEvikiOdZzwyLy9yKyC7giVfuM2O81IvKLiO//TUTen+p9WFwHjBljvgtgjAkAHwc+KCJFIvLP1v/1PhH5cxH5H8Ay4FkReTaVG83E+0/vvQWTNfdfJMm+FxeNaABjwK3GmIuBa4EvW58UANYD/26M2UyoyOX3U7ivdwJPGGOOAb0icrG1finwCWArsBZ4l7VeBBwwxlxmjHk+hfvMRDYDeyMXjDGDhNohfAhYDWw3xlwIPGiM+RrQDlxrjLk2xXvNxPtP772FkU33XyRJvRcXk2gI8L9FZB/wa6ABqLWeazbGvGY93gusSuG+7gB+aD3+ofU9wG5jzCnr08sPgKus9QDwcAr3l8kIEC0nXICrgW8aY/wAxpjeVG4sCpl4/+m9tzCy6f6LJKn3oiMRO8wQ3gNUAzuMMT4ROQ24rOfGI44LAKlyD1QSMnG3iIgBcgndhL9k+s1ofz9m/TGnCz+TP0y4ZjowBRxkyichEXEDy4FTRP+DThcZdf/pvZcQsun+iySp9+JisjRKgXPWL+laYGW6NwS8G3jAGLPSGLPKGLMcaCb0ye5SEVlt+ZP/P0LBykzgDLBJRJwiUgpcn8a9PA0Uisj7IByc/TLwPeAp4E9ExGE9V2GdMwSUpH6rGXf/6b23cLLp/oskqfdi1ouG9Z82DjwI7BSRPYSU9khaNxbiDuCnU9YeBv4QeBH4InCA0B/z1ONSiv17NMa0AA8B+wj9Tl9N155MqF3BrcBtInIcOEbIX/sZ4NuEfMv7ROR1Qr9TCLVLeDxVgcgMvv/03lsg2XD/RZKqezHr24iIyDbgW8aYS9O9l1gRkWuATxpj3pbuvdhk4+8xE8i235vee4uXVP0es9rSEJE/IRTI+5t07yWb0d/j/NDf28LR32FiSOXvMestDUVRFCV1ZLWloSiKoqSWrBINEVkuIs9aVY0HReQvrPUKEfmVhPrD/EpEyq31Suv4YRH5tynXyheRe0XkmIgcEZFUFvwpWUii7j8RKRGR1yK+ekTkX9L1upTsI8HvhXfIRGX7EyJSNevPzib3lIjUA/XGmFdEpIRQcco7gfcDvcaYL4rIp4ByY8xfi0gRsB3YAmwxxvxZxLX+Dsg1xvyNlXpYYYzpSfVrUrKHRN5/U667F/i4MeY3KXkhStaTqHvRyrhqBzYZY3pE5B+BUWPMF2b62VllaRhjOowxr1iPh4DDhKodbwHutw67n9AvD2PMiNUOYSzK5T4I/B/ruKAKhjIXCb7/ABCR9UAN8Nskbl1ZZCTwXhTrq0hEBHATEpEZySrRiEREVhFSzl1ArTGmA0K/TEJ/hLOdW2Y9/F8Saur1XyJSO9s5ihLJQu6/KdwB/Mhkk8mvZBQLuReNMT7gI8B+LIsDuG+2c7JSNCTUvfNh4GNWA7F4cQCNwO+spl4vAv+cwC0qi5gE3H+R3E4oVVJR4mah96KI5BESje2EOvTuAz492zlZJxrWi3yYUFfJn1jLXZaPz/b1nZvjMueBUSYqYf8LuHjmwxUlRILuP/ta2wCHMWbvnAcryhQSdC9eBGCMOWlZuw8Bb5jthKwSDcvndh9w2BjzlYinHgXutB7fCTwy23WsX87PgWuspeuBQwndrLLoSNT9F8EdqJWhzIME3otthPp9VVvf30goPjLzz84mV6qIXEUoYLgfCFrLnyHky3sIWEGoH8xtdqtiCXV4dAP5hPrHv8kYc0hEVgLfB8qAbuADxpizqXs1SraRyPvPeu4U8BZjTLr7VClZRoLfC/8E+AvAR6hp5PuNMedn/NnZJBqKoihKeskq95SiKIqSXlQ0FEVRlJhR0VAURVFiRkVDURRFiRkVDUVRFCVmVDQUJQmIyJ+INVs6xuNXiciBZO5JURKBI90bUJTFhog4jDHfTPc+FCUZqGgoShSsJnBPECqW2g4cA94HNAFfAYqBHkKFUB0i8hzwAnAl8KjVrvr/tXO/oFmFURzHvwdnWXHFajU4RDAZxGy3iMiYVbAummwKMstcmPiniNUipoGCXYZBtBiMc/6rP8N9Xhgv751XudM3fD/pcng4PE+5h/Pcy/me5HZVnQE2gEXgA3AtyW5VnQW26EbavPp3p5P+ntdTUr+TwGaS08BX4DpwD7iUZPLCv7Vv/VKSC0nuTOV5BKy1PG+Bmy3+ALiR5NxhHkIak52G1O9Tktft+QndmIZl4GU3+ocjwOd9659OJ6iqY3TFZLuFHgLPZsQfAxfHP4I0LouG1G96xs43YOeAzuDHH+SuGfmluef1lNTvRFVNCsRl4A1wfBKrqqNVdeqgBEn2gN2qOt9CV4HtJF+AvTZ4DuDK+NuXxmenIfV7B6xU1X3gPd33jBfAerteWgDuAju/ybMCbFTVIvARWG3xVWCrqn62vNLcc8qtNEP7e+p5kuX/vBVprng9JUkazE5DkjSYnYYkaTCLhiRpMIuGJGkwi4YkaTCLhiRpsF+R41Dg9kZU6QAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-280:-180].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Etude de l'incidence annuelle\n", "\n", "Etant donné que l'épidémie reste assez importante au mois de Janvier, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année 𝑁\n", "au 1er septembre de l'année 𝑁+1.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est plutôt faible en septembre, cette modification ne risque moins de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en fin 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "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." ] }, { "cell_type": "code", "execution_count": 20, "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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+pExEDADvAOeOUtcQktZK6pLU1dvbm9kkMzMrV+6S209GxCFJs4FOST8aZV+NEItR4pWWORmI2ARsguLw1CjHZmZm45DV04iIQ+m5B3iS4nzFW2nIifTck3Y/CFxQUnw+cCjF548QH1JGUgtwDtA3Sl1mZlYDYyYNSWdJOntwG1gOvAJsAwZXM3UAT6XtbUB7WhF1MbAQ2J2GsI5KWprmK24YVmawrmuB59K8xzPAckmtaXXW8hQzM7MayBmeOh94Mq2ObQG+ExH/W9ILwBZJa4ADwHUAEbFX0hbgVWAAuCUijqe6bgIeAs4AtqcHwAPAI5K6KfYw2lNdfZLuBl5I+90VEX3jaK+ZmY2Db41uZma+NbqZmVWfk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZtuykIek0SS9K+vv0epakTkn703Nryb7rJXVL2idpRUl8saSX03sbJSnFp0t6IsV3SVpQUqYjfcZ+SR3VaLSZmVWmnJ7GrcBrJa/XATsiYiGwI71G0iKgHbgUWAl8U9Jpqcx9wFpgYXqsTPE1wJGI+DDwdeDeVNcs4E7gSmAJcGdpcjIzs8mVlTQkzQc+C3yrJLwK2Jy2NwOrS+KPR0R/RLwOdANLJM0BZkbEzogI4OFhZQbr2gosS72QFUBnRPRFxBGgk5OJxszMJlluT+OvgNuAEyWx8yPiMEB6np3i84A3S/Y7mGLz0vbw+JAyETEAvAOcO0pdZmZWA2MmDUl/APRExJ7MOjVCLEaJV1qm9BjXSuqS1NXb25t5mGZmVq6cnsYngaslvQE8Dnxa0qPAW2nIifTck/Y/CFxQUn4+cCjF548QH1JGUgtwDtA3Sl1DRMSmiChERKGtrS2jSWZmVokxk0ZErI+I+RGxgOIE93MR8UfANmBwNVMH8FTa3ga0pxVRF1Oc8N6dhrCOSlqa5ituGFZmsK5r02cE8AywXFJrmgBfnmJmZlYDLeMoew+wRdIa4ABwHUBE7JW0BXgVGABuiYjjqcxNwEPAGcD29AB4AHhEUjfFHkZ7qqtP0t3AC2m/uyKibxzHbGZm46DiF/rmUSgUoqurq9aHYWbWUCTtiYjCWPv5inAzM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyjZk0JM2QtFvSDyXtlfRfU3yWpE5J+9Nza0mZ9ZK6Je2TtKIkvljSy+m9jZKU4tMlPZHiuyQtKCnTkT5jv6SOajbezMzKk9PT6Ac+HRG/BVwOrJS0FFgH7IiIhcCO9BpJi4B24FJgJfBNSaeluu4D1gIL02Nliq8BjkTEh4GvA/emumYBdwJXAkuAO0uTk5mZTa4xk0YUvZdenp4eAawCNqf4ZmB12l4FPB4R/RHxOtANLJE0B5gZETsjIoCHh5UZrGsrsCz1QlYAnRHRFxFHgE5OJhozM5tkWXMakk6T9BLQQ/Ef8V3A+RFxGCA9z067zwPeLCl+MMXmpe3h8SFlImIAeAc4d5S6zMysBrKSRkQcj4jLgfkUew2XjbK7RqpilHilZU5+oLRWUpekrt7e3lEOzczMxqOs1VMR8QvgexSHiN5KQ06k556020HggpJi84FDKT5/hPiQMpJagHOAvlHqGn5cmyKiEBGFtra2cppkZmZlyFk91Sbpg2n7DOD3gB8B24DB1UwdwFNpexvQnlZEXUxxwnt3GsI6Kmlpmq+4YViZwbquBZ5L8x7PAMsltaYJ8OUpZmZmNdCSsc8cYHNaATUN2BIRfy9pJ7BF0hrgAHAdQETslbQFeBUYAG6JiOOprpuAh4AzgO3pAfAA8Iikboo9jPZUV5+ku4EX0n53RUTfeBpsZmaVU/ELffMoFArR1dVV68MwM2sokvZERGGs/XxFuJmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDNrAj3vHuNz9++k5+ixCf0cJw0zsyawccd+Xnijj43P7p/Qz2mZ0NrNzGxCXfK17fQPnPjV60d3HeDRXQeY3jKNfRs+U/XPc0/DzKyBPX/bVVx9+VxmnF7853zG6dNYdflcnr/9qgn5PCcNM7MGNnvmDM6e3kL/wAmmt0yjf+AEZ09vYfbZMybk8zw8ZWbW4N5+r5/rr7yILyy5kO/sPkDvBE6GKyJG30G6AHgY+BBwAtgUEX8taRbwBLAAeAP4XEQcSWXWA2uA48CfRcQzKb4YeAg4A3gauDUiQtL09BmLgf8HfD4i3khlOoCvpcPZEBGbRzveQqEQXV1d+f8FzMwMSXsiojDWfjnDUwPAf46IjwJLgVskLQLWATsiYiGwI70mvdcOXAqsBL4p6bRU133AWmBheqxM8TXAkYj4MPB14N5U1yzgTuBKYAlwp6TWjGM2M7MJMGbSiIjDEfGDtH0UeA2YB6wCBr/1bwZWp+1VwOMR0R8RrwPdwBJJc4CZEbEzit2bh4eVGaxrK7BMkoAVQGdE9KVeTCcnE42ZmU2ysibCJS0ArgB2AedHxGEoJhZgdtptHvBmSbGDKTYvbQ+PDykTEQPAO8C5o9RlZmY1kJ00JP0G8L+Ar0TEu6PtOkIsRolXWqb02NZK6pLU1dvbO8qhmZnZeGQlDUmnU0wY346Iv03ht9KQE+m5J8UPAheUFJ8PHErx+SPEh5SR1AKcA/SNUtcQEbEpIgoRUWhra8tpkpmZVWDMpJHmFh4AXouI/17y1jagI213AE+VxNslTZd0McUJ791pCOuopKWpzhuGlRms61rguTTv8QywXFJrmgBfnmJmZlYDOUtuPwU8D7xMccktwFcpzmtsAS4EDgDXRURfKnMHcCPFlVdfiYjtKV7g5JLb7cCX05LbGcAjFOdL+oD2iPhJKnNj+jyAv4iIB8c43l7gp5ntr0fnAW/X+iAmSLO2ze1qPM3atvG066KIGHOoZsykYZNLUlfOWulG1Kxtc7saT7O2bTLa5duImJlZNicNMzPL5qRRfzbV+gAmULO2ze1qPM3atglvl+c0zMwsm3saZmaWzUljEkj6G0k9kl4pif2WpJ2SXpb0d5JmpvgHJD2Y4j+U9LslZb4naZ+kl9Jj9ggfN2kkXSDp/0h6TdJeSbem+CxJnZL2p+fWkjLrJXWndqwoiS9Obe6WtDFdy1MTVW5X3Zyzctsl6dy0/3uSvjGsrro5X+l4qtm2Rj5nvy9pTzo3eyR9uqSu6pyziPBjgh/A7wCfAF4pib0A/Ie0fSNwd9q+BXgwbc8G9gDT0uvvAYVat6ekDXOAT6Tts4F/BRYBfwmsS/F1wL1pexHwQ2A6cDHwY+C09N5u4Lcp3jpmO/CZJmlX3ZyzCtp1FvAp4EvAN4bVVTfnawLa1sjn7Apgbtq+DPhZtc+ZexqTICL+keJFi6UuAf4xbXcCf5i2F1G81TwR0QP8AqjL9eQxOXdAnnTVatfkHvXYym1XRPwyIv4vMOQXfertfEH12lZvKmjXixExeKulvcAMFe/OUbVz5qRRO68AV6ft6zh5j60fAqsktah4G5bFDL3/1oOpy/xfaj0kUEoTdwfkmhpnuwbV3TnLbNep1O35gnG3bVAznLM/BF6MiH6qeM6cNGrnRoo/aLWHYrfz31L8byie0C7gr4B/png7FoDrI+JjwL9Pjy9O6hGfgib2Dsg1U4V2QR2eszLadcoqRojV/HxBVdoGTXDOJF1K8cfs/mQwNMJuFZ0zJ40aiYgfRcTyiFgMPEZxHJyIGIiIP4+IyyNiFfBBYH9672fp+SjwHepgCEQTfwfkmqhSu+runJXZrlOpu/MFVWtbw58zSfOBJ4EbIuLHKVy1c+akUSODKzIkTaP4G+j/I70+U9JZafv3gYGIeDUNV52X4qcDf0BxiKtmUrd9ou+APOmq1a56O2cVtGtE9Xa+oHpta/RzJumDwD8A6yPinwZ3ruo5m4wVAFP9QbEncRh4n2LGXwPcSnElxL8C93DyQssFwD6KE17PUrzzJBRXe+wB/oXiBNdfk1bo1LBdn6LYxf0X4KX0+I8Uf3VxB8Ue0g5gVkmZOyj2qvZRsnqD4mT/K+m9bwz+92jkdtXbOauwXW9QXMTxXvp/d1G9na9qtq3RzxnFL6C/LNn3JWB2Nc+Zrwg3M7NsHp4yM7NsThpmZpbNScPMzLI5aZiZWTYnDTMzy+akYWZm2Zw0zMwsm5OGmZll+/+mYFOfctSjewAAAABJRU5ErkJggg==\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 et les moins élevées. On remarque d'ailleurs que la crise du covid donne une valeur qui semble faible par rapport aux autre données. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "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": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La valeur la plus basse est 2020, sûrement suite aux différentes consignes sanitaires. Le site FUN n'ayant pas été mis à jour, nous devons considérer que la valeur minimale pour l'incidence de la varicelle est plutôt 2002... " ] }, { "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": 2 }