diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..84df5b3baf0e22544feb5d9d542812e666c86d3e 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -261,30 +261,30 @@ "" ], "text/plain": [ - " Date Count Temperature Pressure Malfunction\n", - "0 4/12/81 6 66 50 0\n", - "1 11/12/81 6 70 50 1\n", - "2 3/22/82 6 69 50 0\n", - "3 11/11/82 6 68 50 0\n", - "4 4/04/83 6 67 50 0\n", - "5 6/18/82 6 72 50 0\n", - "6 8/30/83 6 73 100 0\n", - "7 11/28/83 6 70 100 0\n", - "8 2/03/84 6 57 200 1\n", - "9 4/06/84 6 63 200 1\n", - "10 8/30/84 6 70 200 1\n", - "11 10/05/84 6 78 200 0\n", - "12 11/08/84 6 67 200 0\n", - "13 1/24/85 6 53 200 2\n", - "14 4/12/85 6 67 200 0\n", - "15 4/29/85 6 75 200 0\n", - "16 6/17/85 6 70 200 0\n", - "17 7/29/85 6 81 200 0\n", - "18 8/27/85 6 76 200 0\n", - "19 10/03/85 6 79 200 0\n", - "20 10/30/85 6 75 200 2\n", - "21 11/26/85 6 76 200 0\n", - "22 1/12/86 6 58 200 1" + " Date Count Temperature Pressure Malfunction\n", + "0 4/12/81 6 66 50 0\n", + "1 11/12/81 6 70 50 1\n", + "2 3/22/82 6 69 50 0\n", + "3 11/11/82 6 68 50 0\n", + "4 4/04/83 6 67 50 0\n", + "5 6/18/82 6 72 50 0\n", + "6 8/30/83 6 73 100 0\n", + "7 11/28/83 6 70 100 0\n", + "8 2/03/84 6 57 200 1\n", + "9 4/06/84 6 63 200 1\n", + "10 8/30/84 6 70 200 1\n", + "11 10/05/84 6 78 200 0\n", + "12 11/08/84 6 67 200 0\n", + "13 1/24/85 6 53 200 2\n", + "14 4/12/85 6 67 200 0\n", + "15 4/29/85 6 75 200 0\n", + "16 6/17/85 6 70 200 0\n", + "17 7/29/85 6 81 200 0\n", + "18 8/27/85 6 76 200 0\n", + "19 10/03/85 6 79 200 0\n", + "20 10/30/85 6 75 200 2\n", + "21 11/26/85 6 76 200 0\n", + "22 1/12/86 6 58 200 1" ] }, "execution_count": 1, @@ -355,6 +355,14 @@ " \n", " \n", " \n", + " 0\n", + " 4/12/81\n", + " 6\n", + " 66\n", + " 50\n", + " 0\n", + " \n", + " \n", " 1\n", " 11/12/81\n", " 6\n", @@ -363,6 +371,54 @@ " 1\n", " \n", " \n", + " 2\n", + " 3/22/82\n", + " 6\n", + " 69\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 3\n", + " 11/11/82\n", + " 6\n", + " 68\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 4\n", + " 4/04/83\n", + " 6\n", + " 67\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 5\n", + " 6/18/82\n", + " 6\n", + " 72\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 6\n", + " 8/30/83\n", + " 6\n", + " 73\n", + " 100\n", + " 0\n", + " \n", + " \n", + " 7\n", + " 11/28/83\n", + " 6\n", + " 70\n", + " 100\n", + " 0\n", + " \n", + " \n", " 8\n", " 2/03/84\n", " 6\n", @@ -387,6 +443,22 @@ " 1\n", " \n", " \n", + " 11\n", + " 10/05/84\n", + " 6\n", + " 78\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 12\n", + " 11/08/84\n", + " 6\n", + " 67\n", + " 200\n", + " 0\n", + " \n", + " \n", " 13\n", " 1/24/85\n", " 6\n", @@ -395,6 +467,54 @@ " 2\n", " \n", " \n", + " 14\n", + " 4/12/85\n", + " 6\n", + " 67\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 15\n", + " 4/29/85\n", + " 6\n", + " 75\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 16\n", + " 6/17/85\n", + " 6\n", + " 70\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 17\n", + " 7/29/85\n", + " 6\n", + " 81\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 18\n", + " 8/27/85\n", + " 6\n", + " 76\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 19\n", + " 10/03/85\n", + " 6\n", + " 79\n", + " 200\n", + " 0\n", + " \n", + " \n", " 20\n", " 10/30/85\n", " 6\n", @@ -403,6 +523,14 @@ " 2\n", " \n", " \n", + " 21\n", + " 11/26/85\n", + " 6\n", + " 76\n", + " 200\n", + " 0\n", + " \n", + " \n", " 22\n", " 1/12/86\n", " 6\n", @@ -416,12 +544,28 @@ ], "text/plain": [ " Date Count Temperature Pressure Malfunction\n", + "0 4/12/81 6 66 50 0\n", "1 11/12/81 6 70 50 1\n", + "2 3/22/82 6 69 50 0\n", + "3 11/11/82 6 68 50 0\n", + "4 4/04/83 6 67 50 0\n", + "5 6/18/82 6 72 50 0\n", + "6 8/30/83 6 73 100 0\n", + "7 11/28/83 6 70 100 0\n", "8 2/03/84 6 57 200 1\n", "9 4/06/84 6 63 200 1\n", "10 8/30/84 6 70 200 1\n", + "11 10/05/84 6 78 200 0\n", + "12 11/08/84 6 67 200 0\n", "13 1/24/85 6 53 200 2\n", + "14 4/12/85 6 67 200 0\n", + "15 4/29/85 6 75 200 0\n", + "16 6/17/85 6 70 200 0\n", + "17 7/29/85 6 81 200 0\n", + "18 8/27/85 6 76 200 0\n", + "19 10/03/85 6 79 200 0\n", "20 10/30/85 6 75 200 2\n", + "21 11/26/85 6 76 200 0\n", "22 1/12/86 6 58 200 1" ] }, @@ -431,7 +575,7 @@ } ], "source": [ - "data = data[data.Malfunction>0]\n", + "data = data[data.Malfunction>=0]\n", "data" ] }, @@ -453,7 +597,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -469,7 +613,7 @@ "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", "import matplotlib.pyplot as plt\n", "\n", - "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data[\"Frequency\"]= [(1 if d>0 else 0) for d in data.Malfunction] #data.Malfunction/data.Count\n", "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", "plt.grid(True)" ] @@ -509,10 +653,10 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -521,16 +665,16 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 7Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 5Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250Method: IRLS Log-Likelihood: -10.158
Date: Sat, 13 Apr 2019 Deviance: 0.22231Date: Mon, 10 May 2021 Deviance: 20.315
Time: 19:11:24 Pearson chi2: 0.236Time: 12:48:12 Pearson chi2: 23.2
No. Iterations: 4 Covariance Type: nonrobustNo. Iterations: 5 Covariance Type: nonrobust
\n", "\n", @@ -538,10 +682,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953Intercept 15.0429 7.379 2.039 0.041 0.581 29.505
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240Temperature -0.2322 0.108 -2.145 0.032 -0.444 -0.020
" ], @@ -550,19 +694,19 @@ "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", - "Dep. Variable: Frequency No. Observations: 7\n", - "Model: GLM Df Residuals: 5\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", - "Method: IRLS Log-Likelihood: -2.5250\n", - "Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", - "Time: 19:11:24 Pearson chi2: 0.236\n", - "No. Iterations: 4 Covariance Type: nonrobust\n", + "Method: IRLS Log-Likelihood: -10.158\n", + "Date: Mon, 10 May 2021 Deviance: 20.315\n", + "Time: 12:48:12 Pearson chi2: 23.2\n", + "No. Iterations: 5 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", - "Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", - "Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", + "Intercept 15.0429 7.379 2.039 0.041 0.581 29.505\n", + "Temperature -0.2322 0.108 -2.145 0.032 -0.444 -0.020\n", "===============================================================================\n", "\"\"\"" ] @@ -610,7 +754,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -705,7 +849,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..2d829b34d087cdce1cfb23152dd753e97adada79 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,3357 @@ { - "cells": [], + "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 1984 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": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020211774939302068587410FRFrance
120211674780289166697410FRFrance
2202115711215762714803171222FRFrance
3202114711197799414400171222FRFrance
420211379714628913139151020FRFrance
5202112711520841514625171222FRFrance
620211179386667812094141018FRFrance
720211079056645211660141018FRFrance
8202109710988793814038171222FRFrance
9202108711281836114201171321FRFrance
102021077135611031516807211626FRFrance
11202106713401981016992201525FRFrance
12202105712210898815432181323FRFrance
13202104712026882615226181323FRFrance
142021037891363751145113917FRFrance
152021027779554301016012816FRFrance
16202101710525775013300161220FRFrance
17202053711978840615550181323FRFrance
18202052712012828515739181224FRFrance
19202051710564757413554161121FRFrance
20202050770634744938211715FRFrance
2120204975026314569078511FRFrance
22202048766834312905410614FRFrance
2320204774999296370358511FRFrance
242020467375219635541639FRFrance
252020457369620165376639FRFrance
2620204474391237564077410FRFrance
2720204374376250562477410FRFrance
282020427400019796021639FRFrance
292020417396120995823639FRFrance
.................................
15571991267176081130423912312042FRFrance
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15591991247161711007122271281739FRFrance
1560199123711947767116223211329FRFrance
1561199122715452995320951271737FRFrance
1562199121714903897520831261636FRFrance
15631991207190531274225364342345FRFrance
15641991197167391124622232291939FRFrance
15651991187213851388228888382551FRFrance
1566199117713462887718047241632FRFrance
15671991167148571006819646261834FRFrance
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157019911379567604113093171123FRFrance
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1574199109713741878018702241533FRFrance
1575199108713289881317765231531FRFrance
1576199107712337807716597221529FRFrance
1577199106710877701314741191226FRFrance
1578199105710442654414340181125FRFrance
15791991047791345631126314820FRFrance
15801991037153871048420290271836FRFrance
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15841990517190801380724353342543FRFrance
1585199050711079666015498201228FRFrance
15861990497114302610205FRFrance
\n", + "

1587 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202117 7 4939 3020 6858 7 4 \n", + "1 202116 7 4780 2891 6669 7 4 \n", + "2 202115 7 11215 7627 14803 17 12 \n", + "3 202114 7 11197 7994 14400 17 12 \n", + "4 202113 7 9714 6289 13139 15 10 \n", + "5 202112 7 11520 8415 14625 17 12 \n", + "6 202111 7 9386 6678 12094 14 10 \n", + "7 202110 7 9056 6452 11660 14 10 \n", + "8 202109 7 10988 7938 14038 17 12 \n", + "9 202108 7 11281 8361 14201 17 13 \n", + "10 202107 7 13561 10315 16807 21 16 \n", + "11 202106 7 13401 9810 16992 20 15 \n", + "12 202105 7 12210 8988 15432 18 13 \n", + "13 202104 7 12026 8826 15226 18 13 \n", + "14 202103 7 8913 6375 11451 13 9 \n", + "15 202102 7 7795 5430 10160 12 8 \n", + "16 202101 7 10525 7750 13300 16 12 \n", + "17 202053 7 11978 8406 15550 18 13 \n", + "18 202052 7 12012 8285 15739 18 12 \n", + "19 202051 7 10564 7574 13554 16 11 \n", + "20 202050 7 7063 4744 9382 11 7 \n", + "21 202049 7 5026 3145 6907 8 5 \n", + "22 202048 7 6683 4312 9054 10 6 \n", + "23 202047 7 4999 2963 7035 8 5 \n", + "24 202046 7 3752 1963 5541 6 3 \n", + "25 202045 7 3696 2016 5376 6 3 \n", + "26 202044 7 4391 2375 6407 7 4 \n", + "27 202043 7 4376 2505 6247 7 4 \n", + "28 202042 7 4000 1979 6021 6 3 \n", + "29 202041 7 3961 2099 5823 6 3 \n", + "... ... ... ... ... ... ... ... \n", + "1557 199126 7 17608 11304 23912 31 20 \n", + "1558 199125 7 16169 10700 21638 28 18 \n", + "1559 199124 7 16171 10071 22271 28 17 \n", + "1560 199123 7 11947 7671 16223 21 13 \n", + "1561 199122 7 15452 9953 20951 27 17 \n", + "1562 199121 7 14903 8975 20831 26 16 \n", + "1563 199120 7 19053 12742 25364 34 23 \n", + "1564 199119 7 16739 11246 22232 29 19 \n", + "1565 199118 7 21385 13882 28888 38 25 \n", + "1566 199117 7 13462 8877 18047 24 16 \n", + "1567 199116 7 14857 10068 19646 26 18 \n", + "1568 199115 7 13975 9781 18169 25 18 \n", + "1569 199114 7 12265 7684 16846 22 14 \n", + "1570 199113 7 9567 6041 13093 17 11 \n", + "1571 199112 7 10864 7331 14397 19 13 \n", + "1572 199111 7 15574 11184 19964 27 19 \n", + "1573 199110 7 16643 11372 21914 29 20 \n", + "1574 199109 7 13741 8780 18702 24 15 \n", + "1575 199108 7 13289 8813 17765 23 15 \n", + "1576 199107 7 12337 8077 16597 22 15 \n", + "1577 199106 7 10877 7013 14741 19 12 \n", + "1578 199105 7 10442 6544 14340 18 11 \n", + "1579 199104 7 7913 4563 11263 14 8 \n", + "1580 199103 7 15387 10484 20290 27 18 \n", + "1581 199102 7 16277 11046 21508 29 20 \n", + "1582 199101 7 15565 10271 20859 27 18 \n", + "1583 199052 7 19375 13295 25455 34 23 \n", + "1584 199051 7 19080 13807 24353 34 25 \n", + "1585 199050 7 11079 6660 15498 20 12 \n", + "1586 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 10 FR France \n", + "1 10 FR France \n", + "2 22 FR France \n", + "3 22 FR France \n", + "4 20 FR France \n", + "5 22 FR France \n", + "6 18 FR France \n", + "7 18 FR France \n", + "8 22 FR France \n", + "9 21 FR France \n", + "10 26 FR France \n", + "11 25 FR France \n", + "12 23 FR France \n", + "13 23 FR France \n", + "14 17 FR France \n", + "15 16 FR France \n", + "16 20 FR France \n", + "17 23 FR France \n", + "18 24 FR France \n", + "19 21 FR France \n", + "20 15 FR France \n", + "21 11 FR France \n", + "22 14 FR France \n", + "23 11 FR France \n", + "24 9 FR France \n", + "25 9 FR France \n", + "26 10 FR France \n", + "27 10 FR France \n", + "28 9 FR France \n", + "29 9 FR France \n", + "... ... ... ... \n", + "1557 42 FR France \n", + "1558 38 FR France \n", + "1559 39 FR France \n", + "1560 29 FR France \n", + "1561 37 FR France \n", + "1562 36 FR France \n", + "1563 45 FR France \n", + "1564 39 FR France \n", + "1565 51 FR France \n", + "1566 32 FR France \n", + "1567 34 FR France \n", + "1568 32 FR France \n", + "1569 30 FR France \n", + "1570 23 FR France \n", + "1571 25 FR France \n", + "1572 35 FR France \n", + "1573 38 FR France \n", + "1574 33 FR France \n", + "1575 31 FR France \n", + "1576 29 FR France \n", + "1577 26 FR France \n", + "1578 25 FR France \n", + "1579 20 FR France \n", + "1580 36 FR France \n", + "1581 38 FR France \n", + "1582 36 FR France \n", + "1583 45 FR France \n", + "1584 43 FR France \n", + "1585 28 FR France \n", + "1586 5 FR France \n", + "\n", + "[1587 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? **Non! Super!**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020211774939302068587410FRFrance
120211674780289166697410FRFrance
2202115711215762714803171222FRFrance
3202114711197799414400171222FRFrance
420211379714628913139151020FRFrance
5202112711520841514625171222FRFrance
620211179386667812094141018FRFrance
720211079056645211660141018FRFrance
8202109710988793814038171222FRFrance
9202108711281836114201171321FRFrance
102021077135611031516807211626FRFrance
11202106713401981016992201525FRFrance
12202105712210898815432181323FRFrance
13202104712026882615226181323FRFrance
142021037891363751145113917FRFrance
152021027779554301016012816FRFrance
16202101710525775013300161220FRFrance
17202053711978840615550181323FRFrance
18202052712012828515739181224FRFrance
19202051710564757413554161121FRFrance
20202050770634744938211715FRFrance
2120204975026314569078511FRFrance
22202048766834312905410614FRFrance
2320204774999296370358511FRFrance
242020467375219635541639FRFrance
252020457369620165376639FRFrance
2620204474391237564077410FRFrance
2720204374376250562477410FRFrance
282020427400019796021639FRFrance
292020417396120995823639FRFrance
.................................
15571991267176081130423912312042FRFrance
15581991257161691070021638281838FRFrance
15591991247161711007122271281739FRFrance
1560199123711947767116223211329FRFrance
1561199122715452995320951271737FRFrance
1562199121714903897520831261636FRFrance
15631991207190531274225364342345FRFrance
15641991197167391124622232291939FRFrance
15651991187213851388228888382551FRFrance
1566199117713462887718047241632FRFrance
15671991167148571006819646261834FRFrance
1568199115713975978118169251832FRFrance
1569199114712265768416846221430FRFrance
157019911379567604113093171123FRFrance
1571199112710864733114397191325FRFrance
15721991117155741118419964271935FRFrance
15731991107166431137221914292038FRFrance
1574199109713741878018702241533FRFrance
1575199108713289881317765231531FRFrance
1576199107712337807716597221529FRFrance
1577199106710877701314741191226FRFrance
1578199105710442654414340181125FRFrance
15791991047791345631126314820FRFrance
15801991037153871048420290271836FRFrance
15811991027162771104621508292038FRFrance
15821991017155651027120859271836FRFrance
15831990527193751329525455342345FRFrance
15841990517190801380724353342543FRFrance
1585199050711079666015498201228FRFrance
15861990497114302610205FRFrance
\n", + "

1587 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202117 7 4939 3020 6858 7 4 \n", + "1 202116 7 4780 2891 6669 7 4 \n", + "2 202115 7 11215 7627 14803 17 12 \n", + "3 202114 7 11197 7994 14400 17 12 \n", + "4 202113 7 9714 6289 13139 15 10 \n", + "5 202112 7 11520 8415 14625 17 12 \n", + "6 202111 7 9386 6678 12094 14 10 \n", + "7 202110 7 9056 6452 11660 14 10 \n", + "8 202109 7 10988 7938 14038 17 12 \n", + "9 202108 7 11281 8361 14201 17 13 \n", + "10 202107 7 13561 10315 16807 21 16 \n", + "11 202106 7 13401 9810 16992 20 15 \n", + "12 202105 7 12210 8988 15432 18 13 \n", + "13 202104 7 12026 8826 15226 18 13 \n", + "14 202103 7 8913 6375 11451 13 9 \n", + "15 202102 7 7795 5430 10160 12 8 \n", + "16 202101 7 10525 7750 13300 16 12 \n", + "17 202053 7 11978 8406 15550 18 13 \n", + "18 202052 7 12012 8285 15739 18 12 \n", + "19 202051 7 10564 7574 13554 16 11 \n", + "20 202050 7 7063 4744 9382 11 7 \n", + "21 202049 7 5026 3145 6907 8 5 \n", + "22 202048 7 6683 4312 9054 10 6 \n", + "23 202047 7 4999 2963 7035 8 5 \n", + "24 202046 7 3752 1963 5541 6 3 \n", + "25 202045 7 3696 2016 5376 6 3 \n", + "26 202044 7 4391 2375 6407 7 4 \n", + "27 202043 7 4376 2505 6247 7 4 \n", + "28 202042 7 4000 1979 6021 6 3 \n", + "29 202041 7 3961 2099 5823 6 3 \n", + "... ... ... ... ... ... ... ... \n", + "1557 199126 7 17608 11304 23912 31 20 \n", + "1558 199125 7 16169 10700 21638 28 18 \n", + "1559 199124 7 16171 10071 22271 28 17 \n", + "1560 199123 7 11947 7671 16223 21 13 \n", + "1561 199122 7 15452 9953 20951 27 17 \n", + "1562 199121 7 14903 8975 20831 26 16 \n", + "1563 199120 7 19053 12742 25364 34 23 \n", + "1564 199119 7 16739 11246 22232 29 19 \n", + "1565 199118 7 21385 13882 28888 38 25 \n", + "1566 199117 7 13462 8877 18047 24 16 \n", + "1567 199116 7 14857 10068 19646 26 18 \n", + "1568 199115 7 13975 9781 18169 25 18 \n", + "1569 199114 7 12265 7684 16846 22 14 \n", + "1570 199113 7 9567 6041 13093 17 11 \n", + "1571 199112 7 10864 7331 14397 19 13 \n", + "1572 199111 7 15574 11184 19964 27 19 \n", + "1573 199110 7 16643 11372 21914 29 20 \n", + "1574 199109 7 13741 8780 18702 24 15 \n", + "1575 199108 7 13289 8813 17765 23 15 \n", + "1576 199107 7 12337 8077 16597 22 15 \n", + "1577 199106 7 10877 7013 14741 19 12 \n", + "1578 199105 7 10442 6544 14340 18 11 \n", + "1579 199104 7 7913 4563 11263 14 8 \n", + "1580 199103 7 15387 10484 20290 27 18 \n", + "1581 199102 7 16277 11046 21508 29 20 \n", + "1582 199101 7 15565 10271 20859 27 18 \n", + "1583 199052 7 19375 13295 25455 34 23 \n", + "1584 199051 7 19080 13807 24353 34 25 \n", + "1585 199050 7 11079 6660 15498 20 12 \n", + "1586 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 10 FR France \n", + "1 10 FR France \n", + "2 22 FR France \n", + "3 22 FR France \n", + "4 20 FR France \n", + "5 22 FR France \n", + "6 18 FR France \n", + "7 18 FR France \n", + "8 22 FR France \n", + "9 21 FR France \n", + "10 26 FR France \n", + "11 25 FR France \n", + "12 23 FR France \n", + "13 23 FR France \n", + "14 17 FR France \n", + "15 16 FR France \n", + "16 20 FR France \n", + "17 23 FR France \n", + "18 24 FR France \n", + "19 21 FR France \n", + "20 15 FR France \n", + "21 11 FR France \n", + "22 14 FR France \n", + "23 11 FR France \n", + "24 9 FR France \n", + "25 9 FR France \n", + "26 10 FR France \n", + "27 10 FR France \n", + "28 9 FR France \n", + "29 9 FR France \n", + "... ... ... ... \n", + "1557 42 FR France \n", + "1558 38 FR France \n", + "1559 39 FR France \n", + "1560 29 FR France \n", + "1561 37 FR France \n", + "1562 36 FR France \n", + "1563 45 FR France \n", + "1564 39 FR France \n", + "1565 51 FR France \n", + "1566 32 FR France \n", + "1567 34 FR France \n", + "1568 32 FR France \n", + "1569 30 FR France \n", + "1570 23 FR France \n", + "1571 25 FR France \n", + "1572 35 FR France \n", + "1573 38 FR France \n", + "1574 33 FR France \n", + "1575 31 FR France \n", + "1576 29 FR France \n", + "1577 26 FR France \n", + "1578 25 FR France \n", + "1579 20 FR France \n", + "1580 36 FR France \n", + "1581 38 FR France \n", + "1582 36 FR France \n", + "1583 45 FR France \n", + "1584 43 FR France \n", + "1585 28 FR France \n", + "1586 5 FR France \n", + "\n", + "[1587 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 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'." + ] + }, + { + "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 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." + ] + }, + { + "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\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": 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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve **fin** été." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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E/FJenQPb1WZ1ozncStlcewqzBxKQKZF95aRczCFhFRK60R2SYdBcJ9ZSbiV2TebX5t9D5jEHThPR1sYhk9XxY6uFRDPwgxcncWY+WVQJzHaxH7txN/7gHRfYRmGkO2ccShGSpbz+QkwlmO6lXEA6oWg4PLZUuxdTI8xhPObiSwhByCe69ksqxK5zcI05aCXdRb0Rs06EdWctqRyk0srBy8aWw2FUNA6EkK8RQmYJIa86jn2KEHKeEPKi9e9djp99ghBykhBynBByk+P4lYSQV6yffZEQQqzjfkLId6zjzxBCttX2JZbmqz85g9u/9izmE0q9fuWaoZTiX545BwBYLJjHwPzfFw124LfethvWrcWwZRxKLWAAEPGLSKqaXTyWsXa1Wd3Iq3O497lxvP8rBz3XF0jVjbxdelAWq3QrlW68l6ygHABgbNGsPK/GreT38ZgDp/moRjl8HcDNLsf/llK63/p3PwAQQi4GcBuAvdY5XyKEsITzLwO4A8Bu6x97zg8DWKKU7gLwtwA+s8bXsmoeOToDoPRcYC/xyvk4XptaBgAsJvONGVuwWT8lxuaOAESBlPSfA2bMgdLcIsmUg6oZyGq5mEMsnYVuUEy4tKxuJErWyAv+BmWxOrdSmd5K5dxKPWHWlsS8D6XubZ5ysL72iQSEAArvysppAioaB0rpkwAWq3y+WwB8m1KqUErPADgJ4GpCyCCADkrpQWpuUb8B4Bcd59xjff1dADcyVbGRLCQUvDhuumc0DzeWY/zroQkEfAL2DnVgMZk/MIalbnYE8xcqSRSwuSNQ0i8OwB5/yRZJxaEcnG4lNvNhYql4ElojMVti5wreglW6lWzlUKJCupRbqdA4dJRKZZWKlQMhBH6rWyuH43XWE3P4LULIy5bbqds6Ngxg3PGYCevYsPV14fG8cyilGoA4gF63X0gIuYMQcogQcmhubn2xgieOz9m9hVbT6rkRGAbFQ0emccOeTdjWGy4yDqWUAwB85Iad+MBVoyWfO2LVRLA2EhmncnAEpFlWz3nPGQe9wK0kVeVWYjEHVTeKsocqBaQBMyDtE0ne73biNFiBguA0Nw6cZmCtxuHLAHYC2A9gCsDfWMfddvy0zPFy5xQfpPRuSukBSumB/v7+1V1xAY8dy1XRet04vDAew+yKgpv2bkZPWC5WDlbMwW0X+yvXbMU7Lx0s+dwsxZWls7KFS9Vz9Q6KZiCtmsfdZig3EucwHcCsdag2W4nFBZzprGxEaCl3UVAWEfSJ0A2KaMCHUiKXKQdWoOc8zo0DpxlYk3GglM5QSnVKqQHgHwBcbf1oAoBzmzoCYNI6PuJyPO8cQogEoBPVu7HWzNOnF9BnZZ5ourfdSg8fmYZPJLjhwk3oDsu2/58RT2chCcROYV0NLJMpoeS7lVTNsHfXbGgNAG/GHJxuJVlEKltNERxFZ8g0ps50VlYEWC7Di7mWygX6mXEIFCgL063EYw4c77Mm42DFEBj/BQDLZLoPwG1WBtJ2mIHnZymlUwBWCCHXWvGEDwL4geOc262v3wvgMbrBQ3YppVhKqRi2isQ0w7s7OUopHjwyjTfs7ENn0IfesAxKgVgqpx7i6Sw6g6V3seVgMQe2e2YB6axO82IOtlvJc8pBLwpIV4o5sCK/LssN50xnZQqqGuNQ7jFMlRQ2AOQxB06zULF9BiHkWwCuB9BHCJkA8EkA1xNC9sN0/5wF8JsAQCk9Qgi5F8BrADQAH6WUsk/qnTAzn4IAHrD+AcBXAXyTEHISpmK4rRYvrBxJVYdBgR5r58jaRHiRuYSCsYUUbn/DNgC5eQyLSRW9EbP99nI6iw6XeEM1sJhDQtFhGDTniy/IVmJzj70XczDy0karcSsxo8fiB07lwGIvpWIOQO5vUE45OAvz8o+Lnq4453AYFY0DpfQDLoe/WubxdwG4y+X4IQCXuBzPAHhfpeuoJWyCV0/YXFy9rBwylq+fBZt7HcaBEV+HcWCLYFLR8iaUZYtiDuaiuZTKlq0DqDfO9hlAdXUOzDgwt5JTOawo5nujnCrotY1D6Xtuu5UKlYOPu5U4zUFbVkizdhO9TRBzYAu2z1pselyMw3JGs+c1rBancchk833vqsOtlM7q9tQzL7mWlKxeHHOoqBzMvzdzK7kph3LGgSmOamIOsmvMwbubEQ6H0abGwdwdsg+5l2f6sjRLWTRXZmYcFpzGwYo5rAXWyTSp6HmLVjqb328pperYYs1Q9pJrqThbSYKqGXkB+0KYcuhiAWmHckhYyoG1PXejJ2yeV6rGAci5k4pjDjyVldMctKVxYEVj7EPuZeXAFjK2A2UGbalGxkESBQR8ApKqlucLd7ayVrJmttKuTea8BK9kLOkGhWbQAuVg3qd0GdcSM7hd1r189Xwcn7rviN3dFkBZNx1zR1ajHApjDrIk8K6snKbAG47jOrPcRDEHZhx8Ys5NEfVLtnKglK4r5gDkOrNmHL7wVF6nVh1pSznIooAJj7iV2CKbH3PIZV+Vcg3ZMQfrnn3tp2dgUOBXrtmC6bjZmmSgI1Dy97JNxdqzlXjMgeN92lI5sJgD+5BnPawc2ALoc2Tk9ERyhXApVYdm0DUrB8CMO6SUfOXgdLUoWQOprI6wX0R/1I+5ZW80KmSLbF6FtLUYs0C+G+zvze4Z80BNxjOYjqcRDUhVxhwqB6R5thKnWWlz42AqBy9XSKsFbiUgN3AGKF8dXS0hWUJC0fN2tM4g7XImC0rNXXBfRMacR7rYMt+9060Usgf+lC6EY3/vgM+sdma7/MlYGpPxDIY6gyXPBcw50qJA7JbobpTPVvLu+43DYbSpcTAritnuUPOwcWC7XGcuf09IxoI12rNcX6VqifhFK1vJXTnEUubvCMki+iJ++3c3GrYDL2zZDbi34mbYGWAiwZt29+GPbt4DUSCYjKUxHc9gc2dplxIADHUF8bOPvw1v3t1X8jF+kbXpbt0K6clYGp958BiMJmhc6TU2uM63JrSlcVjOZBENSPaC2wxupZLKwQqur9etlFQ1V+VACBBLm78r6DONg1fmX9huJZ+bW6l4AZ6OZ/DwkWm7uE8WBfzDBw/gN968A5s7AjgfS2MqnsZQV3njAJgxiXIV6SWVQwtlKz1ydAZffuKUp1KbvQ6lFH/36Ou46q5H7KxJr9KWxmElo6Ej6INkpYd62a1UGJAGgN6IHzPLGfzJ91/BsWlzxkNhu+7VEJbNOdLORctuIyFLtqIIyiJ6rXiHF3aLZd1KLsbh7x57HXf+8/P2eT6HwR3sDODsfBLzCRWbO8q7laqhdMzBzFZqhp1jJQr7cXEq83ePncTf/OgE5hMqpuKZRl9OWdoyW2kloyEakOwF18vzHHIB6dwu9Vev3YLZlQz+7fkJ++frUQ7RgISVTH4RnG0cAhJWrK+ZctAMM0OKtZFoFGxRcqqqXMyheME6dHYJunXtQL7BHeoK4kFrNvdgFcqhEqJAMNQZwJbeUN5xdq3mBLvVN0r0Euw9kuEB9qowDIovPXESw11BnI+l89LRvUibKocson6fveB6WTm4BaRHukP4wvv3497ffANCVurmegLSHUEf4ulsvnJQixvQhWTJripfSDbeteQWcwiUcCvFU1kcn1kBkGta6DS4Q11B29AOVog5VMvjf3g9fvWarXnHWmmOdG4GCFcO1TC7oiCTNfCWC8xYVcxjI3cLaUvjsJw2lQMhBKJAmqMITiz+U+0b6cI3Pnw1PnL9Trvady10Bn1QNAPL1ptVFIg9Ic051yAoC+i3mv3NrTR+15NzKzmVQ36XWcbhc7ku8EtWgN15T4cdaqFWxsEviRCE/LgEmwrXCumszK1UruCQk2NswZw7ftlIFwBzw+Jl2tStlLVz1CWBeFs5uASknVyxpRtXbOl2/Vm1sAK6uRVTDUQDkv3BdyqHgE+075sXgtK5Oge3VNb8BevQ2SX7a5Z9VehWYmyukMq6HnLKofkX1ESGu5VWAxste9moaRyWUo3fYJWjLZWDGZA2Fz1ZFDydreQWkK41LF4xYxmHiF+yA7rOFhEhWbI7ki54wjgUV0j7JQGEFLuVDp1dgmjt4m23klRsHCoVwK2XlnIrqcw4NL+hqwdji0lIAsHuTRH4RMLdSl7DMCgSqpZTDiLxdPsM1TJckrD6QT7Vwjq6zi5n4BMJgj7RNeYQ9InoDskQCDDvgVoHN7cSIeb1O7OVVM3AixMxXGkprCW3mIOlFioVwK0XpnJaya3EjUN1jC2kMNwdhCQK6ArJeQO7vEjbGYeEqoHS3IIoiYLn3UqyJKxpylu1MOUwl1Dgl0T4RAEs0zLiz8UygrLpQ+8J+xsakDYMip+dmoeSLXYrAaYRc7qVzswnoWoGrt3RA8A95tARlBCWxYoFcOulldxKdrZSC6igejC2kLI7G3cFfbZ706u0nXFgQVfmLmkGt5JbMLqW2MZhWYFfEvLiG3kBaSuY2heRGxqQfvrMAv7rPzyDn51aAJDvVgJMI+Z0K7Espcu3msohl62Urzjes38Yb794YEOvnRmHVujMyrKVFK4cqmJsIYmtVmpzd0j2fMyh7QLSrK9Sh9Ot5HHl4HR/bATMOKwoGoaDwTxjFLXcSpJAbKPRH21slTTL8nh5Ig6guNAsVDDw58T0CkSB4NLhTgA55VAYx/n0rZdu2DUzmCFrhZgDdytVTyylYjmjYVtvGIA5hXB80Rut70vRdsqBGYe8bCUPF8FldaNkplKtcLb7LqUcgo42EL1huaFuJdZanLVtKFRWhW6l4zMr2N4XtrupsmaFG2103bBjDk1uHCilvAhuFZy1MpW4W8nDsH4mzK3kEwW7144XUXVjQzOVAPMesBRQWRLyFk0WkA7IOePQF/FjvoFuJedi5HeJxxS6lU7MrGDPQBSiYAarKTUNw0bGcUrRKjEHRTPszgJcOVSG1ThstZRDd1i2e5Z5lTY0Dkw55IyD19tnbLRyAHKuJb9PdFUOIadxiPqRzupFhWb1wrkYud2bkCzZLbvTqo5ziylcMGBOsWMzszfa4JaiVbKVnF17M01u6DaaeDqLLz1+CtGAZMccOoM+ZKwJi16l7YzDsq0ccjEHL2cr1SMgDeSMQ0AS8hZOFnNwupW6rWrsxQb1hnFW5Lr1J3Kmsp6cTYBSYM/mCACzPTnQQOPQIjGHpGOMbLrMYKV24PlzS/jMg8dw/ytTrokGH/vWCzg9n8CXf+VKu70Lc3F62bXUdsaBvalt5SB4O5U1q9O6LGQsQO9UDgLJzUcIys4qZPPepcvMTNhICt1KhYRk0b42lqnElAO79kYZB2bom92ttKLkFrV2Vw7/+NRpfPmJU/jIPz+Pf3t+Iu9nukHx4xNz+NAbt+NNjvkfrN2NlzOW2s443Hn9Thz/y5ttC+6TvN1bqV5uJRaU9kuCvYD5RMHemTuVQ9hfeaDORuJMnSxMYwWs+RSW2+P12RXIomD7elkMRW5AMBrIXW+zp7I6lUO7p7Kej2Vw7Y4e+ESCsYIMJFZMuinqzzvOjANXDh7D6YqQBMHT2UpmQHrjFzLbreRQDqZxML92xhyCPnOBTTYw5pCbl1DsVmKprJRSLCVV9IRlu3UGM2y+OhhcN3LKodmNg/m3FwXS9tlKU7E0tvSEMNARwFTB4CPWf6qwJUtX0HQrxT0clG67OodCfB6vc8jqxob2+mGwXlN+R8zBJxJ7pxtwUw5K49xK3SEfKHV3K4X9EjSDQtEMJBXdvl72M6BxbiVJFCAJpOndSqzGoScsezqoutGomoG5hILBziCGOoOYLBjgw4xouOAz3B1mbiWuHDyL5LGYwzOnF/Do0Rn7e1UzXBfAWtPpdCtJ5d1KdlvsBi0K6ayOoE/E5Vu6XEd6hh3T4FYULc+4hhsccwCsOdJNvttmi15vWG7rmMPMcgaUAkNdAQx2BTAVz1cOK46hWU6YcnBzK33yB6/iT77/ygZdcfVw5SAJnok5KJqOj337BWSyBp7707dDlkzDVY+FLGccRIdycHcr5ZRD49xKAZ+I/3Xb5XArVWC7tKSiIaloebu2cINjDoAZ9G92txJTDn0RP6aXvT3uciNhoz4HO4PY3BnAdDwDw6D2HI+kS+t7AAj4zE2YW/O9Q2NLnogy1SYXAAAgAElEQVRJtb1y8AkE2QZ0Zc1k9SLXwr+/cB4zywri6Sx+emoeQP3rHAK+nEGQpdzXziK4kB1zaJBbSTPg94kI+ETXmINtHNRi49DoVFbAjDs0u1uJBaR7I+3tVmJKYagrgKHOILI6xbyje0CpmAMhBN0hH+Zc2tAsJVX7OKXmWNvxxVTd5463vXEweyvVXzn8xj2H8Kfff9X+XjcovvLkaVy4OYqoX8L9L08BaEAqqyTaAXBWRfyHN+3Bu/cN2Y9laa3pBgakA2UMJlM5SUVHotCt1OCYA2AuqOOL6coP9DBJVUPAJyAkS20dkJ6M5ZQDmyA4FcspKbehWYyrt/figVemMVOgvJZSWcRSWWR1A391/1Fc9hcP482ffRxPvj6/US/DlbY3Dr4Gtew+M5/ES+Mx+/tXz8dxei6JO96yA2+/eAAPvzaDrG7UpX0GYDYCA8xUS2cqKwB89IZduMRqWgeYikIWhcYph6yeV3dRSKTIreRQPcw4NChbCQCu29mLw2NLDaswrwXM6AZ8Qlunsk7F0+gISAj7JXtglDPuUM44/ME7LoBuUPzNw8ftY2lVt4s8FxIqDo8toc+a284mNdYLbhwa1LJ7OZPF2EIKupVGy6ZCbe0N412XDiKezuLZM4sNCUj7pHzj4EZQFhsbc3BxJzGcc6TNbKVit1IjYw5vuaAfqm7g6dMLDbuG9cLcdQGf2NYzpCdjGdsoMOUw6VAOpbKVAPOzfvt1W/GvhycwaaXAOovi5hMKpuMZ7NlsFnDWexPb9sZBEuqfyqobFCsZDapu2G+KhKPn04XWm2EylrYC0hu/kA1EAwj6RAx15Vp2l2vbES5oi11PMlkDAZfiNwbbpcVSWai6gYjsrWylq7b1IOAT8OSJ+roJakkioyEsSwhIIjSDejodfCOZiqdto9ATluGXhDzlsKJoptIuscF76wWbQCns9t1O4zAdz2BmRcGWHrOAs95Bam4cGqAcWGdYwHQvAUDCakcQ9udmGCcUrX7ZSiEfnv6TG/GOiwdyqaxSaaMUcsyZrjcsW6kUIUsdzFoyPF85NN44BHwirtneiydPzDXsGtaL060EtO80uMlYGoOWciCEYLAzYGcwAaZyKFen1Gu5jFifsqVkbm04Nr0M3aB2sz5uHOqMLNY/W2k5nXPHnLVa+a44shrYYrac1pDVaV2ylQDTtUQIKYo5uBGSxYZVSKcrGAemDligz2sBacB0LZ2eT9rKsdlIqmYsh/0d2jFjKa3qWEplMeQYLTvYGcwzDolMdcZh3jIOiw7lwIZZbbVmQKjcrVRfJGtesl7HFhrxdG53cHrONA4sNTDil+wU0iWXcZb1wFelcWhcbyWjrHEI+AQIxF05sOB0vQxuKXZvMrvETiw1qXGwYjnBNjYObPOxuTNoHxvsym+hkSiIeRXCurMuWKmrS45Ox6+eN43DaA9XDg1Bsvz59Qz2MONASE45JJQsQrJo9wCKBiRbatYjIO2ELZzlYw5SQ7JtdINC1cvHHAghCMsSZq0Pr1v7jEYGpAFz1CqAho5bXQ/MrcTaq7RjOitT+52OSYr9ET8WHAt8Qsnabe/d8IkCukI+LCQst5K1IRzuyrXiGO4KmhMrvaYcCCFfI4TMEkJedRzrIYT8iBDyuvV/t+NnnyCEnCSEHCeE3OQ4fiUh5BXrZ18k1hguQoifEPId6/gzhJBttX2J5WELYD1vPJspsbM/4og5FBZrSR5QDqUXUDNbqf67RVY8Vk45AKYRYMrBi24lZhzqnZ5YC3SDYtFqaNjObiXmVg070qqjAQmKZtjv08LeXm44x+4uJVV0Bn3YbLmqAj7TeMiS4Enl8HUANxcc+ziARymluwE8an0PQsjFAG4DsNc650uEEHZnvgzgDgC7rX/sOT8MYIlSugvA3wL4zFpfzFqQrJ16PQvhmHLYP9qFiaU0VM1AQtHzdhiRgGTvJuq9kMlVpLKayqH+CwKb0xCsYBxCftFeePPcSlY8QmqwcegOyRBIcyqH+YQC3aAY7AraxmFiKY3//oNXm77yezUw5RxyvL9Y63umKhKKhkjAV3yyg96I3/6sL6ay6A750B8xNw+DnUEQQhpSj1XxE0IpfRLAYsHhWwDcY319D4BfdBz/NqVUoZSeAXASwNWEkEEAHZTSg9SsAf9GwTnsub4L4EamKuoBWyTqGZRetozDZaNd0A2K8aUUEplsXnOuiD/nVqq3f9wOSJerQvY3JiDNsmLKuZUA8/6x8a9O5SAKBL/79t145yWbN+4iq0AUCHoj/qZUDiyIPtQZsCvVf/jSJL5xcAzHplYaeWl1hcUJC5UDkPuMm+63apSD+VmPpVR0h2X0Rc1YxOYOU0HIktA0AekBSukUAFj/b7KODwMYdzxuwjo2bH1deDzvHEqpBiAOoHeN17VqfHbMob7KQRSIHZScjmdMt5IjHz8a8NmZC/Woc3BSTczBOTOhnjD3RSW3Un6jwHyf7+++/QJcNtpV+4tbJX1NahxYNs7mzoD9dzg2vQwgP9um1XFTDlF/gXKokK0EmBlLLCC9mFTRE5LRx5SD1XVYFgWoWnP3VnJbxWiZ4+XOKX5yQu4ghBwihByam6tNjjhzndSziCeezqIz6ENv2MpUSKpIKHqecoj6JdvHWO+AtLO3UilCsmQHh+sJMw5uDfecOA1tJZ9vo+iP+j3lVlpKqnYxVjmYcRjqzLmVzi6k7OdoF5hb1akcnG4l3aBIZ8tnKwFAb9iPpVQWmm5gKWkqBxaTYgV2zaQcZixXEaz/Z63jEwBGHY8bATBpHR9xOZ53DiFEAtCJYjcWAIBSejel9ACl9EB/f/8aLz0f261UR+WwnNHQEZDQYxmHpaRalNXgNBTejDk0ZuAPMw7leisBzsAzqWhIGkVfRPaUcvjsQ8fwoa8/V/FxU7G0HShl7j2WCr7YhsYhlKf4LbdSJlu2r5IT1jtpMaViMaWiO+SzlQNLk/WJBFkPBqTduA/A7dbXtwP4geP4bVYG0naYgednLdfTCiHkWiue8MGCc9hzvRfAY7SOvgofC0jXMebAlENXSAYhlnLIFGcr2ddYb+NQTZ2Doy12PWEpk+W6sgI5tVCPKXprxVQOat1dc6WYWEpXpWSm4hk7UFqYGNBOxiGpaPCJJC8mmFMO2ZKzHArpCZuG4PxS2pxyGJaxxapt2G7NPm+Ecqj4ySGEfAvA9QD6CCETAD4J4K8B3EsI+TCAcwDeBwCU0iOEkHsBvAZAA/BRSinbWt4JM/MpCOAB6x8AfBXANwkhJ2Eqhttq8sqqhC2A2Tr685bTWXQEfRAFgq6gD4tJxcpqcFcOdQ9IO+Y5lCJkt+1ujHKomMpq7eYqSfpG0h/xQ9UNLKc1uytuI5lPqPaCVg5nPyF/wd9hqa1iDnqeagCcAWktpxwClWMOAHByNgEA6AnJuGiwA/d/7M24aNDss9aIbKWKnxxK6QdK/OjGEo+/C8BdLscPAbjE5XgGlnFpBHYRXJ2zlYa7TbnYE5YxHVeQ1WneDiPaQOUQlEVEAxI2WX5PN9jiW++23bZyqJjKal6f15UDAMwlFE8Yh4WE+T5UNL2sK24qnsF1O/sAFGeNtZtyCBe4NyOyBEJM5ZAo05HVSV+BceiyqqYvHuqwH2MOiGoOt1LLkAtI1zdbiVVV9oRlnFs0C+GipZRDnY2DXxLx1B/dgFuvGCn5GKYcxhaS+NR9R+pWoMPaQ1eqc2AfWq8rB8AbhXCGVdgGlI8jabqB2RXFnt0ti4I9qnVzR6CtjENK1fMylQBAEAgifgnLGS3XabmKgDQAPHfWDLUyY+GEjQyuJ21vHFgRXL1uPKUUy5lC42BmejgzbCL+3E6yEX2AukKy3crDDSanv/3sOL7+s7M4MVOf/PacW6lSzMH7bqU+D7XQWM5k7bqQhItryTAobv6fT+IzDx6DblC7gpcQgoBktn25ZLizrYxDUi1WDoA5VXHZEXOo9B7stFzMz5+LYWtvCPtd0qzNVFaPuZVaHVboVS/jkM7qyOrUHsvZE5ZtV0lhEZx9jQ3uA+QGa4v9/LklALmWIBuNncpasX0GC0h7M1MJ8JZycBootySD0/MJHJtewbFpcxMw5Gg2F/AJ6IvKGOjw2++HdiClFMccANMDsJLRsFJlQFoQCLpDMuYTCj56wy7X6v1GKAduHIT6upVYu26ncmA45We0gQHpamAqh/lBWdHPRqNUWSFtB6RdPrxeoTPogyQQ1yHz9WY+kdvxuwWlnz8Xy/ueFWcBZvxna08YPWEZsZQK3aBlVWerkFQ1OzDvpCPgW1W2EgAMdPgRkkX8l8uHXX/u48qh/rCAdL1SWVlfpZxxyAV9S6Wy1jvmUA2FdQb1Mg5pVYdAKt+TZnArCQJBX8SPeQ8oh4U841Acc3hxPIZoQMIv7BvEvYcm7NGYAPCey4awa1MECUWDQc2Ei+5wsd+81XDLVgLMjd1UPGPHHKp5D37ml/Yh4BNKJp+YyqG+Kc/e/eTUCfbHUOtw41XNsOV7R9C89T3hXGyhVCprozuIuhEqMA7L6fq5lQI+EZXab7Hr83K2EgBs6vBjejlT+YEbDOsKCrgrhxfOxbB/tAuffPdefODqLbZbFAA+8a6LAAA/ePG89VxqWxgHc462S8wh6MOJ2RVML2fQEZCqUv6XDHeW/bmPZyvVH+bPr0f7jPd/5SA++LVnAbgrh2gp5eBBt5JPzJ+LWyvl8PJEDH/wry8VDV/KZHXc99JkxSlwjEgTKAcAGOkO4rwHBv443UqFAemUquH49DIuH+1CwCdi34h7Xyo2uKZdah3KKYfltIazC0ls74/U5Hf5ebZS/ZHqmMp6bjGFzR0BXLm1G9v7zMrHXscOy7mQ+SXB0ePIm3+mkCxiU9T0ldYqIP3kiTl89/AExqwhSIwHX53Gx771Ap44PlexOhrIKa9ohQKkRjPaHcLEUhpGHScRurGQUOzMvULl8PJEHAYF9m8p36yQxc/aIWOJUloyWykakJBQNJyeS2KH9TlfLz6ReHKeQ0vD2mfUozQ9kdHw7suG8L07r0PUkuVMfhOS76ohhNi7Xy9mKwHmh2DfSKcdgKsFrI6hMDWWNXs7H0tXpRwGO4P43Hv34d37hmpyXRvFSE8Iqm5gZqWxrqX5hGIXZhYWNrIMpP2j3UXnOXH2Cmt1MlkDlKKozgEwA9K6QTEVz2Bbb22MQzM13msZ6tWVVdF0qLpRtJPtsaR4xC8V+dEjAckqMvKmcfjcey/DJ951kZ26VwvSqvl3OD6dyDs+4/DLV2McAOB9B0Y9UXlcDtZDZ3yxsa6lhYSKwc4AJIEUKYdHj87iosGOvMw6N+x5yG1gHNymwDGijnjM9v5aKQcBukHrOuu+7Y1DLltpY286y1woDJAGZRFBn+gaOI34fZ5VDQBw7Y5e7OyPoCPoq5lbKZ0171OhcphdcRqH1nnbjlq79WpaZW8kC0kVfRE/wn4pzzjMLmfw/LmlqoYjsfdyOygHVkXuFnNgySYAauZWkutcjwVw45BrvLfBMYdy7Xt7wrLr8ai/ukyHRlNb5WB+6I4XGodlxe71VK1yaAaGu4MgBBhfaqxxmE8o6Iv4EfFLSDhSWR9+bQaUAjftrW5yXk9YbouBP7ZycMlWciqHbbUyDnZWJTcOdSNnHDb2prPF061DY29Edj0eCUieDUY7iQZ8NUtlZTGHM/PJvHnEMysZvGFnL4a7gnlplM2OXxKxuSNgt1BpBIqmYyWjoS8iWxP+cob+oSPT2N4XxgUD1WXd9Ef99hjRVsaeAlciWwkw70WtUqnZJrGeQWlvp3LUAVEgIGTjYw5MObg14frVa7a6ntMRkJpil9xRQ+XABqjoBsXpuSQuGuwApRQzywoGOgL4vx+6CgGPDu9ZK6PdIUw0MObAsot6LbcSe69msjoOnlrA//Om7VXHvfYOdeC+FydhGBRCC1dJ2/Oj3eocrM3L9hqpBiCnHOrpVmp74wCYLTSyGxxzSJbp7f7+q0aLjgHAR27YlReI9SrRgA8rGQ2U0nUHzzNZHX0RGfMJFcenV3DRYAfi6SxUzcCmqB8XDERrdNXeYaQniIOnFhr2+1l1NHNvsvfqQlKFZtBV+c0vG+nCPz9zDmcXkthRoxx/L1JOOXRYn/HtNcpUAhzFunVUDt73WdQBSSR1Uw6rKcq6YCCKN++uzTjUjaQjKEHVjZpUcKazOi4a7IAkELw4bvbzmbXaSwx0FPexaQVGu0OYXs7kudHqCSta6w2bbiW2K160jMZqqp33jZqVvi9PxGt8ld7CVg4uxqEz5ENHQLLvRS3gAekGYU5Z2ljlsFJlb/dmhAXgapGxlFJ1dAR9uOmSzfjWs+dwbiFlq6dyw4eamS09IVCKhlVKM7dSV8hSDtaueNFhNKplV38EAZ+AlyZilR/cxNjKwcWt5JdEPPXHb8NtV22p2e9jyqGeLTS4cYA1vLtOyqHSyMBmhMnoWsQdMqqOkE/En//8xZAEgk/98AhmlltbObDXtZGtuz/9wFHc/eQp15+x1NOesJyXyrpo9VuqVN/gRBIFXDLU2frKQS2tHIDcjIZa4Zfqk1XphBsHAJIgbHj7jERGg0AqTzBrRlgArhYZS+msjqAsYnNnAB+7cTceOzaLR16bAWA2qWtFWIdblqm1ETx+bBaPHJ11/dliKgtCzAUt5M+5lVgsoje8uvu+b6QLRybjdelX1ihSigZC6ldzw2MODaI7LOPwuSV7kMxGkFA01yroViBaQ+WQUnXbgN529RbIkoCHXptGNCC5Bv9aAfZ6N/L9p2hGyZ5HS0kVXdZONyKb8SPVerwokLyirmrYN9KJTNbAyblE5Qc3KUlVR1iu3+eZxxwaxB/fvAcnZxP4wo9ObNjvWMloecUxrURHsDYxB8OgUDTD3kl3Bn24ae9mUNq6LiUg11MrpW6ccchk9dLGIZVrsc0SJlKqZh4PyateANkAHOeMiFYjpWpFbes3EtYpgSuHOnP9nk34lWu24B+eOo2p+MYEBZOWcmhFaqUcMla2jtP19ktXmJOxWjUYDdTHrZTJGliyprQVwowAkKvgTygaFhLqqoLRDGZg3OZCtApJRa9rO3i7CI4rh/pz40WbQCns4GetSZQYDNIKMEW03s6sbOfsnDL35t39GOkOYkeNGph5Eds4bKByUDQdlAIxl9YWi8msbRxY9k1SMZXGaoLRjHoooUazksnW9fPsb0CFNDcOFmyBS9TAb/4b9zyHh45M5x1bUTREWtStFJZFCCQ3H3utsMXRqRxEgeCHv/Um/Om7Ll7Xc3sZ9no3yjhQSpHJmouKW8fUpaRqTyQMO5TDYlJFT2QdykFtXeVgGs76qdl6tflxwo2DRU5Or2/3m8nqeOToLB4/lp8ZkshkW7LGATBnT0RrMNOBBWQL51N3h+WiY62ETzQHO6U2yK3kdEUUxgEopVh0xBwijpjDQnJ9bqWUyyzqVmExtbZ7s1Ya0VuJGwcL9qFYXqdyiKXMBbKwkVqihWMOgFklvd57l3JRDu1C0CdumHJgqgEwd7yn5xI4OWt2vU1ndaiaYc8VYS6heDqLeDrnbloN7O/Xasrh0w8cxV/+x2sAzOrxtdybtdII5dC6q9UqYUHV9bqV4ukSxiGjtWQBHCPqX79ySJdQDu1AUN4446A4FMliUsEff+8Mjk2v4Psfuc5u7FioHFi1du8a3EqiQBD0iS0XkP7x8TnoBsUfZHUkVX1N92atMOXAK6QbgDNLYz2wgN9UPGNbed2gSKp6SysHs2Hb+hY32zi0oXIIydKGuZWcC8pCUsWJmQRWMho+fM8hjC2Ymxi2C+6L+CEJBM+eWQSwuupoJ2G/WDRutNmZW1Ewu6LYvajWem/WAp/n0EAkUUDQJ65798uUg25QTMXMnkBMXnt92P16CPnz5wCshbRLtlK7EFinW0k3aMk6Bmdx3cnZBOLpLG7aO4CxhRT+6ekxAMgLSB/Y1o0nTsxZx9e2AIZkCakWUg5Z3cBCUkU8ncWk9bluiFtJ4+0zGkIkIK1fOThaSDDXUnINHVmbjbC8/nvHFseQr3XvUylCsmiPSF0L3zs8gbd89nFXA+1UDofOLgEAbrtqCzZ3BPColTjhXOhu2LPJrodYbesMRtgvtZRymE/kUtyPT5vxmnq6lUSBQBQIVL1+95QbBwe1GHe57GIcSs2PbiXCfnHdee3MrRSQ2+9tud6A9JmFJBKK5jqFzakcpq0Otzv6w3jz7j47+8WpEG64cJP9dXd4benXYXn9StJLzDrqn45NLwOor1sJMF1LvPFeg4j6128cYqksBGKWu7O5wCst3JGVEaqhcmjHmENQXp9xZZ1VmcvDCctW6g6ZC70sChjpDuHNF5izQgSCvNGruzdFMNwVtM5Zo1upYBZ1szPr6Jh7bMpUDj11dCsB5prCU1kbRC3cSvF0Fp1BH4a7gkXKoVXrHABTFaVUHZSufWfTzgHpoE9cV+O9JTsRolg5sCFCQ9aCv7U3BFEgeNOuPhBiGgDnSE9CCN55yWaM9gTXPMM8LIstFXOYXckZ3aPTyxCsLrb1RJZEHpBuFFG/ryaprJ1BH0Z7QhhnxqEdlINfhG41zlsr6awOWRQgrXFBamZC61YOpjuznHJgxoG1IukJy7hkqNN10tsf3Xwh/uO33rzm6wnJUku1z2BuJUkgWMloRQa1HvglgSuHRhEJSOvOVoqls+gMydjSE7KVA3vOUoNBWgH22taT255W9br1x/caQVlcV+M9NrVtMpbG48dnsf9/PGxnztnKweqW6pzt/D9u2YtPvru4NYksCegMrX1nHPGLLVUEN7uioDcs2w0g6x1vAOozlMxJe34SSxDxS3Z8YK0w5bC1N4RYKovFpIpTc0nIotDSbadzrZ7XvsClVb1lZzZUYr0BaWd9zVMn5hFLZXFy1pynUKQc+nJNDC/f0r0hc8pDfqml2mfMrWTQH/Wj3/oMN8I4yFw5NI6oFXNYj988njIHp+wb6QIAvDi+hJfGY7hoqMOucmxFwlZtwnpiNmwKXDsSkkVoBl3Th59SiiWrbctkPI2jU2Y2DXNrsljG3qFOCAT2e3MjCcuiPTSoFZhdUbCpI9Bg5SBw5dAoogEJlGJd+dlMOewb6YQoEBweW8Kr5+PYN9xZwyv1Hs4hMWslpep2O4d2g73utbiWljMadIPCJxJMxTI4aqVaMrcmiwMd2NaNF/77O7Bnc7RGV10apgBbJZ11dlnBpqgfAx2NMw6yJDRP+wxCyFlCyCuEkBcJIYesYz2EkB8RQl63/u92PP4ThJCThJDjhJCbHMevtJ7nJCHki6RBszQj/vW17TYMing6i66QDyFZwoWbo/j3FyaRVHVcOtLqxoEph7Ub1kxWr+t0LS/BFtO1uJZYGusFA1Gks3pR80emHGRRqFuGDXs/tEIhnGFQzCdM47AparqV6tmRldGMyuEGSul+SukB6/uPA3iUUrobwKPW9yCEXAzgNgB7AdwM4EuEELYSfBnAHQB2W/9ursF1rRqWTbTWtt0JVYNBcylul2/pwnmrKGlfixsHe6e4XrdSmyqHoFX4txblwNJYLx7ssI8FfEKecpAloa7ZNbV4P3iFxZQKzaB5ysEtw2ujaYVspVsA3GN9fQ+AX3Qc/zalVKGUngFwEsDVhJBBAB2U0oPUdPZ/w3FOXVnvuMu4tWNjM5Wv2GKKpqBPxC5HhkgrUovGhe3sVgr61u6GYcZh71DOOLxld39ezMFf53hXxB740/zKgaWxDnQEbOXQuJgDXVdMdDWs9x1DATxMCDlMCLnDOjZAKZ0CAOt/Vos/DGDcce6EdWzY+rrweBGEkDsIIYcIIYfm5ubWeenFsCK11RiHxaRql9Oz1MGuAuOwd6ij5XP3azEasp3dSiwQv5ZCuEWrxuHiIVOdjvYEcfFQB6aXM8hkdSha/Y2u/X5oAeUwYxXAberw4+KhDmzvC+PSBsQQZVHAYlLFL9/9NJ47u7jhv2+9eYNvpJROEkI2AfgRIeRYmce6aVpa5njxQUrvBnA3ABw4cKDm5jPnVqr+Df33j53EfS9N4tCfvd329TK30tbeELb2hvDGXX21vlTPUYvRkGm1fd1K6zGuLI31goEIJIHgos0d2NITAqXA+VgaStaou3II16gFvheYsBTYaHcImzoCePwPrm/IdfgkAedjacwllLrc13UZB0rppPX/LCHk+wCuBjBDCBmklE5ZLiM2L3MCwKjj9BEAk9bxEZfjdWctc6Sn4mnMJxToVjAaALqsniuEEDz0u29ZcwuCZsIvCRAFsq4iuJSqtW0q63rmSC8mVYgCQWfQhzuv34krt3bbbp1ziylkGqkcWsCtdG4xBb8koD9av5nRbnQFfQj4BHz19qvqsuFc86pFCAkTQqLsawDvAPAqgPsA3G497HYAP7C+vg/AbYQQPyFkO8zA87OW62mFEHKtlaX0Qcc5dSU3KrT6gDQb2L6cziKWNr92ZoQEfCLEOpfZNwJCCEKyuOaBP5RSpLNtHHOQ157KupTKojvkAyEEv/+OPbh+zyZs6QkBMGsdMg1UDq1QJX1uMYUtPSE0KInS5o9u3oNHf//6unki1qMcBgB837phEoB/oZQ+SAh5DsC9hJAPAzgH4H0AQCk9Qgi5F8BrADQAH6WUsk/CnQC+DiAI4AHrX91ZS1CVDViJWTN3AaBrHW0HmhlzGtzaFoOEoiGrU3voTLvBdtprTWUt7J7aH/XDLwk4t5BqSMzBrntpgSrp8cU0Ri1j20iiAZ/t3agHazYOlNLTAC5zOb4A4MYS59wF4C6X44cAXLLWa6kVomDuflfjVmLGIZ7OIpbKwi8Jbbv7XU/zONYSmWWDtBvMrbSW+7eUKjYOhBAMdwcxGU8jkzXq3rOKvZ5mUg5JRcM3Do5hKp7Guy4dxLU7ekEpxfhiCldv72n05dWd9tEJYYUAABaISURBVGxkU4bVDPzRDWqnEcZSKhYSakOKY7xCxC+teTFg6YKbGuzXbRTrcyup2O7ol8Toi/gxn1ChaHrd20uLAkHQt/4BUPXkCz86ga/+5AwEApyYWcG373gDYqksVhTNE8qh3nDjUIDZfK+6mEMspYKlHMfTWSwkFfRG2nNxA8zCp7W6lWYd6YLtiCwKEMjq3EoPvDKF//Xo6xhfTNtp0076I35r9gBpSLfbsF9smmyllUwW33luHLfsH0JPWMa/PHMOqmbYhYSj3cEGX2H9af00mlUy0BFw7YnvhnOgezxtdmBtRHGMVwj71x6QnrPcSv1t6lYyA/qrm4HwyNFZHJteQTqrY7CzePHqjchYSKhWEVz9XZ0hWWqaOofvHp5AQtHwoTdux1XbeqBoBl6djNvTHLf0cuXQ9uzoD+O+FydBKa2YnbDgMA6xVBYLCbXlK6HLEV6PW2lFgV8S0NHCA5EqEfCtbqbD6fkE3rCjF3/68xdhWwm3UjydBSFokHKQPF8hfXY+ib9+4Bieen0OV2zpwv7RLntE6nNnFqFbroHR7vYzDlw5FLCzP4LljIb5hIrPPngMn37gaMnHFioH063UvsrBdCvpUDWjqkpfSin+9+MncWY+idlls19+o9MFG0lIFpGu0rhSSnF6Lokd/WFcMtxpZ9o56bNcnGaiRP2VQ1gW11X3Ug9++NIkHjwyjZv2bsZn37sPgJnptb0vjOfOLmF8MYXesGxnX7UT7feKK7DT2vmfmkvgO8+NY0XR8NEbduUNYGcw5SBLAqbjGWSyBnrC7ekzB6zpX4qGO//pMJ45s4hbrxjGx995YckBPrMrCj730HHE01mzX36bBqMZwVUoh8Wking6mzfVrRDnRsXfAOXQGfRhKl6di7ZRLCRVRAMSvvDL+/OOH9jajYdfm8GmqL8tg9EAVw5FsPm6Txyfw0JShaoZeOjVadfHLiZM47C1J4RTc+bUrXbOVgrJEtJZHT8+MYeOgIRvHBzD06cXSj6eNYY7OrVsGYf2jDcwgqtIBT41lwSQe7+60edIjgg0QDn0hOU8de1F5hJK3n1iXLujF/F0FmcXknjnJZsbcGWNhxuHAoY6gwj4BHz/BbMXYEgWcd9L7t08FpMKOgISeiMyzsybH9Z2diuxHv6aQfHbN+4GACynS7sVWLDv6NQKZpczbZupxBjuCuK1yeWqMpZOW5uRcjGufsei1wjl0BvxYzGp1q2L6FpYSCjoc/nM3rJ/CF//0FU49Gc/h998684GXFnj4cahAEEg2NEXwcyygqBPxO3XbcNPT87bqZZOFpIqeiN+dAVle0JTe2crme6jiF/CWy8w5xKvlGlFMr5ozrqYTyhYzmht71a6/bptWEiquPfQeMXHnp5PQpYEey60G33R3HuxEcqhNyxD1Y11z2XfSMzapOL3nSQKuH7PprrXh3gJbhxcYFJ930gnbtq7GQYFnh+LFT2Opa4622W4vdHahbAVW3jjrl67YrfcwsDcSox2dytdta0bV27txt1Pnq448ev0XALbe8Nl+3aFZMmuVG6McjDfA8z96kXmE+2dRFIObhxcYEHpy7d0Y8QqfplZLlYOzDg4dxc9bfxGY8rh+j2bEPAJkARSttp8fCmV5zPvb3O3EiEEd7xlB87H0jh4qnSsBoCdqVQJph4aFXMAgIWkUvffXQ2abmAplXWNOXC4cXBl5yZmHLrQE5Ihi4Jr1sVC0myX0WkpB78kINymLacB4Mqt3bj18mG865JBEEKsViTl3Ur7hjuxucNUDO3uVgKAy0e7AABnF5KuP59ZzuCv7j+KscVUdcbBWvgaohwsFb3gUeWwaLW+cYs5cHgqqys3XrgJ/9/bL8BbL+iHIBBs6vBjOp7OewylFEsFyqE3LLd1nn5PWM5LCYwGfCWbGGZ1A1PxNEZ7hhFLZzG9nGl7txKQ66Za6HJjfPmJU/jGwbN424UDuP0N2yo+H1ugGxJziDDl4E3jML9iXlc7t7wpBzcOLoT9En7n7bvt7wc7A5gucCstpzVoBjVjDkHzQ9DOLiU3Iv7STQynYhkY1Kw89YkCnh9bautgPoMQgpHuICaW0q4/n0so2NYXxj/efqCq5+tnbqUGdApmf0+vprMydxd3K7nDjUMVbO4M4pWJ/ID067MrAIAtPSHb197OwWg3ynW4ZWmsIz1BvGf/EN575UhbDEWqhpHukH1/CllKqugJVW9EG+lWCvhERPwS5hPejDkwdxcPSLvDYw5VMNgZwFQ8k5ev/eK4aSz2b+nKcytxckQDvpLZSuOOubwBn1g2JbPdGO0J2mm+hSwmVXSv4n3GjEMj3EpA/QrhKKX48Yk5GEb1NRXMaPXxTZ0r3DhUwUBHAIpmIJbKBVdfGI9huCuITdGAbRy4WySfcgHpY9MrEAWCwU4eZyhktDuEeDrrOq52KbU65bCj30x3bdTuuF7G4YXxGG7/2rP48Ym5qs+ZT6jwiQQdQe5AcYMbhypgC5gz7vDiuRj2W5klPWEZPpFgkO9+8yjlVnrw1Sncc/Asbt67GZLI34KFjDrmPzsxkyCyq1IOb9rVh599/G0NU2Z9ERnzdchWGrOyu0plebmxkFDQG27vZo/l4J/MKthsGYcz80l8+oGjOHR2Eedjads4hP0Svv+RN+K/Xr2lkZfpOaIBCQlFy3PHza0o+J1vv4j9o134/PuKpsxykGsPXRiUTqo6VN1Y1ZxtQggGOhqnzkzlsPExhwnLDXe+RCDfDV4AVx5uHKqA5eF/9Sdn8JUfn8av/9/nAJjxBsYlw532qEeOScTvg27QvE6jDx2ZhqIZ+PStl/L7VQJWeFmoHJYs90zhvGgvU6/+Sudjafv/5UwWH7j7aZy0kkYYlFJ84eHjeH3GPL6QVHmmUhm4caiC/qgfAgEOjy2hw9oNiwLBJUOdjb40TxO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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2020-10-05/2020-10-112020417396120995823639FRFrance
2020-10-12/2020-10-182020427400019796021639FRFrance
2020-10-19/2020-10-2520204374376250562477410FRFrance
2020-10-26/2020-11-0120204474391237564077410FRFrance
2020-11-02/2020-11-082020457369620165376639FRFrance
2020-11-09/2020-11-152020467375219635541639FRFrance
2020-11-16/2020-11-2220204774999296370358511FRFrance
2020-11-23/2020-11-29202048766834312905410614FRFrance
2020-11-30/2020-12-0620204975026314569078511FRFrance
2020-12-07/2020-12-13202050770634744938211715FRFrance
2020-12-14/2020-12-20202051710564757413554161121FRFrance
2020-12-21/2020-12-27202052712012828515739181224FRFrance
2020-12-28/2021-01-03202053711978840615550181323FRFrance
2021-01-04/2021-01-10202101710525775013300161220FRFrance
2021-01-11/2021-01-172021027779554301016012816FRFrance
2021-01-18/2021-01-242021037891363751145113917FRFrance
2021-01-25/2021-01-31202104712026882615226181323FRFrance
2021-02-01/2021-02-07202105712210898815432181323FRFrance
2021-02-08/2021-02-14202106713401981016992201525FRFrance
2021-02-15/2021-02-212021077135611031516807211626FRFrance
2021-02-22/2021-02-28202108711281836114201171321FRFrance
2021-03-01/2021-03-07202109710988793814038171222FRFrance
2021-03-08/2021-03-1420211079056645211660141018FRFrance
2021-03-15/2021-03-2120211179386667812094141018FRFrance
2021-03-22/2021-03-28202112711520841514625171222FRFrance
2021-03-29/2021-04-0420211379714628913139151020FRFrance
2021-04-05/2021-04-11202114711197799414400171222FRFrance
2021-04-12/2021-04-18202115711215762714803171222FRFrance
2021-04-19/2021-04-2520211674780289166697410FRFrance
2021-04-26/2021-05-0220211774939302068587410FRFrance
\n", + "

1587 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", + "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", + "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", + "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", + "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", + "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", + "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", + "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", + "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", + "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", + "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", + "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", + "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", + "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", + "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", + "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", + "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", + "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", + "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", + "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", + "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", + "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", + "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", + "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", + "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", + "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", + "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", + "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", + "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", + "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", + "... ... ... ... ... ... ... \n", + "2020-10-05/2020-10-11 202041 7 3961 2099 5823 6 \n", + "2020-10-12/2020-10-18 202042 7 4000 1979 6021 6 \n", + "2020-10-19/2020-10-25 202043 7 4376 2505 6247 7 \n", + "2020-10-26/2020-11-01 202044 7 4391 2375 6407 7 \n", + "2020-11-02/2020-11-08 202045 7 3696 2016 5376 6 \n", + "2020-11-09/2020-11-15 202046 7 3752 1963 5541 6 \n", + "2020-11-16/2020-11-22 202047 7 4999 2963 7035 8 \n", + "2020-11-23/2020-11-29 202048 7 6683 4312 9054 10 \n", + "2020-11-30/2020-12-06 202049 7 5026 3145 6907 8 \n", + "2020-12-07/2020-12-13 202050 7 7063 4744 9382 11 \n", + "2020-12-14/2020-12-20 202051 7 10564 7574 13554 16 \n", + "2020-12-21/2020-12-27 202052 7 12012 8285 15739 18 \n", + "2020-12-28/2021-01-03 202053 7 11978 8406 15550 18 \n", + "2021-01-04/2021-01-10 202101 7 10525 7750 13300 16 \n", + "2021-01-11/2021-01-17 202102 7 7795 5430 10160 12 \n", + "2021-01-18/2021-01-24 202103 7 8913 6375 11451 13 \n", + "2021-01-25/2021-01-31 202104 7 12026 8826 15226 18 \n", + "2021-02-01/2021-02-07 202105 7 12210 8988 15432 18 \n", + "2021-02-08/2021-02-14 202106 7 13401 9810 16992 20 \n", + "2021-02-15/2021-02-21 202107 7 13561 10315 16807 21 \n", + "2021-02-22/2021-02-28 202108 7 11281 8361 14201 17 \n", + "2021-03-01/2021-03-07 202109 7 10988 7938 14038 17 \n", + "2021-03-08/2021-03-14 202110 7 9056 6452 11660 14 \n", + "2021-03-15/2021-03-21 202111 7 9386 6678 12094 14 \n", + "2021-03-22/2021-03-28 202112 7 11520 8415 14625 17 \n", + "2021-03-29/2021-04-04 202113 7 9714 6289 13139 15 \n", + "2021-04-05/2021-04-11 202114 7 11197 7994 14400 17 \n", + "2021-04-12/2021-04-18 202115 7 11215 7627 14803 17 \n", + "2021-04-19/2021-04-25 202116 7 4780 2891 6669 7 \n", + "2021-04-26/2021-05-02 202117 7 4939 3020 6858 7 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1990-12-03/1990-12-09 0 5 FR France \n", + "1990-12-10/1990-12-16 12 28 FR France \n", + "1990-12-17/1990-12-23 25 43 FR France \n", + "1990-12-24/1990-12-30 23 45 FR France \n", + "1990-12-31/1991-01-06 18 36 FR France \n", + "1991-01-07/1991-01-13 20 38 FR France \n", + "1991-01-14/1991-01-20 18 36 FR France \n", + "1991-01-21/1991-01-27 8 20 FR France \n", + "1991-01-28/1991-02-03 11 25 FR France \n", + "1991-02-04/1991-02-10 12 26 FR France \n", + "1991-02-11/1991-02-17 15 29 FR France \n", + "1991-02-18/1991-02-24 15 31 FR France \n", + "1991-02-25/1991-03-03 15 33 FR France \n", + "1991-03-04/1991-03-10 20 38 FR France \n", + "1991-03-11/1991-03-17 19 35 FR France \n", + "1991-03-18/1991-03-24 13 25 FR France \n", + "1991-03-25/1991-03-31 11 23 FR France \n", + "1991-04-01/1991-04-07 14 30 FR France \n", + "1991-04-08/1991-04-14 18 32 FR France \n", + "1991-04-15/1991-04-21 18 34 FR France \n", + "1991-04-22/1991-04-28 16 32 FR France \n", + "1991-04-29/1991-05-05 25 51 FR France \n", + "1991-05-06/1991-05-12 19 39 FR France \n", + "1991-05-13/1991-05-19 23 45 FR France \n", + "1991-05-20/1991-05-26 16 36 FR France \n", + "1991-05-27/1991-06-02 17 37 FR France \n", + "1991-06-03/1991-06-09 13 29 FR France \n", + "1991-06-10/1991-06-16 17 39 FR France \n", + "1991-06-17/1991-06-23 18 38 FR France \n", + "1991-06-24/1991-06-30 20 42 FR France \n", + "... ... ... ... ... \n", + "2020-10-05/2020-10-11 3 9 FR France \n", + "2020-10-12/2020-10-18 3 9 FR France \n", + "2020-10-19/2020-10-25 4 10 FR France \n", + "2020-10-26/2020-11-01 4 10 FR France \n", + "2020-11-02/2020-11-08 3 9 FR France \n", + "2020-11-09/2020-11-15 3 9 FR France \n", + "2020-11-16/2020-11-22 5 11 FR France \n", + "2020-11-23/2020-11-29 6 14 FR France \n", + "2020-11-30/2020-12-06 5 11 FR France \n", + "2020-12-07/2020-12-13 7 15 FR France \n", + "2020-12-14/2020-12-20 11 21 FR France \n", + "2020-12-21/2020-12-27 12 24 FR France \n", + "2020-12-28/2021-01-03 13 23 FR France \n", + "2021-01-04/2021-01-10 12 20 FR France \n", + "2021-01-11/2021-01-17 8 16 FR France \n", + "2021-01-18/2021-01-24 9 17 FR France \n", + "2021-01-25/2021-01-31 13 23 FR France \n", + "2021-02-01/2021-02-07 13 23 FR France \n", + "2021-02-08/2021-02-14 15 25 FR France \n", + "2021-02-15/2021-02-21 16 26 FR France \n", + "2021-02-22/2021-02-28 13 21 FR France \n", + "2021-03-01/2021-03-07 12 22 FR France \n", + "2021-03-08/2021-03-14 10 18 FR France \n", + "2021-03-15/2021-03-21 10 18 FR France \n", + "2021-03-22/2021-03-28 12 22 FR France \n", + "2021-03-29/2021-04-04 10 20 FR France \n", + "2021-04-05/2021-04-11 12 22 FR France \n", + "2021-04-12/2021-04-18 12 22 FR France \n", + "2021-04-19/2021-04-25 4 10 FR France \n", + "2021-04-26/2021-05-02 4 10 FR France \n", + "\n", + "[1587 rows x 10 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle\n", + "\n", + "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 **septembre** de l'année $N$ au\n", + "1er **septembre** 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 **septembre** de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er **septembre**.\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 en août **1990**, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en **1991**." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n", + " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", + " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", + " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", + " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", + " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", + " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", + " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", + " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", + " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", + " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", + " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", + " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", + " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", + " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", + " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", + " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", + " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", + " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", + " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", + " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", + " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", + " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", + " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", + " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", + " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", + " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", + " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", + " Period('2019-08-26/2019-09-01', 'W-SUN'),\n", + " Period('2020-08-31/2020-09-06', 'W-SUN')]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]\n", + "first_august_week" + ] + }, + { + "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": 17, + "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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'année ayant recensée le plus de cas de varicelle est l'année 2009. Et à l'inverse, l'année en ayant eu le moins est l'année **2020**. Mais puique le QCM a été réalisé avant cette date, la vraie réponse est l'année **2002** ;)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +3368,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -