module3:exports html et csv données varicelle, module4: exo 1 en cours

module3/exo1/incidence-PAY-3.csv

+# @source="rseau Sentinelles, INSERM, Sorbonne Universit, http://www.sentiweb.fr", @meta={"period":[198444,202016],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-04-24T19:31:32+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202016,3,0,0,0,0,0,0,FR,France
+202015,3,0,0,0,0,0,0,FR,France
+202014,3,0,0,0,0,0,0,FR,France
+202013,3,0,0,0,0,0,0,FR,France
+202012,3,8321,5873,10769,13,9,17,FR,France
+202011,3,101704,93652,109756,154,142,166,FR,France
+202010,3,104977,96650,113304,159,146,172,FR,France
+202009,3,110696,102066,119326,168,155,181,FR,France
+202008,3,143753,133984,153522,218,203,233,FR,France
+202007,3,183610,172812,194408,279,263,295,FR,France
+202006,3,206669,195481,217857,314,297,331,FR,France
+202005,3,187957,177445,198469,285,269,301,FR,France
+202004,3,122331,113492,131170,186,173,199,FR,France
+202003,3,78413,71330,85496,119,108,130,FR,France
+202002,3,53614,47654,59574,81,72,90,FR,France
+202001,3,36850,31608,42092,56,48,64,FR,France
+201952,3,28135,23220,33050,43,36,50,FR,France
+201951,3,29786,25042,34530,45,38,52,FR,France
+201950,3,34223,29156,39290,52,44,60,FR,France
+201949,3,25662,21414,29910,39,33,45,FR,France
+201948,3,22367,18055,26679,34,27,41,FR,France
+201947,3,18669,14759,22579,28,22,34,FR,France
+201946,3,16030,12567,19493,24,19,29,FR,France
+201945,3,10138,7160,13116,15,10,20,FR,France
+201944,3,7822,5010,10634,12,8,16,FR,France
+201943,3,9487,6448,12526,14,9,19,FR,France
+201942,3,7747,5243,10251,12,8,16,FR,France
+201941,3,7122,4720,9524,11,7,15,FR,France
+201940,3,8505,5784,11226,13,9,17,FR,France
+201939,3,7091,4462,9720,11,7,15,FR,France
+201938,3,4897,2891,6903,7,4,10,FR,France
+201937,3,3172,1367,4977,5,2,8,FR,France
+201936,3,2295,728,3862,3,1,5,FR,France
+201935,3,1010,2,2018,2,0,4,FR,France
+201934,3,1672,279,3065,3,1,5,FR,France
+201933,3,1593,68,3118,2,0,4,FR,France
+201932,3,1592,71,3113,2,0,4,FR,France
+201931,3,2337,695,3979,4,2,6,FR,France
+201930,3,1944,415,3473,3,1,5,FR,France
+201929,3,2096,420,3772,3,0,6,FR,France
+201928,3,1195,125,2265,2,0,4,FR,France
+201927,3,2466,848,4084,4,2,6,FR,France
+201926,3,1798,402,3194,3,1,5,FR,France
+201925,3,1544,389,2699,2,0,4,FR,France
+201924,3,876,17,1735,1,0,2,FR,France
+201923,3,2004,748,3260,3,1,5,FR,France
+201922,3,2181,602,3760,3,1,5,FR,France
+201921,3,2585,1102,4068,4,2,6,FR,France
+201920,3,2891,1202,4580,4,1,7,FR,France
+201919,3,3221,1509,4933,5,2,8,FR,France
+201918,3,1385,278,2492,2,0,4,FR,France
+201917,3,3166,1348,4984,5,2,8,FR,France
+201916,3,3842,1973,5711,6,3,9,FR,France
+201915,3,5932,3669,8195,9,6,12,FR,France
+201914,3,12675,9167,16183,19,14,24,FR,France
+201913,3,16513,12530,20496,25,19,31,FR,France
+201912,3,22184,17786,26582,34,27,41,FR,France
+201911,3,31553,26556,36550,48,40,56,FR,France
+201910,3,49742,43268,56216,76,66,86,FR,France
+201909,3,88354,79564,97144,134,121,147,FR,France
+201908,3,172604,160024,185184,262,243,281,FR,France
+201907,3,307338,291220,323456,467,443,491,FR,France
+201906,3,394286,376782,411790,599,572,626,FR,France
+201905,3,355785,339295,372275,540,515,565,FR,France
+201904,3,241090,227261,254919,366,345,387,FR,France
+201903,3,147063,135890,158236,223,206,240,FR,France
+201902,3,75548,67632,83464,115,103,127,FR,France
+201901,3,50295,43525,57065,76,66,86,FR,France
+201852,3,37903,31375,44431,58,48,68,FR,France
+201851,3,39259,32977,45541,60,50,70,FR,France
+201850,3,27781,22638,32924,42,34,50,FR,France
+201849,3,19738,15481,23995,30,24,36,FR,France
+201848,3,19501,15275,23727,30,24,36,FR,France
+201847,3,15949,12105,19793,24,18,30,FR,France
+201846,3,11278,7957,14599,17,12,22,FR,France
+201845,3,11065,7791,14339,17,12,22,FR,France
+201844,3,6586,3875,9297,10,6,14,FR,France
+201843,3,6550,3988,9112,10,6,14,FR,France
+201842,3,7787,5129,10445,12,8,16,FR,France
+201841,3,8048,5098,10998,12,8,16,FR,France
+201840,3,7409,4717,10101,11,7,15,FR,France
+201839,3,7174,4235,10113,11,7,15,FR,France
+201838,3,7349,4399,10299,11,7,15,FR,France
+201837,3,4915,2386,7444,7,3,11,FR,France
+201836,3,3215,1349,5081,5,2,8,FR,France
+201835,3,1506,239,2773,2,0,4,FR,France
+201834,3,1368,116,2620,2,0,4,FR,France
+201833,3,1962,5,3919,3,0,6,FR,France
+201832,3,1839,183,3495,3,0,6,FR,France
+201831,3,2048,242,3854,3,0,6,FR,France
+201830,3,1951,202,3700,3,0,6,FR,France
+201829,3,1951,252,3650,3,0,6,FR,France
+201828,3,1654,52,3256,3,1,5,FR,France
+201827,3,3266,1145,5387,5,2,8,FR,France
+201826,3,3758,1493,6023,6,3,9,FR,France
+201825,3,4580,2220,6940,7,3,11,FR,France
+201824,3,3223,1351,5095,5,2,8,FR,France
+201823,3,1207,136,2278,2,0,4,FR,France
+201822,3,3202,1330,5074,5,2,8,FR,France
+201821,3,2537,763,4311,4,1,7,FR,France
+201820,3,2694,967,4421,4,1,7,FR,France
+201819,3,1025,0,2098,2,0,4,FR,France
+201818,3,3541,1416,5666,5,2,8,FR,France
+201817,3,2573,1003,4143,4,2,6,FR,France
+201816,3,4818,2724,6912,7,4,10,FR,France
+201815,3,16311,12168,20454,25,19,31,FR,France
+201814,3,22666,18092,27240,35,28,42,FR,France
+201813,3,32680,25536,39824,50,39,61,FR,France
+201812,3,52040,44590,59490,79,68,90,FR,France
+201811,3,65159,56506,73812,99,86,112,FR,France
+201810,3,93512,83989,103035,142,128,156,FR,France
+201809,3,108545,98645,118445,165,150,180,FR,France
+201808,3,131870,120950,142790,201,184,218,FR,France
+201807,3,141138,130177,152099,215,198,232,FR,France
+201806,3,138810,128164,149456,211,195,227,FR,France
+201805,3,157839,146646,169032,240,223,257,FR,France
+201804,3,175483,163835,187131,267,249,285,FR,France
+201803,3,178329,166640,190018,271,253,289,FR,France
+201802,3,198079,185663,210495,302,283,321,FR,France
+201801,3,235493,220941,250045,358,336,380,FR,France
+201752,3,299114,279359,318869,459,429,489,FR,France
+201751,3,229448,214366,244530,352,329,375,FR,France
+201750,3,129535,118787,140283,199,183,215,FR,France
+201749,3,63789,56120,71458,98,86,110,FR,France
+201748,3,37568,31541,43595,58,49,67,FR,France
+201747,3,24972,19923,30021,38,30,46,FR,France
+201746,3,13398,9948,16848,21,16,26,FR,France
+201745,3,14035,8091,19979,22,13,31,FR,France
+201744,3,8544,5318,11770,13,8,18,FR,France
+201743,3,7300,4402,10198,11,7,15,FR,France
+201742,3,9966,6503,13429,15,10,20,FR,France
+201741,3,12434,8718,16150,19,13,25,FR,France
+201740,3,12725,8690,16760,20,14,26,FR,France
+201739,3,14949,10534,19364,23,16,30,FR,France
+201738,3,11463,7438,15488,18,12,24,FR,France
+201737,3,9716,6364,13068,15,10,20,FR,France
+201736,3,2815,917,4713,4,1,7,FR,France
+201735,3,2794,850,4738,4,1,7,FR,France
+201734,3,2497,879,4115,4,2,6,FR,France
+201733,3,2406,766,4046,4,1,7,FR,France
+201732,3,2667,879,4455,4,1,7,FR,France
+201731,3,3256,1158,5354,5,2,8,FR,France
+201730,3,3759,1299,6219,6,2,10,FR,France
+201729,3,5014,1989,8039,8,3,13,FR,France
+201728,3,5271,2576,7966,8,4,12,FR,France
+201727,3,3924,1432,6416,6,2,10,FR,France
+201726,3,3171,1166,5176,5,2,8,FR,France
+201725,3,837,0,1721,1,0,2,FR,France
+201724,3,1566,248,2884,2,0,4,FR,France
+201723,3,1664,203,3125,3,1,5,FR,France
+201722,3,1305,92,2518,2,0,4,FR,France
+201721,3,971,0,2046,1,0,3,FR,France
+201720,3,2686,793,4579,4,1,7,FR,France
+201719,3,3461,1490,5432,5,2,8,FR,France
+201718,3,2102,515,3689,3,1,5,FR,France
+201717,3,2071,428,3714,3,0,6,FR,France
+201716,3,1380,222,2538,2,0,4,FR,France
+201715,3,479,0,1242,1,0,2,FR,France
+201714,3,1110,0,2549,2,0,4,FR,France
+201713,3,7594,3808,11380,12,6,18,FR,France
+201712,3,8780,4834,12726,13,7,19,FR,France
+201711,3,7814,4329,11299,12,7,17,FR,France
+201710,3,11802,7964,15640,18,12,24,FR,France
+201709,3,13111,9099,17123,20,14,26,FR,France
+201708,3,29545,23136,35954,45,35,55,FR,France
+201707,3,59590,49764,69416,91,76,106,FR,France
+201706,3,93628,82560,104696,144,127,161,FR,France
+201705,3,193677,179255,208099,297,275,319,FR,France
+201704,3,256428,240618,272238,394,370,418,FR,France
+201703,3,267276,251345,283207,410,386,434,FR,France
+201702,3,260588,245070,276106,400,376,424,FR,France
+201701,3,255535,239743,271327,392,368,416,FR,France
+201652,3,224489,207799,241179,345,319,371,FR,France
+201651,3,187704,172655,202753,288,265,311,FR,France
+201650,3,126570,115081,138059,194,176,212,FR,France
+201649,3,76390,67374,85406,117,103,131,FR,France
+201648,3,40502,33949,47055,62,52,72,FR,France
+201647,3,22270,17612,26928,34,27,41,FR,France
+201646,3,20816,16125,25507,32,25,39,FR,France
+201645,3,15957,11448,20466,24,17,31,FR,France
+201644,3,15145,10357,19933,23,16,30,FR,France
+201643,3,9378,5830,12926,14,9,19,FR,France
+201642,3,12920,9044,16796,20,14,26,FR,France
+201641,3,13909,9709,18109,21,15,27,FR,France
+201640,3,13379,8969,17789,21,14,28,FR,France
+201639,3,10598,6814,14382,16,10,22,FR,France
+201638,3,5969,3073,8865,9,5,13,FR,France
+201637,3,4065,1538,6592,6,2,10,FR,France
+201636,3,2372,473,4271,4,1,7,FR,France
+201635,3,3420,1350,5490,5,2,8,FR,France
+201634,3,2233,0,4643,3,0,7,FR,France
+201633,3,1567,0,3938,2,0,6,FR,France
+201632,3,2100,93,4107,3,0,6,FR,France
+201631,3,3691,0,9120,6,0,14,FR,France
+201630,3,3209,644,5774,5,1,9,FR,France
+201629,3,1469,0,3073,2,0,4,FR,France
+201628,3,2725,357,5093,4,0,8,FR,France
+201627,3,5905,2343,9467,9,4,14,FR,France
+201626,3,4186,1636,6736,6,2,10,FR,France
+201625,3,4611,1560,7662,7,2,12,FR,France
+201624,3,4977,2404,7550,8,4,12,FR,France
+201623,3,4893,2416,7370,8,4,12,FR,France
+201622,3,5667,3015,8319,9,5,13,FR,France
+201621,3,5603,2475,8731,9,4,14,FR,France
+201620,3,3908,1679,6137,6,3,9,FR,France
+201619,3,4738,2308,7168,7,3,11,FR,France
+201618,3,8204,4394,12014,13,7,19,FR,France
+201617,3,13385,8822,17948,21,14,28,FR,France
+201616,3,25670,19632,31708,39,30,48,FR,France
+201615,3,48441,39824,57058,74,61,87,FR,France
+201614,3,92603,81261,103945,142,125,159,FR,France
+201613,3,170654,156387,184921,262,240,284,FR,France
+201612,3,272174,254338,290010,418,391,445,FR,France
+201611,3,304543,286453,322633,467,439,495,FR,France
+201610,3,261822,245799,277845,402,377,427,FR,France
+201609,3,229943,214533,245353,353,329,377,FR,France
+201608,3,195067,180874,209260,299,277,321,FR,France
+201607,3,207359,193223,221495,318,296,340,FR,France
+201606,3,196143,182672,209614,301,280,322,FR,France
+201605,3,178963,166296,191630,275,256,294,FR,France
+201604,3,148473,136591,160355,228,210,246,FR,France
+201603,3,75277,66630,83924,116,103,129,FR,France
+201602,3,44617,37821,51413,68,58,78,FR,France
+201601,3,42263,35556,48970,65,55,75,FR,France
+201553,3,24835,19106,30564,38,29,47,FR,France
+201552,3,19495,14319,24671,30,22,38,FR,France
+201551,3,29080,23014,35146,45,36,54,FR,France
+201550,3,25043,19812,30274,39,31,47,FR,France
+201549,3,21479,16686,26272,33,26,40,FR,France
+201548,3,16428,12273,20583,25,19,31,FR,France
+201547,3,14959,10856,19062,23,17,29,FR,France
+201546,3,12599,8237,16961,20,13,27,FR,France
+201545,3,13577,9192,17962,21,14,28,FR,France
+201544,3,14596,10025,19167,23,16,30,FR,France
+201543,3,16749,11745,21753,26,18,34,FR,France
+201542,3,18675,13613,23737,29,21,37,FR,France
+201541,3,13585,9830,17340,21,15,27,FR,France
+201540,3,13100,9349,16851,20,14,26,FR,France
+201539,3,8595,5671,11519,13,8,18,FR,France
+201538,3,8402,4929,11875,13,8,18,FR,France
+201537,3,8817,4089,13545,14,7,21,FR,France
+201536,3,1922,440,3404,3,1,5,FR,France
+201535,3,229,0,644,0,0,1,FR,France
+201534,3,760,0,1618,1,0,2,FR,France
+201533,3,1537,208,2866,2,0,4,FR,France
+201532,3,1594,66,3122,2,0,4,FR,France
+201531,3,2845,475,5215,4,0,8,FR,France
+201530,3,2447,353,4541,4,1,7,FR,France
+201529,3,2389,332,4446,4,1,7,FR,France
+201528,3,2089,105,4073,3,0,6,FR,France
+201527,3,2257,393,4121,3,0,6,FR,France
+201526,3,2536,774,4298,4,1,7,FR,France
+201525,3,2607,680,4534,4,1,7,FR,France
+201524,3,2849,1000,4698,4,1,7,FR,France
+201523,3,2702,857,4547,4,1,7,FR,France
+201522,3,4262,1989,6535,7,3,11,FR,France
+201521,3,5547,2875,8219,9,5,13,FR,France
+201520,3,4594,2040,7148,7,3,11,FR,France
+201519,3,6307,3256,9358,10,5,15,FR,France
+201518,3,3664,1544,5784,6,3,9,FR,France
+201517,3,6382,3523,9241,10,6,14,FR,France
+201516,3,12098,7913,16283,19,13,25,FR,France
+201515,3,19458,14192,24724,30,22,38,FR,France
+201514,3,30940,24617,37263,48,38,58,FR,France
+201513,3,49563,41986,57140,77,65,89,FR,France
+201512,3,70024,61079,78969,109,95,123,FR,France
+201511,3,99295,88838,109752,154,138,170,FR,France
+201510,3,143931,131682,156180,223,204,242,FR,France
+201509,3,238947,222902,254992,370,345,395,FR,France
+201508,3,409972,388670,431274,635,602,668,FR,France
+201507,3,517750,494301,541199,802,766,838,FR,France
+201506,3,533317,510627,556007,827,792,862,FR,France
+201505,3,457911,436964,478858,710,678,742,FR,France
+201504,3,292161,275491,308831,453,427,479,FR,France
+201503,3,153931,141290,166572,239,219,259,FR,France
+201502,3,104096,93358,114834,161,144,178,FR,France
+201501,3,70962,60505,81419,110,94,126,FR,France
+201452,3,63168,52705,73631,98,82,114,FR,France
+201451,3,45346,38002,52690,71,60,82,FR,France
+201450,3,42182,35049,49315,66,55,77,FR,France
+201449,3,33537,27281,39793,52,42,62,FR,France
+201448,3,28156,21896,34416,44,34,54,FR,France
+201447,3,18158,13186,23130,28,20,36,FR,France
+201446,3,14764,10174,19354,23,16,30,FR,France
+201445,3,16172,11449,20895,25,18,32,FR,France
+201444,3,15500,10464,20536,24,16,32,FR,France
+201443,3,16011,10653,21369,25,17,33,FR,France
+201442,3,21651,15828,27474,34,25,43,FR,France
+201441,3,22486,16295,28677,35,25,45,FR,France
+201440,3,16526,11583,21469,26,18,34,FR,France
+201439,3,8583,5054,12112,13,8,18,FR,France
+201438,3,10567,6496,14638,16,10,22,FR,France
+201437,3,7714,3780,11648,12,6,18,FR,France
+201436,3,5018,1949,8087,8,3,13,FR,France
+201435,3,2268,355,4181,4,1,7,FR,France
+201434,3,2367,250,4484,4,1,7,FR,France
+201433,3,2358,0,4875,4,0,8,FR,France
+201432,3,3229,447,6011,5,1,9,FR,France
+201431,3,4143,349,7937,6,0,12,FR,France
+201430,3,4631,902,8360,7,1,13,FR,France
+201429,3,5122,1258,8986,8,2,14,FR,France
+201428,3,4145,1200,7090,6,1,11,FR,France
+201427,3,4823,1997,7649,8,4,12,FR,France
+201426,3,3858,1235,6481,6,2,10,FR,France
+201425,3,4564,1276,7852,7,2,12,FR,France
+201424,3,2007,249,3765,3,0,6,FR,France
+201423,3,5164,1853,8475,8,3,13,FR,France
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+200817,3,10150,5835,14465,17,10,24,FR,France
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+200229,3,5576,379,10773,9,0,18,FR,France
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+199628,3,3109,541,5677,5,1,9,FR,France
+199627,3,4456,1595,7317,8,3,13,FR,France
+199626,3,2871,1138,4604,5,2,8,FR,France
+199625,3,2308,856,3760,4,1,7,FR,France
+199624,3,2490,1067,3913,4,2,6,FR,France
+199623,3,2819,1262,4376,5,2,8,FR,France
+199622,3,4005,1984,6026,7,4,10,FR,France
+199621,3,7042,4291,9793,12,7,17,FR,France
+199620,3,10619,7365,13873,18,12,24,FR,France
+199619,3,11898,8065,15731,21,14,28,FR,France
+199618,3,18338,13352,23324,32,23,41,FR,France
+199617,3,24954,19100,30808,43,33,53,FR,France
+199616,3,29313,23868,34758,51,42,60,FR,France
+199615,3,51703,43644,59762,89,75,103,FR,France
+199614,3,44414,36774,52054,77,64,90,FR,France
+199613,3,30622,25062,36182,53,43,63,FR,France
+199612,3,31818,25852,37784,55,45,65,FR,France
+199611,3,24427,19499,29355,42,33,51,FR,France
+199610,3,20910,16198,25622,36,28,44,FR,France
+199609,3,19594,15228,23960,34,26,42,FR,France
+199608,3,19046,14850,23242,33,26,40,FR,France
+199607,3,23041,18338,27744,40,32,48,FR,France
+199606,3,21810,17153,26467,38,30,46,FR,France
+199605,3,25275,20410,30140,44,36,52,FR,France
+199604,3,43359,36948,49770,75,64,86,FR,France
+199603,3,54691,48016,61366,94,82,106,FR,France
+199602,3,105606,96106,115106,182,166,198,FR,France
+199601,3,223830,209274,238386,386,361,411,FR,France
+199552,3,397122,378252,415992,688,655,721,FR,France
+199551,3,749988,725434,774542,1299,1256,1342,FR,France
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+199545,3,29791,24523,35059,52,43,61,FR,France
+199544,3,13168,9820,16516,23,17,29,FR,France
+199543,3,16188,12389,19987,28,21,35,FR,France
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+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
+199539,3,18499,13844,23154,32,24,40,FR,France
+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
+199535,3,2496,644,4348,4,1,7,FR,France
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+199533,3,905,0,2007,2,0,4,FR,France
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+199531,3,1946,92,3800,3,0,6,FR,France
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+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
+199511,3,81730,73346,90114,142,127,157,FR,France
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+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
+199507,3,30550,25479,35621,53,44,62,FR,France
+199506,3,37351,32007,42695,65,56,74,FR,France
+199505,3,27876,23218,32534,48,40,56,FR,France
+199504,3,27643,22818,32468,48,40,56,FR,France
+199503,3,24844,20391,29297,43,35,51,FR,France
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+199501,3,26556,21916,31196,46,38,54,FR,France
+199452,3,22036,17495,26577,38,30,46,FR,France
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+199450,3,28831,23470,34192,50,41,59,FR,France
+199449,3,21161,16559,25763,37,29,45,FR,France
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+199446,3,14252,10525,17979,25,19,31,FR,France
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+199444,3,11307,7688,14926,20,14,26,FR,France
+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
+199436,3,2806,1133,4479,5,2,8,FR,France
+199435,3,4556,2358,6754,8,4,12,FR,France
+199434,3,3459,1444,5474,6,2,10,FR,France
+199433,3,4272,977,7567,7,1,13,FR,France
+199432,3,4786,1613,7959,8,2,14,FR,France
+199431,3,5027,1580,8474,9,3,15,FR,France
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+199429,3,1188,0,2384,2,0,4,FR,France
+199428,3,1581,316,2846,3,1,5,FR,France
+199427,3,713,0,1459,1,0,2,FR,France
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+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
+199419,3,1483,406,2560,3,1,5,FR,France
+199418,3,3705,1940,5470,6,3,9,FR,France
+199417,3,2288,1064,3512,4,2,6,FR,France
+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
+199411,3,5399,3327,7471,9,5,13,FR,France
+199410,3,4548,2529,6567,8,4,12,FR,France
+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
+199404,3,23712,19048,28376,41,33,49,FR,France
+199403,3,29882,24817,34947,52,43,61,FR,France
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+199401,3,118119,108182,128056,205,188,222,FR,France
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+199340,3,13805,10348,17262,24,18,30,FR,France
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+199338,3,10492,7329,13655,18,12,24,FR,France
+199337,3,7023,4503,9543,12,8,16,FR,France
+199336,3,4361,2445,6277,8,5,11,FR,France
+199335,3,5589,3066,8112,10,6,14,FR,France
+199334,3,2984,1209,4759,5,2,8,FR,France
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+199332,3,3755,1051,6459,7,2,12,FR,France
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+199330,3,4055,1907,6203,7,3,11,FR,France
+199329,3,3714,1668,5760,6,2,10,FR,France
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+199327,3,2574,918,4230,4,1,7,FR,France
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+199321,3,5942,3670,8214,10,6,14,FR,France
+199320,3,5137,3157,7117,9,6,12,FR,France
+199319,3,4593,2725,6461,8,5,11,FR,France
+199318,3,5027,3075,6979,9,6,12,FR,France
+199317,3,8111,5138,11084,14,9,19,FR,France
+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
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+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
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+199249,3,39146,33847,44445,69,60,78,FR,France
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+199244,3,27619,22945,32293,48,40,56,FR,France
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+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
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+199232,3,4963,2215,7711,9,4,14,FR,France
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+199221,3,5059,2889,7229,9,5,13,FR,France
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+199218,3,3887,1777,5997,7,3,11,FR,France
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+199147,3,35691,28224,43158,63,50,76,FR,France
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+199145,3,19565,13551,25579,34,23,45,FR,France
+199144,3,21571,15477,27665,38,27,49,FR,France
+199143,3,28842,21958,35726,51,39,63,FR,France
+199142,3,27483,20385,34581,48,36,60,FR,France
+199141,3,28185,20655,35715,50,37,63,FR,France
+199140,3,26740,16049,37431,47,28,66,FR,France
+199139,3,14373,8734,20012,25,15,35,FR,France
+199138,3,7460,3700,11220,13,6,20,FR,France
+199137,3,2429,434,4424,4,0,8,FR,France
+199136,3,2380,0,4882,4,0,8,FR,France
+199135,3,2372,0,5025,4,0,9,FR,France
+199134,3,2099,0,5104,4,0,9,FR,France
+199133,3,1654,0,4102,3,0,7,FR,France
+199132,3,625,0,2190,1,0,4,FR,France
+199131,3,2464,0,5830,4,0,10,FR,France
+199130,3,2382,0,6857,4,0,12,FR,France
+199129,3,3837,40,7634,7,0,14,FR,France
+199128,3,3297,152,6442,6,0,12,FR,France
+199127,3,3164,934,5394,6,2,10,FR,France
+199126,3,6376,3324,9428,11,6,16,FR,France
+199125,3,8218,4776,11660,14,8,20,FR,France
+199124,3,8223,4660,11786,14,8,20,FR,France
+199123,3,6801,3596,10006,12,6,18,FR,France
+199122,3,6050,2682,9418,11,5,17,FR,France
+199121,3,6391,3385,9397,11,6,16,FR,France
+199120,3,8633,4683,12583,15,8,22,FR,France
+199119,3,7785,3986,11584,14,7,21,FR,France
+199118,3,10856,6457,15255,19,11,27,FR,France
+199117,3,15603,10950,20256,27,19,35,FR,France
+199116,3,15665,11184,20146,28,20,36,FR,France
+199115,3,15062,10715,19409,26,18,34,FR,France
+199114,3,27862,21583,34141,49,38,60,FR,France
+199113,3,32179,25829,38529,57,46,68,FR,France
+199112,3,27873,22312,33434,49,39,59,FR,France
+199111,3,46186,38876,53496,81,68,94,FR,France
+199110,3,88750,77784,99716,156,137,175,FR,France
+199109,3,139093,124326,153860,245,219,271,FR,France
+199108,3,216369,198630,234108,381,350,412,FR,France
+199107,3,208836,191789,225883,367,337,397,FR,France
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+199102,3,29759,22044,37474,52,38,66,FR,France
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+199051,3,46924,37544,56304,83,66,100,FR,France
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+199048,3,29571,22315,36827,52,39,65,FR,France
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+199041,3,19723,13330,26116,35,24,46,FR,France
+199040,3,20433,14614,26252,36,26,46,FR,France
+199039,3,15618,10315,20921,28,19,37,FR,France
+199038,3,11146,6197,16095,20,11,29,FR,France
+199037,3,4051,1022,7080,7,2,12,FR,France
+199036,3,2542,124,4960,4,0,8,FR,France
+199035,3,934,0,2681,2,0,5,FR,France
+199034,3,201,0,595,0,0,1,FR,France
+199033,3,0,0,0,0,0,0,FR,France
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module3/exo2/exercice_python_fr.html

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+<title>Module 3 - exercice 2</title>
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+</head>
+<body>
+<div id="content">
+<h1 class="title">Module 3 - exercice 2</h1>
+<div id="table-of-contents">
+<h2>Table des matières</h2>
+<div id="text-table-of-contents">
+<ul>
+<li><a href="#orgc31a0c5">1. Import des données, vérification, préparation</a>
+<ul>
+<li><a href="#orgbe3316d">1.1. Import et vérification rapide</a></li>
+<li><a href="#orgf31b999">1.2. Extraction des colonnes utilisées</a></li>
+<li><a href="#org1471b0d">1.3. Conversion des dates</a></li>
+</ul>
+</li>
+<li><a href="#org6ff0822">2. Analyse des données</a>
+<ul>
+<li><a href="#org008933f">2.1. Visualisation au pas semaine</a></li>
+<li><a href="#org183bb13">2.2. Analyse année par année</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+
+<div id="outline-container-orgc31a0c5" class="outline-2">
+<h2 id="orgc31a0c5"><span class="section-number-2">1</span> Import des données, vérification, préparation</h2>
+<div class="outline-text-2" id="text-1">
+</div>
+<div id="outline-container-orgbe3316d" class="outline-3">
+<h3 id="orgbe3316d"><span class="section-number-3">1.1</span> Import et vérification rapide</h3>
+<div class="outline-text-3" id="text-1-1">
+<p>
+Les données au format csv sont téléchargées depuis l'adresse :
+<a href="http://www.sentiweb.fr/datasets/incidence-PAY-7.csv">http://www.sentiweb.fr/datasets/incidence-PAY-7.csv</a>
+</p>
+
+<p>
+Code source adapté de :
+</p>
+<p>
+<a href="https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-orgmode.org">https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-orgmode.org</a>
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a020f0;">from</span> urllib.request <span style="color: #a020f0;">import</span> urlretrieve
+<span style="color: #a020f0;">import</span> os
+
+<span style="color: #a020f0;">if</span> os.path.isfile(<span style="color: #8b2252;">"incidence-PAY-7.csv"</span>):
+    <span style="color: #a020f0;">print</span>(<span style="color: #8b2252;">"Chargement du fichier local"</span>)
+<span style="color: #a020f0;">else</span>:
+    urlretrieve(<span style="color: #8b2252;">"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv"</span>, <span style="color: #8b2252;">"incidence-PAY-7.csv"</span>)
+<span style="color: #a0522d;">data</span> = <span style="color: #483d8b;">open</span>(<span style="color: #8b2252;">"incidence-PAY-7.csv"</span>, encoding=<span style="color: #8b2252;">"iso-8859-1"</span>).read()
+<span style="color: #a0522d;">lines</span> = data.strip().split(<span style="color: #8b2252;">'\n'</span>)
+<span style="color: #a0522d;">data_lines</span> = lines[1:]
+<span style="color: #a0522d;">table</span> = [line.split(<span style="color: #8b2252;">','</span>) <span style="color: #a020f0;">for</span> line <span style="color: #a020f0;">in</span> data_lines]
+</pre>
+</div>
+
+<pre class="example">
+Chargement du fichier local
+
+</pre>
+
+<p>
+Vérification visuelle des premières lignes.
+</p>
+<div class="org-src-container">
+<pre class="src src-python">table[:5]
+</pre>
+</div>
+
+<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
+
+
+<colgroup>
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-right" />
+
+<col  class="org-left" />
+
+<col  class="org-left" />
+</colgroup>
+<tbody>
+<tr>
+<td class="org-right">week</td>
+<td class="org-right">indicator</td>
+<td class="org-right">inc</td>
+<td class="org-right">inc<sub>low</sub></td>
+<td class="org-right">inc<sub>up</sub></td>
+<td class="org-right">inc100</td>
+<td class="org-right">inc100<sub>low</sub></td>
+<td class="org-right">inc100<sub>up</sub></td>
+<td class="org-left">geo<sub>insee</sub></td>
+<td class="org-left">geo<sub>name</sub></td>
+</tr>
+
+<tr>
+<td class="org-right">202016</td>
+<td class="org-right">7</td>
+<td class="org-right">803</td>
+<td class="org-right">83</td>
+<td class="org-right">1523</td>
+<td class="org-right">1</td>
+<td class="org-right">0</td>
+<td class="org-right">2</td>
+<td class="org-left">FR</td>
+<td class="org-left">France</td>
+</tr>
+
+<tr>
+<td class="org-right">202015</td>
+<td class="org-right">7</td>
+<td class="org-right">1918</td>
+<td class="org-right">675</td>
+<td class="org-right">3161</td>
+<td class="org-right">3</td>
+<td class="org-right">1</td>
+<td class="org-right">5</td>
+<td class="org-left">FR</td>
+<td class="org-left">France</td>
+</tr>
+
+<tr>
+<td class="org-right">202014</td>
+<td class="org-right">7</td>
+<td class="org-right">3879</td>
+<td class="org-right">2227</td>
+<td class="org-right">5531</td>
+<td class="org-right">6</td>
+<td class="org-right">3</td>
+<td class="org-right">9</td>
+<td class="org-left">FR</td>
+<td class="org-left">France</td>
+</tr>
+
+<tr>
+<td class="org-right">202013</td>
+<td class="org-right">7</td>
+<td class="org-right">7326</td>
+<td class="org-right">5236</td>
+<td class="org-right">9416</td>
+<td class="org-right">11</td>
+<td class="org-right">8</td>
+<td class="org-right">14</td>
+<td class="org-left">FR</td>
+<td class="org-left">France</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+
+<div id="outline-container-orgf31b999" class="outline-3">
+<h3 id="orgf31b999"><span class="section-number-3">1.2</span> Extraction des colonnes utilisées</h3>
+<div class="outline-text-3" id="text-1-2">
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a0522d;">week</span> = [row[0] <span style="color: #a020f0;">for</span> row <span style="color: #a020f0;">in</span> table]
+<span style="color: #a020f0;">assert</span> week[0] == <span style="color: #8b2252;">'week'</span>
+<span style="color: #a020f0;">del</span> week[0]
+<span style="color: #a0522d;">inc</span> = [row[2] <span style="color: #a020f0;">for</span> row <span style="color: #a020f0;">in</span> table]
+<span style="color: #a020f0;">assert</span> inc[0] == <span style="color: #8b2252;">'inc'</span>
+<span style="color: #a020f0;">del</span> inc[0]
+<span style="color: #a0522d;">data</span> = <span style="color: #483d8b;">list</span>(<span style="color: #483d8b;">zip</span>(week, inc))
+</pre>
+</div>
+
+<p>
+Vérification visuelle des premières et dernières lignes.
+</p>
+<div class="org-src-container">
+<pre class="src src-python">[(<span style="color: #8b2252;">'week'</span>, <span style="color: #8b2252;">'inc'</span>), <span style="color: #008b8b;">None</span>] + data[:5] + [<span style="color: #008b8b;">None</span>] + data[-5:]
+</pre>
+</div>
+
+<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
+
+
+<colgroup>
+<col  class="org-right" />
+
+<col  class="org-right" />
+</colgroup>
+<thead>
+<tr>
+<th scope="col" class="org-right">week</th>
+<th scope="col" class="org-right">inc</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="org-right">202016</td>
+<td class="org-right">803</td>
+</tr>
+
+<tr>
+<td class="org-right">202015</td>
+<td class="org-right">1918</td>
+</tr>
+
+<tr>
+<td class="org-right">202014</td>
+<td class="org-right">3879</td>
+</tr>
+
+<tr>
+<td class="org-right">202013</td>
+<td class="org-right">7326</td>
+</tr>
+
+<tr>
+<td class="org-right">202012</td>
+<td class="org-right">8123</td>
+</tr>
+</tbody>
+<tbody>
+<tr>
+<td class="org-right">199101</td>
+<td class="org-right">15565</td>
+</tr>
+
+<tr>
+<td class="org-right">199052</td>
+<td class="org-right">19375</td>
+</tr>
+
+<tr>
+<td class="org-right">199051</td>
+<td class="org-right">19080</td>
+</tr>
+
+<tr>
+<td class="org-right">199050</td>
+<td class="org-right">11079</td>
+</tr>
+
+<tr>
+<td class="org-right">199049</td>
+<td class="org-right">1143</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+
+<div id="outline-container-org1471b0d" class="outline-3">
+<h3 id="org1471b0d"><span class="section-number-3">1.3</span> Conversion des dates</h3>
+<div class="outline-text-3" id="text-1-3">
+<p>
+Code source du  mooc.
+</p>
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a020f0;">import</span> datetime
+<span style="color: #a0522d;">converted_data</span> = [(datetime.datetime.strptime(year_and_week + <span style="color: #8b2252;">":1"</span> , <span style="color: #8b2252;">'%G%V:%u'</span>).date(),
+                  <span style="color: #483d8b;">int</span>(inc))
+                  <span style="color: #a020f0;">for</span> year_and_week, inc <span style="color: #a020f0;">in</span> data]
+converted_data.sort(key = <span style="color: #a020f0;">lambda</span> record: record[0])
+</pre>
+</div>
+
+<p>
+Visualisation premières lignes. Code source tjrs repris de
+<a href="#org58821c0">1</a>
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a0522d;">str_data</span> = [(<span style="color: #483d8b;">str</span>(date), <span style="color: #483d8b;">str</span>(inc)) <span style="color: #a020f0;">for</span> date, inc <span style="color: #a020f0;">in</span> converted_data]
+[(<span style="color: #8b2252;">'date'</span>, <span style="color: #8b2252;">'inc'</span>), <span style="color: #008b8b;">None</span>] + str_data[:5] + [<span style="color: #008b8b;">None</span>] + str_data[-5:]
+</pre>
+</div>
+
+<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
+
+
+<colgroup>
+<col  class="org-right" />
+
+<col  class="org-right" />
+</colgroup>
+<thead>
+<tr>
+<th scope="col" class="org-right">date</th>
+<th scope="col" class="org-right">inc</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="org-right">1990-12-03</td>
+<td class="org-right">1143</td>
+</tr>
+
+<tr>
+<td class="org-right">1990-12-10</td>
+<td class="org-right">11079</td>
+</tr>
+
+<tr>
+<td class="org-right">1990-12-17</td>
+<td class="org-right">19080</td>
+</tr>
+
+<tr>
+<td class="org-right">1990-12-24</td>
+<td class="org-right">19375</td>
+</tr>
+
+<tr>
+<td class="org-right">1990-12-31</td>
+<td class="org-right">15565</td>
+</tr>
+</tbody>
+<tbody>
+<tr>
+<td class="org-right">2020-03-16</td>
+<td class="org-right">8123</td>
+</tr>
+
+<tr>
+<td class="org-right">2020-03-23</td>
+<td class="org-right">7326</td>
+</tr>
+
+<tr>
+<td class="org-right">2020-03-30</td>
+<td class="org-right">3879</td>
+</tr>
+
+<tr>
+<td class="org-right">2020-04-06</td>
+<td class="org-right">1918</td>
+</tr>
+
+<tr>
+<td class="org-right">2020-04-13</td>
+<td class="org-right">803</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+Vérification des dates : voyons si il y a des données manquantes,
+i.e. plus de 7 jours d'écart entre deux dates.
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a0522d;">dates</span> = [date <span style="color: #a020f0;">for</span> date, _ <span style="color: #a020f0;">in</span> converted_data]
+<span style="color: #a020f0;">for</span> date1, date2 <span style="color: #a020f0;">in</span> <span style="color: #483d8b;">zip</span>(dates[:-1], dates[1:]):
+    <span style="color: #a020f0;">if</span> date2-date1 != datetime.timedelta(weeks=1):
+        <span style="color: #a020f0;">print</span>(f<span style="color: #8b2252;">"Il y a {date2-date1} entre {date1} et {date2}"</span>)
+</pre>
+</div>
+
+<p>
+Visiblement aucune ligne manquante.
+</p>
+</div>
+</div>
+</div>
+
+
+<div id="outline-container-org6ff0822" class="outline-2">
+<h2 id="org6ff0822"><span class="section-number-2">2</span> Analyse des données</h2>
+<div class="outline-text-2" id="text-2">
+<p>
+Contrairement au mooc, je fais l'analyse entièrement avec python,
+librairie pandas.
+</p>
+</div>
+
+<div id="outline-container-org008933f" class="outline-3">
+<h3 id="org008933f"><span class="section-number-3">2.1</span> Visualisation au pas semaine</h3>
+<div class="outline-text-3" id="text-2-1">
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a020f0;">import</span> pandas
+<span style="color: #a0522d;">data</span> = pandas.DataFrame(converted_data, columns=(<span style="color: #8b2252;">"Date"</span>, <span style="color: #8b2252;">"Incidence"</span>))
+data.set_index(pandas.to_datetime(data[<span style="color: #8b2252;">"Date"</span>]), inplace=<span style="color: #008b8b;">True</span>)
+<span style="color: #a0522d;">fig</span> = data.plot()
+plt.savefig(<span style="color: #8b2252;">"incidence_hebdo_varicelle.png"</span>)
+</pre>
+</div>
+
+<pre class="example">
+Traceback (most recent call last):
+  File "&lt;stdin&gt;", line 1, in &lt;module&gt;
+  File "/tmp/babel-EgMhYL/python-1puHgN", line 5, in &lt;module&gt;
+    plt.savefig("incidence_hebdo_varicelle.png")
+NameError: name 'plt' is not defined
+
+</pre>
+
+
+<div class="figure">
+<p><img src="./incidence_hebdo_varicelle.png" alt="incidence_hebdo_varicelle.png" />
+</p>
+</div>
+
+<p>
+Zoom sur une année pour situer à quel mois se situe de pic annuel.
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #b22222;">#</span><span style="color: #b22222;">print(data.index)</span>
+<span style="color: #a0522d;">fig2</span> = data[<span style="color: #8b2252;">"2002-01-01"</span>:<span style="color: #8b2252;">"2002-12-31"</span>].plot()
+plt.savefig(<span style="color: #8b2252;">"incidence_hebdo_varicelle_2002.png"</span>)
+</pre>
+</div>
+
+<pre class="example">
+Traceback (most recent call last):
+  File "&lt;stdin&gt;", line 1, in &lt;module&gt;
+  File "/tmp/babel-EgMhYL/python-MvQYNv", line 3, in &lt;module&gt;
+    plt.savefig("incidence_hebdo_varicelle_2002.png")
+NameError: name 'plt' is not defined
+
+</pre>
+
+<p>
+<a href=""incidence_hebdo_varicelle_2002.png"">"incidence_hebdo_varicelle_2002.png"</a>
+</p>
+
+<p>
+Le pic semble être au printemps de chaque année, légèrement plus tard
+que le pic de grippe.
+</p>
+</div>
+</div>
+
+<div id="outline-container-org183bb13" class="outline-3">
+<h3 id="org183bb13"><span class="section-number-3">2.2</span> Analyse année par année</h3>
+<div class="outline-text-3" id="text-2-2">
+<p>
+La période de référence commence au 1er septembre. La période
+commençant en septembre de l'année N a l'étiquette N+1 car le pic
+annuel se situe en début/printemps de l'année N+1 (sur le modèle
+de l'analyse du syndrome grippal dans le mooc). 
+De plus, on supprime les données antérieures au 01-09-1991 car pas de
+données pour la période du 01-09-1990 au 03-12-1990, qui compte pour
+l'année 1991. L'année 1991 n'est donc pas analysée.
+De manière similaire, on ne tient pas compte des données postérieures
+au 01-09-2019 car les données s'arrêtent au 13-04-2020. L'année 2020
+n'est donc pas analysée non plus.
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a0522d;">data</span> = data[<span style="color: #8b2252;">"1991-09-01"</span>:<span style="color: #8b2252;">"2019-09-01"</span>]
+
+<span style="color: #a0522d;">ann&#233;es</span> = [N <span style="color: #a020f0;">for</span> N <span style="color: #a020f0;">in</span> <span style="color: #483d8b;">range</span>(1992,2020,1)]
+<span style="color: #a0522d;">incidence_annuelle</span> = []
+
+<span style="color: #a020f0;">for</span> N <span style="color: #a020f0;">in</span> ann&#233;es :
+    incidence_annuelle.append(data[<span style="color: #8b2252;">"{}-09-01"</span>.<span style="color: #483d8b;">format</span>(N-1):<span style="color: #8b2252;">"{}-08-31"</span>.<span style="color: #483d8b;">format</span>(N)][<span style="color: #8b2252;">"Incidence"</span>].<span style="color: #483d8b;">sum</span>())
+
+<span style="color: #a0522d;">donn</span> = pandas.DataFrame()
+<span style="color: #a0522d;">donn</span>[<span style="color: #8b2252;">"Ann&#233;e"</span>] = ann&#233;es
+donn.set_index(<span style="color: #8b2252;">"Ann&#233;e"</span>, inplace=<span style="color: #008b8b;">True</span>)
+<span style="color: #a0522d;">donn</span>[<span style="color: #8b2252;">"Incidence annuelle"</span>] = incidence_annuelle
+
+</pre>
+</div>
+
+<p>
+Epidémie la plus forte.
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a020f0;">print</span>(<span style="color: #8b2252;">"incidence max : "</span>, donn.<span style="color: #483d8b;">max</span>())
+<span style="color: #a020f0;">print</span>(<span style="color: #8b2252;">"ann&#233;e : "</span>, donn.idxmax())
+</pre>
+</div>
+
+<pre class="example">
+incidence max :  Incidence annuelle    844054
+dtype: int64
+année :  Incidence annuelle    2009
+dtype: int64
+
+</pre>
+
+<p>
+Epidémie la plus faible.
+</p>
+
+<div class="org-src-container">
+<pre class="src src-python"><span style="color: #a020f0;">print</span>(<span style="color: #8b2252;">"incidence min : "</span>, donn.<span style="color: #483d8b;">min</span>())
+<span style="color: #a020f0;">print</span>(<span style="color: #8b2252;">"ann&#233;e : "</span>, donn.idxmin())
+</pre>
+</div>
+
+<pre class="example">
+incidence min :  Incidence annuelle    515343
+dtype: int64
+année :  Incidence annuelle    2002
+dtype: int64
+
+</pre>
+</div>
+</div>
+</div>
+</div>
+<div id="postamble" class="status">
+<p class="date">Date: La date du jour</p>
+<p class="author">Auteur: Votre nom</p>
+<p class="date">Created: 2020-04-28 mar. 14:05</p>
+<p class="validation"><a href="http://validator.w3.org/check?uri=referer">Validate</a></p>
+</div>
+</body>
+</html>

module3/exo2/incidence-PAY-7.csv

+# @source="rseau Sentinelles, INSERM, Sorbonne Universit, http://www.sentiweb.fr", @meta={"period":[199049,202016],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-04-27T14:24:08+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202016,7,803,83,1523,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
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+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
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+201710,7,10218,6896,13540,16,11,21,FR,France
+201709,7,12248,8571,15925,19,13,25,FR,France
+201708,7,14721,10712,18730,23,17,29,FR,France
+201707,7,10991,7538,14444,17,12,22,FR,France
+201706,7,9627,6502,12752,15,10,20,FR,France
+201705,7,8885,6064,11706,14,10,18,FR,France
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+199851,7,13919,9478,18360,24,16,32,FR,France
+199850,7,20556,15164,25948,35,26,44,FR,France
+199849,7,16232,11327,21137,28,20,36,FR,France
+199848,7,11998,7691,16305,21,14,28,FR,France
+199847,7,13617,8934,18300,23,15,31,FR,France
+199846,7,7646,4419,10873,13,7,19,FR,France
+199845,7,15930,10713,21147,27,18,36,FR,France
+199844,7,14145,9390,18900,24,16,32,FR,France
+199843,7,16707,11447,21967,29,20,38,FR,France
+199842,7,12475,7806,17144,21,13,29,FR,France
+199841,7,13018,8151,17885,22,14,30,FR,France
+199840,7,7396,3653,11139,13,7,19,FR,France
+199839,7,4467,1896,7038,8,4,12,FR,France
+199838,7,3421,1176,5666,6,2,10,FR,France
+199837,7,4489,2136,6842,8,4,12,FR,France
+199836,7,6355,2914,9796,11,5,17,FR,France
+199835,7,7689,3867,11511,13,6,20,FR,France
+199834,7,7235,3191,11279,12,5,19,FR,France
+199833,7,11431,6018,16844,20,11,29,FR,France
+199832,7,20493,9237,31749,35,16,54,FR,France
+199831,7,15619,9125,22113,27,16,38,FR,France
+199830,7,19360,12617,26103,33,21,45,FR,France
+199829,7,17816,11414,24218,31,20,42,FR,France
+199828,7,20757,14385,27129,36,25,47,FR,France
+199827,7,20714,14671,26757,36,26,46,FR,France
+199826,7,24479,17611,31347,42,30,54,FR,France
+199825,7,24462,17924,31000,42,31,53,FR,France
+199824,7,18928,13945,23911,32,23,41,FR,France
+199823,7,13478,8876,18080,23,15,31,FR,France
+199822,7,13213,8997,17429,23,16,30,FR,France
+199821,7,10741,6627,14855,18,11,25,FR,France
+199820,7,23223,17217,29229,40,30,50,FR,France
+199819,7,16130,10667,21593,28,19,37,FR,France
+199818,7,15802,10374,21230,27,18,36,FR,France
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+199816,7,11744,7632,15856,20,13,27,FR,France
+199815,7,17422,12372,22472,30,21,39,FR,France
+199814,7,20962,15699,26225,36,27,45,FR,France
+199813,7,17029,12181,21877,29,21,37,FR,France
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+199739,7,3242,851,5633,6,2,10,FR,France
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+199734,7,6971,2145,11797,12,4,20,FR,France
+199733,7,11167,5245,17089,19,9,29,FR,France
+199732,7,12349,6916,17782,21,12,30,FR,France
+199731,7,13276,8068,18484,23,14,32,FR,France
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+199305,7,15954,12443,19465,28,22,34,FR,France
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module4/challenger.org

+* Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure
+  :PROPERTIES:
+  :CUSTOM_ID: risk-analysis-of-the-space-shuttle-pre-challenger-prediction-of-failure
+  :END:
+
+NDT: This document is based on the
+[[https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/blob/master/src/Python3/challenger.ipynb][original
+ipynb notebook]] from the
+mooc, which was converted into an org notebook thanks to pandoc. Then code
+blocks were manually edited (to a minimal extent) to retrieve
+locally-executed results. Edits include: indentation removal, adding
+plt.savefig(), etc.
+
+In this document we reperform some of the analysis provided in /Risk
+Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure/ by
+/Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley/ published in
+/Journal of the American Statistical Association/, Vol. 84, No. 408
+(Dec., 1989), pp. 945-957 and available at
+[[http://www.jstor.org/stable/2290069]].
+
+On the fourth page of this article, they indicate that the maximum
+likelihood estimates of the logistic regression using only temperature
+are: $\hat{\alpha}=5.085$ and $\hat{\beta}=-0.1156$ and their asymptotic
+standard errors are $s_{\hat{\alpha}}=3.052$ and
+$s_{\hat{\beta}}=0.047$. The Goodness of fit indicated for this model
+was $G^2=18.086$ with 21 degrees of freedom. Our goal is to reproduce
+the computation behind these values and the Figure 4 of this article,
+possibly in a nicer looking way.
+
+** Technical information on the computer on which the analysis is run
+   :PROPERTIES:
+   :CUSTOM_ID: technical-information-on-the-computer-on-which-the-analysis-is-run
+   :END:
+
+We will be using the python3 language using the pandas, statsmodels,
+numpy, matplotlib and seaborn libraries.
+
+#+BEGIN_SRC python :session :export both :results output 
+def print_imported_modules():
+   import sys
+   for name, val in sorted(sys.modules.items()):
+    if(hasattr(val, '__version__')): 
+           print(val.__name__, val.__version__)
+#        else:
+#            print(val.__name__, "(unknown version)")
+def print_sys_info():
+   import sys
+   import platform
+   print(sys.version)
+   print(platform.uname())
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+import seaborn as sns
+
+print_sys_info()
+print_imported_modules()
+#+END_SRC
+
+#+RESULTS:
+#+begin_example
+3.8.2 | packaged by conda-forge | (default, Apr 16 2020, 18:04:51) 
+[GCC 7.3.0]
+uname_result(system='Linux', node='hpisor', release='4.9.0-12-amd64', version='#1 SMP Debian 4.9.210-1 (2020-01-20)', machine='x86_64', processor='')
+_csv 1.0
+_ctypes 1.1.0
+decimal 1.70
+argparse 1.1
+csv 1.0
+ctypes 1.1.0
+cycler 0.10.0
+dateutil 2.8.1
+decimal 1.70
+distutils 3.8.2
+json 2.0.9
+kiwisolver 1.0.1
+logging 0.5.1.2
+matplotlib 3.1.3
+matplotlib.backends.backend_agg 3.1.3
+matplotlib.backends.backend_qt5 5.9.2
+matplotlib.backends.qt_compat 5.9.2
+matplotlib.backends.qt_editor._formlayout 1.0.10
+mkl 2.3.0
+numpy 1.18.1
+numpy.core 1.18.1
+numpy.core._multiarray_umath 3.1
+numpy.lib 1.18.1
+numpy.linalg._umath_linalg b'0.1.5'
+pandas 1.0.3
+patsy 0.5.1
+patsy.version 0.5.1
+platform 1.0.8
+pyparsing 2.4.6
+pytz 2019.3
+re 2.2.1
+scipy 1.4.1
+scipy._lib._uarray 0.5.1+5.ga864a57.scipy
+scipy._lib.decorator 4.0.5
+scipy._lib.six 1.2.0
+scipy.integrate._dop b'$Revision: $'
+scipy.integrate._ode $Id$
+scipy.integrate._odepack  1.9 
+scipy.integrate._quadpack  1.13 
+scipy.integrate.lsoda b'$Revision: $'
+scipy.integrate.vode b'$Revision: $'
+scipy.interpolate._fitpack  1.7 
+scipy.interpolate.dfitpack b'$Revision: $'
+scipy.linalg 0.4.9
+scipy.linalg._fblas b'$Revision: $'
+scipy.linalg._flapack b'$Revision: $'
+scipy.linalg._flinalg b'$Revision: $'
+scipy.ndimage 2.0
+scipy.optimize._cobyla b'$Revision: $'
+scipy.optimize._lbfgsb b'$Revision: $'
+scipy.optimize._minpack  1.10 
+scipy.optimize._nnls b'$Revision: $'
+scipy.optimize._slsqp b'$Revision: $'
+scipy.optimize.minpack2 b'$Revision: $'
+scipy.signal.spline 0.2
+scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'
+scipy.sparse.linalg.isolve._iterative b'$Revision: $'
+scipy.special.specfun b'$Revision: $'
+scipy.stats.mvn b'$Revision: $'
+scipy.stats.statlib b'$Revision: $'
+seaborn 0.10.1
+seaborn.external.husl 2.1.0
+six 1.14.0
+statsmodels 0.11.0
+statsmodels.__init__ 0.11.0
+statsmodels.api 0.11.0
+statsmodels.tools.web 0.11.0
+urllib.request 3.8
+zlib 1.0
+#+end_example
+
+** Loading and inspecting data
+   :PROPERTIES:
+   :CUSTOM_ID: loading-and-inspecting-data
+   :END:
+
+Let's start by reading data.
+
+Note: url corrected based on forum post at https://www.fun-mooc.fr/courses/course-v1:inria+41016+self-paced/courseware/7bf2267c336246f9b6518db624692e14/96b7ce47bd11466a9a2e63d8e8a93d99/
+
+#+BEGIN_SRC python :session :export both :results value 
+data = pd.read_csv("https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv")
+data
+#+END_SRC
+
+#+RESULTS:
+#+begin_example
+         Date  Count  Temperature  Pressure  Malfunction
+0     4/12/81      6           66        50            0
+1    11/12/81      6           70        50            1
+2     3/22/82      6           69        50            0
+3    11/11/82      6           68        50            0
+4     4/04/83      6           67        50            0
+5     6/18/82      6           72        50            0
+6     8/30/83      6           73       100            0
+7    11/28/83      6           70       100            0
+8     2/03/84      6           57       200            1
+9     4/06/84      6           63       200            1
+10    8/30/84      6           70       200            1
+11   10/05/84      6           78       200            0
+12   11/08/84      6           67       200            0
+13    1/24/85      6           53       200            2
+14    4/12/85      6           67       200            0
+15    4/29/85      6           75       200            0
+16    6/17/85      6           70       200            0
+17  7/2903/85      6           81       200            0
+18    8/27/85      6           76       200            0
+19   10/03/85      6           79       200            0
+20   10/30/85      6           75       200            2
+21   11/26/85      6           76       200            0
+22    1/12/86      6           58       200            1
+#+end_example
+
+
+We know from our previous experience on this data set that filtering
+data is a really bad idea. We will therefore process it as such.
+
+#+BEGIN_SRC python :session :export both :results output 
+#%matplotlib inline
+pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas
+import matplotlib.pyplot as plt
+
+data["Frequency"]=data.Malfunction/data.Count
+data.plot(x="Temperature",y="Frequency",kind="scatter",ylim=[0,1])
+plt.grid(True)
+plt.savefig("fig1.png")
+#+END_SRC
+
+#+RESULTS:
+: 'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.
+
+[[./fig1.png]]
+
+** Logistic regression
+   :PROPERTIES:
+   :CUSTOM_ID: logistic-regression
+   :END:
+
+Let's assume O-rings independently fail with the same probability which
+solely depends on temperature. A logistic regression should allow us to
+estimate the influence of temperature.
+
+#+BEGIN_SRC python :session :export both :results value
+import statsmodels.api as sm
+
+data["Success"]=data.Count-data.Malfunction
+data["Intercept"]=1
+
+logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
+                family=sm.families.Binomial(sm.families.links.logit)).fit()
+
+logmodel.summary()
+#+END_SRC
+
+#+RESULTS:
+#+begin_example
+                 Generalized Linear Model Regression Results                  
+==============================================================================
+Dep. Variable:              Frequency   No. Observations:                   23
+Model:                            GLM   Df Residuals:                       21
+Model Family:                Binomial   Df Model:                            1
+Link Function:                  logit   Scale:                          1.0000
+Method:                          IRLS   Log-Likelihood:                -3.9210
+Date:               mar., 05 mai 2020   Deviance:                       3.0144
+Time:                        19:13:17   Pearson chi2:                     5.00
+No. Iterations:                     6                                         
+Covariance Type:            nonrobust                                         
+===============================================================================
+                  coef    std err          z      P>|z|      [0.025      0.975]
+-------------------------------------------------------------------------------
+Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740
+Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110
+===============================================================================
+#+end_example
+
+The maximum likelyhood estimator of the intercept and of Temperature are
+thus $\hat{\alpha}=5.0849$ and $\hat{\beta}=-0.1156$. This *corresponds*
+to the values from the article of Dalal /et al./ The standard errors are
+$s_{\hat{\alpha}} = 7.477$ and $s_{\hat{\beta}} = 0.115$, which is
+*different* from the $3.052$ and $0.04702$ reported by Dallal /et al./
+The deviance is $3.01444$ with 21 degrees of freedom. I cannot find any
+value similar to the Goodness of fit ($G^2=18.086$) reported by Dalal
+/et al./ There seems to be something wrong. Oh I know, I haven't
+indicated that my observations are actually the result of 6 observations
+for each rocket launch. Let's indicate these weights (since the weights
+are always the same throughout all experiments, it does not change the
+estimates of the fit but it does influence the variance estimates).
+
+#+BEGIN_SRC python :session :export both :results value
+logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], 
+                family=sm.families.Binomial(sm.families.links.logit),
+                var_weights=data['Count']).fit()
+
+logmodel.summary()
+#+END_SRC
+
+#+RESULTS:
+#+begin_example
+                 Generalized Linear Model Regression Results                  
+==============================================================================
+Dep. Variable:              Frequency   No. Observations:                   23
+Model:                            GLM   Df Residuals:                       21
+Model Family:                Binomial   Df Model:                            1
+Link Function:                  logit   Scale:                          1.0000
+Method:                          IRLS   Log-Likelihood:                -23.526
+Date:               mar., 05 mai 2020   Deviance:                       18.086
+Time:                        19:13:17   Pearson chi2:                     30.0
+No. Iterations:                     6                                         
+Covariance Type:            nonrobust                                         
+===============================================================================
+                  coef    std err          z      P>|z|      [0.025      0.975]
+-------------------------------------------------------------------------------
+Intercept       5.0850      3.052      1.666      0.096      -0.898      11.068
+Temperature    -0.1156      0.047     -2.458      0.014      -0.208      -0.023
+===============================================================================
+#+end_example
+
+Good, now I have recovered the asymptotic standard errors
+$s_{\hat{\alpha}}=3.052$ and $s_{\hat{\beta}}=0.047$. The Goodness of
+fit (Deviance) indicated for this model is $G^2=18.086$ with 21 degrees
+of freedom (Df Residuals).
+
+*I have therefore managed to fully replicate the results of the Dalal
+/et al./ article*.
+
+** Predicting failure probability
+   :PROPERTIES:
+   :CUSTOM_ID: predicting-failure-probability
+   :END:
+
+The temperature when launching the shuttle was 31°F. Let's try to
+estimate the failure probability for such temperature using our model.:
+
+#+BEGIN_SRC python :session :results output :export both
+#%matplotlib inline
+data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})
+data_pred['Frequency'] = logmodel.predict(data_pred)
+data_pred.plot(x="Temperature",y="Frequency",kind="line",ylim=[0,1])
+plt.scatter(x=data["Temperature"],y=data["Frequency"])
+plt.grid(True)
+plt.savefig("fig2.png")
+#+END_SRC
+
+#+RESULTS:
+
+[[./fig2.png]]
+
+This figure is very similar to the Figure 4 of Dalal /et al./ *I have
+managed to replicate the Figure 4 of the Dalal /et al./ article.*
+
+** Computing and plotting uncertainty
+   :PROPERTIES:
+   :CUSTOM_ID: computing-and-plotting-uncertainty
+   :END:
+
+Following the documentation of
+[[https://seaborn.pydata.org/generated/seaborn.regplot.html][Seaborn]],
+I use regplot.
+
+#+BEGIN_SRC python :session :results output :export both
+sns.set(color_codes=True)
+plt.xlim(30,90)
+plt.ylim(0,1)
+sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)
+plt.savefig("fig3.png")
+#+END_SRC
+
+#+RESULTS:
+
+[[./fig3.png]]
+
+*I think I have managed to correctly compute and plot the uncertainty of
+my prediction.* Although the shaded area seems very similar to
+[[https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/tree/master/challenger.pdf][the
+one obtained by with R]], I can spot a few differences (e.g., the blue
+point for temperature 63 is outside)... Could this be a numerical error
+? Or a difference in the statistical method ? It is not clear which one
+is "right".