From 95937bb7ab612b8f10862a2f59c54ce49c5e4862 Mon Sep 17 00:00:00 2001 From: 364204affd8dd7e07e30248eb3c751a8 <364204affd8dd7e07e30248eb3c751a8@app-learninglab.inria.fr> Date: Wed, 22 Nov 2023 13:22:27 +0000 Subject: [PATCH] =?UTF-8?q?analyse=20termin=C3=A9e?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- module3/exo1/analyse-varicelle.ipynb | 2502 +++++++++++++++++++------- 1 file changed, 1832 insertions(+), 670 deletions(-) diff --git a/module3/exo1/analyse-varicelle.ipynb b/module3/exo1/analyse-varicelle.ipynb index 32ef95b..10c676b 100644 --- a/module3/exo1/analyse-varicelle.ipynb +++ b/module3/exo1/analyse-varicelle.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - 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1719 rows × 10 columns

\n", + "" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202345 7 6118 3049 9187 9 4 \n", + "1 202344 7 3758 1702 5814 6 3 \n", + "2 202343 7 3891 1675 6107 6 3 \n", + "3 202342 7 3968 1212 6724 6 2 \n", + "4 202341 7 3356 1764 4948 5 3 \n", + "5 202340 7 2845 1410 4280 4 2 \n", + "6 202339 7 1739 629 2849 3 1 \n", + "7 202338 7 1663 274 3052 3 1 \n", + "8 202337 7 1122 223 2021 2 1 \n", + "9 202336 7 726 10 1442 1 0 \n", + "10 202335 7 961 96 1826 1 0 \n", + "11 202334 7 1168 9 2327 2 0 \n", + "12 202333 7 3308 1184 5432 5 2 \n", + "13 202332 7 7996 1120 14872 12 2 \n", + "14 202331 7 3318 1398 5238 5 2 \n", + "15 202330 7 5821 3269 8373 9 5 \n", + "16 202329 7 13558 8297 18819 20 12 \n", + "17 202328 7 6700 4043 9357 10 6 \n", + "18 202327 7 7253 4599 9907 11 7 \n", + "19 202326 7 9192 6223 12161 14 10 \n", + "20 202325 7 11498 8257 14739 17 12 \n", + "21 202324 7 11115 7968 14262 17 12 \n", + "22 202323 7 12563 6134 18992 19 9 \n", + "23 202322 7 12184 8125 16243 18 12 \n", + "24 202321 7 11349 7598 15100 17 11 \n", + "25 202320 7 9000 4615 13385 14 7 \n", + "26 202319 7 9344 6091 12597 14 9 \n", + "27 202318 7 10671 7291 14051 16 11 \n", + "28 202317 7 9184 6162 12206 14 9 \n", + "29 202316 7 11387 8014 14760 17 12 \n", + "... ... ... ... ... ... ... ... \n", + "1689 199126 7 17608 11304 23912 31 20 \n", + "1690 199125 7 16169 10700 21638 28 18 \n", + "1691 199124 7 16171 10071 22271 28 17 \n", + "1692 199123 7 11947 7671 16223 21 13 \n", + "1693 199122 7 15452 9953 20951 27 17 \n", + "1694 199121 7 14903 8975 20831 26 16 \n", + "1695 199120 7 19053 12742 25364 34 23 \n", + "1696 199119 7 16739 11246 22232 29 19 \n", + "1697 199118 7 21385 13882 28888 38 25 \n", + "1698 199117 7 13462 8877 18047 24 16 \n", + "1699 199116 7 14857 10068 19646 26 18 \n", + "1700 199115 7 13975 9781 18169 25 18 \n", + "1701 199114 7 12265 7684 16846 22 14 \n", + "1702 199113 7 9567 6041 13093 17 11 \n", + "1703 199112 7 10864 7331 14397 19 13 \n", + "1704 199111 7 15574 11184 19964 27 19 \n", + "1705 199110 7 16643 11372 21914 29 20 \n", + "1706 199109 7 13741 8780 18702 24 15 \n", + "1707 199108 7 13289 8813 17765 23 15 \n", + "1708 199107 7 12337 8077 16597 22 15 \n", + "1709 199106 7 10877 7013 14741 19 12 \n", + "1710 199105 7 10442 6544 14340 18 11 \n", + "1711 199104 7 7913 4563 11263 14 8 \n", + "1712 199103 7 15387 10484 20290 27 18 \n", + "1713 199102 7 16277 11046 21508 29 20 \n", + "1714 199101 7 15565 10271 20859 27 18 \n", + "1715 199052 7 19375 13295 25455 34 23 \n", + "1716 199051 7 19080 13807 24353 34 25 \n", + "1717 199050 7 11079 6660 15498 20 12 \n", + "1718 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 14 FR France \n", + "1 9 FR France \n", + "2 9 FR France \n", + "3 10 FR France \n", + "4 7 FR France \n", + "5 6 FR France \n", + "6 5 FR France \n", + "7 5 FR France \n", + "8 3 FR France \n", + "9 2 FR France \n", + "10 2 FR France \n", + "11 4 FR France \n", + "12 8 FR France \n", + "13 22 FR France \n", + "14 8 FR France \n", + "15 13 FR France \n", + "16 28 FR France \n", + "17 14 FR France \n", + "18 15 FR France \n", + "19 18 FR France \n", + "20 22 FR France \n", + "21 22 FR France \n", + "22 29 FR France \n", + "23 24 FR France \n", + "24 23 FR France \n", + "25 21 FR France \n", + "26 19 FR France \n", + "27 21 FR France \n", + "28 19 FR France \n", + "29 22 FR France \n", + "... ... ... ... \n", + "1689 42 FR France \n", + "1690 38 FR France \n", + "1691 39 FR France \n", + "1692 29 FR France \n", + "1693 37 FR France \n", + "1694 36 FR France \n", + "1695 45 FR France \n", + "1696 39 FR France \n", + "1697 51 FR France \n", + "1698 32 FR France \n", + "1699 34 FR France \n", + "1700 32 FR France \n", + "1701 30 FR France \n", + "1702 23 FR France \n", + "1703 25 FR France \n", + "1704 35 FR France \n", + "1705 38 FR France \n", + "1706 33 FR France \n", + "1707 31 FR France \n", + "1708 29 FR France \n", + "1709 26 FR France \n", + "1710 25 FR France \n", + "1711 20 FR France \n", + "1712 36 FR France \n", + "1713 38 FR France \n", + "1714 36 FR France \n", + "1715 45 FR France \n", + "1716 43 FR France \n", + "1717 28 FR France \n", + "1718 5 FR France \n", + "\n", + "[1719 rows x 10 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(local_path, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Ici non, comme on peut le voir ci-dessous" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "1689 False\n", + "1690 False\n", + "1691 False\n", + "1692 False\n", + "1693 False\n", + "1694 False\n", + "1695 False\n", + "1696 False\n", + "1697 False\n", + "1698 False\n", + "1699 False\n", + "1700 False\n", + "1701 False\n", + "1702 False\n", + "1703 False\n", + "1704 False\n", + "1705 False\n", + "1706 False\n", + "1707 False\n", + "1708 False\n", + "1709 False\n", + "1710 False\n", + "1711 False\n", + "1712 False\n", + "1713 False\n", + "1714 False\n", + "1715 False\n", + "1716 False\n", + "1717 False\n", + "1718 False\n", + "Length: 1719, dtype: bool" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data.isnull().any(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous n'avons pas de point à éliminer." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234576118304991879414FRFrance
12023447375817025814635032343108.057538.07665.087.09FRFrance
22023437389116756107634498238170.051794.06858.078.09FRFrance
320234235684249277.064407.08675.097.073968121267246210FRFrance
42023417335617644948535835751032.065682.08877.099.07FRFrance
520234036889460069.077719.010491.0117.07284514104280426FRFrance
620233971739629284937200363452.080554.010895.0121.015FRFrance
720233871663274305236321855227.071209.09583.0107.015FRFrance
82023377112222320212134908542079.056091.07463.085.0FRFrance
920233633824732237.044257.05849.067.07726101442102FRFrance
1020233533169526013.037377.04839.057.07961961826102FRFrance
1120233432666321057.032269.04032.048.07116892327204FRFrance
1220233331914413161.025127.02920.038.07330811845432528FRFrance
1320233231464110285.018997.0779961120148721222215.029.0FRFrance
1420233131528610705.019867.02316.030.07331813985238528FRFrance
152023303132058647.017763.02013.027.075821326983739513FRFrance
162023293111227113.015131.01711.023.0713558829718819201228FRFrance
17202328391795703.012655.07670040439357106149.019.0FRFrance
18202327389995763.012235.0149.019.0772534599990711715FRFrance
19202326390235934.012112.079192622312161149.019.01018FRFrance
202023253100906739.013441.01510.020.0711498825714739171222FRFrance
212023243113087639.014977.07111157968142621711.023.01222FRFrance
2220232331430010661.017939.02217.027.071256361341899219929FRFrance
2320232231830313822.022784.02821.035.0712184812516243181224FRFrance
2420232131646012188.020732.02519.031.0711349759815100171123FRFrance
2520232031616211963.020361.02418.030.07900046151338514721FRFrance
2620231931690112577.021225.02518.032.07934460911259714919FRFrance
2720231831992915402.024456.03023.037.0710671729114051161121FRFrance
2820231732700721779.032235.04133.049.07918461621220614919FRFrance
2920231632787522767.032983.04234.050.0711387801414760171222FRFrance
...
200719852132609619621.032571.04735.059.016891991267176081130423912312042FRFrance
200819852032789620885.034907.05138.064.016901991257161691070021638281838FRFrance
200919851934315432821.053487.07859.097.016911991247161711007122271281739FRFrance
201019851834055529935.051175.07455.093.01692199123711947767116223211329FRFrance
201119851733405324366.043740.06244.080.01693199122715452995320951271737FRFrance
201219851635036236451.064273.09166.0116.01694199121714903897520831261636FRFrance
201319851536388145538.082224.011683.0149.016951991207190531274225364342345FRFrance
20141985143134545114400.0154690.0244207.0281.016961991197167391124622232291939FRFrance
20151985133197206176080.0218332.0357319.0395.016971991187213851388228888382551FRFrance
20161985123245240223304.0267176.0445405.0485.01698199117713462887718047241632FRFrance
20171985113276205252399.0300011.0501458.0544.016991991167148571006819646261834FRFrance
20181985103353231326279.0380183.0640591.0689.01700199115713975978118169251832FRFrance
20191985093369895341109.0398681.0670618.0722.01701199114712265768416846221430FRFrance
20201985083389886359529.0420243.0707652.0762.0170219911379567604113093171123FRFrance
20211985073471852432599.0511105.0855784.0926.01703199112710864733114397191325FRFrance
20221985063565825518011.0613639.01026939.01113.017041991117155741118419964271935FRFrance
20231985053637302592795.0681809.011551074.01236.017051991107166431137221914292038FRFrance
20241985043424937390794.0459080.0770708.0832.01706199109713741878018702241533FRFrance
20251985033213901174689.0253113.0388317.0459.01707199108713289881317765231531FRFrance
202619850239758680949.0114223.0177147.0207.01708199107712337807716597221529FRFrance
202719850138548965918.0105060.0155120.0190.01709199106710877701314741191226FRFrance
202819845238483060602.0109058.0154110.0198.01710199105710442654414340181125FRFrance
2029198451310172680242.0123210.0185146.0224.017111991047791345631126314820FRFrance
20301984503123680101401.0145959.0225184.0266.017121991037153871048420290271836FRFrance
2031198449310107381684.0120462.0184149.0219.017131991027162771104621508292038FRFrance
203219844837862060634.096606.0143110.0176.017141991017155651027120859271836FRFrance
203319844737202954274.089784.013199.0163.017151990527193751329525455342345FRFrance
203419844638733067686.0106974.0159123.0195.017161990517190801380724353342543FRFrance
20351984453135223101414.0169032.0246184.0308.01717199050711079666015498201228FRFrance
203619844436842220056.0116788.012537.0213.017181990497114302610205FRFrance
\n", - "

2037 rows × 10 columns

\n", + "

1719 rows × 10 columns

\n", "
" ], "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202345 3 81678 70566.0 92790.0 123 106.0 \n", - "1 202344 3 50323 43108.0 57538.0 76 65.0 \n", - "2 202343 3 44982 38170.0 51794.0 68 58.0 \n", - "3 202342 3 56842 49277.0 64407.0 86 75.0 \n", - "4 202341 3 58357 51032.0 65682.0 88 77.0 \n", - "5 202340 3 68894 60069.0 77719.0 104 91.0 \n", - "6 202339 3 72003 63452.0 80554.0 108 95.0 \n", - "7 202338 3 63218 55227.0 71209.0 95 83.0 \n", - "8 202337 3 49085 42079.0 56091.0 74 63.0 \n", - "9 202336 3 38247 32237.0 44257.0 58 49.0 \n", - "10 202335 3 31695 26013.0 37377.0 48 39.0 \n", - "11 202334 3 26663 21057.0 32269.0 40 32.0 \n", - "12 202333 3 19144 13161.0 25127.0 29 20.0 \n", - "13 202332 3 14641 10285.0 18997.0 22 15.0 \n", - "14 202331 3 15286 10705.0 19867.0 23 16.0 \n", - "15 202330 3 13205 8647.0 17763.0 20 13.0 \n", - "16 202329 3 11122 7113.0 15131.0 17 11.0 \n", - "17 202328 3 9179 5703.0 12655.0 14 9.0 \n", - "18 202327 3 8999 5763.0 12235.0 14 9.0 \n", - "19 202326 3 9023 5934.0 12112.0 14 9.0 \n", - "20 202325 3 10090 6739.0 13441.0 15 10.0 \n", - "21 202324 3 11308 7639.0 14977.0 17 11.0 \n", - "22 202323 3 14300 10661.0 17939.0 22 17.0 \n", - "23 202322 3 18303 13822.0 22784.0 28 21.0 \n", - "24 202321 3 16460 12188.0 20732.0 25 19.0 \n", - "25 202320 3 16162 11963.0 20361.0 24 18.0 \n", - "26 202319 3 16901 12577.0 21225.0 25 18.0 \n", - "27 202318 3 19929 15402.0 24456.0 30 23.0 \n", - "28 202317 3 27007 21779.0 32235.0 41 33.0 \n", - "29 202316 3 27875 22767.0 32983.0 42 34.0 \n", - "... ... ... ... ... ... ... ... \n", - "2007 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2008 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2009 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2010 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2011 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2012 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2013 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2014 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2015 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2016 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2017 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2018 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2019 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2020 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2021 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2022 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2023 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2024 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2025 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2026 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2027 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2028 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2029 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2030 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2031 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2032 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2033 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2034 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2035 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2036 198444 3 68422 20056.0 116788.0 125 37.0 \n", + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202345 7 6118 3049 9187 9 4 \n", + "1 202344 7 3758 1702 5814 6 3 \n", + "2 202343 7 3891 1675 6107 6 3 \n", + "3 202342 7 3968 1212 6724 6 2 \n", + "4 202341 7 3356 1764 4948 5 3 \n", + "5 202340 7 2845 1410 4280 4 2 \n", + "6 202339 7 1739 629 2849 3 1 \n", + "7 202338 7 1663 274 3052 3 1 \n", + "8 202337 7 1122 223 2021 2 1 \n", + "9 202336 7 726 10 1442 1 0 \n", + "10 202335 7 961 96 1826 1 0 \n", + "11 202334 7 1168 9 2327 2 0 \n", + "12 202333 7 3308 1184 5432 5 2 \n", + "13 202332 7 7996 1120 14872 12 2 \n", + "14 202331 7 3318 1398 5238 5 2 \n", + "15 202330 7 5821 3269 8373 9 5 \n", + "16 202329 7 13558 8297 18819 20 12 \n", + "17 202328 7 6700 4043 9357 10 6 \n", + "18 202327 7 7253 4599 9907 11 7 \n", + "19 202326 7 9192 6223 12161 14 10 \n", + "20 202325 7 11498 8257 14739 17 12 \n", + "21 202324 7 11115 7968 14262 17 12 \n", + "22 202323 7 12563 6134 18992 19 9 \n", + "23 202322 7 12184 8125 16243 18 12 \n", + "24 202321 7 11349 7598 15100 17 11 \n", + "25 202320 7 9000 4615 13385 14 7 \n", + "26 202319 7 9344 6091 12597 14 9 \n", + "27 202318 7 10671 7291 14051 16 11 \n", + "28 202317 7 9184 6162 12206 14 9 \n", + "29 202316 7 11387 8014 14760 17 12 \n", + "... ... ... ... ... ... ... ... \n", + "1689 199126 7 17608 11304 23912 31 20 \n", + "1690 199125 7 16169 10700 21638 28 18 \n", + "1691 199124 7 16171 10071 22271 28 17 \n", + "1692 199123 7 11947 7671 16223 21 13 \n", + "1693 199122 7 15452 9953 20951 27 17 \n", + "1694 199121 7 14903 8975 20831 26 16 \n", + "1695 199120 7 19053 12742 25364 34 23 \n", + "1696 199119 7 16739 11246 22232 29 19 \n", + "1697 199118 7 21385 13882 28888 38 25 \n", + "1698 199117 7 13462 8877 18047 24 16 \n", + "1699 199116 7 14857 10068 19646 26 18 \n", + "1700 199115 7 13975 9781 18169 25 18 \n", + "1701 199114 7 12265 7684 16846 22 14 \n", + "1702 199113 7 9567 6041 13093 17 11 \n", + "1703 199112 7 10864 7331 14397 19 13 \n", + "1704 199111 7 15574 11184 19964 27 19 \n", + "1705 199110 7 16643 11372 21914 29 20 \n", + "1706 199109 7 13741 8780 18702 24 15 \n", + "1707 199108 7 13289 8813 17765 23 15 \n", + "1708 199107 7 12337 8077 16597 22 15 \n", + "1709 199106 7 10877 7013 14741 19 12 \n", + "1710 199105 7 10442 6544 14340 18 11 \n", + "1711 199104 7 7913 4563 11263 14 8 \n", + "1712 199103 7 15387 10484 20290 27 18 \n", + "1713 199102 7 16277 11046 21508 29 20 \n", + "1714 199101 7 15565 10271 20859 27 18 \n", + "1715 199052 7 19375 13295 25455 34 23 \n", + "1716 199051 7 19080 13807 24353 34 25 \n", + "1717 199050 7 11079 6660 15498 20 12 \n", + "1718 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", - "0 140.0 FR France \n", - "1 87.0 FR France \n", - "2 78.0 FR France \n", - "3 97.0 FR France \n", - "4 99.0 FR France \n", - "5 117.0 FR France \n", - "6 121.0 FR France \n", - "7 107.0 FR France \n", - "8 85.0 FR France \n", - "9 67.0 FR France \n", - "10 57.0 FR France \n", - "11 48.0 FR France \n", - "12 38.0 FR France \n", - "13 29.0 FR France \n", - "14 30.0 FR France \n", - "15 27.0 FR France \n", - "16 23.0 FR France \n", - "17 19.0 FR France \n", - "18 19.0 FR France \n", - "19 19.0 FR France \n", - "20 20.0 FR France \n", - "21 23.0 FR France \n", - "22 27.0 FR France \n", - "23 35.0 FR France \n", - "24 31.0 FR France \n", - "25 30.0 FR France \n", - "26 32.0 FR France \n", - "27 37.0 FR France \n", - "28 49.0 FR France \n", - "29 50.0 FR France \n", + "0 14 FR France \n", + "1 9 FR France \n", + "2 9 FR France \n", + "3 10 FR France \n", + "4 7 FR France \n", + "5 6 FR France \n", + "6 5 FR France \n", + "7 5 FR France \n", + "8 3 FR France \n", + "9 2 FR France \n", + "10 2 FR France \n", + "11 4 FR France \n", + "12 8 FR France \n", + "13 22 FR France \n", + "14 8 FR France \n", + "15 13 FR France \n", + "16 28 FR France \n", + "17 14 FR France \n", + "18 15 FR France \n", + "19 18 FR France \n", + "20 22 FR France \n", + "21 22 FR France \n", + "22 29 FR France \n", + "23 24 FR France \n", + "24 23 FR France \n", + "25 21 FR France \n", + "26 19 FR France \n", + "27 21 FR France \n", + "28 19 FR France \n", + "29 22 FR France \n", "... ... ... ... \n", - "2007 59.0 FR France \n", - "2008 64.0 FR France \n", - "2009 97.0 FR France \n", - "2010 93.0 FR France \n", - "2011 80.0 FR France \n", - "2012 116.0 FR France \n", - "2013 149.0 FR France \n", - "2014 281.0 FR France \n", - "2015 395.0 FR France \n", - "2016 485.0 FR France \n", - "2017 544.0 FR France \n", - "2018 689.0 FR France \n", - "2019 722.0 FR France \n", - "2020 762.0 FR France \n", - "2021 926.0 FR France \n", - "2022 1113.0 FR France \n", - "2023 1236.0 FR France \n", - "2024 832.0 FR France \n", - "2025 459.0 FR France \n", - "2026 207.0 FR France \n", - "2027 190.0 FR France \n", - "2028 198.0 FR France \n", - "2029 224.0 FR France \n", - "2030 266.0 FR France \n", - "2031 219.0 FR France \n", - "2032 176.0 FR France \n", - "2033 163.0 FR France \n", - "2034 195.0 FR France \n", - "2035 308.0 FR France \n", - "2036 213.0 FR France \n", + "1689 42 FR France \n", + "1690 38 FR France \n", + "1691 39 FR France \n", + "1692 29 FR France \n", + "1693 37 FR France \n", + "1694 36 FR France \n", + "1695 45 FR France \n", + "1696 39 FR France \n", + "1697 51 FR France \n", + "1698 32 FR France \n", + "1699 34 FR France \n", + "1700 32 FR France \n", + "1701 30 FR France \n", + "1702 23 FR France \n", + "1703 25 FR France \n", + "1704 35 FR France \n", + "1705 38 FR France \n", + "1706 33 FR France \n", + "1707 31 FR France \n", + "1708 29 FR France \n", + "1709 26 FR France \n", + "1710 25 FR France \n", + "1711 20 FR France \n", + "1712 36 FR France \n", + "1713 38 FR France \n", + "1714 36 FR France \n", + "1715 45 FR France \n", + "1716 43 FR France \n", + "1717 28 FR France \n", + "1718 5 FR France \n", "\n", - "[2037 rows x 10 columns]" + "[1719 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "raw_data = pd.read_csv(local_path, skiprows=1)\n", - "raw_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "raw_data[raw_data.isnull().any(axis=1)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = raw_data.dropna().copy()\n", + "data = raw_data\n", "data" ] }, @@ -1089,7 +2128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1119,10 +2158,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -1137,16 +2174,12 @@ "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 !" + "Ceci s'avère tout à fait juste ici, ce qui confirme qu'aucune donnée n'est manquante." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1166,9 +2199,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "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()" ] @@ -1177,14 +2233,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + "Un zoom sur les dernières années montre mieux la situation des pics en début d'année et d'été. Le creux des incidences se trouve en début d'automne (sept/oct)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "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'][-200:].plot()" ] @@ -1200,33 +2279,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", - "entre deux années civiles, nous définissons la période de référence\n", - "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", - "1er août de l'année $N+1$.\n", + "Etant donné que le pic de l'épidémie se situe généralement d'avril à juillet, nous définissons la période de référence 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 août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", - "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "Comme l'incidence de la varicelle est très faible après l'été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", - "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", - "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + "Encore un petit détail: les données commencent en décembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ - "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", - " for y in range(1985,\n", + "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, @@ -1234,14 +2309,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "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": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1265,9 +2340,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "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='*')" ] @@ -1281,9 +2379,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\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", + "2022 641397\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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -1292,15 +2432,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", - " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + "Enfin, un histogramme montre bien que l'épidémie n'a pas été forte au cours de ces dernières décénnies, c'est-à-dire qu'elle n'a jamais touché 10% de la population française." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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