diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 4933e016e5d0241312815f3bee24f6b3e7bfad67..27fda540e12f0aece26827a8c2bebf10bdf276a5 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -20,28 +20,42 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": {}, + "outputs": [], "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." + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_file = \"syndrome-grippal.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json) :\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", @@ -61,68 +75,2034 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231434882941324.056334.07362.084.0FRFrance
120231336485956800.072918.09886.0110.0FRFrance
220231237275064499.081001.010997.0121.0FRFrance
320231137463866420.082856.0112100.0124.0FRFrance
420231037636868243.084493.0115103.0127.0FRFrance
520230936206254778.069346.09382.0104.0FRFrance
620230837639168065.084717.0115102.0128.0FRFrance
720230738985180397.099305.0135121.0149.0FRFrance
820230639736887636.0107100.0146131.0161.0FRFrance
920230539546986268.0104670.0144130.0158.0FRFrance
1020230437490166916.082886.0113101.0125.0FRFrance
1120230336957061893.077247.010593.0117.0FRFrance
1220230237826070090.086430.0118106.0130.0FRFrance
132023013121773111024.0132522.0183167.0199.0FRFrance
142022523155371142004.0168738.0234214.0254.0FRFrance
152022513248319232128.0264510.0374350.0398.0FRFrance
162022503234143219402.0248884.0353331.0375.0FRFrance
172022493163384151691.0175077.0246228.0264.0FRFrance
182022483121691111744.0131638.0184169.0199.0FRFrance
1920224739641687230.0105602.0145131.0159.0FRFrance
2020224636773560075.075395.010290.0114.0FRFrance
2120224534530638909.051703.06858.078.0FRFrance
2220224433471328880.040546.05243.061.0FRFrance
2320224334476936884.052654.06856.080.0FRFrance
2420224234746240773.054151.07262.082.0FRFrance
2520224134858342388.054778.07364.082.0FRFrance
2620224034192736115.047739.06354.072.0FRFrance
2720223933990234168.045636.06051.069.0FRFrance
2820223832878123733.033829.04335.051.0FRFrance
2920223732139517076.025714.03225.039.0FRFrance
.................................
197619852132609619621.032571.04735.059.0FRFrance
197719852032789620885.034907.05138.064.0FRFrance
197819851934315432821.053487.07859.097.0FRFrance
197919851834055529935.051175.07455.093.0FRFrance
198019851733405324366.043740.06244.080.0FRFrance
198119851635036236451.064273.09166.0116.0FRFrance
198219851536388145538.082224.011683.0149.0FRFrance
19831985143134545114400.0154690.0244207.0281.0FRFrance
19841985133197206176080.0218332.0357319.0395.0FRFrance
19851985123245240223304.0267176.0445405.0485.0FRFrance
19861985113276205252399.0300011.0501458.0544.0FRFrance
19871985103353231326279.0380183.0640591.0689.0FRFrance
19881985093369895341109.0398681.0670618.0722.0FRFrance
19891985083389886359529.0420243.0707652.0762.0FRFrance
19901985073471852432599.0511105.0855784.0926.0FRFrance
19911985063565825518011.0613639.01026939.01113.0FRFrance
19921985053637302592795.0681809.011551074.01236.0FRFrance
19931985043424937390794.0459080.0770708.0832.0FRFrance
19941985033213901174689.0253113.0388317.0459.0FRFrance
199519850239758680949.0114223.0177147.0207.0FRFrance
199619850138548965918.0105060.0155120.0190.0FRFrance
199719845238483060602.0109058.0154110.0198.0FRFrance
1998198451310172680242.0123210.0185146.0224.0FRFrance
19991984503123680101401.0145959.0225184.0266.0FRFrance
2000198449310107381684.0120462.0184149.0219.0FRFrance
200119844837862060634.096606.0143110.0176.0FRFrance
200219844737202954274.089784.013199.0163.0FRFrance
200319844638733067686.0106974.0159123.0195.0FRFrance
20041984453135223101414.0169032.0246184.0308.0FRFrance
200519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2006 rows × 10 columns

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175077.0 246 228.0 \n", + "18 202248 3 121691 111744.0 131638.0 184 169.0 \n", + "19 202247 3 96416 87230.0 105602.0 145 131.0 \n", + "20 202246 3 67735 60075.0 75395.0 102 90.0 \n", + "21 202245 3 45306 38909.0 51703.0 68 58.0 \n", + "22 202244 3 34713 28880.0 40546.0 52 43.0 \n", + "23 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "24 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "25 202241 3 48583 42388.0 54778.0 73 64.0 \n", + "26 202240 3 41927 36115.0 47739.0 63 54.0 \n", + "27 202239 3 39902 34168.0 45636.0 60 51.0 \n", + "28 202238 3 28781 23733.0 33829.0 43 35.0 \n", + "29 202237 3 21395 17076.0 25714.0 32 25.0 \n", + "... ... ... ... ... ... ... ... \n", + "1976 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1977 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1978 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1979 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1980 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1981 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1982 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1983 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1984 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1985 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1986 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1987 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1988 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1989 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1990 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1991 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1992 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1993 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1994 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1995 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1996 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1997 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1998 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1999 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2000 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2001 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2002 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2003 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2004 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2005 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 84.0 FR France \n", + "1 110.0 FR France \n", + "2 121.0 FR France \n", + "3 124.0 FR France \n", + "4 127.0 FR France \n", + "5 104.0 FR France \n", + "6 128.0 FR France \n", + "7 149.0 FR France \n", + "8 161.0 FR France \n", + "9 158.0 FR France \n", + "10 125.0 FR France \n", + "11 117.0 FR France \n", + "12 130.0 FR France \n", + "13 199.0 FR France \n", + "14 254.0 FR France \n", + "15 398.0 FR France \n", + "16 375.0 FR France \n", + "17 264.0 FR France \n", + "18 199.0 FR France \n", + "19 159.0 FR France \n", + "20 114.0 FR France \n", + "21 78.0 FR France \n", + "22 61.0 FR France \n", + "23 80.0 FR France \n", + "24 82.0 FR France \n", + "25 82.0 FR France \n", + "26 72.0 FR France \n", + "27 69.0 FR France \n", + "28 51.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1976 59.0 FR France \n", + "1977 64.0 FR France \n", + "1978 97.0 FR France \n", + "1979 93.0 FR France \n", + "1980 80.0 FR France \n", + "1981 116.0 FR France \n", + "1982 149.0 FR France \n", + "1983 281.0 FR France \n", + "1984 395.0 FR France \n", + "1985 485.0 FR France \n", + "1986 544.0 FR France \n", + "1987 689.0 FR France \n", + "1988 722.0 FR France \n", + "1989 762.0 FR France \n", + "1990 926.0 FR France \n", + "1991 1113.0 FR France \n", + "1992 1236.0 FR France \n", + "1993 832.0 FR France \n", + "1994 459.0 FR France \n", + "1995 207.0 FR France \n", + "1996 190.0 FR France \n", + "1997 198.0 FR France \n", + "1998 224.0 FR France \n", + "1999 266.0 FR France \n", + "2000 219.0 FR France \n", + "2001 176.0 FR France \n", + "2002 163.0 FR France \n", + "2003 195.0 FR France \n", + "2004 308.0 FR France \n", + "2005 213.0 FR France \n", + "\n", + "[2006 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
176919891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1769 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1769 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "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, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231434882941324.056334.07362.084.0FRFrance
120231336485956800.072918.09886.0110.0FRFrance
220231237275064499.081001.010997.0121.0FRFrance
320231137463866420.082856.0112100.0124.0FRFrance
420231037636868243.084493.0115103.0127.0FRFrance
520230936206254778.069346.09382.0104.0FRFrance
620230837639168065.084717.0115102.0128.0FRFrance
720230738985180397.099305.0135121.0149.0FRFrance
820230639736887636.0107100.0146131.0161.0FRFrance
920230539546986268.0104670.0144130.0158.0FRFrance
1020230437490166916.082886.0113101.0125.0FRFrance
1120230336957061893.077247.010593.0117.0FRFrance
1220230237826070090.086430.0118106.0130.0FRFrance
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142022523155371142004.0168738.0234214.0254.0FRFrance
152022513248319232128.0264510.0374350.0398.0FRFrance
162022503234143219402.0248884.0353331.0375.0FRFrance
172022493163384151691.0175077.0246228.0264.0FRFrance
182022483121691111744.0131638.0184169.0199.0FRFrance
1920224739641687230.0105602.0145131.0159.0FRFrance
2020224636773560075.075395.010290.0114.0FRFrance
2120224534530638909.051703.06858.078.0FRFrance
2220224433471328880.040546.05243.061.0FRFrance
2320224334476936884.052654.06856.080.0FRFrance
2420224234746240773.054151.07262.082.0FRFrance
2520224134858342388.054778.07364.082.0FRFrance
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2920223732139517076.025714.03225.039.0FRFrance
.................................
197619852132609619621.032571.04735.059.0FRFrance
197719852032789620885.034907.05138.064.0FRFrance
197819851934315432821.053487.07859.097.0FRFrance
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198019851733405324366.043740.06244.080.0FRFrance
198119851635036236451.064273.09166.0116.0FRFrance
198219851536388145538.082224.011683.0149.0FRFrance
19831985143134545114400.0154690.0244207.0281.0FRFrance
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19861985113276205252399.0300011.0501458.0544.0FRFrance
19871985103353231326279.0380183.0640591.0689.0FRFrance
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2005 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202314 3 48829 41324.0 56334.0 73 62.0 \n", + "1 202313 3 64859 56800.0 72918.0 98 86.0 \n", + "2 202312 3 72750 64499.0 81001.0 109 97.0 \n", + "3 202311 3 74638 66420.0 82856.0 112 100.0 \n", + "4 202310 3 76368 68243.0 84493.0 115 103.0 \n", + "5 202309 3 62062 54778.0 69346.0 93 82.0 \n", + "6 202308 3 76391 68065.0 84717.0 115 102.0 \n", + "7 202307 3 89851 80397.0 99305.0 135 121.0 \n", + "8 202306 3 97368 87636.0 107100.0 146 131.0 \n", + "9 202305 3 95469 86268.0 104670.0 144 130.0 \n", + "10 202304 3 74901 66916.0 82886.0 113 101.0 \n", + "11 202303 3 69570 61893.0 77247.0 105 93.0 \n", + "12 202302 3 78260 70090.0 86430.0 118 106.0 \n", + "13 202301 3 121773 111024.0 132522.0 183 167.0 \n", + "14 202252 3 155371 142004.0 168738.0 234 214.0 \n", + "15 202251 3 248319 232128.0 264510.0 374 350.0 \n", + "16 202250 3 234143 219402.0 248884.0 353 331.0 \n", + "17 202249 3 163384 151691.0 175077.0 246 228.0 \n", + "18 202248 3 121691 111744.0 131638.0 184 169.0 \n", + "19 202247 3 96416 87230.0 105602.0 145 131.0 \n", + "20 202246 3 67735 60075.0 75395.0 102 90.0 \n", + "21 202245 3 45306 38909.0 51703.0 68 58.0 \n", + "22 202244 3 34713 28880.0 40546.0 52 43.0 \n", + "23 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "24 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "25 202241 3 48583 42388.0 54778.0 73 64.0 \n", + "26 202240 3 41927 36115.0 47739.0 63 54.0 \n", + "27 202239 3 39902 34168.0 45636.0 60 51.0 \n", + "28 202238 3 28781 23733.0 33829.0 43 35.0 \n", + "29 202237 3 21395 17076.0 25714.0 32 25.0 \n", + "... ... ... ... ... ... ... ... \n", + "1976 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1977 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1978 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1979 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1980 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1981 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1982 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1983 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1984 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1985 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1986 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1987 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1988 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1989 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1990 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1991 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1992 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1993 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1994 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1995 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1996 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1997 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1998 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1999 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2000 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2001 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2002 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2003 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2004 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2005 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 84.0 FR France \n", + "1 110.0 FR France \n", + "2 121.0 FR France \n", + "3 124.0 FR France \n", + "4 127.0 FR France \n", + "5 104.0 FR France \n", + "6 128.0 FR France \n", + "7 149.0 FR France \n", + "8 161.0 FR France \n", + "9 158.0 FR France \n", + "10 125.0 FR France \n", + "11 117.0 FR France \n", + "12 130.0 FR France \n", + "13 199.0 FR France \n", + "14 254.0 FR France \n", + "15 398.0 FR France \n", + "16 375.0 FR France \n", + "17 264.0 FR France \n", + "18 199.0 FR France \n", + "19 159.0 FR France \n", + "20 114.0 FR France \n", + "21 78.0 FR France \n", + "22 61.0 FR France \n", + "23 80.0 FR France \n", + "24 82.0 FR France \n", + "25 82.0 FR France \n", + "26 72.0 FR France \n", + "27 69.0 FR France \n", + "28 51.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1976 59.0 FR France \n", + "1977 64.0 FR France \n", + "1978 97.0 FR France \n", + "1979 93.0 FR France \n", + "1980 80.0 FR France \n", + "1981 116.0 FR France \n", + "1982 149.0 FR France \n", + "1983 281.0 FR France \n", + "1984 395.0 FR France \n", + "1985 485.0 FR France \n", + "1986 544.0 FR France \n", + "1987 689.0 FR France \n", + "1988 722.0 FR France \n", + "1989 762.0 FR France \n", + "1990 926.0 FR France \n", + "1991 1113.0 FR France \n", + "1992 1236.0 FR France \n", + "1993 832.0 FR France \n", + "1994 459.0 FR France \n", + "1995 207.0 FR France \n", + "1996 190.0 FR France \n", + "1997 198.0 FR France \n", + "1998 224.0 FR France \n", + "1999 266.0 FR France \n", + "2000 219.0 FR France \n", + "2001 176.0 FR France \n", + "2002 163.0 FR France \n", + "2003 195.0 FR France \n", + "2004 308.0 FR France \n", + "2005 213.0 FR France \n", + "\n", + "[2005 rows x 10 columns]" + ] + }, + "execution_count": 6, + "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\n", - "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", - "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\n", - "semaine. Il faut lui fournir les dates de début et de fin de\n", - "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", - "\n", - "Comme la conversion des semaines est devenu assez complexe, nous\n", - "écrivons une petite fonction Python pour cela. Ensuite, nous\n", - "l'appliquons à tous les points de nos donnés. Les résultats vont\n", - "dans une nouvelle colonne 'period'." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -136,52 +2116,28 @@ "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\n", - "comme nouvel index de notre jeux de données. Ceci en fait\n", - "une suite chronologique, ce qui sera pratique par la suite.\n", - "\n", - "Deuxièmement, nous trions les points par période, dans\n", - "le sens chronologique." - ] - }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "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": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -190,72 +2146,74 @@ " print(p1, p2)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un premier regard sur les données !" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'].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 en été." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "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'][-200:].plot()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Etude de l'incidence annuelle" - ] - }, - { - "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", - "\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", - "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." - ] - }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", @@ -263,18 +2221,9 @@ " sorted_data.index[-1].year)]" ] }, - { - "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", - "\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": 13, "metadata": {}, "outputs": [], "source": [ @@ -289,63 +2238,126 @@ "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voici les incidences annuelles." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JvvHES9x3/SV12T4bvUb7DDaTZvk+eQ2lTjTafGytp0Usv0b7DDaTVvs++YqNVdAIpwrx6TeaWyN8BptJq36fPOVlgKdFzHJqtu+Tp7xsVDwtYpZPq36fHFBazEjHInirpFk+rfh98pRXi/FpYsxstMqd8vKifIto1UXC8WqWbZ9m1eAprxbhS+eOTatt+zQbD49QWkSrLhKOlUd0ZqPnEUoLacVFwrHyiM5s9DxCaSG+dG75PKIzGz0HFLNh+Ohys9HxtmEzMxuRj5Q3M7OqckAxM7MsHFDMmoAvMV197vOPc0AxawI+ALP63Ocf50V5swbmy/tWXyv2uRflzVqAD8Csvpx93mzTZg4oZhVWyR8NH4BZfTn7vNmmzXxgo1mFFf9oVOKSAT4As/rG2+fNeq44r6GYVUgrzrXn0AqXDGi0SwR7DcWsxry+MTbNNg00lGadqvSUl1mFNOuPRqU06zTQcJpxqtIBxayCmvFHo1JevGPhsNNAzagZz/7tgGJWQc34o1EpHtE1Pq+hmFnd8EXg8qvmsS7e5WVm1sRWP/0qj+08wA0Lzh/ztvVsu7wknSdpq6Q3JO2RdFtKnypps6TudD+lqMwqSfsk7ZW0uCj9UkmvptfWSVJKb5f0ZErfIamjqMzy9B7dkpYXpc9NebtT2dPL7Rwzs0YwntHF/NUb6Vj5LI/uOEBEYZNDx8pnmb96YwVaWlDOlFc/8EcR8RngcuBWSRcCK4EtETEP2JKek15bBlwELAG+K+m0VNf9wApgXrotSek3AUcj4tPAvcDaVNdU4C7gMmABcFdR4FoL3Jve/2iqw8ysaYxnC3Uttq2fclE+Ig4Bh9Lj45LeAGYDS4ErU7ZHgG3AnSl9fUT0Ab+QtA9YIGk/cGZEbAeQ9APgWmBjKvOtVNePgPvS6GUxsDkijqQym4ElktYDVwHXF73/tygELDOzhpZjC3UtNjmMalE+TUVdAuwAzk3BZiDozEjZZgNvFRU7mNJmp8el6YPKREQ/8B4wbYS6pgHvpryldZW2eYWkLkldvb29o/nnmpnVRK7RRbU3OZS9bVjSJ4G/BP4wIo6l5Y8hsw6RFiOkj6XMSHUNTox4EHgQCovyQ+UxM6snuUYX1d62XtYIRdJECsHksYj4cUp+R9LM9PpMoCelHwTOKyo+B3g7pc8ZIn1QGUltwFnAkRHqOgycnfKW1mVm1vAacQv1KUcoaS3jIeCNiPhO0UsbgOXAPen+maL0xyV9B5hFYfF9Z0R8IOm4pMspTJndCPz3krq2A9cBL0RESNoE/JeihfhFwKr02taUd33J+5uZNbxGPCi2nBHKFcDXgKskvZxuv00hkFwtqRu4Oj0nIvYATwGvA88Bt0bEB6muW4DvAfuANyksyEMhYE1LC/i3k3aMpcX4u4Fd6fbtgQV6ChsAbk9lpqU6rA4020WDzKw8PrDRsstxIJWZ1Y9yD2z0ubwsm1Y7W6yZDeZzeVk2vv6HWWtzQLFsfLZYs9bmKS/Lytf/MGtdXpQ3M7MR+ZryZmZWVQ4oTcTHf5hZLTmgNJHxnOrazGy8vCjfBHz8h9lgPcdO8I0nXuK+6y/xLsMq8gilCfj4D7PBPFqvDY9QmoCP/zAr8Gi9tjxCaRKNeKprs9w8Wq8tj1CaRCOe6tosN4/Wa8sBxcyais/WUDs+Ut7MzEbkI+XNzKyqHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsCwcUMzPLwgHFzMyycEAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLItTBhRJD0vqkfRaUdpUSZsldaf7KUWvrZK0T9JeSYuL0i+V9Gp6bZ0kpfR2SU+m9B2SOorKLE/v0S1peVH63JS3O5U9ffxdYWZm41HOCOUvgCUlaSuBLRExD9iSniPpQmAZcFEq811Jp6Uy9wMrgHnpNlDnTcDRiPg0cC+wNtU1FbgLuAxYANxVFLjWAvem9z+a6jAzsxo6ZUCJiJ8BR0qSlwKPpMePANcWpa+PiL6I+AWwD1ggaSZwZkRsj4gAflBSZqCuHwFfSKOXxcDmiDgSEUeBzcCS9NpVKW/p+5uZWY2MdQ3l3Ig4BJDuZ6T02cBbRfkOprTZ6XFp+qAyEdEPvAdMG6GuacC7KW9pXR8jaYWkLkldvb29o/xnmplZuXIvymuItBghfSxlRqrr4y9EPBgRnRHROX369OGymZnZOI01oLyTprFI9z0p/SBwXlG+OcDbKX3OEOmDykhqA86iMMU2XF2HgbNT3tK6zMysRsYaUDYAA7uulgPPFKUvSzu35lJYfN+ZpsWOS7o8rYHcWFJmoK7rgBfSOssmYJGkKWkxfhGwKb22NeUtfX8zM6uRtlNlkPQEcCVwjqSDFHZe3QM8Jekm4ADwFYCI2CPpKeB1oB+4NSI+SFXdQmHH2CeAjekG8BDwQ0n7KIxMlqW6jki6G9iV8n07IgY2B9wJrJe0Bngp1WFmZjWkwh/8raGzszO6urpq3Qwzs4YiaXdEdJ4qn4+UNzOzLBxQzMwsCwcUM7MG1nPsBF99YDs9x0/UuikOKGZmjWzdlm527T/Cuue7a92UU+/yMjOz+jN/9Ub6+k9++PzRHQd4dMcB2tsmsHfNF2vSJo9QzMwa0It3LOSai2cxaWLhZ3zSxAksvXgWL965sGZtckAxM2tAM86cxOT2Nvr6T9LeNoG+/pNMbm9jxuRJNWuTp7zMzBrU4ff7uOGyC7h+wfk8vvMAvTVemPeBjWZmNiIf2GhmZlXlgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWbTUtmFJvcDfD/PyORQuL1zv3M68GqWd0DhtdTvzqod2XhAR00+VqaUCykgkdZWzz7rW3M68GqWd0DhtdTvzapR2gqe8zMwsEwcUMzPLwgHlIw/WugFlcjvzapR2QuO01e3Mq1Ha6TUUMzPLwyMUMzPLomkDiqSHJfVIeq0o7Z9L2i7pVUn/U9KZKX2ipEdS+huSVhWV2SZpr6SX021GDdt5uqTvp/RXJF1ZVObSlL5P0jpJytnOzG2tWJ9KOk/S1vT/uEfSbSl9qqTNkrrT/ZSiMqtSv+2VtLgovaJ9mrmtddOnkqal/O9Luq+kror1aeZ21lN/Xi1pd+q33ZKuKqqr4t/7UYmIprwB/wr4LeC1orRdwL9Oj38fuDs9vh5Ynx7/BrAf6EjPtwGdddLOW4Hvp8czgN3AhPR8J/B5QMBG4It13NaK9SkwE/it9Hgy8HfAhcB/BVam9JXA2vT4QuAVoB2YC7wJnFaNPs3c1nrq0zOAfwH8AXBfSV0V69PM7ayn/rwEmJUefxb4h2r051huTTtCiYifAUdKkucDP0uPNwO/O5AdOENSG/AJ4B+BY3XYzguBLalcD/Au0ClpJnBmRGyPwqfsB8C19djW3G0aoo2HIuL/pMfHgTeA2cBS4JGU7RE+6p+lFP6Y6IuIXwD7gAXV6NNcbc3ZphztjIhfRcRfA4Ou9lTpPs3VzkobQztfioi3U/oeYJKk9mp970ejaQPKMF4DrkmPvwKclx7/CPgVcAg4APy3iCj+4fx+Gvb+pyoNKYdr5yvAUkltkuYCl6bXZgMHi8ofTGnVMNq2Dqh4n0rqoPDX3Q7g3Ig4BIUvNIVRExT66a2iYgN9V9U+HWdbB9RLnw6nan06znYOqMf+/F3gpYjoo7bf+yG1WkD5feBWSbspDDX/MaUvAD4AZlGYSvgjSf8kvXZDRHwO+Jfp9rUatvNhCh+aLuBPgf8F9FMY7paq1va90bYVqtCnkj4J/CXwhxEx0mhzuL6rWp9maCvUV58OW8UQadn7NEM7oQ77U9JFwFrg5oGkIbLVdNtuSwWUiPh5RCyKiEuBJyjMQUNhDeW5iPh1mp75G9L0TET8Q7o/DjxOdaYYhmxnRPRHxH+IiIsjYilwNtBN4Yd7TlEVc4C3S+utk7ZWvE8lTaTwRX0sIn6ckt9JUwQDUy89Kf0gg0dOA31XlT7N1NZ669PhVLxPM7Wz7vpT0hzgaeDGiBj43arZ9344LRVQBnZqSJoArAb+PL10ALhKBWcAlwM/T9M156QyE4EvUZjiqUk7Jf1Gah+Srgb6I+L1NDw+LunyNDS/EXim0u0cS1sr3afp3/8Q8EZEfKfopQ3A8vR4OR/1zwZgWZqTngvMA3ZWo09ztbUO+3RIle7TXO2st/6UdDbwLLAqIv5mIHMtv/fDyr3KXy83Cn8tHwJ+TSGS3wTcRmFHxd8B9/DRgZ2fBP4HhQWv14H/GB/tAtkN/G167c9Iu2pq1M4OYC+FRbznKZwBdKCeTgof+jeB+wbK1FtbK92nFHbtRKr/5XT7bWAahU0C3el+alGZb6Z+20vRLplK92muttZpn+6nsIHj/fRZubDSfZqrnfXWnxT+UPtVUd6XgRnV+t6P5uYj5c3MLIuWmvIyM7PKcUAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy+L/A9t1yP3IuE0bAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] }, - { - "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." - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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