diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..80a906595729eeaee588babb9e0d8c79bb0de98c 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2312 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020224534711639170.055062.07159.083.0FRFrance
120224433392328110.039736.05142.060.0FRFrance
220224334476936884.052654.06856.080.0FRFrance
320224234746240773.054151.07262.082.0FRFrance
420224134858342388.054778.07364.082.0FRFrance
520224034192736115.047739.06354.072.0FRFrance
620223933990234168.045636.06051.069.0FRFrance
720223832878123733.033829.04335.051.0FRFrance
820223732139517076.025714.03225.039.0FRFrance
920223631412010487.017753.02116.026.0FRFrance
10202235392836485.012081.01410.018.0FRFrance
11202234374984731.010265.0117.015.0FRFrance
12202233375864442.010730.0116.016.0FRFrance
132022323122227749.016695.01811.025.0FRFrance
142022313132578905.017609.02013.027.0FRFrance
1520223031500610738.019274.02317.029.0FRFrance
1620222932080115829.025773.03124.038.0FRFrance
1720222832338717970.028804.03527.043.0FRFrance
1820222733601529709.042321.05444.064.0FRFrance
1920222632942124314.034528.04436.052.0FRFrance
2020222532288718582.027192.03529.041.0FRFrance
2120222431929415406.023182.02923.035.0FRFrance
2220222331715913450.020868.02620.032.0FRFrance
2320222231423910930.017548.02116.026.0FRFrance
242022213118048686.014922.01813.023.0FRFrance
2520222031735513600.021110.02620.032.0FRFrance
2620221931717813462.020894.02620.032.0FRFrance
2720221832756922584.032554.04234.050.0FRFrance
2820221733595030255.041645.05445.063.0FRFrance
2920221635440546976.061834.08271.093.0FRFrance
.................................
195519852132609619621.032571.04735.059.0FRFrance
195619852032789620885.034907.05138.064.0FRFrance
195719851934315432821.053487.07859.097.0FRFrance
195819851834055529935.051175.07455.093.0FRFrance
195919851733405324366.043740.06244.080.0FRFrance
196019851635036236451.064273.09166.0116.0FRFrance
196119851536388145538.082224.011683.0149.0FRFrance
19621985143134545114400.0154690.0244207.0281.0FRFrance
19631985133197206176080.0218332.0357319.0395.0FRFrance
19641985123245240223304.0267176.0445405.0485.0FRFrance
19651985113276205252399.0300011.0501458.0544.0FRFrance
19661985103353231326279.0380183.0640591.0689.0FRFrance
19671985093369895341109.0398681.0670618.0722.0FRFrance
19681985083389886359529.0420243.0707652.0762.0FRFrance
19691985073471852432599.0511105.0855784.0926.0FRFrance
19701985063565825518011.0613639.01026939.01113.0FRFrance
19711985053637302592795.0681809.011551074.01236.0FRFrance
19721985043424937390794.0459080.0770708.0832.0FRFrance
19731985033213901174689.0253113.0388317.0459.0FRFrance
197419850239758680949.0114223.0177147.0207.0FRFrance
197519850138548965918.0105060.0155120.0190.0FRFrance
197619845238483060602.0109058.0154110.0198.0FRFrance
1977198451310172680242.0123210.0185146.0224.0FRFrance
19781984503123680101401.0145959.0225184.0266.0FRFrance
1979198449310107381684.0120462.0184149.0219.0FRFrance
198019844837862060634.096606.0143110.0176.0FRFrance
198119844737202954274.089784.013199.0163.0FRFrance
198219844638733067686.0106974.0159123.0195.0FRFrance
19831984453135223101414.0169032.0246184.0308.0FRFrance
198419844436842220056.0116788.012537.0213.0FRFrance
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1985 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202245 3 47116 39170.0 55062.0 71 59.0 \n", + "1 202244 3 33923 28110.0 39736.0 51 42.0 \n", + "2 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "3 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "4 202241 3 48583 42388.0 54778.0 73 64.0 \n", + "5 202240 3 41927 36115.0 47739.0 63 54.0 \n", + "6 202239 3 39902 34168.0 45636.0 60 51.0 \n", + "7 202238 3 28781 23733.0 33829.0 43 35.0 \n", + "8 202237 3 21395 17076.0 25714.0 32 25.0 \n", + "9 202236 3 14120 10487.0 17753.0 21 16.0 \n", + "10 202235 3 9283 6485.0 12081.0 14 10.0 \n", + "11 202234 3 7498 4731.0 10265.0 11 7.0 \n", + "12 202233 3 7586 4442.0 10730.0 11 6.0 \n", + "13 202232 3 12222 7749.0 16695.0 18 11.0 \n", + "14 202231 3 13257 8905.0 17609.0 20 13.0 \n", + "15 202230 3 15006 10738.0 19274.0 23 17.0 \n", + "16 202229 3 20801 15829.0 25773.0 31 24.0 \n", + "17 202228 3 23387 17970.0 28804.0 35 27.0 \n", + "18 202227 3 36015 29709.0 42321.0 54 44.0 \n", + "19 202226 3 29421 24314.0 34528.0 44 36.0 \n", + "20 202225 3 22887 18582.0 27192.0 35 29.0 \n", + "21 202224 3 19294 15406.0 23182.0 29 23.0 \n", + "22 202223 3 17159 13450.0 20868.0 26 20.0 \n", + "23 202222 3 14239 10930.0 17548.0 21 16.0 \n", + "24 202221 3 11804 8686.0 14922.0 18 13.0 \n", + "25 202220 3 17355 13600.0 21110.0 26 20.0 \n", + "26 202219 3 17178 13462.0 20894.0 26 20.0 \n", + "27 202218 3 27569 22584.0 32554.0 42 34.0 \n", + "28 202217 3 35950 30255.0 41645.0 54 45.0 \n", + "29 202216 3 54405 46976.0 61834.0 82 71.0 \n", + "... ... ... ... ... ... ... ... \n", + "1955 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1956 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1957 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1958 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1959 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1960 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1961 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1962 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1963 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1964 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1965 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1966 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1967 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1968 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1969 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1970 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1971 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1972 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1973 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1974 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1975 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1976 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1977 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1978 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1979 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1980 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1981 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1982 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1983 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1984 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 83.0 FR France \n", + "1 60.0 FR France \n", + "2 80.0 FR France \n", + "3 82.0 FR France \n", + "4 82.0 FR France \n", + "5 72.0 FR France \n", + "6 69.0 FR France \n", + "7 51.0 FR France \n", + "8 39.0 FR France \n", + "9 26.0 FR France \n", + "10 18.0 FR France \n", + "11 15.0 FR France \n", + "12 16.0 FR France \n", + "13 25.0 FR France \n", + "14 27.0 FR France \n", + "15 29.0 FR France \n", + "16 38.0 FR France \n", + "17 43.0 FR France \n", + "18 64.0 FR France \n", + "19 52.0 FR France \n", + "20 41.0 FR France \n", + "21 35.0 FR France \n", + "22 32.0 FR France \n", + "23 26.0 FR France \n", + "24 23.0 FR France \n", + "25 32.0 FR France \n", + "26 32.0 FR France \n", + "27 50.0 FR France \n", + "28 63.0 FR France \n", + "29 93.0 FR France \n", + "... ... ... ... \n", + "1955 59.0 FR France \n", + "1956 64.0 FR France \n", + "1957 97.0 FR France \n", + "1958 93.0 FR France \n", + "1959 80.0 FR France \n", + "1960 116.0 FR France \n", + "1961 149.0 FR France \n", + "1962 281.0 FR France \n", + "1963 395.0 FR France \n", + "1964 485.0 FR France \n", + "1965 544.0 FR France \n", + "1966 689.0 FR France \n", + "1967 722.0 FR France \n", + "1968 762.0 FR France \n", + "1969 926.0 FR France \n", + "1970 1113.0 FR France \n", + "1971 1236.0 FR France \n", + "1972 832.0 FR France \n", + "1973 459.0 FR France \n", + "1974 207.0 FR France \n", + "1975 190.0 FR France \n", + "1976 198.0 FR France \n", + "1977 224.0 FR France \n", + "1978 266.0 FR France \n", + "1979 219.0 FR France \n", + "1980 176.0 FR France \n", + "1981 163.0 FR France \n", + "1982 195.0 FR France \n", + "1983 308.0 FR France \n", + "1984 213.0 FR France \n", + "\n", + "[1985 rows x 10 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020224534711639170.055062.07159.083.0FRFrance
120224433392328110.039736.05142.060.0FRFrance
220224334476936884.052654.06856.080.0FRFrance
320224234746240773.054151.07262.082.0FRFrance
420224134858342388.054778.07364.082.0FRFrance
520224034192736115.047739.06354.072.0FRFrance
620223933990234168.045636.06051.069.0FRFrance
720223832878123733.033829.04335.051.0FRFrance
820223732139517076.025714.03225.039.0FRFrance
920223631412010487.017753.02116.026.0FRFrance
10202235392836485.012081.01410.018.0FRFrance
11202234374984731.010265.0117.015.0FRFrance
12202233375864442.010730.0116.016.0FRFrance
132022323122227749.016695.01811.025.0FRFrance
142022313132578905.017609.02013.027.0FRFrance
1520223031500610738.019274.02317.029.0FRFrance
1620222932080115829.025773.03124.038.0FRFrance
1720222832338717970.028804.03527.043.0FRFrance
1820222733601529709.042321.05444.064.0FRFrance
1920222632942124314.034528.04436.052.0FRFrance
2020222532288718582.027192.03529.041.0FRFrance
2120222431929415406.023182.02923.035.0FRFrance
2220222331715913450.020868.02620.032.0FRFrance
2320222231423910930.017548.02116.026.0FRFrance
242022213118048686.014922.01813.023.0FRFrance
2520222031735513600.021110.02620.032.0FRFrance
2620221931717813462.020894.02620.032.0FRFrance
2720221832756922584.032554.04234.050.0FRFrance
2820221733595030255.041645.05445.063.0FRFrance
2920221635440546976.061834.08271.093.0FRFrance
.................................
195519852132609619621.032571.04735.059.0FRFrance
195619852032789620885.034907.05138.064.0FRFrance
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195919851733405324366.043740.06244.080.0FRFrance
196019851635036236451.064273.09166.0116.0FRFrance
196119851536388145538.082224.011683.0149.0FRFrance
19621985143134545114400.0154690.0244207.0281.0FRFrance
19631985133197206176080.0218332.0357319.0395.0FRFrance
19641985123245240223304.0267176.0445405.0485.0FRFrance
19651985113276205252399.0300011.0501458.0544.0FRFrance
19661985103353231326279.0380183.0640591.0689.0FRFrance
19671985093369895341109.0398681.0670618.0722.0FRFrance
19681985083389886359529.0420243.0707652.0762.0FRFrance
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19701985063565825518011.0613639.01026939.01113.0FRFrance
19711985053637302592795.0681809.011551074.01236.0FRFrance
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19731985033213901174689.0253113.0388317.0459.0FRFrance
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197519850138548965918.0105060.0155120.0190.0FRFrance
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1984 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202245 3 47116 39170.0 55062.0 71 59.0 \n", + "1 202244 3 33923 28110.0 39736.0 51 42.0 \n", + "2 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "3 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "4 202241 3 48583 42388.0 54778.0 73 64.0 \n", + "5 202240 3 41927 36115.0 47739.0 63 54.0 \n", + "6 202239 3 39902 34168.0 45636.0 60 51.0 \n", + "7 202238 3 28781 23733.0 33829.0 43 35.0 \n", + "8 202237 3 21395 17076.0 25714.0 32 25.0 \n", + "9 202236 3 14120 10487.0 17753.0 21 16.0 \n", + "10 202235 3 9283 6485.0 12081.0 14 10.0 \n", + "11 202234 3 7498 4731.0 10265.0 11 7.0 \n", + "12 202233 3 7586 4442.0 10730.0 11 6.0 \n", + "13 202232 3 12222 7749.0 16695.0 18 11.0 \n", + "14 202231 3 13257 8905.0 17609.0 20 13.0 \n", + "15 202230 3 15006 10738.0 19274.0 23 17.0 \n", + "16 202229 3 20801 15829.0 25773.0 31 24.0 \n", + "17 202228 3 23387 17970.0 28804.0 35 27.0 \n", + "18 202227 3 36015 29709.0 42321.0 54 44.0 \n", + "19 202226 3 29421 24314.0 34528.0 44 36.0 \n", + "20 202225 3 22887 18582.0 27192.0 35 29.0 \n", + "21 202224 3 19294 15406.0 23182.0 29 23.0 \n", + "22 202223 3 17159 13450.0 20868.0 26 20.0 \n", + "23 202222 3 14239 10930.0 17548.0 21 16.0 \n", + "24 202221 3 11804 8686.0 14922.0 18 13.0 \n", + "25 202220 3 17355 13600.0 21110.0 26 20.0 \n", + "26 202219 3 17178 13462.0 20894.0 26 20.0 \n", + "27 202218 3 27569 22584.0 32554.0 42 34.0 \n", + "28 202217 3 35950 30255.0 41645.0 54 45.0 \n", + "29 202216 3 54405 46976.0 61834.0 82 71.0 \n", + "... ... ... ... ... ... ... ... \n", + "1955 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1956 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1957 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1958 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1959 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1960 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1961 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1962 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1963 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1964 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1965 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1966 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1967 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1968 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1969 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1970 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1971 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1972 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1973 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1974 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1975 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1976 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1977 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1978 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1979 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1980 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1981 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1982 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1983 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1984 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 83.0 FR France \n", + "1 60.0 FR France \n", + "2 80.0 FR France \n", + "3 82.0 FR France \n", + "4 82.0 FR France \n", + "5 72.0 FR France \n", + "6 69.0 FR France \n", + "7 51.0 FR France \n", + "8 39.0 FR France \n", + "9 26.0 FR France \n", + "10 18.0 FR France \n", + "11 15.0 FR France \n", + "12 16.0 FR France \n", + "13 25.0 FR France \n", + "14 27.0 FR France \n", + "15 29.0 FR France \n", + "16 38.0 FR France \n", + "17 43.0 FR France \n", + "18 64.0 FR France \n", + "19 52.0 FR France \n", + "20 41.0 FR France \n", + "21 35.0 FR France \n", + "22 32.0 FR France \n", + "23 26.0 FR France \n", + "24 23.0 FR France \n", + "25 32.0 FR France \n", + "26 32.0 FR France \n", + "27 50.0 FR France \n", + "28 63.0 FR France \n", + "29 93.0 FR France \n", + "... ... ... ... \n", + "1955 59.0 FR France \n", + "1956 64.0 FR France \n", + "1957 97.0 FR France \n", + "1958 93.0 FR France \n", + "1959 80.0 FR France \n", + "1960 116.0 FR France \n", + "1961 149.0 FR France \n", + "1962 281.0 FR France \n", + "1963 395.0 FR France \n", + "1964 485.0 FR France \n", + "1965 544.0 FR France \n", + "1966 689.0 FR France \n", + "1967 722.0 FR France \n", + "1968 762.0 FR France \n", + "1969 926.0 FR France \n", + "1970 1113.0 FR France \n", + "1971 1236.0 FR France \n", + "1972 832.0 FR France \n", + "1973 459.0 FR France \n", + "1974 207.0 FR France \n", + "1975 190.0 FR France \n", + "1976 198.0 FR France \n", + "1977 224.0 FR France \n", + "1978 266.0 FR France \n", + "1979 219.0 FR France \n", + "1980 176.0 FR France \n", + "1981 163.0 FR France \n", + "1982 195.0 FR France \n", + "1983 308.0 FR France \n", + "1984 213.0 FR France \n", + "\n", + "[1984 rows x 10 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "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", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc100'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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+ ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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