{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Supprimer les données pas valables" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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42020093110696102066.0119326.0168155.0181.0FRFrance
52020083143753133984.0153522.0218203.0233.0FRFrance
62020073183610172812.0194408.0279263.0295.0FRFrance
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92020043122331113492.0131170.0186173.0199.0FRFrance
1020200337841371330.085496.0119108.0130.0FRFrance
1120200235361447654.059574.08172.090.0FRFrance
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1520195033422329156.039290.05244.060.0FRFrance
1620194932566221414.029910.03933.045.0FRFrance
1720194832236718055.026679.03427.041.0FRFrance
1820194731866914759.022579.02822.034.0FRFrance
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21201944378225010.010634.0128.016.0FRFrance
22201943394876448.012526.0149.019.0FRFrance
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24201941371224720.09524.0117.015.0FRFrance
25201940385055784.011226.0139.017.0FRFrance
26201939370914462.09720.0117.015.0FRFrance
27201938348972891.06903.074.010.0FRFrance
28201937331721367.04977.052.08.0FRFrance
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.................................
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1848 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202013 3 0 0.0 0.0 0 0.0 \n", "1 202012 3 8321 5873.0 10769.0 13 9.0 \n", "2 202011 3 101704 93652.0 109756.0 154 142.0 \n", "3 202010 3 104977 96650.0 113304.0 159 146.0 \n", "4 202009 3 110696 102066.0 119326.0 168 155.0 \n", "5 202008 3 143753 133984.0 153522.0 218 203.0 \n", "6 202007 3 183610 172812.0 194408.0 279 263.0 \n", "7 202006 3 206669 195481.0 217857.0 314 297.0 \n", "8 202005 3 187957 177445.0 198469.0 285 269.0 \n", "9 202004 3 122331 113492.0 131170.0 186 173.0 \n", "10 202003 3 78413 71330.0 85496.0 119 108.0 \n", "11 202002 3 53614 47654.0 59574.0 81 72.0 \n", "12 202001 3 36850 31608.0 42092.0 56 48.0 \n", "13 201952 3 28135 23220.0 33050.0 43 36.0 \n", "14 201951 3 29786 25042.0 34530.0 45 38.0 \n", "15 201950 3 34223 29156.0 39290.0 52 44.0 \n", "16 201949 3 25662 21414.0 29910.0 39 33.0 \n", "17 201948 3 22367 18055.0 26679.0 34 27.0 \n", "18 201947 3 18669 14759.0 22579.0 28 22.0 \n", "19 201946 3 16030 12567.0 19493.0 24 19.0 \n", "20 201945 3 10138 7160.0 13116.0 15 10.0 \n", "21 201944 3 7822 5010.0 10634.0 12 8.0 \n", "22 201943 3 9487 6448.0 12526.0 14 9.0 \n", "23 201942 3 7747 5243.0 10251.0 12 8.0 \n", "24 201941 3 7122 4720.0 9524.0 11 7.0 \n", "25 201940 3 8505 5784.0 11226.0 13 9.0 \n", "26 201939 3 7091 4462.0 9720.0 11 7.0 \n", "27 201938 3 4897 2891.0 6903.0 7 4.0 \n", "28 201937 3 3172 1367.0 4977.0 5 2.0 \n", "29 201936 3 2295 728.0 3862.0 3 1.0 \n", "... ... ... ... ... ... ... ... \n", "1818 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1819 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1820 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1821 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1822 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1823 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1824 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1825 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1826 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1827 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1828 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1829 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1830 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1831 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1832 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1833 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1834 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1835 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1836 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1837 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1838 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1839 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1840 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1841 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1842 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1843 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1844 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1845 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1846 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1847 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 0.0 FR France \n", "1 17.0 FR France \n", "2 166.0 FR France \n", "3 172.0 FR France \n", "4 181.0 FR France \n", "5 233.0 FR France \n", "6 295.0 FR France \n", "7 331.0 FR France \n", "8 301.0 FR France \n", "9 199.0 FR France \n", "10 130.0 FR France \n", "11 90.0 FR France \n", "12 64.0 FR France \n", "13 50.0 FR France \n", "14 52.0 FR France \n", "15 60.0 FR France \n", "16 45.0 FR France \n", "17 41.0 FR France \n", "18 34.0 FR France \n", "19 29.0 FR France \n", "20 20.0 FR France \n", "21 16.0 FR France \n", "22 19.0 FR France \n", "23 16.0 FR France \n", "24 15.0 FR France \n", "25 17.0 FR France \n", "26 15.0 FR France \n", "27 10.0 FR France \n", "28 8.0 FR France \n", "29 5.0 FR France \n", "... ... ... ... \n", "1818 59.0 FR France \n", "1819 64.0 FR France \n", "1820 97.0 FR France \n", "1821 93.0 FR France \n", "1822 80.0 FR France \n", "1823 116.0 FR France \n", "1824 149.0 FR France \n", "1825 281.0 FR France \n", "1826 395.0 FR France \n", "1827 485.0 FR France \n", "1828 544.0 FR France \n", "1829 689.0 FR France \n", "1830 722.0 FR France \n", "1831 762.0 FR France \n", "1832 926.0 FR France \n", "1833 1113.0 FR France \n", "1834 1236.0 FR France \n", "1835 832.0 FR France \n", "1836 459.0 FR France \n", "1837 207.0 FR France \n", "1838 190.0 FR France \n", "1839 198.0 FR France \n", "1840 224.0 FR France \n", "1841 266.0 FR France \n", "1842 219.0 FR France \n", "1843 176.0 FR France \n", "1844 163.0 FR France \n", "1845 195.0 FR France \n", "1846 308.0 FR France \n", "1847 213.0 FR France \n", "\n", "[1848 rows x 10 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\",skiprows=1)\n", "raw_data\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
161119891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1611 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1611 FR France " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous supprimons cette ligne qui ne contient pas de données valables." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202013300.00.000.00.0FRFrance
1202012383215873.010769.0139.017.0FRFrance
2202011310170493652.0109756.0154142.0166.0FRFrance
3202010310497796650.0113304.0159146.0172.0FRFrance
42020093110696102066.0119326.0168155.0181.0FRFrance
52020083143753133984.0153522.0218203.0233.0FRFrance
62020073183610172812.0194408.0279263.0295.0FRFrance
72020063206669195481.0217857.0314297.0331.0FRFrance
82020053187957177445.0198469.0285269.0301.0FRFrance
92020043122331113492.0131170.0186173.0199.0FRFrance
1020200337841371330.085496.0119108.0130.0FRFrance
1120200235361447654.059574.08172.090.0FRFrance
1220200133685031608.042092.05648.064.0FRFrance
1320195232813523220.033050.04336.050.0FRFrance
1420195132978625042.034530.04538.052.0FRFrance
1520195033422329156.039290.05244.060.0FRFrance
1620194932566221414.029910.03933.045.0FRFrance
1720194832236718055.026679.03427.041.0FRFrance
1820194731866914759.022579.02822.034.0FRFrance
1920194631603012567.019493.02419.029.0FRFrance
202019453101387160.013116.01510.020.0FRFrance
21201944378225010.010634.0128.016.0FRFrance
22201943394876448.012526.0149.019.0FRFrance
23201942377475243.010251.0128.016.0FRFrance
24201941371224720.09524.0117.015.0FRFrance
25201940385055784.011226.0139.017.0FRFrance
26201939370914462.09720.0117.015.0FRFrance
27201938348972891.06903.074.010.0FRFrance
28201937331721367.04977.052.08.0FRFrance
2920193632295728.03862.031.05.0FRFrance
.................................
181819852132609619621.032571.04735.059.0FRFrance
181919852032789620885.034907.05138.064.0FRFrance
182019851934315432821.053487.07859.097.0FRFrance
182119851834055529935.051175.07455.093.0FRFrance
182219851733405324366.043740.06244.080.0FRFrance
182319851635036236451.064273.09166.0116.0FRFrance
182419851536388145538.082224.011683.0149.0FRFrance
18251985143134545114400.0154690.0244207.0281.0FRFrance
18261985133197206176080.0218332.0357319.0395.0FRFrance
18271985123245240223304.0267176.0445405.0485.0FRFrance
18281985113276205252399.0300011.0501458.0544.0FRFrance
18291985103353231326279.0380183.0640591.0689.0FRFrance
18301985093369895341109.0398681.0670618.0722.0FRFrance
18311985083389886359529.0420243.0707652.0762.0FRFrance
18321985073471852432599.0511105.0855784.0926.0FRFrance
18331985063565825518011.0613639.01026939.01113.0FRFrance
18341985053637302592795.0681809.011551074.01236.0FRFrance
18351985043424937390794.0459080.0770708.0832.0FRFrance
18361985033213901174689.0253113.0388317.0459.0FRFrance
183719850239758680949.0114223.0177147.0207.0FRFrance
183819850138548965918.0105060.0155120.0190.0FRFrance
183919845238483060602.0109058.0154110.0198.0FRFrance
1840198451310172680242.0123210.0185146.0224.0FRFrance
18411984503123680101401.0145959.0225184.0266.0FRFrance
1842198449310107381684.0120462.0184149.0219.0FRFrance
184319844837862060634.096606.0143110.0176.0FRFrance
184419844737202954274.089784.013199.0163.0FRFrance
184519844638733067686.0106974.0159123.0195.0FRFrance
18461984453135223101414.0169032.0246184.0308.0FRFrance
184719844436842220056.0116788.012537.0213.0FRFrance
\n", "

1847 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202013 3 0 0.0 0.0 0 0.0 \n", "1 202012 3 8321 5873.0 10769.0 13 9.0 \n", "2 202011 3 101704 93652.0 109756.0 154 142.0 \n", "3 202010 3 104977 96650.0 113304.0 159 146.0 \n", "4 202009 3 110696 102066.0 119326.0 168 155.0 \n", "5 202008 3 143753 133984.0 153522.0 218 203.0 \n", "6 202007 3 183610 172812.0 194408.0 279 263.0 \n", "7 202006 3 206669 195481.0 217857.0 314 297.0 \n", "8 202005 3 187957 177445.0 198469.0 285 269.0 \n", "9 202004 3 122331 113492.0 131170.0 186 173.0 \n", "10 202003 3 78413 71330.0 85496.0 119 108.0 \n", "11 202002 3 53614 47654.0 59574.0 81 72.0 \n", "12 202001 3 36850 31608.0 42092.0 56 48.0 \n", "13 201952 3 28135 23220.0 33050.0 43 36.0 \n", "14 201951 3 29786 25042.0 34530.0 45 38.0 \n", "15 201950 3 34223 29156.0 39290.0 52 44.0 \n", "16 201949 3 25662 21414.0 29910.0 39 33.0 \n", "17 201948 3 22367 18055.0 26679.0 34 27.0 \n", "18 201947 3 18669 14759.0 22579.0 28 22.0 \n", "19 201946 3 16030 12567.0 19493.0 24 19.0 \n", "20 201945 3 10138 7160.0 13116.0 15 10.0 \n", "21 201944 3 7822 5010.0 10634.0 12 8.0 \n", "22 201943 3 9487 6448.0 12526.0 14 9.0 \n", "23 201942 3 7747 5243.0 10251.0 12 8.0 \n", "24 201941 3 7122 4720.0 9524.0 11 7.0 \n", "25 201940 3 8505 5784.0 11226.0 13 9.0 \n", "26 201939 3 7091 4462.0 9720.0 11 7.0 \n", "27 201938 3 4897 2891.0 6903.0 7 4.0 \n", "28 201937 3 3172 1367.0 4977.0 5 2.0 \n", "29 201936 3 2295 728.0 3862.0 3 1.0 \n", "... ... ... ... ... ... ... ... \n", "1818 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1819 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1820 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1821 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1822 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1823 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1824 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1825 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1826 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1827 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1828 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1829 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1830 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1831 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1832 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1833 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1834 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1835 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1836 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1837 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1838 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1839 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1840 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1841 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1842 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1843 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1844 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1845 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1846 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1847 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 0.0 FR France \n", "1 17.0 FR France \n", "2 166.0 FR France \n", "3 172.0 FR France \n", "4 181.0 FR France \n", "5 233.0 FR France \n", "6 295.0 FR France \n", "7 331.0 FR France \n", "8 301.0 FR France \n", "9 199.0 FR France \n", "10 130.0 FR France \n", "11 90.0 FR France \n", "12 64.0 FR France \n", "13 50.0 FR France \n", "14 52.0 FR France \n", "15 60.0 FR France \n", "16 45.0 FR France \n", "17 41.0 FR France \n", "18 34.0 FR France \n", "19 29.0 FR France \n", "20 20.0 FR France \n", "21 16.0 FR France \n", "22 19.0 FR France \n", "23 16.0 FR France \n", "24 15.0 FR France \n", "25 17.0 FR France \n", "26 15.0 FR France \n", "27 10.0 FR France \n", "28 8.0 FR France \n", "29 5.0 FR France \n", "... ... ... ... \n", "1818 59.0 FR France \n", "1819 64.0 FR France \n", "1820 97.0 FR France \n", "1821 93.0 FR France \n", "1822 80.0 FR France \n", "1823 116.0 FR France \n", "1824 149.0 FR France \n", "1825 281.0 FR France \n", "1826 395.0 FR France \n", "1827 485.0 FR France \n", "1828 544.0 FR France \n", "1829 689.0 FR France \n", "1830 722.0 FR France \n", "1831 762.0 FR France \n", "1832 926.0 FR France \n", "1833 1113.0 FR France \n", "1834 1236.0 FR France \n", "1835 832.0 FR France \n", "1836 459.0 FR France \n", "1837 207.0 FR France \n", "1838 190.0 FR France \n", "1839 198.0 FR France \n", "1840 224.0 FR France \n", "1841 266.0 FR France \n", "1842 219.0 FR France \n", "1843 176.0 FR France \n", "1844 163.0 FR France \n", "1845 195.0 FR France \n", "1846 308.0 FR France \n", "1847 213.0 FR France \n", "\n", "[1847 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# copy de données\n", "raw_data.dropna().copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Transformation des données" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_add_week_int):\n", " year_add_week_str = str(year_add_week_int)\n", " year = int(year_add_week_str[:4])\n", " week = int(year_add_week_str[4:])\n", " w = isoweek.Week(year,week)\n", " return pd.Period(w.day(0),'W')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data = raw_data\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1984-10-29/1984-11-0419844436842220056.0116788.012537.0213.0FRFrance
1984-11-05/1984-11-111984453135223101414.0169032.0246184.0308.0FRFrance
1984-11-12/1984-11-1819844638733067686.0106974.0159123.0195.0FRFrance
1984-11-19/1984-11-2519844737202954274.089784.013199.0163.0FRFrance
1984-11-26/1984-12-0219844837862060634.096606.0143110.0176.0FRFrance
1984-12-03/1984-12-09198449310107381684.0120462.0184149.0219.0FRFrance
1984-12-10/1984-12-161984503123680101401.0145959.0225184.0266.0FRFrance
1984-12-17/1984-12-23198451310172680242.0123210.0185146.0224.0FRFrance
1984-12-24/1984-12-3019845238483060602.0109058.0154110.0198.0FRFrance
1984-12-31/1985-01-0619850138548965918.0105060.0155120.0190.0FRFrance
1985-01-07/1985-01-1319850239758680949.0114223.0177147.0207.0FRFrance
1985-01-14/1985-01-201985033213901174689.0253113.0388317.0459.0FRFrance
1985-01-21/1985-01-271985043424937390794.0459080.0770708.0832.0FRFrance
1985-01-28/1985-02-031985053637302592795.0681809.011551074.01236.0FRFrance
1985-02-04/1985-02-101985063565825518011.0613639.01026939.01113.0FRFrance
1985-02-11/1985-02-171985073471852432599.0511105.0855784.0926.0FRFrance
1985-02-18/1985-02-241985083389886359529.0420243.0707652.0762.0FRFrance
1985-02-25/1985-03-031985093369895341109.0398681.0670618.0722.0FRFrance
1985-03-04/1985-03-101985103353231326279.0380183.0640591.0689.0FRFrance
1985-03-11/1985-03-171985113276205252399.0300011.0501458.0544.0FRFrance
1985-03-18/1985-03-241985123245240223304.0267176.0445405.0485.0FRFrance
1985-03-25/1985-03-311985133197206176080.0218332.0357319.0395.0FRFrance
1985-04-01/1985-04-071985143134545114400.0154690.0244207.0281.0FRFrance
1985-04-08/1985-04-1419851536388145538.082224.011683.0149.0FRFrance
1985-04-15/1985-04-2119851635036236451.064273.09166.0116.0FRFrance
1985-04-22/1985-04-2819851733405324366.043740.06244.080.0FRFrance
1985-04-29/1985-05-0519851834055529935.051175.07455.093.0FRFrance
1985-05-06/1985-05-1219851934315432821.053487.07859.097.0FRFrance
1985-05-13/1985-05-1919852032789620885.034907.05138.064.0FRFrance
1985-05-20/1985-05-2619852132609619621.032571.04735.059.0FRFrance
.................................
2019-09-02/2019-09-0820193632295728.03862.031.05.0FRFrance
2019-09-09/2019-09-15201937331721367.04977.052.08.0FRFrance
2019-09-16/2019-09-22201938348972891.06903.074.010.0FRFrance
2019-09-23/2019-09-29201939370914462.09720.0117.015.0FRFrance
2019-09-30/2019-10-06201940385055784.011226.0139.017.0FRFrance
2019-10-07/2019-10-13201941371224720.09524.0117.015.0FRFrance
2019-10-14/2019-10-20201942377475243.010251.0128.016.0FRFrance
2019-10-21/2019-10-27201943394876448.012526.0149.019.0FRFrance
2019-10-28/2019-11-03201944378225010.010634.0128.016.0FRFrance
2019-11-04/2019-11-102019453101387160.013116.01510.020.0FRFrance
2019-11-11/2019-11-1720194631603012567.019493.02419.029.0FRFrance
2019-11-18/2019-11-2420194731866914759.022579.02822.034.0FRFrance
2019-11-25/2019-12-0120194832236718055.026679.03427.041.0FRFrance
2019-12-02/2019-12-0820194932566221414.029910.03933.045.0FRFrance
2019-12-09/2019-12-1520195033422329156.039290.05244.060.0FRFrance
2019-12-16/2019-12-2220195132978625042.034530.04538.052.0FRFrance
2019-12-23/2019-12-2920195232813523220.033050.04336.050.0FRFrance
2019-12-30/2020-01-0520200133685031608.042092.05648.064.0FRFrance
2020-01-06/2020-01-1220200235361447654.059574.08172.090.0FRFrance
2020-01-13/2020-01-1920200337841371330.085496.0119108.0130.0FRFrance
2020-01-20/2020-01-262020043122331113492.0131170.0186173.0199.0FRFrance
2020-01-27/2020-02-022020053187957177445.0198469.0285269.0301.0FRFrance
2020-02-03/2020-02-092020063206669195481.0217857.0314297.0331.0FRFrance
2020-02-10/2020-02-162020073183610172812.0194408.0279263.0295.0FRFrance
2020-02-17/2020-02-232020083143753133984.0153522.0218203.0233.0FRFrance
2020-02-24/2020-03-012020093110696102066.0119326.0168155.0181.0FRFrance
2020-03-02/2020-03-08202010310497796650.0113304.0159146.0172.0FRFrance
2020-03-09/2020-03-15202011310170493652.0109756.0154142.0166.0FRFrance
2020-03-16/2020-03-22202012383215873.010769.0139.017.0FRFrance
2020-03-23/2020-03-29202013300.00.000.00.0FRFrance
\n", "

1848 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", "1984-12-03/1984-12-09 198449 3 101073 81684.0 120462.0 184 \n", "1984-12-10/1984-12-16 198450 3 123680 101401.0 145959.0 225 \n", "1984-12-17/1984-12-23 198451 3 101726 80242.0 123210.0 185 \n", "1984-12-24/1984-12-30 198452 3 84830 60602.0 109058.0 154 \n", "1984-12-31/1985-01-06 198501 3 85489 65918.0 105060.0 155 \n", "1985-01-07/1985-01-13 198502 3 97586 80949.0 114223.0 177 \n", "1985-01-14/1985-01-20 198503 3 213901 174689.0 253113.0 388 \n", "1985-01-21/1985-01-27 198504 3 424937 390794.0 459080.0 770 \n", "1985-01-28/1985-02-03 198505 3 637302 592795.0 681809.0 1155 \n", "1985-02-04/1985-02-10 198506 3 565825 518011.0 613639.0 1026 \n", "1985-02-11/1985-02-17 198507 3 471852 432599.0 511105.0 855 \n", "1985-02-18/1985-02-24 198508 3 389886 359529.0 420243.0 707 \n", "1985-02-25/1985-03-03 198509 3 369895 341109.0 398681.0 670 \n", "1985-03-04/1985-03-10 198510 3 353231 326279.0 380183.0 640 \n", "1985-03-11/1985-03-17 198511 3 276205 252399.0 300011.0 501 \n", "1985-03-18/1985-03-24 198512 3 245240 223304.0 267176.0 445 \n", "1985-03-25/1985-03-31 198513 3 197206 176080.0 218332.0 357 \n", "1985-04-01/1985-04-07 198514 3 134545 114400.0 154690.0 244 \n", "1985-04-08/1985-04-14 198515 3 63881 45538.0 82224.0 116 \n", "1985-04-15/1985-04-21 198516 3 50362 36451.0 64273.0 91 \n", "1985-04-22/1985-04-28 198517 3 34053 24366.0 43740.0 62 \n", "1985-04-29/1985-05-05 198518 3 40555 29935.0 51175.0 74 \n", "1985-05-06/1985-05-12 198519 3 43154 32821.0 53487.0 78 \n", "1985-05-13/1985-05-19 198520 3 27896 20885.0 34907.0 51 \n", "1985-05-20/1985-05-26 198521 3 26096 19621.0 32571.0 47 \n", "... ... ... ... ... ... ... \n", "2019-09-02/2019-09-08 201936 3 2295 728.0 3862.0 3 \n", "2019-09-09/2019-09-15 201937 3 3172 1367.0 4977.0 5 \n", "2019-09-16/2019-09-22 201938 3 4897 2891.0 6903.0 7 \n", "2019-09-23/2019-09-29 201939 3 7091 4462.0 9720.0 11 \n", "2019-09-30/2019-10-06 201940 3 8505 5784.0 11226.0 13 \n", "2019-10-07/2019-10-13 201941 3 7122 4720.0 9524.0 11 \n", "2019-10-14/2019-10-20 201942 3 7747 5243.0 10251.0 12 \n", "2019-10-21/2019-10-27 201943 3 9487 6448.0 12526.0 14 \n", "2019-10-28/2019-11-03 201944 3 7822 5010.0 10634.0 12 \n", "2019-11-04/2019-11-10 201945 3 10138 7160.0 13116.0 15 \n", "2019-11-11/2019-11-17 201946 3 16030 12567.0 19493.0 24 \n", "2019-11-18/2019-11-24 201947 3 18669 14759.0 22579.0 28 \n", "2019-11-25/2019-12-01 201948 3 22367 18055.0 26679.0 34 \n", "2019-12-02/2019-12-08 201949 3 25662 21414.0 29910.0 39 \n", "2019-12-09/2019-12-15 201950 3 34223 29156.0 39290.0 52 \n", "2019-12-16/2019-12-22 201951 3 29786 25042.0 34530.0 45 \n", "2019-12-23/2019-12-29 201952 3 28135 23220.0 33050.0 43 \n", "2019-12-30/2020-01-05 202001 3 36850 31608.0 42092.0 56 \n", "2020-01-06/2020-01-12 202002 3 53614 47654.0 59574.0 81 \n", "2020-01-13/2020-01-19 202003 3 78413 71330.0 85496.0 119 \n", "2020-01-20/2020-01-26 202004 3 122331 113492.0 131170.0 186 \n", "2020-01-27/2020-02-02 202005 3 187957 177445.0 198469.0 285 \n", "2020-02-03/2020-02-09 202006 3 206669 195481.0 217857.0 314 \n", "2020-02-10/2020-02-16 202007 3 183610 172812.0 194408.0 279 \n", "2020-02-17/2020-02-23 202008 3 143753 133984.0 153522.0 218 \n", "2020-02-24/2020-03-01 202009 3 110696 102066.0 119326.0 168 \n", "2020-03-02/2020-03-08 202010 3 104977 96650.0 113304.0 159 \n", "2020-03-09/2020-03-15 202011 3 101704 93652.0 109756.0 154 \n", "2020-03-16/2020-03-22 202012 3 8321 5873.0 10769.0 13 \n", "2020-03-23/2020-03-29 202013 3 0 0.0 0.0 0 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1984-10-29/1984-11-04 37.0 213.0 FR France \n", "1984-11-05/1984-11-11 184.0 308.0 FR France \n", "1984-11-12/1984-11-18 123.0 195.0 FR France \n", "1984-11-19/1984-11-25 99.0 163.0 FR France \n", "1984-11-26/1984-12-02 110.0 176.0 FR France \n", "1984-12-03/1984-12-09 149.0 219.0 FR France \n", "1984-12-10/1984-12-16 184.0 266.0 FR France \n", "1984-12-17/1984-12-23 146.0 224.0 FR France \n", "1984-12-24/1984-12-30 110.0 198.0 FR France \n", "1984-12-31/1985-01-06 120.0 190.0 FR France \n", "1985-01-07/1985-01-13 147.0 207.0 FR France \n", "1985-01-14/1985-01-20 317.0 459.0 FR France \n", "1985-01-21/1985-01-27 708.0 832.0 FR France \n", "1985-01-28/1985-02-03 1074.0 1236.0 FR France \n", "1985-02-04/1985-02-10 939.0 1113.0 FR France \n", "1985-02-11/1985-02-17 784.0 926.0 FR France \n", "1985-02-18/1985-02-24 652.0 762.0 FR France \n", "1985-02-25/1985-03-03 618.0 722.0 FR France \n", "1985-03-04/1985-03-10 591.0 689.0 FR France \n", "1985-03-11/1985-03-17 458.0 544.0 FR France \n", "1985-03-18/1985-03-24 405.0 485.0 FR France \n", "1985-03-25/1985-03-31 319.0 395.0 FR France \n", "1985-04-01/1985-04-07 207.0 281.0 FR France \n", "1985-04-08/1985-04-14 83.0 149.0 FR France \n", "1985-04-15/1985-04-21 66.0 116.0 FR France \n", "1985-04-22/1985-04-28 44.0 80.0 FR France \n", "1985-04-29/1985-05-05 55.0 93.0 FR France \n", "1985-05-06/1985-05-12 59.0 97.0 FR France \n", "1985-05-13/1985-05-19 38.0 64.0 FR France \n", "1985-05-20/1985-05-26 35.0 59.0 FR France \n", "... ... ... ... ... \n", "2019-09-02/2019-09-08 1.0 5.0 FR France \n", "2019-09-09/2019-09-15 2.0 8.0 FR France \n", "2019-09-16/2019-09-22 4.0 10.0 FR France \n", "2019-09-23/2019-09-29 7.0 15.0 FR France \n", "2019-09-30/2019-10-06 9.0 17.0 FR France \n", "2019-10-07/2019-10-13 7.0 15.0 FR France \n", "2019-10-14/2019-10-20 8.0 16.0 FR France \n", "2019-10-21/2019-10-27 9.0 19.0 FR France \n", "2019-10-28/2019-11-03 8.0 16.0 FR France \n", "2019-11-04/2019-11-10 10.0 20.0 FR France \n", "2019-11-11/2019-11-17 19.0 29.0 FR France \n", "2019-11-18/2019-11-24 22.0 34.0 FR France \n", "2019-11-25/2019-12-01 27.0 41.0 FR France \n", "2019-12-02/2019-12-08 33.0 45.0 FR France \n", "2019-12-09/2019-12-15 44.0 60.0 FR France \n", "2019-12-16/2019-12-22 38.0 52.0 FR France \n", "2019-12-23/2019-12-29 36.0 50.0 FR France \n", "2019-12-30/2020-01-05 48.0 64.0 FR France \n", "2020-01-06/2020-01-12 72.0 90.0 FR France \n", "2020-01-13/2020-01-19 108.0 130.0 FR France \n", "2020-01-20/2020-01-26 173.0 199.0 FR France \n", "2020-01-27/2020-02-02 269.0 301.0 FR France \n", "2020-02-03/2020-02-09 297.0 331.0 FR France \n", "2020-02-10/2020-02-16 263.0 295.0 FR France \n", "2020-02-17/2020-02-23 203.0 233.0 FR France \n", "2020-02-24/2020-03-01 155.0 181.0 FR France \n", "2020-03-02/2020-03-08 146.0 172.0 FR France \n", "2020-03-09/2020-03-15 142.0 166.0 FR France \n", "2020-03-16/2020-03-22 9.0 17.0 FR France \n", "2020-03-23/2020-03-29 0.0 0.0 FR France \n", "\n", "[1848 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Vérification et inspection" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1],periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time # différence temporel\n", " if delta > pd.Timedelta('1s'):\n", " print(p1,p2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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'] = sorted_data['inc'].astype('int')\n", "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# regarder en détaille seulemnt les dernières années\n", "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "first_august_week = [pd.Period(pd.Timestamp(y,8,1),'w') for y in range(sorted_data.index[0].year,sorted_data.index[-1].year)]\n", "for week1,week2 in zip(first_august_week[:-1],first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " #assert abs(len(one_year)-52) < 2 # Afficher une erreur quand nb_week not =51,52,53\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "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": [ "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n", "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }