diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..0358d03b321ec5c1997377254d1db46862a6506a 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": 6, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,11 +59,999 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "data_file = \"syndromegrippal.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": [ + "On utilise une copie locale des données afin de se protéger du cas où la source soit effacée ou corrompue." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214431940615338.023474.02923.035.0FRFrance
120214332698721881.032093.04133.049.0FRFrance
220214232834323382.033304.04335.051.0FRFrance
320214132504320586.029500.03831.045.0FRFrance
420214032628621842.030730.04033.047.0FRFrance
520213932215518014.026296.03428.040.0FRFrance
620213831561412310.018918.02419.029.0FRFrance
720213731367310404.016942.02116.026.0FRFrance
82021363102897505.013073.01612.020.0FRFrance
92021353126099282.015936.01914.024.0FRFrance
102021343130159485.016545.02015.025.0FRFrance
112021333103927042.013742.01611.021.0FRFrance
1220213231558611009.020163.02417.031.0FRFrance
1320213131885513664.024046.02921.037.0FRFrance
142021303139919695.018287.02114.028.0FRFrance
152021293136269618.017634.02115.027.0FRFrance
16202128386365430.011842.0138.018.0FRFrance
172021273106936838.014548.01610.022.0FRFrance
18202126370864109.010063.0116.016.0FRFrance
19202125379425540.010344.0128.016.0FRFrance
20202124348553011.06699.074.010.0FRFrance
21202123367104455.08965.0107.013.0FRFrance
22202122378795495.010263.0128.016.0FRFrance
23202121378275403.010251.0128.016.0FRFrance
242021203102787540.013016.01612.020.0FRFrance
25202119395396860.012218.01410.018.0FRFrance
262021183121359165.015105.01814.022.0FRFrance
272021173120588891.015225.01813.023.0FRFrance
2820211631650512735.020275.02519.031.0FRFrance
2920211531930615398.023214.02923.035.0FRFrance
.................................
190219852132609619621.032571.04735.059.0FRFrance
190319852032789620885.034907.05138.064.0FRFrance
190419851934315432821.053487.07859.097.0FRFrance
190519851834055529935.051175.07455.093.0FRFrance
190619851733405324366.043740.06244.080.0FRFrance
190719851635036236451.064273.09166.0116.0FRFrance
190819851536388145538.082224.011683.0149.0FRFrance
19091985143134545114400.0154690.0244207.0281.0FRFrance
19101985133197206176080.0218332.0357319.0395.0FRFrance
19111985123245240223304.0267176.0445405.0485.0FRFrance
19121985113276205252399.0300011.0501458.0544.0FRFrance
19131985103353231326279.0380183.0640591.0689.0FRFrance
19141985093369895341109.0398681.0670618.0722.0FRFrance
19151985083389886359529.0420243.0707652.0762.0FRFrance
19161985073471852432599.0511105.0855784.0926.0FRFrance
19171985063565825518011.0613639.01026939.01113.0FRFrance
19181985053637302592795.0681809.011551074.01236.0FRFrance
19191985043424937390794.0459080.0770708.0832.0FRFrance
19201985033213901174689.0253113.0388317.0459.0FRFrance
192119850239758680949.0114223.0177147.0207.0FRFrance
192219850138548965918.0105060.0155120.0190.0FRFrance
192319845238483060602.0109058.0154110.0198.0FRFrance
1924198451310172680242.0123210.0185146.0224.0FRFrance
19251984503123680101401.0145959.0225184.0266.0FRFrance
1926198449310107381684.0120462.0184149.0219.0FRFrance
192719844837862060634.096606.0143110.0176.0FRFrance
192819844737202954274.089784.013199.0163.0FRFrance
192919844638733067686.0106974.0159123.0195.0FRFrance
19301984453135223101414.0169032.0246184.0308.0FRFrance
193119844436842220056.0116788.012537.0213.0FRFrance
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1932 rows × 10 columns

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10063.0 11 6.0 \n", + "19 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "20 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "21 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "22 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "23 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "24 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "25 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "26 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "27 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "28 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "29 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "... ... ... ... ... ... ... ... \n", + "1902 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1903 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1904 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1905 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1906 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1907 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1908 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1909 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1910 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1911 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1912 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1913 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1914 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1915 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1916 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1917 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1918 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1919 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1920 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1921 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1922 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1923 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1924 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1925 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1926 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1927 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1928 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1929 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1930 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1931 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 35.0 FR France \n", + "1 49.0 FR France \n", + "2 51.0 FR France \n", + "3 45.0 FR France \n", + "4 47.0 FR France \n", + "5 40.0 FR France \n", + "6 29.0 FR France \n", + "7 26.0 FR France \n", + "8 20.0 FR France \n", + "9 24.0 FR France \n", + "10 25.0 FR France \n", + "11 21.0 FR France \n", + "12 31.0 FR France \n", + "13 37.0 FR France \n", + "14 28.0 FR France \n", + "15 27.0 FR France \n", + "16 18.0 FR France \n", + "17 22.0 FR France \n", + "18 16.0 FR France \n", + "19 16.0 FR France \n", + "20 10.0 FR France \n", + "21 13.0 FR France \n", + "22 16.0 FR France \n", + "23 16.0 FR France \n", + "24 20.0 FR France \n", + "25 18.0 FR France \n", + "26 22.0 FR France \n", + "27 23.0 FR France \n", + "28 31.0 FR France \n", + "29 35.0 FR France \n", + "... ... ... ... \n", + "1902 59.0 FR France \n", + "1903 64.0 FR France \n", + "1904 97.0 FR France \n", + "1905 93.0 FR France \n", + "1906 80.0 FR France \n", + "1907 116.0 FR France \n", + "1908 149.0 FR France \n", + "1909 281.0 FR France \n", + "1910 395.0 FR France \n", + "1911 485.0 FR France \n", + "1912 544.0 FR France \n", + "1913 689.0 FR France \n", + "1914 722.0 FR France \n", + "1915 762.0 FR France \n", + "1916 926.0 FR France \n", + "1917 1113.0 FR France \n", + "1918 1236.0 FR France \n", + "1919 832.0 FR France \n", + "1920 459.0 FR France \n", + "1921 207.0 FR France \n", + "1922 190.0 FR France \n", + "1923 198.0 FR France \n", + "1924 224.0 FR France \n", + "1925 266.0 FR France \n", + "1926 219.0 FR France \n", + "1927 176.0 FR France \n", + "1928 163.0 FR France \n", + "1929 195.0 FR France \n", + "1930 308.0 FR France \n", + "1931 213.0 FR France \n", + "\n", + "[1932 rows x 10 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1064,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "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
020214431940615338.023474.02923.035.0FRFrance
120214332698721881.032093.04133.049.0FRFrance
220214232834323382.033304.04335.051.0FRFrance
320214132504320586.029500.03831.045.0FRFrance
420214032628621842.030730.04033.047.0FRFrance
520213932215518014.026296.03428.040.0FRFrance
620213831561412310.018918.02419.029.0FRFrance
720213731367310404.016942.02116.026.0FRFrance
82021363102897505.013073.01612.020.0FRFrance
92021353126099282.015936.01914.024.0FRFrance
102021343130159485.016545.02015.025.0FRFrance
112021333103927042.013742.01611.021.0FRFrance
1220213231558611009.020163.02417.031.0FRFrance
1320213131885513664.024046.02921.037.0FRFrance
142021303139919695.018287.02114.028.0FRFrance
152021293136269618.017634.02115.027.0FRFrance
16202128386365430.011842.0138.018.0FRFrance
172021273106936838.014548.01610.022.0FRFrance
18202126370864109.010063.0116.016.0FRFrance
19202125379425540.010344.0128.016.0FRFrance
20202124348553011.06699.074.010.0FRFrance
21202123367104455.08965.0107.013.0FRFrance
22202122378795495.010263.0128.016.0FRFrance
23202121378275403.010251.0128.016.0FRFrance
242021203102787540.013016.01612.020.0FRFrance
25202119395396860.012218.01410.018.0FRFrance
262021183121359165.015105.01814.022.0FRFrance
272021173120588891.015225.01813.023.0FRFrance
2820211631650512735.020275.02519.031.0FRFrance
2920211531930615398.023214.02923.035.0FRFrance
.................................
190219852132609619621.032571.04735.059.0FRFrance
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19111985123245240223304.0267176.0445405.0485.0FRFrance
19121985113276205252399.0300011.0501458.0544.0FRFrance
19131985103353231326279.0380183.0640591.0689.0FRFrance
19141985093369895341109.0398681.0670618.0722.0FRFrance
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1931 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202144 3 19406 15338.0 23474.0 29 23.0 \n", + "1 202143 3 26987 21881.0 32093.0 41 33.0 \n", + "2 202142 3 28343 23382.0 33304.0 43 35.0 \n", + "3 202141 3 25043 20586.0 29500.0 38 31.0 \n", + "4 202140 3 26286 21842.0 30730.0 40 33.0 \n", + "5 202139 3 22155 18014.0 26296.0 34 28.0 \n", + "6 202138 3 15614 12310.0 18918.0 24 19.0 \n", + "7 202137 3 13673 10404.0 16942.0 21 16.0 \n", + "8 202136 3 10289 7505.0 13073.0 16 12.0 \n", + "9 202135 3 12609 9282.0 15936.0 19 14.0 \n", + "10 202134 3 13015 9485.0 16545.0 20 15.0 \n", + "11 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "12 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "13 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "14 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "15 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "16 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "17 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "18 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "19 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "20 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "21 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "22 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "23 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "24 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "25 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "26 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "27 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "28 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "29 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "... ... ... ... ... ... ... ... \n", + "1902 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1903 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1904 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1905 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1906 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1907 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1908 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1909 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1910 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1911 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1912 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1913 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1914 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1915 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1916 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1917 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1918 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1919 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1920 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1921 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1922 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1923 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1924 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1925 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1926 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1927 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1928 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1929 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1930 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1931 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 35.0 FR France \n", + "1 49.0 FR France \n", + "2 51.0 FR France \n", + "3 45.0 FR France \n", + "4 47.0 FR France \n", + "5 40.0 FR France \n", + "6 29.0 FR France \n", + "7 26.0 FR France \n", + "8 20.0 FR France \n", + "9 24.0 FR France \n", + "10 25.0 FR France \n", + "11 21.0 FR France \n", + "12 31.0 FR France \n", + "13 37.0 FR France \n", + "14 28.0 FR France \n", + "15 27.0 FR France \n", + "16 18.0 FR France \n", + "17 22.0 FR France \n", + "18 16.0 FR France \n", + "19 16.0 FR France \n", + "20 10.0 FR France \n", + "21 13.0 FR France \n", + "22 16.0 FR France \n", + "23 16.0 FR France \n", + "24 20.0 FR France \n", + "25 18.0 FR France \n", + "26 22.0 FR France \n", + "27 23.0 FR France \n", + "28 31.0 FR France \n", + "29 35.0 FR France \n", + "... ... ... ... \n", + "1902 59.0 FR France \n", + "1903 64.0 FR France \n", + "1904 97.0 FR France \n", + "1905 93.0 FR France \n", + "1906 80.0 FR France \n", + "1907 116.0 FR France \n", + "1908 149.0 FR France \n", + "1909 281.0 FR France \n", + "1910 395.0 FR France \n", + "1911 485.0 FR France \n", + "1912 544.0 FR France \n", + "1913 689.0 FR France \n", + "1914 722.0 FR France \n", + "1915 762.0 FR France \n", + "1916 926.0 FR France \n", + "1917 1113.0 FR France \n", + "1918 1236.0 FR France \n", + "1919 832.0 FR France \n", + "1920 459.0 FR France \n", + "1921 207.0 FR France \n", + "1922 190.0 FR France \n", + "1923 198.0 FR France \n", + "1924 224.0 FR France \n", + "1925 266.0 FR France \n", + "1926 219.0 FR France \n", + "1927 176.0 FR France \n", + "1928 163.0 FR France \n", + "1929 195.0 FR France \n", + "1930 308.0 FR France \n", + "1931 213.0 FR France \n", + "\n", + "[1931 rows x 10 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2169,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2194,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "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", @@ -199,9 +2222,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+198450,3,123680,101401,145959,225,184,266,FR,France +198449,3,101073,81684,120462,184,149,219,FR,France +198448,3,78620,60634,96606,143,110,176,FR,France +198447,3,72029,54274,89784,131,99,163,FR,France +198446,3,87330,67686,106974,159,123,195,FR,France +198445,3,135223,101414,169032,246,184,308,FR,France +198444,3,68422,20056,116788,125,37,213,FR,France diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0b452077a64584cf84c574b31a630ff13e5b0de0 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2277 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"varicelle.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": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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.................................
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1614 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2021-04-12/2021-04-18202115711215762714803171222FRFrance
2021-04-19/2021-04-2520211674780289166697410FRFrance
2021-04-26/2021-05-0220211774686287864947410FRFrance
2021-05-03/2021-05-092021187391221105714639FRFrance
2021-05-10/2021-05-16202119766544370893810713FRFrance
2021-05-17/2021-05-232021207748546011036911715FRFrance
2021-05-24/2021-05-3020212176092345887269513FRFrance
2021-05-31/2021-06-0620212274817275268827410FRFrance
2021-06-07/2021-06-1320212379116642011812141018FRFrance
2021-06-14/2021-06-20202124712034893715131181323FRFrance
2021-06-21/2021-06-2720212579351654012162141018FRFrance
2021-06-28/2021-07-042021267728441081046011616FRFrance
2021-07-05/2021-07-112021277902643161373614721FRFrance
2021-07-12/2021-07-182021287973402173115033FRFrance
2021-07-19/2021-07-25202129768004109949110614FRFrance
2021-07-26/2021-08-012021307719041911018911616FRFrance
2021-08-02/2021-08-0820213174793230172857311FRFrance
2021-08-09/2021-08-152021327410818956321639FRFrance
2021-08-16/2021-08-222021337382918305828639FRFrance
2021-08-23/2021-08-29202134714293782480204FRFrance
2021-08-30/2021-09-052021357256211074017426FRFrance
2021-09-06/2021-09-122021367344117305152528FRFrance
2021-09-13/2021-09-19202137719647543174315FRFrance
2021-09-20/2021-09-2620213874325226763837410FRFrance
2021-09-27/2021-10-032021397229110563526315FRFrance
2021-10-04/2021-10-1020214074441245464287410FRFrance
2021-10-11/2021-10-172021417402122395803639FRFrance
2021-10-18/2021-10-242021427944360371284914919FRFrance
2021-10-25/2021-10-312021437816451791114912717FRFrance
2021-11-01/2021-11-072021447868053431201713818FRFrance
\n", + "

1614 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", + "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", + "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", + "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", + "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", + "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", + "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", + "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", + "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", + "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", + "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", + "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", + "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", + "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", + "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", + "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", + "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", + "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", + "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", + "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", + "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", + "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", + "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", + "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", + "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", + "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", + "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", + "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", + "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", + "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", + "... ... ... ... ... ... ... \n", + "2021-04-12/2021-04-18 202115 7 11215 7627 14803 17 \n", + "2021-04-19/2021-04-25 202116 7 4780 2891 6669 7 \n", + "2021-04-26/2021-05-02 202117 7 4686 2878 6494 7 \n", + "2021-05-03/2021-05-09 202118 7 3912 2110 5714 6 \n", + "2021-05-10/2021-05-16 202119 7 6654 4370 8938 10 \n", + "2021-05-17/2021-05-23 202120 7 7485 4601 10369 11 \n", + "2021-05-24/2021-05-30 202121 7 6092 3458 8726 9 \n", + "2021-05-31/2021-06-06 202122 7 4817 2752 6882 7 \n", + "2021-06-07/2021-06-13 202123 7 9116 6420 11812 14 \n", + "2021-06-14/2021-06-20 202124 7 12034 8937 15131 18 \n", + "2021-06-21/2021-06-27 202125 7 9351 6540 12162 14 \n", + "2021-06-28/2021-07-04 202126 7 7284 4108 10460 11 \n", + "2021-07-05/2021-07-11 202127 7 9026 4316 13736 14 \n", + "2021-07-12/2021-07-18 202128 7 9734 0 21731 15 \n", + "2021-07-19/2021-07-25 202129 7 6800 4109 9491 10 \n", + "2021-07-26/2021-08-01 202130 7 7190 4191 10189 11 \n", + "2021-08-02/2021-08-08 202131 7 4793 2301 7285 7 \n", + "2021-08-09/2021-08-15 202132 7 4108 1895 6321 6 \n", + "2021-08-16/2021-08-22 202133 7 3829 1830 5828 6 \n", + "2021-08-23/2021-08-29 202134 7 1429 378 2480 2 \n", + "2021-08-30/2021-09-05 202135 7 2562 1107 4017 4 \n", + "2021-09-06/2021-09-12 202136 7 3441 1730 5152 5 \n", + "2021-09-13/2021-09-19 202137 7 1964 754 3174 3 \n", + "2021-09-20/2021-09-26 202138 7 4325 2267 6383 7 \n", + "2021-09-27/2021-10-03 202139 7 2291 1056 3526 3 \n", + "2021-10-04/2021-10-10 202140 7 4441 2454 6428 7 \n", + "2021-10-11/2021-10-17 202141 7 4021 2239 5803 6 \n", + "2021-10-18/2021-10-24 202142 7 9443 6037 12849 14 \n", + "2021-10-25/2021-10-31 202143 7 8164 5179 11149 12 \n", + "2021-11-01/2021-11-07 202144 7 8680 5343 12017 13 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1990-12-03/1990-12-09 0 5 FR France \n", + "1990-12-10/1990-12-16 12 28 FR France \n", + "1990-12-17/1990-12-23 25 43 FR France \n", + "1990-12-24/1990-12-30 23 45 FR France \n", + "1990-12-31/1991-01-06 18 36 FR France \n", + "1991-01-07/1991-01-13 20 38 FR France \n", + "1991-01-14/1991-01-20 18 36 FR France \n", + "1991-01-21/1991-01-27 8 20 FR France \n", + "1991-01-28/1991-02-03 11 25 FR France \n", + "1991-02-04/1991-02-10 12 26 FR France \n", + "1991-02-11/1991-02-17 15 29 FR France \n", + "1991-02-18/1991-02-24 15 31 FR France \n", + "1991-02-25/1991-03-03 15 33 FR France \n", + "1991-03-04/1991-03-10 20 38 FR France \n", + "1991-03-11/1991-03-17 19 35 FR France \n", + "1991-03-18/1991-03-24 13 25 FR France \n", + "1991-03-25/1991-03-31 11 23 FR France \n", + "1991-04-01/1991-04-07 14 30 FR France \n", + "1991-04-08/1991-04-14 18 32 FR France \n", + "1991-04-15/1991-04-21 18 34 FR France \n", + "1991-04-22/1991-04-28 16 32 FR France \n", + "1991-04-29/1991-05-05 25 51 FR France \n", + "1991-05-06/1991-05-12 19 39 FR France \n", + "1991-05-13/1991-05-19 23 45 FR France \n", + "1991-05-20/1991-05-26 16 36 FR France \n", + "1991-05-27/1991-06-02 17 37 FR France \n", + "1991-06-03/1991-06-09 13 29 FR France \n", + "1991-06-10/1991-06-16 17 39 FR France \n", + "1991-06-17/1991-06-23 18 38 FR France \n", + "1991-06-24/1991-06-30 20 42 FR France \n", + "... ... ... ... ... \n", + "2021-04-12/2021-04-18 12 22 FR France \n", + "2021-04-19/2021-04-25 4 10 FR France \n", + "2021-04-26/2021-05-02 4 10 FR France \n", + "2021-05-03/2021-05-09 3 9 FR France \n", + "2021-05-10/2021-05-16 7 13 FR France \n", + "2021-05-17/2021-05-23 7 15 FR France \n", + "2021-05-24/2021-05-30 5 13 FR France \n", + "2021-05-31/2021-06-06 4 10 FR France \n", + "2021-06-07/2021-06-13 10 18 FR France \n", + "2021-06-14/2021-06-20 13 23 FR France \n", + "2021-06-21/2021-06-27 10 18 FR France \n", + "2021-06-28/2021-07-04 6 16 FR France \n", + "2021-07-05/2021-07-11 7 21 FR France \n", + "2021-07-12/2021-07-18 0 33 FR France \n", + "2021-07-19/2021-07-25 6 14 FR France \n", + "2021-07-26/2021-08-01 6 16 FR France \n", + "2021-08-02/2021-08-08 3 11 FR France \n", + "2021-08-09/2021-08-15 3 9 FR France \n", + "2021-08-16/2021-08-22 3 9 FR France \n", + "2021-08-23/2021-08-29 0 4 FR France \n", + "2021-08-30/2021-09-05 2 6 FR France \n", + "2021-09-06/2021-09-12 2 8 FR France \n", + "2021-09-13/2021-09-19 1 5 FR France \n", + "2021-09-20/2021-09-26 4 10 FR France \n", + "2021-09-27/2021-10-03 1 5 FR France \n", + "2021-10-04/2021-10-10 4 10 FR France \n", + "2021-10-11/2021-10-17 3 9 FR France \n", + "2021-10-18/2021-10-24 9 19 FR France \n", + "2021-10-25/2021-10-31 7 17 FR France \n", + "2021-11-01/2021-11-07 8 18 FR France \n", + "\n", + "[1614 rows x 10 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "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['inc'][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": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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}, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -