diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 8d8f7392affccec6d6664d99b01d89b2f4f7f88b..26d486a32082f91cee56cb6ce7698377465b8cfa 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": 30, "metadata": {}, "outputs": [], "source": [ @@ -28,43 +28,75 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" - ] + "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Maintenant, je vais tester si un fichier local existe déjà avant de telecharger les donnés pour eviter d'avoir des donnés répétées." + "Maintenant, je vais tester si un fichier local existe déjà avant de telecharger les donnés pour eviter d'avoir des donnés répétées. Si le fichier n'existe pas, je le telecharge." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The file https://app-learninglab.inria.fr/moocrr/jupyter/user/4bb0f49ad7e02c93b7804cb2c6776886/notebooks/work/module3/exo1/incidence-PAY-3.csv does not exist\n" + "The file incidence-PAY-3.csv exists\n" ] + }, + { + "data": { + "text/plain": [ + "88317" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "\n", - "path_to_file = 'https://app-learninglab.inria.fr/moocrr/jupyter/user/4bb0f49ad7e02c93b7804cb2c6776886/notebooks/work/module3/exo1/incidence-PAY-3.csv'\n", + "path_to_file = 'incidence-PAY-3.csv'\n", "path = Path(path_to_file)\n", "\n", "if path.is_file():\n", " print(f'The file {path_to_file} exists')\n", "else:\n", - " print(f'The file {path_to_file} does not exist')" + " print(f'The file {path_to_file} does not exist')\n", + " import requests\n", + "\n", + "\n", + "url = 'http://www.sentiweb.fr/datasets/incidence-PAY-3.csv'\n", + "r = requests.get(url, allow_redirects=True)\n", + "\n", + "open('incidence-PAY-3.csv', 'wb').write(r.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintenant je vais lire le fichier local\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "data = \"incidence-PAY-3.csv\"" ] }, { @@ -91,11 +123,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020222032481519537.030093.03729.045.0FRFrance
120221931805914211.021907.02721.033.0FRFrance
220221833035325089.035617.04638.054.0FRFrance
320221733600630373.041639.05446.062.0FRFrance
420221634994942836.057062.07564.086.0FRFrance
5202215310080690824.0110788.0152137.0167.0FRFrance
62022143155441143891.0166991.0234217.0251.0FRFrance
72022133191914179558.0204270.0289270.0308.0FRFrance
82022123166224155035.0177413.0251234.0268.0FRFrance
92022113122849113306.0132392.0185171.0199.0FRFrance
1020221038790479741.096067.0133121.0145.0FRFrance
1120220935018243958.056406.07667.085.0FRFrance
1220220833096325942.035984.04739.055.0FRFrance
1320220733488229446.040318.05345.061.0FRFrance
1420220634662340398.052848.07061.079.0FRFrance
1520220536297056043.069897.09585.0105.0FRFrance
1620220437220964804.079614.010998.0120.0FRFrance
1720220337461367144.082082.0113102.0124.0FRFrance
1820220235592049511.062329.08474.094.0FRFrance
1920220135762950699.064559.08777.097.0FRFrance
2020215235434947029.061669.08271.093.0FRFrance
2120215134169835359.048037.06353.073.0FRFrance
2220215033811732497.043737.05849.067.0FRFrance
2320214934016834716.045620.06153.069.0FRFrance
2420214834184236364.047320.06355.071.0FRFrance
2520214733659831338.041858.05547.063.0FRFrance
2620214633005925302.034816.04639.053.0FRFrance
2720214532036416564.024164.03125.037.0FRFrance
2820214431899915042.022956.02923.035.0FRFrance
2920214332704021935.032145.04133.049.0FRFrance
.................................
193019852132609619621.032571.04735.059.0FRFrance
193119852032789620885.034907.05138.064.0FRFrance
193219851934315432821.053487.07859.097.0FRFrance
193319851834055529935.051175.07455.093.0FRFrance
193419851733405324366.043740.06244.080.0FRFrance
193519851635036236451.064273.09166.0116.0FRFrance
193619851536388145538.082224.011683.0149.0FRFrance
19371985143134545114400.0154690.0244207.0281.0FRFrance
19381985133197206176080.0218332.0357319.0395.0FRFrance
19391985123245240223304.0267176.0445405.0485.0FRFrance
19401985113276205252399.0300011.0501458.0544.0FRFrance
19411985103353231326279.0380183.0640591.0689.0FRFrance
19421985093369895341109.0398681.0670618.0722.0FRFrance
19431985083389886359529.0420243.0707652.0762.0FRFrance
19441985073471852432599.0511105.0855784.0926.0FRFrance
19451985063565825518011.0613639.01026939.01113.0FRFrance
19461985053637302592795.0681809.011551074.01236.0FRFrance
19471985043424937390794.0459080.0770708.0832.0FRFrance
19481985033213901174689.0253113.0388317.0459.0FRFrance
194919850239758680949.0114223.0177147.0207.0FRFrance
195019850138548965918.0105060.0155120.0190.0FRFrance
195119845238483060602.0109058.0154110.0198.0FRFrance
1952198451310172680242.0123210.0185146.0224.0FRFrance
19531984503123680101401.0145959.0225184.0266.0FRFrance
1954198449310107381684.0120462.0184149.0219.0FRFrance
195519844837862060634.096606.0143110.0176.0FRFrance
195619844737202954274.089784.013199.0163.0FRFrance
195719844638733067686.0106974.0159123.0195.0FRFrance
19581984453135223101414.0169032.0246184.0308.0FRFrance
195919844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1960 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202220 3 24815 19537.0 30093.0 37 29.0 \n", + "1 202219 3 18059 14211.0 21907.0 27 21.0 \n", + "2 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "3 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "4 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "5 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "6 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "7 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "8 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "9 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "10 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "11 202209 3 50182 43958.0 56406.0 76 67.0 \n", + "12 202208 3 30963 25942.0 35984.0 47 39.0 \n", + "13 202207 3 34882 29446.0 40318.0 53 45.0 \n", + "14 202206 3 46623 40398.0 52848.0 70 61.0 \n", + "15 202205 3 62970 56043.0 69897.0 95 85.0 \n", + "16 202204 3 72209 64804.0 79614.0 109 98.0 \n", + "17 202203 3 74613 67144.0 82082.0 113 102.0 \n", + "18 202202 3 55920 49511.0 62329.0 84 74.0 \n", + "19 202201 3 57629 50699.0 64559.0 87 77.0 \n", + "20 202152 3 54349 47029.0 61669.0 82 71.0 \n", + "21 202151 3 41698 35359.0 48037.0 63 53.0 \n", + "22 202150 3 38117 32497.0 43737.0 58 49.0 \n", + "23 202149 3 40168 34716.0 45620.0 61 53.0 \n", + "24 202148 3 41842 36364.0 47320.0 63 55.0 \n", + "25 202147 3 36598 31338.0 41858.0 55 47.0 \n", + "26 202146 3 30059 25302.0 34816.0 46 39.0 \n", + "27 202145 3 20364 16564.0 24164.0 31 25.0 \n", + "28 202144 3 18999 15042.0 22956.0 29 23.0 \n", + "29 202143 3 27040 21935.0 32145.0 41 33.0 \n", + "... ... ... ... ... ... ... ... \n", + "1930 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1931 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1932 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1933 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1934 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1935 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1936 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1937 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1938 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1939 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1940 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1941 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1942 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1943 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1944 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1945 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1946 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1947 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1948 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1949 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1950 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1951 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1952 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1953 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1954 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1955 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1956 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1957 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1958 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1959 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 45.0 FR France \n", + "1 33.0 FR France \n", + "2 54.0 FR France \n", + "3 62.0 FR France \n", + "4 86.0 FR France \n", + "5 167.0 FR France \n", + "6 251.0 FR France \n", + "7 308.0 FR France \n", + "8 268.0 FR France \n", + "9 199.0 FR France \n", + "10 145.0 FR France \n", + "11 85.0 FR France \n", + "12 55.0 FR France \n", + "13 61.0 FR France \n", + "14 79.0 FR France \n", + "15 105.0 FR France \n", + "16 120.0 FR France \n", + "17 124.0 FR France \n", + "18 94.0 FR France \n", + "19 97.0 FR France \n", + "20 93.0 FR France \n", + "21 73.0 FR France \n", + "22 67.0 FR France \n", + "23 69.0 FR France \n", + "24 71.0 FR France \n", + "25 63.0 FR France \n", + "26 53.0 FR France \n", + "27 37.0 FR France \n", + "28 35.0 FR France \n", + "29 49.0 FR France \n", + "... ... ... ... \n", + "1930 59.0 FR France \n", + "1931 64.0 FR France \n", + "1932 97.0 FR France \n", + "1933 93.0 FR France \n", + "1934 80.0 FR France \n", + "1935 116.0 FR France \n", + "1936 149.0 FR France \n", + "1937 281.0 FR France \n", + "1938 395.0 FR France \n", + "1939 485.0 FR France \n", + "1940 544.0 FR France \n", + "1941 689.0 FR France \n", + "1942 722.0 FR France \n", + "1943 762.0 FR France \n", + "1944 926.0 FR France \n", + "1945 1113.0 FR France \n", + "1946 1236.0 FR France \n", + "1947 832.0 FR France \n", + "1948 459.0 FR France \n", + "1949 207.0 FR France \n", + "1950 190.0 FR France \n", + "1951 198.0 FR France \n", + "1952 224.0 FR France \n", + "1953 266.0 FR France \n", + "1954 219.0 FR France \n", + "1955 176.0 FR France \n", + "1956 163.0 FR France \n", + "1957 195.0 FR France \n", + "1958 308.0 FR France \n", + "1959 213.0 FR France \n", + "\n", + "[1960 rows x 10 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data, skiprows=1)\n", "raw_data" ] }, @@ -108,9 +1107,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "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
020222032481519537.030093.03729.045.0FRFrance
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220221833035325089.035617.04638.054.0FRFrance
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5202215310080690824.0110788.0152137.0167.0FRFrance
62022143155441143891.0166991.0234217.0251.0FRFrance
72022133191914179558.0204270.0289270.0308.0FRFrance
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92022113122849113306.0132392.0185171.0199.0FRFrance
1020221038790479741.096067.0133121.0145.0FRFrance
1120220935018243958.056406.07667.085.0FRFrance
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1920220135762950699.064559.08777.097.0FRFrance
2020215235434947029.061669.08271.093.0FRFrance
2120215134169835359.048037.06353.073.0FRFrance
2220215033811732497.043737.05849.067.0FRFrance
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2520214733659831338.041858.05547.063.0FRFrance
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2720214532036416564.024164.03125.037.0FRFrance
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193019852132609619621.032571.04735.059.0FRFrance
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193219851934315432821.053487.07859.097.0FRFrance
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193419851733405324366.043740.06244.080.0FRFrance
193519851635036236451.064273.09166.0116.0FRFrance
193619851536388145538.082224.011683.0149.0FRFrance
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1959 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202220 3 24815 19537.0 30093.0 37 29.0 \n", + "1 202219 3 18059 14211.0 21907.0 27 21.0 \n", + "2 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "3 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "4 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "5 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "6 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "7 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "8 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "9 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "10 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "11 202209 3 50182 43958.0 56406.0 76 67.0 \n", + "12 202208 3 30963 25942.0 35984.0 47 39.0 \n", + "13 202207 3 34882 29446.0 40318.0 53 45.0 \n", + "14 202206 3 46623 40398.0 52848.0 70 61.0 \n", + "15 202205 3 62970 56043.0 69897.0 95 85.0 \n", + "16 202204 3 72209 64804.0 79614.0 109 98.0 \n", + "17 202203 3 74613 67144.0 82082.0 113 102.0 \n", + "18 202202 3 55920 49511.0 62329.0 84 74.0 \n", + "19 202201 3 57629 50699.0 64559.0 87 77.0 \n", + "20 202152 3 54349 47029.0 61669.0 82 71.0 \n", + "21 202151 3 41698 35359.0 48037.0 63 53.0 \n", + "22 202150 3 38117 32497.0 43737.0 58 49.0 \n", + "23 202149 3 40168 34716.0 45620.0 61 53.0 \n", + "24 202148 3 41842 36364.0 47320.0 63 55.0 \n", + "25 202147 3 36598 31338.0 41858.0 55 47.0 \n", + "26 202146 3 30059 25302.0 34816.0 46 39.0 \n", + "27 202145 3 20364 16564.0 24164.0 31 25.0 \n", + "28 202144 3 18999 15042.0 22956.0 29 23.0 \n", + "29 202143 3 27040 21935.0 32145.0 41 33.0 \n", + "... ... ... ... ... ... ... ... \n", + "1930 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1931 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1932 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1933 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1934 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1935 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1936 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1937 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1938 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1939 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1940 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1941 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1942 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1943 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1944 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1945 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1946 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1947 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1948 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1949 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1950 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1951 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1952 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1953 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1954 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1955 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1956 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1957 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1958 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1959 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 45.0 FR France \n", + "1 33.0 FR France \n", + "2 54.0 FR France \n", + "3 62.0 FR France \n", + "4 86.0 FR France \n", + "5 167.0 FR France \n", + "6 251.0 FR France \n", + "7 308.0 FR France \n", + "8 268.0 FR France \n", + "9 199.0 FR France \n", + "10 145.0 FR France \n", + "11 85.0 FR France \n", + "12 55.0 FR France \n", + "13 61.0 FR France \n", + "14 79.0 FR France \n", + "15 105.0 FR France \n", + "16 120.0 FR France \n", + "17 124.0 FR France \n", + "18 94.0 FR France \n", + "19 97.0 FR France \n", + "20 93.0 FR France \n", + "21 73.0 FR France \n", + "22 67.0 FR France \n", + "23 69.0 FR France \n", + "24 71.0 FR France \n", + "25 63.0 FR France \n", + "26 53.0 FR France \n", + "27 37.0 FR France \n", + "28 35.0 FR France \n", + "29 49.0 FR France \n", + "... ... ... ... \n", + "1930 59.0 FR France \n", + "1931 64.0 FR France \n", + "1932 97.0 FR France \n", + "1933 93.0 FR France \n", + "1934 80.0 FR France \n", + "1935 116.0 FR France \n", + "1936 149.0 FR France \n", + "1937 281.0 FR France \n", + "1938 395.0 FR France \n", + "1939 485.0 FR France \n", + "1940 544.0 FR France \n", + "1941 689.0 FR France \n", + "1942 722.0 FR France \n", + "1943 762.0 FR France \n", + "1944 926.0 FR France \n", + "1945 1113.0 FR France \n", + "1946 1236.0 FR France \n", + "1947 832.0 FR France \n", + "1948 459.0 FR France \n", + "1949 207.0 FR France \n", + "1950 190.0 FR France \n", + "1951 198.0 FR France \n", + "1952 224.0 FR France \n", + "1953 266.0 FR France \n", + "1954 219.0 FR France \n", + "1955 176.0 FR France \n", + "1956 163.0 FR France \n", + "1957 195.0 FR France \n", + "1958 308.0 FR France \n", + "1959 213.0 FR France \n", + "\n", + "[1959 rows x 10 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -152,7 +2182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -182,10 +2212,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -209,9 +2237,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "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", @@ -229,9 +2265,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -361,9 +2511,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] diff --git a/module3/exo1/incidence-PAY-3.csv b/module3/exo1/incidence-PAY-3.csv new file mode 100644 index 0000000000000000000000000000000000000000..004dac4c2e1786655ad5e8647e36f27994a0ce93 --- /dev/null +++ b/module3/exo1/incidence-PAY-3.csv @@ -0,0 +1,1962 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202220],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2022-05-27T10:18:20+02:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202220,3,24815,19537,30093,37,29,45,FR,France +202219,3,18059,14211,21907,27,21,33,FR,France +202218,3,30353,25089,35617,46,38,54,FR,France +202217,3,36006,30373,41639,54,46,62,FR,France +202216,3,49949,42836,57062,75,64,86,FR,France 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