diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0f2319043257d3be895b68788b0a593c5f7c4d9 --- /dev/null +++ b/module3/exo1/influenza-like-illness-analysis.ipynb @@ -0,0 +1,2500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence of influenza-like illness in France" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Column name | Description |\n", + "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", + "| `week` | ISO8601 Yearweek number as numeric (year times 100 + week nubmer) |\n", + "| `indicator` | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n", + "| `inc` | Estimated incidence value for the time step, in the geographic level |\n", + "| `inc_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", + "| `inc_up` | Upper bound of the estimated incidence 95% Confidence Interval |\n", + "| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n", + "| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", + "| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n", + "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", + "| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", + "\n", + "The first line of the CSV file is a comment, which we ignore with `skip=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"syndrome-grippal.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2120 rows × 10 columns

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202506 3 273519 258159.0 288879.0 408 385.0 \n", + "19 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "20 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "21 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "22 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "23 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "24 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "25 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "26 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "27 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "28 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "29 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "... ... ... ... ... ... ... ... \n", + "2090 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2091 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2092 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2093 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2094 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2095 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2096 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2097 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2098 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2099 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2100 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2101 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2102 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2103 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2104 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2105 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2106 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2107 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2108 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2109 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2110 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2111 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2112 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2113 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2114 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2115 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2116 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2117 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2118 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2119 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 42.0 FR France \n", + "1 45.0 FR France \n", + "2 35.0 FR France \n", + "3 43.0 FR France \n", + "4 37.0 FR France \n", + "5 30.0 FR France \n", + "6 33.0 FR France \n", + "7 40.0 FR France \n", + "8 52.0 FR France \n", + "9 62.0 FR France \n", + "10 65.0 FR France \n", + "11 68.0 FR France \n", + "12 88.0 FR France \n", + "13 100.0 FR France \n", + "14 101.0 FR France \n", + "15 140.0 FR France \n", + "16 220.0 FR France \n", + "17 331.0 FR France \n", + "18 431.0 FR France \n", + "19 523.0 FR France \n", + "20 548.0 FR France \n", + "21 398.0 FR France \n", + "22 405.0 FR France \n", + "23 370.0 FR France \n", + "24 326.0 FR France \n", + "25 323.0 FR France \n", + "26 220.0 FR France \n", + "27 177.0 FR France \n", + "28 144.0 FR France \n", + "29 127.0 FR France \n", + "... ... ... ... \n", + "2090 59.0 FR France \n", + "2091 64.0 FR France \n", + "2092 97.0 FR France \n", + "2093 93.0 FR France \n", + "2094 80.0 FR France \n", + "2095 116.0 FR France \n", + "2096 149.0 FR France \n", + "2097 281.0 FR France \n", + "2098 395.0 FR France \n", + "2099 485.0 FR France \n", + "2100 544.0 FR France \n", + "2101 689.0 FR France \n", + "2102 722.0 FR France \n", + "2103 762.0 FR France \n", + "2104 926.0 FR France \n", + "2105 1113.0 FR France \n", + "2106 1236.0 FR France \n", + "2107 832.0 FR France \n", + "2108 459.0 FR France \n", + "2109 207.0 FR France \n", + "2110 190.0 FR France \n", + "2111 198.0 FR France \n", + "2112 224.0 FR France \n", + "2113 266.0 FR France \n", + "2114 219.0 FR France \n", + "2115 176.0 FR France \n", + "2116 163.0 FR France \n", + "2117 195.0 FR France \n", + "2118 308.0 FR France \n", + "2119 213.0 FR France \n", + "\n", + "[2120 rows x 10 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Are there missing data points? Yes, week 19 of year 1989 does not have any observed values." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18831989193-NaNNaN-NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1883 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1883 FR France " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We delete this point, which does not have big consequence for our rather simple analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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202025043350043332885.0367201.0522496.0548.0FRFrance
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222025023257247242991.0271503.0384363.0405.0FRFrance
232025013231549214627.0248471.0345320.0370.0FRFrance
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.................................
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2119 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202524 3 22816 17621.0 28011.0 34 26.0 \n", + "1 202523 3 24564 19382.0 29746.0 37 29.0 \n", + "2 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "3 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "4 202520 3 20265 15814.0 24716.0 30 23.0 \n", + "5 202519 3 16264 12394.0 20134.0 24 18.0 \n", + "6 202518 3 18115 13975.0 22255.0 27 21.0 \n", + "7 202517 3 22150 17291.0 27009.0 33 26.0 \n", + "8 202516 3 28564 22550.0 34578.0 43 34.0 \n", + "9 202515 3 35721 29592.0 41850.0 53 44.0 \n", + "10 202514 3 37579 31232.0 43926.0 56 47.0 \n", + "11 202513 3 39673 33686.0 45660.0 59 50.0 \n", + "12 202512 3 52543 45627.0 59459.0 78 68.0 \n", + "13 202511 3 59469 52154.0 66784.0 89 78.0 \n", + "14 202510 3 60334 53048.0 67620.0 90 79.0 \n", + "15 202509 3 84531 74994.0 94068.0 126 112.0 \n", + "16 202508 3 136020 124824.0 147216.0 203 186.0 \n", + "17 202507 3 208952 195988.0 221916.0 312 293.0 \n", + "18 202506 3 273519 258159.0 288879.0 408 385.0 \n", + "19 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "20 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "21 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "22 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "23 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "24 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "25 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "26 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "27 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "28 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "29 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "... ... ... ... ... ... ... ... \n", + "2090 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2091 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2092 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2093 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2094 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2095 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2096 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2097 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2098 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2099 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2100 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2101 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2102 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2103 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2104 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2105 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2106 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2107 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2108 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2109 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2110 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2111 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2112 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2113 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2114 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2115 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2116 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2117 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2118 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2119 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 42.0 FR France \n", + "1 45.0 FR France \n", + "2 35.0 FR France \n", + "3 43.0 FR France \n", + "4 37.0 FR France \n", + "5 30.0 FR France \n", + "6 33.0 FR France \n", + "7 40.0 FR France \n", + "8 52.0 FR France \n", + "9 62.0 FR France \n", + "10 65.0 FR France \n", + "11 68.0 FR France \n", + "12 88.0 FR France \n", + "13 100.0 FR France \n", + "14 101.0 FR France \n", + "15 140.0 FR France \n", + "16 220.0 FR France \n", + "17 331.0 FR France \n", + "18 431.0 FR France \n", + "19 523.0 FR France \n", + "20 548.0 FR France \n", + "21 398.0 FR France \n", + "22 405.0 FR France \n", + "23 370.0 FR France \n", + "24 326.0 FR France \n", + "25 323.0 FR France \n", + "26 220.0 FR France \n", + "27 177.0 FR France \n", + "28 144.0 FR France \n", + "29 127.0 FR France \n", + "... ... ... ... \n", + "2090 59.0 FR France \n", + "2091 64.0 FR France \n", + "2092 97.0 FR France \n", + "2093 93.0 FR France \n", + "2094 80.0 FR France \n", + "2095 116.0 FR France \n", + "2096 149.0 FR France \n", + "2097 281.0 FR France \n", + "2098 395.0 FR France \n", + "2099 485.0 FR France \n", + "2100 544.0 FR France \n", + "2101 689.0 FR France \n", + "2102 722.0 FR France \n", + "2103 762.0 FR France \n", + "2104 926.0 FR France \n", + "2105 1113.0 FR France \n", + "2106 1236.0 FR France \n", + "2107 832.0 FR France \n", + "2108 459.0 FR France \n", + "2109 207.0 FR France \n", + "2110 190.0 FR France \n", + "2111 198.0 FR France \n", + "2112 224.0 FR France \n", + "2113 266.0 FR France \n", + "2114 219.0 FR France \n", + "2115 176.0 FR France \n", + "2116 163.0 FR France \n", + "2117 195.0 FR France \n", + "2118 308.0 FR France \n", + "2119 213.0 FR France \n", + "\n", + "[2119 rows x 10 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dataset uses an uncommon encoding; the week number is attached\n", + "to the year number, leaving the impression of a six-digit integer.\n", + "That is how Pandas interprets it.\n", + "\n", + "A second problem is that Pandas does not know about week numbers.\n", + "It needs to be given the dates of the beginning and end of the week.\n", + "We use the library `isoweek` for that.\n", + "\n", + "Since the conversion is a bit lengthy, we write a small Python \n", + "function for doing it. Then we apply it to all points in our dataset. \n", + "The results go into a new column 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "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": "markdown", + "metadata": {}, + "source": [ + "There are two more small changes to make.\n", + "\n", + "First, we define the observation periods as the new index of\n", + "our dataset. That turns it into a time series, which will be\n", + "convenient later on.\n", + "\n", + "Second, we sort the points chronologically." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check the consistency of the data. Between the end of a period and\n", + "the beginning of the next one, the difference should be zero, or very small.\n", + "We tolerate an error of one second.\n", + "\n", + "This is OK except for one pair of consecutive periods between which\n", + "a whole week is missing.\n", + "\n", + "We recognize the dates: it's the week without observations that we\n", + "have deleted earlier!" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "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": "markdown", + "metadata": {}, + "source": [ + "A first look at the data!" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "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": "markdown", + "metadata": {}, + "source": [ + "A zoom on the last few years shows more clearly that the peaks are situated in winter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study of the annual incidence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the peaks of the epidemic happen in winter, near the transition\n", + "between calendar years, we define the reference period for the annual\n", + "incidence from August 1st of year $N$ to August 1st of year $N+1$. We\n", + "label this period as year $N+1$ because the peak is always located in\n", + "year $N+1$. The very low incidence in summer ensures that the arbitrariness\n", + "of the choice of reference period has no impact on our conclusions.\n", + "\n", + "Our task is a bit complicated by the fact that a year does not have an\n", + "integer number of weeks. Therefore we modify our reference period a bit:\n", + "instead of August 1st, we use the first day of the week containing August 1st.\n", + "\n", + "A final detail: the dataset starts in October 1984, the first peak is thus\n", + "incomplete, We start the analysis with the first full peak." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "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": "markdown", + "metadata": {}, + "source": [ + "Starting from this list of weeks that contain August 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n", + "\n", + "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "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": "markdown", + "metadata": {}, + "source": [ + "And here are the annual incidences." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "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.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A sorted list makes it easier to find the highest values (at the end)." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2023 2873501\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2024 3670417\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population,\n", + "but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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