diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..b6b54d2da082b6a5d4b7bc221c69c85171fa05e1 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,3447 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence of Chicken Pox in France" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [ + "The data on the incidence of Chicen Pox 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 1991 and ending with a recent week, is available for download." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We made a copy of the data in the url and run this code with these data (14 of november of 2024)" + ] + }, + { + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020244573899152362756210FRFrance
1202444722086923724315FRFrance
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32024427262112463996426FRFrance
4202441720353813689315FRFrance
5202440721257253525315FRFrance
62024397289813334463426FRFrance
7202438775101513102FRFrance
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10202435716202852955204FRFrance
11202434725606224498417FRFrance
12202433719715363406315FRFrance
1320243274399194468547311FRFrance
1420243174500221367877410FRFrance
15202430770044278973011715FRFrance
1620242979270630312237141018FRFrance
1720242879364649812230141018FRFrance
18202427710247709013404151020FRFrance
192024267143681039918337221628FRFrance
20202425711174803914309171222FRFrance
21202424712621935715885191424FRFrance
222024237146571133917975221727FRFrance
23202422711628836114895171222FRFrance
2420242179701685112551151119FRFrance
252024207136611020917113201525FRFrance
2620241971008364131375315921FRFrance
27202418713438951417362201426FRFrance
282024177153031121919387231729FRFrance
292024167181381354022736272034FRFrance
.................................
17411991267176081130423912312042FRFrance
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1745199122715452995320951271737FRFrance
1746199121714903897520831261636FRFrance
17471991207190531274225364342345FRFrance
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17491991187213851388228888382551FRFrance
1750199117713462887718047241632FRFrance
17511991167148571006819646261834FRFrance
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1760199107712337807716597221529FRFrance
1761199106710877701314741191226FRFrance
1762199105710442654414340181125FRFrance
17631991047791345631126314820FRFrance
17641991037153871048420290271836FRFrance
17651991027162771104621508292038FRFrance
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17671990527193751329525455342345FRFrance
17681990517190801380724353342543FRFrance
1769199050711079666015498201228FRFrance
17701990497114302610205FRFrance
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1771 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202445 7 3899 1523 6275 6 2 \n", + "1 202444 7 2208 692 3724 3 1 \n", + "2 202443 7 2124 641 3607 3 1 \n", + "3 202442 7 2621 1246 3996 4 2 \n", + "4 202441 7 2035 381 3689 3 1 \n", + "5 202440 7 2125 725 3525 3 1 \n", + "6 202439 7 2898 1333 4463 4 2 \n", + "7 202438 7 751 0 1513 1 0 \n", + "8 202437 7 916 28 1804 1 0 \n", + "9 202436 7 2235 870 3600 3 1 \n", + "10 202435 7 1620 285 2955 2 0 \n", + "11 202434 7 2560 622 4498 4 1 \n", + "12 202433 7 1971 536 3406 3 1 \n", + "13 202432 7 4399 1944 6854 7 3 \n", + "14 202431 7 4500 2213 6787 7 4 \n", + "15 202430 7 7004 4278 9730 11 7 \n", + "16 202429 7 9270 6303 12237 14 10 \n", + "17 202428 7 9364 6498 12230 14 10 \n", + "18 202427 7 10247 7090 13404 15 10 \n", + "19 202426 7 14368 10399 18337 22 16 \n", + "20 202425 7 11174 8039 14309 17 12 \n", + "21 202424 7 12621 9357 15885 19 14 \n", + "22 202423 7 14657 11339 17975 22 17 \n", + "23 202422 7 11628 8361 14895 17 12 \n", + "24 202421 7 9701 6851 12551 15 11 \n", + "25 202420 7 13661 10209 17113 20 15 \n", + "26 202419 7 10083 6413 13753 15 9 \n", + "27 202418 7 13438 9514 17362 20 14 \n", + "28 202417 7 15303 11219 19387 23 17 \n", + "29 202416 7 18138 13540 22736 27 20 \n", + "... ... ... ... ... ... ... ... \n", + "1741 199126 7 17608 11304 23912 31 20 \n", + "1742 199125 7 16169 10700 21638 28 18 \n", + "1743 199124 7 16171 10071 22271 28 17 \n", + "1744 199123 7 11947 7671 16223 21 13 \n", + "1745 199122 7 15452 9953 20951 27 17 \n", + "1746 199121 7 14903 8975 20831 26 16 \n", + "1747 199120 7 19053 12742 25364 34 23 \n", + "1748 199119 7 16739 11246 22232 29 19 \n", + "1749 199118 7 21385 13882 28888 38 25 \n", + "1750 199117 7 13462 8877 18047 24 16 \n", + "1751 199116 7 14857 10068 19646 26 18 \n", + "1752 199115 7 13975 9781 18169 25 18 \n", + "1753 199114 7 12265 7684 16846 22 14 \n", + "1754 199113 7 9567 6041 13093 17 11 \n", + "1755 199112 7 10864 7331 14397 19 13 \n", + "1756 199111 7 15574 11184 19964 27 19 \n", + "1757 199110 7 16643 11372 21914 29 20 \n", + "1758 199109 7 13741 8780 18702 24 15 \n", + "1759 199108 7 13289 8813 17765 23 15 \n", + "1760 199107 7 12337 8077 16597 22 15 \n", + "1761 199106 7 10877 7013 14741 19 12 \n", + "1762 199105 7 10442 6544 14340 18 11 \n", + "1763 199104 7 7913 4563 11263 14 8 \n", + "1764 199103 7 15387 10484 20290 27 18 \n", + "1765 199102 7 16277 11046 21508 29 20 \n", + "1766 199101 7 15565 10271 20859 27 18 \n", + "1767 199052 7 19375 13295 25455 34 23 \n", + "1768 199051 7 19080 13807 24353 34 25 \n", + "1769 199050 7 11079 6660 15498 20 12 \n", + "1770 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 10 FR France \n", + "1 5 FR France \n", + "2 5 FR France \n", + "3 6 FR France \n", + "4 5 FR France \n", + "5 5 FR France \n", + "6 6 FR France \n", + "7 2 FR France \n", + "8 2 FR France \n", + "9 5 FR France \n", + "10 4 FR France \n", + "11 7 FR France \n", + "12 5 FR France \n", + "13 11 FR France \n", + "14 10 FR France \n", + "15 15 FR France \n", + "16 18 FR France \n", + "17 18 FR France \n", + "18 20 FR France \n", + "19 28 FR France \n", + "20 22 FR France \n", + "21 24 FR France \n", + "22 27 FR France \n", + "23 22 FR France \n", + "24 19 FR France \n", + "25 25 FR France \n", + "26 21 FR France \n", + "27 26 FR France \n", + "28 29 FR France \n", + "29 34 FR France \n", + "... ... ... ... \n", + "1741 42 FR France \n", + "1742 38 FR France \n", + "1743 39 FR France \n", + "1744 29 FR France \n", + "1745 37 FR France \n", + "1746 36 FR France \n", + "1747 45 FR France \n", + "1748 39 FR France \n", + "1749 51 FR France \n", + "1750 32 FR France \n", + "1751 34 FR France \n", + "1752 32 FR France \n", + "1753 30 FR France \n", + "1754 23 FR France \n", + "1755 25 FR France \n", + "1756 35 FR France \n", + "1757 38 FR France \n", + "1758 33 FR France \n", + "1759 31 FR France \n", + "1760 29 FR France \n", + "1761 26 FR France \n", + "1762 25 FR France \n", + "1763 20 FR France \n", + "1764 36 FR France \n", + "1765 38 FR France \n", + "1766 36 FR France \n", + "1767 45 FR France \n", + "1768 43 FR France \n", + "1769 28 FR France \n", + "1770 5 FR France \n", + "\n", + "[1771 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_file = \"inc-7-PAY.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)\n", + "\n", + "raw_data = pd.read_csv(data_file, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Are there missing data points? " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is no missing points" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020244573899152362756210FRFrance
1202444722086923724315FRFrance
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5202440721257253525315FRFrance
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192024267143681039918337221628FRFrance
20202425711174803914309171222FRFrance
21202424712621935715885191424FRFrance
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252024207136611020917113201525FRFrance
2620241971008364131375315921FRFrance
27202418713438951417362201426FRFrance
282024177153031121919387231729FRFrance
292024167181381354022736272034FRFrance
.................................
17411991267176081130423912312042FRFrance
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17701990497114302610205FRFrance
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1771 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202445 7 3899 1523 6275 6 2 \n", + "1 202444 7 2208 692 3724 3 1 \n", + "2 202443 7 2124 641 3607 3 1 \n", + "3 202442 7 2621 1246 3996 4 2 \n", + "4 202441 7 2035 381 3689 3 1 \n", + "5 202440 7 2125 725 3525 3 1 \n", + "6 202439 7 2898 1333 4463 4 2 \n", + "7 202438 7 751 0 1513 1 0 \n", + "8 202437 7 916 28 1804 1 0 \n", + "9 202436 7 2235 870 3600 3 1 \n", + "10 202435 7 1620 285 2955 2 0 \n", + "11 202434 7 2560 622 4498 4 1 \n", + "12 202433 7 1971 536 3406 3 1 \n", + "13 202432 7 4399 1944 6854 7 3 \n", + "14 202431 7 4500 2213 6787 7 4 \n", + "15 202430 7 7004 4278 9730 11 7 \n", + "16 202429 7 9270 6303 12237 14 10 \n", + "17 202428 7 9364 6498 12230 14 10 \n", + "18 202427 7 10247 7090 13404 15 10 \n", + "19 202426 7 14368 10399 18337 22 16 \n", + "20 202425 7 11174 8039 14309 17 12 \n", + "21 202424 7 12621 9357 15885 19 14 \n", + "22 202423 7 14657 11339 17975 22 17 \n", + "23 202422 7 11628 8361 14895 17 12 \n", + "24 202421 7 9701 6851 12551 15 11 \n", + "25 202420 7 13661 10209 17113 20 15 \n", + "26 202419 7 10083 6413 13753 15 9 \n", + "27 202418 7 13438 9514 17362 20 14 \n", + "28 202417 7 15303 11219 19387 23 17 \n", + "29 202416 7 18138 13540 22736 27 20 \n", + "... ... ... ... ... ... ... ... \n", + "1741 199126 7 17608 11304 23912 31 20 \n", + "1742 199125 7 16169 10700 21638 28 18 \n", + "1743 199124 7 16171 10071 22271 28 17 \n", + "1744 199123 7 11947 7671 16223 21 13 \n", + "1745 199122 7 15452 9953 20951 27 17 \n", + "1746 199121 7 14903 8975 20831 26 16 \n", + "1747 199120 7 19053 12742 25364 34 23 \n", + "1748 199119 7 16739 11246 22232 29 19 \n", + "1749 199118 7 21385 13882 28888 38 25 \n", + "1750 199117 7 13462 8877 18047 24 16 \n", + "1751 199116 7 14857 10068 19646 26 18 \n", + "1752 199115 7 13975 9781 18169 25 18 \n", + "1753 199114 7 12265 7684 16846 22 14 \n", + "1754 199113 7 9567 6041 13093 17 11 \n", + "1755 199112 7 10864 7331 14397 19 13 \n", + "1756 199111 7 15574 11184 19964 27 19 \n", + "1757 199110 7 16643 11372 21914 29 20 \n", + "1758 199109 7 13741 8780 18702 24 15 \n", + "1759 199108 7 13289 8813 17765 23 15 \n", + "1760 199107 7 12337 8077 16597 22 15 \n", + "1761 199106 7 10877 7013 14741 19 12 \n", + "1762 199105 7 10442 6544 14340 18 11 \n", + "1763 199104 7 7913 4563 11263 14 8 \n", + "1764 199103 7 15387 10484 20290 27 18 \n", + "1765 199102 7 16277 11046 21508 29 20 \n", + "1766 199101 7 15565 10271 20859 27 18 \n", + "1767 199052 7 19375 13295 25455 34 23 \n", + "1768 199051 7 19080 13807 24353 34 25 \n", + "1769 199050 7 11079 6660 15498 20 12 \n", + "1770 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 10 FR France \n", + "1 5 FR France \n", + "2 5 FR France \n", + "3 6 FR France \n", + "4 5 FR France \n", + "5 5 FR France \n", + "6 6 FR France \n", + "7 2 FR France \n", + "8 2 FR France \n", + "9 5 FR France \n", + "10 4 FR France \n", + "11 7 FR France \n", + "12 5 FR France \n", + "13 11 FR France \n", + "14 10 FR France \n", + "15 15 FR France \n", + "16 18 FR France \n", + "17 18 FR France \n", + "18 20 FR France \n", + "19 28 FR France \n", + "20 22 FR France \n", + "21 24 FR France \n", + "22 27 FR France \n", + "23 22 FR France \n", + "24 19 FR France \n", + "25 25 FR France \n", + "26 21 FR France \n", + "27 26 FR France \n", + "28 29 FR France \n", + "29 34 FR France \n", + "... ... ... ... \n", + "1741 42 FR France \n", + "1742 38 FR France \n", + "1743 39 FR France \n", + "1744 29 FR France \n", + "1745 37 FR France \n", + "1746 36 FR France \n", + "1747 45 FR France \n", + "1748 39 FR France \n", + "1749 51 FR France \n", + "1750 32 FR France \n", + "1751 34 FR France \n", + "1752 32 FR France \n", + "1753 30 FR France \n", + "1754 23 FR France \n", + "1755 25 FR France \n", + "1756 35 FR France \n", + "1757 38 FR France \n", + "1758 33 FR France \n", + "1759 31 FR France \n", + "1760 29 FR France \n", + "1761 26 FR France \n", + "1762 25 FR France \n", + "1763 20 FR France \n", + "1764 36 FR France \n", + "1765 38 FR France \n", + "1766 36 FR France \n", + "1767 45 FR France \n", + "1768 43 FR France \n", + "1769 28 FR France \n", + "1770 5 FR France \n", + "\n", + "[1771 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.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": 6, + "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": 7, + "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
.................................
2024-04-15/2024-04-212024167181381354022736272034FRFrance
2024-04-22/2024-04-282024177153031121919387231729FRFrance
2024-04-29/2024-05-05202418713438951417362201426FRFrance
2024-05-06/2024-05-1220241971008364131375315921FRFrance
2024-05-13/2024-05-192024207136611020917113201525FRFrance
2024-05-20/2024-05-2620242179701685112551151119FRFrance
2024-05-27/2024-06-02202422711628836114895171222FRFrance
2024-06-03/2024-06-092024237146571133917975221727FRFrance
2024-06-10/2024-06-16202424712621935715885191424FRFrance
2024-06-17/2024-06-23202425711174803914309171222FRFrance
2024-06-24/2024-06-302024267143681039918337221628FRFrance
2024-07-01/2024-07-07202427710247709013404151020FRFrance
2024-07-08/2024-07-1420242879364649812230141018FRFrance
2024-07-15/2024-07-2120242979270630312237141018FRFrance
2024-07-22/2024-07-28202430770044278973011715FRFrance
2024-07-29/2024-08-0420243174500221367877410FRFrance
2024-08-05/2024-08-1120243274399194468547311FRFrance
2024-08-12/2024-08-18202433719715363406315FRFrance
2024-08-19/2024-08-25202434725606224498417FRFrance
2024-08-26/2024-09-01202435716202852955204FRFrance
2024-09-02/2024-09-08202436722358703600315FRFrance
2024-09-09/2024-09-152024377916281804102FRFrance
2024-09-16/2024-09-22202438775101513102FRFrance
2024-09-23/2024-09-292024397289813334463426FRFrance
2024-09-30/2024-10-06202440721257253525315FRFrance
2024-10-07/2024-10-13202441720353813689315FRFrance
2024-10-14/2024-10-202024427262112463996426FRFrance
2024-10-21/2024-10-27202443721246413607315FRFrance
2024-10-28/2024-11-03202444722086923724315FRFrance
2024-11-04/2024-11-1020244573899152362756210FRFrance
\n", + "

1771 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", + "2024-04-15/2024-04-21 202416 7 18138 13540 22736 27 \n", + "2024-04-22/2024-04-28 202417 7 15303 11219 19387 23 \n", + "2024-04-29/2024-05-05 202418 7 13438 9514 17362 20 \n", + "2024-05-06/2024-05-12 202419 7 10083 6413 13753 15 \n", + "2024-05-13/2024-05-19 202420 7 13661 10209 17113 20 \n", + "2024-05-20/2024-05-26 202421 7 9701 6851 12551 15 \n", + "2024-05-27/2024-06-02 202422 7 11628 8361 14895 17 \n", + "2024-06-03/2024-06-09 202423 7 14657 11339 17975 22 \n", + "2024-06-10/2024-06-16 202424 7 12621 9357 15885 19 \n", + "2024-06-17/2024-06-23 202425 7 11174 8039 14309 17 \n", + "2024-06-24/2024-06-30 202426 7 14368 10399 18337 22 \n", + "2024-07-01/2024-07-07 202427 7 10247 7090 13404 15 \n", + "2024-07-08/2024-07-14 202428 7 9364 6498 12230 14 \n", + "2024-07-15/2024-07-21 202429 7 9270 6303 12237 14 \n", + "2024-07-22/2024-07-28 202430 7 7004 4278 9730 11 \n", + "2024-07-29/2024-08-04 202431 7 4500 2213 6787 7 \n", + "2024-08-05/2024-08-11 202432 7 4399 1944 6854 7 \n", + "2024-08-12/2024-08-18 202433 7 1971 536 3406 3 \n", + "2024-08-19/2024-08-25 202434 7 2560 622 4498 4 \n", + "2024-08-26/2024-09-01 202435 7 1620 285 2955 2 \n", + "2024-09-02/2024-09-08 202436 7 2235 870 3600 3 \n", + "2024-09-09/2024-09-15 202437 7 916 28 1804 1 \n", + "2024-09-16/2024-09-22 202438 7 751 0 1513 1 \n", + "2024-09-23/2024-09-29 202439 7 2898 1333 4463 4 \n", + "2024-09-30/2024-10-06 202440 7 2125 725 3525 3 \n", + "2024-10-07/2024-10-13 202441 7 2035 381 3689 3 \n", + "2024-10-14/2024-10-20 202442 7 2621 1246 3996 4 \n", + "2024-10-21/2024-10-27 202443 7 2124 641 3607 3 \n", + "2024-10-28/2024-11-03 202444 7 2208 692 3724 3 \n", + "2024-11-04/2024-11-10 202445 7 3899 1523 6275 6 \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", + "2024-04-15/2024-04-21 20 34 FR France \n", + "2024-04-22/2024-04-28 17 29 FR France \n", + "2024-04-29/2024-05-05 14 26 FR France \n", + "2024-05-06/2024-05-12 9 21 FR France \n", + "2024-05-13/2024-05-19 15 25 FR France \n", + "2024-05-20/2024-05-26 11 19 FR France \n", + "2024-05-27/2024-06-02 12 22 FR France \n", + "2024-06-03/2024-06-09 17 27 FR France \n", + "2024-06-10/2024-06-16 14 24 FR France \n", + "2024-06-17/2024-06-23 12 22 FR France \n", + "2024-06-24/2024-06-30 16 28 FR France \n", + "2024-07-01/2024-07-07 10 20 FR France \n", + "2024-07-08/2024-07-14 10 18 FR France \n", + "2024-07-15/2024-07-21 10 18 FR France \n", + "2024-07-22/2024-07-28 7 15 FR France \n", + "2024-07-29/2024-08-04 4 10 FR France \n", + "2024-08-05/2024-08-11 3 11 FR France \n", + "2024-08-12/2024-08-18 1 5 FR France \n", + "2024-08-19/2024-08-25 1 7 FR France \n", + "2024-08-26/2024-09-01 0 4 FR France \n", + "2024-09-02/2024-09-08 1 5 FR France \n", + "2024-09-09/2024-09-15 0 2 FR France \n", + "2024-09-16/2024-09-22 0 2 FR France \n", + "2024-09-23/2024-09-29 2 6 FR France \n", + "2024-09-30/2024-10-06 1 5 FR France \n", + "2024-10-07/2024-10-13 1 5 FR France \n", + "2024-10-14/2024-10-20 2 6 FR France \n", + "2024-10-21/2024-10-27 1 5 FR France \n", + "2024-10-28/2024-11-03 1 5 FR France \n", + "2024-11-04/2024-11-10 2 10 FR France \n", + "\n", + "[1771 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": [ + "We check the consistency of the data. Between the end of a period and the beginning of the next one, the difference should be zero, or very small. We tolerate an error of one second.\n", + "\n", + "This is OK except for one pair of consecutive periods between which a whole week is missing.\n", + "\n", + "We recognize the dates: it's the week without observations that we have deleted earlier!" + ] + }, + { + "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\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": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "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": [ + "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": "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": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "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 between calendar years, we define the reference period for the annual incidence from August 1st of year 𝑁\n", + " to August 1st of year 𝑁+1\n", + " . We label this period as year 𝑁+1\n", + " because the peak is always located in year 𝑁+1\n", + " . The very low incidence in summer ensures that the arbitrariness 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 integer number of weeks. Therefore we modify our reference period a bit: 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 incomplete, We start the analysis with the first full peak." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\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": 17, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2023 366227\n", + "2021 376290\n", + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2022 641397\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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https:\/\/www.sentiweb.fr","meta":{"period":[198444,202445],"geo":["PAY",0],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2024-11-13T18:41:04+01:00","bundle":"1731519663"} +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202445,3,61119,51039,71199,92,77,107,FR,France +202444,3,36671,30600,42742,55,46,64,FR,France +202443,3,46572,39928,53216,70,60,80,FR,France +202442,3,67785,60009,75561,102,90,114,FR,France +202441,3,79435,71386,87484,119,107,131,FR,France +202440,3,84965,76555,93375,127,114,140,FR,France +202439,3,91660,82937,100383,137,124,150,FR,France +202438,3,91786,82903,100669,138,125,151,FR,France +202437,3,56460,49319,63601,85,74,96,FR,France +202436,3,33657,27906,39408,50,41,59,FR,France +202435,3,27404,22036,32772,41,33,49,FR,France +202434,3,26717,21003,32431,40,31,49,FR,France +202433,3,20623,15349,25897,31,23,39,FR,France 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