{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os\n", "import urllib.request\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", "data_file = \"syndrome_vericelle.csv\"\n", "if os.path.exists(data_file):\n", " pass\n", "else:\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data = pd.read_csv(data_file, skiprows=1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020220479487665812316141018FRFrance
12022037139721068017264211626FRFrance
22022027849560261096413917FRFrance
32022017137931059716989211626FRFrance
4202152713239961116867201525FRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202204 7 9487 6658 12316 14 10 18 \n", "1 202203 7 13972 10680 17264 21 16 26 \n", "2 202202 7 8495 6026 10964 13 9 17 \n", "3 202201 7 13793 10597 16989 21 16 26 \n", "4 202152 7 13239 9611 16867 20 15 25 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
16211991017155651027120859271836FRFrance
16221990527193751329525455342345FRFrance
16231990517190801380724353342543FRFrance
1624199050711079666015498201228FRFrance
16251990497114302610205FRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1621 199101 7 15565 10271 20859 27 18 \n", "1622 199052 7 19375 13295 25455 34 23 \n", "1623 199051 7 19080 13807 24353 34 25 \n", "1624 199050 7 11079 6660 15498 20 12 \n", "1625 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "1621 36 FR France \n", "1622 45 FR France \n", "1623 43 FR France \n", "1624 28 FR France \n", "1625 5 FR France " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "week int64\n", "indicator int64\n", "inc int64\n", "inc_low int64\n", "inc_up int64\n", "inc100 int64\n", "inc100_low int64\n", "inc100_up int64\n", "geo_insee object\n", "geo_name object\n", "dtype: object\n" ] }, { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_up
count1626.0000001626.01626.0000001626.0000001626.0000001626.0000001626.0000001626.000000
mean200626.7207877.012433.8394837840.61623617064.53075020.40651912.86531428.009840
std899.6915900.06628.8654345146.6379978398.87272510.9977428.52686313.953634
min199049.0000007.0161.0000000.000000597.0000000.0000000.0000001.000000
25%199838.2500007.07162.2500003514.75000010573.25000012.0000006.00000017.000000
50%200626.5000007.012366.0000007760.00000017028.00000020.00000013.00000028.000000
75%201415.7500007.016966.75000011521.75000022474.25000028.00000019.00000037.000000
max202204.0000007.036298.00000025490.00000054240.00000061.00000044.00000090.000000
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" ], "text/plain": [ " week indicator inc inc_low inc_up \\\n", "count 1626.000000 1626.0 1626.000000 1626.000000 1626.000000 \n", "mean 200626.720787 7.0 12433.839483 7840.616236 17064.530750 \n", "std 899.691590 0.0 6628.865434 5146.637997 8398.872725 \n", "min 199049.000000 7.0 161.000000 0.000000 597.000000 \n", "25% 199838.250000 7.0 7162.250000 3514.750000 10573.250000 \n", "50% 200626.500000 7.0 12366.000000 7760.000000 17028.000000 \n", "75% 201415.750000 7.0 16966.750000 11521.750000 22474.250000 \n", "max 202204.000000 7.0 36298.000000 25490.000000 54240.000000 \n", "\n", " inc100 inc100_low inc100_up \n", "count 1626.000000 1626.000000 1626.000000 \n", "mean 20.406519 12.865314 28.009840 \n", "std 10.997742 8.526863 13.953634 \n", "min 0.000000 0.000000 1.000000 \n", "25% 12.000000 6.000000 17.000000 \n", "50% 20.000000 13.000000 28.000000 \n", "75% 28.000000 19.000000 37.000000 \n", "max 61.000000 44.000000 90.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(data.dtypes)\n", "data.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missing_data = data.loc[data.isna().any(axis=1), :]\n", "missing_data" ] }, { "cell_type": "markdown", "metadata": { "hidePrompt": false }, "source": [ "We confirmed that there was no missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing week format: from YYYYWW to time period" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def from_int_to_period(weekdate):\n", " date = str(weekdate)\n", " date = isoweek.Week(int(date[:4]), int(date[4:]))\n", " return pd.Period(date.day(0), \"W\")\n", "\n", "period = data.loc[:, \"week\"].apply(from_int_to_period)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "data.loc[:, \"week\"] = period\n", "data = data.rename(columns={\"week\": \"period\"})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index(\"period\").sort_index()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-097114302610205FRFrance
1990-12-10/1990-12-16711079666015498201228FRFrance
1990-12-17/1990-12-237190801380724353342543FRFrance
1990-12-24/1990-12-307193751329525455342345FRFrance
1990-12-31/1991-01-067155651027120859271836FRFrance
\n", "
" ], "text/plain": [ " indicator inc inc_low inc_up inc100 inc100_low \\\n", "period \n", "1990-12-03/1990-12-09 7 1143 0 2610 2 0 \n", "1990-12-10/1990-12-16 7 11079 6660 15498 20 12 \n", "1990-12-17/1990-12-23 7 19080 13807 24353 34 25 \n", "1990-12-24/1990-12-30 7 19375 13295 25455 34 23 \n", "1990-12-31/1991-01-06 7 15565 10271 20859 27 18 \n", "\n", " inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 5 FR France \n", "1990-12-10/1990-12-16 28 FR France \n", "1990-12-17/1990-12-23 43 FR France \n", "1990-12-24/1990-12-30 45 FR France \n", "1990-12-31/1991-01-06 36 FR France " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
2021-12-27/2022-01-02713239961116867201525FRFrance
2022-01-03/2022-01-097137931059716989211626FRFrance
2022-01-10/2022-01-167849560261096413917FRFrance
2022-01-17/2022-01-237139721068017264211626FRFrance
2022-01-24/2022-01-3079487665812316141018FRFrance
\n", "
" ], "text/plain": [ " indicator inc inc_low inc_up inc100 inc100_low \\\n", "period \n", "2021-12-27/2022-01-02 7 13239 9611 16867 20 15 \n", "2022-01-03/2022-01-09 7 13793 10597 16989 21 16 \n", "2022-01-10/2022-01-16 7 8495 6026 10964 13 9 \n", "2022-01-17/2022-01-23 7 13972 10680 17264 21 16 \n", "2022-01-24/2022-01-30 7 9487 6658 12316 14 10 \n", "\n", " inc100_up geo_insee geo_name \n", "period \n", "2021-12-27/2022-01-02 25 FR France \n", "2022-01-03/2022-01-09 26 FR France \n", "2022-01-10/2022-01-16 17 FR France \n", "2022-01-17/2022-01-23 26 FR France \n", "2022-01-24/2022-01-30 18 FR France " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checking data periods" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "for p1, p2 in zip(sorted_data.index[:-1], sorted_data.index[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, there is only one instance where the time difference between the end and start of 2 consecutive periods is > 0: it corresponds to the week that was removed from the dataset due to missing values.\n", "Everything seems consistent, we can now analyze the sorted dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Analysis" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), \"W\") for y in range(1991, sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "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": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1992 832939\n", "1993 643387\n", "1994 661409\n", "1995 652478\n", "1996 564901\n", "1997 683434\n", "1998 677775\n", "1999 756456\n", "2000 617597\n", "2001 619041\n", "2002 516689\n", "2003 758363\n", "2004 777388\n", "2005 628464\n", "2006 632833\n", "2007 717352\n", "2008 749478\n", "2009 842373\n", "2010 829911\n", "2011 642368\n", "2012 624573\n", "2013 698332\n", "2014 685769\n", "2015 604382\n", "2016 782114\n", "2017 551041\n", "2018 542312\n", "2019 584066\n", "2020 221186\n", "2021 376290\n", "dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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