{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence du syndrome grippal\n", "\n", "Dans un premier temps nous allons inspecter les données et dans un deuxième temps nous allons les analyser et en traire une conclusion. \n", "**Remember the first step is to take a manual look at all the data all together!!**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Importation des bibliothèques principales\n", " #Demander à python de garder les fichier inside the document and not on outside widows.\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202011310170493652.0109756.0154142.0166.0FRFrance
1202010310497796650.0113304.0159146.0172.0FRFrance
22020093110696102066.0119326.0168155.0181.0FRFrance
32020083143753133984.0153522.0218203.0233.0FRFrance
42020073183610172812.0194408.0279263.0295.0FRFrance
.................................
184119844837862060634.096606.0143110.0176.0FRFrance
184219844737202954274.089784.013199.0163.0FRFrance
184319844638733067686.0106974.0159123.0195.0FRFrance
18441984453135223101414.0169032.0246184.0308.0FRFrance
184519844436842220056.0116788.012537.0213.0FRFrance
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1846 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", "... ... ... ... ... ... ... ... \n", "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 166.0 FR France \n", "1 172.0 FR France \n", "2 181.0 FR France \n", "3 233.0 FR France \n", "4 295.0 FR France \n", "... ... ... ... \n", "1841 176.0 FR France \n", "1842 163.0 FR France \n", "1843 195.0 FR France \n", "1844 308.0 FR France \n", "1845 213.0 FR France \n", "\n", "[1846 rows x 10 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_url= \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "raw_data = pd.read_csv(data_url, skiprows=1) #Otherwise unable to read the first raw which is messy\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing data wrangling\n", "Now that we have seen a line with missing data we are going to supress it well8" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202011310170493652.0109756.0154142.0166.0FRFrance
1202010310497796650.0113304.0159146.0172.0FRFrance
22020093110696102066.0119326.0168155.0181.0FRFrance
32020083143753133984.0153522.0218203.0233.0FRFrance
42020073183610172812.0194408.0279263.0295.0FRFrance
.................................
184119844837862060634.096606.0143110.0176.0FRFrance
184219844737202954274.089784.013199.0163.0FRFrance
184319844638733067686.0106974.0159123.0195.0FRFrance
18441984453135223101414.0169032.0246184.0308.0FRFrance
184519844436842220056.0116788.012537.0213.0FRFrance
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1845 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", "... ... ... ... ... ... ... ... \n", "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 166.0 FR France \n", "1 172.0 FR France \n", "2 181.0 FR France \n", "3 233.0 FR France \n", "4 295.0 FR France \n", "... ... ... ... \n", "1841 176.0 FR France \n", "1842 163.0 FR France \n", "1843 195.0 FR France \n", "1844 308.0 FR France \n", "1845 213.0 FR France \n", "\n", "[1845 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#raw_data[raw_data.dropna().copy()]\n", "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque isoweek.Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2020-03-09/2020-03-15\n", "1 2020-03-02/2020-03-08\n", "2 2020-02-24/2020-03-01\n", "3 2020-02-17/2020-02-23\n", "4 2020-02-10/2020-02-16\n", " ... \n", "1841 1984-11-26/1984-12-02\n", "1842 1984-11-19/1984-11-25\n", "1843 1984-11-12/1984-11-18\n", "1844 1984-11-05/1984-11-11\n", "1845 1984-10-29/1984-11-04\n", "Name: period, Length: 1845, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def convert_week(year_week_int):\n", " year_week_str= str(year_week_int)\n", " year = int(year_week_str[:4])\n", " week = int(year_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']]\n", "data['period']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Sort data to make human sense\n", "sorted_data= data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "#Verify that each of the period is subsequent to the other\n", "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": [ "Ceci est du au fait que nous avons enlevé une semaine de l'année 1989 parce que nous trouvions pas de données pertinentes. Nous avons ici un bon exemple d'elimination des donnéees qui est pertinante'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "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": [ "## Questions et réponses\n", "\n", "### Quelles ont été les épidémies les plus fortes ? \n", "\n", "Epidemics are calculated in yearly incidence not in added months so the problem is to find a convention to define and add up all the weekly incidences. Here is one way " ] }, { "cell_type": "code", "execution_count": 9, "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": "code", "execution_count": 10, "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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": [ "yearly_incidence.plot(style=\"*\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quelle est la distribution des épidémies? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }