diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 540b20939cfeebc96cda4e28b191f190beb383df..26cbfc47160cc4370a72cac5c34c104b20b9a626 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1045,7 +1045,7 @@ "[1539 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1064,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -1111,7 +1111,7 @@ "Index: []" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1136,7 +1136,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -1164,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1197,16 +1197,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -1236,16 +1236,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -1270,14 +1270,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Etude de la variabilité moyenne sur un an.\n", + "## Etude de la variabilité moyenne sur un an\n", "\n", "contrairement à la grippe il n'y a pas de période large où l'on peut dire qu'il n'y a rien. Il y a bien des chutes importantes vers le 3e trimestre mais il faudra être plus précis pour découper les données en années." ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1286,13 +1286,13 @@ "[]" ] }, - "execution_count": 44, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] @@ -1307,9 +1307,9 @@ "import statistics\n", "\n", "weeks_t = dict()\n", - "for i in range(len(sorted_data[\"week\"])):\n", - " num_week = int(str(sorted_data[\"week\"][i])[-2:])\n", - " num_inc = int(sorted_data[\"inc\"][i])\n", + "for i in range(len(raw_data[\"week\"])): # sorted_data cause des répétitions et suppressions de lignes très bizares, de troutes façon on a pas besoin d'avoir un tri donc on ne trie pas.\n", + " num_week = int(str(raw_data[\"week\"][i])[-2:])\n", + " num_inc = int(raw_data[\"inc\"][i])\n", " if not num_week in weeks_t:\n", " weeks_t[num_week] = []\n", " weeks_t[num_week].append(num_inc)\n", @@ -1331,17 +1331,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Le minimum est de 2896.9 durant la semaine 36.\n", - "Semaine 35 : 3435.7\n", - "Semaine 36 : 2896.9\n", - "Semaine 37 : 2912.3\n" + "Le minimum est de 2631.9 durant la semaine 36.\n", + "Semaine 35 : 3182.0\n", + "Semaine 36 : 2631.9\n", + "Semaine 37 : 2655.5\n" ] } ], @@ -1364,6 +1364,298 @@ "source": [ "On va donc couper l'année entre la semaine 36 et 37." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle (coupe après la semaine 36)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L'année 2019 n'a que 38 semaines avec des données.\n", + "2018 : 584834 cas\n", + "2017 : 538421 cas\n", + "2016 : 553188 cas\n", + "2015 : 781437 cas\n", + "2014 : 605469 cas\n", + "2013 : 681567 cas\n", + "2012 : 697854 cas\n", + "2011 : 622706 cas\n", + "2010 : 644204 cas\n", + "2009 : 835706 cas\n", + "2008 : 841233 cas\n", + "2007 : 747248 cas\n", + "2006 : 720969 cas\n", + "2005 : 627774 cas\n", + "2004 : 629836 cas\n", + "2003 : 778914 cas\n", + "2002 : 758094 cas\n", + "2001 : 517623 cas\n", + "2000 : 614635 cas\n", + "1999 : 621746 cas\n", + "1998 : 753288 cas\n", + "1997 : 681363 cas\n", + "1996 : 679000 cas\n", + "1995 : 567254 cas\n", + "1994 : 651659 cas\n", + "1993 : 661527 cas\n", + "1992 : 646135 cas\n", + "1991 : 831882 cas\n", + "L'année 1990 n'a que 40 semaines avec des données.\n" + ] + } + ], + "source": [ + "cas_par_annee_t = dict()\n", + "for i in range(len(raw_data[\"week\"])): # Usage de raw_data particulièrement critique ici\n", + " num_year = int(str(raw_data[\"week\"][i])[:4])\n", + " num_week = int(str(raw_data[\"week\"][i])[-2:])\n", + " num_inc = int(raw_data[\"inc\"][i])\n", + "\n", + " # Fin de l'année précédente ?\n", + " if num_week <= week_min:\n", + " num_year = num_year - 1\n", + "\n", + " #print(\"%d-%d => %d : %d\" % (int(str(raw_data[\"week\"][i])[:4]), num_week, num_year, num_inc))\n", + "\n", + " # Rajoute aux cas\n", + " if not num_year in cas_par_annee_t:\n", + " cas_par_annee_t[num_year] = []\n", + " cas_par_annee_t[num_year].append(num_inc)\n", + "\n", + "# Sommes par année\n", + "cas_par_annee = dict()\n", + "for year in cas_par_annee_t:\n", + " nb_sem = len(cas_par_annee_t[year])\n", + " #print(cas_par_annee_t[year])\n", + " if nb_sem >= 51:\n", + " somme = sum(cas_par_annee_t[year])\n", + " print(\"%d : %d cas\" % (year, somme))\n", + " cas_par_annee[year] = somme\n", + " else:\n", + " print(\"L'année %d n'a que %d semaines avec des données.\" % (year, nb_sem))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(list(cas_par_annee.keys()), list(cas_par_annee.values()), align='center')\n", + "plt.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calcul des extrèmes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le minimum est de 517623 durant l'année' 2001-2002.\n", + "Le maximum est de 841233 durant l'année' 2008-2009.\n" + ] + } + ], + "source": [ + "val_annee_min = min(cas_par_annee.values())\n", + "val_annee_max = max(cas_par_annee.values())\n", + "annee_min = None\n", + "annee_max = None\n", + "for annee in cas_par_annee:\n", + " if val_annee_min == cas_par_annee[annee]:\n", + " annee_min = annee\n", + " if val_annee_max == cas_par_annee[annee]:\n", + " annee_max = annee\n", + "\n", + "print(\"Le minimum est de %d durant l'année' %i-%i.\" % (val_annee_min, annee_min, annee_min+1))\n", + "print(\"Le maximum est de %d durant l'année' %i-%i.\" % (val_annee_max, annee_max, annee_max+1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sauf que voila ... en fait il fallait couper au 1er septembre. Bon, du coup je vais pouvoir reprendre le code préexistant que le syndrôme gripal. En plus c'est mieux vu ma maitrise de ces libs ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle (coupe au 1er septembre)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On reprend donc le code du syndrome grippal mais cette fois il y a un bug, l'assertion ne passe pas car l'année 1990 à des données pour 38 semaines. Je l'ai donc exclue de l'analyse." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1990,\n", + " sorted_data.index[-1].year)]\n", + "\n", + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1], first_sept_week[1:]):\n", + " if week1.year != 1990: # Année 1990 exclue car données partielles.\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2, \"L'année %d à des données pour %d semaines !\" % (week1.year, len(one_year))\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": [ + "On regarde les 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut maintenant faire une liste des incidence par nombre de cas par année :" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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", + "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": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On voit l'année avec le minimum et le maximum." + ] } ], "metadata": { diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..c6f02f6f7b4b693b04a99b716e995581b4d8254d 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,13 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +24,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -