From 543580ed7b13d07aa3d84bbd1ccc1a63ffab92a6 Mon Sep 17 00:00:00 2001 From: fcf61da24638f93d44cd057b11a6abdc Date: Wed, 10 Dec 2025 13:56:56 +0000 Subject: [PATCH] Corrected to meet requirement --- module3/exo1/analyse-syndrome-grippal.ipynb | 470 +++++++++++--------- 1 file changed, 270 insertions(+), 200 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 9719176..4c6e3ed 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "# Incidence du syndrome grippal" @@ -12,10 +12,10 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 93, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -33,8 +33,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." @@ -42,10 +42,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 94, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -56,38 +56,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true - }, - "source": [ - "Afin de verifier que la copie locale est identique au valeurs disponible en ligne à l'URL fournis, on procede avec : \n", - "1. Les données sont elles encore disponible à l'URL indiqué `data_url`\n", - "2. Le hash (sha2556) de ces données est-il identique a celle deja presente en local `local_file`\n", - " 1. Si oui -> pas d'action\n", - " 2. Si non -> télecharger les nouvelles datas, il faudrat aussi indiquer que le fichier a ete mis à jour et conserver le numero de version avec `%y%m%d-sentinelle-syndrome-grippal.csv`" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "hideCode": true, - "hidePrompt": true - }, - "outputs": [], - "source": [ - "# fonctions permettant de calculer le sha256\n", - "def computesha256(content): \n", - " sh256 = hashlib.sha256()\n", - " sh256.update(content)\n", - " return sh256.hexdigest()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", @@ -108,12 +78,26 @@ "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Afin de verifier que la copie locale est disponible identique au valeurs disponible en ligne à l'URL fournis, on procede avec : \n", + "1. Les données sont elles encore disponible à l'URL indiqué `data_url`\n", + "2. Le hash (sha2556) de ces données est-il identique a celle deja presente en local `local_file`\n", + " 1. Si oui -> pas d'action\n", + " 2. Si non -> télecharger les nouvelles datas, il faudrat aussi indiquer que le fichier a ete mis à jour et conserver le numero de version avec `%y%m%d-sentinelle-syndrome-grippal.csv`" + ] + }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 125, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -121,24 +105,7 @@ "output_type": "stream", "text": [ "Detected 1 file(s) with pattern -sentinelle-syndrome-grippal.csv\n", - "Using latest : 20251210-sentinelle-syndrome-grippal.csv\n", - "Checking data version :\n", - "Local file sentinelle-syndrome-grippal.csv is identical to provided the one at provided URL, no need to update data\n", - "\n", - "RangeIndex: 2144 entries, 0 to 2143\n", - "Data columns (total 10 columns):\n", - "week 2144 non-null int64\n", - "indicator 2144 non-null int64\n", - "inc 2144 non-null object\n", - "inc_low 2143 non-null float64\n", - "inc_up 2143 non-null float64\n", - "inc100 2144 non-null object\n", - "inc100_low 2143 non-null float64\n", - "inc100_up 2143 non-null float64\n", - "geo_insee 2144 non-null object\n", - "geo_name 2144 non-null object\n", - "dtypes: float64(4), int64(2), object(4)\n", - "memory usage: 167.6+ KB\n" + "Using latest : ./20251210-sentinelle-syndrome-grippal.csv\n" ] }, { @@ -260,62 +227,44 @@ "4 FR France " ] }, - "execution_count": 80, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# checking if URL is still active \n", - "url_file = requests.get(data_url, timeout = 50)\n", - "assert url_file.raise_for_status() != 200, f'Provided URL is unreachable or returned {url_file.status_code}'\n", - "\n", - "# loading file from URL\n", - "url_content = url_file.content\n", - "\n", - "# compute sha256 \n", - "url_sha256 = computesha256(url_content)\n", - "\n", - "\n", "# detect latest version of local data \n", "files = glob.glob(f'*{local_file_pattern}')\n", "\n", - "\n", "if files == [] : \n", - " print(f'No local file detected using pattern {local_file_pattern}, ... Downloading up to date')\n", + " print(f'No local file detected using pattern {local_file_pattern}, ... Downloading up to date file')\n", + " \n", + " # checking if URL is still active \n", + " url_file = requests.get(data_url, timeout = 50)\n", + " assert url_file.raise_for_status() != 200, f'Provided URL is unreachable or returned {url_file.status_code}'\n", + "\n", + " # loading file from URL\n", + " url_content = url_file.content\n", + "\n", + "\n", " with open(f'{datetime.today().strftime(\"%Y%m%d\")}-{local_file_pattern}', 'wb') as f:\n", " f.write(url_content)\n", "else : \n", " print(f'Detected {len(files)} file(s) with pattern {local_file_pattern}')\n", " latest = max(files) \n", " print(f'Using latest : {latest}')\n", - " with open(latest, 'rb') as f:\n", - " data= f.read()\n", - " local_sha256 = computesha256(data)\n", - "\n", - " print('Checking data version :')\n", - " if url_sha256 == local_sha256 : \n", - " print(f'Local file {local_file} is identical to provided the one at provided URL, no need to update data')\n", - " raw_data = local_file\n", + " raw_data = latest\n", " \n", - " else : \n", - " print(f'The current file {local_file}, is not up to date compared to URL, ... Downloading the new one')\n", - " update = f'{datetime.today().strftime(\"%Y%m%d\")}-{local_file_pattern}'\n", - " with open(update, 'wb') as f:\n", - " f.write(url_content)\n", - " raw_data = update\n", - "\n", "raw_data = pd.read_csv(raw_data, encoding = 'iso-8859-1', skiprows=1) \n", "\n", - "raw_data.info()\n", "raw_data.head()" ] }, { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." @@ -323,10 +272,10 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 100, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -388,7 +337,7 @@ "1907 FR France " ] }, - "execution_count": 81, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -400,8 +349,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." @@ -409,10 +358,10 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 101, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -534,7 +483,7 @@ "4 FR France " ] }, - "execution_count": 83, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -547,8 +496,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", @@ -567,10 +516,10 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 102, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -587,8 +536,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Il restent deux petites modifications à faire.\n", @@ -603,10 +552,10 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 103, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -616,8 +565,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", @@ -634,10 +583,10 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 104, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { @@ -659,8 +608,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Reformater `inc` en integer car il a ete apellé en tant que object " @@ -668,21 +617,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "metadata": { - "collapsed": true, - "hideCode": true, - "hideOutput": true, - "hidePrompt": true + "hideCode": false, + "hideOutput": false, + "hidePrompt": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "68422" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data['inc']= pd.to_numeric(sorted_data['inc'])\n", + "sorted_data['inc'][0]" + ] }, { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Un premier regard sur les données !" @@ -690,27 +652,33 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 109, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { - "ename": "TypeError", - "evalue": "Empty 'DataFrame': no numeric data to plot", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -720,8 +688,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." @@ -729,27 +697,33 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 112, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { - "ename": "TypeError", - "evalue": "Empty 'DataFrame': no numeric data to plot", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -759,8 +733,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "## Etude de l'incidence annuelle" @@ -769,8 +743,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", @@ -792,11 +766,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": { - "collapsed": true, - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -808,8 +781,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", @@ -819,10 +792,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -840,8 +813,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Voici les incidences annuelles." @@ -849,12 +822,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 115, + "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='*')" ] @@ -862,8 +858,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." @@ -871,12 +867,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, - "outputs": [], + "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": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -884,8 +930,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", @@ -894,12 +940,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "metadata": { - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 119, + "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.hist(xrot=20)" ] @@ -909,14 +978,15 @@ "execution_count": null, "metadata": { "collapsed": true, - "hideCode": true, - "hidePrompt": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [] } ], "metadata": { + "celltoolbar": "Hide code", "hide_code_all_hidden": true, "kernelspec": { "display_name": "Python 3", -- 2.18.1