From b4243da3b8e0de8e5d64cbc83c66ed8e4420da02 Mon Sep 17 00:00:00 2001 From: f7c25f85c0dbc37b267c9f7a62882323 Date: Wed, 6 May 2020 19:44:53 +0000 Subject: [PATCH] Exercice Module 3 : sujet 2 le pouvoir d'achat des ouvriers anglais --- module3/exo1/analyse-syndrome-grippal.ipynb | 89 ++++- module3/exo3/exercice_fr.ipynb | 391 +++++++++++++++++++- 2 files changed, 461 insertions(+), 19 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 667a71e..8e9e3c2 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ "data_csv=\"./incidence-PAY-3.csv\"\n", "if not os.path.isfile(data_csv):\n", " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", - " dataset=pd.read_csv(data_url, skiprows=1)\n", + " dataset=pd.read_csv(data_url)\n", " dataset.to_csv(data_csv)" ] }, @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1098,7 +1098,7 @@ "[1849 rows x 11 columns]" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1117,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1178,7 +1178,7 @@ "1612 1613 198919 3 0 NaN NaN 0 NaN NaN FR France" ] }, - "execution_count": 10, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1196,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2223,7 +2223,7 @@ "[1848 rows x 11 columns]" ] }, - "execution_count": 11, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2253,9 +2253,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'week'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'week'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'W'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'week'\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/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\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/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'week'" + ] + } + ], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", @@ -2283,11 +2312,39 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'period'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'period'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\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[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_index\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/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m 3144\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3145\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3146\u001b[0;31m \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3147\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3148\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdrop\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/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\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/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'period'" + ] + } + ], "source": [ "sorted_data = data.set_index('period').sort_index()" ] diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 0bbbe37..5e25e1f 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -1,5 +1,391 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 1 : reproduire le graphe de Playfair (une seule ordonnée)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lecture du fichier :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import pandas as pd\n", + "f='https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n", + "dataset = pd.read_csv(f, delimiter=\",\", header=0, index_col=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On visualise que le fichier est bien lu :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearWheatWages
1156541.05.00
2157045.05.05
3157542.05.08
4158049.05.12
5158541.55.15
\n", + "
" + ], + "text/plain": [ + " Year Wheat Wages\n", + "1 1565 41.0 5.00\n", + "2 1570 45.0 5.05\n", + "3 1575 42.0 5.08\n", + "4 1580 49.0 5.12\n", + "5 1585 41.5 5.15" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Réaliser le 1er graphique (histogramme + line, 1 seul axe) :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "\n", + "x=np.arange(len(dataset['Year']))\n", + "plt.bar(x,dataset['Wheat'],color='blue')\n", + "plt.plot(x,dataset['Wages'],color='red',linewidth=3)\n", + "plt.ylabel(\"Wheat\", multialignment='center',color='blue',fontsize=14)\n", + "plt.xlabel(\"Years\", multialignment='center',color='blue',fontsize=14)\n", + "plt.title(\"The price of the quarter of wheat & wages of labour by the week\")\n", + "plt.xticks(range(len(dataset['Year'])),list(dataset['Year']),rotation=90,fontsize=10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie2 : ajouter un second axe (avoir 2 ordonnées) :" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig=plt.figure(figsize=(16,10))\n", + "a1 = fig.add_axes([0,0,1,1])\n", + "plt.bar(x,dataset['Wheat'],color='blue')\n", + "plt.title(\"The price of the quarter of wheat & wages of labour by the week\",fontsize=16)\n", + "plt.xticks(range(len(dataset['Year'])),list(dataset['Year']),rotation=90,fontsize=10)\n", + "plt.xlabel(\"Years\", multialignment='center',color='black',fontsize=14)\n", + "a2 = a1.twinx()\n", + "a2.plot(x,dataset['Wages'],color='red',linewidth=3)\n", + "a1.set_ylabel(\"Wheat\", multialignment='center',color='blue',fontsize=16)\n", + "a2.set_ylabel(\"Wages\", multialignment='center',color='red',fontsize=16)\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partie 3 : Montrer autrement que le pouvoir d'achat des ouvriers a augmenté au cours du temps\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour chaque année, on a le prix d'une unité de blé et le salaire heddomadaire d'un ouvrier. Pour connaitre la quantité de blé qu'un ouvrier peut acheter avec son salaire, on divise ce dernier la prix d'une unité de blé. On obtient :" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 0.121951\n", + "2 0.112222\n", + "3 0.120952\n", + "4 0.104490\n", + "5 0.124096\n", + "6 0.111702\n", + "7 0.086563\n", + "8 0.207778\n", + "9 0.172424\n", + "10 0.180625\n", + "11 0.180000\n", + "12 0.171714\n", + "13 0.185455\n", + "14 0.138222\n", + "15 0.190909\n", + "16 0.163333\n", + "17 0.121698\n", + "18 0.154762\n", + "19 0.162963\n", + "20 0.145161\n", + "21 0.212500\n", + "22 0.186486\n", + "23 0.162791\n", + "24 0.208571\n", + "25 0.281481\n", + "26 0.200000\n", + "27 0.170000\n", + "28 0.300000\n", + "29 0.312500\n", + "30 0.250000\n", + "31 0.356061\n", + "32 0.431034\n", + "33 0.333333\n", + "34 0.511538\n", + "35 0.425000\n", + "36 0.518519\n", + "37 0.527273\n", + "38 0.483871\n", + "39 0.442254\n", + "40 0.532258\n", + "41 0.409302\n", + "42 0.393617\n", + "43 0.443182\n", + "44 0.456522\n", + "45 0.547619\n", + "46 0.536842\n", + "47 0.361842\n", + "48 0.360759\n", + "49 0.364198\n", + "50 0.303030\n", + "51 NaN\n", + "52 NaN\n", + "53 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[\"Wages\"]/dataset[\"Wheat\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et sous forme de graphique on obtient :" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "x=np.arange(len(dataset['Year']))\n", + "plt.bar(x,dataset[\"Wages\"]/dataset[\"Wheat\"],color='green')\n", + "plt.ylabel(\"Pouvoir d\\'achat\", multialignment='center',color='green',fontsize=14)\n", + "plt.xlabel(\"Years\", multialignment='center',color='green',fontsize=14)\n", + "plt.title(\"Le pouvoir d'achat des ouvriers\")\n", + "plt.xticks(range(len(dataset['Year'])),list(dataset['Year']),rotation=90,fontsize=10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une autre façon de montrer que le prix du blé et le salaire sont corrélés : Afficher le salaire en fonction du prix du blé avec un dégradé de couleur en fonction de l'année" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "x=np.arange(len(dataset['Year']))\n", + "COLORS = [plt.get_cmap('plasma')(i) for i in np.linspace(0,1,len(dataset))]\n", + "b1=plt.bar(x,dataset[\"Wages\"],color=COLORS,label=list(dataset['Year']))\n", + "plt.ylabel(\"Wages\", multialignment='center',color='magenta',fontsize=14)\n", + "plt.xlabel(\"Wheat\", multialignment='center',color='magenta',fontsize=14)\n", + "plt.title(\"Le salaire des ouvriers en fonction du prix du blé de 1565 à 1821\")\n", + "plt.xticks(range(len(dataset['Wheat'])),list(dataset['Wheat']),rotation=90,fontsize=10)\n", + "y=list(dataset['Year'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +402,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1