From 94bfef77986df2f9a1075011265a0548e6f35317 Mon Sep 17 00:00:00 2001 From: 26c634550904aba62520384fc8aa7dec <26c634550904aba62520384fc8aa7dec@app-learninglab.inria.fr> Date: Fri, 24 Apr 2020 11:17:16 +0000 Subject: [PATCH] no commit message --- module3/exo3/exercice.ipynb | 273 +++++++++++++++++++++++++++--------- 1 file changed, 207 insertions(+), 66 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index b17100c..5394988 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -2,7 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "# Exercice pratique : pouvoir d'achat en Angleterre\n", "\n", @@ -14,7 +17,10 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [], "source": [ "# Importation des librairies\n", @@ -26,7 +32,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Pour accéder aux données, je vais prévoir 2 versions\n", "- Soit une version locale chargée sur Gitlab\n", @@ -35,8 +44,11 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 2, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [], "source": [ "url_ext=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", @@ -45,7 +57,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Récupération des données\n", "La première ligne contient le nom des données , je la garde, ca va permetre de mettre directement les entetes pour le tableau panda." @@ -53,8 +68,11 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 3, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "name": "stdout", @@ -139,7 +157,7 @@ "4 5 1585 41.5 5.15" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -156,7 +174,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Le chargement à partir des données GIT ne fonctionne pas \n", "A regarder plus tard, surement un problème dans le lien !!\n", @@ -168,8 +189,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 4, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { @@ -271,7 +295,7 @@ "max 53.000000 1821.000000 99.000000 30.000000" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -282,15 +306,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "On va maintenant regarder si il y a des données manquantes." ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 5, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { @@ -352,7 +382,7 @@ "52 53 1821 54.0 NaN" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -363,7 +393,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Il y a 3 lignes avec des données manquantes...uniqument sur les salaires. On va donc les garder pour l'instant.\n", "On va donc supprimer la première colonne et paaser la colonne Year en index." @@ -371,8 +404,11 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "execution_count": 6, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { @@ -444,7 +480,7 @@ "1585 41.5 5.15" ] }, - "execution_count": 37, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -463,7 +499,10 @@ } }, "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "On va maintenant essayer de refaire un graphique qui ressemble à celui la :\n", "![image.png](attachment:image.png)\n", @@ -474,8 +513,9 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true, - "hideOutput": true + "hideCode": false, + "hideOutput": true, + "hidePrompt": false }, "source": [ "Première étape : graphique standard de Pandas\n", @@ -484,25 +524,21 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, + "execution_count": 7, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AxesSubplot(0.125,0.125;0.775x0.755)\n" - ] - }, { "data": { "text/plain": [ - "array([,\n", - " ],\n", + "array([,\n", + " ],\n", " dtype=object)" ] }, - "execution_count": 60, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -538,7 +574,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "On va maintenant essayer de travailler sur le graphique pour arriver à avoir ce que l'on veut !\n", "Après beaucoup de labeur, j'ai réussi à modifier certains élements du graphique grace au code ci-dessous.\n" @@ -546,16 +585,19 @@ }, { "cell_type": "code", - "execution_count": 67, - "metadata": {}, + "execution_count": 8, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 67, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -576,12 +618,15 @@ "graphique1 = plt.figure()\n", "ax_1_1 = graphique1.add_subplot(111)\n", "plt.ylabel('Livres')\n", - "sorted_data.plot(title= \"Evolution des salaires et du prix du blé\",ax=ax)\n" + "sorted_data.plot(title= \"Evolution des salaires et du prix du blé\",ax=ax_1_1)\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "On va maintenant essayer de raffiner !\n", "En n'utilisant pas les fonctions graphique des Panda mais en appelant directement matplotlib" @@ -589,24 +634,26 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 9, "metadata": { + "hideCode": false, + "hidePrompt": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 67, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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pQYiISColCBERSaUEISIiqSo+QeTrPbGhQ10K7ep78uTJnHDCCY1Sxocffph58+alzrvqqqvo2bMngwcPZsCAAUyaNCl1uUmTJnHttdc2SjwiUhkqPkEUWxZdfW8rQUBIWjNnzuTBBx/kvPPOY9OmTVvN37BhA2PGjOGKK65o1LhEpLwpQTSyQrr63n///YFwQ9w//dM/0a9fP84666zNN8vMmDGDww8/nKFDh3LMMcewePFiAG677TYOOOAAqqurOfXUU/noo4+YMmUKkyZN4tvf/jaDBw9mwYIFeWPr378/LVu2ZPny5ZxzzjlceumlHHnkkVx++eXccccdXHjhhQAsWbKEk08+merqaqqrqzeX5+6772b48OEMHjyYr3zlK2zcWLY9sotIAZQgGllaV98jRoxg6tSpTJ8+nUGDBtG6dWsAXnrpJW666SbmzZvHG2+8wXPPPcf69eu56KKLmDhxIjNmzOC8887je9/7HgCnnHIKL7zwArNmzaJ///7cfvvtHHzwwYwZM4brrruOmTNnstdee+WNbdq0abRo0YLck/j+/ve/8/jjj3PDDTdstdw3vvENDj/8cGbNmsWLL77Ifvvtx/z587n//vt57rnnmDlzJlVVVUyYMKGJfkURKQW6Ua4J1NXVd87w4cPp1asXAIMHD+bNN99k5513Zs6cOYwePRqAjRs30r17dwDmzJnDv/7rv7Jy5UrWrFnDMcccU1A8N954I3fffTft27fn/vvv33wu5bTTTqOqqupTyz/55JPcddddAFRVVdGxY0d++9vfMmPGDA444AAAPv74Y7p169bAX0hEyoESRBOoq6vvnDZt2mwer6qqYsOGDbg7++23H1OnTv3Ues855xwefvhhqqurueOOO5g8eXJB8VxyySVcdtlln5q+4447Flwmd2fcuHH88Ic/LPgzIlLe1MTUBOrq6ntb9tlnH5YtW7Y5Qaxfv37zSe7Vq1fTvXt31q9fv1XzTvv27Vm9enWjxT9q1ChuvvlmINRgVq1axahRo5g4cSJLly4F4L333tvcRbmIVKaKTxDu3qhDIerq6ntbWrduzcSJE7n88suprq5m8ODBm08SX3PNNYwYMYLRo0dv7h4c4IwzzuC6665j//333+ZJ6kL99Kc/5amnnmLgwIEMHTqUuXPnsu+++/KDH/yAz33ucwwaNIjRo0dvPnkuIpVJ3X1Lo9LvL5JfuXX3XfE1CBERaRglCBERSVWRCaKcm83KmX53kcpScQmibdu2rFixQjurInN3VqxYQdu2bbMORUQaScXdB9GrVy9qampYtmxZ1qE0O23btt1845+IlL+KSxCtWrWiT58+WYchIlL2Kq6JSUREGocShIiIpFKCEBGRVEoQIiKSSglCRERSKUGIiEgqJQgREUmVSYIws0vMbK6ZzTGze82srZl1MrO/mtlr8XWXLGITEZGg6AnCzHoC3wCGufsAoAo4A7gCeMLd+wJPxPciImXHzFKHcpNVE1NLoJ2ZtQR2AN4BxgJ3xvl3AidlFJuIiJBBgnD3RcD1wNvAYuADd/8LsKu7L47LLAa6pX3ezMab2XQzm67+lkREmk4WTUy7EGoLfYAewI5mdnahn3f3W919mLsP69q1a1OFKSLS7GXRxHQ08A93X+bu64HfAwcDS8ysO0B8XZpBbCIiEmWRIN4GDjSzHSyctRkFzAcmAePiMuOARzKITUSkXirhZHQ+Re/u292nmdlE4EVgA/AScCuwE/CAmZ1PSCKnFTs2ERHZIpPnQbj7fwD/UWvyOkJtQkSkWclX68j6yZi6k1pERFIpQYiISColCBERSVVxz6QWEWkKlXR1UqFUgxARkVRKECIikkoJQkREUilBiIhIKiUIERFJpQQhIiKplCBERCSVEoSIiKRSghARkVRKECIikkoJQkREUilBiIhIKiUIERFJpQQhIiKplCBERCSVEoSIiKRSghARkVRKECIikkoJQkREUilBiIhIKiUIERFJpQQhIiKplCBERCSVEoSIiKRqmXUAEpjZp6a5ewaRiIgEqkGIiEgqJQgREUmlBCEiIqmUIEREJFUmCcLMdjaziWb2ipnNN7ODzKyTmf3VzF6Lr7tkEZuIiARZ1SB+CvzJ3fsB1cB84ArgCXfvCzwR34uISEaKniDMrANwGHA7gLt/4u4rgbHAnXGxO4GTih2biIhskUUNYk9gGfAbM3vJzH5lZjsCu7r7YoD42i3tw2Y23symm9n0ZcuWFS9qEZFmJosE0RIYAtzs7vsDH1KP5iR3v9Xdh7n7sK5duzZVjCIizV5BCcLMLjazDhbcbmYvmtnnGvidNUCNu0+L7ycSEsYSM+sev687sLSB6xcRkUZQaA3iPHdfBXwO6AqcC1zbkC9093eBhWa2T5w0CpgHTALGxWnjgEcasn4REWkchfbFlOso6HjgN+4+y9I6DyrcRcAEM2sNvEFIOC2AB8zsfOBt4LTtWL+IiGynQhPEDDP7C9AHuNLM2gObGvql7j4TGJYya1RD1ykiIo2r0ARxPjAYeMPdPzKzzoSjfhERqVDbTBBmNqTWpD23r2VJRETKRV01iBvia1tgKPAy4XzEIGAacEjThSYiIlna5lVM7n6kux8JvAUMjfcfDAX2B14vRoAiIpKNQi9z7efus3Nv3H0O4ZyEiIhUqEJPUs83s18BdwMOnE3oYE9ERCpUoQniXOCrwMXx/TPAzU0SkYiIlISCEoS7rwVujIOIiBTBtq4adfcm//6CEoSZjQSuAvZIfsbd92yasEREJGuFNjHdDlwCzAA2Nl04UunyHREV42hIROqn0ATxgbv/sUkjERGRklJognjKzK4Dfg+sy0109xebJCoREclcoQliRHxNdrDnwFGNG46IiJSKQq9iOrKpAxERkdJS6FVM/5423d2vbtxwRESkVBTaxPRhYrwtcAK6k1okL12tJZWg0CamG5Lvzex6wiNCRUSkQhVag6htB0A3yaEjRRGpXIWeg5hNuGoJoAroCuj8g4hIBSu0BnFCYnwDsMTdNzRBPCIiUiIKPQfxlplVA4fGSc8Qni4nIlJR9FjlLQp6YJCZXQxMALrFYYKZXdSUgVUqM0sdRKRw+h8qjkKbmM4HRrj7hwBm9iNgKvDzpgpMRESyVWiCMLbuxXVjnCZSdLpyTKQ4Ck0QvwGmmdlD8f1JhC7ARURKmg4oGq7Qk9Q/MbPJwCGEmsO57v5SUwYmIiLZ2maCMLO2wAXAZ4HZwC8r/fJWHW2k0+8i0vzUdRXTnYQuvmcDxwHXN3lEIiJSEupqYtrX3QcCmNntwPNNH5KI5KTV3FRrS6dLXRtfXTWI9bmRSm9aEhGRrdVVg6g2s1Vx3IB28b0B7u4dmjQ6kSak8yqlT7WCbG0zQbh7VbECERGR0tLQ7r6bHR3JNC21tUux6X+6bgX1xdQUzKzKzF4ys0fj+05m9lczey2+7pJVbI1BfS6JSLnLLEEAF7P1Y0uvAJ5w977AE/F9k9IOXEQkv0wShJn1Aj4P/CoxeSzhvgvi60nFjktKi2phItnKqgZxE/AdYFNi2q7uvhggvnZL+6CZjTez6WY2fdmyZU0fqcg2KIlJJSt6gjCzE4Cl7j6jIZ9391vdfZi7D+vatWsjRyciIjlZXMU0EhhjZscDbYEOZnY3sMTMurv7YjPrDizNIDZpJDqKLh2630Maqug1CHe/0t17uXtv4AzgSXc/G5gEjIuLjQMeKXZsIiKyRZZXMdV2LTDazF4DRsf3IiKSkUxvlHP3ycDkOL4CGJVlPCIisoXupJaKp/MhIg1TSk1MIiJSQpQgREQklRKEiIik0jkIESka3ZNRXpQgmjH9s6ZT1+MigZqYREQklRKEiIikUoIQEZFUOgdRhup77qC+N4rVZ3ndhFZ8OnckxaIahIiIpFINooI096P5ci5/KcWuGorkqAYhIiKpVIMQkUbXlOe9pHiUIEREiqAjMBAYFIedgLMzjahuShAiRaQj5eajE3A6cDwhIeyRmPceMD2LoOpJCUJEpJG0BU4g1AyOA1oDfweeBX4JvByHd7IKsJ6UIEQqhGon2WgBHE5ICqcSmpIWAT8FJgCzsgttuylBiIg0wEBCUvgC0AtYBfwOuJvwHOVNmUXWeJQgREQK1BK4EDiXcF5hPfBH4FLgD8Da7EJrEkoQItJgzalZay/gHmA4MBX4GvAAsCLLoJqYEkQJa07/fKVO26J5/wbjgF8AnxDOM/w+23CKRndSi4jk0RG4D7gDeAGopvkkB1ANQqTZas41gkKMJFyF1BO4EvgxlXHiuT6UIEREavkCodbwNiFRPJ9pNNlRE5OISMKlhJrDs8BQmm9yACUIEREADLghDg8AxwIfZBpR9pQgRKTZa02oNVxKuAP6DMIVS82dzkGISLPWHngIGAV8B7gu23BKihKEiDRbg4B7gb7AvxC6yZAt1MQkIs2OARcTTkDvDByDkkMa1SBEpFnpRriE9ThgEnA+sDzLgEqYahAi0mwcB8wGjgC+CoxFyWFbip4gzGw3M3vKzOab2VwzuzhO72RmfzWz1+LrLsWOTUQqUy/g18D/AouBYcAtmUZUHrKoQWwAvuXu/YEDga+b2b7AFcAT7t4XeCK+FxFpsF2AHwGvEe6O/hEwApiXZVBlpOgJwt0Xu/uLcXw1MJ/Q3clY4M642J3AScWOTUQqQ1vCJasLgMsIHe7tTTjqXJdhXOUm05PUZtYb2B+YBuzq7oshJBEz65bnM+OB8QC77757cQIVkbLQGzgLuIDQrPQooaO9ORnGVM4yO0ltZjsRntD3TXdfVejn3P1Wdx/m7sO6du3adAGKSFnoREgIfwP+AfwA+DvhOdEnouSwPTKpQZhZK0JymODuue7Vl5hZ91h76A4szSI2ESl9HQk7/9MIfSa1BuYSmpDuARZmF1pFKXqCsNAJ/e3AfHf/SWLWJMKDm66Nr48UOzYRKV1dCCcmTyV0i9EKqAF+RrjJbVZ2oVWsLGoQIwl3tc82s5lx2ncJieEBMzuf0A37aRnEJiIlpC0hIZxHaDKqAt4AbiI0QTwPeGbRVb6iJwh3f5Zwp3uaUcWMRURK0wDgy8DZhHMMC4D/IiQF1RSKR11tiEhJ6Al8HjiXcIPUOsLzn28DJqOaQhaUIEQkEy0J7c3HAccDA+P0+YTnMtwFrMgmNImUIESkKIyQBI4AjoxDR2A94RLVy4A/orucS4kShIg0iR0I5xKGE5LB4UDnOG8B4bGe/0voV2d1FgFKnZQgRGS7GLAn4eE7uWEgsBdb7sR9g3Dd+lPA0+g+hXKhBCEiBfsMoU+jgWydDHaM8zcR7mKeSTiH8DLwEkoI5UoJQkQ260Dow6gnsBuhFtA3Dp8Fdkosu5yQAG6Lr7MJdzN/XMR4pWkpQYg0Ex0JO/xcAkh73anWZ9YT+jd6jXCp6WvA64RksLgYQUumlCBEKkwLQjNQrgmoOr7W7vt4PWEnv4iww/9jHK+JQ258Q1GillKkBCFSAXoDo+NwFFuuFloPvEK4jPRlwvmBhYSd/1LCOQORfJQgRMrQzoREMBo4mnB+AMIR/yRCc9BMQnL4JIP4pDIoQYiUgVbAQWypJQwjdFy3mnDp6E+BxwkJQaSxKEGIlKgqQi3hbOBkoD3hfMDzhIfi/JXwKEadI5CmogQhUmKGEJLCGUB3YCVwP+HxmU8BBT9+UWQ7KUGIlIDewBcIiaE/4bzBo4QH4fwvoWdTkWJTghDJSCfCU7HOBg6J054BbgQmAu9nFJdIjhKESBG0Bvqx5d6EwYTO61oTei+9kvAs5bezClAkhRKESCPrwdYd1w0iJIdWcf5aQpcUPyc0Ic1MWYdIKVCCEGmA1oQeTPsmhlwNoVNiubcIdylPItyo9jKhu4qNxQxWpIGUIERqqSJcPZSvv6LdCN1WVCU+s4Jwl/KDbEkEs4EPiha1SONTgpCy1ZbQzXRLQvNN8rUN0C4OOyTG873flS1J4DNsvfOH0CyU659oCqEr69cSg04oSyVSgpAGa5MY2hKaXdrE1+R4bn6bWuOtEkPLxHhb0nfi7QndUeeGXJt+Q20kdE39MbCELZ3W5TqpS77q2cjSHClBVKDWbNmZdkwMHQg727QdeG7NZ0zWAAAHA0lEQVQnnBx2JP9Rd5tGjnkToWO59YSj9Y/YsvP+OL5fRrhJbHV8XQV8mPjchsTrulqfr72uj+OyIpKfEkSG2hKueOmZeN2VLUfjtXfitY/Ak6/tEq/1tZ6w0/yIsMPNjX9M6PGz9g52bWJYF4e1hJu71tV6/SQxf12t8U/YslP3BsQtIk2r2SeI3YHj+XRTR8s4VCWGFonx5Py0ZZPL53bcyWFHQo+cta0j7JyTO9fceG7H+gFb72hzO+3kzntNXG5V4nVVYt3J9WrnLCJpmn2CGADcnGfeRsIR7qY4nhw2xGFjrdfay24i7LBX8Olmjly79yLgnfiqq15EpFQ0+wTxBKFZJ9l+nXsVEWnOmn2CWEdoZxcRka21yDoAEREpTUoQIiKSSglCRERSKUGIiEgqJQgREUlVcgnCzI41s1fN7HUzuyLreEREmquSShBmVgX8N3AcsC9wppntm21UIiLNU0klCGA48Lq7v+HunwD3AWMzjklEpFkqtRvlegILE+9rgBHJBcxsPDA+vl1jZiuA5dv3td06w67dt28dTe29KujUTB5E1lzKqnJWnvqU1TfBq3+H9Q3quMHMGvKxnD0KWajUEkRaibfqS87dbwVu3fwBs+nuPqypA8taKGdNxZcTmk9ZVc7KU2llLbUmphrCEx1zehH6sRMRkSIrtQTxAtDXzPqYWWvgDMLz3kVEpMhKqonJ3TeY2YXAnwmPUvi1u8+t42O31jG/UjSXckLzKavKWXkqqqzmrsfFiIjIp5VaE5OIiJQIJQgREUlVcgnCzH5tZkvNbE5i2lVmtsjMZsbh+Di9t5l9nJh+S+IzQ81sduyy42e2nRcNN4W0ssbpF8XuRuaa2Y8T06+M5XnVzI5JTC/pstannOW8TfP87d6fKMubZjYzMa8styfUr6wVuE0Hm9n/xbJMN7PhiXllu01TuXtJDcBhwBBgTmLaVcBlKcv2Ti5Xa97zwEGEeyv+CByXddkKLOuRwONAm/i+W3zdF5gFtAH6AAuAqnIoaz3LWbbbNK2ctebfAPx7uW/PBpS1orYp8JdcnMDxwORK2KZpQ8nVINz9GeC97VmHmXUHOrj7VA9b5y7gpMaIrzHlKetXgWvdfV1cJvdE1LHAfe6+zt3/AbwODC+HstaznKnKuJwAxCPG04F746Sy3Z5Q77KmKoey5imnAx3ieEe23KtV1ts0TckliG240MxejlW+XRLT+5jZS2b2tJkdGqf1JNx0l1MTp5WDvYFDzWxaLNMBcXpaNyQ9Kd+y5isnVN42BTgUWOLur8X3lbY9k2qXFSprm34TuM7MFgLXA1fG6RW3TcslQdwM7AUMBhYTqq/E8d3dfX/gUuAeM+tAAV12lLCWwC7AgcC3gQfiEVm+MpVrWfOVsxK3KcCZbH1EXWnbM6l2WSttm34VuMTddwMuAW6P0ytum5bUjXL5uPuS3LiZ3QY8GqevA3JNFDPMbAHhyLSG0E1HTjl12VED/D5WRZ83s01AF/J3Q1KuZU0tp7svo8K2qZm1BE4BhiYmV9r2BNLLWoH/p+OAi+P4g8Cv4njFbdOyqEHENryck4E5cXpXC8+QwMz2BPoCb7j7YmC1mR0Yj0q/CDxS5LAb6mHgKAAz2xtoTeitdhJwhpm1MbM+hLI+X8ZlTS1nhW7To4FX3D3ZzFBp2zPnU2WtwG36DnB4HD8KyDWlVd42zfosee2BUDVdDKwnZN7zgd8Cs4GXCRuhe1z2VGAu4cqBF4ETE+sZRkgkC4BfEO8aL6UhT1lbA3fH2F8Ejkos/71YnldJXAVR6mWtTznLeZumlTNOvwO4IGX5stye9S1rpW1T4BBgRizPNGBoJWzTtEFdbYiISKqyaGISEZHiU4IQEZFUShAiIpJKCUJERFIpQYiISColCJECWfCsmR2XmHa6mf0py7hEmooucxWpBzMbQLh7dn/CY3FnAse6+4LtWGdLd9/QSCGKNBolCJF6svDsig+BHYHV7n6NmY0Dvk64AXAKcKG7bzKzWwndRbcD7nf3q+M6aoD/AY4FbnL3BzMoisg2lUVfTCIl5vuEO4I/AYbFWsXJwMHuviEmhTOAe4Ar3P292EfRU2Y20d3nxfV86O4jsyiASCGUIETqyd0/NLP7gTXuvs7MjgYOAKbHB4W1Y0u3z2ea2fmE/7UehIfK5BLE/cWNXKR+lCBEGmZTHCB05/xrd/+35AJm1pfQ6+dwd19pZncDbROLfFiUSEUaSFcxiWy/x4HTzawLgJl1NrPdCU8dWw2sij0SH7ONdYiUHNUgRLaTu882s+8Dj5tZC0LPnxcA0wnNSXOAN4DnsotSpP50FZOIiKRSE5OIiKRSghARkVRKECIikkoJQkREUilBiIhIKiUIERFJpQQhIiKp/h8bj3vsRzWAUAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -634,7 +681,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Le bleu du remplissage entre la ligne rouge des salaires et l'axe est masqué par le noir de l'histogramme !!\n", "J'ai trouvé une méthode dans la doc en ligne de matplotlib pour faire un diagramme en barre avec un gradient de couleur (ce qui pourrait permettre de se rapprocher de la version originale) mais ca me parait bien compliqué à implanter\n", @@ -646,22 +696,25 @@ }, { "cell_type": "code", - "execution_count": 82, - "metadata": {}, + "execution_count": 12, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 82, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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El770pYb+G2+8kZ///OdRhCYi0mxKEO2gOc19jx8/PqrwRESaRQmiHTSnue/jjz+e8847j9GjR3PCCScwZ86chvlvvfVWhg4dysSJE7nyyiv52c9+BsCqVau44IILOPnkkznzzDNZsWIFAI888ggjRoxg5MiRTJgwoeMLLCI5Kacb6+P666GNm/tm1Chog+a+u3TpwhNPPEHXrl0bmuS4+OKLWbRoEY899hhvvPEG8Xic0aNHc/LJJwMwffp07rjjDo499lgWLFjA1772NZ5//nluueUWnnnmGfr378+OHTvatrwi0mnldoKI0MGa+3Z3vve97/Hiiy+Sl5fHhg0b2Lx5My+//DKTJ0+mpKQEgIsuugiAqqoq5s+fz+WXX96wjpqamoZ1TZs2jSuuuIIpU6Z0fGFFJCfldoLI4Oa+Z82aRWVlJYsWLaKgoICysjKqq6tpqm2sZDLJYYcdlvYFSHfccQcLFizgqaeeYtSoUSxevJgePXq0dxFFJMfpGkQ7OVhz3x988AG9e/emoKCAv/3tb6xduxaAM844gz/+8Y9UV1dTVVXFU089BUDXrl0ZPHgwjzzyCADuzptvvgkE1ybGjRvHLbfcQs+ePVm/fn00hRaRnKIE0U4O1tz3VVddxcKFCxkzZgyzZs1qaL77lFNO4eKLL2bkyJFMmTKFMWPG0K1bNwBmzZrF3XffzciRIxk+fHjDhe1vf/vbnHDCCYwYMYIJEyYwcuTIji+wiOSc3K5iilAsFmPnzp37DUttwrdnz55p3xoH8K1vfYubb76ZPXv2MGHCBG688UYABg8ezNNPP33A9I8//njbBS4iElKCyEDTp09n2bJlVFdXc/XVVzN69OioQxKRTkgJIgPdf//9UYcgIpKb1yCy+S157UHbQ0RaI+cSRHFxMdu2bdNOMeTubNu2jeLi4qhDEZEsk3NVTAMGDKC8vJzKysqoQ8kYxcXFDBgwIOowRCTL5FyCKCgoYPDgwVGHISKS9XKuiklERNqGEoSIiKSlBCEiImkpQYiISFpKECIiklYkCcLMbjCzpWa2xMweMLNiM+tuZn81s3fC78OjiE1ERAIdfpurmfUH/hUY5u57zexhYCowDHjO3Wea2QxgBnBTR8cnItKWZsyYQUVFxQHDd+/eHUE0LRPVcxD5QImZ1QFdgI3Ad4Gzw/H3AfNQghCRLFdRUUFZWVnUYbRKh1cxufsG4GfAOmAT8IG7/wU4wt03hdNsAnqnm9/MppvZQjNbqKelRUTaT4cniPDawmRgMNAPOMTMPtvc+d39Tncf4+5jevXq1V5hioh0elFUMZ0PvOfulQBm9jhwOrDZzPq6+yYz6wtsiSA2EZFWyeZrDU2JIkGsA041sy7AXuA8YCGwG7gamBl+z4kgNhGRVsnmaw1N6fAE4e4LzOxR4HUgDrwB3AkcCjxsZtcQJJHLOzo2EZGo7d69m2nTph0wvE+fPsycObNDY4nkLiZ3/3fg3xsNriE4mxAR6bSGDx+edviaNWs6NhD0JLWIiDRBCUJERNLKuRcGiYi0p1y8W6kpShAiIi2Qi3crNUVVTCIikpYShIiIpKUEISIiaSlBiIhIWkoQIiKSlhKEiIikpQQhIiJpKUGIiEhaShAiIpKWEoSIiKSlBCEiImkpQYiISFpKECIikpYShIiIpKUEISIiaSlBiIhIWkoQIiKSlhKEiIikpQQhIiJpKUGIiEhaShAiIpKWEoSIiKSVH3UAndmMGTOoqKg4YHifPn2YOXNmBBGJiOyjBBGhiooKysrKDhi+Zs2aDo9FRKQxVTGJiEhaShAiIpKWEoSIiKQVSYIws8PM7FEzW2Fmy83sNDPrbmZ/NbN3wu/Do4hNREQCUZ1B/CfwtLsPBUYCy4EZwHPufizwXNgvIiIR6fAEYWZdgQnA3QDuXuvuO4DJwH3hZPcBl3R0bCIisk8UZxBHAZXA78zsDTO7y8wOAY5w900A4XfvdDOb2XQzW2hmCysrKzsuahGRTiaKBJEPjAZ+4+4nAbtpQXWSu9/p7mPcfUyvXr3aK0YRkU4vigRRDpS7+4Kw/1GChLHZzPoChN9bIohNRERCHZ4g3L0CWG9mQ8JB5wHLgLnA1eGwq4E5HR2biIjsE1VTG9cBs8ysEFgNfIEgWT1sZtcA64DLI4pNRESIKEG4+2JgTJpR53V0LCIikl6zqpjM7Btm1tUCd5vZ62b2sfYOTkREotPcaxBfdPedwMeAXgRVQmqPWkQkhzU3QVj4/Qngd+7+ZsowERHJQc1NEIvM7C8ECeIZMysFku0XloiIRK25F6mvAUYBq919j5n1IKhmEhGRHPWhCcLMRjcadJSZapZERDqDg51B3BZ+FwMnA28RXHs4EVgAnNF+oYmISL3du3czbdq0hv6OeHf9hyYIdz8HwMweBKa7+9th/wjgW+0amYiINBg+fPh+/R3x7vrmXoMYWp8cANx9iZmNaqeYJEfNmDGDioqKA4Z3xJGQiLRccxPEcjO7C/gD4MBnCV7yI9JsFRUVlJWVHTC8I46ERKTlmpsgvgB8FfhG2P8i8Jt2iUhERDJCsxKEu1cDvwg/IiLSCTQrQZjZeOBm4MjUedz9qPYJS0REotbcKqa7gRuARUCi/cIREZFM0dwE8YG7/7ldIxHJIbpjS3JBcxPE38zsp8DjQE39QHd/vV2iEslyumNLckFzE8S48Dv1JT8OnNu24WQHHR2KSGfQ3LuYzmnvQLKJjg5FpDNo7l1M/5ZuuLvf0rbhiIhIpmhuFdPulO5i4EL0JLWI5LCmqpJ3796dZurc1NwqpttS+83sZ8DcdokoB+kPTaTttfe1wKaqkjuT5p5BNNYF0ENyzaQ/tLaVbsegGwQ6H10LbH/NvQbxNsFdSwAxoBeg6w8SiXQ7Bu0U5GB092HLNfcM4sKU7jiw2d3j7RCPiEi70BlHyzX3GsRaMxsJnBkOepHg7XI5Q9UWTdORl0jn1Nwqpm8A1xI8SQ0wy8zudPf/arfIOpiqLZqmIy+Rzqm5VUzXAOPcfTeAmf0YeBXImQQhkol09tZyjd/dnDpcWqa5CcLYvxXXRDhMRNqRzt5arvG7m6X1mpsgfgcsMLMnwv5LCJoAF8lKOjLPHnqOKDrNvUj9czObB5xBcObwBXd/oz0DE2lPOjLPHnqOKDofmiDMrBj4CnAM8Dbw6850e6vqMjtGU9tZR/PSEfR/3rSDnUHcB9QBLwEfB44Hrm+LFZtZDFgIbHD3C82sO/AQUAasAa5w9+1tsa7Wamldpv7QWqep7ayjeekIumbRtIMliGHufgKAmd0NvNaG6/4GQYN/XcP+GcBz7j7TzGaE/Te14foOkFq32RY7cf2hiUguOViCqKvvcPe4WdvcuGRmA4BPAj8CvhkOngycHXbfB8yjnROE6jYzm87IRKJ1sAQx0sx2ht0GlIT9Bri7d2161g91O/AdoDRl2BHuvolgwZvMrHe6Gc1sOjAdYNCgQa1cvWSDbDgj0x02kss+NEG4e6ytV2hmFwJb3H2RmZ3d0vnd/U7gToAxY8b4QSYXaVc6C5Vc1trmvj+K8cDFZvYJgpcPdTWzPwCbzaxvePbQF9gSQWzSBnRUnXn03Ie0RocnCHf/LvBdgPAM4lvu/lkz+ylwNTAz/J7T0bFJ29BRdebRcx/SGnlRB5BiJjDRzN4BJob9IiISkSiqmBq4+zyCu5Vw923AeVHGIyIi+0SaIETam66HiLSeEoTkNF0PEWm9TLoGISIiGURnECLSoXTLbfZQguiE9A/aNLUs2/50y232UILohPQP2jS1LCuyj65BiIhIWkoQIiKSlqqYskhLrx209BmAljavrea4o6NrJdIRlCCySEuvHbT0GYCWNq+dDc1x5ypdK5GOoASRA3Qkn/3bINOe+NadbgJKEDlBR/LZvw0y7Ylv3ekmoAQhIu0o3ZmIrmllDyUIEWk3LTkzyvazwObKr6uj59at9K6spFf4WX3UUbw2dmzUoR1ACUKkA+koufPJj8c55t13GbZsGf3KN9B9x3byPHhbch35vGvHsLDiZMi8/KAEIdKROstRcmeXH49z9KpVDF+6lCErVlJUV8u2vO7MS57NsrzjWZ53PG8mTuQdhlDnhXT1D7iBX0Qd9gGUIERyiM5QohOLxzl69eqGpFBcW8P2vMP4vX+Ox2JTeDbxMRLkQzLqSJtPCUIkh+gMpWPFEgmOSkkKJTXV7MjrxgM+lcfyL+OZ+CTiFEAi6khbRwlCRKSFCmprOWfePE56/Q1KaqrZaaU8xBU8nn8pT8cvoI5CiEcd5UenBCEiH1mmPejXngatW8fkOXPo8f77PGCf5qH8qfwp/omcSQqplCAykOqRM49+k8CHbYdcr97Kr6vj3Oef59S/L2B9bCCfjj3Ic4mJOZcUUilBZKBc/0fLRvpNAp11OwwoL+eS2bPpuW0bv8n7Mt9J/IQqukYdVrtTghDpxHRm9OHMnbPnzePMl15iY14/JsX+zF8SF0QdVodRghDpxDrrGUFzFNTWMuWJJzh+xQrus8/xr4n/ZCeHRx1Wh1KCEBFppHTnTq584AGOqNjCjXk/4+fJbwIWdVgdTglCRCRFv40bmfrAg8T2JJgSe4y5iUuiDikyShAiIqFhy5ZxyROz2exHcLHNYXFidNQhRUoJQkQ6vVgiwVnz5jHh5ZdZkDeWS3iCimS/qMOKnBKEiHRqfTZt4pI5c+izeTO/s2l8Nfnf1NAl6rAyghKEiHRKeYkEZ770EhNeeolt1oNPxR5jdmJK1GFlFCUIEel0jti8mcmzZ9OvooL786ZyXeKXvE+vqMPKOB2eIMxsIPB7oA9Bw7d3uvt/mll34CGgDFgDXOHu2zs6PhHJXSV79jB+/nxOffXvfEA3Lo89zKOJy6MOK2NFcQYRB25099fNrBRYZGZ/BaYBz7n7TDObAcwAboogPhHJMSV793La/PmMe+01CmrreNCmcgM/Z0uyT9ShZbQOTxDuvgnYFHbvMrPlQH9gMnB2ONl9wDyUIETkIyjeu5fTXn2VcQteo7i2hkftUm6N/YC3EqOiDi0rRHoNwszKgJOABcARYfLA3TeZWe8m5pkOTAcYNGhQxwQqIlnD3Bm4bh3Dly5l5FtvUVxTw+P2KW6N/SB4riFLX94ThcgShJkdCjwGXO/uO82a9xi7u98J3AkwZswYb78IRSRbmDsD1q9n+NKlDFu2nK5Vu6i2IuZyET+O3cTriTFKDK0QSYIwswKC5DDL3R8PB282s77h2UNfYEsUsYlIdojF45StXcuQlSsZsmIl3XbtpMYK+TMX8FhsCrMTU6iiVInhI4jiLiYD7gaWu/vPU0bNBa4GZobfczo6NhHJbF327OHYd95hyMqVHP3uKorqatlrxfyFiTyR/ykej1/KLroqKbSRKM4gxgOfA942s8XhsO8RJIaHzewaYB2ge89EhIK6OoauWMHIN9/kqNWryXNnU14f7vPP86f8T/JMfBLVlOT0m92iEsVdTC/TdLu553VkLCKSmcydQWvXMvLNNxm2bDnFtTWsyxvATG5iTt5k/pEci5OnpNDO9CS1iGSEopoaytas4ahVqzjun+9w+Ac7qLIuPMBU/hD7LH9LnBskBd2a0mGUIEQkErFEgn4bNnD06tUMXr2aAeUbiHmSvVbMPM7modgVPJK4gj0comsKEVGCEJEOUVRTw8D16xm0bh2D1q2jf/kGChJxkhiv543mAa7k2bzzeCl5FrUUKSlkACUIEWlT5k63HTvoXVlJry1b6F1ZSe/Nmzliyxby3IkT400byRwmMz//NP4a/xjbk92DmVV9lFGUIESkddzp9sEH+yWCXpWV9KzcSlFdbcNkm6wPb/uJ3JU3jpftdF5JnsluPzQYqYvMGU0JQkSaZO6U7tzJ4du3N3y6b9/O4e9vp+fWrRTX1jRMW2FHsMRHsCzveFbmD+WtxAiW+Ins8MODCZIRFUJaTQlCpDNyp2TvXkqrqijdtWu/z6FVVZTu3MWhu6oordpFfnLfxYA4McptACt8KCtsKCvyh/J2cgRvJ09ku4fVREmUDHKEEoRIrnLnkN2796sC6llZSdcPdlFatYuCxIH1OzusGxvpywofQoX1oSKvD2u2RPFRAAAK+0lEQVTzj+Td5DG8kzyOdQwi7gXh8lEVUY5TghDJEbFEgv4bNlC2Zg1la9ZwRMVmDtm7p2H8DuvGUobzspdRkdeHilgfNtKP9YmBbKQ/G+lHtZfsW6CjO4k6OSUIkSyVl0jQf+PGhoQwcN16CuN1ALyVdwLP+zmsiA1liY/greRINnlfGhoxUBWQNIMShEiWyEsm6ZeSEAatXdeQEJbkDecO/zIv5Z/J8/HzeD/ZI5hJZwDyEShBiGSwopoajlu5kuHLllG2+j2Kw9tHl+Udz51+LS/ln8Hz8fPZmuwVzKBrAtKGlCBEMkxhTQ1D/vlPhi9dytHvrqIgEWdDXj9+51/gxfwzeS4+kcpk+MJFJQRpR0oQIhmgsLaW48KkcMw771KQiLMxry+/9q/xaOxSXkmcodZLpcMpQYhEpKC2luPeeYfhS5dy7DvvUBCPU5F3BHf4l3k0dhkvJSYESUHXESQiShAiHaCouppelZX7PZNQf9fR5rze/I9P57HYpbyQOFtJQTKGEoRIGyqqqaFno0TQc8tWDtv1QcM0e62Y5QzlGb+Gx2KXMi9xDkliSgqScZQgRFqoqLp6X5tEqW0UbX2fw3fuaJiumiJW2FD+7qeyIjaEpQznzcQo1nhZcJYASgqS0ZQgREIFdXUcmtouUaN2ig7dWcWhVVWU1FbvN982684qjmaBj2NFbChLGcabiVG8x2CSHgsmUiKQLKQEIRkvL5mksKaGotrafd/hJy+RIC+ZJC+ZJBZ+5yUS5MfjFMTj5Mfj5NfV7d8fj1MQDovVxSmsqaW0qoouNXsPWHc1RWyyPmzyfmykHxV5fdgY68sqjuafieNYzdHs9G77ZlAikByiBCH7MXfy6+qCnWnqjjXszo/HyU8kiMXjxBKJ/brTffJTu+vniccbdup5iZTvRDKYJp6gIF6/7lryvfXtQiTIYy/F1FgxeylhLyVUezG7KGUPXdhLCXvowua8I9gU68tG+rLBB7A+OZBN9GU7h4PbvgWqiQrpRJQgMpU7sWQy2Lmm7JRTv4Od6b6j4voj46Kamn2f2lqKamooqKsjlkiQl0ju+44Hyyqoi1MYr6UgXpe2hc/WqKGQOvKptSJq2Pep9UJ204VaColTQB0FxImRIJ86Cqi2YmoootqKqbYiqvOCnXoVh7CLUnbRlZ3elR3JblRRGi4nf79PHQVUEySEOPmAHfxNZdrxixxACaKFzJ28ZBKrPwJ2x5LJYLg7FvbnJZMHVHMUxOMU1tRQWlUV1HVXBXXah+yqomRP9b4j7WR8vzb4WyOJsctK2UUpO+nKB96VGoKdby0F1FFIHQXUWBF76EK1FbHHulAdK2Evxey1EvZ6CdWUsMdL2ONd2OMlVIc799SdfsPOn8KGHXZDo3AtfYWkN/oWkch0zgSxZAn+mc/wH2vLKYrF9lVzJJPEkolwZ5/ct7PHsaST721XwVxDIRXWh03el42M5H3rTq0VUmuF1FkhtbHCcIdb0LADrqaIWoqp9iKqvbhhhx2MK6aaYvbQhZ10ZTeH7F810hTtkEWkCZ0zQRQX817e0SzcOYSE7aveSFiMOPkkLI8keSSINXx7Xl5DFUaCWFgtsv8ndZ44Marpwm4vYW+yhD0cQjUlVHEom+jLDg7bfwfuaCctIhmlcyaIY47hsaue4Ka34rinbALtoEVEGuRFHYCIiGQmJQgREUlLCUJERNJSghARkbSUIEREJK2MSxBmdoGZrTSzd81sRtTxiIh0VhmVIMwsBvw38HFgGHClmQ2LNioRkc4p056DGAu86+6rAczsQWAysKytVzR37vW4v0FDkxAiIhHZvTvOvfeWt2ieLl26tFM0+2RagugPrE/pLwfGpU5gZtOB6QCDBg1q9YoOPRTMHHc9HSci0YrFaqiurj74hCl69uzZTtHsk2kJIt3h/H57cHe/E7gTYMyYMa3eu//5z7e3dlYRkU4ho65BEJwxDEzpHwBsjCgWEZFOLdMSxD+AY81ssJkVAlOBuRHHJCLSKWVUFZO7x83s68AzQAy4x92XRhyWiEinlFEJAsDd/wT8Keo4REQ6u0yrYhIRkQyhBCEiImkpQYiISFpKECIikpZl85PEZlYJrI06jnbWE9gadRAdqDOVV2XNXZle3iPdvdfBJsrqBNEZmNlCdx8TdRwdpTOVV2XNXblSXlUxiYhIWkoQIiKSlhJE5rsz6gA6WGcqr8qau3KivLoGISIiaekMQkRE0lKCEBGRtJQgImBm95jZFjNb0mj4dWa20syWmtlPUoZ/18zeDcdNShl+spm9HY77pZll3PtTW1JWMyszs71mtjj83JEyfVaW1cweSinPGjNbnDIua39XaFl5c/S3HWVmfw/Ls9DMxqaMy+rftoG769PBH2ACMBpYkjLsHOBZoCjs7x1+DwPeBIqAwcAqIBaOew04jeBNfH8GPh512T5iWctSp2u0nKwsa6PxtwH/lgu/ayvKm3O/LfCX+liBTwDzcuW3rf/oDCIC7v4i8H6jwV8FZrp7TTjNlnD4ZOBBd69x9/eAd4GxZtYX6Orur3rwl/d74JKOKUHztbCsaWV5WQEIjxSvAB4IB2X17wotLm9a2VLeJsrqQNewuxv73n6Z9b9tPSWIzHEccKaZLTCzF8zslHB4f2B9ynTl4bD+YXfj4dmgqbICDDazN8LhZ4bDsrms9c4ENrv7O2F/Lv6uqRqXF3Lvt70e+KmZrQd+Bnw3HJ4zv23GvTCoE8sHDgdOBU4BHjazowhORRvzDxmeDZoq6yZgkLtvM7OTgdlmNpzsLmu9K9n/aDoXf9dUjcubi7/tV4Eb3P0xM7sCuBs4nxz6bZUgMkc58Hh46vmamSUJGvwqBwamTDeA4FS2POxuPDwbpC2ru1cC9dVOi8xsFcHZRjaXFTPLB6YAJ6cMzsXfFUhf3rA6Mdd+26uBb4TdjwB3hd0589uqiilzzAbOBTCz44BCgtYg5wJTzazIzAYDxwKvufsmYJeZnRrW934emBNN6C2Wtqxm1svMYuHwowjKujrLywrBUeUKd0+tXsjF37XeAeXN0d92I3BW2H0uUF+dlju/bdRXyTvjh+DUexNQR3BUcQ3BTvIPwBLgdeDclOm/T3AnxEpS7noAxoTTrwJ+RfhkfCZ9WlJW4FJgKcEdIK8DF2V7WcPh9wJfSTN91v6uLS1vLv62wBnAorBMC4CTc+W3rf+oqQ0REUlLVUwiIpKWEoSIiKSlBCEiImkpQYiISFpKECIikpYShEgzWeBlM/t4yrArzOzpKOMSaS+6zVWkBcxsBMFTsycBMWAxcIG7r/oIy8x393gbhSjSZpQgRFrIgvdX7AYOAXa5+61mdjXwLwQPAc4Hvu7uSTO7k6CZ6BLgIXe/JVxGOfA/wAXA7e7+SARFEflQaotJpOV+SPA0cC0wJjyr+BRwurvHw6QwFbgfmOHu74ftE/3NzB5192Xhcna7+/goCiDSHEoQIi3k7rvN7CGgyt1rzOx8glZpF4YvCCthX3PPV5rZNQT/a/0IXiZTnyAe6tjIRVpGCUKkdZLhB4JmnO9x9/+TOoGZHUvQ2udYd99hZn8AilMm2d0hkYq0ku5iEvnongWuMLOeAGbWw8wGEbxtbBewM3yb2KQPWYZIxtEZhMhH5O5vm9kPgWfNLI+gxc+vAAsJqpOWAKuBV6KLUqTldBeTiIikpSomERFJSwlCRETSUoIQEZG0lCBERCQtJQgREUlLCUJERNJSghARkbT+Pw5/ChNgqC3cAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -678,10 +731,10 @@ "y_wheat=list(raw_data[\"Wheat\"])\n", "zero=[0 for i in x]\n", "graphique3 = plt.figure()\n", - "ax3_1 = graphique3.add_subplot(111)\n", + "ax_3_1 = graphique3.add_subplot(111)\n", "ax3_1.set(xbound=[1550,1850],ybound=[0,100],ylabel=\"Shillings\",xlabel=\"Year\",Title=\"Evolution of wheat prices and wages\")\n", "ax3_1.xaxis.set_major_locator(plt.MultipleLocator(50))\n", - "ax3_1.plot(x,zero,color=\"black\",label=\"Wheat\")\n", + "ax_3_1.plot(x,zero,color=\"black\",label=\"Wheat\")\n", "ax_3_1.plot(x,y_wages,color=\"red\",label=\"Wages\")\n", "ax_3_1.fill_between(x,y_wheat,0,color=\"black\",step=\"mid\",alpha=0.5)#alpha permet d'avoir un remplissage semi-transparent\n", "ax_3_1.fill_between(x,y_wages,0,color=\"blue\")\n", @@ -690,7 +743,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "### Deuxième exercice\n", "Il s'agit maintenant d'avoir 2 axes Y différents pour les courbes.\n", @@ -699,16 +755,19 @@ }, { "cell_type": "code", - "execution_count": 98, - "metadata": {}, + "execution_count": 13, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 98, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -746,20 +805,50 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ - "Si j'essaie de mettre la meme échelle à droite et à gauche pour ne pas avoir de superposition des coubres, il y a un décalage qui apparait ! Il doit manquer une option quelque part !!" + "Si j'essaie de mettre la meme échelle à droite et à gauche pen utilisant l'attribut ybound, ca ne fonctionne pas !!\n", + "En fait il faut utiliser à la place l'attribut ylim." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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": [ "graphique5 = plt.figure()\n", "ax_5_1 = graphique5.add_subplot(111)\n", - "ax_5_1.set(xbound=[1550,1850],ybound=[0,100],ylabel=\"Wheat Price (shilling per 1/4 bushel)\",xlabel=\"Year\",Title=\"Evolution of wheat prices and wages\")\n", + "ax_5_1.set(xbound=[1550,1850],ylim=[0,100],ylabel=\"Wheat Price (shilling per 1/4 bushel)\",xlabel=\"Year\",Title=\"Evolution of wheat prices and wages\")\n", "ax_5_1.xaxis.set_major_locator(plt.MultipleLocator(50))\n", "\n", "ax_5_1.plot(x,zero,color=\"red\",label=\"Wages\")\n", @@ -771,12 +860,64 @@ "ax_5_2.plot(x,y_wages,color=\"red\",label=\"Wages\")\n", "ax_5_2.fill_between(x,y_wages,0,color=\"blue\",alpha=0.25)\n", "ax_5_2.set_ylabel(\"Wages (shilling per week)\", color=\"red\")\n", + "ax_5_2.set_ylim(0,100)\n", "ax_5_2.tick_params(axis='y', labelcolor=\"red\")\n", "ax_5_1.legend(loc='upper left')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] } ], "metadata": { + "hide_code_all_hidden": false, "kernelspec": { "display_name": "Python 3", "language": "python", -- 2.18.1