{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[William Playfair](https://fr.wikipedia.org/wiki/William_Playfair) était un des pionniers de la présentation graphique des données. Il est notamment considéré comme l'inventeur de l'histogramme. Un de ses graphes célèbres, tiré de son livre **\"A Letter on Our Agricultural Distresses, Their Causes and Remedies\"**, montre l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Playfair n'a pas publié les données numériques brutes qu'il a utilisées, car à son époque la réplicabilité n'était pas encore considérée comme essentielle. Des valeurs obtenues par numérisation du graphe sont aujourd'hui téléchargeables, [la version en format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv) étant la plus pratique." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", "
Unnamed: 0YearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
89160533.05.69
910161032.05.78
1011161533.05.94
1112162035.06.01
1213162533.06.12
1314163045.06.22
1415163533.06.30
1516164039.06.37
1617164553.06.45
1718165042.06.50
1819165540.56.60
1920166046.56.75
2021166532.06.80
2122167037.06.90
2223167543.07.00
2324168035.07.30
2425168527.07.60
2526169040.08.00
2627169550.08.50
2728170030.09.00
2829170532.010.00
2930171044.011.00
3031171533.011.75
3132172029.012.50
3233172539.013.00
3334173026.013.30
3435173532.013.60
3536174027.014.00
3637174527.514.50
3738175031.015.00
3839175535.515.70
3940176031.016.50
4041176543.017.60
4142177047.018.50
4243177544.019.50
4344178046.021.00
4445178542.023.00
4546179047.525.50
4647179576.027.50
4748180079.028.50
4849180581.029.50
4950181099.030.00
5051181578.0NaN
5152182054.0NaN
5253182154.0NaN
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages\n", "0 1 1565 41.0 5.00\n", "1 2 1570 45.0 5.05\n", "2 3 1575 42.0 5.08\n", "3 4 1580 49.0 5.12\n", "4 5 1585 41.5 5.15\n", "5 6 1590 47.0 5.25\n", "6 7 1595 64.0 5.54\n", "7 8 1600 27.0 5.61\n", "8 9 1605 33.0 5.69\n", "9 10 1610 32.0 5.78\n", "10 11 1615 33.0 5.94\n", "11 12 1620 35.0 6.01\n", "12 13 1625 33.0 6.12\n", "13 14 1630 45.0 6.22\n", "14 15 1635 33.0 6.30\n", "15 16 1640 39.0 6.37\n", "16 17 1645 53.0 6.45\n", "17 18 1650 42.0 6.50\n", "18 19 1655 40.5 6.60\n", "19 20 1660 46.5 6.75\n", "20 21 1665 32.0 6.80\n", "21 22 1670 37.0 6.90\n", "22 23 1675 43.0 7.00\n", "23 24 1680 35.0 7.30\n", "24 25 1685 27.0 7.60\n", "25 26 1690 40.0 8.00\n", "26 27 1695 50.0 8.50\n", "27 28 1700 30.0 9.00\n", "28 29 1705 32.0 10.00\n", "29 30 1710 44.0 11.00\n", "30 31 1715 33.0 11.75\n", "31 32 1720 29.0 12.50\n", "32 33 1725 39.0 13.00\n", "33 34 1730 26.0 13.30\n", "34 35 1735 32.0 13.60\n", "35 36 1740 27.0 14.00\n", "36 37 1745 27.5 14.50\n", "37 38 1750 31.0 15.00\n", "38 39 1755 35.5 15.70\n", "39 40 1760 31.0 16.50\n", "40 41 1765 43.0 17.60\n", "41 42 1770 47.0 18.50\n", "42 43 1775 44.0 19.50\n", "43 44 1780 46.0 21.00\n", "44 45 1785 42.0 23.00\n", "45 46 1790 47.5 25.50\n", "46 47 1795 76.0 27.50\n", "47 48 1800 79.0 28.50\n", "48 49 1805 81.0 29.50\n", "49 50 1810 99.0 30.00\n", "50 51 1815 78.0 NaN\n", "51 52 1820 54.0 NaN\n", "52 53 1821 54.0 NaN" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read the csv file\n", "data = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 4, "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", "
Unnamed: 0YearWheatWages
5051181578.0NaN
5152182054.0NaN
5253182154.0NaN
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages\n", "50 51 1815 78.0 NaN\n", "51 52 1820 54.0 NaN\n", "52 53 1821 54.0 NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Voir les lignes avec des données manquantes\n", "\n", "data[data.isnull().any(axis = 1)]" ] }, { "cell_type": "code", "execution_count": 5, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0YearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
89160533.05.69
910161032.05.78
1011161533.05.94
1112162035.06.01
1213162533.06.12
1314163045.06.22
1415163533.06.30
1516164039.06.37
1617164553.06.45
1718165042.06.50
1819165540.56.60
1920166046.56.75
2021166532.06.80
2122167037.06.90
2223167543.07.00
2324168035.07.30
2425168527.07.60
2526169040.08.00
2627169550.08.50
2728170030.09.00
2829170532.010.00
2930171044.011.00
3031171533.011.75
3132172029.012.50
3233172539.013.00
3334173026.013.30
3435173532.013.60
3536174027.014.00
3637174527.514.50
3738175031.015.00
3839175535.515.70
3940176031.016.50
4041176543.017.60
4142177047.018.50
4243177544.019.50
4344178046.021.00
4445178542.023.00
4546179047.525.50
4647179576.027.50
4748180079.028.50
4849180581.029.50
4950181099.030.00
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages\n", "0 1 1565 41.0 5.00\n", "1 2 1570 45.0 5.05\n", "2 3 1575 42.0 5.08\n", "3 4 1580 49.0 5.12\n", "4 5 1585 41.5 5.15\n", "5 6 1590 47.0 5.25\n", "6 7 1595 64.0 5.54\n", "7 8 1600 27.0 5.61\n", "8 9 1605 33.0 5.69\n", "9 10 1610 32.0 5.78\n", "10 11 1615 33.0 5.94\n", "11 12 1620 35.0 6.01\n", "12 13 1625 33.0 6.12\n", "13 14 1630 45.0 6.22\n", "14 15 1635 33.0 6.30\n", "15 16 1640 39.0 6.37\n", "16 17 1645 53.0 6.45\n", "17 18 1650 42.0 6.50\n", "18 19 1655 40.5 6.60\n", "19 20 1660 46.5 6.75\n", "20 21 1665 32.0 6.80\n", "21 22 1670 37.0 6.90\n", "22 23 1675 43.0 7.00\n", "23 24 1680 35.0 7.30\n", "24 25 1685 27.0 7.60\n", "25 26 1690 40.0 8.00\n", "26 27 1695 50.0 8.50\n", "27 28 1700 30.0 9.00\n", "28 29 1705 32.0 10.00\n", "29 30 1710 44.0 11.00\n", "30 31 1715 33.0 11.75\n", "31 32 1720 29.0 12.50\n", "32 33 1725 39.0 13.00\n", "33 34 1730 26.0 13.30\n", "34 35 1735 32.0 13.60\n", "35 36 1740 27.0 14.00\n", "36 37 1745 27.5 14.50\n", "37 38 1750 31.0 15.00\n", "38 39 1755 35.5 15.70\n", "39 40 1760 31.0 16.50\n", "40 41 1765 43.0 17.60\n", "41 42 1770 47.0 18.50\n", "42 43 1775 44.0 19.50\n", "43 44 1780 46.0 21.00\n", "44 45 1785 42.0 23.00\n", "45 46 1790 47.5 25.50\n", "46 47 1795 76.0 27.50\n", "47 48 1800 79.0 28.50\n", "48 49 1805 81.0 29.50\n", "49 50 1810 99.0 30.00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# delete line without variable data\n", "# Copy data\n", "my_data = data.dropna().copy()\n", "my_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique du prix du blé " ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Wheat')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a barplot\n", "plt.bar(my_data[\"Year\"], my_data[\"Wheat\"])\n", "\n", "# set axis label\n", "plt.xlabel(\"Year\", fontsize = 14)\n", "plt.ylabel(\"Wheat\", fontsize = 14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation grahique des salaires" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEOCAYAAACNY7BQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl4VOXd//H3lz0QVgkURIoo7rsBV0QQ3Cta1IpWtPURaesu11NQWxeenyJUW61apVWk1spjBUT5iRQRxQXZUTYVsChLICEhEAJJSPJ9/jiHNhMTyGAyZ5J8Xtc115y55wzzva+j+cy5z3KbuyMiIrJXg6gLEBGR5KJgEBGRGAoGERGJoWAQEZEYCgYREYmhYBARkRgKBhERiaFgEBGRGAoGERGJ0SjqAg5E+/btvVu3blGXISJSqyxatGiru6ftb71aGQzdunVj4cKFUZchIlKrmNk3VVlPQ0kiIhJDwSAiIjEUDCIiEkPBICIiMRQMIiISI2HBYGbNzGy+mX1mZivM7KGwvZ2ZzTSz1eFz20TVJCIi35XIPYZCoJ+7nwicBFxoZqcDI4BZ7t4DmBW+FhGRiCQsGDywM3zZOHw4MBCYELZPAC5PVE0iIrXGpk1w552Qm1vjX5XQYwxm1tDMlgKZwEx3nwd0dPcMgPC5QyWfHWpmC81sYVZWVuKKFhGJ0rZtMGIEfvjhlD79DF+8Pr3GvzKhweDuJe5+EtAF6GVmx8Xx2XHunu7u6Wlp+72iW0Skdtu1C0aPxrt3x8eM4eOT+3HbI5OYd0LvGv/qSG6J4e65ZvY+cCGwxcw6uXuGmXUi2JsQEamfiovhz3/GH34Y27yZpSedw8RBv2TtwT0SVkLCgsHM0oA9YSikAP2Bx4A3gRuA0eHz1ETVJCKSVIqLYfBgeP11Vh95Mq/e9AjLDjsx4WUkco+hEzDBzBoSDGG95u7TzGwu8JqZ3QR8C1yVwJpERJJDaSkMHQqvv87ff3IHk8+/HswiKSVhweDunwMnV9CeDZyXqDpERJKOO9x1F4wfz6SBNzP5giGRlqMrn0VEovbAA/DUU7x9/rW8etktUVejYBARidTjj8OoUbx/zkBe/MndkQ0flaVgEBGJyrhxMHw483oN4Nkh9yVFKEAtncFNRKTWmzgRHzaMz088i9//1yhKGzSMuqJ/0x6DiEiiTZuGX389Xx15CmOGPUZxo8ZRVxRDwSAikkizZ+NXXsk3PzySR25/gsKmKVFX9B0KBhGRRJk3D7/sMjZ3OIRRdz5FfrPUqCuqkIJBRCQRPv8cv+giclLb8vA9z7C9RZuoK6qUgkFEpKatXo2ffz47GjThoeHPktWqfdQV7ZPOShIRqUnr1+P9+7O7oIhRI/7Cpnado65ov7THICJSU0pL8YEDKcrexqh7nmFdx25RV1QlCgYRkZoybRq2ZAkv/PTXrO5yZNTVVJmCQUSkJrjDo4+S0+Fg3k8fEHU1cVEwiIjUhDlz4NNPmXrhEEob1q7DuQoGEZGa8Oij7GxzEDPPvDTqSuKmYBARqW5LlsCMGUzrP5iiJs2iriZuCgYRkeo2ejQFzVOZfu6VUVdyQBQMIiLVafVq/PXXmdH3KvKbt4y6mgOiYBARqU5jx1LSqBHTBlwTdSUHTMEgIlJdNm3CJ0xg9tkD2Zbkt73YFwWDiEh1eeIJvLiYty68PupKvhcFg4hIdcjJwZ9/nrmnXcCm9gdHXc33omAQEakOzzyD7dzJm5fcGHUl31vtuhxPRCQZ7d6NP/kkS0/qzdrOh0ddzfeWsD0GMzvEzGab2SozW2Fmd4TtD5rZRjNbGj4uTlRNIiLV4o03sOxspl9wXdSVVItE7jEUA/e4+2IzawksMrOZ4Xu/d/ffJbAWEZHq89JL5KR1ZkmPU6OupFokbI/B3TPcfXG4nAesAmr3ERoRkfXr8ZkzmX3mJXiDunHYNpJemFk34GRgXth0q5l9bmYvmlnbSj4z1MwWmtnCrKysBFUqIrIfL7+MufN+LbxZXmUSHgxmlgpMAu509x3An4DDgJOADODxij7n7uPcPd3d09PS0hJWr4hIpdzx8eNZddSpZKR1ibqaapPQYDCzxgSh8Iq7TwZw9y3uXuLupcCfgV6JrElE5IB98gm2Zg1zel8WdSXVKpFnJRnwArDK3Z8o096pzGpXAMsTVZOIyPcyfjxFzZrz4cn9oq6kWiXyrKSzgOuBZWa2NGy7FxhsZicBDqwDbklgTSIiByY/H//f/+WT9PMoaNY86mqqVcKCwd0/AqyCt95OVA0iItVm8mRs504+PGdg1JVUu7pxbpWISKKNH8/Wjl347LCToq6k2ikYRETitW4dzJ7N7LN+BFbRQEjtpmAQEYnXhAm4GbPPvCTqSmqEgkFEJB6lpfhLL7HymJ5ktuu0//VrIQWDiEg85szB1q3jg95176DzXgoGEZF4jB9PQfNUPjqxT9SV1BgFg4hIVeXl4a+/zsc9B1DUNCXqamqMgkFEpKomTMB27WJOHbx2oSzN4CYiUhV79uBjx7LmiJNYcejxUVdTo7THICJSFRMnYt9+y9RLfhZ1JTVOewwiIvtTWoqPHs2mQw7n0+POirqaGqc9BhGR/XnrLWzlymBvoQ5e6VyegkFEZF/c4dFHye7YhfdP7R91NQmhYBAR2ZcPPoB585h64fWUNqwfo+8KBhGRfRk9mp1tDuLdM+rOnM77o2AQEanM4sUwYwZvDbiWoibNoq4mYRQMIiKVGT2aguapvNNnUNSVJJSCQUSkIqtX46+/zoy+V5HfvGXU1SSUgkFEpCJjxlDSuAnTBgyOupKEUzCIiJS3cSM+YQLv9b6Mba0OirqahFMwiIiU9/DDeGkpb104JOpKIqFgEBEp66OPYNw4pg8YTMZBnaOuJhL142oNEZGqKCrCb7mF3LROTLzslqiriUzC9hjM7BAzm21mq8xshZndEba3M7OZZrY6fG6bqJpERGKMHYutXMlfrh/J7mbNo64mMokcSioG7nH3o4HTgV+Z2THACGCWu/cAZoWvRUQSa/VqfNQo5vUawLx6cAfVfUlYMLh7hrsvDpfzgFXAwcBAYEK42gTg8kTVJCICBDfKGzaMwkZNGH/tPVFXE7lIDj6bWTfgZGAe0NHdMyAID6BDFDWJSD328svw3nu8cuVtbG2VFnU1kUt4MJhZKjAJuNPdd8TxuaFmttDMFmZlZdVcgSJSv2zdit99N2t6nMg7va+IupqkkNBgMLPGBKHwirtPDpu3mFmn8P1OQGZFn3X3ce6e7u7paWlKdBGpJsOH47nbGXfjfXgDncEPiT0ryYAXgFXu/kSZt94EbgiXbwCmJqomEann3nsPJkzgrYuu5+tOh0VdTdJI5HUMZwHXA8vMbGnYdi8wGnjNzG4CvgWuSmBNIlJfrV6NX3st2T84hNcu/a+oq0kqCQsGd/8IqGyy1PMSVYeICOvX4/37s7ugiNEjnqWwHs21UBUaUBOR+mXLFrx/fwqztzHqnmdY17Fb1BUlHd0SQ0Tqj23b8AsuoPjb9Tw2/FlWdzky6oqSkvYYRKR+2LkTLr6Y0pWr+N3tT7Cs+wlRV5S0tMcgInVfQQFcfjmlCxbw1C/HsOioXlFXlNS0xyAidVthIVx9NcyaxfM/f4CPTz436oqSnvYYRKTu2rULfvxjmDGDF4eMZNYZl0RdUa2gYBCRuikvDy69FP/wQ8bd9FtmnjUw6opqjSoPJZnZMWZ2ZJnXA8zsb2Y20swa1kx5IiIHYNs26N+f0o8/5ulhjygU4hTPMYYXCO6Iipl1Ibh1RTvgV8D/VH9pIiIHIDMT79uXkiVL+cOtY/mg5/lRV1TrxBMMRwOLw+WrgHnufjHBbS4GV3dhIiJx27gR79OHki++ZMxdT/LJiX2irqhWiucYQ0OgKFw+D3g7XF4LdKzOokRE4paRgffuzZ4tWYy+52k+P/zkqCuqteLZY1gO/MLMehMEwzth+8HA1uouTESkyvLz8R/9iOLNWxg1/FmFwvcUTzD8GrgZeB941d2Xhe2XAfOruS4RkaopKYGf/hSWLOHJYY+yqtuxUVdU61V5KMnd55hZGtDK3beVeet5YFe1VyYiUhW//jW88QYTfvrffHr82VFXUyfEdeWzu5cADc3sNDNrGratc/cKZ10TEalRzz0Hjz/OjAHXMK3fT6Kups6I5zqGlmb2D4KpNz8hOLaAmT1nZg/WTHkiIpWYMQO/9VaWnnwOL/zk7qirqVPi2WN4DOgMnALsLtM+DdAM2iKSOMuW4VddxYZDevD40P9HaQNdY1ud4jld9TLgCndfamZepn0V0L16yxIRqcT69fgll5DXJIXRt/+e3U2bR11RnRPPHkNbILuC9pZASfWUIyKyDwsW4L16UZSzjdF3/IEtbTpEXVGdFE8wLCDYa9hr717DLQTHHEREas6kSXifPmwrbcj9947nK82+VmPiGUq6F5hhZseGn7s7XO4FnFMTxYmI4A6PPQYjR/J1jxN57LbfkZPaLuqq6rQq7zG4+yfAmUATgttgnAdsAs5w98X7+qyIyAEpKoKbboKRI5l7+gX8ZvifFAoJENd8DOHVzjfUUC0iIv+RnQ2DBsEHHzBp4M28etktYBZ1VfVClYPBzLpW8pYDBe6eVT0liUi9N3cufs01lGZs5rlb/ofZp10UdUX1SjwHn9cB/6rgsQ7YbGbbzOwJM6swbMzsRTPLNLPlZdoeNLONZrY0fFx8oB0RkTqgtBTGjMF79yansJQH731BoRCBeIaSBgNjgOeAeWHbacBQ4EGgDXA/kAc8UMHnXwKeBv5arv337v67OOoQkbooKwuGDIF33mFBz/48e+P97ExpGXVV9VI8wfAL4C53n1ym7T0z+xK4w937mFkm8BAVBEN4E75u36dYEamjPvgAv/ZaSrZm89chI3m7zyAdT4hQPENJpwHLKmhfDvQMl+cCXeKs4VYz+zwcamob52dFpDbbvh1GjsT79SOLJvz2/gm8fe6VCoWIxRMM3xAMG5V3M/BtuJwG5MTxb/4JOAw4CcgAHq9sRTMbamYLzWxhVpaOc4vUart3w+OP4927w+jRzDnrUobf/1e+6tIj6sqE+IaS7gEmhQeIFxCcjdST4A/7oHCdnsBrVf0H3X3L3mUz+zPBDfkqW3ccMA4gPT3dK1tPRJJYcTG89BL+0EPYhg0sO/5MJt7xK7465KioK5My4pmo5/+bWQ/gl8CRgAFvAs+5+7fhOs/G8+Vm1sndM8KXVxAMS4lIXeMe3NLi/vuxL7/k68OPZ+KI37LkiFOjrkwqEO8FbuuBkQfyRWb2KnAu0N7MNhAcoD7XzE4i2PtYR3DfJRGpS959Fx8xAlu0iM1dDuPVO57gkxPO0XGEJBZXMACYWWegK8GtMf7N3efs63PuPriC5hfi/X4RqSXmz4eRI+G998hN68RrNz/ErNMu0twJtUA8Vz53Bv5OcMM8JxhKKjvWr60tIrBqFdx/P0yeTH6rtky6bjhvnzOI4sZN9v9ZSQrx7DH8gWDehWMIDj5fCHQEHgbuqv7SRKTWcId334U//hGfNo2ilBa8ecUw3ux/LbtTWkRdncQpnmDoA1zi7l+EM7hlufvHZlYIjAJm1kiFIpK88vJgwgT86aexL79kZ+t2zLz050zrP5jtLXVZUm0VTzCkAFvD5RygA/AVsBI4oZrrEpFkVVoKCxfCyy/jEyZgeXmsO+xY3rllFB+c0l9DRnVAPMHwBXAUwdlDS4FhZrYe+BWwsfpLE5GkUVwMc+bAlCn4lCnYxo2UNGrMJ70G8M/+17Cq27FRVyjVKJ5geBL4Qbj8MPAOwY31CtEcDSJ1z+7dMHNmEAZvvonl5LCnaTOWHnsGiy4dxtzjzia/Rauoq5QasN9gMLNzgU/c/ZW9be6+OLwh3lHAt+6+teJPi0itsn07vP02TJ6MT5+O5eezu0VLFp7Qm8U9+zH/6NMpbJoSdZVSw6qyx/AeUGBmc4HZ4et57r4L0JSeIrVdSQm89lpwzODdd7E9e9jRtj3zel3EovR+LO1xCsWNGkddpSRQVYLhCKAvwVXLvyAYRso3s48IQmI2sMjddf8ikdrEHd56C7/vPmz5crZ26MKn513DgvR+rOx2HN4gnntsSl2y32Bw9zXAGuDPAGZ2FEFQ9CG4sd5oYDugGbpFaos5c2DECJg7l6xOP2Tirx7jw5P7KQwEOIBbYoTXMeQQnLK6HbgGSK3uwkSkBnz2WXCbiunT2dGuA6/97DfMPOMSSjRUJGVUKRjM7CCCoaS+QD+gO7AI+AC4GviohuoTkergDk8+iQ8fTkHzVKb85A6m9b2aoibNoq5MklBVzkr6jOA4w0KCILgD+Dg8+CwiyW7XLhg6FF55hUWn9uWZn/2WvOY6zVQqV5U9hh7ANuBrYC2wRqEgUkusW4dfcQV89hn/+PEv+cfFP9NxBNmvqgRDa4L5nvsC1wPPmFkW8P7eh7v/q6YKFJEDNHMmfs01FBbu4am7n2T+sWdFXZHUEvv96eDue9z9I3cf5e79gLYEVzr/C7gRWGlm62q0ShGpOncYOxa/8EIyWrRj5AMvKxQkLnGflQSUlnnsnZfhkOosSkQOUH4+/Pzn8NprLOjZnz/+/AF2N20edVVSy1Tl4HMjoBfBUFJf4AygGfAtwcVtL4TPIhKlNWuC4wkrVzLx6tuZdMEQTZ8pB6Qqewy5BLfcziAIgNuA2TquIJJEpk/Hr72WghL4w/CnWXTUaVFXJLVYVYLhHuA9d19d08WISJxKS+HRR/Hf/IaNXY9gzG2/Y1O7zlFXJbVcVW6J8XwiChGROOXlwQ03wJQpzD3jIp4Zcp/ufCrV4kAOPotI1HbtgksvpfTjj3ll8N1M7X+tjidItVEwiNQ2RUUwaBD+4Yc8/YtHmJN+ftQVSR2jSyBFapPiYrjuOnjnHf584/0KBakRCQsGM3vRzDLNbHmZtnZmNtPMVofPbRNVj0itU1oa3PPo9df52+C7+Wfvy6OuSOqoRO4xvARcWK5tBDDL3XsAs8LXIlKeO9x1F4wfz6TLh/LGgOuirkjqsIQFg7vPIZjDoayBwIRweQKgn0AiFXngAXjqKaZfcB2v/mho1NVIHRf1MYaO7p4BED53iLgekeTz+OMwahTvnzOQF66+S2cfSY2LOhiqzMyGmtlCM1uYlZUVdTkiiTFuHAwfzrxeA3h2yH0KBUmIqINhi5l1AgifMytb0d3HuXu6u6enpaUlrECRyLz6Kj5sGJ+deBa//69RlDZoGHVFUk9EHQxvEtzCm/B5aoS1iCSPadPwIUP46shTGDPsMYo1J7MkUCJPV30VmAscaWYbzOwmYDQwwMxWAwPC1yL12+zZ+JVX8s0Pj+SR25/QbS4k4RJ25bO7D67krfMSVYNI0ps3D7/sMjZ3OIRRdz5FfrPUqCuSeijqoSQR2WvZMvyii8hJbcvD9zzD9hZtoq5I6ikFg0jUduyABx7AzziDHQ2a8NDwZ8lq1T7qqqQe0030RKJSUADPPos/8giWnc38XgN45erbNZ+CRE7BIJJoxcUwYQL+4IPYhg0sP/4MJt72K77senTUlYkACgaRxHGHyZPx++/HvviCfx12HK+O+A1LjkiPujKRGAoGkUSYNQsfORJbsIDNXboz8fbH+fjEPrqSWZKSgkGkJi1YAPfeC+++S25aJ167+SFmnXaRrmKWpKZgEKkJCxbAY4/BpEnkt27LpOuG8/Y5gyhu3CTqykT2S8EgUl0KC+Ef/8D/+Eds/nwKm7fgrctvYeqA69id0iLq6kSqTMEg8n1t3AjPPYePG4dlZpLZuRvv/PS/mXXGJexK0ZXLUvsoGEQOxJYt8OabMGUKPnMmlJSw5MTe/POGB1l0ZE+8ga4dldpLwSBSVevWwZQpQRh89BHmTlbHLsw9/zrePXcQm9ofHHWFItVCwSBSGXdYuTK49mDKFGzJEgDWdz2C+QNvZv6p/Vjb+XCdcip1joJBpKzS0uCMoilT8MmTsdWrAVjb4wQW/OROPjnlXDLSDom4SJGapWCQ+sMdtm4NDhaXfWzYABs34hs34uvX02D7dkoaNmLl0eksvPEqPjnhHLa10ayBUn8oGKR2274dvvgC1q8P7lK6Ywfk5f3neds2fNMmfP0GLGMTVlQU8/FSM3a0aU9OmzS2tklje/oxrD38eOYedzb5LVpF1CmRaCkYJLm5Q27uf37Zr10Lq1bBqlWUfvEFDTZtqvBjRU2aUZDSnF0pqWS3TmNbp6PJPe5ctrVNI7ttBzJbpZHdNo3cVgdRomkzRWIoGCRxSkogJweysiAzM3iu6Ff+9u2weTOl4VBPg927Y/6ZgpQWbOx0KOsPPZUtZw9iY6dD2diuM/kpqexOaUFB0xT9sRf5HhQMcuDy8//zR37vH/rs7O88SrduhcwsLHsrVlpa6T9X2DSFgmbNKWjWnG2tDiKnXXdyDz+d3HYdyW6TRmbr9mxpfzA5bdJ0JpBIDVIwyHcVFATDNuFB2bIP37gRz9gMWZk02LWrwo+XNGzEztTW7ExtzY4WrclLTSPvuKPY2aotO1q1I7dlO7a1aE1uy7bsSkllV0oqBU1TKG2o/xxFkoH+T6wvSkuDsfryv+i3bg0O3H77Lb5+Pf7NtzTI3PKdj+9unkpOmw5kt0kj9+Bj2XnM2eS1asf2Vu3ITW1LTos27GjZlrzU1uxu1kK/6EVqMQVDXVNYCF99BStWwPLlsGIFvmIFrF1b6TBOYbMUsg/qRGbbjuQcfSY5vTuRfVBHMtt0IKt1GjltOlCgm8CJ1BsKhtrAPRjPLzuWv3lz8MjIgM2b8YwMPGMztmE9VlICQGmDhmz5QVe+6dydzEt6k9eyLTtatmFH89bkNm9FXmob8lJbk9+8pX7hi8i/KRiilpEBH34Y/MEvN8zj2dl4ZlYwnl/uzJy98lNbs61Ne3JaHcT2zseQc+J5bOhyGF937M7GH/xQ9/8XkbglRTCY2TogDygBit297k6C6x6chz91Kj51KjZvXszbu5q3ZGdqa/JahAduDzmenccGB213tGpHbos25KS2Ibf1QeS2Okh/+EWk2iVFMIT6uvvWqIuoVrt2xZ7Vs2RJEAZr1gCwrvuxLBz0S5Ycdyab23Ykv3lLnX8vIpFLpmCIRnFxcFFVXl5wmmZFj/z8ih9739+9+9/LXlCAZ2cHF2bl5sZ+VePGrDi6J4tvvJpPT+hNdpsOEXVaRKRyyRIMDvzTzBx43t3H1ci3jB2LT5yIb9+B5+VheTsqHbvfl5IGDSlq2oyiJs3Y07gpRY2bsKdxEwrD5V3N08hNP47ctmnktO3A1jZpZLZOY2u7H1DUNKUGOiYiUn2SJRjOcvdNZtYBmGlmX7j7nLIrmNlQYChA165dD+xbUlJYZS3ZltaBgq4tKEhpQUGz4Hl3sxQKGzcNHo2aUNSoCUXhH/rCJikUNm1GYZMUCpqmUNyosc7iEZE6KymCwd03hc+ZZjYF6AXMKbfOOGAcQHp6uh/QF916K0+m9SF7157vV7CISB0W+cS0ZtbCzFruXQbOB5ZHW5WISP2VDHsMHYEpFgzNNAL+7u7vRFuSiEj9FXkwuPvXwIlR1yEiIoHIh5JERCS5KBhERCSGgkFERGIoGEREJIaCQUREYigYREQkhoJBRERiKBhERCSGgkFERGIoGEREJIaCQUREYigYREQkhoJBRERiKBhERCSGgkFERGIoGEREJIaCQUREYigYREQkhoJBRERiKBhERCSGgkFERGIoGEREJIaCQUREYigYREQkRlIEg5ldaGZfmtkaMxsRdT0iIvVZ5MFgZg2BZ4CLgGOAwWZ2TLRViYjUX5EHA9ALWOPuX7t7ETARGBhxTSIi9VYyBMPBwPoyrzeEbSIiEoFGURcAWAVt/p2VzIYCQwG6du16wF/WuXVT8otKDvjzIiJRSkttUuPfkQzBsAE4pMzrLsCm8iu5+zhgHEB6evp3gqOqHrjwiAP9qIhIvZAMQ0kLgB5mdqiZNQGuAd6MuCYRkXor8j0Gdy82s1uBGUBD4EV3XxFxWSIi9VbkwQDg7m8Db0ddh4iIJMdQkoiIJBEFg4iIxFAwiIhIDAWDiIjEUDCIiEgMcz/ga8UiY2ZZwDdR15Eg7YGtURcRAfW7flG/E+OH7p62v5VqZTDUJ2a20N3To64j0dTv+kX9Ti4aShIRkRgKBhERiaFgSH7joi4gIup3/aJ+JxEdYxARkRjaYxARkRgKhgQzsxfNLNPMlpdrv83MvjSzFWY2pkz7SDNbE753QZn2U81sWfjeU2ZW0YRHSSOefptZNzPbbWZLw8dzZdav9f02s/8t07d1Zra0zHt1dntX1u96sL1PMrNPw74tNLNeZd5Lzu3t7nok8AGcA5wCLC/T1hd4F2gavu4QPh8DfAY0BQ4F1gINw/fmA2cQzIA3Hbgo6r5VY7+7lV2v3L9T6/td7v3Hgd/Wh+29j37X6e0N/HNv3cDFwPvJvr21x5Bg7j4HyCnX/AtgtLsXhutkhu0DgYnuXuju/wLWAL3MrBPQyt3nevBf0V+ByxPTgwMTZ78rVIf6DUD4K/Bq4NWwqa5vb6DCfleoDvXbgVbhcmv+M0Nl0m5vBUNyOALobWbzzOwDM+sZth8MrC+z3oaw7eBwuXx7bVNZvwEONbMlYXvvsK2u9Huv3sAWd18dvq7r23uv8v2Gur297wTGmtl64HfAyLA9abd3UkzUIzQC2gKnAz2B18ysO8FuZHm+j/baprJ+ZwBd3T3bzE4F3jCzY6k7/d5rMLG/muv69t6rfL/r+vb+BXCXu08ys6uBF4D+JPH2VjAkhw3A5HC3cb6ZlRLcQ2UDcEiZ9boQ7IZuCJfLt9c2Ffbb3bOAvcNLi8xsLcHeRV3pN2bWCPgxcGqZ5rq+vSvsdziUWJe39w3AHeHyP4C/hMtJu701lJQc3gD6AZjZEUATghtrvQlcY2ZNzexQoAcw390zgDwzOz0crx0CTI2m9O+lwn6bWZqZNQzbuxP0++s61G8IfjF+4e5lhwzq+vaGCvpdD7b3JqBPuNwP2DuElrzbO+qj+PXtQbALnQHsIfhlcBPBH8S/AcuBxUAiIfIkAAACeklEQVS/MuvfR3C2wpeUOTMBSA/XXws8TXixYrI+4uk3MAhYQXDGxmLgR3Wp32H7S8CwCtavs9u7sn7X9e0NnA0sCvs3Dzg12be3rnwWEZEYGkoSEZEYCgYREYmhYBARkRgKBhERiaFgEBGRGAoGERGJoWAQKcfM/hbeIrlJufbzzGyPmZ0ZVW0iiaBgEPmuW4GDgAf2NphZK+BFYKy7f1ITX1o+iESiomAQKcfdc4GfAf9dZlKV3wPbgAcBzOw4M5tuZnnhxCyvmFnHvf+GmZ1mZjPNbKuZbTezD8tN0NLIzNzMhpnZVDPLBx5OWCdF9kHBIFIBd38X+BPwVzO7ErgOuN7di8zsYOADYAnBXWEHAG2AKWVm2moJTCC4xfTpwDJgupm1LfdVDxHcB+d44DlEkoBuiSFSCTNLIfjj3wMY4e5jw/ZHCO53U3YqxvZAVti+uIJ/y4BM4DZ3nxjeZXQP8Ad3v6vmeyNSddpjEKmEu+8mmFilkGAqyr1OBfqa2c69D2Bd+N5hAGbW0czGmdlXZrYdyCM4btG13NcsrMk+iBwIzccgsm/FQKm7l5ZpawC8Bfy6gvU3h89/IxheuhP4hiBc3ie4o2xZ+dVZrEh1UDCIxG8xwXy969y9uJJ1zgaGuvvb8O/5i3+QoPpEvhcNJYnE748EM+y9ama9zKy7mQ0ws7+ExyUAvgKuN7Ojw7ORJhLOUiaS7BQMInHyYPaxs4CGwAyCSWaeBnYRHFAGuJFgKGkJ8HfgeWInfhdJWjorSUREYmiPQUREYigYREQkhoJBRERiKBhERCSGgkFERGIoGEREJIaCQUREYigYREQkhoJBRERi/B+7KfCBIy8wKQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a plot\n", "plt.plot(my_data[\"Year\"], my_data[\"Wages\"], \"r\")\n", "\n", "# set axis label\n", "plt.xlabel(\"Year\", fontsize = 14)\n", "plt.ylabel(\"Wages\", fontsize = 14)\n", "\n", "# fill area between curve and axis\n", "x = my_data[\"Year\"] \n", "y = my_data[\"Wages\"] \n", "plt.fill_between(x, y, color='#539ecd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Superposition des deux graphiques" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create two plots in the same graph\n", "p = plt.bar(my_data[\"Year\"], my_data[\"Wheat\"], color = \"black\", label = \"Wheat\"), \n", "plt.plot(my_data[\"Year\"], my_data[\"Wages\"], \"r\", label = \"Wages\")\n", "plt.legend()\n", "\n", "# set x-axis label\n", "plt.xlabel(\"Year\", fontsize = 14)\n", "\n", "# fill area between curve and axis\n", "x = my_data[\"Year\"]\n", "y = my_data[\"Wages\"]\n", "plt.fill_between(x, y, color='#539ecd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utlisaion de 2 axes d'ordonnées" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create figure and axis objects\n", "fig,ax = plt.subplots()\n", "\n", "# make a plot\n", "ax.plot(my_data[\"Year\"], my_data[\"Wheat\"], color = \"red\", marker = \"o\")\n", "\n", "# set x-axis label\n", "ax.set_xlabel(\"Year\",fontsize = 14)\n", "# set x-axis label\n", "ax.set_ylabel(\"Wheat\", color = \"red\", fontsize = 14)\n", "\n", "# twin object for two different y-axis on the sample plot\n", "ax2 = ax.twinx()\n", "\n", "# make a plot with different y-axis using second axis object\n", "ax2.plot(my_data[\"Year\"],my_data[\"Wages\"] ,color = \"blue\",marker = \"o\")\n", "ax2.set_ylabel(\"Wages\",color = \"blue\", fontsize = 14)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Pouvoir d'achat** : la quantité de blé qu’un ouvrier peut acheter avec son salaire hebdomadaire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Avec un salaire donné, combien puis-je acheter de quantité de blé ?\n", "- Quelle est la quantité de travail nécessaire pour acheter une unité de blé donnée ?" ] }, { "cell_type": "code", "execution_count": 10, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", "
Unnamed: 0YearWheatWagesPurchase_Power
01156541.05.000.121951
12157045.05.050.112222
23157542.05.080.120952
34158049.05.120.104490
45158541.55.150.124096
56159047.05.250.111702
67159564.05.540.086563
78160027.05.610.207778
89160533.05.690.172424
910161032.05.780.180625
1011161533.05.940.180000
1112162035.06.010.171714
1213162533.06.120.185455
1314163045.06.220.138222
1415163533.06.300.190909
1516164039.06.370.163333
1617164553.06.450.121698
1718165042.06.500.154762
1819165540.56.600.162963
1920166046.56.750.145161
2021166532.06.800.212500
2122167037.06.900.186486
2223167543.07.000.162791
2324168035.07.300.208571
2425168527.07.600.281481
2526169040.08.000.200000
2627169550.08.500.170000
2728170030.09.000.300000
2829170532.010.000.312500
2930171044.011.000.250000
3031171533.011.750.356061
3132172029.012.500.431034
3233172539.013.000.333333
3334173026.013.300.511538
3435173532.013.600.425000
3536174027.014.000.518519
3637174527.514.500.527273
3738175031.015.000.483871
3839175535.515.700.442254
3940176031.016.500.532258
4041176543.017.600.409302
4142177047.018.500.393617
4243177544.019.500.443182
4344178046.021.000.456522
4445178542.023.000.547619
4546179047.525.500.536842
4647179576.027.500.361842
4748180079.028.500.360759
4849180581.029.500.364198
4950181099.030.000.303030
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages Purchase_Power\n", "0 1 1565 41.0 5.00 0.121951\n", "1 2 1570 45.0 5.05 0.112222\n", "2 3 1575 42.0 5.08 0.120952\n", "3 4 1580 49.0 5.12 0.104490\n", "4 5 1585 41.5 5.15 0.124096\n", "5 6 1590 47.0 5.25 0.111702\n", "6 7 1595 64.0 5.54 0.086563\n", "7 8 1600 27.0 5.61 0.207778\n", "8 9 1605 33.0 5.69 0.172424\n", "9 10 1610 32.0 5.78 0.180625\n", "10 11 1615 33.0 5.94 0.180000\n", "11 12 1620 35.0 6.01 0.171714\n", "12 13 1625 33.0 6.12 0.185455\n", "13 14 1630 45.0 6.22 0.138222\n", "14 15 1635 33.0 6.30 0.190909\n", "15 16 1640 39.0 6.37 0.163333\n", "16 17 1645 53.0 6.45 0.121698\n", "17 18 1650 42.0 6.50 0.154762\n", "18 19 1655 40.5 6.60 0.162963\n", "19 20 1660 46.5 6.75 0.145161\n", "20 21 1665 32.0 6.80 0.212500\n", "21 22 1670 37.0 6.90 0.186486\n", "22 23 1675 43.0 7.00 0.162791\n", "23 24 1680 35.0 7.30 0.208571\n", "24 25 1685 27.0 7.60 0.281481\n", "25 26 1690 40.0 8.00 0.200000\n", "26 27 1695 50.0 8.50 0.170000\n", "27 28 1700 30.0 9.00 0.300000\n", "28 29 1705 32.0 10.00 0.312500\n", "29 30 1710 44.0 11.00 0.250000\n", "30 31 1715 33.0 11.75 0.356061\n", "31 32 1720 29.0 12.50 0.431034\n", "32 33 1725 39.0 13.00 0.333333\n", "33 34 1730 26.0 13.30 0.511538\n", "34 35 1735 32.0 13.60 0.425000\n", "35 36 1740 27.0 14.00 0.518519\n", "36 37 1745 27.5 14.50 0.527273\n", "37 38 1750 31.0 15.00 0.483871\n", "38 39 1755 35.5 15.70 0.442254\n", "39 40 1760 31.0 16.50 0.532258\n", "40 41 1765 43.0 17.60 0.409302\n", "41 42 1770 47.0 18.50 0.393617\n", "42 43 1775 44.0 19.50 0.443182\n", "43 44 1780 46.0 21.00 0.456522\n", "44 45 1785 42.0 23.00 0.547619\n", "45 46 1790 47.5 25.50 0.536842\n", "46 47 1795 76.0 27.50 0.361842\n", "47 48 1800 79.0 28.50 0.360759\n", "48 49 1805 81.0 29.50 0.364198\n", "49 50 1810 99.0 30.00 0.303030" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the purchase_power \n", "# purchase_power = wages / wheat\n", "my_data[\"Purchase_Power\"] = my_data[\"Wages\"] / my_data[\"Wheat\"]\n", "\n", "my_data" ] }, { "cell_type": "code", "execution_count": 11, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", "
Unnamed: 0YearWheatWagesPurchase_Power
01156541.05.000.121951
12157045.05.050.112222
23157542.05.080.120952
34158049.05.120.104490
45158541.55.150.124096
56159047.05.250.111702
67159564.05.540.086563
78160027.05.610.207778
89160533.05.690.172424
910161032.05.780.180625
1011161533.05.940.180000
1112162035.06.010.171714
1213162533.06.120.185455
1314163045.06.220.138222
1415163533.06.300.190909
1516164039.06.370.163333
1617164553.06.450.121698
1718165042.06.500.154762
1819165540.56.600.162963
1920166046.56.750.145161
2021166532.06.800.212500
2122167037.06.900.186486
2223167543.07.000.162791
2324168035.07.300.208571
2425168527.07.600.281481
2526169040.08.000.200000
2627169550.08.500.170000
2728170030.09.000.300000
2829170532.010.000.312500
2930171044.011.000.250000
3031171533.011.750.356061
3132172029.012.500.431034
3233172539.013.000.333333
3334173026.013.300.511538
3435173532.013.600.425000
3536174027.014.000.518519
3637174527.514.500.527273
3738175031.015.000.483871
3839175535.515.700.442254
3940176031.016.500.532258
4041176543.017.600.409302
4142177047.018.500.393617
4243177544.019.500.443182
4344178046.021.000.456522
4445178542.023.000.547619
4546179047.525.500.536842
4647179576.027.500.361842
4748180079.028.500.360759
4849180581.029.500.364198
4950181099.030.000.303030
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages Purchase_Power\n", "0 1 1565 41.0 5.00 0.121951\n", "1 2 1570 45.0 5.05 0.112222\n", "2 3 1575 42.0 5.08 0.120952\n", "3 4 1580 49.0 5.12 0.104490\n", "4 5 1585 41.5 5.15 0.124096\n", "5 6 1590 47.0 5.25 0.111702\n", "6 7 1595 64.0 5.54 0.086563\n", "7 8 1600 27.0 5.61 0.207778\n", "8 9 1605 33.0 5.69 0.172424\n", "9 10 1610 32.0 5.78 0.180625\n", "10 11 1615 33.0 5.94 0.180000\n", "11 12 1620 35.0 6.01 0.171714\n", "12 13 1625 33.0 6.12 0.185455\n", "13 14 1630 45.0 6.22 0.138222\n", "14 15 1635 33.0 6.30 0.190909\n", "15 16 1640 39.0 6.37 0.163333\n", "16 17 1645 53.0 6.45 0.121698\n", "17 18 1650 42.0 6.50 0.154762\n", "18 19 1655 40.5 6.60 0.162963\n", "19 20 1660 46.5 6.75 0.145161\n", "20 21 1665 32.0 6.80 0.212500\n", "21 22 1670 37.0 6.90 0.186486\n", "22 23 1675 43.0 7.00 0.162791\n", "23 24 1680 35.0 7.30 0.208571\n", "24 25 1685 27.0 7.60 0.281481\n", "25 26 1690 40.0 8.00 0.200000\n", "26 27 1695 50.0 8.50 0.170000\n", "27 28 1700 30.0 9.00 0.300000\n", "28 29 1705 32.0 10.00 0.312500\n", "29 30 1710 44.0 11.00 0.250000\n", "30 31 1715 33.0 11.75 0.356061\n", "31 32 1720 29.0 12.50 0.431034\n", "32 33 1725 39.0 13.00 0.333333\n", "33 34 1730 26.0 13.30 0.511538\n", "34 35 1735 32.0 13.60 0.425000\n", "35 36 1740 27.0 14.00 0.518519\n", "36 37 1745 27.5 14.50 0.527273\n", "37 38 1750 31.0 15.00 0.483871\n", "38 39 1755 35.5 15.70 0.442254\n", "39 40 1760 31.0 16.50 0.532258\n", "40 41 1765 43.0 17.60 0.409302\n", "41 42 1770 47.0 18.50 0.393617\n", "42 43 1775 44.0 19.50 0.443182\n", "43 44 1780 46.0 21.00 0.456522\n", "44 45 1785 42.0 23.00 0.547619\n", "45 46 1790 47.5 25.50 0.536842\n", "46 47 1795 76.0 27.50 0.361842\n", "47 48 1800 79.0 28.50 0.360759\n", "48 49 1805 81.0 29.50 0.364198\n", "49 50 1810 99.0 30.00 0.303030" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# delete a duplicate column\n", "# del my_data[\"purchase_power\"]\n", "my_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique du pouvoir d'achat au cours du temps" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create figure and axis objects\n", "fig,ax = plt.subplots()\n", "\n", "# make a plot\n", "ax.plot(my_data[\"Year\"], my_data[\"Purchase_Power\"], color = \"green\", marker = \"o\")\n", "\n", "# set x-axis l# set x-axis label\n", "ax.set_xlabel(\"Year\",fontsize = 14)\n", "# set y-axis l# set x-axis label\n", "ax.set_ylabel(\"Purchsae_Power\", color = \"green\", fontsize = 14)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique du prix du blé et du salaire sur deux axes différents, sans l'axe du temps." ] }, { "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": [ "# create figure and axis objects\n", "fig,ax = plt.subplots()\n", "\n", "# make a plot\n", "ax.plot(my_data[\"Wheat\"], color = \"red\", marker = \"o\")\n", "\n", "\n", "# set y-axis l# set x-axis label\n", "ax.set_ylabel(\"Wheat\", color = \"red\", fontsize = 14)\n", "\n", "# twin object for two different y-axis on the sample plot\n", "ax2 = ax.twinx()\n", "\n", "\n", "# make a plot with different y-axis using second axis object\n", "ax2.plot(my_data[\"Wages\"] ,color = \"blue\", marker = \"o\")\n", "ax2.set_ylabel(\"Wages\",color = \"blue\", fontsize = 14)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Progression du temps dans la représentation graphique du prix du blé et du salaire" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create figure and axis objects\n", "fig, ax = plt.subplots()\n", "\n", "# twin object for three different y-axis on the sample plot\n", "ax3 = ax.twinx()\n", "\n", "# Set the position of the spine\n", "rspine = ax3.spines['right']\n", "rspine.set_position(('axes', 1.15))\n", "\n", "# make a plot with different y-axis \n", "my_data.Wheat.plot(ax = ax, style ='r-', marker = \"o\")\n", "my_data.Wages.plot(ax = ax, style ='b-', secondary_y = True)\n", "my_data.Year.plot(ax = ax3, style ='g--')\n", "\n", "# add legend\n", "ax.legend([ax.get_lines()[0], ax.right_ax.get_lines()[0], ax3.get_lines()[0]],\n", " ['Wheat','Wages','Year'], bbox_to_anchor = (1.75, 1))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }