{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n", "\n", "On utilise le [jeu de données de William Playfair](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html) pour étudier l'évolution du pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle.\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importation du jeu de données\n", "\n", "On importe le jeu de données d'après l'url suivant :" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWages
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23157542.05.08
34158049.05.12
45158541.55.15
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67159564.05.54
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89160533.05.69
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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
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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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", "data = pd.read_csv(data_url)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Premier graphique \n", "\n", "Pour ce premier graphique, l'objectif est de reproduire quelque chose de similaire au [graphique produit par William Playfair](https://upload.wikimedia.org/wikipedia/commons/3/3a/Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat%2C_and_Wages_of_Labour_by_the_Week%2C_from_1565_to_1821.png). \n", "On commence par créer les deux premier graphiques bruts :" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evolution du prix du blé :" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.bar(data['Year'],data['Wheat'], 5, color='grey')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evolution du salaire hebdomadaire :" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data['Year'],data['Wages'], color='r')\n", "plt.fill_between(data['Year'], data['Wages'], color='#539ecd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut maintenant superposer les deux graphiques, en ajoutant un échelle à droite et les légendes." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'The price of the quarter of wheat in shillings')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig1, ax1 = plt.subplots()\n", "ax1.set_title(\"The price of the quarter of wheat and wages of labour by the week\")\n", "ax1.bar(data['Year'], data['Wheat'],5, color='grey')\n", "ax1.set_xlabel(\"Years\")\n", "\n", "ax2 = ax1.twinx()\n", "ax2.set_ylim((0,100))\n", "ax2.plot(data['Year'],data['Wages'], color='r')\n", "ax2.fill_between(data['Year'], data['Wages'], color='#539ecd')\n", "ax2.set_ylabel(\"The price of the quarter of wheat in shillings\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Amélioration de la présentation des données\n", "On ajuste les unités du graphique, on remplace shillings par shillings par quart de boisseau de bé ou shillings par semaine." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Weekly wages (in shillings by week)')" ] }, "execution_count": 52, "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": [ "fig1, ax1 = plt.subplots()\n", "ax1.bar(data['Year'], data['Wheat'],5, color='grey')\n", "ax1.set_title(\"The price of the quarter of wheat and wages of labour by the week\")\n", "ax1.set_xlabel(\"Years\")\n", "ax1.set_ylabel(\"Price of the quarter of wheat (in shillings by quarter of wheat)\")\n", "\n", "ax2 = ax1.twinx()\n", "ax2.set_ylim((0,100))\n", "ax2.plot(data['Year'],data['Wages'], color='r')\n", "ax2.fill_between(data['Year'], data['Wages'], color='#539ecd')\n", "ax2.set_ylabel(\"Weekly wages (in shillings by week)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ajustement des données pour observer l'évolution du pouvoir d'achat\n", "\n", "On souhaite représenter le pouvoir d'achat au cours du temps, défini comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire. \n", "On crée une nouvelle colonne au tableau : la colonne Power qui représente le pouvoir d'achat de l'année, la quantité de quart de boisseaux de blé qu'un ouvrier peut acheter par semaine." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWagesPower
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
5051181578.0NaNNaN
5152182054.0NaNNaN
5253182154.0NaNNaN
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages 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\n", "50 51 1815 78.0 NaN NaN\n", "51 52 1820 54.0 NaN NaN\n", "52 53 1821 54.0 NaN NaN" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Power'] = data['Wages']/data['Wheat']\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On représente maintenant l'évolution de ce pouvoir d'achat dans le temps" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Buying power')" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(data['Year'],data['Power'], color='green')\n", "plt.xlabel('Year')\n", "plt.ylabel('Buying power')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evolution du prix du blé en fonction du salaire\n", "\n", "Dans un dernier graphique, on veut montrer l'évolution du prix du blé en fonction du salaire, sans l'axe du temps. On indique la progression du temps avec la couleur : plus l'année est récente, plus la couleur du point est foncée." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Price of the quarter of wheat (in shillings by quarter of wheat)')" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(data['Wages'],data['Wheat'], c=data['Year'], cmap = 'Blues')\n", "plt.xlabel('Weekly wages (in shillings by week)')\n", "plt.ylabel('Price of the quarter of wheat (in shillings by quarter of wheat)')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Parmi tous ces graphiques, le plus adapté pour moi est le graphique qui représente directement l'évolution du pouvoir d'achat dans le temps. On voit bien l'augmentation progressive du pouvoir d'achat jusqu'aux années 1700, puis une augmentation brutale jusqu'en 1730 avant une stabilisation puis une diminution. \n", "C'est le comportement global, mais en regardant chaque année, on constate que l'évolution est \"en dents de scies\" : une année à fort pouvoir d'achat est souvent suivie d'une année à plus faible pouvoir d'achat, et inversement. Grace aux autres graphiques, on comprend que ce comportement est directement du à la fluctuation forte d'une année sur l'autre du prix du blé, car le salaire est lui en constante augmentation sur la période de temps étudiée." ] } ], "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 }