{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n", "\n", "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.\n", "\n", "Quelques remarques pour la compréhension des données :\n", "- Jusqu'en 1971, la livre sterling était divisée en 20 shillings, et un shilling en 12 pences.\n", "- Le prix du blé est donné en shillings pour un quart de boisseau de blé. Un quart de boisseau équivaut 15 livres britanniques ou 6,8 kg.\n", "- Les salaires sont donnés en shillings par semaine." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mission 0 : preparer les données\n", "\n", "Le document csv est téléchargé et est enregistré localement." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il existe des données vides.\n", "Suppression des lignes contenant des données inexistantes (année 1815, 1820, 1821)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Représentez, comme Playfair, le prix du blé par des barres\n", "2. Représentez, comme Playfair, les salaires par une surface bleue délimitée par une courbe rouge. \n", "3. Superposez les deux de la même façon dans un seul graphique. Le style de votre graphique pourra rester différent par rapport à l'original, mais l'impression globale devrait être la même." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Représentation du prix du blé par des barres." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'Year')" ] }, "execution_count": 5, "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.bar( data['Year'],data['Wheat'] )\n", "plt.ylabel('Wheat')\n", "plt.xlabel('Year')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Représentation des salaires par une surface bleue délimitée par une courbe rouge" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "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'], 'r-')\n", "plt.fill_between(data['Year'],data['Wages'], color='blue')\n", "\n", "plt.ylabel('Wages')\n", "plt.xlabel('Year')\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Superposition des deux dans un seul graphique" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "p1 = plt.bar( data['Year'],data['Wheat'] )\n", "\n", "p2 = plt.plot( data['Year'],data['Wages'], 'r-')\n", "p2 = plt.fill_between(data['Year'],data['Wages'], color='blue')\n", "\n", "plt.ylabel('Value')\n", "plt.xlabel('Year')\n", "plt.legend([p1, p2], [\"Wheat\", \"Wages\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mission 2 : Améliorez la présentation de ces données\n", "\n", "Pour commencer, Playfair a combiné les deux quantités dans un même graphique en simplifiant les unités \"shillings par quart de boisseau de blé\" et \"shillings par semaine\" à un simple \"shillings\", ce qui aujourd'hui n'est plus admissible. \n", "\n", "Utilisez deux ordonnées différentes, une à gauche et une à droite, et indiquez les unités correctes. À cette occasion, n'hésitez pas à proposer d'autres représentations que des barres et des surface/courbes pour les deux jeux de données si ceci vous paraît judicieux." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "color = 'tab:orange'\n", "ax1.set_xlabel('Year')\n", "ax1.set_ylabel('Shillings par semaine', color=color)\n", "\n", "ax2 = ax1.twinx()\n", "\n", "color = 'tab:blue'\n", "ax2.set_ylabel('shillings par quart de boisseau de blé', color=color)\n", "\n", "p1 = plt.bar( data['Year'],data['Wheat'] )\n", "\n", "p2 = plt.bar( data['Year'],data['Wages'])\n", "\n", "\n", "plt.legend([p1, p2], [\"Wheat\", \"Wages\"])\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mission 3 : Pouvoir d'achat\n", "\n", "L'objectif de Playfair était de montrer que le pouvoir d'achat des ouvriers avait augmenté au cours du temps. \n", "Essayez de mieux faire ressortir cette information. \n", "\n", "1. Faites une représentation graphique du pouvoir d'achat au cours du temps, définie comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire.\n", "\n", "2. Dans un autre graphique, montrez les deux quantités (prix du blé, salaire) sur deux axes différents, sans l'axe du temps.Trouvez une autre façon d'indiquer la progression du temps dans ce graphique.\n", "\n", "3. Quelle représentation des données vous paraît la plus claire ?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Représentation graphique du pouvoir d'achat au cours du temps\n", "\n", "Les salaires sont données en shillings par semaine, et les données sont espacées de 5 ans. La première etapes est donc de calculer le salaire sur 5 ans. Il est ensuite ajouté au tableau de données." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages WagesFor5Year\n", "0 1 1565 41.0 5.00 1300.0\n", "1 2 1570 45.0 5.05 1313.0\n", "2 3 1575 42.0 5.08 1320.8\n", "3 4 1580 49.0 5.12 1331.2\n", "4 5 1585 41.5 5.15 1339.0\n", "5 6 1590 47.0 5.25 1365.0\n", "6 7 1595 64.0 5.54 1440.4\n", "7 8 1600 27.0 5.61 1458.6\n", "8 9 1605 33.0 5.69 1479.4\n", "9 10 1610 32.0 5.78 1502.8\n", "10 11 1615 33.0 5.94 1544.4\n", "11 12 1620 35.0 6.01 1562.6\n", "12 13 1625 33.0 6.12 1591.2\n", "13 14 1630 45.0 6.22 1617.2\n", "14 15 1635 33.0 6.30 1638.0\n", "15 16 1640 39.0 6.37 1656.2\n", "16 17 1645 53.0 6.45 1677.0\n", "17 18 1650 42.0 6.50 1690.0\n", "18 19 1655 40.5 6.60 1716.0\n", "19 20 1660 46.5 6.75 1755.0\n", "20 21 1665 32.0 6.80 1768.0\n", "21 22 1670 37.0 6.90 1794.0\n", "22 23 1675 43.0 7.00 1820.0\n", "23 24 1680 35.0 7.30 1898.0\n", "24 25 1685 27.0 7.60 1976.0\n", "25 26 1690 40.0 8.00 2080.0\n", "26 27 1695 50.0 8.50 2210.0\n", "27 28 1700 30.0 9.00 2340.0\n", "28 29 1705 32.0 10.00 2600.0\n", "29 30 1710 44.0 11.00 2860.0\n", "30 31 1715 33.0 11.75 3055.0\n", "31 32 1720 29.0 12.50 3250.0\n", "32 33 1725 39.0 13.00 3380.0\n", "33 34 1730 26.0 13.30 3458.0\n", "34 35 1735 32.0 13.60 3536.0\n", "35 36 1740 27.0 14.00 3640.0\n", "36 37 1745 27.5 14.50 3770.0\n", "37 38 1750 31.0 15.00 3900.0\n", "38 39 1755 35.5 15.70 4082.0\n", "39 40 1760 31.0 16.50 4290.0\n", "40 41 1765 43.0 17.60 4576.0\n", "41 42 1770 47.0 18.50 4810.0\n", "42 43 1775 44.0 19.50 5070.0\n", "43 44 1780 46.0 21.00 5460.0\n", "44 45 1785 42.0 23.00 5980.0\n", "45 46 1790 47.5 25.50 6630.0\n", "46 47 1795 76.0 27.50 7150.0\n", "47 48 1800 79.0 28.50 7410.0\n", "48 49 1805 81.0 29.50 7670.0\n", "49 50 1810 99.0 30.00 7800.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Wages5year = (data['Wages']*52*5)\n", "data.insert(4, \"Wages for 5 year\", Wages5year, True)\n", "\n", "data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "purchasingPower = data['WagesFor5Year']/data['Wheat']\n", "data.insert(4, \"Purchasing power\", purchasingPower, True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages Purchasing power WagesFor5Year\n", "0 1 1565 41.0 5.00 31.707317 1300.0\n", "1 2 1570 45.0 5.05 29.177778 1313.0\n", "2 3 1575 42.0 5.08 31.447619 1320.8\n", "3 4 1580 49.0 5.12 27.167347 1331.2\n", "4 5 1585 41.5 5.15 32.265060 1339.0\n", "5 6 1590 47.0 5.25 29.042553 1365.0\n", "6 7 1595 64.0 5.54 22.506250 1440.4\n", "7 8 1600 27.0 5.61 54.022222 1458.6\n", "8 9 1605 33.0 5.69 44.830303 1479.4\n", "9 10 1610 32.0 5.78 46.962500 1502.8\n", "10 11 1615 33.0 5.94 46.800000 1544.4\n", "11 12 1620 35.0 6.01 44.645714 1562.6\n", "12 13 1625 33.0 6.12 48.218182 1591.2\n", "13 14 1630 45.0 6.22 35.937778 1617.2\n", "14 15 1635 33.0 6.30 49.636364 1638.0\n", "15 16 1640 39.0 6.37 42.466667 1656.2\n", "16 17 1645 53.0 6.45 31.641509 1677.0\n", "17 18 1650 42.0 6.50 40.238095 1690.0\n", "18 19 1655 40.5 6.60 42.370370 1716.0\n", "19 20 1660 46.5 6.75 37.741935 1755.0\n", "20 21 1665 32.0 6.80 55.250000 1768.0\n", "21 22 1670 37.0 6.90 48.486486 1794.0\n", "22 23 1675 43.0 7.00 42.325581 1820.0\n", "23 24 1680 35.0 7.30 54.228571 1898.0\n", "24 25 1685 27.0 7.60 73.185185 1976.0\n", "25 26 1690 40.0 8.00 52.000000 2080.0\n", "26 27 1695 50.0 8.50 44.200000 2210.0\n", "27 28 1700 30.0 9.00 78.000000 2340.0\n", "28 29 1705 32.0 10.00 81.250000 2600.0\n", "29 30 1710 44.0 11.00 65.000000 2860.0\n", "30 31 1715 33.0 11.75 92.575758 3055.0\n", "31 32 1720 29.0 12.50 112.068966 3250.0\n", "32 33 1725 39.0 13.00 86.666667 3380.0\n", "33 34 1730 26.0 13.30 133.000000 3458.0\n", "34 35 1735 32.0 13.60 110.500000 3536.0\n", "35 36 1740 27.0 14.00 134.814815 3640.0\n", "36 37 1745 27.5 14.50 137.090909 3770.0\n", "37 38 1750 31.0 15.00 125.806452 3900.0\n", "38 39 1755 35.5 15.70 114.985915 4082.0\n", "39 40 1760 31.0 16.50 138.387097 4290.0\n", "40 41 1765 43.0 17.60 106.418605 4576.0\n", "41 42 1770 47.0 18.50 102.340426 4810.0\n", "42 43 1775 44.0 19.50 115.227273 5070.0\n", "43 44 1780 46.0 21.00 118.695652 5460.0\n", "44 45 1785 42.0 23.00 142.380952 5980.0\n", "45 46 1790 47.5 25.50 139.578947 6630.0\n", "46 47 1795 76.0 27.50 94.078947 7150.0\n", "47 48 1800 79.0 28.50 93.797468 7410.0\n", "48 49 1805 81.0 29.50 94.691358 7670.0\n", "49 50 1810 99.0 30.00 78.787879 7800.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.121951\n", "1 0.112222\n", "2 0.120952\n", "3 0.104490\n", "4 0.124096\n", "5 0.111702\n", "6 0.086563\n", "7 0.207778\n", "8 0.172424\n", "9 0.180625\n", "10 0.180000\n", "11 0.171714\n", "12 0.185455\n", "13 0.138222\n", "14 0.190909\n", "15 0.163333\n", "16 0.121698\n", "17 0.154762\n", "18 0.162963\n", "19 0.145161\n", "20 0.212500\n", "21 0.186486\n", "22 0.162791\n", "23 0.208571\n", "24 0.281481\n", "25 0.200000\n", "26 0.170000\n", "27 0.300000\n", "28 0.312500\n", "29 0.250000\n", "30 0.356061\n", "31 0.431034\n", "32 0.333333\n", "33 0.511538\n", "34 0.425000\n", "35 0.518519\n", "36 0.527273\n", "37 0.483871\n", "38 0.442254\n", "39 0.532258\n", "40 0.409302\n", "41 0.393617\n", "42 0.443182\n", "43 0.456522\n", "44 0.547619\n", "45 0.536842\n", "46 0.361842\n", "47 0.360759\n", "48 0.364198\n", "49 0.303030\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "purchasingPower = data['Wages']/data['Wheat']\n", "purchasingPower" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }