{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Données de Playfair" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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rownamesYearWheatWages
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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
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" ], "text/plain": [ " rownames 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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", "c=pd.read_csv(url)\n", "c" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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rownamesYearWheatWages
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
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5051181578.0NaN
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" ], "text/plain": [ " rownames 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": [ "c1 = pd.DataFrame(c,\n", " columns=['rownames','Year','Wheat','Wages'])\n", "c1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "x=c1['Year']\n", "y1=c1['Wheat']\n", "y2=c1['Wages']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Wages')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots(figsize=(9, 6))\n", "plt.plot(x,y2, color='r')\n", "plt.fill_between(x, y2) \n", "plt.bar(x,y1,width = 5, color='black')\n", "ax2 = ax1.twinx() \n", "plt.xlabel('year')\n", "ax1.set_ylabel('Wheat')\n", "ax2.set_ylabel('Wages')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Changement des unités" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Wheat price \n", "Wheat2=c1['Wheat']/6.8\n", "c1.insert(3, 'Wheat2', Wheat2 )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# shilling per month\n", "Wages2=4*c1['Wages']\n", "c1.insert(5, 'Wages/month', Wages2 )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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67159564.09.4117655.5422.16
78160027.03.9705885.6122.44
89160533.04.8529415.6922.76
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1011161533.04.8529415.9423.76
1112162035.05.1470596.0124.04
1213162533.04.8529416.1224.48
1314163045.06.6176476.2224.88
1415163533.04.8529416.3025.20
1516164039.05.7352946.3725.48
1617164553.07.7941186.4525.80
1718165042.06.1764716.5026.00
1819165540.55.9558826.6026.40
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2122167037.05.4411766.9027.60
2223167543.06.3235297.0028.00
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2425168527.03.9705887.6030.40
2526169040.05.8823538.0032.00
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2829170532.04.70588210.0040.00
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3435173532.04.70588213.6054.40
3536174027.03.97058814.0056.00
3637174527.54.04411814.5058.00
3738175031.04.55882415.0060.00
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5051181578.011.470588NaNNaN
5152182054.07.941176NaNNaN
5253182154.07.941176NaNNaN
\n", "
" ], "text/plain": [ " rownames Year Wheat Wheat2 Wages Wages/month\n", "0 1 1565 41.0 6.029412 5.00 20.00\n", "1 2 1570 45.0 6.617647 5.05 20.20\n", "2 3 1575 42.0 6.176471 5.08 20.32\n", "3 4 1580 49.0 7.205882 5.12 20.48\n", "4 5 1585 41.5 6.102941 5.15 20.60\n", "5 6 1590 47.0 6.911765 5.25 21.00\n", "6 7 1595 64.0 9.411765 5.54 22.16\n", "7 8 1600 27.0 3.970588 5.61 22.44\n", "8 9 1605 33.0 4.852941 5.69 22.76\n", "9 10 1610 32.0 4.705882 5.78 23.12\n", "10 11 1615 33.0 4.852941 5.94 23.76\n", "11 12 1620 35.0 5.147059 6.01 24.04\n", "12 13 1625 33.0 4.852941 6.12 24.48\n", "13 14 1630 45.0 6.617647 6.22 24.88\n", "14 15 1635 33.0 4.852941 6.30 25.20\n", "15 16 1640 39.0 5.735294 6.37 25.48\n", "16 17 1645 53.0 7.794118 6.45 25.80\n", "17 18 1650 42.0 6.176471 6.50 26.00\n", "18 19 1655 40.5 5.955882 6.60 26.40\n", "19 20 1660 46.5 6.838235 6.75 27.00\n", "20 21 1665 32.0 4.705882 6.80 27.20\n", "21 22 1670 37.0 5.441176 6.90 27.60\n", "22 23 1675 43.0 6.323529 7.00 28.00\n", "23 24 1680 35.0 5.147059 7.30 29.20\n", "24 25 1685 27.0 3.970588 7.60 30.40\n", "25 26 1690 40.0 5.882353 8.00 32.00\n", "26 27 1695 50.0 7.352941 8.50 34.00\n", "27 28 1700 30.0 4.411765 9.00 36.00\n", "28 29 1705 32.0 4.705882 10.00 40.00\n", "29 30 1710 44.0 6.470588 11.00 44.00\n", "30 31 1715 33.0 4.852941 11.75 47.00\n", "31 32 1720 29.0 4.264706 12.50 50.00\n", "32 33 1725 39.0 5.735294 13.00 52.00\n", "33 34 1730 26.0 3.823529 13.30 53.20\n", "34 35 1735 32.0 4.705882 13.60 54.40\n", "35 36 1740 27.0 3.970588 14.00 56.00\n", "36 37 1745 27.5 4.044118 14.50 58.00\n", "37 38 1750 31.0 4.558824 15.00 60.00\n", "38 39 1755 35.5 5.220588 15.70 62.80\n", "39 40 1760 31.0 4.558824 16.50 66.00\n", "40 41 1765 43.0 6.323529 17.60 70.40\n", "41 42 1770 47.0 6.911765 18.50 74.00\n", "42 43 1775 44.0 6.470588 19.50 78.00\n", "43 44 1780 46.0 6.764706 21.00 84.00\n", "44 45 1785 42.0 6.176471 23.00 92.00\n", "45 46 1790 47.5 6.985294 25.50 102.00\n", "46 47 1795 76.0 11.176471 27.50 110.00\n", "47 48 1800 79.0 11.617647 28.50 114.00\n", "48 49 1805 81.0 11.911765 29.50 118.00\n", "49 50 1810 99.0 14.558824 30.00 120.00\n", "50 51 1815 78.0 11.470588 NaN NaN\n", "51 52 1820 54.0 7.941176 NaN NaN\n", "52 53 1821 54.0 7.941176 NaN NaN" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Wages/Month')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y3=c1['Wheat2']\n", "y4=c1['Wages/month']\n", "fig, ax1 = plt.subplots(figsize=(9, 6))\n", "plt.bar(x,y3,width = 5, color='r')\n", "plt.plot(x,y4/10)\n", "\n", "ax2 = ax1.twinx() \n", "ax1.set_xlabel('year')\n", "ax1.set_ylabel('shillings/kg')\n", "ax2.set_ylabel('Wages/Month')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pouvoir d'achat " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "p=c1['Wages']/c1['Wheat2']" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Wages/Wheat')" ] }, "execution_count": 21, "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": [ "plt.figure()\n", "plt.plot(x,p)\n", "plt.xlabel('Years')\n", "plt.ylabel('Wages/Wheat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }