{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" ] }, { "cell_type": "code", "execution_count": 1, "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": 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": [ "# Voir les lignes avec des données manquantes\n", "\n", "data[data.isnull().any(axis = 1)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Supprimer la ligne qui ne contient pas de données valables\n", "# Copier les données\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": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(my_data[\"Year\"], my_data[\"Wheat\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation grahique des salaires" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "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(my_data[\"Year\"], my_data[\"Wages\"], \"r\")\n", "\n", " \n", "y1 = my_data[\"Wages\"]\n", "x = my_data[\"Year\"]\n", " \n", "plt.fill_between(x, y1, color='#539ecd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Superposition des deux graphiques" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "\n", "p = plt.bar(my_data[\"Year\"], my_data[\"Wheat\"]), plt.plot(my_data[\"Year\"], my_data[\"Wages\"], \"r\")\n", "\n", "\n", "x = my_data[\"Year\"]\n", "y2 = my_data[\"Wages\"]\n", "\n", "\n", "plt.fill_between(x, y2, color='#539ecd')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utlisaion de 2 axes d'ordonnées" ] }, { "cell_type": "code", "execution_count": 8, "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 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(\"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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWagespurchase_powerPurchase_Power
01156541.05.000.1219510.121951
12157045.05.050.1122220.112222
23157542.05.080.1209520.120952
34158049.05.120.1044900.104490
45158541.55.150.1240960.124096
56159047.05.250.1117020.111702
67159564.05.540.0865630.086563
78160027.05.610.2077780.207778
89160533.05.690.1724240.172424
910161032.05.780.1806250.180625
1011161533.05.940.1800000.180000
1112162035.06.010.1717140.171714
1213162533.06.120.1854550.185455
1314163045.06.220.1382220.138222
1415163533.06.300.1909090.190909
1516164039.06.370.1633330.163333
1617164553.06.450.1216980.121698
1718165042.06.500.1547620.154762
1819165540.56.600.1629630.162963
1920166046.56.750.1451610.145161
2021166532.06.800.2125000.212500
2122167037.06.900.1864860.186486
2223167543.07.000.1627910.162791
2324168035.07.300.2085710.208571
2425168527.07.600.2814810.281481
2526169040.08.000.2000000.200000
2627169550.08.500.1700000.170000
2728170030.09.000.3000000.300000
2829170532.010.000.3125000.312500
2930171044.011.000.2500000.250000
3031171533.011.750.3560610.356061
3132172029.012.500.4310340.431034
3233172539.013.000.3333330.333333
3334173026.013.300.5115380.511538
3435173532.013.600.4250000.425000
3536174027.014.000.5185190.518519
3637174527.514.500.5272730.527273
3738175031.015.000.4838710.483871
3839175535.515.700.4422540.442254
3940176031.016.500.5322580.532258
4041176543.017.600.4093020.409302
4142177047.018.500.3936170.393617
4243177544.019.500.4431820.443182
4344178046.021.000.4565220.456522
4445178542.023.000.5476190.547619
4546179047.525.500.5368420.536842
4647179576.027.500.3618420.361842
4748180079.028.500.3607590.360759
4849180581.029.500.3641980.364198
4950181099.030.000.3030300.303030
\n", "
" ], "text/plain": [ " Unnamed: 0 Year Wheat Wages purchase_power Purchase_Power\n", "0 1 1565 41.0 5.00 0.121951 0.121951\n", "1 2 1570 45.0 5.05 0.112222 0.112222\n", "2 3 1575 42.0 5.08 0.120952 0.120952\n", "3 4 1580 49.0 5.12 0.104490 0.104490\n", "4 5 1585 41.5 5.15 0.124096 0.124096\n", "5 6 1590 47.0 5.25 0.111702 0.111702\n", "6 7 1595 64.0 5.54 0.086563 0.086563\n", "7 8 1600 27.0 5.61 0.207778 0.207778\n", "8 9 1605 33.0 5.69 0.172424 0.172424\n", "9 10 1610 32.0 5.78 0.180625 0.180625\n", "10 11 1615 33.0 5.94 0.180000 0.180000\n", "11 12 1620 35.0 6.01 0.171714 0.171714\n", "12 13 1625 33.0 6.12 0.185455 0.185455\n", "13 14 1630 45.0 6.22 0.138222 0.138222\n", "14 15 1635 33.0 6.30 0.190909 0.190909\n", "15 16 1640 39.0 6.37 0.163333 0.163333\n", "16 17 1645 53.0 6.45 0.121698 0.121698\n", "17 18 1650 42.0 6.50 0.154762 0.154762\n", "18 19 1655 40.5 6.60 0.162963 0.162963\n", "19 20 1660 46.5 6.75 0.145161 0.145161\n", "20 21 1665 32.0 6.80 0.212500 0.212500\n", "21 22 1670 37.0 6.90 0.186486 0.186486\n", "22 23 1675 43.0 7.00 0.162791 0.162791\n", "23 24 1680 35.0 7.30 0.208571 0.208571\n", "24 25 1685 27.0 7.60 0.281481 0.281481\n", "25 26 1690 40.0 8.00 0.200000 0.200000\n", "26 27 1695 50.0 8.50 0.170000 0.170000\n", "27 28 1700 30.0 9.00 0.300000 0.300000\n", "28 29 1705 32.0 10.00 0.312500 0.312500\n", "29 30 1710 44.0 11.00 0.250000 0.250000\n", "30 31 1715 33.0 11.75 0.356061 0.356061\n", "31 32 1720 29.0 12.50 0.431034 0.431034\n", "32 33 1725 39.0 13.00 0.333333 0.333333\n", "33 34 1730 26.0 13.30 0.511538 0.511538\n", "34 35 1735 32.0 13.60 0.425000 0.425000\n", "35 36 1740 27.0 14.00 0.518519 0.518519\n", "36 37 1745 27.5 14.50 0.527273 0.527273\n", "37 38 1750 31.0 15.00 0.483871 0.483871\n", "38 39 1755 35.5 15.70 0.442254 0.442254\n", "39 40 1760 31.0 16.50 0.532258 0.532258\n", "40 41 1765 43.0 17.60 0.409302 0.409302\n", "41 42 1770 47.0 18.50 0.393617 0.393617\n", "42 43 1775 44.0 19.50 0.443182 0.443182\n", "43 44 1780 46.0 21.00 0.456522 0.456522\n", "44 45 1785 42.0 23.00 0.547619 0.547619\n", "45 46 1790 47.5 25.50 0.536842 0.536842\n", "46 47 1795 76.0 27.50 0.361842 0.361842\n", "47 48 1800 79.0 28.50 0.360759 0.360759\n", "48 49 1805 81.0 29.50 0.364198 0.364198\n", "49 50 1810 99.0 30.00 0.303030 0.303030" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# purchase_power = wages / wheat\n", "my_data[\"Purchase_Power\"] = my_data[\"Wages\"] / my_data[\"Wheat\"]\n", "\n", "my_data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Effacer une colonne en double\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": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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": "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 }