{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle\n", "\n", "Dans le cadre de cette exercice évalué par les pairs (Mooc RR, mod3) j'ai choisi le sujet n°2 intitulé :\n", "**Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle**\n", "\n", "## Contexte de l'étude\n", "\n", "William Playfair un des pionnier de la représentation graphique des données, a réalisé un graphique montrant l'évolution du prix du blé et du salaire moyen entre 1565 et 1821. Ce graphique a été publié en 1822 dans son livre *A Letter on our Agricultural Distresses, Their Causes and Remedies*. Ci-dessous une reproduction hébergée sur [Wikipédia][graph original].\n", "\n", "![Chart Showing at One View the Price of the Quarter of Wheat, and Wages of Labour by the Week, from 1565 to 1821](playfair_ori_prixble_salaire.png)\n", "\n", "[graph original]:https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png\n", "\n", "Le premier objectif de l'étude est de reproduire ce graphe, puis dans un second temps de corriger le graphique.Een effet W. Playfair a utilisé la même unité pour représenter deux quantités différentes sur l'axe des ordonnées. Enfin dans un troisième temps le but sera d'améliorer la représentation du pouvoir d'achat des agriculteurs anglais sur cette période.\n", "\n", "## Les données\n", "\n", "### Sources\n", "\n", "W. Playfair n'a pas publié les données numériques brutes de son étude. Néanmoins une version numérisée est diponible [ici][data_url], réalisé par [Vincent Arel-Bundock] et publié sur son site [R datasets][vab r datasets].\n", "\n", "[data_url]: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\n", "[Vincent Arel-Bundock]: https://github.com/vincentarelbundock\n", "[vab r datasets]: https://vincentarelbundock.github.io/Rdatasets/\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# import des bibliothèques\n", "import urllib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from os import listdir\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fichier déjà téléchargé\n" ] }, { "data": { "text/plain": [ "['exercice_python_en.org',\n", " 'exercice_fr.ipynb',\n", " 'Wheat.csv',\n", " 'exercice.ipynb',\n", " 'exercice_fr.Rmd',\n", " 'playfair_ori_prixble_salaire.png',\n", " 'exercice_python_fr.org',\n", " 'exercice_R_en.org',\n", " 'exercice_R_fr.org',\n", " 'exercice_en.Rmd',\n", " 'exercice_en.ipynb',\n", " '.ipynb_checkpoints']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# téléchargement du fichier\n", "data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n", "filename = 'Wheat.csv'\n", "\n", "curFiles = set(listdir())\n", "\n", "# téléchargement automatique du fichier\n", "# si non présent dans le répertoire\n", "if not(filename in curFiles):\n", " print('Téléchargement du fichier')\n", " urllib.request.urlretrieve(data_url, filename)\n", "else:\n", " print('Fichier déjà téléchargé')\n", "\n", "listdir()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# lecture du fichier\n", "rawdata = pd.read_csv(filename, index_col=0)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Year Wheat Wages\n", "1 1565 41.0 5.00\n", "2 1570 45.0 5.05\n", "3 1575 42.0 5.08\n", "4 1580 49.0 5.12\n", "5 1585 41.5 5.15" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vérification des données\n", "\n", "Les données sont vérifiées sur les critères suivants :\n", "\n", "* présence de lignes vides\n", "* rupture de date, ou division non régulière\n", "\n", "Il est à noter les données sont enregistrées tous les 5 ans." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearWheatWages
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" ], "text/plain": [ " Year Wheat Wages\n", "51 1815 78.0 NaN\n", "52 1820 54.0 NaN\n", "53 1821 54.0 NaN" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# présence de lignes vides\n", "rawdata[rawdata.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les 3 dernières lignes sont vides pour la colonne *Wages*, ce qui explique l'arrêt de la ligne rouge et de la surface bleue dans le document originale." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Années de rupture :\n", "1820\n" ] } ], "source": [ "# rupture de date, ou division non régulière\n", "print(\"Années de rupture :\")\n", "expect_delta = 5\n", "for y1, y2 in zip(rawdata['Year'][:-1], rawdata['Year'][1:]):\n", " delta_y = y2 - y1\n", " if delta_y != expect_delta :\n", " print(y1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seule l'année 1820 présente une rupture. En effet l'enregistrement suivant est 1821. Ce qui explique la barre moins large sur le graphique original, pour les années 1820 à 1821.\n", "\n", "### Création des unités\n", "\n", "Pour rappel les untités originales des données sont les suivantes :" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "wheat_unit = 'Shillings pour un quart de boisseau'\n", "wages_unit = 'Shillings par semaine'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reproduction du graphique original\n", "\n", "Ci-dessous le graphique original est reproduit à l'aide de `Matplotlib`.\n", "Pour représenter des dégradés voir ce lien : https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/gradient_bar.html?highlight=gradient" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib.ticker import MultipleLocator\n", "\n", "fig1, ax1 = plt.subplots(1,1)\n", "\n", "fig1.set_size_inches(2.54*6, 2.54*3)\n", "\n", "# === représentation du prix du blé === #\n", "ax1.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='dimgrey')\n", "ax1.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='dimgrey')\n", "\n", "# graduation proche de l'original, avec les siècles comme majeure\n", "# et une graduation mineure tous les 5 ans\n", "# aide sur les ticks locator :\n", "# https://matplotlib.org/gallery/ticks_and_spines/tick-locators.html#sphx-glr-gallery-ticks-and-spines-tick-locators-py\n", "ax1.xaxis.set_major_locator(MultipleLocator(100))\n", "ax1.xaxis.set_minor_locator(MultipleLocator(5))\n", "\n", "ax1.grid(True, which='both')\n", "\n", "# les marges de l'axe x sont diminuées\n", "ax1.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n", "\n", "ax1.set_xlabel('5 Years each division')\n", "\n", "# === représentation du salaire === #\n", "# l'axe 2 partage l'axe x de l'axe 1\n", "ax2 = ax1.twinx()\n", "ax2.fill_between(rawdata['Year'], rawdata['Wages'])\n", "ax2.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n", "\n", "# les deux axes ont les mêmes limites, pour reproduire l'original\n", "myylim = ax1.get_ylim()\n", "ax2.set_ylim(myylim)\n", "\n", "ax2.set_ylabel('Price of the Quarter of Wheat in Shillings')\n", "\n", "ax1.set_title(\"\"\"Chart Showing at One View\n", "the Price of the Quarter of Wheat, and Wages of Labour\n", "by the Week, from 1565 to 1821\"\"\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphique avec deux axes des ordonnées\n", "Le graphique précédent est repris et amélioré avec deux axes distincts cette fois-ci.\n", "Le jeu de couleur est changé pour un affichage plus lisible." ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Chart Showing at One View\\nthe Price of the Quarter of Wheat, and Wages of Labour\\nby the Week, from 1565 to 1821')" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig2, ax11 = plt.subplots(1,1)\n", "\n", "fig2.set_size_inches(2.54*6, 2.54*3)\n", "\n", "# === représentation du prix du blé === #\n", "ax11.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='lightgrey')\n", "ax11.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='lightgrey')\n", "\n", "# graduation proche de l'original, avec les siècles comme majeure\n", "# et une graduation mineure tous les 5 ans\n", "ax11.xaxis.set_major_locator(MultipleLocator(100))\n", "ax11.xaxis.set_minor_locator(MultipleLocator(5))\n", "# grille\n", "ax11.grid(True, which='both')\n", "\n", "ax11.set_xlabel('5 Years each division')\n", "ax11.set_ylabel('Price of the Quarter of Wheat in Shillings')\n", "\n", "# les marges de l'axe x sont diminuées\n", "ax11.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n", "\n", "# === représentation du salaire === #\n", "# l'axe 2 partage l'axe x de l'axe 1\n", "ax12 = ax11.twinx()\n", "ax12.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n", "\n", "# les deux axes ont des limites différentes pour montrer que les axes sont différents\n", "myylim = ax11.get_ylim()\n", "ax12.set_ylim([0,myylim[1]/2])\n", "\n", "ax12.set_ylabel('Wages in Shillings/Week')\n", "ax12.yaxis.label.set_color('r')\n", "ax12.tick_params(axis='y', colors='r')\n", "\n", "ax11.set_title(\"\"\"Chart Showing at One View\n", "the Price of the Quarter of Wheat, and Wages of Labour\n", "by the Week, from 1565 to 1821\"\"\")\n" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "53 1821\n", "Name: Year, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata['Year'][-1:]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearWheatWages
1156541.05.00
2157045.05.05
3157542.05.08
4158049.05.12
5158541.55.15
6159047.05.25
7159564.05.54
8160027.05.61
9160533.05.69
10161032.05.78
11161533.05.94
12162035.06.01
13162533.06.12
14163045.06.22
15163533.06.30
16164039.06.37
17164553.06.45
18165042.06.50
19165540.56.60
20166046.56.75
21166532.06.80
22167037.06.90
23167543.07.00
24168035.07.30
25168527.07.60
26169040.08.00
27169550.08.50
28170030.09.00
29170532.010.00
30171044.011.00
31171533.011.75
32172029.012.50
33172539.013.00
34173026.013.30
35173532.013.60
36174027.014.00
37174527.514.50
38175031.015.00
39175535.515.70
40176031.016.50
41176543.017.60
42177047.018.50
43177544.019.50
44178046.021.00
45178542.023.00
46179047.525.50
47179576.027.50
48180079.028.50
49180581.029.50
50181099.030.00
51181578.0NaN
52182054.0NaN
53182154.0NaN
\n", "
" ], "text/plain": [ " Year Wheat Wages\n", "1 1565 41.0 5.00\n", "2 1570 45.0 5.05\n", "3 1575 42.0 5.08\n", "4 1580 49.0 5.12\n", "5 1585 41.5 5.15\n", "6 1590 47.0 5.25\n", "7 1595 64.0 5.54\n", "8 1600 27.0 5.61\n", "9 1605 33.0 5.69\n", "10 1610 32.0 5.78\n", "11 1615 33.0 5.94\n", "12 1620 35.0 6.01\n", "13 1625 33.0 6.12\n", "14 1630 45.0 6.22\n", "15 1635 33.0 6.30\n", "16 1640 39.0 6.37\n", "17 1645 53.0 6.45\n", "18 1650 42.0 6.50\n", "19 1655 40.5 6.60\n", "20 1660 46.5 6.75\n", "21 1665 32.0 6.80\n", "22 1670 37.0 6.90\n", "23 1675 43.0 7.00\n", "24 1680 35.0 7.30\n", "25 1685 27.0 7.60\n", "26 1690 40.0 8.00\n", "27 1695 50.0 8.50\n", "28 1700 30.0 9.00\n", "29 1705 32.0 10.00\n", "30 1710 44.0 11.00\n", "31 1715 33.0 11.75\n", "32 1720 29.0 12.50\n", "33 1725 39.0 13.00\n", "34 1730 26.0 13.30\n", "35 1735 32.0 13.60\n", "36 1740 27.0 14.00\n", "37 1745 27.5 14.50\n", "38 1750 31.0 15.00\n", "39 1755 35.5 15.70\n", "40 1760 31.0 16.50\n", "41 1765 43.0 17.60\n", "42 1770 47.0 18.50\n", "43 1775 44.0 19.50\n", "44 1780 46.0 21.00\n", "45 1785 42.0 23.00\n", "46 1790 47.5 25.50\n", "47 1795 76.0 27.50\n", "48 1800 79.0 28.50\n", "49 1805 81.0 29.50\n", "50 1810 99.0 30.00\n", "51 1815 78.0 NaN\n", "52 1820 54.0 NaN\n", "53 1821 54.0 NaN" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata" ] }, { "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 }