From 393bb20929e391a78089a8fde69e9d207a3fcf6e Mon Sep 17 00:00:00 2001 From: 50b5daa9521febd9b1bedafb5c2ad791 <50b5daa9521febd9b1bedafb5c2ad791@app-learninglab.inria.fr> Date: Mon, 30 Nov 2020 19:11:25 +0000 Subject: [PATCH] Replace exercice.ipynb --- module3/exo3/exercice.ipynb | 966 +++++++++++++++++++++++++++++++++++- 1 file changed, 963 insertions(+), 3 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe37..58bec2e 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,966 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Purchasing power of English workers from the 16th to the 19th century\n", + "\n", + "### Author: Ana Granizo\n", + "\n", + "The goal of this document is to present an analysis of the purchasing power of English workers from the 16th to the 19th century. To this end, we are going to analyze [William Playfair's](https://en.wikipedia.org/wiki/William_Playfair) data on Wages and the Price of Wheat. Although Playfair did not publish his raw data we can obtain the numbers from a scan of the graph [here](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html).\n", + "\n", + "A few remarks for understanding the data:\n", + "- Until 1971, the pound sterling was divided into 20 shillings, and a shilling into 12 pence.\n", + "- The wheat price is given in shillings per quarter, a quarter being 15 British pounds or about 6,8 kg.\n", + "- Salaries are given in shillings per week.\n", + "\n", + "**Format of the data** \n", + "The data frame has 53 observations on 3 variables: Year, Wheat, and Wages. \n", + "* Variable Year is in intervals of 5 years from 1565 to 1821: a numeric value \n", + "* Variable Wheat is the price of Wheat (Shillings/Quarter bushel\\*): a numeric value \n", + "* Variable Wage is the weekly wage (Shillings): a numeric value \n", + "\n", + "\\* A bushel is an imperial and US customary unit of volume, based upon an earlier measure of dry capacity. Source: [https://en.wikipedia.org/wiki/Bushel](https://en.wikipedia.org/wiki/Bushel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get and clean the data\n", + "First, we need to import the libraries that we are going to use" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import os.path\n", + "import urllib.request\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we are going to download and present the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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\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": [ + "data_url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", + "file = \"Wheat.csv\"\n", + "if not os.path.exists(file):\n", + " urllib.request.urlretrieve(data_url, file)\n", + "df = pd.read_csv(file)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, we have the first column labeled as \"Unnamed: 0\" and an extra column for the index. So we are going to rename the \"Unnamed: 0\" row as “index” and set it as the index column." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Year Wheat Wages\n", + "index \n", + "1 1565 41.0 5.00\n", + "2 1570 45.0 5.05\n", + "3 1575 42.0 5.08" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.rename({'Unnamed: 0': 'index'}, axis=1)\n", + "df = df.set_index('index')\n", + "df.iloc[:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a quick look at the data, we can see that there are some missing values in the Wages column. We could simply delete those 3 affected rows, but it is better if we look up and delete all the rows that have an empty value. With this, we generalize the solution and we do not expose ourselves to the fact that there may be more empty values that we did not see." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.dropna().copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we review the information in our data frame to check that everything is in order. As we can see, the “Years” column is in integers and the columns “Wheat” and “Wages” are float. And we have now only 50 entries because we deleted the 3 rows that had empty values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 50 entries, 1 to 50\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Year 50 non-null int64 \n", + " 1 Wheat 50 non-null float64\n", + " 2 Wages 50 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 1.6 KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the data, we can see that apparently the data is sampled every 5 years. But we must make sure that this is the case for all the data-frame and determine if maybe there are some years missing." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "first_year = df['Year'].iloc[0]\n", + "for year in df[\"Year\"].iloc[1:]:\n", + " diff_year = year - first_year\n", + " first_year = year\n", + " if (diff_year!=5):\n", + " print(\"There are some years missing\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we check that all the data is every 5 years we can start analyzing the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Reproduce Playfair's graph from the numerical data.\n", + "We are first going to reproduce the original Playfair's graph. Where the wheat price is presented in grey bars and the salarie per weak by a blue surface delimited by a red curve. \n", + "As we can see in this graps, the wheat price was a bit regular with little ups and downs from 1600 to 1795. From 1795 there is a peak of growth, going from 50 Shillings/Quarter bushel to around 80 Shillings/Quarter bushel. \n", + "Regarding the weekly work wage, we can see that the curve is growing uniformly over the years. \n", + "\n", + "We can see the original graph after the plot we created. As we can observe, our plot differs a bit from the original in that it does not have the last 3 values of the price of wheat, because we deleted those rows due to the fact that they did not have the values corresponding to the salary." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (15, 10))\n", + "plt.bar(df['Year'], df['Wheat'], color ='grey', width = 5,zorder=1) \n", + "plt.plot(df['Year'], df['Wages'], color ='red',zorder=2)\n", + "plt.fill_between(df['Year'], df['Wages'], color ='blue',zorder=2)\n", + " \n", + "plt.xlabel(\"5 Years each division\") \n", + "plt.ylabel(\"Price of the Quarter of Wheat in Shillings\") \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Improve the presentation of the data.\n", + "\n", + "Because the original graph had a failure at using only a generalized Y-axis as “shillings”, both for “shillings per quarter” and for “shillings per week”, we are going to improve this problem and give their own scale for each value. The left axis shows the scale for \"shillings per quarter\" and the right one for \"shillings per week\"." + ] + }, + { + "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(figsize = (10, 7))\n", + "ax1.set_xlabel('Years')\n", + "ax1.set_ylabel('Shillings per quarter', color=\"blue\")\n", + "ax1.bar(df['Year'], df['Wheat'], color=\"blue\", width = 4)\n", + "ax1.tick_params(axis='y', labelcolor=\"blue\")\n", + "ax1.xaxis.set_ticks(np.arange(1565, 1820, 20))\n", + "ax1.yaxis.set_ticks(np.arange(0, 110, 10))\n", + "ax2 = ax1.twinx()\n", + "ax2.set_ylabel('Shillings per week', color=\"red\")\n", + "ax2.plot(df['Year'], df['Wages'], color=\"red\")\n", + "ax2.tick_params(axis='y', labelcolor=\"red\")\n", + "ax2.yaxis.set_ticks(np.arange(0, 110, 10))\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, now each of the values has its own axis marked on the graph, but we must consider that this does not mean that there is a relationship between the two scale-axes since one is in \"shillings per quarter\" and the other \"Shillings per week\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Highlight the feature that the workers' purchasing power had increased over time.\n", + "\n", + "In order to highlight the fact that the workers' purchasing power had increased over time, first, we need to calculate the purchasing power, which is defined as the quantity of wheat a worker can buy with a weekly salary, as a function of time. So, we must divide the weekly wage by the price of wheat. Since this price of the wheat is given in Shillings per quarter bushel and, as we mentioned at the beginning of the document, a quarter is about 6.8 kg, we need to divide the value of wheat by 6.8 to obtain the value of wheat in Shillings per kg. And by dividing the weekly wage by the price of wheat in kg we obtain the number of kg of wheat that can be bought weekly." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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index
1156541.05.000.829268
2157045.05.050.763111
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\n", + "
" + ], + "text/plain": [ + " Year Wheat Wages PurchasingPower\n", + "index \n", + "1 1565 41.0 5.00 0.829268\n", + "2 1570 45.0 5.05 0.763111\n", + "3 1575 42.0 5.08 0.822476" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['PurchasingPower'] = df['Wages']/(df['Wheat']/6.8)\n", + "df.iloc[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (10, 7))\n", + "ax.plot(df['Year'], df['PurchasingPower'], color ='red') \n", + "ax.xaxis.set_ticks(np.arange(1565, 1820, 20))\n", + "ax.yaxis.set_ticks(np.arange(0, 4, 0.5))\n", + "ax.set_xlabel(\"Years\") \n", + "ax.set_ylabel(\"Purchasing Power with a Weekly Salary \") \n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at this graph we can notice that the curve is in fact increasing, which means that the Purchasing Power did increase over the years, but around 1785 it seems to be starting to decrease. \n", + "To do a deeper analysis, now we are going to plot a graph to show booth the wheat price and the salary on two different axes, without an explicit time axis. But in order not to lose the information of the advancement of time, we are going to plot the points with the color corresponding to the years." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (10, 7))\n", + "plt.scatter(df['Wheat'], df['Wages'], c=df['Year'], alpha=1.5, cmap='viridis')\n", + "plt.xlabel(\"Wheat price\") \n", + "plt.ylabel(\"Salary\")\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph shows us that as the years go by, the salary increases. But the wheat remains around the same values, between 25 and 50, except for the last values from around 1785 when the price of wheat increases over 70 shillings per kg. \n", + "This corroborates the conclusion we got from the previous graph for the Purchasing Power. As the years went by, the salaries increased and the wheat price remained the same, which generated more purchasing power, with a decrease around 1785. \n", + "\n", + "The Purchasing Power graph looks clearest because it is easier to get the conclusion that the purchasing power is rising when the curve on the graph rises. While in the other graph it is not so intuitive without an analysis of the graph." + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +977,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1