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| { | |
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| "source": [ | |
| "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n", | |
| "\n", | |
| "<h1 align=center><font size = 5>Area Plots, Histograms, and Bar Plots</font></h1>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
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| }, | |
| "source": [ | |
| "## Introduction\n", | |
| "\n", | |
| "In this lab, we will continue exploring the Matplotlib library and will learn how to create additional plots, namely area plots, histograms, and bar charts." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
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| }, | |
| "source": [ | |
| "## Table of Contents\n", | |
| "\n", | |
| "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n", | |
| "\n", | |
| "1. [Exploring Datasets with *pandas*](#0)<br>\n", | |
| "2. [Downloading and Prepping Data](#2)<br>\n", | |
| "3. [Visualizing Data using Matplotlib](#4) <br>\n", | |
| "4. [Area Plots](#6) <br>\n", | |
| "5. [Histograms](#8) <br>\n", | |
| "6. [Bar Charts](#10) <br>\n", | |
| "</div>\n", | |
| "<hr>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
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| } | |
| }, | |
| "source": [ | |
| "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n", | |
| "\n", | |
| "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library that we are exploring in the course is [Matplotlib](http://matplotlib.org/).\n", | |
| "\n", | |
| "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n", | |
| "\n", | |
| "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
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| "deletable": true, | |
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| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Downloading and Prepping Data <a id=\"2\"></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "source": [ | |
| "Import Primary Modules. The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np # useful for many scientific computing in Python\n", | |
| "import pandas as pd # primary data structure library" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n", | |
| "```\n", | |
| "!conda install -c anaconda xlrd --yes\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Download the dataset and read it into a *pandas* dataframe." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Data downloaded and read into a dataframe!\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n", | |
| " sheet_name='Canada by Citizenship',\n", | |
| " skiprows=range(20),\n", | |
| " skipfooter=2\n", | |
| " )\n", | |
| "\n", | |
| "print('Data downloaded and read into a dataframe!')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
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| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "source": [ | |
| "Let's take a look at the first five items in our dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
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| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
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| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>OdName</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>AreaName</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>RegName</th>\n", | |
| " <th>DEV</th>\n", | |
| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2004</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Afghanistan</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>16</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2978</td>\n", | |
| " <td>3436</td>\n", | |
| " <td>3009</td>\n", | |
| " <td>2652</td>\n", | |
| " <td>2111</td>\n", | |
| " <td>1746</td>\n", | |
| " <td>1758</td>\n", | |
| " <td>2203</td>\n", | |
| " <td>2635</td>\n", | |
| " <td>2004</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Albania</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Algeria</td>\n", | |
| " <td>903</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>912</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3616</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>American Samoa</td>\n", | |
| " <td>909</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>957</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Andorra</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 43 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage OdName AREA AreaName REG \\\n", | |
| "0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n", | |
| "1 Immigrants Foreigners Albania 908 Europe 925 \n", | |
| "2 Immigrants Foreigners Algeria 903 Africa 912 \n", | |
| "3 Immigrants Foreigners American Samoa 909 Oceania 957 \n", | |
| "4 Immigrants Foreigners Andorra 908 Europe 925 \n", | |
| "\n", | |
| " RegName DEV DevName 1980 ... 2004 2005 2006 \\\n", | |
| "0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n", | |
| "1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n", | |
| "2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n", | |
| "3 Polynesia 902 Developing regions 0 ... 0 0 1 \n", | |
| "4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n", | |
| "\n", | |
| " 2007 2008 2009 2010 2011 2012 2013 \n", | |
| "0 2652 2111 1746 1758 2203 2635 2004 \n", | |
| "1 702 560 716 561 539 620 603 \n", | |
| "2 3623 4005 5393 4752 4325 3774 4331 \n", | |
| "3 0 0 0 0 0 0 0 \n", | |
| "4 1 0 0 0 0 1 1 \n", | |
| "\n", | |
| "[5 rows x 43 columns]" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Let's find out how many entries there are in our dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# print the dimensions of the dataframe\n", | |
| "print(df_can.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to `Introduction to Matplotlib and Line Plots` lab for the rational and detailed description of the changes." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 1. Clean up the dataset to remove columns that are not informative to us for visualization (eg. Type, AREA, REG)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
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| } | |
| }, | |
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| " <th>0</th>\n", | |
| " <td>Afghanistan</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Albania</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
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| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Algeria</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>67</td>\n", | |
| " <td>71</td>\n", | |
| " <td>69</td>\n", | |
| " <td>63</td>\n", | |
| " <td>44</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3616</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>American Samoa</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Andorra</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 38 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " OdName AreaName RegName DevName 1980 1981 \\\n", | |
| "0 Afghanistan Asia Southern Asia Developing regions 16 39 \n", | |
| "1 Albania Europe Southern Europe Developed regions 1 0 \n", | |
| "2 Algeria Africa Northern Africa Developing regions 80 67 \n", | |
| "3 American Samoa Oceania Polynesia Developing regions 0 1 \n", | |
| "4 Andorra Europe Southern Europe Developed regions 0 0 \n", | |
| "\n", | |
| " 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n", | |
| "0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n", | |
| "1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n", | |
| "2 71 69 63 44 ... 3616 3626 4807 3623 4005 5393 4752 \n", | |
| "3 0 0 0 0 ... 0 0 1 0 0 0 0 \n", | |
| "4 0 0 0 0 ... 0 0 1 1 0 0 0 \n", | |
| "\n", | |
| " 2011 2012 2013 \n", | |
| "0 2203 2635 2004 \n", | |
| "1 539 620 603 \n", | |
| "2 4325 3774 4331 \n", | |
| "3 0 0 0 \n", | |
| "4 0 1 1 \n", | |
| "\n", | |
| "[5 rows x 38 columns]" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n", | |
| "\n", | |
| "# let's view the first five elements and see how the dataframe was changed\n", | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 2. Rename some of the columns so that they make sense." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>Country</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>Continent</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>Region</th>\n", | |
| " <th>DEV</th>\n", | |
| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2004</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Afghanistan</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>16</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2978</td>\n", | |
| " <td>3436</td>\n", | |
| " <td>3009</td>\n", | |
| " <td>2652</td>\n", | |
| " <td>2111</td>\n", | |
| " <td>1746</td>\n", | |
| " <td>1758</td>\n", | |
| " <td>2203</td>\n", | |
| " <td>2635</td>\n", | |
| " <td>2004</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Albania</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Algeria</td>\n", | |
| " <td>903</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>912</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3616</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>American Samoa</td>\n", | |
| " <td>909</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>957</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Andorra</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 43 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage Country AREA Continent REG \\\n", | |
| "0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n", | |
| "1 Immigrants Foreigners Albania 908 Europe 925 \n", | |
| "2 Immigrants Foreigners Algeria 903 Africa 912 \n", | |
| "3 Immigrants Foreigners American Samoa 909 Oceania 957 \n", | |
| "4 Immigrants Foreigners Andorra 908 Europe 925 \n", | |
| "\n", | |
| " Region DEV DevName 1980 ... 2004 2005 2006 \\\n", | |
| "0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n", | |
| "1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n", | |
| "2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n", | |
| "3 Polynesia 902 Developing regions 0 ... 0 0 1 \n", | |
| "4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n", | |
| "\n", | |
| " 2007 2008 2009 2010 2011 2012 2013 \n", | |
| "0 2652 2111 1746 1758 2203 2635 2004 \n", | |
| "1 702 560 716 561 539 620 603 \n", | |
| "2 3623 4005 5393 4752 4325 3774 4331 \n", | |
| "3 0 0 0 0 0 0 0 \n", | |
| "4 1 0 0 0 0 1 1 \n", | |
| "\n", | |
| "[5 rows x 43 columns]" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n", | |
| "\n", | |
| "# let's view the first five elements and see how the dataframe was changed\n", | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the column names now make much more sense, even to an outsider." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 3. For consistency, ensure that all column labels of type string." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "False" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# let's examine the types of the column labels\n", | |
| "all(isinstance(column, str) for column in df_can.columns)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the above line of code returned *False* when we tested if all the column labels are of type **string**. So let's change them all to **string** type." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df_can.columns = list(map(str, df_can.columns))\n", | |
| "\n", | |
| "# let's check the column labels types now\n", | |
| "all(isinstance(column, str) for column in df_can.columns)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 4. Set the country name as index - useful for quickly looking up countries using .loc method." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>Continent</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>Region</th>\n", | |
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| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>1981</th>\n", | |
| " <th>...</th>\n", | |
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| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Country</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
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| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
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| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Afghanistan</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>16</td>\n", | |
| " <td>39</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2978</td>\n", | |
| " <td>3436</td>\n", | |
| " <td>3009</td>\n", | |
| " <td>2652</td>\n", | |
| " <td>2111</td>\n", | |
| " <td>1746</td>\n", | |
| " <td>1758</td>\n", | |
| " <td>2203</td>\n", | |
| " <td>2635</td>\n", | |
| " <td>2004</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Albania</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Algeria</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>903</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>912</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>67</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3616</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>American Samoa</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>909</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>957</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Andorra</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 42 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage AREA Continent REG Region \\\n", | |
| "Country \n", | |
| "Afghanistan Immigrants Foreigners 935 Asia 5501 Southern Asia \n", | |
| "Albania Immigrants Foreigners 908 Europe 925 Southern Europe \n", | |
| "Algeria Immigrants Foreigners 903 Africa 912 Northern Africa \n", | |
| "American Samoa Immigrants Foreigners 909 Oceania 957 Polynesia \n", | |
| "Andorra Immigrants Foreigners 908 Europe 925 Southern Europe \n", | |
| "\n", | |
| " DEV DevName 1980 1981 ... 2004 2005 2006 \\\n", | |
| "Country ... \n", | |
| "Afghanistan 902 Developing regions 16 39 ... 2978 3436 3009 \n", | |
| "Albania 901 Developed regions 1 0 ... 1450 1223 856 \n", | |
| "Algeria 902 Developing regions 80 67 ... 3616 3626 4807 \n", | |
| "American Samoa 902 Developing regions 0 1 ... 0 0 1 \n", | |
| "Andorra 901 Developed regions 0 0 ... 0 0 1 \n", | |
| "\n", | |
| " 2007 2008 2009 2010 2011 2012 2013 \n", | |
| "Country \n", | |
| "Afghanistan 2652 2111 1746 1758 2203 2635 2004 \n", | |
| "Albania 702 560 716 561 539 620 603 \n", | |
| "Algeria 3623 4005 5393 4752 4325 3774 4331 \n", | |
| "American Samoa 0 0 0 0 0 0 0 \n", | |
| "Andorra 1 0 0 0 0 1 1 \n", | |
| "\n", | |
| "[5 rows x 42 columns]" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.set_index('Country', inplace=True)\n", | |
| "\n", | |
| "# let's view the first five elements and see how the dataframe was changed\n", | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the country names now serve as indices." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 5. Add total column." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>Continent</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>Region</th>\n", | |
| " <th>DEV</th>\n", | |
| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>1981</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " <th>Total</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Country</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Afghanistan</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>16</td>\n", | |
| " <td>39</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3436</td>\n", | |
| " <td>3009</td>\n", | |
| " <td>2652</td>\n", | |
| " <td>2111</td>\n", | |
| " <td>1746</td>\n", | |
| " <td>1758</td>\n", | |
| " <td>2203</td>\n", | |
| " <td>2635</td>\n", | |
| " <td>2004</td>\n", | |
| " <td>65977</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Albania</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " <td>18433</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Algeria</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>903</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>912</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>67</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " <td>72156</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>American Samoa</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>909</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>957</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2774</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Andorra</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2749</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 43 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage AREA Continent REG Region \\\n", | |
| "Country \n", | |
| "Afghanistan Immigrants Foreigners 935 Asia 5501 Southern Asia \n", | |
| "Albania Immigrants Foreigners 908 Europe 925 Southern Europe \n", | |
| "Algeria Immigrants Foreigners 903 Africa 912 Northern Africa \n", | |
| "American Samoa Immigrants Foreigners 909 Oceania 957 Polynesia \n", | |
| "Andorra Immigrants Foreigners 908 Europe 925 Southern Europe \n", | |
| "\n", | |
| " DEV DevName 1980 1981 ... 2005 2006 2007 \\\n", | |
| "Country ... \n", | |
| "Afghanistan 902 Developing regions 16 39 ... 3436 3009 2652 \n", | |
| "Albania 901 Developed regions 1 0 ... 1223 856 702 \n", | |
| "Algeria 902 Developing regions 80 67 ... 3626 4807 3623 \n", | |
| "American Samoa 902 Developing regions 0 1 ... 0 1 0 \n", | |
| "Andorra 901 Developed regions 0 0 ... 0 1 1 \n", | |
| "\n", | |
| " 2008 2009 2010 2011 2012 2013 Total \n", | |
| "Country \n", | |
| "Afghanistan 2111 1746 1758 2203 2635 2004 65977 \n", | |
| "Albania 560 716 561 539 620 603 18433 \n", | |
| "Algeria 4005 5393 4752 4325 3774 4331 72156 \n", | |
| "American Samoa 0 0 0 0 0 0 2774 \n", | |
| "Andorra 0 0 0 0 1 1 2749 \n", | |
| "\n", | |
| "[5 rows x 43 columns]" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can['Total'] = df_can.sum(axis=1)\n", | |
| "\n", | |
| "# let's view the first five elements and see how the dataframe was changed\n", | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Now the dataframe has an extra column that presents the total number of immigrants from each country in the dataset from 1980 - 2013. So if we print the dimension of the data, we get:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "print ('data dimensions:', df_can.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "So now our dataframe has 38 columns instead of 37 columns that we had before." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "[1980,\n", | |
| " 1981,\n", | |
| " 1982,\n", | |
| " 1983,\n", | |
| " 1984,\n", | |
| " 1985,\n", | |
| " 1986,\n", | |
| " 1987,\n", | |
| " 1988,\n", | |
| " 1989,\n", | |
| " 1990,\n", | |
| " 1991,\n", | |
| " 1992,\n", | |
| " 1993,\n", | |
| " 1994,\n", | |
| " 1995,\n", | |
| " 1996,\n", | |
| " 1997,\n", | |
| " 1998,\n", | |
| " 1999,\n", | |
| " 2000,\n", | |
| " 2001,\n", | |
| " 2002,\n", | |
| " 2003,\n", | |
| " 2004,\n", | |
| " 2005,\n", | |
| " 2006,\n", | |
| " 2007,\n", | |
| " 2008,\n", | |
| " 2009,\n", | |
| " 2010,\n", | |
| " 2011,\n", | |
| " 2012,\n", | |
| " 2013]" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# finally, let's create a list of years from 1980 - 2013\n", | |
| "# this will come in handy when we start plotting the data\n", | |
| "years = list(map(int, range(1980, 2014)))\n", | |
| "\n", | |
| "years" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Visualizing Data using Matplotlib<a id=\"4\"></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Import `Matplotlib` and **Numpy**." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Matplotlib version: 3.0.3\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# use the inline backend to generate the plots within the browser\n", | |
| "%matplotlib inline \n", | |
| "\n", | |
| "import matplotlib as mpl\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "mpl.style.use('ggplot') # optional: for ggplot-like style\n", | |
| "\n", | |
| "# check for latest version of Matplotlib\n", | |
| "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Area Plots<a id=\"6\"></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "In the last module, we created a line plot that visualized the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a **Stacked Line Plot** or **Area plot**." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Country</th>\n", | |
| " <th>India</th>\n", | |
| " <th>China</th>\n", | |
| " <th>United Kingdom of Great Britain and Northern Ireland</th>\n", | |
| " <th>Philippines</th>\n", | |
| " <th>Pakistan</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1980</th>\n", | |
| " <td>8880</td>\n", | |
| " <td>5123</td>\n", | |
| " <td>22045</td>\n", | |
| " <td>6051</td>\n", | |
| " <td>978</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1981</th>\n", | |
| " <td>8670</td>\n", | |
| " <td>6682</td>\n", | |
| " <td>24796</td>\n", | |
| " <td>5921</td>\n", | |
| " <td>972</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1982</th>\n", | |
| " <td>8147</td>\n", | |
| " <td>3308</td>\n", | |
| " <td>20620</td>\n", | |
| " <td>5249</td>\n", | |
| " <td>1201</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1983</th>\n", | |
| " <td>7338</td>\n", | |
| " <td>1863</td>\n", | |
| " <td>10015</td>\n", | |
| " <td>4562</td>\n", | |
| " <td>900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1984</th>\n", | |
| " <td>5704</td>\n", | |
| " <td>1527</td>\n", | |
| " <td>10170</td>\n", | |
| " <td>3801</td>\n", | |
| " <td>668</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Country India China United Kingdom of Great Britain and Northern Ireland \\\n", | |
| "1980 8880 5123 22045 \n", | |
| "1981 8670 6682 24796 \n", | |
| "1982 8147 3308 20620 \n", | |
| "1983 7338 1863 10015 \n", | |
| "1984 5704 1527 10170 \n", | |
| "\n", | |
| "Country Philippines Pakistan \n", | |
| "1980 6051 978 \n", | |
| "1981 5921 972 \n", | |
| "1982 5249 1201 \n", | |
| "1983 4562 900 \n", | |
| "1984 3801 668 " | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.sort_values(['Total'], ascending=False, axis=0, inplace=True)\n", | |
| "\n", | |
| "# get the top 5 entries\n", | |
| "df_top5 = df_can.head()\n", | |
| "\n", | |
| "# transpose the dataframe\n", | |
| "df_top5 = df_top5[years].transpose() \n", | |
| "\n", | |
| "df_top5.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Area plots are stacked by default. And to produce a stacked area plot, each column must be either all positive or all negative values (any NaN values will defaulted to 0). To produce an unstacked plot, pass `stacked=False`. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n", | |
| "df_top5.plot(kind='area', \n", | |
| " stacked=False,\n", | |
| " figsize=(20, 10), # pass a tuple (x, y) size\n", | |
| " )\n", | |
| "\n", | |
| "plt.title('Immigration Trend of Top 5 Countries')\n", | |
| "plt.ylabel('Number of Immigrants')\n", | |
| "plt.xlabel('Years')\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "The unstacked plot has a default transparency (alpha value) at 0.5. We can modify this value by passing in the `alpha` parameter." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_top5.plot(kind='area', \n", | |
| " alpha=0.25, # 0-1, default value a= 0.5\n", | |
| " stacked=False,\n", | |
| " figsize=(20, 10),\n", | |
| " )\n", | |
| "\n", | |
| "plt.title('Immigration Trend of Top 5 Countries')\n", | |
| "plt.ylabel('Number of Immigrants')\n", | |
| "plt.xlabel('Years')\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "### Two types of plotting\n", | |
| "\n", | |
| "As we discussed in the video lectures, there are two styles/options of ploting with `matplotlib`. Plotting using the Artist layer and plotting using the scripting layer.\n", | |
| "\n", | |
| "**Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as 'plt' **\n", | |
| "\n", | |
| "You can use `plt` i.e. `matplotlib.pyplot` and add more elements by calling different methods procedurally; for example, `plt.title(...)` to add title or `plt.xlabel(...)` to add label to the x-axis.\n", | |
| "```python\n", | |
| " # Option 1: This is what we have been using so far\n", | |
| " df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10)) \n", | |
| " plt.title('Immigration trend of top 5 countries')\n", | |
| " plt.ylabel('Number of immigrants')\n", | |
| " plt.xlabel('Years')\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Option 2: Artist layer (Object oriented method) - using an `Axes` instance from Matplotlib (preferred) **\n", | |
| "\n", | |
| "You can use an `Axes` instance of your current plot and store it in a variable (eg. `ax`). You can add more elements by calling methods with a little change in syntax (by adding \"*set_*\" to the previous methods). For example, use `ax.set_title()` instead of `plt.title()` to add title, or `ax.set_xlabel()` instead of `plt.xlabel()` to add label to the x-axis. \n", | |
| "\n", | |
| "This option sometimes is more transparent and flexible to use for advanced plots (in particular when having multiple plots, as you will see later). \n", | |
| "\n", | |
| "In this course, we will stick to the **scripting layer**, except for some advanced visualizations where we will need to use the **artist layer** to manipulate advanced aspects of the plots." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# option 2: preferred option with more flexibility\n", | |
| "ax = df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10))\n", | |
| "\n", | |
| "ax.set_title('Immigration Trend of Top 5 Countries')\n", | |
| "ax.set_ylabel('Number of Immigrants')\n", | |
| "ax.set_xlabel('Years')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Question**: Use the scripting layer to create a stacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.45." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import matplotlib as mpl\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "df = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n", | |
| " sheet_name='Canada by Citizenship',\n", | |
| " skiprows=range(20),\n", | |
| " skipfooter=2\n", | |
| " )\n", | |
| "df.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n", | |
| "df.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n", | |
| "\n", | |
| "df.set_index('Country',inplace=True)\n", | |
| "df['Total'] = df.sum(axis=1)\n", | |
| "years = list(map(int,range(1980,2014)))\n", | |
| "\n", | |
| "df.sort_values(['Total'],axis=0,inplace=True)\n", | |
| "df_least5 = df.head()\n", | |
| "df_least5 = df_least5[years].transpose()\n", | |
| "df_least5.index = df_least5.index.map(int)\n", | |
| "df_least5.plot(kind='area', \n", | |
| " alpha=0.45, # 0-1, default value a= 0.5\n", | |
| " stacked=False,\n", | |
| " figsize=(20, 10),\n", | |
| " )\n", | |
| "\n", | |
| "plt.title('Least Immigration to Canada')\n", | |
| "plt.ylabel('Countries')\n", | |
| "plt.xlabel('Years')\n", | |
| " \n", | |
| "\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click __here__ for the solution.\n", | |
| "<!-- The correct answer is:\n", | |
| "\\\\ # get the 5 countries with the least contribution\n", | |
| "df_least5 = df_can.tail(5)\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # transpose the dataframe\n", | |
| "df_least5 = df_least5[years].transpose() \n", | |
| "df_least5.head()\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n", | |
| "df_least5.plot(kind='area', alpha=0.45, figsize=(20, 10)) \n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "plt.title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n", | |
| "plt.ylabel('Number of Immigrants')\n", | |
| "plt.xlabel('Years')\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "plt.show()\n", | |
| "-->" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Question**: Use the artist layer to create an unstacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.55." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5, 0, 'Years')" | |
| ] | |
| }, | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "ax = df_least5.plot(kind='area',stacked=False,alpha=0.55,figsize=(20,10),)\n", | |
| "\n", | |
| "ax.set_title(\"Least Immigrations\")\n", | |
| "ax.set_ylabel('Number of Immigrants')\n", | |
| "ax.set_xlabel('Years')\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click __here__ for the solution.\n", | |
| "<!-- The correct answer is:\n", | |
| "\\\\ # get the 5 countries with the least contribution\n", | |
| "df_least5 = df_can.tail(5)\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # transpose the dataframe\n", | |
| "df_least5 = df_least5[years].transpose() \n", | |
| "df_least5.head()\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "ax = df_least5.plot(kind='area', alpha=0.55, stacked=False, figsize=(20, 10))\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "ax.set_title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n", | |
| "ax.set_ylabel('Number of Immigrants')\n", | |
| "ax.set_xlabel('Years')\n", | |
| "-->" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Histograms<a id=\"8\"></a>\n", | |
| "\n", | |
| "A histogram is a way of representing the *frequency* distribution of numeric dataset. The way it works is it partitions the x-axis into *bins*, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Question:** What is the frequency distribution of the number (population) of new immigrants from the various countries to Canada in 2013?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Before we proceed with creating the histogram plot, let's first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts as follows:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Country\n", | |
| "India 33087\n", | |
| "China 34129\n", | |
| "United Kingdom of Great Britain and Northern Ireland 5827\n", | |
| "Philippines 29544\n", | |
| "Pakistan 12603\n", | |
| "Name: 2013, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 42, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# let's quickly view the 2013 data\n", | |
| "df_can[2013].head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[178 11 1 2 0 0 0 0 1 2]\n", | |
| "[ 0. 3412.9 6825.8 10238.7 13651.6 17064.5 20477.4 23890.3 27303.2\n", | |
| " 30716.1 34129. ]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# np.histogram returns 2 values\n", | |
| "count, bin_edges = np.histogram(df_can[2013])\n", | |
| "\n", | |
| "print(count) # frequency count\n", | |
| "print(bin_edges) # bin ranges, default = 10 bins" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of immigration in 2013. We can see that in 2013:\n", | |
| "* 178 countries contributed between 0 to 3412.9 immigrants \n", | |
| "* 11 countries contributed between 3412.9 to 6825.8 immigrants\n", | |
| "* 1 country contributed between 6285.8 to 10238.7 immigrants, and so on..\n", | |
| "\n", | |
| "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod2Fig1-Histogram.JPG\" align=\"center\" width=800>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "We can easily graph this distribution by passing `kind=hist` to `plot()`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 576x360 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_can[2013].plot(kind='hist', figsize=(8, 5))\n", | |
| "\n", | |
| "plt.title('Histogram of Immigration from 195 Countries in 2013') # add a title to the histogram\n", | |
| "plt.ylabel('Number of Countries') # add y-label\n", | |
| "plt.xlabel('Number of Immigrants') # add x-label\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "In the above plot, the x-axis represents the population range of immigrants in intervals of 3412.9. The y-axis represents the number of countries that contributed to the aforementioned population. \n", | |
| "\n", | |
| "Notice that the x-axis labels do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 576x360 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 'bin_edges' is a list of bin intervals\n", | |
| "count, bin_edges = np.histogram(df_can[2013])\n", | |
| "\n", | |
| "df_can[2013].plot(kind='hist', figsize=(8, 5), xticks=bin_edges)\n", | |
| "\n", | |
| "plt.title('Histogram of Immigration from 195 countries in 2013') # add a title to the histogram\n", | |
| "plt.ylabel('Number of Countries') # add y-label\n", | |
| "plt.xlabel('Number of Immigrants') # add x-label\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "*Side Note:* We could use `df_can['2013'].plot.hist()`, instead. In fact, throughout this lesson, using `some_data.plot(kind='type_plot', ...)` is equivalent to `some_data.plot.type_plot(...)`. That is, passing the type of the plot as argument or method behaves the same. \n", | |
| "\n", | |
| "See the *pandas* documentation for more info http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "We can also plot multiple histograms on the same plot. For example, let's try to answer the following questions using a histogram.\n", | |
| "\n", | |
| "**Question**: What is the immigration distribution for Denmark, Norway, and Sweden for years 1980 - 2013?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 48, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>1980</th>\n", | |
| " <th>1981</th>\n", | |
| " <th>1982</th>\n", | |
| " <th>1983</th>\n", | |
| " <th>1984</th>\n", | |
| " <th>1985</th>\n", | |
| " <th>1986</th>\n", | |
| " <th>1987</th>\n", | |
| " <th>1988</th>\n", | |
| " <th>1989</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2004</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Country</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Denmark</th>\n", | |
| " <td>272</td>\n", | |
| " <td>293</td>\n", | |
| " <td>299</td>\n", | |
| " <td>106</td>\n", | |
| " <td>93</td>\n", | |
| " <td>73</td>\n", | |
| " <td>93</td>\n", | |
| " <td>109</td>\n", | |
| " <td>129</td>\n", | |
| " <td>129</td>\n", | |
| " <td>...</td>\n", | |
| " <td>89</td>\n", | |
| " <td>62</td>\n", | |
| " <td>101</td>\n", | |
| " <td>97</td>\n", | |
| " <td>108</td>\n", | |
| " <td>81</td>\n", | |
| " <td>92</td>\n", | |
| " <td>93</td>\n", | |
| " <td>94</td>\n", | |
| " <td>81</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Norway</th>\n", | |
| " <td>116</td>\n", | |
| " <td>77</td>\n", | |
| " <td>106</td>\n", | |
| " <td>51</td>\n", | |
| " <td>31</td>\n", | |
| " <td>54</td>\n", | |
| " <td>56</td>\n", | |
| " <td>80</td>\n", | |
| " <td>73</td>\n", | |
| " <td>76</td>\n", | |
| " <td>...</td>\n", | |
| " <td>73</td>\n", | |
| " <td>57</td>\n", | |
| " <td>53</td>\n", | |
| " <td>73</td>\n", | |
| " <td>66</td>\n", | |
| " <td>75</td>\n", | |
| " <td>46</td>\n", | |
| " <td>49</td>\n", | |
| " <td>53</td>\n", | |
| " <td>59</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sweden</th>\n", | |
| " <td>281</td>\n", | |
| " <td>308</td>\n", | |
| " <td>222</td>\n", | |
| " <td>176</td>\n", | |
| " <td>128</td>\n", | |
| " <td>158</td>\n", | |
| " <td>187</td>\n", | |
| " <td>198</td>\n", | |
| " <td>171</td>\n", | |
| " <td>182</td>\n", | |
| " <td>...</td>\n", | |
| " <td>129</td>\n", | |
| " <td>205</td>\n", | |
| " <td>139</td>\n", | |
| " <td>193</td>\n", | |
| " <td>165</td>\n", | |
| " <td>167</td>\n", | |
| " <td>159</td>\n", | |
| " <td>134</td>\n", | |
| " <td>140</td>\n", | |
| " <td>140</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>3 rows × 34 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n", | |
| "Country ... \n", | |
| "Denmark 272 293 299 106 93 73 93 109 129 129 ... \n", | |
| "Norway 116 77 106 51 31 54 56 80 73 76 ... \n", | |
| "Sweden 281 308 222 176 128 158 187 198 171 182 ... \n", | |
| "\n", | |
| " 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n", | |
| "Country \n", | |
| "Denmark 89 62 101 97 108 81 92 93 94 81 \n", | |
| "Norway 73 57 53 73 66 75 46 49 53 59 \n", | |
| "Sweden 129 205 139 193 165 167 159 134 140 140 \n", | |
| "\n", | |
| "[3 rows x 34 columns]" | |
| ] | |
| }, | |
| "execution_count": 48, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# let's quickly view the dataset \n", | |
| "df_can.loc[['Denmark', 'Norway', 'Sweden'], years]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 49, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e08491898>" | |
| ] | |
| }, | |
| "execution_count": 49, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# generate histogram\n", | |
| "df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "That does not look right! \n", | |
| "\n", | |
| "Don't worry, you'll often come across situations like this when creating plots. The solution often lies in how the underlying dataset is structured.\n", | |
| "\n", | |
| "Instead of plotting the population frequency distribution of the population for the 3 countries, *pandas* instead plotted the population frequency distribution for the `years`.\n", | |
| "\n", | |
| "This can be easily fixed by first transposing the dataset, and then plotting as shown below.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Country</th>\n", | |
| " <th>Denmark</th>\n", | |
| " <th>Norway</th>\n", | |
| " <th>Sweden</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1980</th>\n", | |
| " <td>272</td>\n", | |
| " <td>116</td>\n", | |
| " <td>281</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1981</th>\n", | |
| " <td>293</td>\n", | |
| " <td>77</td>\n", | |
| " <td>308</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1982</th>\n", | |
| " <td>299</td>\n", | |
| " <td>106</td>\n", | |
| " <td>222</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1983</th>\n", | |
| " <td>106</td>\n", | |
| " <td>51</td>\n", | |
| " <td>176</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1984</th>\n", | |
| " <td>93</td>\n", | |
| " <td>31</td>\n", | |
| " <td>128</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Country Denmark Norway Sweden\n", | |
| "1980 272 116 281\n", | |
| "1981 293 77 308\n", | |
| "1982 299 106 222\n", | |
| "1983 106 51 176\n", | |
| "1984 93 31 128" | |
| ] | |
| }, | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# transpose dataframe\n", | |
| "df_t = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n", | |
| "df_t.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# generate histogram\n", | |
| "df_t.plot(kind='hist', figsize=(10, 6))\n", | |
| "\n", | |
| "plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n", | |
| "plt.ylabel('Number of Years')\n", | |
| "plt.xlabel('Number of Immigrants')\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Let's make a few modifications to improve the impact and aesthetics of the previous plot:\n", | |
| "* increase the bin size to 15 by passing in `bins` parameter\n", | |
| "* set transparency to 60% by passing in `alpha` paramemter\n", | |
| "* label the x-axis by passing in `x-label` paramater\n", | |
| "* change the colors of the plots by passing in `color` parameter" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# let's get the x-tick values\n", | |
| "count, bin_edges = np.histogram(df_t, 15)\n", | |
| "\n", | |
| "# un-stacked histogram\n", | |
| "df_t.plot(kind ='hist', \n", | |
| " figsize=(10, 6),\n", | |
| " bins=15,\n", | |
| " alpha=0.6,\n", | |
| " xticks=bin_edges,\n", | |
| " color=['coral', 'darkslateblue', 'mediumseagreen']\n", | |
| " )\n", | |
| "\n", | |
| "plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n", | |
| "plt.ylabel('Number of Years')\n", | |
| "plt.xlabel('Number of Immigrants')\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Tip:\n", | |
| "For a full listing of colors available in Matplotlib, run the following code in your python shell:\n", | |
| "```python\n", | |
| "import matplotlib\n", | |
| "for name, hex in matplotlib.colors.cnames.items():\n", | |
| " print(name, hex)\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "If we do no want the plots to overlap each other, we can stack them using the `stacked` paramemter. Let's also adjust the min and max x-axis labels to remove the extra gap on the edges of the plot. We can pass a tuple (min,max) using the `xlim` paramater, as show below." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "count, bin_edges = np.histogram(df_t, 15)\n", | |
| "xmin = bin_edges[0] - 10 # first bin value is 31.0, adding buffer of 10 for aesthetic purposes \n", | |
| "xmax = bin_edges[-1] + 10 # last bin value is 308.0, adding buffer of 10 for aesthetic purposes\n", | |
| "\n", | |
| "# stacked Histogram\n", | |
| "df_t.plot(kind='hist',\n", | |
| " figsize=(10, 6), \n", | |
| " bins=15,\n", | |
| " xticks=bin_edges,\n", | |
| " color=['coral', 'darkslateblue', 'mediumseagreen'],\n", | |
| " stacked=True,\n", | |
| " xlim=(xmin, xmax)\n", | |
| " )\n", | |
| "\n", | |
| "plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n", | |
| "plt.ylabel('Number of Years')\n", | |
| "plt.xlabel('Number of Immigrants') \n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Question**: Use the scripting layer to display the immigration distribution for Greece, Albania, and Bulgaria for years 1980 - 2013? Use an overlapping plot with 15 bins and a transparency value of 0.35." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": true, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fc9dac5f8>" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "import matplotlib as mpl\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "df = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n", | |
| " sheet_name='Canada by Citizenship',\n", | |
| " skiprows=range(20),\n", | |
| " skipfooter=2\n", | |
| " )\n", | |
| "df.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n", | |
| "df.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n", | |
| "\n", | |
| "df.set_index('Country',inplace=True)\n", | |
| "years = list(map(int,range(1980,2014)))\n", | |
| "\n", | |
| "df_imm = df.loc[['Greece','Albania','Bulgaria'],years].transpose()\n", | |
| "\n", | |
| "count, bin_edges = np.histogram(df_imm,15)\n", | |
| "\n", | |
| "df_imm.plot(kind=\"hist\",alpha=0.35,xticks = bin_edges,bins=15)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click __here__ for the solution.\n", | |
| "<!-- The correct answer is:\n", | |
| "\\\\ # create a dataframe of the countries of interest (cof)\n", | |
| "df_cof = df_can.loc[['Greece', 'Albania', 'Bulgaria'], years]\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # transpose the dataframe\n", | |
| "df_cof = df_cof.transpose() \n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # let's get the x-tick values\n", | |
| "count, bin_edges = np.histogram(df_cof, 15)\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # Un-stacked Histogram\n", | |
| "df_cof.plot(kind ='hist',\n", | |
| " figsize=(10, 6),\n", | |
| " bins=15,\n", | |
| " alpha=0.35,\n", | |
| " xticks=bin_edges,\n", | |
| " color=['coral', 'darkslateblue', 'mediumseagreen']\n", | |
| " )\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "plt.title('Histogram of Immigration from Greece, Albania, and Bulgaria from 1980 - 2013')\n", | |
| "plt.ylabel('Number of Years')\n", | |
| "plt.xlabel('Number of Immigrants')\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "plt.show()\n", | |
| "-->" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Bar Charts (Dataframe) <a id=\"10\"></a>\n", | |
| "\n", | |
| "A bar plot is a way of representing data where the *length* of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \n", | |
| "\n", | |
| "To create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\n", | |
| "\n", | |
| "* `kind=bar` creates a *vertical* bar plot\n", | |
| "* `kind=barh` creates a *horizontal* bar plot" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "**Vertical bar plot**\n", | |
| "\n", | |
| "In vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. \n", | |
| "\n", | |
| "**Let's start off by analyzing the effect of Iceland's Financial Crisis:**\n", | |
| "\n", | |
| "The 2008 - 2011 Icelandic Financial Crisis was a major economic and political event in Iceland. Relative to the size of its economy, Iceland's systemic banking collapse was the largest experienced by any country in economic history. The crisis led to a severe economic depression in 2008 - 2011 and significant political unrest.\n", | |
| "\n", | |
| "**Question:** Let's compare the number of Icelandic immigrants (country = 'Iceland') to Canada from year 1980 to 2013. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1980 17\n", | |
| "1981 33\n", | |
| "1982 10\n", | |
| "1983 9\n", | |
| "1984 13\n", | |
| "Name: Iceland, dtype: object" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# step 1: get the data\n", | |
| "df_iceland = df_can.loc['Iceland', years]\n", | |
| "df_iceland.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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pLnvRV2amOF9E2Vld03y+m1B2ar9u2n0JZUe244Dt7W1NPNcyxGUveurbnzJO609NG3/a1LV9M3+LZpv5U/PZv6NZJ+cO2GbWOwGBsjP8bLOMiyiJ/tE0lwBp3nMjynjK6Z3+EVz/shd3pexgr6QM1H8WMww6n6Gd5zLzoP6ZztrrvezFZcxy2Yu+Mi/vXSfNtEOA8wfE9Squf1mJmvX/t/+dktSva9bdu+g7y5nSQ/ZmStL2V8rJOO+kOaO3r22nN++5uFm/KwbEeKtmfUwPjN+zmX57yv/E9GUvTmHAgPlZ2lr3f35t4mw+05nqmv7fvDdlLOXvKf/7v6H0DN+pzf+Gj+4fVfNBSppQVVX9A+XLfN+6rv+v43AkSTMwIZMmTFXuq3kBpTfqHyiHfLelXJbiqrnKSpK64RgyafJsTTlEdnPKmaxfp1zPyGRMksaUPWSSJEkd87IXkiRJHTMhkyRJ6thSHEPmMVZJkrSUDLx7zFJMyLjwwgtnnTc1NcXatRt+/c5hyxvD+MQwCW0YhxgmoQ3jEMMktMEYRlPeGEZTfqnEsGrVqlnn9fKQpSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyu7DkCSJGmpuObAfdd7vaZv/oojT96geu0hkyRJ6pgJmSRJUsdMyCRJkjq2KGPIIuL2wPE9k24FvAJ4fzN9J+BcIDLz0sWISZIkaVwsSg9ZZp6VmTtn5s7ArsBfgBOBQ4BTM/O2wKnNa0mSpGWli0OWDwLOyczfAI8Ejm2mHws8qoN4JEmSOtVFQvYE4MPN820z8yKA5u82HcQjSZLUqaqu60VbWERsDFwI3Dkz10TEZZm5Zc/8SzNzqxnKHQQcBJCZu65bt27WZaxcuZKrr756g2MctrwxjE8Mk9CGcYhhEtowDjFMQhuMYTTljWE05buKYc2j7zvn/G1PPH291xtvvDFANTCWeUUxvIcC38/M6euorYmI7TPzoojYHrhkpkKZeQRwRPOyXrt27awLmJqaYq75gwxb3hjGJ4ZJaMM4xDAJbRiHGCahDcYwmvLGMJry4xJDv/76Vq1a1arcYh+yfCLXHa4EOBnYv3m+P3DSIscjSZLUuUVLyCJiU2Bv4OM9kw8F9o6IXzbzDl2seCRJksbFoh2yzMy/AFv3Tfs95axLSZKkZcsr9UuSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdW7lYC4qILYGjgLsANfAvwFnA8cBOwLlAZOalixWTJEnSOFjMHrLDgc9k5h2AuwNnAocAp2bmbYFTm9eSJEnLyqIkZBFxE2AP4GiAzFyXmZcBjwSObd52LPCoxYhHkiRpnFR1XS/4QiJiZ+AI4AxK79j3gOcBF2Tmlj3vuzQzt5qh/EHAQQCZueu6detmXdbKlSu5+uqrNzjWYcsbw/jEMAltGIcYJqEN4xDDJLTBGEZT3hhGU76rGNY8+r5zzt/2xNPXe73xxhsDVANjmVcUG24lcA/gOZn5rYg4nHkcnszMIygJHUC9du3aWd87NTXFXPMHGba8MYxPDJPQhnGIYRLaMA4xTEIbjGE05Y1hNOXHJYZ+/fWtWrWqVbnFGkN2PnB+Zn6reX0CJUFbExHbAzR/L1mkeCRJksbGoiRkmXkxcF5E3L6Z9CDK4cuTgf2bafsDJy1GPJIkSeNk0S57ATwHOC4iNgZ+BTydkhBmRBwA/BZYvYjxSJIkjYVFS8gy84fAbjPMetBixSBJkjSOvFK/JElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnq2Mo2b4qIFwBfzMwfRsR9gASuBp6cmd9YyAAlSZImXdsesv8H/Lp5/gbgMOB1wNsWIihJkqTlpG1CtkVm/jEiNgfuDvxPZh4N3H7hQpMkSVoeWh2yBM6LiPsCdwa+kpnXRMRNgGsWLjRJkqTloW1C9kLgBGAd8Nhm2iOAby9EUJIkSctJq4QsMz8FrOqb/FHK4H5JkiQNodUYsoj4Q/+0zPwbcOHII5IkSVpm2g7qv0H/hIi4AbBitOFIkiQtP3MesoyIrwI1sElEfKVv9g7A6QsVmCRJ0nIxaAzZUUAF3BM4umd6DawBvrhAcUmSJC0bcyZkmXksQER8MzN/PsyCIuJc4M+US2VcnZm7RcRNgeOBnYBzgcjMS4dZjiRJ0lLT9izLn0fEPwE7A5v1zXvFPJa3V2au7Xl9CHBqZh4aEYc0r188j/okSZKWvLb3svxfIIAvAX/pmVUPufxHAns2z48FTsOETJIkLTNtLwz7RGDnzDxviGXVwOciogbek5lHANtm5kUAmXlRRGwzRP2SJElLUtuE7PfAZUMu636ZeWGTdH0+IlqPSYuIg4CDADKTqampWd+7cuXKOecPMmx5YxifGCahDeMQwyS0YRximIQ2GMNoyhvDaMp3FcOaAfM3NJ6qrgcfdYyIZwIPB97QH0tm/mq+C42IVwGXAwcCeza9Y9sDp2XmoBuW1xdeOPv1aKempli7du2s8wcZtrwxjE8Mk9CGcYhhEtowDjFMQhuMYTTljWE05buK4ZoD951z/oojT17v9apVq6BcsWJObS8M+y7KvSu/Dpzd8/hlm8IRceOI2Hz6OfBPwE+Bk4H9m7ftD5zUMh5JkqSJ0fYsy7aJ22y2BU6MiOllfigzPxMR3wEyIg4AfgusHnI5kiRJS07bMWRDaQ5r3n2G6b8HHrQYMUiSJI2rtpe9WAk8C3gAMEXPsdDM3GNhQpMkSVoe2h6KfCvwTOArwK7Ax4Bt8NZJkiRJQ2ubkD0GeGhmHk657dHhwKOAvRYsMkmSpGWibUK2KTB9UdgrI2LT5t6WuyxMWJIkSctH20H9ZwL3BL4NfBd4VUT8CbhgoQKTJElaLtomZM8Drm6ev4ByXbLNaa6eL0mSpA03MCGLiBXAXYHjADLzl8CDFzguSZKkZWPgGLLMvAY4LDOvWoR4JEmSlp22g/r/LyL+eUEjkSRJWqbajiHbBDghIr5BOdvy73ckz8ynLkRgkiRJy0XbhOynzUOSJEkj1vbm4v+10IFIkiQtV23vZfnAWWZdBZyfmb8ZXUiSJEnLS9tDlkcDq5rnvwe2bp5fAmwXET8GntBcEkOSJEnz0PYsy6OBtwNbZuYqYEvgcODdzfPvAO9ckAglSZImXNuE7HnASzLzSoDm78uA52fmFcDBwG4LE6IkSdJka5uQXUG5l2WvXYG/NM+vHVlEkiRJy0zbMWSvAD4XESdTrkO2A/DPwHOa+Q8CThh9eJIkSZOv7WUv3h8R3wUeSxnc/wvgHzPzjGb+KcApCxalJEnSBGvbQ0aTfJ2xgLFIkiQtS7MmZBFxRGYe1Dz/AD23S+rlrZMkSZKGM1cP2a97np+90IFIkiQtV7MmZJn5hp7n3jpJkiRpgbQeQxYROwF3AzbrnZ6ZHxpxTJIkSctK23tZvoRy6YufAVf2zKoBEzJJkqQhtO0hOxjYdfoyF5IkSRqdtlfq/z1w7gLGIUmStGy17SF7PnBERLwNuKR3Rmb+duRRSZIkLSNtE7KNgX8CntQ3vQZWjDQiSZKkZaZtQvZO4KXAR1h/UL8kSdKScM2B+673ek3f/BVHnrx4wfRpm5CtBN6XmdcsZDCSJEnLUdtB/W8BDomIaiGDkSRJWo7a9pA9F9gOeGlE/L53RmbeYuRRSZIkLSNtE7KnLGgUkiRJy1irhCwzvzyKhUXECuC7wAWZ+YiIuCXlRIGbAt8H9svMdaNYliRJ0lLR9tZJK4EnArtw/XtZHjSP5T0POBO4SfP6jcBbM/MjEfFu4ADgXfOoT5IkaclrO6j/g8AhwLWUs0R7H61ExA7Aw4GjmtcV8EDghOYtxwKPalufJEnSpGg7hmwfYMfM/PMQy3ob8CJg8+b11sBlmXl18/p84OZD1C9JkrQktU3IzqCM89qghCwiHgFckpnfi4g9m8kzXUKjnqX8QcBBAJnJ1NTUrMtauXLlnPMHGba8MYxPDJPQhnGIYRLaMA4xTEIbjGE05Y1hNOU3pI5Bh/Xa1DWKOmYyn7Msj4qIz/XHkpnvb1H+fsC+EfEwYBPKGLK3AVtGxMqml2wH4MKZCmfmEcARzct67dq1sy5oamqKueYPMmx5YxifGCahDeMQwyS0YRximIQ2GMNoyhvDaMqPqo5eo6irv45Vq1a1Ktc2IXsacH9gK9a/dVINDEzIMvMlwEsAmh6y/8jMJ0fER4HHUc603B84qWU8kiRJE6NtQvY8YJfMPHPEy38x8JGIeC3wA+DoEdcvSZI09tomZGuA345igZl5GnBa8/xXwL1GUa8kSdJS1TYheytwXEQcClzSO6NJqiRJkrSB2iZk72j+7ts3vQZWjC4cSZKk5aftrZPaXkBWkiRJ89S2h0ySJKlT1xy4/oG6/muCrTjy5MULZsRmTcgi4jOZuU/z/KvMctHWzNxjgWKTJElaFubqIeu9vthRCx2IJEnScjVrQpaZH+p5fuzihCNJkrT8OFhfkiSpYyZkkiRJHTMhkyRJ6tisCVlEfLPn+SsXJxxJkqTlZ64esttFxCbN84MXIxhJkqTlaK7LXpwE/CIizgVuFBFfmelNXodMkiRpOHNd9uLpEbE7sBNwT+DoxQpKkiRpOZnz1kmZ+TXgaxGxsdcikyRJWhhtby7+3ojYC9gPuDlwAfDBzPziQgYnSZK0HLS67EVEPAM4HrgY+DhwEfChiDhwAWOTJElaFlr1kAEvAvbOzB9NT4iI44GPAUcuRGCSJEnLRdsLw24NnNE37SzgpqMNR5Ikaflpm5B9DTgsIjYFiIgbA28GTl+owCRJkpaLtgnZvwJ3A/4YEWuAy4C7A89cqMAkSZKWi7ZnWV4EPCAidgBWARdm5vkLGpkkSdIy0XZQPwBNEmYiJkmSNEJtD1lKkiRpgZiQSZIkdWzgIcuI2AjYE/haZq5b8IgW2TUH7rve6zUzvGfFkScvTjCSJGlZGthDlpnXAidNYjImSZI0DtoesvxKRNxnQSORJElaptqeZfkb4NMRcRJwHlBPz8jMVyxEYJIkSctF24TsRsAnmuc7LFAskiRJy1LbC8M+faEDkSRJWq5aXxg2Iu4IPA7YNjOfHRG3B26YmT9esOgkSZKWgVaD+iNiNfAV4ObAU5vJmwOHLVBckiRJy0bbsyxfDeydmf8KXNNM+xHlBuOSJEkaQttDlttQEjC47gzLuuf5nCJiE0oP2w2bZZ6Qma+MiFsCHwFuCnwf2M/rnUmSpOWmbQ/Z94D9+qY9Afh2y/JXAQ/MzLsDOwP7NNc1eyPw1sy8LXApcEDL+iRJkiZG2x6y5wKfi4gDgBtHxGeB2wH/1KZwZtbA5c3LGzSPGngg8KRm+rHAq4B3tYxJkiRpIrS97MXPI+IOwCOAUygXhz0lMy+fu+R1ImIFpaftNsA7gHOAyzLz6uYt51NOGpAkSVpWWl/2IjP/EhFfB34NXDifZKwpfw2wc0RsCZwI3HGGt804Ji0iDgIOauphampq1uWsXLlyzvn9ZrqZeL/51LchMYy6vDGMprwxjKa8MYymvDGMTwyT0IYuYljz6Puu/7pv/rYnnj64jgHzB8UzbPlR1TGTVglZRNwCOA64D2Ws11YR8S3gyZn5m/nlrSmPAAAfB0lEQVQsMDMvi4jTmrq2jIiVTS/ZDsCFs5Q5AjiieVmvXbt21vqnpqaYa/6GmG99w8YwijYYw2S0YRximIQ2jEMMk9AGYxhNeWOY2SjqGraOhYhh1apVrcq1HdR/LOVw45aZuQ2wFfCdZvpAEXGzpmeMiLgR8GDgTOBLlIvNAuwPnNQyHkmSpInRNiHbFXhhZl4B0ByufHEzvY3tgS9FxI8pidznM/OUpo4XRMTZwNbA0fMJXpIkaRK0HUP2TeBewNd7pu0GfKNN4eb2SrvMMP1XTb2SJEnL1qwJWUS8uuflOcCnIuKTlDMsdwQeBnxoYcOTJEmafHP1kO3Y9/rjzd9tKBd6PRHYZCGCkiRJWk5mTcgy8+mLGYgkSdJy1fo6ZBGxKeWirpv1Ts/MwRcOkSRJ0qzaXofsqcD/AuuAK3tm1cAtFiAuSZKkZaNtD9mbgMdm5ucXMhhJkqTlqO11yNYBpy1gHJIkSctW24TsP4HDImK4G2dJkiTpetoesvwF8GrgWRExPa0C6sxcsRCBSZIkLRdtE7IPAO8Hjmf9Qf2SJEkaUtuEbGvgFZlZL2QwkiRJy1HbMWTvA/ZbyEAkSZKWq7Y9ZPcCnh0RLwPW9M7IzD1GHpUkaSxcc+C+671e0zd/xZEnL14w0gRrm5Ad2TwkSZI0Yq0Sssw8dqEDkSRJWq7a3jrpX2abl5nvHV04kiRJy0/bQ5b9A/q3A24NfB0wIZMkSRpC20OWe/VPa3rN7jjyiCRJkpaZtpe9mMkxwAEjikOSJGnZajuGrD9x2xR4CnDZyCOSJElaZtqOIbsa6L9K/wXAgaMNR5Ikaflpm5Ddsu/1FZm5dtTBSJIkLUdtB/X/ZqEDkSRJWq7mTMgi4ktc/1BlrzozHzTakCRJkpaXQT1kH5xl+s2B51IG90uSJGkIcyZkmXl07+uI2Bp4CWUw//HAqxcuNEmShucN0rUUtL3sxU2AFwLPBk4B7pGZ5yxkYJIkScvFoDFkNwKeDxwMnAbsnpk/W4S4JEmSlo1BPWS/BlYAbwK+C2wbEdv2viEzv7hAsUmSJC0LgxKyv1LOsvy3WebXwK1GGpEkSdIyM2hQ/06LFIckSdKyNczNxSVJkjQCJmSSJEkdMyGTJEnqWNubiw8lInYE3g9sB1wLHJGZh0fETSkXmN0JOBeIzLx0MWKSJEkaF4vVQ3Y1cHBm3hG4D/DvEXEn4BDg1My8LXBq81qSJGlZWZSELDMvyszvN8//DJxJuR/mI4Fjm7cdCzxqMeKRJEkaJ4s+hiwidgJ2Ab4FbJuZF0FJ2oBtFjseSZKkri3KGLJpEbEZ8DHg+Zn5p4hoW+4g4CCAzGRqamrW965cuXLO+f36bzI7k/nUtyExjLq8MYymvDGMpvxyjWHNo++7/usZ3rPtiacvaAyjKD/oO3Kxvx83pI5JaMMkxDCKz2HYOsYhhtksWkIWETegJGPHZebHm8lrImL7zLwoIrYHLpmpbGYeARzRvKzXrl0763KmpqaYa/6GmG99w8YwijYYw2S0YRximIQ2jEsM/Zbid0u/xW7DqOrotRTbMCkx9BpFXcPWsRAxrFq1qlW5RTlkGREVcDRwZmYe1jPrZGD/5vn+wEmLEY8kSdI4WawesvsB+wE/iYgfNtNeChwKZEQcAPwWWL1I8UiSJI2NRUnIMvNrQDXL7ActRgySJEnjyiv1S5IkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkji3qzcUXwjUH7rve6/6bfq448uTFC0YaA/5PTA4/y+uvA1ie60GTzx4ySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUsSV/YVhJo+XFSKXxMw7/l+MQwySzh0ySJKljJmSSJEkdMyGTJEnqmGPIJE0kx7tIWkrsIZMkSeqYCZkkSVLHTMgkSZI65hiyCeF4GUnSbPr3EeB+YtzYQyZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjq2KBeGjYj3Ao8ALsnMuzTTbgocD+wEnAtEZl66GPFo9EZx0UEvbjs5F2+chM9yEtowDlyPUjuL1UN2DLBP37RDgFMz87bAqc1rSZKkZWdRErLM/Arwh77JjwSObZ4fCzxqMWKRJEkaN12OIds2My8CaP5u02EskiRJnVkSNxePiIOAgwAyk6mpqb/P6x+P0K/3vTMZVL5VHY++75x1bnvi6S2Wcp2VK1cOXOb1Yhgwf771zTeGkazHIcv325D1OOo6luJ6HMXnMA6fZdfrYRw+y36uxw0sP+LveBj9d8OSWI9Dlp+UGGbTZUK2JiK2z8yLImJ74JLZ3piZRwBHNC/rtWvXtl7IfN67UHXMt/zU1NRI4l7qMQxbfhRtGLaOSViPoy6/IXVM4nrsIgbX4/iUH/VnsVzX41KIYdWqVa3KdXnI8mRg/+b5/sBJHcYiSZLUmcW67MWHgT2BqYg4H3glcCiQEXEA8Ftg9WLEIkmSNG4WJSHLzCfOMutBi7H8ced1eqT1+T8xGq5HaenwSv2SJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljS+Lm4lIbXgRTkrRU2UMmSZLUMRMySZKkjpmQSZIkdcwxZAIcfyVJUpfsIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMW8uLvXwJuuSpC7YQyZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYw7ql0bIkwIkzcTvBg1iD5kkSVLHTMgkSZI6ZkImSZLUsc7HkEXEPsDhwArgqMw8tOOQJEmSFlWnPWQRsQJ4B/BQ4E7AEyPiTl3GJEmStNi6PmR5L+DszPxVZq4DPgI8suOYJEmSFlXXCdnNgfN6Xp/fTJMkSVo2qrquO1t4RKwGHpKZz2he7wfcKzOf0/e+g4CDADJz10UPVJIkacNVg97QdQ/Z+cCOPa93AC7sf1NmHpGZu2XmbpRGzfqIiO8Nes9CljeG8YlhEtowDjFMQhvGIYZJaIMxTE4bxiGGSWjDPOoYqOuzLL8D3DYibglcADwBeFK3IUmSJC2uTnvIMvNq4NnAZ4Ezy6T8WZcxSZIkLbaue8jIzE8BnxphlUd0XN4YxieGSWjDOMQwCW0YhxgmoQ3GMJryxjCa8pMSA0C3g/olSZLU/aB+SZKkZc+ETJIkqWMmZJIkSR3rfFC/JGlhRMQWwD6UO6DUlOs8fjYzL+s0sHmIiO0AMvPiiLgZcH/grGHOyI+I12fmS0cV41IQEXsAazLzrIjYHbgPcGZmfrLj0NRwUL8kTaCIeCrwSuBzlOs8Qrn49t7Af2Xm+4eoe+/M/HzL994EuFlmntM3/W6Z+eMBZZ8JHEK5sOYbgacBPwPuB7wpM49usfy3902qgP2A9wNk5nPbtKOnvlsCuwBnZObPW7z/FsAlmfnXiKiaNtwDOAM4srn806A69gU+l5l/nU+sPeXfRrl39ErKZaYeBHwaeADwg8x8Yct6NqMk+DsCVwO/bOK6tmX5O1DuV937A+HkzDxzXg2aue6nZ+b7WsZwc+BbmXl5z/R9MvMzLcrfC6gz8zsRcSfK+vh5c8WIoSz5HjI/4FnrfH9mPnUDy+5O+ef9aWZ+rmWZe1N+bf0pIm5E+RKd/tJ5fWb+cUD55wInZuZ5c71vjvIbUy4sfGFmfiEingTcl3J9uyMy828t67k18GjW/8L58KD4e8o/BHgU62+PJ7XZDlrU/YrMfHXLGHYATs3Mc3um/0tmvrdF+QpYTYn/BOCBlP+xnwPvbvvl21fnFzPzgS3fO5WZa3teP4Vme6TswAb+ioyIRwNfzsw/NL0q/02zEwUOzszzB5Q/DPhYZn69Tcyz1HFTynUWLwSOBl4K/CNlm3x9Zl7aoo69gMey/vZ4VGae3SKElwG79veGRcRWwLdoEpINdDRwi0FviogA3gZcEhE3AJ6Wmd9pZh9D+Y6Yy7OBOwM3An4D3KbpKdsK+FITxyCPAU6jJKbTV0x/AvC9FmWJiE9k5qOa549s2nMa8IaIeENmHjOgik9Rtl+AQ4FbA5+g/F/dE/iXFmEcD1wREZ8GPkzp5bymTfyNvYG7UNbjBcDNM/MvEXEo8ANgYELWfJYvBH4E7AWcTulle1NEPDkzfzKg/IuBJwIfAb7dTN4B+HBEfCQzD51He2byX8Cc++tmP/PvlP/BoyPieZl5UjP79cCc39MR8UrgocDKiPg8cG/KtnBIROySma8bpgFLOiHzA/57HSf3TaqAvSJiS4DM3HdA+W9n5r2a5wc27TkReGVE3KPlenwvcPfm+eHAXyi/aB9EWYePGVD+NZQ2n0P5wvloZv6uxXKnvY+yPW8aEfsDmwEfb5Z/L2D/QRU0n+U/A1+mfFH+kLIj/EZEPCszTxtQ/m3A7Sg7uukd/g7AcyPioZn5vHm0ZybPAOZMyCLi9cDuwPeBl0bE2zLzf5rZz6Z8ToO8A9gG2JiSiN0Q+D/gYcDtgTnbERH9vR4VcLvp6Zl5twHL/xzNjjoiXk45RPUh4BHAHYH/16INr8vMOzXP/xf4JiUhejBlW9l7QPn9gD2aZO54SlL+gxbL7fVB4CfArsBTmudvbJZ9DGXdzqrZWW4LnApsB/waOAf4aHPI7aMDll9Rkup+19LiVi4zfK/01rv1oPKNl1KSwouaH54fiIiXZubH28QA/C0z/wL8JSLOycyLATLz0ohoe3jnjpTvl32AF2bmBRHxysw8tmX5f+h5/mLggZn564iYonw2xwwov1HTBijb3z2bHzUfjIgftYzh55QE7nHAwcD7IuJEynb55Rbl68ysI2L6x9T0uruW9mPJXw7cp0nkpoDjMvMhEXE34D2UH8BzOQC4c/+P4+bHz88oyeqcZvhumVZR/lcGOZCyPV4eETsBJ0TETpl5OO22x8cBO1O+Ey8Gdmg6Id5M+ZGzfBMy/ICn7UD55X8U5R+tAnaj9Aq0cYOe5wcBe2fm7yLiLZQdWZuEbKOervfdMnP6l+/XIuKHLcr/irLjejDweOC/mvuDfRj4eGb+eUD5u2bm3SJiJeUX4KrMvCYiPkj5RdfGgcDOTbnDgE9l5p4R8R7gJEoPy1welpm3658YEccDv2BAItO890+zzKoov24H+Wdgl8y8OiJeBXwoIm6Vmf+PlvdTA+6fmXdtejQuBrbPzHUR8SHKr+lBzgX+BLwWuLJZ7leb2NrojfMxTTxXNMv/fss6VvQ8v01mPr55fkxEPL9F+fMzc7eIuC2lN+WDEbGCsj1+ODN/0aKOVZn5sKbH8fzM3LOZ/tWW/xMPz8y7AkTERyg9fi+MiBMo63NQQvY64PsR8Tlguuf5FpSE8DUtln9/SiJ5ed/0iut6fAZZkZkXAWTmt5sev1MiYgdmThb7XRsRN2i+4x8+PTEiNqFlItF8dzw/InalfI6fbFu20Rvnysz8dVPv2p4EZy7nRcQDM/OLlP+NHYHfRETbpBZKQnUpcCRwZJRxdQEcGhE7ZOaOcxfnkxHxVWATyn4iI+KblEOWX2kZQ0X5fwa4gvKjjcz8cZTD0oNcC6yi9HT22r6Z18a2wEOA/t7litJjN8iK6aNYmXluROxJ2Wf/A+2+H69ueianfyD8qanrypbbwpyW+lmW0x9wv/l+wE+l7Cz6H79vUX69DxjYE3hos0Nv/QE3v6DW+4Dn0YbdKN3vLwP+2PTkXJmZX27562mjiNiq+YKopnumMvMKymGSNn4aEU9vnv8oInYDiIjbAW0OF9aZeW1mfi4zD6B8ru+k/Kr9Vcs2bAxsDmwKbNFMvyHrJ5yDTP9IuWFTF5n525Z1/LXpBeh3T6Dt2I/LgNtm5k36HpsDF7Uov3I6MW4OVf0zcJOI+Cilx6uN6fJ/A76Tmeua11cDAw+TND2yH6Ncvfruzf/F3zLzN5nZ/2U8kxtFxC7NDnRFsx1Ox9P2MM1pEfHqKIfPT4uI6UNOewFtDj/XzTJ/mZmvycw7U3aAm9D+ziIbNYfWdgQ2a36w0fyftfksro1y2BPK/8OKJqZLafHd0vQA7Ubp8b0KWEfpfd+txWE2KD/G/jL9PdLzOA04q0V5gD9HGQYwHdNFlO/IR1IORQ7y9571XP8w89aUnqLWMvN7lF6mK4GvzaPo3SPiTxHxZ2DnJhmaHiaxYu6iQOnZ/s+I+Arlc/9hRHwR+ALwgpYxrPd5Z+bFmfn2zPxHSo/4nDLzxcCLgOdkGS/2fMo2cRTwrJYxfAr4TES8lNKL/VH4+6H5Nvu65wOnRsSnI+KI5vEZSi9j26MHpwCbTX+X9DzOpWzbg1wcETtPv2j23Y8ApoC7tii/LiI2bZ7vOj0xyskzQydkS72HbPoD/iXr/wK8DeXwTBvTH/D1frFGxGktyl8cETtPl296yh5BOTTU+gNuErIN+oCb7u+3Njvdt0bEGub32W5BSegqoI6I7bKM09iM9r0qzwAObw4xraUc5juP8rk8o0X5/i+cvwEnAyc3O9VBjqZ066+gJKYfjYhfUcY4fKRlG44CvtP8ctyDcniJ5rDVH1qUfxrwrojYnOsOWe5I6S16WssY3k85RLJmhnkfalH+nIh4wHQi3vyaOyAiXksZi9TGxRGxWWZenpn7TE9sdkTr2lSQmSc2PTOviYhn0D4ZhJJ4HtY8/0NEbN8c8tqa9j8Qnk3ZDqYTh/8XEVdQDr3u16L89bb7LAPQfwy8pGUMb6Bsk1DGCR3VHGa7E2U4xCCvB34QEWcBdwD+Df6+Pbbq9W0O7X2JnjGN2WLsWlP2oXPM26NNHZSY1/vhn5l/joh9KAnuoBh+CxAR27J+Gy7guhMVWump43TK2MhWMnO2pGtT4Jktyp9HGUJyR8qQhmMo3w/fyfbjMWc9TN/yRw6Z+Y2I2DYi7kFZjx/IzJm+Z2Yr/+KIeBhl+311XndSx2UMHgtIZn6m+YF+L8rnUHHdemj1Q6v5sT7bvCe1qOKp9H2HND80n9ocCRlkj8y8qinX+9ndgBbDYgZZ8mdZRsRGDPEBj2D5O1B6uS6eYd79csCg4Ii44fQH3Dd9inKoaM6BkrPU+XDgfjnkad3NL4Ftp7voW5bZHLgVJSE8v+0/fETcruVhoLnqWAWQmRdGGT/3YOC3mfntuUuuV8edKWNOfpotzqCapY7t6NkeZ9o2Fsp08tr0sPbPu3mzI9vQum8M3DgzL5lnubsD/5iZ797QZTf1rABumNeNx2lbbgtKz2GbHu/pMptlzwk6G6qJucpyCHklZXjCBU1PUZvyN6X8P52d87xURdMT8G7KD67zKdvjDpQd6LMys9Xh3xmSodY78WHr6GtD75milwH/li3G9Q2oY9HWwxivx9brYJgYBtQ59P/bsHV0XR6Wfg8ZlI3q55n5zeaQwG7AnyljyFqJclrynzLzsp46fp6ZPx1UNjPPj4hbRMSWM5QfeIZWZl41x/JbJ2O9dVDafqOIuEubNvSXn+86mKGOHzV13D8i2q7HX4wghpWU3iiALZu/89p5U7adLwyzHijb5PRZcZdTxmHNS3PI9+9n1rVNDqcTsZnKzzcZmyWGKzaw/GnzWfYcy59vMvb3OiJiPuvx8jliaC3LeMTdIqK3jlbJWFP+DxFxK0oPy3xjOAZ4ZmZ+q3diRNyHcmLD3Wcq1PO+XYB30bcTj4jWO/HZEoGmjjYJ1VxtOGZQG1rUMex6GNiGAetgFOuxTR3HMMQ6GFU75nAGLc7aXeA6ui6/tHvIIuIQSpfxVcBbgP8Avk45THV0Zh42R/GR1NF1eWMYqzY8gHIixWWUw89fB7aijKHbL1tc0mPYOiYhhklowzjE0CSgt51l3tmZeZsB5X/I7Dvx92Rmm534UHUM24ZR1DGCNkzKehw2htnGy1XAyzLzprPMH1kdXZcfZKkP6t+Pcjz7fsBbKWdjHUA5hNnm2i6jqKPr8sYwPm14G/DQzHwwZUzF3zLzfpSz3dpcL2kUdUxCDJPQhnGI4dMR8cmIeHxE3Ld5PD7KWYZtrot34/6dL0BmfhO4cYvyo6hj2DaMoo5h2zAp63HYGF5P+UGxed9jM9rnIsPW0XX5OS31Q5bXZDnddB3lzJnfQzk7MGLgeNFR1dF1eWMYnzasyOuunfZbmusXZebno1yjbDHqmIQYJqENnceQmc+NiIdy3YWzp8fYviPbXXT6080O+/1cd9LUjpSB0fNKhja0jhG0YRzWw0SsxxG04/vAJ7Kc7bqeKCf+tDFsHV2Xn9NST8i+H+XaRDemnDp7bJTTaB9IOZ67GHV0Xd4YxqcN342Io5uyj6QZNxXl5Ig2p8ePoo5JiGES2jAWMWTmpym3yJm3MUmGhmrDKOoYtg2Tsh5HEMPTmf1s9d1ahjFsHV2Xn9NST8iewfq3eLk35cr9Z1GuNr4YdXRd3hjGpw3PpFxc9r6UawxNXxW/plzMsI1h65iEGCahDZ3HEOXs0pdQdqDbNJMvoVzk+NBscdZm18nQKNowDuthEtbjsDFk5qzXrsuWZ2oOW0fX5QdZ0oP6JUkzi4jPAl8Ejs3m0itRLsnyNOBBmTnn7aPGIRkatg2jqGMEbZiU9Tiq9fAo4GbzLT+KOrouP8iS7iGLcuHSF1EueLkD5aKV5wDvypb3KRu2jq7LG8NYtuExlLEV0+Xfne2ujD50HZMQwyS0YUxi2Ckz39g7odkZHxrX3VVjLknZie81w078owy+H+go6hi2DaOoY9g2TMp6HNV62LOv/P4ty4+ijq7Lz2mpn2V5HOW2Og+hXPn67ZQz5R4Y5SbLi1FH1+WNYfzasE9f+b02IIYNrWMSYpiENoxDDL+JiBdFuZAnAFGu1P5irhuUPZedMvON2XNh4yy37DmU9tdbGraOYdswijqGbcOkrMeFWg9vbFl+FHV0XX5OS7qHjLJyjmmeHxYR38nM1zQZ/xlAmyvVD1tH1+WNYXLaYAyT04ZxiOHxwCHAl5sdcU25JdfJMPi2RTQ7ccphrjVQduKUHpF5JUND1DFsG0ZRx7BtmJT1OAnroevyc1rqPWRXRMTuABHxzzRnP2S5x1TbezAOW0fX5Y1hctpgDJPThs5jyHLPyvdR7uu5Y2beNDPvmOVG0/dqsfzHU27i/eWIuDQi/kA50/OmzC8Z2uA6RtCGcVgPE7EeR9COztfDGJSf01LvIftXyg17bwf8lObinVFuvtv2zLph6+i6vDFMThuMYXLa0HkMEfFc4N+BM5t6npeZJzWzX8+Aa0dluTH5+4DPA9/Mnvv0Rbk5eJvrXw1Vx7BtGEUdw7ZhUtbjJKyHrssPVNf1RD5Wr1799K7r6Lq8MUxOG4xhctqwWDGsXr36J6tXr96seb7T6tWrv7t69ernNa9/0KL8c1evXn3W6tWrP7F69epzV69e/cieed9vGedQdQzbhnFYDxO0HidhPXTehrkeS/2Q5Vz+awzq6Lq8MYymvDGMTwyT0IbFimHF9C/4zDwX2BN4aEQcRrtDpgcCu2bmo5qy/xkRz2vmtT1sO2wdw7ZhFHUM24ZJWY+TsB66Lj+nJX3IMiJ+PMusCth2lnkjraPr8sYwmvLGMD4xTEIbxiSGiyNi58z8IUBmXh4Rj6BcYPauLcqvtxOPiD2BEyLiH9jAZGgD6hi2DaOoY9g2TMp6nIT10HX5OS3phIzypfQQ4NK+6RVw+iLV0XV5YxhNeWMYnxgmoQ3jEMNTgat7J2Tm1cBTI+I9LcqPQzI0bBtGUcewbZiU9TgJ66Hr8nNa6gnZKcBm0yunV0Sctkh1dF3eGEZT3hjGJ4ZJaEPnMWTm+XPM+3qL5XeeDI2gDeOwHiZiPQ4bwwjKj0MMo2jDrLx1kiRJUscmeVC/JEnSkmBCJkmS1DETMkmSpI4t9UH9krSeiDgOuCoz/6Vn2gOAjwN3ycyLOgtOkmZhD5mkSfNc4GERsTdARGwCHAkcPMpkLCJWjKouSfIsS0kTJyJWA28C7gK8HNg5Mx8aERsBhwAHAFsAXwD+rblH3UZAArsDmwA/bOad2dT5QeCPwK2B+wMPz8zTFrVhkiaWPWSSJk5mfhT4HvBh4CDgmc2sFwAPB/YAdgCuAN7eU/QU4LbAdpQben+gr+onUW5btDnwjQUKX9IyZA+ZpIkUEdsC5wAvy8zDm2m/BJ6RmV9uXu8InA3cKDOv7Ss/BfyOcnHWK5oesnW9Y9MkaVQc1C9pImXmmohYC/ysZ/ItgP+LiN7kqwa2iYjfAW8AHgdMAdPvmaL0pAGct7BRS1quTMgkLSfnA0/KzG/1z4iIpwMPAx4I/AbYmtJD1nvTYA8pSFoQjiGTtJy8G3h9RNwCICK2iYh9m3mbA1cBvwc2BV7XTYiSliMTMknLyWHAZ4BTI+LPwOnAPZt57wMubB4/a+ZJ0qJwUL8kSVLH7CGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnq2P8HKo4KYG22Hg0AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# step 2: plot data\n", | |
| "df_iceland.plot(kind='bar', figsize=(10, 6))\n", | |
| "\n", | |
| "plt.xlabel('Year') # add to x-label to the plot\n", | |
| "plt.ylabel('Number of immigrants') # add y-label to the plot\n", | |
| "plt.title('Icelandic immigrants to Canada from 1980 to 2013') # add title to the plot\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "The bar plot above shows the total number of immigrants broken down by each year. We can clearly see the impact of the financial crisis; the number of immigrants to Canada started increasing rapidly after 2008. \n", | |
| "\n", | |
| "Let's annotate this on the plot using the `annotate` method of the **scripting layer** or the **pyplot interface**. We will pass in the following parameters:\n", | |
| "- `s`: str, the text of annotation.\n", | |
| "- `xy`: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).\n", | |
| "- `xytext`: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).\n", | |
| "- `xycoords`: The coordinate system that xy is given in - 'data' uses the coordinate system of the object being annotated (default).\n", | |
| "- `arrowprops`: Takes a dictionary of properties to draw the arrow:\n", | |
| " - `arrowstyle`: Specifies the arrow style, `'->'` is standard arrow.\n", | |
| " - `connectionstyle`: Specifies the connection type. `arc3` is a straight line.\n", | |
| " - `color`: Specifes color of arror.\n", | |
| " - `lw`: Specifies the line width.\n", | |
| "\n", | |
| "I encourage you to read the Matplotlib documentation for more details on annotations: \n", | |
| "http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) # rotate the bars by 90 degrees\n", | |
| "\n", | |
| "plt.xlabel('Year')\n", | |
| "plt.ylabel('Number of Immigrants')\n", | |
| "plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n", | |
| "\n", | |
| "# Annotate arrow\n", | |
| "plt.annotate('', # s: str. Will leave it blank for no text\n", | |
| " xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\n", | |
| " xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\n", | |
| " xycoords='data', # will use the coordinate system of the object being annotated \n", | |
| " arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n", | |
| " )\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Let's also annotate a text to go over the arrow. We will pass in the following additional parameters:\n", | |
| "- `rotation`: rotation angle of text in degrees (counter clockwise)\n", | |
| "- `va`: vertical alignment of text [‘center’ | ‘top’ | ‘bottom’ | ‘baseline’]\n", | |
| "- `ha`: horizontal alignment of text [‘center’ | ‘right’ | ‘left’]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) \n", | |
| "\n", | |
| "plt.xlabel('Year')\n", | |
| "plt.ylabel('Number of Immigrants')\n", | |
| "plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n", | |
| "\n", | |
| "# Annotate arrow\n", | |
| "plt.annotate('', # s: str. will leave it blank for no text\n", | |
| " xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\n", | |
| " xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\n", | |
| " xycoords='data', # will use the coordinate system of the object being annotated \n", | |
| " arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n", | |
| " )\n", | |
| "\n", | |
| "# Annotate Text\n", | |
| "plt.annotate('2008 - 2011 Financial Crisis', # text to display\n", | |
| " xy=(28, 30), # start the text at at point (year 2008 , pop 30)\n", | |
| " rotation=72.5, # based on trial and error to match the arrow\n", | |
| " va='bottom', # want the text to be vertically 'bottom' aligned\n", | |
| " ha='left', # want the text to be horizontally 'left' algned.\n", | |
| " )\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
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| } | |
| }, | |
| "source": [ | |
| "**Horizontal Bar Plot**\n", | |
| "\n", | |
| "Sometimes it is more practical to represent the data horizontally, especially if you need more room for labelling the bars. In horizontal bar graphs, the y-axis is used for labelling, and the length of bars on the x-axis corresponds to the magnitude of the variable being measured. As you will see, there is more room on the y-axis to label categetorical variables.\n", | |
| "\n", | |
| "\n", | |
| "**Question:** Using the scripting layter and the `df_can` dataset, create a *horizontal* bar plot showing the *total* number of immigrants to Canada from the top 15 countries, for the period 1980 - 2013. Label each country with the total immigrant count." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
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| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Step 1: Get the data pertaining to the top 15 countries." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "button": false, | |
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| "deletable": true, | |
| "new_sheet": false, | |
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| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>Continent</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>Region</th>\n", | |
| " <th>DEV</th>\n", | |
| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>1981</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " <th>Total</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Country</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>India</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>8880</td>\n", | |
| " <td>8670</td>\n", | |
| " <td>...</td>\n", | |
| " <td>36210</td>\n", | |
| " <td>33848</td>\n", | |
| " <td>28742</td>\n", | |
| " <td>28261</td>\n", | |
| " <td>29456</td>\n", | |
| " <td>34235</td>\n", | |
| " <td>27509</td>\n", | |
| " <td>30933</td>\n", | |
| " <td>33087</td>\n", | |
| " <td>699242</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>China</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>906</td>\n", | |
| " <td>Eastern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>5123</td>\n", | |
| " <td>6682</td>\n", | |
| " <td>...</td>\n", | |
| " <td>42584</td>\n", | |
| " <td>33518</td>\n", | |
| " <td>27642</td>\n", | |
| " <td>30037</td>\n", | |
| " <td>29622</td>\n", | |
| " <td>30391</td>\n", | |
| " <td>28502</td>\n", | |
| " <td>33024</td>\n", | |
| " <td>34129</td>\n", | |
| " <td>662705</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>United Kingdom of Great Britain and Northern Ireland</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>924</td>\n", | |
| " <td>Northern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>22045</td>\n", | |
| " <td>24796</td>\n", | |
| " <td>...</td>\n", | |
| " <td>7258</td>\n", | |
| " <td>7140</td>\n", | |
| " <td>8216</td>\n", | |
| " <td>8979</td>\n", | |
| " <td>8876</td>\n", | |
| " <td>8724</td>\n", | |
| " <td>6204</td>\n", | |
| " <td>6195</td>\n", | |
| " <td>5827</td>\n", | |
| " <td>554233</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Philippines</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>920</td>\n", | |
| " <td>South-Eastern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>6051</td>\n", | |
| " <td>5921</td>\n", | |
| " <td>...</td>\n", | |
| " <td>18139</td>\n", | |
| " <td>18400</td>\n", | |
| " <td>19837</td>\n", | |
| " <td>24887</td>\n", | |
| " <td>28573</td>\n", | |
| " <td>38617</td>\n", | |
| " <td>36765</td>\n", | |
| " <td>34315</td>\n", | |
| " <td>29544</td>\n", | |
| " <td>514148</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Pakistan</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>978</td>\n", | |
| " <td>972</td>\n", | |
| " <td>...</td>\n", | |
| " <td>14314</td>\n", | |
| " <td>13127</td>\n", | |
| " <td>10124</td>\n", | |
| " <td>8994</td>\n", | |
| " <td>7217</td>\n", | |
| " <td>6811</td>\n", | |
| " <td>7468</td>\n", | |
| " <td>11227</td>\n", | |
| " <td>12603</td>\n", | |
| " <td>248938</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>United States of America</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>905</td>\n", | |
| " <td>Northern America</td>\n", | |
| " <td>905</td>\n", | |
| " <td>Northern America</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>9378</td>\n", | |
| " <td>10030</td>\n", | |
| " <td>...</td>\n", | |
| " <td>8394</td>\n", | |
| " <td>9613</td>\n", | |
| " <td>9463</td>\n", | |
| " <td>10190</td>\n", | |
| " <td>8995</td>\n", | |
| " <td>8142</td>\n", | |
| " <td>7676</td>\n", | |
| " <td>7891</td>\n", | |
| " <td>8501</td>\n", | |
| " <td>243833</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Iran (Islamic Republic of)</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>1172</td>\n", | |
| " <td>1429</td>\n", | |
| " <td>...</td>\n", | |
| " <td>5837</td>\n", | |
| " <td>7480</td>\n", | |
| " <td>6974</td>\n", | |
| " <td>6475</td>\n", | |
| " <td>6580</td>\n", | |
| " <td>7477</td>\n", | |
| " <td>7479</td>\n", | |
| " <td>7534</td>\n", | |
| " <td>11291</td>\n", | |
| " <td>183261</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sri Lanka</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>185</td>\n", | |
| " <td>371</td>\n", | |
| " <td>...</td>\n", | |
| " <td>4930</td>\n", | |
| " <td>4714</td>\n", | |
| " <td>4123</td>\n", | |
| " <td>4756</td>\n", | |
| " <td>4547</td>\n", | |
| " <td>4422</td>\n", | |
| " <td>3309</td>\n", | |
| " <td>3338</td>\n", | |
| " <td>2394</td>\n", | |
| " <td>155696</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Republic of Korea</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>906</td>\n", | |
| " <td>Eastern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>1011</td>\n", | |
| " <td>1456</td>\n", | |
| " <td>...</td>\n", | |
| " <td>5832</td>\n", | |
| " <td>6215</td>\n", | |
| " <td>5920</td>\n", | |
| " <td>7294</td>\n", | |
| " <td>5874</td>\n", | |
| " <td>5537</td>\n", | |
| " <td>4588</td>\n", | |
| " <td>5316</td>\n", | |
| " <td>4509</td>\n", | |
| " <td>145324</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Poland</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>923</td>\n", | |
| " <td>Eastern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>863</td>\n", | |
| " <td>2930</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1405</td>\n", | |
| " <td>1263</td>\n", | |
| " <td>1235</td>\n", | |
| " <td>1267</td>\n", | |
| " <td>1013</td>\n", | |
| " <td>795</td>\n", | |
| " <td>720</td>\n", | |
| " <td>779</td>\n", | |
| " <td>852</td>\n", | |
| " <td>141973</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Lebanon</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>922</td>\n", | |
| " <td>Western Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>1409</td>\n", | |
| " <td>1119</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3709</td>\n", | |
| " <td>3802</td>\n", | |
| " <td>3467</td>\n", | |
| " <td>3566</td>\n", | |
| " <td>3077</td>\n", | |
| " <td>3432</td>\n", | |
| " <td>3072</td>\n", | |
| " <td>1614</td>\n", | |
| " <td>2172</td>\n", | |
| " <td>118118</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>France</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>926</td>\n", | |
| " <td>Western Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1729</td>\n", | |
| " <td>2027</td>\n", | |
| " <td>...</td>\n", | |
| " <td>4429</td>\n", | |
| " <td>4002</td>\n", | |
| " <td>4290</td>\n", | |
| " <td>4532</td>\n", | |
| " <td>5051</td>\n", | |
| " <td>4646</td>\n", | |
| " <td>4080</td>\n", | |
| " <td>6280</td>\n", | |
| " <td>5623</td>\n", | |
| " <td>111826</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Jamaica</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>904</td>\n", | |
| " <td>Latin America and the Caribbean</td>\n", | |
| " <td>915</td>\n", | |
| " <td>Caribbean</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>3198</td>\n", | |
| " <td>2634</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1945</td>\n", | |
| " <td>1722</td>\n", | |
| " <td>2141</td>\n", | |
| " <td>2334</td>\n", | |
| " <td>2456</td>\n", | |
| " <td>2321</td>\n", | |
| " <td>2059</td>\n", | |
| " <td>2182</td>\n", | |
| " <td>2479</td>\n", | |
| " <td>109152</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Viet Nam</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>920</td>\n", | |
| " <td>South-Eastern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>1191</td>\n", | |
| " <td>1829</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1852</td>\n", | |
| " <td>3153</td>\n", | |
| " <td>2574</td>\n", | |
| " <td>1784</td>\n", | |
| " <td>2171</td>\n", | |
| " <td>1942</td>\n", | |
| " <td>1723</td>\n", | |
| " <td>1731</td>\n", | |
| " <td>2112</td>\n", | |
| " <td>99903</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Romania</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>923</td>\n", | |
| " <td>Eastern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>375</td>\n", | |
| " <td>438</td>\n", | |
| " <td>...</td>\n", | |
| " <td>5048</td>\n", | |
| " <td>4468</td>\n", | |
| " <td>3834</td>\n", | |
| " <td>2837</td>\n", | |
| " <td>2076</td>\n", | |
| " <td>1922</td>\n", | |
| " <td>1776</td>\n", | |
| " <td>1588</td>\n", | |
| " <td>1512</td>\n", | |
| " <td>96317</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>15 rows × 43 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage \\\n", | |
| "Country \n", | |
| "India Immigrants Foreigners \n", | |
| "China Immigrants Foreigners \n", | |
| "United Kingdom of Great Britain and Northern Ir... Immigrants Foreigners \n", | |
| "Philippines Immigrants Foreigners \n", | |
| "Pakistan Immigrants Foreigners \n", | |
| "United States of America Immigrants Foreigners \n", | |
| "Iran (Islamic Republic of) Immigrants Foreigners \n", | |
| "Sri Lanka Immigrants Foreigners \n", | |
| "Republic of Korea Immigrants Foreigners \n", | |
| "Poland Immigrants Foreigners \n", | |
| "Lebanon Immigrants Foreigners \n", | |
| "France Immigrants Foreigners \n", | |
| "Jamaica Immigrants Foreigners \n", | |
| "Viet Nam Immigrants Foreigners \n", | |
| "Romania Immigrants Foreigners \n", | |
| "\n", | |
| " AREA \\\n", | |
| "Country \n", | |
| "India 935 \n", | |
| "China 935 \n", | |
| "United Kingdom of Great Britain and Northern Ir... 908 \n", | |
| "Philippines 935 \n", | |
| "Pakistan 935 \n", | |
| "United States of America 905 \n", | |
| "Iran (Islamic Republic of) 935 \n", | |
| "Sri Lanka 935 \n", | |
| "Republic of Korea 935 \n", | |
| "Poland 908 \n", | |
| "Lebanon 935 \n", | |
| "France 908 \n", | |
| "Jamaica 904 \n", | |
| "Viet Nam 935 \n", | |
| "Romania 908 \n", | |
| "\n", | |
| " Continent \\\n", | |
| "Country \n", | |
| "India Asia \n", | |
| "China Asia \n", | |
| "United Kingdom of Great Britain and Northern Ir... Europe \n", | |
| "Philippines Asia \n", | |
| "Pakistan Asia \n", | |
| "United States of America Northern America \n", | |
| "Iran (Islamic Republic of) Asia \n", | |
| "Sri Lanka Asia \n", | |
| "Republic of Korea Asia \n", | |
| "Poland Europe \n", | |
| "Lebanon Asia \n", | |
| "France Europe \n", | |
| "Jamaica Latin America and the Caribbean \n", | |
| "Viet Nam Asia \n", | |
| "Romania Europe \n", | |
| "\n", | |
| " REG Region \\\n", | |
| "Country \n", | |
| "India 5501 Southern Asia \n", | |
| "China 906 Eastern Asia \n", | |
| "United Kingdom of Great Britain and Northern Ir... 924 Northern Europe \n", | |
| "Philippines 920 South-Eastern Asia \n", | |
| "Pakistan 5501 Southern Asia \n", | |
| "United States of America 905 Northern America \n", | |
| "Iran (Islamic Republic of) 5501 Southern Asia \n", | |
| "Sri Lanka 5501 Southern Asia \n", | |
| "Republic of Korea 906 Eastern Asia \n", | |
| "Poland 923 Eastern Europe \n", | |
| "Lebanon 922 Western Asia \n", | |
| "France 926 Western Europe \n", | |
| "Jamaica 915 Caribbean \n", | |
| "Viet Nam 920 South-Eastern Asia \n", | |
| "Romania 923 Eastern Europe \n", | |
| "\n", | |
| " DEV DevName \\\n", | |
| "Country \n", | |
| "India 902 Developing regions \n", | |
| "China 902 Developing regions \n", | |
| "United Kingdom of Great Britain and Northern Ir... 901 Developed regions \n", | |
| "Philippines 902 Developing regions \n", | |
| "Pakistan 902 Developing regions \n", | |
| "United States of America 901 Developed regions \n", | |
| "Iran (Islamic Republic of) 902 Developing regions \n", | |
| "Sri Lanka 902 Developing regions \n", | |
| "Republic of Korea 902 Developing regions \n", | |
| "Poland 901 Developed regions \n", | |
| "Lebanon 902 Developing regions \n", | |
| "France 901 Developed regions \n", | |
| "Jamaica 902 Developing regions \n", | |
| "Viet Nam 902 Developing regions \n", | |
| "Romania 901 Developed regions \n", | |
| "\n", | |
| " 1980 1981 ... 2005 \\\n", | |
| "Country ... \n", | |
| "India 8880 8670 ... 36210 \n", | |
| "China 5123 6682 ... 42584 \n", | |
| "United Kingdom of Great Britain and Northern Ir... 22045 24796 ... 7258 \n", | |
| "Philippines 6051 5921 ... 18139 \n", | |
| "Pakistan 978 972 ... 14314 \n", | |
| "United States of America 9378 10030 ... 8394 \n", | |
| "Iran (Islamic Republic of) 1172 1429 ... 5837 \n", | |
| "Sri Lanka 185 371 ... 4930 \n", | |
| "Republic of Korea 1011 1456 ... 5832 \n", | |
| "Poland 863 2930 ... 1405 \n", | |
| "Lebanon 1409 1119 ... 3709 \n", | |
| "France 1729 2027 ... 4429 \n", | |
| "Jamaica 3198 2634 ... 1945 \n", | |
| "Viet Nam 1191 1829 ... 1852 \n", | |
| "Romania 375 438 ... 5048 \n", | |
| "\n", | |
| " 2006 2007 2008 \\\n", | |
| "Country \n", | |
| "India 33848 28742 28261 \n", | |
| "China 33518 27642 30037 \n", | |
| "United Kingdom of Great Britain and Northern Ir... 7140 8216 8979 \n", | |
| "Philippines 18400 19837 24887 \n", | |
| "Pakistan 13127 10124 8994 \n", | |
| "United States of America 9613 9463 10190 \n", | |
| "Iran (Islamic Republic of) 7480 6974 6475 \n", | |
| "Sri Lanka 4714 4123 4756 \n", | |
| "Republic of Korea 6215 5920 7294 \n", | |
| "Poland 1263 1235 1267 \n", | |
| "Lebanon 3802 3467 3566 \n", | |
| "France 4002 4290 4532 \n", | |
| "Jamaica 1722 2141 2334 \n", | |
| "Viet Nam 3153 2574 1784 \n", | |
| "Romania 4468 3834 2837 \n", | |
| "\n", | |
| " 2009 2010 2011 \\\n", | |
| "Country \n", | |
| "India 29456 34235 27509 \n", | |
| "China 29622 30391 28502 \n", | |
| "United Kingdom of Great Britain and Northern Ir... 8876 8724 6204 \n", | |
| "Philippines 28573 38617 36765 \n", | |
| "Pakistan 7217 6811 7468 \n", | |
| "United States of America 8995 8142 7676 \n", | |
| "Iran (Islamic Republic of) 6580 7477 7479 \n", | |
| "Sri Lanka 4547 4422 3309 \n", | |
| "Republic of Korea 5874 5537 4588 \n", | |
| "Poland 1013 795 720 \n", | |
| "Lebanon 3077 3432 3072 \n", | |
| "France 5051 4646 4080 \n", | |
| "Jamaica 2456 2321 2059 \n", | |
| "Viet Nam 2171 1942 1723 \n", | |
| "Romania 2076 1922 1776 \n", | |
| "\n", | |
| " 2012 2013 Total \n", | |
| "Country \n", | |
| "India 30933 33087 699242 \n", | |
| "China 33024 34129 662705 \n", | |
| "United Kingdom of Great Britain and Northern Ir... 6195 5827 554233 \n", | |
| "Philippines 34315 29544 514148 \n", | |
| "Pakistan 11227 12603 248938 \n", | |
| "United States of America 7891 8501 243833 \n", | |
| "Iran (Islamic Republic of) 7534 11291 183261 \n", | |
| "Sri Lanka 3338 2394 155696 \n", | |
| "Republic of Korea 5316 4509 145324 \n", | |
| "Poland 779 852 141973 \n", | |
| "Lebanon 1614 2172 118118 \n", | |
| "France 6280 5623 111826 \n", | |
| "Jamaica 2182 2479 109152 \n", | |
| "Viet Nam 1731 2112 99903 \n", | |
| "Romania 1588 1512 96317 \n", | |
| "\n", | |
| "[15 rows x 43 columns]" | |
| ] | |
| }, | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "df_can.sort_values('Total',inplace=True,ascending=False)\n", | |
| "df_total = df_can.head(15)\n", | |
| "df_total" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click __here__ for the solution.\n", | |
| "<!-- The correct answer is:\n", | |
| "\\\\ # sort dataframe on 'Total' column (descending)\n", | |
| "df_can.sort_values(by='Total', ascending=True, inplace=True)\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # get top 15 countries\n", | |
| "df_top15 = df_can['Total'].tail(15)\n", | |
| "df_top15\n", | |
| "-->" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Step 2: Plot data:\n", | |
| " 1. Use `kind='barh'` to generate a bar chart with horizontal bars.\n", | |
| " 2. Make sure to choose a good size for the plot and to label your axes and to give the plot a title.\n", | |
| " 3. Loop through the countries and annotate the immigrant population using the anotate function of the scripting interface." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": true, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "ename": "ValueError", | |
| "evalue": "Cannot specify ',' with 's'.", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-33-b1f92e764716>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdf_total\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'barh'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_total\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# format int with commas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mValueError\u001b[0m: Cannot specify ',' with 's'." | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 864x864 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "\n", | |
| "\n", | |
| "df_total.plot(kind='barh',figsize=(12,12))\n", | |
| "for index, value in enumerate(df_total): \n", | |
| " label = format((value), ',') # format int with commas\n", | |
| " \n", | |
| " # place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\n", | |
| " plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click __here__ for the solution.\n", | |
| "<!-- The correct answer is:\n", | |
| "\\\\ # generate plot\n", | |
| "df_top15.plot(kind='barh', figsize=(12, 12), color='steelblue')\n", | |
| "plt.xlabel('Number of Immigrants')\n", | |
| "plt.title('Top 15 Conuntries Contributing to the Immigration to Canada between 1980 - 2013')\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "\\\\ # annotate value labels to each country\n", | |
| "for index, value in enumerate(df_top15): \n", | |
| " label = format(int(value), ',') # format int with commas\n", | |
| " \n", | |
| " # place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\n", | |
| " plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\n", | |
| "-->\n", | |
| "\n", | |
| "<!--\n", | |
| "plt.show()\n", | |
| "-->" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "### Thank you for completing this lab!\n", | |
| "\n", | |
| "This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n", | |
| "\n", | |
| "This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "This notebook is part of the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course online by clicking [here](https://cocl.us/DV0101EN_Lab2)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "deletable": true, | |
| "editable": true, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "<hr>\n", | |
| "\n", | |
| "Copyright © 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)." | |
| ] | |
| } | |
| ], | |
| "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": "3.6.8" | |
| }, | |
| "widgets": { | |
| "state": {}, | |
| "version": "1.1.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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