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Michy01/DV0101EN-2-2-1-Area-Plots-Histograms-and-Bar-Charts-py-v2.0.ipynb

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DV0101EN-2-2-1-Area-Plots-Histograms-and-Bar-Charts-py-v2.0.ipynb
{
"cells": [
{
"cell_type": "markdown",
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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>"
]
},
{
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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,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": 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": {
"button": false,
"deletable": true,
"editable": true,
"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": {
"read_only": false
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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": {
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"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
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},
"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,
"jupyter": {
"outputs_hidden": false
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"new_sheet": false,
"run_control": {
"read_only": false
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},
"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,
"editable": true,
"new_sheet": false,
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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,
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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",
" 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>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": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(195, 43)\n"
]
}
],
"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": {
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}
},
"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": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"outputs": [
{
"data": {
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"<div>\n",
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" 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>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
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" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
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" <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>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
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" <td>3436</td>\n",
" <td>3009</td>\n",
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" <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>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",
" <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>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": 5,
"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": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>Country</th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</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>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</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>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",
" <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>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": [
" Country Continent Region 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": 6,
"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": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 7,
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</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",
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" <th></th>\n",
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" <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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</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>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>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>Algeria</th>\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>69</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>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>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>Andorra</th>\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>2</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 × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2004 2005 2006 2007 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 2978 3436 3009 2652 \n",
"Albania 0 0 0 0 1 ... 1450 1223 856 702 \n",
"Algeria 71 69 63 44 69 ... 3616 3626 4807 3623 \n",
"American Samoa 0 0 0 0 0 ... 0 0 1 0 \n",
"Andorra 0 0 0 0 2 ... 0 0 1 1 \n",
"\n",
" 2008 2009 2010 2011 2012 2013 \n",
"Country \n",
"Afghanistan 2111 1746 1758 2203 2635 2004 \n",
"Albania 560 716 561 539 620 603 \n",
"Algeria 4005 5393 4752 4325 3774 4331 \n",
"American Samoa 0 0 0 0 0 0 \n",
"Andorra 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 9,
"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": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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",
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" 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>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</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",
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" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</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>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <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>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\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>69</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>69439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Samoa</th>\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>0</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>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Andorra</th>\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>2</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>15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2005 2006 2007 2008 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 3436 3009 2652 2111 \n",
"Albania 0 0 0 0 1 ... 1223 856 702 560 \n",
"Algeria 71 69 63 44 69 ... 3626 4807 3623 4005 \n",
"American Samoa 0 0 0 0 0 ... 0 1 0 0 \n",
"Andorra 0 0 0 0 2 ... 0 1 1 0 \n",
"\n",
" 2009 2010 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 1746 1758 2203 2635 2004 58639 \n",
"Albania 716 561 539 620 603 15699 \n",
"Algeria 5393 4752 4325 3774 4331 69439 \n",
"American Samoa 0 0 0 0 0 6 \n",
"Andorra 0 0 0 1 1 15 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 10,
"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": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n"
]
}
],
"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": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 12,
"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(str, 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": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\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": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>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": 14,
"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": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"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": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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": {},
"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": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Years')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\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",
"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": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\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": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# let's quickly view the 2013 data\n",
"df_can['2013'].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"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"
]
},
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"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>"
]
},
{
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"source": [
"We can easily graph this distribution by passing `kind=hist` to `plot()`."
]
},
{
"cell_type": "code",
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"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,
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"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",
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"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,
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"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."
]
},
{
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"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": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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"outputs_hidden": false
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"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# let's quickly view the dataset \n",
"df_can.loc[['Denmark', 'Norway', 'Sweden'], years]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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"outputs_hidden": false
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}
},
"outputs": [],
"source": [
"# generate histogram\n",
"df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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"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": null,
"metadata": {
"button": false,
"collapsed": false,
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},
"outputs": [],
"source": [
"# transpose dataframe\n",
"df_t = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
"df_t.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
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"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,
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"run_control": {
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"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": null,
"metadata": {
"button": false,
"collapsed": false,
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"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()"
]
},
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"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
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},
"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",
"```"
]
},
{
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"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
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}
},
"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."
]
},
{
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"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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"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()"
]
},
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"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"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": null,
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"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
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"metadata": {
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"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",
"-->"
]
},
{
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"button": false,
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"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"
]
},
{
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"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",
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"source": [
"# step 1: get the data\n",
"df_iceland = df_can.loc['Iceland', years]\n",
"df_iceland.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"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,
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"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",
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"outputs": [],
"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": {
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}
},
"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,
"jupyter": {
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},
"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,
"editable": true,
"new_sheet": false,
"run_control": {
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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,
"editable": true,
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},
"source": [
"Step 1: Get the data pertaining to the top 15 countries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
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"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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"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": {
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}
},
"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": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"jupyter": {
"outputs_hidden": true
},
"new_sheet": false,
"run_control": {
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}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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"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",
"-->"
]
},
{
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"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": {
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},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week2_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
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},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 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",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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