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bkee06/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",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<center>\n",
" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%202/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"\n",
"# Area Plots, Histograms, and Bar Plots\n",
"\n",
"\n",
"Estimated time needed: **30** minutes\n",
" \n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"* Create additional labs namely area plots, histogram and bar charts"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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,
"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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded and read into a dataframe!\n"
]
}
],
"source": [
"\n",
"df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2\n",
" )\n",
"print('Data downloaded and read into a dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": 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>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,
"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,
"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,
"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,
"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,
"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>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",
" <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": [
" 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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</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",
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" <th>1982</th>\n",
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" <th>1985</th>\n",
" <th>...</th>\n",
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" <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,
"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,
"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,
"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,
"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,
"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,
"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,
"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",
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" <th></th>\n",
" <th></th>\n",
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" <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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the country names now serve as indices."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 5. Add total column."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": 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>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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" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>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,
"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,
"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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Visualizing Data using Matplotlib<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import `Matplotlib` and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.3.4\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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Area Plots<a id=\"6\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"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,
"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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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"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,
"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,
"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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" # get the 5 countries with the least contribution\n",
" df_least5 = df_can.tail(5)\n",
" \n",
" # transpose the dataframe\n",
" df_least5 = df_least5[years].transpose() \n",
" df_least5.head()\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",
" plt.title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n",
" plt.ylabel('Number of Immigrants')\n",
" plt.xlabel('Years')\n",
"\n",
" plt.show()\n",
" \n",
"```\n",
"\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" \n",
" # get the 5 countries with the least contribution\n",
" df_least5 = df_can.tail(5)\n",
"\n",
" # transpose the dataframe\n",
" df_least5 = df_least5[years].transpose() \n",
" \n",
" df_least5.head()\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",
" ax = df_least5.plot(kind='area', alpha=0.55, stacked=False, figsize=(20, 10))\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",
"\n",
" \n",
"```\n",
"\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"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,
"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,
"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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%202/images/Mod2Fig1-Histogram.JPG\" align=\"center\" width=800>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily graph this distribution by passing `kind=hist` to `plot()`."
]
},
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"execution_count": null,
"metadata": {
"button": false,
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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,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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,
"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": null,
"metadata": {
"button": false,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# generate histogram\n",
"df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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": null,
"metadata": {
"button": 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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" \n",
" # create a dataframe of the countries of interest (cof)\n",
" df_cof = df_can.loc[['Greece', 'Albania', 'Bulgaria'], years]\n",
"\n",
" # transpose the dataframe\n",
" df_cof = df_cof.transpose() \n",
"\n",
" # let's get the x-tick values\n",
" count, bin_edges = np.histogram(df_cof, 15)\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",
" 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",
" plt.show()\n",
"\n",
" \n",
"```\n",
"\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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,
"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": null,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) # rotate the xticks(labelled points on x-axis) 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,
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data pertaining to the top 15 countries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": 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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" \n",
" # sort dataframe on 'Total' column (descending)\n",
" df_can.sort_values(by='Total', ascending=True, inplace=True)\n",
"\n",
" # get top 15 countries\n",
" df_top15 = df_can['Total'].tail(15)\n",
" df_top15\n",
"\n",
"```\n",
"\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": null,
"metadata": {
"button": 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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" \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",
" # 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",
" plt.show()\n",
"\n",
"```\n",
"\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
"\n",
"\n",
"### Other Contributors\n",
"[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan)\n",
"[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani)\n",
"[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"\n",
"## Change Log\n",
"\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"|---|---|---|---|\n",
"| 2021-01-20 | 2.3 | Lakshmi Holla | Changed TOC cell markdown |\n",
"| 2021-01-05 | 2.2 | Lakshmi Holla | Changed solution code for annotate |\n",
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed the URL of excel file |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"\n",
"\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>"
]
}
],
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