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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"<center>\n",
" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Data Visualization\n",
"\n",
"Estimated time needed: **30** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Create Data Visualization with Python\n",
"- Use various Python libraries for visualization\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no _best_ data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in _pandas_ dataframes. Therefore, in this lab, we provide a brief crash course on _pandas_. However, if you are interested in learning more about the _pandas_ library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a _pandas_ dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n",
"\n",
"* * *\n"
]
},
{
"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",
" 1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
" 1.2 [_pandas_ Basics](#4) <br>\n",
" 1.3 [_pandas_ Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
" 2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
" </div>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Exploring Datasets with _pandas_ <a id=\"0\"></a>\n",
"\n",
"_pandas_ is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740):\n",
"\n",
"> _pandas_ is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on _pandas_ for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the _pandas_ API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants 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. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png\" align=\"center\" width=900>\n",
"\n",
" The Canada Immigration dataset can be fetched from <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx\">here</a>.\n",
"\n",
"* * *\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## _pandas_ Basics<a id=\"4\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The first thing we'll do is import two key data analysis modules: _pandas_ and **Numpy**.\n"
]
},
{
"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",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data.\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"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 read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe tbody tr th {\n",
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" }\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()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" }\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",
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" <th>OdName</th>\n",
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" <th>AreaName</th>\n",
" <th>REG</th>\n",
" <th>RegName</th>\n",
" <th>DEV</th>\n",
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" <th>1980</th>\n",
" <th>...</th>\n",
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" <th>2008</th>\n",
" <th>2009</th>\n",
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" <th>2012</th>\n",
" <th>2013</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Viet Nam</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
" <td>1816</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Western Sahara</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>0</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>192</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Yemen</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
" <td>434</td>\n",
" <td>437</td>\n",
" <td>407</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",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method.\n",
"\n",
"This method can be used to get a short summary of the dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Columns: 43 entries, Type to 2013\n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info(verbose=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list.\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in _pandas_ objects are _float_, _int_, _bool_, _datetime64[ns]_ and _datetime64[ns, tz] (in >= 0.17.0)_, _timedelta[ns]_, _category (in >= 0.15.0)_, and _object_ (string). In addition these dtypes have item sizes, e.g. int64 and int32. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use _pandas_ `drop()` method as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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",
" </tbody>\n",
"</table>\n",
"<p>2 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",
"\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",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"## _pandas_ Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python\n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"\n",
"* * *\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country').\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985.\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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>Country</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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</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",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</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",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</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",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</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>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</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>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>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",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
"\n",
"```\n",
"1. The full row data (all columns)\n",
"2. For year 2013\n",
"3. For years 1980 to 1985\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"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": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\n",
"\n",
"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
"\n",
"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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",
" </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>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</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>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing 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>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing 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>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
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" <th>2013</th>\n",
" <th>Total</th>\n",
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" </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",
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" <td>58639</td>\n",
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" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
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" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
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" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
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" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>0</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",
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" <td>0</td>\n",
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" <td>4</td>\n",
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" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
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" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
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" <td>13127</td>\n",
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" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 \n",
"Bangladesh Asia Southern Asia Developing regions 83 \n",
"Bhutan Asia Southern Asia Developing regions 0 \n",
"India Asia Southern Asia Developing regions 8880 \n",
"Iran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \n",
"Maldives Asia Southern Asia Developing regions 0 \n",
"Nepal Asia Southern Asia Developing regions 1 \n",
"Pakistan Asia Southern Asia Developing regions 978 \n",
"Sri Lanka Asia Southern Asia Developing regions 185 \n",
"\n",
" 1981 1982 1983 1984 1985 1986 ... 2005 \\\n",
"Afghanistan 39 39 47 71 340 496 ... 3436 \n",
"Bangladesh 84 86 81 98 92 486 ... 4171 \n",
"Bhutan 0 0 0 1 0 0 ... 5 \n",
"India 8670 8147 7338 5704 4211 7150 ... 36210 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \n",
"Maldives 0 0 1 0 0 0 ... 0 \n",
"Nepal 1 6 1 2 4 13 ... 607 \n",
"Pakistan 972 1201 900 668 514 691 ... 14314 \n",
"Sri Lanka 371 290 197 1086 845 1838 ... 4930 \n",
"\n",
" 2006 2007 2008 2009 2010 2011 2012 \\\n",
"Afghanistan 3009 2652 2111 1746 1758 2203 2635 \n",
"Bangladesh 4014 2897 2939 2104 4721 2694 2640 \n",
"Bhutan 10 7 36 865 1464 1879 1075 \n",
"India 33848 28742 28261 29456 34235 27509 30933 \n",
"Iran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \n",
"Maldives 0 2 1 7 4 3 1 \n",
"Nepal 540 511 581 561 1392 1129 1185 \n",
"Pakistan 13127 10124 8994 7217 6811 7468 11227 \n",
"Sri Lanka 4714 4123 4756 4547 4422 3309 3338 \n",
"\n",
" 2013 Total \n",
"Afghanistan 2004 58639 \n",
"Bangladesh 3789 65568 \n",
"Bhutan 487 5876 \n",
"India 33087 691904 \n",
"Iran (Islamic Republic of) 11291 175923 \n",
"Maldives 1 30 \n",
"Nepal 1308 10222 \n",
"Pakistan 12603 241600 \n",
"Sri Lanka 2394 148358 \n",
"\n",
"[9 rows x 38 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"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",
" </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",
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" <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",
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" <td>...</td>\n",
" <td>1223</td>\n",
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" <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",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
"\n",
"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740). As mentioned on their website: \n",
"\n",
"> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
"\n",
"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Matplotlib.Pyplot\n",
"\n",
"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded.\n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in _pandas_\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in _pandas_ is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740#plotting)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740#api-dataframe-plotting)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti.\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hoKCAkpISlFJBeWFCCDFiYUr6weTzmP4zzzxDVlaW9+fy8nLy8vJYv349eXl5lJeXA1BbW0tlZSXr1q3j9ttv5+GHH8Y0TQA2bdrEsmXLWL9+PfX19ezYsSOgL0YIIQKq2Q3jE1Dj48MdScD4lPSdTifbt2/nkksu8W6rqqqiqKgIgKKiIqqqqrzbFy5cSFxcHGlpaaSnp1NdXY3b7aajo4PZs2ejlGLRokXefYQQYlRqaYLJSeGOIqB8Gt759a9/zbXXXktHR4d3W3NzM8nJ1s2N5ORkWlpaAHC5XMyaNcv7OJvNhsvlIiYmhpSUFO/2lJQUXC5Xv8+3ZcsWtmzZAsCaNWuw2+3DfFmW2NhYv/cNJ4k7tCTu0BpLcbs62iElDVtvvGMp9oEMmfTfeustkpKSmDFjBrt27RrygFrrYW3vT3FxMcXFxd6fGxsbfd73WHa73e99w0niDi2JO7TGUtw9TgdkZHvjHUuxZ2Zm9rt9yKS/Z88e3nzzTd5++208Hg8dHR2sX7+epKQk3G43ycnJuN1uJk+eDFhX8E6n07u/y+XCZrOdsN3pdGKz2Ub6uoQQInhamlCnnBHuKAJqyDH9a665hgcffJANGzbwgx/8gDPOOIPly5dTWFhIRUUFABUVFcybNw+AwsJCKisr6erqoqGhgbq6OnJzc0lOTiYhIYG9e/eitWbbtm0UFhYG99UJIYSfdHcXtIehBUOQ+V2nf8UVV1BWVsbWrVux2+2UlpYCMG3aNBYsWEBpaSmGYbB06VIMw/rbcsMNN7Bx40Y8Hg/5+fkUFBQE5lUIIUSgtYSnBUOwKT2cwfYwOXTIv25zY2n87VgSd2hJ3KE1VuLWH1dj3l2KcfN/oQrmA2Mndhh4TF967wghRH8icGIWSNIXQoh+6ebevjuS9IUQIgp4r/Qj60auJH0hhOhPSxPEJ6DGjw93JAElSV8IIfoT6gXRQySqk77eXon56wfQ7a3hDkUIMcpoSfqRx3ztn+hXX8S8uxR9cH+4wxHDoGv2fHqjTYhgaHZH3Hg+RHnSx9UAGdOguxtzza2Yb2wLd0TCB7qnB3Pdj9F/+1O4QxGRrKUp9MskhkCUJ30HavbpGHesg5xc9KafYz7xKLqnJ9yRicHU18LRTvShA+GOREQo3dUFR9ogaUq4Qwm4qE36+mgntLVCShoqKRnj/9yFuugy9PObMR+4E93WEu4QxQD0x/usb+pqwxuIiFytTdZXudKPIM4G66vNWodSxcZhXPNd1JLl8OFua5z/QE0YAxQDOtj779LaLDfhRXD01ujL8E4kcTmA3gWIj2GcV4xx2xowTcz/exvm6xXhiE4MQh/YB71N/ORqXwRFhE7MgihO+tppJX1saSf8Tk2fhXHHWjh5FvqhtdY4/+jvSxcVtGnCwf0wZ671c70kfRF4kdqCAaI46eNsgJgYmNL/X3I1ORljxV2oRf+Gfn4z7B161TARAo310HEEVbAAYuOsm7pCBFqENluDqE76DpiSgjJiBnyIio1FXbUU4hPQr24JYXBiQL33WdTJuTA1E13/SZgDEhGptRkSJqDGRVYLBojipK9dDkg5cWjns9T48ah5F6DfehXdeSQEkYnB6AP7rE9omSeh0rNlTF8ER7MbJk0JdxRBEbVJH1fDCTdxB6LOKwbPUXTVK0EOSgxFH6iBjBxUXBxkZENjvVVTLUQARWoLBojSpK+7u8Ht8pZrDmnGKZCeja58MbiBiUFpreFADeqkGdaG9GwwTXDUhTcwEXlamiJyYhZEadKn2QXa9Gl4B0AphTrvEqh+X6pFwqnJZY21TpsJYA3vgNzMFYHX4o7IGn2I1qTfOzHL1+EdADX/IjAMudoPpwPWTFzvlf5Uaw1QLeP6IoCsFgztMrwTSQar0R+ImmKDM85GV74kvXnCRB+oAaUgezoAKj4BbHaQCh4RSBE8MQuiNOn3zcbFZh/WbsZ5xdbQ0O63gxCUGIo+sA+mZlrJvk96tgy5icCK4BYMEK1J39kAk5KGX4N7ZiEkTsZ8RWr2w+LAPlTOzOM2qfRsqK+VGdMicPqu9JPkSj9iaKdvNfqfpWLjrLH9nW+gW6ULZyjp1hZwNULOjON/kZ4NnR3WJzAhAkC3RG4LBojSpI+rwfdyzc9Q510CPd3o1/8Z2JjE4A723sQ94Uo/y/pGbuaKQIngFgwQhUlfa20tnjKMyp1jqeyT4aRc9KtbZEghhLxtrj97pZ9hlW1KOwYRMC1NkDARFTcu3JEERdQlfdpawOPxa3injzqvGGo/8vaBESFwoMZa8GbipOO3J9kgPkFq9UXgNLsj9iofojHp99Xo+zm8A6DOWQSxcdKELYT0gRqYNuOE7UopqeARAaVbm2ByUrjDCJroS/p95Zp+Du8AqImJqIL56Ncr0F2eAAUmBqI7j8DhTz6dlPUZfRU8QgRESxMqQmv0AWKHeoDH42H16tV0d3fT09PD/Pnzueqqq2hra6OsrAyHw0FqaiorVqwgMTERgM2bN7N161YMw6CkpIT8/HwAampq2LBhAx6Ph4KCAkpKSqwrtRDyTswawfAOgDq/GF31MnrHG6h55wcgMjGggx8BoKbN7P/36Vnwr5fQnR3H1/AL4Y/mJpgzJdxRBM2QV/pxcXGsXr2a++67j3vvvZcdO3awd+9eysvLycvLY/369eTl5VFeXg5AbW0tlZWVrFu3jttvv52HH34Y0zQB2LRpE8uWLWP9+vXU19ezY8eOYL62/jkbYHwCTEgc2XFOPRNsdvSrLwQmLjEg3dt+gYGu9Htv5nL4UIgiEpFKd3mgI3JbMIAPSV8pRXx8PAA9PT309PSglKKqqoqioiIAioqKqKqqAqCqqoqFCxcSFxdHWloa6enpVFdX43a76ejoYPbs2SilWLRokXefUNJOB9jsI/6EoYwY1IKLYfcOqze/CJ4DNTApybpp25/0vgoeGeIRI9TSbH2N0IlZ4MPwDoBpmvznf/4n9fX1fP7zn2fWrFk0NzeTnGydmOTkZFparMlKLpeLWbNmefe12Wy4XC5iYmJISUnxbk9JScHl6n9CzZYtW9iyxbpJumbNGuz24bVL8L642NgT9nW2uDEyskn285jH6v7ilTj//jgJO18n8etLRny8Pv3FPRYEK27noY8xcueQnNr/fRidNJkGI4YJzS4S/Xh+Od+hNZrj7nI14AKSsnMY30+Mozl2X/mU9A3D4L777qO9vZ2f//znHDhwYMDHDlS7Ppya9uLiYoqLi70/NzY2+rzvsex2+wn79jQcQk2b7vcxjxMXD7PPoP2Fv9Jx4RcDdn+iv7jHgmDErbs8mAf3o+bkD35s+1SO1Oyl04/nl/MdWqM5bt17/6gFA9VPjKM59s/KzMzsd/uwqncmTpzIaaedxo4dO0hKSsLttqYru91uJk+eDFhX8E6n07uPy+XCZrOdsN3pdGKzDfBxPUj00U5oa/V7Nm5/1HnF4KiHD2Xh9KD45GPo6UGdNMBN3D4ZUrYpRk4397VgiNzhnSGTfktLC+3t7YBVyfPuu++SlZVFYWEhFRUVAFRUVDBv3jwACgsLqayspKuri4aGBurq6sjNzSU5OZmEhAT27t2L1ppt27ZRWFgYxJfWD1dgKneOpc5e2LtwuvTZD4ZPZ+IOnvRVehYcPoQ2pe21GAFvC4bIrdMfcnjH7XazYcMGTNNEa82CBQs4++yzmT17NmVlZWzduhW73U5paSkA06ZNY8GCBZSWlmIYBkuXLsUwrL8tN9xwAxs3bsTj8ZCfn09BQUFwX91n+bF4ylDU+Hhr4fTXK9BXfwcVPyFgxxZYC6ckTAT71MEfl54N3V3gdEBqemhiE5EnwlswgA9J/6STTuLee+89YfukSZNYtWpVv/ssXryYxYsXn7B95syZrF271o8wA8OfxVN8oRZcjH75efS726VmfwjaNFGG76OK+kAN5MwY8n6JSs9GgzVJS5K+8JNucUfs2rh9omtGrssBMTEwJcDjdSfPAsOw+vGIAZlVL2Pedr3PzdF0Tw/UfoTqp/3CCXq7bcrSiWJEWpoiukYfoi3pOxtgSgrKiAnoYVVcHKRloA8NXNUU7bTnKPqJR6HZhfnko77tVF8LXZ4BJ2UdSyVOtmr55WauGImW5ohuwQBRlvT9XTzFJ5k5IEl/QHrr38DdCPnzrUVo3t859D69N3EHbL/wWelZUsEjRqYlsjtsQpQlfVwNI+quORiVmQOOemnA1g/d3op+9knIK8S48YeQkob5+CNDV9oc2AfjxnmHboZiNV6TvvrCP1YLhiOS9COF7ukBt2tE3TUHlXkSaFNWcOqHfvZJ6DiCsfhbqLhxqK9dB7X7hyxz1QdqIOtkVIyPw3Hp2dDajG6TpSyFHyJ8xaw+UZP0aXJaSTlIwzsqMwdAxvU/Q7sc6Bf/hpp/ESp7OgCq8HyYeSr66d9bbZP728804WDN0JOyjuFtvCZX+8IfvROzZEw/UgRg8ZRBTc2wKoMOfRyc449R+uk/AKC+8k3vNqUUxlVLodmNfvap/ndsPGx91PalcqePNF4TIyFX+pFFB2DxlMGo2DhIy0QfOhiU449FuvYj9GtbURd/8YQJcWrGKahzitAvlH86f+JYve2Uh3OlT0oqxMZJBY/wi+5L+lKnHyG8E7OCdKVP7xCPDO94mZsfg/gJqC9c2e/v1eJvA6Cf+u0Jv9MHaqxPTpkn+fx8yoiBqZmySLrwT0tv351JU8IaRrBFUdJvgElJqHHjg/ccmTnQeBh99GjwnmOM0Hvfg3eqUF+40qqh74dKSUVdegX6jQp0zZ7j9z+wDzJyrDkQw6DSs+VmuvBPSxNMmDjs99xYEzVJ31o8JXhX+QAqKwe0hvroHuLRWmP+5TfWRLhLLh/0seoLX4OkZMzHH/a239Zaw4GaAdfEHVRGNjTWo7u6/AldRDHd0hTR3TX7RE3SxxXEiVl9eoci9CdRPsTz9mtQswf15auH/GSl4hOsm7z7PkC/+Yq1sckFrc3g66SsY6Vng2mCo86PwEVUa26K+Ju4ECVJX2ttTcwKVo1+n7QMiI2N6nF93dOD+dRjkDENtfASn/ZR510C06aj//Iba4JM30xcP670VXpf2aYM8QjQR49+2iN/KC1NKEn6EaKtBTye4A/vxMTA1KyortXXr74Ahz/BWPxtnydVKSMG4+vXg7MB/cLT1ni+UtBb1z8sU63VgqTxmgDQTz6Cuepmq3vmUFrcEb02bp/oSPq95Zoq2MM7RHcFj+7sQP/1T5A7B+aeM6x91Zy5MPcc9DNPondth6mZqPiEYceg4hMg2S4TtHoNZ5nSSKO1Rr9TBUfa+60QO+6xnqPQ2WE17Ytw0ZH0eydmBa0Fw7Eyc6wr1s6O4D/XKNP+v3+GZhfG167za71g48oS6PbAvg98a6c8EGm8BoBucmL+6DuYL/093KGEx+FPwNUIqenoV188oULsON4afbnSjwjeyT+hutIHqIuuCh7d2sKRzb+D/HNRuaf5dQyVnoW68DLrh+FMyjrhONlQXxvVV7kA+u1/WRcgf/glZuXWcIcTcnrXDgCMm38ESTbMP/7Kau/RH28LhimhCS6Mhlw5KyK4HDA+HiYkBv+5junBo6bPDv7zhYD5SBn6ve1WOarWVg8jrcE89vse0Brjq98a0XOpL10Nrc2oggX+HyQj2/qo3uyCKSkjimcs0zvesIoLUtLQv16Pjk9AnTWC8zrG6N1vQ2o6Kns66srr0A+XoV/dgrrgcyc+uLXJ+ipJPzJoZwPYUv0achi2tHSrFUCEjOvrAzXo116C0/JRaZnWDVbDsL4qBar3e0ORVHAurX2fdPykJiaivvPDkR2jb+nEutqoTfq64wjseRd1yeWoL12NWbYKc9N9GP/xY9RpIV6bOgx0dxfseQ+14EIA1LkXoiueQz/1W/RZC1ETj78A9LZgiII6/ahI+gRz8ZTPUEYMZGRHTAWP3vI0jI/HWHYbaohPSuPtdlobG0MU2SC8jdc+sW4QR6Pdb0NPN2ruOaj4BIzlqzHv+xHmhp9ilN6FmnlquCMMrpo9cLTD+wdOKYVx9Y2Yd5ei//oH1NU3Hv/45ibr62S5kRsZgrh4Sn9UZg5EwAQt3eRCv/Ey6rziIRP+qDLFBuMTorpWX+98AyZOgplzAOsTlLHiJzDFhrn+v9EH94c5wuDSu3ZYn0hPyfNuUzkzUEX/hn7pGXTtZ15/SxNMSLQaJ0a4iE/6+mgntLWGpnKnT2YOuBvRR9pD95xBoF96Bswe1CVfCncow6KUiuoKHm32oN99E5V39nFzJVRSMkbpXTA+AbNsFfrwoTBGGVz6/R0wfTZqwsTjtqsrvgkTJ1o3dY+50a9bmqKicgeiIOnjCl3lTp9IqODRR4+iK56Fueei0jLCHc6wqYzs6L3S37cH2lpR/cyVUClp1hU/YK77cf9trcc43d4KH33Y770LNXES6opvwd5d6KqXP/1FFKyN2yfyk36wF0/pTwSsoqX/9RK0t2Jc+uVwh+Kf9GxwNUblfAm983WIiYXTz+r39yojG+MHd0JHu3XF33cTM1K8vxO0Rp2W3++v1QWXQs5M9BOPfvr+iJIWDBAFST/Yi6f0yz7VWtB7jCZ9bZrWDdyTcmHW6eEOxy/eHjwRPIQxEL3zDTjlDFTChAEfo3JmYvzHKnA7MO9fjT7SFsIIg0vv3gEJE2CAkmllxGBcswyanOhnHrc2tjTJlX7EcDqsxTim2EL2lMowICNn7F7p79oO9Z+gir8cmjLXYIjSpRN1/SfWv92ZQ7fBULNOw7jpv+DQQcz777TKPMc4rbWV9E89c9DeT2rmqagFF6Gff9q6qd3ZIUk/YjgbrL7uhm/NvwJFZU4L65W+bm/F/Pvj6PbhX8GZLzxtnbPC84IQWYikZVhzCKIt6b/zBgBq7jyfHq/OOAtj2W1wYJ91xT/WE//hQ+BsGHBo51jqa0sgLg7zkfutDZL0I4N2OkI7tNMnMweaXH4l3UDQf/wVuvx3mA/9HG32+L5f7X54f6e1ru0YLl9TcXGQOhX98T50j++vf6zTO6sg+2SUfarP+6iC+Rg33gYfV2M+MLav+PX7OwB8moCmkpKtGeC95ZtKqncihMuBsoWucqePt4InDFf7+t230K9XWN0u39uOLv+97/u+8FcYNx616PNBjDA01Cl58O6bmLeVYP5pE3r/3ojux6PbW6F6t09DO5+lzlqAceOt8NGHVuLvHJuJX+96G+xTfa44UxdfDhnTrB+i5Ep/yBm5jY2NbNiwgaamJpRSFBcXc9lll9HW1kZZWRkOh4PU1FRWrFhBYqI1gWfz5s1s3boVwzAoKSkhPz8fgJqaGjZs2IDH46GgoICSkpKgjhnrnm5ocobvSp/eHjyz/GtA5g/d2YH5u42QMQ2j9G70n36FfvZJdM4MVOH5g+/b7Ea/UYE6/3OoiZNCFHHwqKuXoc44C/P1CnTFs+gX/xfSMlDnFKHOXfTpzd4Iod99C0wTlT/8pA+gzlqI8Z1bMX91L+b9d2L84E5U/MA3g0cb3d1ttZ44p8jnfVRsLMa3b8Hc/DuYmhXE6EaPIa/0Y2Ji+Na3vkVZWRn33HMPzz33HLW1tZSXl5OXl8f69evJy8ujvLwcgNraWiorK1m3bh233347Dz/8MGZvZ7tNmzaxbNky1q9fT319PTt27Ajma8N0Oqyl80JZrtnHlmo1eQvxlb4u/x24GzG+/T1UXBzqGzfCzFMxH30AXfvR4Pv+8xnoGXuTsQai4uJQZy0k5qYfYaz9Leq6/7Caj/39z5g/vpmeu0sxny+nJ1Jq1Xe+YU0wOinX70OosxdaQz3792I+8N9j64p//17o7ECdnj+s3VTuacTc+lO/1m8Yi4ZM+snJycyYYfU2T0hIICsrC5fLRVVVFUVF1l/UoqIiqqqqAKiqqmLhwoXExcWRlpZGeno61dXVuN1uOjo6mD17NkopFi1a5N0nWHoaDwOhWTzls6wKnmkhreDR+z5Ab/0b6sIvoHJ7p9/HxWF8dyUkTMTc+FNrCKC/fT1H0f98Fs6ch0qPvCseNSER4/xLiSm9C+PeR1BXLQVAP/EIjTd8hZ47bsL87S8wX3tpTE5Y0t1d6PfeQp05z3rvjYA6eyHGd34INXswH/jJmEn8evfb1s37U88Mdyij2rAarjU0NLB//35yc3Npbm4mOdm68ZGcnExLSwsALpeLWbNmefex2Wy4XC5iYmJISfm042FKSgoul6vf59myZQtbtmwBYM2aNdjt9uG9ql6e99+24sudTayfxxiJ5hmz8Wx/bdjxx8bGDnsf3dWF8/f/g2FLJeU7KzASjpl+brfj+dEa3HfcQuyvH2DKHWtPKGc78vzTtLa1kHzltxnn57nyJ+6wsNsh9xS4eindn3xM11uVdL73Nl3bX0O//DwaMFLTGXd6PnGn5TPutHxiMqeNuvLVY8/30Z1VNHV2kHRBMeMD8W/wb1fQOWkSzevuJGbjz5jy47UYg9T9D0ew3ieuve/BrDnYck4O+LH7jJn3+CB8TvqdnZ2sXbuWJUuWMGHCwP/4A90oG84NtOLiYoqLi70/N/rZuTG+d8k8t4pFhaH7o5mShm5y4fioBpU42ef97Hb7sF+z+bc/oQ/ux/jej3G1d0D7Z2aipqSjrlmG57e/wLGpDOPKJd5faa0xy/8A06bTPHWa3+fKn7jDbvxE7F++mvaFl6JME3XoY/SeXZgfvkfnW6/R+c9/WI+z2TFuuR2V4//iLoF27Pk2t70AceNoyZweuPf6KXNRN5TS9dBaHKuXYyxfHZAhkGC8T3R7G+aH76O+eFVQ34Nj6T2emZnZ73afPgd2d3ezdu1aLrjgAs4991wAkpKScLut1WbcbjeTJ1tJLSUlBafT6d3X5XJhs9lO2O50OrHZgjthymw8DJOSUOPGB/V5BhKqCh5ddxD998dR8y4YtD7buOBzVpfB557CPLbvyK7tUHcQdekVo+5qNpSUYaCyp2Nccjkx312Jsfa3GHdtRH3rFtBg/s+aAYfHwklrbc3CPS0fNT6w73Vj3gWoG/4PVH+A+Yu70V1dAT1+wHzwDmjTp/r8aDdk0tda8+CDD5KVlcXll1/u3V5YWEhFRQUAFRUVzJs3z7u9srKSrq4uGhoaqKurIzc3l+TkZBISEti71yqb27ZtG4WFhUF6WZYeR314buL26avgCWKbZW2amL/9BYxPQH3jO0M+Xn3jO5A7x1pJqbe9rvnC05BkQ80bvLon2iilUOnZGIs+j3HTSmhyYj60dljzHkLik4+tCUln+jYha7iMeRegSr4Pe9613jejsOxV794B8QkDtl4QnxpyeGfPnj1s27aNnJwcbr31VgCuvvpqrrjiCsrKyti6dSt2u53S0lIApk2bxoIFCygtLcUwDJYuXYrRe2PphhtuYOPGjXg8HvLz8ykoCO4KPj0N9eEtw0q2Wz1Agnilr7f9A6rfR5V836eGUSrWurFr3l2KufGnGEu+D7t3oK64dkxPxgo2NX026hs3on+3Ef2/f0Z95Zpwh+Sld/bOwg1S0gcwFlyE6XJY1WH2NNQIl8UMJK01etd2OCUPFRsd60KNxJBn6NRTT+Xxxx/v93erVq3qd/vixYtZvHjxCdtnzpzJ2rVrhxmif7TWmI561Jz8kDxff5RSQa3g0a5G9F9+A3PmohZc7HtcSckYN620VlK6fxWMG4cq+regxBhJ1KLPw/496L/9CX3yLJ9bHQSb3vkGnDwLFeT+Uuqyr1sLrT/zBGZKKsaiUfKecdRZn3Q+/9VwRzImRO6M3LZW8BwNz8SsY6jMnKBc6WutMf/wIJgmxrduGfZYvJpxCuqbN0F3N2r+xcO60RytlFKoa74LOTMxH16Hbgh/B0/d7Ib9e/vtnR9o3td/xlno3z9oTQYbBfTuHYBvrRdEJCd9V28f/TDU6B8nMwfaWgLes1y/+SrsfAP1lW+iUtP9OoZx/qUYt/7UW7MuhqbGjbfG9w0Dc+PP0EePhjUe/Y4118XfWbjDpWJjrQZtWSdh/vL/oj/eF5LnHYzetcNaJGkMLvYTDpGb9HsXTwnrjVxAZQW+gke3t6L/+Es4KXfEs2fV7DMCXvER6ZR9qjV56dAB9GO/COuNTb3zDes9nnVyyJ5TxU/AWL4KJiZi/r+foPv+XwsD3dMDe95BnZYf1ZVnwxGxST8si6f0JwiraOnNj1mrWl33H4P2DBfBo04vQH3lm+jXK9Bb/x6WGPTRo/D+DtTcc0Ke8NSUFIzld4LHY7VrCNciLPv3QscR1OkytOOriE36OB3WRJJwNw5LssGEiRCgsk39yQH0tudRF16GmjY9IMcU/lFfuBLmnoN+4mH0h7tD/vyed94Ejyck4/n9UVk51lBXQ5011NUd+hp+q/WCktYLwxCxSV87GzBS08P+kU8pBZmBW0XLfPIRSEhAfekbATme8J8yDIzrfwApadb4dlP/bUWC5WjVy1Zt+uwzQvq8x1Jz5lqN7Pa8i/7N/+t3qEsfPWpNIHz3Lcx/Pkvb44+i330zID199O4dVuVSuC/uxpDILWp1OoixT2U0TKNRmTnoN19Faz2iP0L6vbfgve2or18v1TajhJqQiHHTjzB/ZrUkNkrvDkmtuDZNjr5ZiTr9LGvBmDAyFlyE6WxAP/17UAZ6UpJV2ulssO6ttTYf9/h2744GTJ+NOvVM1KlnwsxTUXHjfH5efaTNqlz6wpWBezFRIGKTviqYz/isaYyK/oCZOXDkOWh2+71Wr+7pwXziUUhNR130xQAHKEZCZZ+M+vb30A+tRT/9e9TXrgv+k364G9PdiJo7OiZJqS9eZS00XvEPiBtn3UuzpaFyZliVNSlpViWdPQ17dg6NVZXoD96x/nv2SfTfH7f2y53z6R+BqVkwYeLAF0ofvGutHyCtF4YlYpO+cfm/M8Fu58goaI6kMnPQYFXw+Jv0X3kBDh3AuGll2K/sxImMc4sw97yLfu4pdF4havbpQXsuvf01zEfux5higyDOwh0OpRTq2pvRi78NCYMkakDFJ1jDQnPmAqCPtMOHu9Ef7LT+CGx+DO8gUUwMTErq/W8KavKn3+sP3oHxCTDjlOC/wAgSsUl/VMk6ZhUtP65KdMcR66PzrNOgYEGAgxOBoq5aiv7gHcxHyjBWr0cFqBVxH232oJ/+A/qZJ2D6bGz/dS/uUXZbTk1I9GOfiTB3nneGs25thr270G4HtDRDa7M1z6W1GX34E2htAo/H2vnshdI+ZJgk6YfCpCmQOMnvWn397JPQ2oyxfFXYb0yLgan4BIzrV2De+yP0nx9CLVkesGPrthbMTWth99uoCz6HunoZMfY0GAWfZANNTUqykvkgj9FHO6Glye9PztFMkn4IjKSCRzsb0C88jZp/EerkWUPvIMJK5c5BfeFK9DOPo+eegyqYP+Jj6gP7MDf+DJpdqG/dghEBi9aPlBofD37ORI92o+uzYQTr68Ez3Nmb+qnfgqFQX702SJGJQFNf+nfImYH521+gW9wjOpb5r5cw1/yn1WPptjWS8MWISdIPlcwc6DgCbufQj+2l932AfmMb6nNfRYW5nYTwnYqNw1haCp0dmL/xr02D7u7G/OOv0A+XwfTZGHesQ0mveBEAkvRDZLiraGmtMZ94BJKSUZ8/sU21GN1UZo5VuvlOlVV5NQy6yYm59g5rkftLv4JRepdPayUI4QtJ+qHS14Png50+rbyk33wV9n1gLW4SgHVJReipiy+HOXPRf34I3VA35ON1dzfmC09j/vhmOLAP9Z0fYly1VPoriYCSpB8ialISTJ+Nfm4z5o9utBYyH2CoR3uOov/ya8iejlro++IoYnRRhoGxZDkYMZiPlFkdIQeg39+J+ZPvox9/GHLnYKx6AOOcRSGMVkQLqd4JIeO2n8GO1zG3PWfVW//1T3BmoXVz7oyzUIZ1RXfk70+As8H6WG/IVd5YpmypqG9+15qt+4+/WDNXj6GdDvQTj6DfehXsUzFuuR3C0DVTRA9J+iGkYuOg8HxiCs9HN9ShX3ke/coWzJ1vgM2OOv9zqLnzaH/yN9b/+L0zFsXYps5ZBDvfQP/vH9F5Z6NyZqK7POjny9HPPA4a1FeusW7Yj5O1DURwSdIPE5WWgVp8HfrL18DOKsxt/0D/9Q/ov/4BYmIwrlwS7hBFgCil4JvfRX+4C/OhdRhXXIv55KPgqIezFmB8/XqUfWq4wxRRQpJ+mKnYODh7ITFnL0Q76tGvbCHxpOkcSc8Od2gigNTESRhLvo95/2rM//kZpGdjrPhvWddVhJwk/VFEpaajvnrtqGkUJwJLnV6A+uZ3rcXoL/yC9IwRYSFJX4gQMi68LNwhiCgnJZtCCBFFJOkLIUQUkaQvhBBRRJK+EEJEEUn6QggRRSTpCyFEFJGkL4QQUUSSvhBCRBGl/VnWRwghxJgU0Vf6K1euDHcIfpG4Q0viDq2xGjeM7dj7RHTSF0IIcTxJ+kIIEUUiOukXFxeHOwS/SNyhJXGH1liNG8Z27H3kRq4QQkSRiL7SF0IIcTxJ+kIIEUXG1CIqGzduZPv27SQlJbF27VoAPvroIzZt2kRnZyepqaksX76cCRMm0N3dzYMPPsj+/fsxTZNFixbx1a9+FYCamho2bNiAx+OhoKCAkpISax3TUR73nXfeidvtZty4cQDccccdJCUlBS1uf2L/1a9+xb59+zAMgyVLlnD66acDo/+cDxR3KM95Y2MjGzZsoKmpCaUUxcXFXHbZZbS1tVFWVobD4SA1NZUVK1aQmJgIwObNm9m6dSuGYVBSUkJ+fj4Q2vMdyLhD/R4fbuytra2sW7eO6upqLrzwQpYuXeo9Vqjf437TY8iuXbv0vn37dGlpqXfbypUr9a5du7TWWr/44ov6j3/8o9Za65dfflmXlZVprbXu7OzUN998sz58+LB3nz179mjTNPU999yjt2/fPibiXr16ta6urg5qrCOJ/dlnn9UbNmzQWmvd1NSkb7vtNt3T0+PdZ7Se88HiDuU5d7lcet++fVprrY8cOaKXL1+uDx48qB977DG9efNmrbXWmzdv1o899pjWWuuDBw/qH/7wh9rj8ejDhw/r733ve2E534GMO9Tv8eHG3tHRod9//3393HPP6Yceeui4Y4X6Pe6vMTW8c9ppp3mvFPocOnSIOXPmAHDmmWfy+uuve3/X2dlJT08PHo+H2NhYJkyYgNvtpqOjg9mzZ6OUYtGiRVRVVY36uMNlOLHX1tZyxhlnAJCUlMTEiROpqakZ9ed8oLhDLTk5mRkzZgCQkJBAVlYWLpeLqqoqioqKACgqKvKeu6qqKhYuXEhcXBxpaWmkp6dTXV0d8vMdqLjDYbixx8fHc+qpp3o/ifQJx3vcX2Mq6fdn2rRpvPnmmwD861//wul0AjB//nzi4+O58cYbufnmm/nSl75EYmIiLpeLlJQU7/4pKSm4XK5RH3efjRs3cuutt/Lkk0+iw1R4NVDsJ598Mm+++SY9PT00NDRQU1NDY2PjqD/nA8XdJxznvKGhgf3795Obm0tzczPJycmAlaRaWloATjivNpsNl8sV1vM9krj7hOs97kvsAxkt73FfjKkx/f7cdNNNPProozz55JMUFhYSG2u9pOrqagzD4Je//CXt7e2sWrWKvLy8sCXKzxpu3FOnTmX58uXYbDY6OjpYu3Yt27Zt816NjIbYL7roImpra1m5ciWpqamccsopxMTEjPpzPlDcQFjOeWdnJ2vXrmXJkiWDfsob6LyG63yPNG4Iz/kG32MfyGh5j/tizCf9rKws7rjjDsD6+L59+3YAXnnlFfLz84mNjSUpKYlTTjmFffv2MWfOHO8VHoDT6cRms436uKdOneqNMyEhgfPPP5/q6uqwJP2BYo+JiWHJkiXex91xxx1kZGQwceLEUX3OB4obCPk57+7uZu3atVxwwQWce+65gDXk5Ha7SU5Oxu12M3nyZMC6mjz2vLpcLmw22wnbQ3G+AxE3hP58Dzf2gYTjnPtrzA/vNDc3A2CaJk899RSXXnopAHa7nffeew+tNZ2dnXz44YdkZWWRnJxMQkICe/fuRWvNtm3bKCwsHPVx9/T0eD9idnd389ZbbzFt2rSQxz1Y7EePHqWzsxOAd955h5iYGLKzs0f9OR8o7lCfc601Dz74IFlZWVx++eXe7YWFhVRUVABQUVHBvHnzvNsrKyvp6uqioaGBuro6cnNzQ36+AxV3ON7jw419IKPlPe6LMTUj9/7772f37t20traSlJTEVVddRWdnJ8899xwA55xzDtdccw1KKTo7O9m4cSO1tbVorbnooov48pe/DMC+ffvYuHEjHo+H/Px8rr/++qCWVgUi7s7OTlavXk1PTw+maZKXl8d1112HYQT37/ZwYm9oaOCee+7BMAxsNhvf/e53SU1NBUb3OR8o7lCf8w8++IBVq1aRk5PjPTdXX301s2bNoqysjMbGRux2O6Wlpd77PE899RQvvfSSt9S0oKAACO35DlTc4XiP+xP7LbfcwpEjR+ju7mbixInccccdZGdnh/w97q8xlfSFEEKMzJgf3hFCCOE7SfpCCBFFJOkLIUQUkaQvhBBRRJK+EEJEEUn6QggRRSTpCyFEFPn/zr2JE/uFXMUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type _string_. Therefore, let's change the type of the index values to _integer_ for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
"\n",
"```\n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
"```\n",
"\n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```\n",
"\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"We will cover advanced annotation methods in later modules.\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI=df_can.loc[['India', 'China'], years]\n",
"df_CI.head()"
]
},
{
"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",
" df_CI = df_can.loc[['India', 'China'], years]\n",
" df_CI.head()\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI.plot(kind='line')"
]
},
{
"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",
" df_CI.plot(kind='line')\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that _pandas_ plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>India</th>\n",
" <th>China</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8880</td>\n",
" <td>5123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>8670</td>\n",
" <td>6682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>8147</td>\n",
" <td>3308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>7338</td>\n",
" <td>1863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>5704</td>\n",
" <td>1527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" India China\n",
"1980 8880 5123\n",
"1981 8670 6682\n",
"1982 8147 3308\n",
"1983 7338 1863\n",
"1984 5704 1527"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI.index=df_CI.index.map(int)\n",
"df_CI.plot(kind='line')\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()\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",
" df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
" df_CI.plot(kind='line')\n",
"\n",
" plt.title('Immigrants from China and India')\n",
" plt.ylabel('Number of Immigrants')\n",
" plt.xlabel('Years')\n",
"\n",
" plt.show()\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<br>From the above plot, we can observe that the China and India have very similar immigration trends through the years. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_Note_: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"\n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
"\n",
"> class 'pandas.core.series.Series' <br>\n",
"> 1980 1666 <br>\n",
"> 1981 3692 <br>\n",
"> 1982 3498 <br>\n",
"> 1983 2860 <br>\n",
"> 1984 1418 <br>\n",
"> Name: Haiti, dtype: int64 <br>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
" Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"inplace=True\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"\n",
"df_top5=df_can.head(5)\n",
"\n",
"df_top5=df_top5[years].transpose()\n",
"print(df_top5)\n",
"\n",
"df_top5.index=df_top5.index.map(int)\n",
"df_top5.plot(kind='line', figsize=(14,8))\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrations')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"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",
" #Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n",
" #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
" \n",
" inplace = True paramemter saves the changes to the original df_can dataframe\n",
" df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"\n",
" # get the top 5 entries\n",
" df_top5 = df_can.head(5)\n",
"\n",
" # transpose the dataframe\n",
" df_top5 = df_top5[years].transpose() \n",
"\n",
" print(df_top5)\n",
"\n",
"\n",
" #Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
" 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='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"\n",
"\n",
"\n",
" plt.title('Immigration Trend of Top 5 Countries')\n",
" plt.ylabel('Number of Immigrants')\n",
" plt.xlabel('Years')\n",
"\n",
"\n",
" plt.show()\n",
"\n",
"```\n",
"\n",
"</details>\n"
]
},
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"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"- `bar` for vertical bar plots\n",
"- `barh` for horizontal bar plots\n",
"- `hist` for histogram\n",
"- `box` for boxplot\n",
"- `kde` or `density` for density plots\n",
"- `area` for area plots\n",
"- `pie` for pie plots\n",
"- `scatter` for scatter plots\n",
"- `hexbin` for hexbin plot\n"
]
},
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"source": [
"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
"\n",
"### Other Contributors\n",
"\n",
"[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740)\n",
"[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740)\n",
"[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n",
"\n",
"## Change Log\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",
"| 2020-11-20 | 2.2 | Lakshmi Holla | Changed IBM box URL |\n",
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL and info method |\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/>\n"
]
}
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