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HovigA/DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots-py.ipynb

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DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots-py.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
},
{
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"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) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python).\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).\n",
"\n",
"------------"
]
},
{
"cell_type": "markdown",
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},
"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"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",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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"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/):\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."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"new_sheet": false,
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},
"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).\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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/coursera/Images/Mod1Fig1-Dataset.png\" align=\"center\" width=900>\n",
"\n",
"For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"new_sheet": false,
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"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
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},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"collapsed": false,
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" <th>RegName</th>\n",
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" <th>DevName</th>\n",
" <th>1980</th>\n",
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" <th>2007</th>\n",
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" <th>2013</th>\n",
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" </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",
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" <td>...</td>\n",
" <td>2978</td>\n",
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" <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",
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" <td>...</td>\n",
" <td>1450</td>\n",
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" <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",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" </tr>\n",
" </tbody>\n",
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"<p>5 rows × 43 columns</p>\n",
"</div>"
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"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"outputs": [
{
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" vertical-align: top;\n",
" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
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" <thead>\n",
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" <th>190</th>\n",
" <td>Immigrants</td>\n",
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" <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",
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" <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",
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" <td>Africa</td>\n",
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" <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",
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" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
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" <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",
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],
"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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Type 195 non-null object\n",
" 1 Coverage 195 non-null object\n",
" 2 OdName 195 non-null object\n",
" 3 AREA 195 non-null int64 \n",
" 4 AreaName 195 non-null object\n",
" 5 REG 195 non-null int64 \n",
" 6 RegName 195 non-null object\n",
" 7 DEV 195 non-null int64 \n",
" 8 DevName 195 non-null object\n",
" 9 1980 195 non-null int64 \n",
" 10 1981 195 non-null int64 \n",
" 11 1982 195 non-null int64 \n",
" 12 1983 195 non-null int64 \n",
" 13 1984 195 non-null int64 \n",
" 14 1985 195 non-null int64 \n",
" 15 1986 195 non-null int64 \n",
" 16 1987 195 non-null int64 \n",
" 17 1988 195 non-null int64 \n",
" 18 1989 195 non-null int64 \n",
" 19 1990 195 non-null int64 \n",
" 20 1991 195 non-null int64 \n",
" 21 1992 195 non-null int64 \n",
" 22 1993 195 non-null int64 \n",
" 23 1994 195 non-null int64 \n",
" 24 1995 195 non-null int64 \n",
" 25 1996 195 non-null int64 \n",
" 26 1997 195 non-null int64 \n",
" 27 1998 195 non-null int64 \n",
" 28 1999 195 non-null int64 \n",
" 29 2000 195 non-null int64 \n",
" 30 2001 195 non-null int64 \n",
" 31 2002 195 non-null int64 \n",
" 32 2003 195 non-null int64 \n",
" 33 2004 195 non-null int64 \n",
" 34 2005 195 non-null int64 \n",
" 35 2006 195 non-null int64 \n",
" 36 2007 195 non-null int64 \n",
" 37 2008 195 non-null int64 \n",
" 38 2009 195 non-null int64 \n",
" 39 2010 195 non-null int64 \n",
" 40 2011 195 non-null int64 \n",
" 41 2012 195 non-null int64 \n",
" 42 2013 195 non-null int64 \n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 11,
"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,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 12,
"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,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\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",
"```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",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country')."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
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"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": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 17,
"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,
"deletable": true,
"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",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"deletable": true,
"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": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>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": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
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},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
" 1. The full row data (all columns)\n",
" 2. For year 2013\n",
" 3. For years 1980 to 1985"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
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"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": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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,
"deletable": true,
"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'."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"deletable": true,
"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,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"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": 25,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\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)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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"run_control": {
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}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
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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",
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" <td>58639</td>\n",
" </tr>\n",
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" <td>Asia</td>\n",
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" <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",
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" <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",
" <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>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>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>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": 28,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"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",
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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>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>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
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" <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",
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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",
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" <td>0</td>\n",
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" <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",
" </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": 29,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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/). As mentioned on their website: \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."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['classic', 'seaborn-pastel', 'seaborn-notebook', 'bmh', 'seaborn-muted', 'seaborn-whitegrid', 'seaborn-poster', 'tableau-colorblind10', 'fast', 'seaborn', 'seaborn-dark', 'fivethirtyeight', 'seaborn-paper', 'seaborn-ticks', 'seaborn-bright', 'seaborn-dark-palette', 'seaborn-white', 'ggplot', 'seaborn-darkgrid', 'Solarize_Light2', 'seaborn-deep', '_classic_test', 'seaborn-talk', 'seaborn-colorblind', 'dark_background', 'grayscale']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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": 33,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7efeaf461978>"
]
},
"execution_count": 34,
"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": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 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,
"deletable": true,
"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",
" 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",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\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",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" We will cover advanced annotation methods in later modules."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": 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>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",
" <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",
" </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",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_china_India.index = df_china_India.index.map(int)\n",
"df_china_India = df_can.loc[['China','India'], years]\n",
"df_china_India.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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()`."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7efeadd79710>"
]
},
"execution_count": 51,
"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",
"df_china_India.plot(kind='line')\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_china_India = df_china_India.transpose()\n",
"df_china_India.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_china_India.plot(kind='line')\n",
"\n",
"plt.title('Immigration from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"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",
"\n",
"<!--\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"--> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"From the above plot, we can observe that the China and India have very similar immigration trends through the years. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\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>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"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",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"df_top5 = df_can.head(5)\n",
"#print(df_top5)\n",
"\n",
"#df_top5.drop(['Total'], axis = 1, inplace = True)\n",
"\n",
"\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",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\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",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\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",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"### 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"
]
},
{
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"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
{
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"source": [
"This notebook is part of a course on **edX** called *Visualizing Data with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_edX_LAB1)."
]
},
{
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"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
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"display_name": "Python",
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"version": "1.1.2"
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