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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb
{
"cells": [
{
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"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 <a href='http://cocl.us/PY0101EN_DV0101EN_LAB1_Coursera'>**Python for Data Science**</a> and <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which are part of this specialization. \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 <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which is also part of this specialization. \n",
"\n",
"------------"
]
},
{
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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,
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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",
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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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/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",
"---"
]
},
{
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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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"scrolled": 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,
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},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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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"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": {
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},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
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" <th>RegName</th>\n",
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" <th>2010</th>\n",
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" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
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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",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
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" <td>539</td>\n",
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" </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",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
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"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
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{
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" }\n",
"\n",
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" }\n",
"</style>\n",
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" <thead>\n",
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" <td>Immigrants</td>\n",
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" <td>935</td>\n",
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" <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",
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" <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",
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" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
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" <td>611</td>\n",
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" <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,
"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",
"Type 195 non-null object\n",
"Coverage 195 non-null object\n",
"OdName 195 non-null object\n",
"AREA 195 non-null int64\n",
"AreaName 195 non-null object\n",
"REG 195 non-null int64\n",
"RegName 195 non-null object\n",
"DEV 195 non-null int64\n",
"DevName 195 non-null object\n",
"1980 195 non-null int64\n",
"1981 195 non-null int64\n",
"1982 195 non-null int64\n",
"1983 195 non-null int64\n",
"1984 195 non-null int64\n",
"1985 195 non-null int64\n",
"1986 195 non-null int64\n",
"1987 195 non-null int64\n",
"1988 195 non-null int64\n",
"1989 195 non-null int64\n",
"1990 195 non-null int64\n",
"1991 195 non-null int64\n",
"1992 195 non-null int64\n",
"1993 195 non-null int64\n",
"1994 195 non-null int64\n",
"1995 195 non-null int64\n",
"1996 195 non-null int64\n",
"1997 195 non-null int64\n",
"1998 195 non-null int64\n",
"1999 195 non-null int64\n",
"2000 195 non-null int64\n",
"2001 195 non-null int64\n",
"2002 195 non-null int64\n",
"2003 195 non-null int64\n",
"2004 195 non-null int64\n",
"2005 195 non-null int64\n",
"2006 195 non-null int64\n",
"2007 195 non-null int64\n",
"2008 195 non-null int64\n",
"2009 195 non-null int64\n",
"2010 195 non-null int64\n",
"2011 195 non-null int64\n",
"2012 195 non-null int64\n",
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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, 'Total'],\n",
" dtype='object')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can['Total'] = df_can.sum(axis=1)\n",
"df_can.columns"
]
},
{
"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,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
"5 Angola\n",
"6 Antigua and Barbuda\n",
"7 Argentina\n",
"8 Armenia\n",
"9 Australia\n",
"10 Austria\n",
"11 Azerbaijan\n",
"12 Bahamas\n",
"13 Bahrain\n",
"14 Bangladesh\n",
"15 Barbados\n",
"16 Belarus\n",
"17 Belgium\n",
"18 Belize\n",
"19 Benin\n",
"20 Bhutan\n",
"21 Bolivia (Plurinational State of)\n",
"22 Bosnia and Herzegovina\n",
"23 Botswana\n",
"24 Brazil\n",
"25 Brunei Darussalam\n",
"26 Bulgaria\n",
"27 Burkina Faso\n",
"28 Burundi\n",
"29 Cabo Verde\n",
" ... \n",
"165 Suriname\n",
"166 Swaziland\n",
"167 Sweden\n",
"168 Switzerland\n",
"169 Syrian Arab Republic\n",
"170 Tajikistan\n",
"171 Thailand\n",
"172 The former Yugoslav Republic of Macedonia\n",
"173 Togo\n",
"174 Tonga\n",
"175 Trinidad and Tobago\n",
"176 Tunisia\n",
"177 Turkey\n",
"178 Turkmenistan\n",
"179 Tuvalu\n",
"180 Uganda\n",
"181 Ukraine\n",
"182 United Arab Emirates\n",
"183 United Kingdom of Great Britain and Northern I...\n",
"184 United Republic of Tanzania\n",
"185 United States of America\n",
"186 Uruguay\n",
"187 Uzbekistan\n",
"188 Vanuatu\n",
"189 Venezuela (Bolivarian Republic of)\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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Angola</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Argentina</td>\n",
" <td>368</td>\n",
" <td>426</td>\n",
" <td>626</td>\n",
" <td>241</td>\n",
" <td>237</td>\n",
" <td>196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Armenia</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>9</th>\n",
" <td>Australia</td>\n",
" <td>702</td>\n",
" <td>639</td>\n",
" <td>484</td>\n",
" <td>317</td>\n",
" <td>317</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Austria</td>\n",
" <td>234</td>\n",
" <td>238</td>\n",
" <td>201</td>\n",
" <td>117</td>\n",
" <td>127</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Azerbaijan</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>12</th>\n",
" <td>Bahamas</td>\n",
" <td>26</td>\n",
" <td>23</td>\n",
" <td>38</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Bahrain</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",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Bangladesh</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",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Barbados</td>\n",
" <td>372</td>\n",
" <td>376</td>\n",
" <td>299</td>\n",
" <td>244</td>\n",
" <td>265</td>\n",
" <td>285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Belarus</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>17</th>\n",
" <td>Belgium</td>\n",
" <td>511</td>\n",
" <td>540</td>\n",
" <td>519</td>\n",
" <td>297</td>\n",
" <td>183</td>\n",
" <td>181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Belize</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>13</td>\n",
" <td>21</td>\n",
" <td>37</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Benin</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bhutan</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",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Bolivia (Plurinational State of)</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>42</td>\n",
" <td>49</td>\n",
" <td>38</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Bosnia and Herzegovina</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>23</th>\n",
" <td>Botswana</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Brazil</td>\n",
" <td>211</td>\n",
" <td>220</td>\n",
" <td>192</td>\n",
" <td>139</td>\n",
" <td>145</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Brunei Darussalam</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",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Bulgaria</td>\n",
" <td>24</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>33</td>\n",
" <td>11</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Burkina Faso</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Burundi</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Cabo Verde</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1</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>165</th>\n",
" <td>Suriname</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Swaziland</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>Sweden</td>\n",
" <td>281</td>\n",
" <td>308</td>\n",
" <td>222</td>\n",
" <td>176</td>\n",
" <td>128</td>\n",
" <td>158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Switzerland</td>\n",
" <td>806</td>\n",
" <td>811</td>\n",
" <td>634</td>\n",
" <td>370</td>\n",
" <td>326</td>\n",
" <td>314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>Syrian Arab Republic</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",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Tajikistan</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>171</th>\n",
" <td>Thailand</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",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>The former Yugoslav Republic of Macedonia</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>173</th>\n",
" <td>Togo</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>Tonga</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>Trinidad and Tobago</td>\n",
" <td>958</td>\n",
" <td>947</td>\n",
" <td>972</td>\n",
" <td>766</td>\n",
" <td>606</td>\n",
" <td>699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Tunisia</td>\n",
" <td>58</td>\n",
" <td>51</td>\n",
" <td>55</td>\n",
" <td>46</td>\n",
" <td>51</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>Turkey</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",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>Turkmenistan</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>179</th>\n",
" <td>Tuvalu</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>Uganda</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>38</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>Ukraine</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>182</th>\n",
" <td>United Arab Emirates</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",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>United Kingdom of Great Britain and Northern I...</td>\n",
" <td>22045</td>\n",
" <td>24796</td>\n",
" <td>20620</td>\n",
" <td>10015</td>\n",
" <td>10170</td>\n",
" <td>9564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>United Republic of Tanzania</td>\n",
" <td>635</td>\n",
" <td>832</td>\n",
" <td>621</td>\n",
" <td>474</td>\n",
" <td>473</td>\n",
" <td>460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>United States of America</td>\n",
" <td>9378</td>\n",
" <td>10030</td>\n",
" <td>9074</td>\n",
" <td>7100</td>\n",
" <td>6661</td>\n",
" <td>6543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>Uruguay</td>\n",
" <td>128</td>\n",
" <td>132</td>\n",
" <td>146</td>\n",
" <td>105</td>\n",
" <td>90</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>Uzbekistan</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>188</th>\n",
" <td>Vanuatu</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>189</th>\n",
" <td>Venezuela (Bolivarian Republic of)</td>\n",
" <td>103</td>\n",
" <td>117</td>\n",
" <td>174</td>\n",
" <td>124</td>\n",
" <td>142</td>\n",
" <td>165</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 \\\n",
"0 Afghanistan 16 39 39 \n",
"1 Albania 1 0 0 \n",
"2 Algeria 80 67 71 \n",
"3 American Samoa 0 1 0 \n",
"4 Andorra 0 0 0 \n",
"5 Angola 1 3 6 \n",
"6 Antigua and Barbuda 0 0 0 \n",
"7 Argentina 368 426 626 \n",
"8 Armenia 0 0 0 \n",
"9 Australia 702 639 484 \n",
"10 Austria 234 238 201 \n",
"11 Azerbaijan 0 0 0 \n",
"12 Bahamas 26 23 38 \n",
"13 Bahrain 0 2 1 \n",
"14 Bangladesh 83 84 86 \n",
"15 Barbados 372 376 299 \n",
"16 Belarus 0 0 0 \n",
"17 Belgium 511 540 519 \n",
"18 Belize 16 27 13 \n",
"19 Benin 2 5 4 \n",
"20 Bhutan 0 0 0 \n",
"21 Bolivia (Plurinational State of) 44 52 42 \n",
"22 Bosnia and Herzegovina 0 0 0 \n",
"23 Botswana 10 1 3 \n",
"24 Brazil 211 220 192 \n",
"25 Brunei Darussalam 79 6 8 \n",
"26 Bulgaria 24 20 12 \n",
"27 Burkina Faso 2 1 3 \n",
"28 Burundi 0 0 0 \n",
"29 Cabo Verde 1 1 2 \n",
".. ... ... ... ... \n",
"165 Suriname 15 10 21 \n",
"166 Swaziland 4 1 1 \n",
"167 Sweden 281 308 222 \n",
"168 Switzerland 806 811 634 \n",
"169 Syrian Arab Republic 315 419 409 \n",
"170 Tajikistan 0 0 0 \n",
"171 Thailand 56 53 113 \n",
"172 The former Yugoslav Republic of Macedonia 0 0 0 \n",
"173 Togo 5 5 2 \n",
"174 Tonga 2 4 7 \n",
"175 Trinidad and Tobago 958 947 972 \n",
"176 Tunisia 58 51 55 \n",
"177 Turkey 481 874 706 \n",
"178 Turkmenistan 0 0 0 \n",
"179 Tuvalu 0 1 0 \n",
"180 Uganda 13 16 17 \n",
"181 Ukraine 0 0 0 \n",
"182 United Arab Emirates 0 2 2 \n",
"183 United Kingdom of Great Britain and Northern I... 22045 24796 20620 \n",
"184 United Republic of Tanzania 635 832 621 \n",
"185 United States of America 9378 10030 9074 \n",
"186 Uruguay 128 132 146 \n",
"187 Uzbekistan 0 0 0 \n",
"188 Vanuatu 0 0 0 \n",
"189 Venezuela (Bolivarian Republic of) 103 117 174 \n",
"190 Viet Nam 1191 1829 2162 \n",
"191 Western Sahara 0 0 0 \n",
"192 Yemen 1 2 1 \n",
"193 Zambia 11 17 11 \n",
"194 Zimbabwe 72 114 102 \n",
"\n",
" 1983 1984 1985 \n",
"0 47 71 340 \n",
"1 0 0 0 \n",
"2 69 63 44 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 6 4 3 \n",
"6 0 42 52 \n",
"7 241 237 196 \n",
"8 0 0 0 \n",
"9 317 317 319 \n",
"10 117 127 165 \n",
"11 0 0 0 \n",
"12 12 21 28 \n",
"13 1 1 3 \n",
"14 81 98 92 \n",
"15 244 265 285 \n",
"16 0 0 0 \n",
"17 297 183 181 \n",
"18 21 37 26 \n",
"19 3 4 3 \n",
"20 0 1 0 \n",
"21 49 38 44 \n",
"22 0 0 0 \n",
"23 3 7 4 \n",
"24 139 145 130 \n",
"25 2 2 4 \n",
"26 33 11 24 \n",
"27 2 3 2 \n",
"28 0 1 2 \n",
"29 0 11 1 \n",
".. ... ... ... \n",
"165 12 5 16 \n",
"166 0 10 7 \n",
"167 176 128 158 \n",
"168 370 326 314 \n",
"169 269 264 385 \n",
"170 0 0 0 \n",
"171 65 82 66 \n",
"172 0 0 0 \n",
"173 3 6 5 \n",
"174 1 2 5 \n",
"175 766 606 699 \n",
"176 46 51 57 \n",
"177 280 338 202 \n",
"178 0 0 0 \n",
"179 0 1 0 \n",
"180 38 32 29 \n",
"181 0 0 0 \n",
"182 1 2 0 \n",
"183 10015 10170 9564 \n",
"184 474 473 460 \n",
"185 7100 6661 6543 \n",
"186 105 90 92 \n",
"187 0 0 0 \n",
"188 0 0 0 \n",
"189 124 142 165 \n",
"190 3404 7583 5907 \n",
"191 0 0 0 \n",
"192 6 0 18 \n",
"193 7 16 9 \n",
"194 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,
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"run_control": {
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"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,
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"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": {
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"collapsed": true,
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"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,
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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",
" <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,
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"deletable": true,
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"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": {
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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,
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"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n"
]
},
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"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",
"Angola False\n",
"Antigua and Barbuda False\n",
"Argentina False\n",
"Armenia True\n",
"Australia False\n",
"Austria False\n",
"Azerbaijan True\n",
"Bahamas False\n",
"Bahrain True\n",
"Bangladesh True\n",
"Barbados False\n",
"Belarus False\n",
"Belgium False\n",
"Belize False\n",
"Benin False\n",
"Bhutan True\n",
"Bolivia (Plurinational State of) False\n",
"Bosnia and Herzegovina False\n",
"Botswana False\n",
"Brazil False\n",
"Brunei Darussalam True\n",
"Bulgaria False\n",
"Burkina Faso False\n",
"Burundi False\n",
"Cabo Verde False\n",
" ... \n",
"Suriname False\n",
"Swaziland False\n",
"Sweden False\n",
"Switzerland False\n",
"Syrian Arab Republic True\n",
"Tajikistan True\n",
"Thailand True\n",
"The former Yugoslav Republic of Macedonia False\n",
"Togo False\n",
"Tonga False\n",
"Trinidad and Tobago False\n",
"Tunisia False\n",
"Turkey True\n",
"Turkmenistan True\n",
"Tuvalu False\n",
"Uganda False\n",
"Ukraine False\n",
"United Arab Emirates True\n",
"United Kingdom of Great Britain and Northern Ireland False\n",
"United Republic of Tanzania False\n",
"United States of America False\n",
"Uruguay False\n",
"Uzbekistan True\n",
"Vanuatu False\n",
"Venezuela (Bolivarian Republic of) False\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,
"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",
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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",
" <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",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <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",
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" <td>11</td>\n",
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" <td>5</td>\n",
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" <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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
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" <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",
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" <td>Developing regions</td>\n",
" <td>16</td>\n",
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" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <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",
" <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>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,
"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",
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" }\n",
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" }\n",
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" .dataframe thead th {\n",
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" }\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",
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" <th>...</th>\n",
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" <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",
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" <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",
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" <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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.0.3\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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'tableau-colorblind10']\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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd460bc82e8>"
]
},
"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": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
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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,
"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": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI=df_can.loc[['China','India'],years]\n",
"df_CI.head()"
]
},
{
"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": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd46086a940>"
]
},
"execution_count": 38,
"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_CI.plot(kind='line')\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": 39,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.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": 40,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd4607f7940>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"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",
"df_CI.plot(kind='line')"
]
},
{
"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": 41,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
" Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
"image/png": 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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",
"df_top5 = df_top5[years].transpose()\n",
"print(df_top5)\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"\n",
"\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"\n",
"plt.show()\n"
]
},
{
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently 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!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week1_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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