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

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DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots-py.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely [**Python for Data Science**](https://cognitiveclass.ai/courses/python-for-data-science/).\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 [**Data Analysis with Python**](https://cognitiveclass.ai/courses/data-analysis-python/).\n",
"\n",
"------------"
]
},
{
"cell_type": "markdown",
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"button": false,
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
"1.2 [*pandas* Basics](#4) <br>\n",
"1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
"2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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"source": [
"# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
"\n",
"*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/coursera/Images/Mod1Fig1-Dataset.png\" align=\"center\" width=900>\n",
"\n",
"For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
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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": 84,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [
{
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" text-align: right;\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>Type</th>\n",
" <th>Coverage</th>\n",
" <th>OdName</th>\n",
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" <th>REG</th>\n",
" <th>RegName</th>\n",
" <th>DEV</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
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" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
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" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 86,
"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": 87,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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"run_control": {
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},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>190</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Viet Nam</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
" <td>1816</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Western Sahara</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
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" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Yemen</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
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" <td>437</td>\n",
" <td>407</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 87,
"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": 88,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Type 195 non-null object\n",
" 1 Coverage 195 non-null object\n",
" 2 OdName 195 non-null object\n",
" 3 AREA 195 non-null int64 \n",
" 4 AreaName 195 non-null object\n",
" 5 REG 195 non-null int64 \n",
" 6 RegName 195 non-null object\n",
" 7 DEV 195 non-null int64 \n",
" 8 DevName 195 non-null object\n",
" 9 1980 195 non-null int64 \n",
" 10 1981 195 non-null int64 \n",
" 11 1982 195 non-null int64 \n",
" 12 1983 195 non-null int64 \n",
" 13 1984 195 non-null int64 \n",
" 14 1985 195 non-null int64 \n",
" 15 1986 195 non-null int64 \n",
" 16 1987 195 non-null int64 \n",
" 17 1988 195 non-null int64 \n",
" 18 1989 195 non-null int64 \n",
" 19 1990 195 non-null int64 \n",
" 20 1991 195 non-null int64 \n",
" 21 1992 195 non-null int64 \n",
" 22 1993 195 non-null int64 \n",
" 23 1994 195 non-null int64 \n",
" 24 1995 195 non-null int64 \n",
" 25 1996 195 non-null int64 \n",
" 26 1997 195 non-null int64 \n",
" 27 1998 195 non-null int64 \n",
" 28 1999 195 non-null int64 \n",
" 29 2000 195 non-null int64 \n",
" 30 2001 195 non-null int64 \n",
" 31 2002 195 non-null int64 \n",
" 32 2003 195 non-null int64 \n",
" 33 2004 195 non-null int64 \n",
" 34 2005 195 non-null int64 \n",
" 35 2006 195 non-null int64 \n",
" 36 2007 195 non-null int64 \n",
" 37 2008 195 non-null int64 \n",
" 38 2009 195 non-null int64 \n",
" 39 2010 195 non-null int64 \n",
" 40 2011 195 non-null int64 \n",
" 41 2012 195 non-null int64 \n",
" 42 2013 195 non-null int64 \n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 89,
"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": 90,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 90,
"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": 91,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 93,
"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": 94,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 94,
"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": 95,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 95,
"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": 96,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 97,
"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": 98,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 98,
"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": 99,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 99,
"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": 100,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 104,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"run_control": {
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},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\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, 1985]])\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": 107,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 108,
"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": 109,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
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" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <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": 111,
"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": 112,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
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" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>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",
" </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": 112,
"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": 113,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded."
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['grayscale', 'seaborn-notebook', 'seaborn-white', 'seaborn-muted', 'seaborn-dark-palette', 'seaborn-paper', 'seaborn-dark', 'seaborn-poster', 'ggplot', 'bmh', 'seaborn-darkgrid', 'seaborn-whitegrid', 'seaborn-deep', 'fast', 'seaborn-ticks', 'seaborn', 'dark_background', 'seaborn-talk', 'seaborn-bright', 'seaborn-colorblind', 'tableau-colorblind10', 'fivethirtyeight', '_classic_test', 'Solarize_Light2', 'seaborn-pastel', 'classic']\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": 116,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 116,
"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": 117,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc5b714f5f8>"
]
},
"execution_count": 117,
"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": [
"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": 118,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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e2BqLQTdn4KAdq2ntNFd77UuVuD1L4vas9ho31I9df/U55pL5GH94CJU6yKlrVC29F/Lz8PvLcjdFafHqGIcQQoiGaUdVXOdaHAAqsSfkZaPNKjdF1TRJHEII4U152RAYCGHhzp8TkwCVlVDQ8HIEd5PEIYQQXqTzciAqDmU4/+vYMQMrt2Vd8K0liUMIIbwpN9upGVV1VCcORzeXh0niEEIIL9GmCUdznFvDUVu3SAgI/HnXQA+TxCGEEN5SVAB2e7MGxgGrWys6TlocQgjR4eRZv/ib3eIAq7tKxjiEEKJj0XnVVXGbO8ZBdbLJy7G6uzzMqcTx2muvceDAAQB2797N9ddfz0033cTu3bvdGZsQQvi23GwwDIiIbv650fFQYbd2DvQwpxLH66+/TkxMDAD/+te/mDx5MlOnTuXpp592Z2xCCOHb8nIgMgbl3/zqTyq2elV3nufHOZxKHMeOHaNTp06Ul5dz4MABzjvvPCZNmtTiMh5CCCGqp9M2c2DcoWZK7k+e/z3sVJqLjIzku+++49ChQ/Tv3x/DMDh27Jij8KAQQogWyMtG9U459fMaEh4J/v5emZLrVOKYNm0aixYtwt/fn9tvvx2Azz77jOTkZLcGJ4QQvkqXlcCxsha3OJThB1FxaC90VTmVOE4//XT+/ve/1zk2atQoRo8e7ZaghBDC5+XWTMVt/owqh9gE8EJXlVN9TTNmzKh3zN/fn9mzZ7s8ICGE6AhaUhX3ZCo63qqS6+HdMZxKHFVV9Uv3VlZWYnph/rAQQviEmjUcUa1occTEWyvPizw7JbfJrqp58+ahlKKiooL58+fXeSw/P5/U1FS3BieEED4rNxu6RaCCglp8CRUTj3ZcK9JloZ1Kk4lj0qRJAOzZs4eJEyc6jiulCAsLY9Ag53arEkIIUZfOy3F6u9hG1ZqS6+zuga7QZOKYMGECACkpKSQmJnoiHiGE6BjyclCDhrXuGhHR4Ofv8UWATs2qSkxMZOfOnRw4cIDjx4/XeeySSy5xS2BCCOGr9Inj1rhEKwbGAZSfH0TFerxKrlOJY/Xq1Xz44YcMHDiQoFb0xwkhhODngfHWdlXVXKMtJo6tW7fy6KOPEhUV5e54hBDC99Ws4WhBVdyTqZh49O5daK1RSrX6es5wajpuly5dCA0NdXcsQgjRIfxcTt1FLY4Tx6G4sPXXcpJTiWPy5MksW7aM3bt389NPP9X5I4QQopnysiG0Cyq0c6sv5dgEyoPdVU51Va1atQqw6lOd7IUXXnBtREII4eOsqrit76YCfp6Sm5uNShngmmueglOJQ5KDEEK4UF4OqreLFlBHxoKfn0e3kZW66EII4UG6ogLy81wzo4rqKbmRMW2vq6qqqoo333yTr7/+mpKSkjqP3XfffW4JTAghfFFVXg5o0zUD4zVi4j26lsOpFsczzzzDxo0bGTBgAPv27WPkyJEUFRUxcOBAd8cnhBA+pSrnR8A1U3FreLpKrlMtjo8//pgHH3yQqKgo1q1bx/nnn8/QoUP5xz/+4fSNbrzxRoKDgzEMAz8/PxYsWEBpaSmLFy8mLy+P6OhobrvtNjp3tmYZrF+/nk2bNmEYBjNmzCAtLQ2Affv2sXz5cux2O8OGDWPGjBkem7sshBCtVZM4XNVVBVj7cpQfg5Ii6NrNdddthFOJw263ExlpVV4MDAzkxIkTJCYmcuDAgWbdbP78+XTt2tXxfWZmJoMHD2bKlClkZmaSmZnJtGnTOHz4MNu2bWPRokUUFBRw//33s3TpUgzDYOXKlcyePZuUlBQefvhhduzYwbBhraz3IoQQHlKVcxgCgyAs3GXXrFMl1wOJw6muqsTERPbu3QtAnz59ePHFF/nPf/5DREREq26elZXF+PHjARg/fjxZWVmO42PGjCEgIICYmBji4uLYs2cPBQUFlJeXk5qailKKcePGOc4RQoj2oDLnR4iOc21PSfTPU3I9wakWx/Tp0/Hz8wPg6quvZtWqVZSXl3Pdddc162YPPvggAOeccw4ZGRkUFRURHm5l3fDwcIqLiwGw2WykpPy8gXtERAQ2mw0/Pz9HywcgMjISm63hDUw2btzIxo0bAViwYEGLy6X4+/u3y1IrErdnSdye1V7jBsjP+ZGgpJ50c2H8OiyMXMOgU2kRnT3wvpwycZimycGDBznrrLMAiI+P55577mn2je6//34iIiIoKirigQceICEhodHnNjbA05yBn4yMDDIyMhzfHz161Plga4mKimrxud4kcXuWxO1Z7TVubZqYOT9S1X+o6+OPjOHYgT0cd+F1G/s9fcquKsMwWLt2LQEBAa0KoKZbKywsjOHDh7Nnzx7CwsIoKCgAoKCgwDH+ERkZSX5+vuNcm81GREREveP5+fmt7i4TQgiPKbRBhd21U3FrRHtuSq5TYxxnnHEGn3zySYtvcvz4ccrLyx1ff/HFF/To0YP09HQ2b94MwObNmxk+fDgA6enpbNu2jYqKCnJzc8nOziY5OZnw8HBCQkLYvXs3Wmu2bNlCenp6i+MSQgiPqt5wScW4bipuDVVdXt0TU3KdGuOoqKhg0aJFpKamEhkZWWdQ56abbjrl+UVFRfz1r38FrMWEY8eOJS0tjb59+7J48WI2bdpEVFQUc+fOBaB79+6MHj2auXPnYhgGM2fOxDCsHDdr1ixWrFiB3W4nLS1NZlQJIdoNR4vAHS2OmHgoL4OyEujc9dTPbwWnEkf37t3p3r17i28SGxvLY489Vu94ly5dmDdvXoPnTJ06lalTp9Y73rdvXxYuXNjiWIQQwmvycqy6UhHRLr+0ikmwpuT+dKRtJI7f/e53bg1CCCE6hNxs/GLireThajVVcvOyUX37uf76tTiVOHbt2tXwyf7+REZGEh3t+uwphBC+Rufl4BeXSJU7Lh4VC0p5pNihU4njiSeecMx+6tKli6PQYVhYGIWFhfTo0YNbb72V+Hg39NsJIYQP0FpDXjZ+A9PckjhUQIDVBdZWEsekSZM4duwYl1xyCYGBgdjtdtatW0enTp04//zzWbt2LatWrWrR+g4hhOgQSkug/Bh+cYnuu4eHquQ6NR13w4YNXH755QQGBgJWvapLL72U119/neDgYK666ir27dvn1kCFEKJdK7AW5vlFx7rtFjVTct3NqcQRHBzsqFVVY9++fQQFBVkXMWQ/KCGEaFKx1d1vdIs8xRNbISYeykrQZSWnfm4rONVVdfHFF/PAAw+Qnp7uWL396aefcs011wDw5ZdfMnLkSLcGKoQQ7ZkuLgTA6Oa+ahc/V8nNgd5d3HYfpxLH+PHj6du3Lx999BEFBQUkJCQwdepUkpKSAGtl+RlnnOG2IIUQot2rnTjKjrnnHjFWbSmdewTVO+UUT245pxIHQFJSEhdddJHbAhFCCJ9WVAiBQRghndyXOKLjPDIlt9HE8fe//53Zs2cD8PjjjzdaO96ZkiNCCNHhFRe6fZMlFRAI4ZHeSxwxMTGOr+PiXF+QSwghOhJdUujSXf8aFR2PzvNS4vjNb37j+FpKjgghRCsVFTjGINxJxSagP/vQrfdweowjNzeXgwcPcvz48TrHx44d6/KghBDC5xQXolIGuP8+MfFQWow+Vorq1Nktt3Aqcaxfv56XXnqJ7t27OxYBAiilJHEIIcQp6MpKKC12+xgHgIqunpKblwM9k91yD6cSx2uvvcYjjzzimH4rhBCiGUqLrL89kDgcVXJzs1FuShxOLfnu3LmzVMAVQoiWql7Dobp6ZnAcsPblcBOnWhzTp0/n73//OxdccAFhYWF1HouKinJLYEII4TOKrMThka6qoCDo5t4puU4ljsrKSr744gu2bt1a77EXXnjB5UEJIYQvqSk34pGuKrCq5LpxSq5TiWPVqlVcdtllnHnmmXUGx4UQQjjBw4lDxcSjv8hy2/WdShymaTJx4kSpgiuEEC1RXABBwajgEM/cLyYBigvR5cdQIZ1cfnmnMsGvfvUrMjMzrR2shBBCNI8Hyo3UpqpnVuGm7iqnWhxvvPEGhYWFrF+/ns6d6y4oeeKJJ9wSmBBC+Art4cRRMyWX3Gzo0dfll3cqcdx8880uv7EQQnQYRQXgzi1jTxZt1RfUudk0XJ62dZxKHAMGeGCZvBBC+KqSQlTqQI/dTgWHQFgE5LpnLYdTiaOqqoqtW7eyf//+erWqakqvCyGEqM8qN1ICnlj8V1tMHNpNazmcShyPP/44Bw8eJC0trd4CQCGEEE0o8WC5kVpUTDx61+duubZTiWPHjh088cQThIR4aCqZEEL4Cke5Ec8mDqLjoegd9InjqKBgl17aqcSRlJREaWmpJA4hhGiu4gLrb09s4lSLGjURNXAY+Ae4/NpOz6p68sknGTp0aL2uqvHjx7s8KCGE8BUeLzdSTUVGQ6R7itM6lTjee+89vv32W8rKyurtx9GcxGGaJnfeeScRERHceeedlJaWsnjxYvLy8oiOjua2225zrBNZv349mzZtwjAMZsyYQVpaGgD79u1j+fLl2O12hg0bxowZMxrdD10IIbzOS4nDnZxKHBs2bHDJfhwbNmwgMTGR8vJyADIzMxk8eDBTpkwhMzOTzMxMpk2bxuHDh9m2bRuLFi2ioKCA+++/n6VLl2IYBitXrmT27NmkpKTw8MMPs2PHDoYNG9aquIQQwm2KCiAoxOXjDN7kVMmRbt26tbp8en5+Pp999hlnn32241hWVpajxTJ+/HiysrIcx8eMGUNAQAAxMTHExcWxZ88eCgoKKC8vJzU1FaUU48aNc5wjhBBtUnEhdPWt2ahOtTguuOACli1bxpQpU+qNccTGxjp1o6effppp06Y5WhsARUVFhIdbA0bh4eEUFxcDYLPZSElJcTwvIiICm82Gn58fkZGRjuORkZHYbLYG77dx40Y2btwIwIIFC1qc+Pz9/dvlniMSt2dJ3J7VnuK2lZdBZAwR1fG2p9gb41TiWL16NQCffvppvcec2Y/j008/JSwsjD59+vDVV1+d8vmNFVNsTpHFjIwMMjIyHN8fPXrU6XNri4qKavG53iRxe5bE7VntKe6q/DyIT3LE255iT0hIaPC4U4mjtZs1fffdd3zyySd8/vnn2O12ysvLWbZsGWFhYRQUFBAeHk5BQQFdu3YFrJZEfn6+43ybzUZERES94/n5+URERLQqNiGEcKviQtRpg7wdhUt5ZIONyy+/nCeffJLly5dz6623MmjQIObMmUN6ejqbN28GYPPmzQwfPhyA9PR0tm3bRkVFBbm5uWRnZ5OcnEx4eDghISHs3r0brTVbtmwhPT3dEy9BCCGaTVdWQJkXyo24WaMtjgcffJC77roLgHnz5jU65fW+++5r8c2nTJnC4sWL2bRpE1FRUcydOxeA7t27M3r0aObOnYthGMycOdOxidSsWbNYsWIFdrudtLQ0mVElhGi7ir1TbsTdGk0ctddnTJo0yWU3HDhwIAMHWlUiu3Tpwrx58xp83tSpU5k6dWq943379mXhwoUui0cIIdymxEvlRtys0cQxduxYx9cTJkzwRCxCCOFbfHDxH3hojEMIIToiXVRdp0oShxBCCKc4Why+NTguiUMIIdyluBCCQ1BBQd6OxKUaTRw1M6oAXnzxRY8EI4QQPqW40Oe6qaCJxHHkyBHsdjsAr732mscCam/0Z9swn16KLivxdihCiDZG+2jiaHRW1fDhw7nllluIiYnBbrczf/78Bp/XmnUcvsD88D3Y8RH6u10YN/wZ1b23t0MSTtL7voPIGJSHN9gRHUhRAST08HYULtdo4rjhhhv49ttvyc3NZc+ePUycONGTcbUftlyI7w7lxzAX/AF19RyMEeO8HZU4BV1VhbnoHtToiagrrvd2OMJXFRei+g3xdhQu12Stqn79+tGvXz8qKytlLUdjbHmoM85E/eoyzCcfQa/8K+YPe1FTr0L5+Xk7OtGYnMNw4jj6yEFvRyJ8lK6ogGOlENaBuqpqmzRpErt27WLLli2OooTjxo1j0CDfKtzVXPrEcSgtcXR3GLffj163Gv3WevShfRjX/QHVuau3wxQN0D/stb7IPuzdQITvKvHNxX/g5HTcd955hyVLltCtWzdGjBhBeHg4S5cudex30WHl51p/R1j7+ir/AA9zSJoAACAASURBVIzLf4+aPge+/xrzgbnog/u8GKBo1KHqf5eSIpnYINyj2DfLjYCTLY7//ve/3H333fTq1ctxbMyYMSxcuLDOnhcdji0PqN4UvhbjzAx0Qk/MJx7GfOQO1FU3Y4x0fm924X764F4wDDBNq9WR3N/bIQlf46OL/8DJFkdJSUm9/cYTEhIoLS11S1Dthc63EgcRMfUeU71TMO5eCL1S0KsWYr64plkbUQn30aYJh/ZD/6HW9znSXSVcz1fLjYCTiaNfv36sXbuWEydOAHD8+HGeffZZUlNT3Rpcm5efC35+0K3hTxSqazjGbfejxv0S/dZ62H3q3Q+FBxzNgfJjqGGjwT/AGigXwtV8tMAhONlVde2117JkyRKmT59O586dKS0tJTU1lVtuucXd8bVt+XnQLRJlND57Svn7w8Uz0ds3o7du9LmdwNql6nEn1SsZHZuAzvnRu/EI31RSBCGdUIG+VW4EnEwc4eHh3HfffeTn5ztmVUVGRro7tjZP2/Igsn431clUUBBq+FnojzejL78OFdzJA9GJxuiDe62WYkJPVFwS+tB+b4ckfFFRAXTxvdYGNLPIYWRkJMnJyZI0athy6w2MN0admQH2E+isD9wclDgVfXAfxPdABQRAfBIczbHm3AvhQr5abgSkOm6L6cpKKLA5puKeUp/TIC4Jve0d9wYmmqS1hoP7UD37WAfikqyZVXnZ3g1M+J7iQp9c/AeSOFquyAbadKqrCkAphTrzbNjzjczi8aZCm9X33L0vACquerag/JsIVysu8Mk1HOBE4jBNk127dlFZWemJeNqP6sV/znZVAahRE8EwpNXhTQetFeOOFkdsAgBaVpALF7LKjZR13K4qwzB49NFH8fd3ahy9w2hqDUdjVLcIGHQGetu76KoqN0UmmqIP7gOlIMmqYqyCQyAiCmRmlXAlH178B052VfXv35/du3e7O5b2xVaTOKKadZpxZobVzfX1524ISpyKPrgXYhOshFEjLkm6D4Vr+XC5EXByOm50dDQPP/ww6enpREZGopRyPHbJJZe4Lbg2LT8XuoQ1f472kHTo3BXzg434DU53T2yicQf3opIH1DmkqictaK3r/GwL0WI1LQ4f3evFqcRht9sZPnw4ADabza0BtRc637k1HCdT/gGoURPR776OLilGdZHquZ6iS4rBdhR69Kn7QFwSHC+3WoLdZKq5aD1d7LvlRsDJxHHDDTe4O472x5YLCT1bdKo682z0xlfQH7+Hyvi1iwMTjTpUPTDeo2+dwyouEQ1WsUNJHMIVfLjcCDRjOu7hw4d56aWXWL16NWDtSf7DDz+4LbC2TGttbeDUjBlVtamkXtAzGb11oxQ+9CBHifuTWxzx1pRcKT0iXKa4EEJCUQGB3o7ELZxKHB9++CHz58/HZrOxZcsWAMrLy1m7dq1bg2uzSovBbm9RV1UNdWYGHD7gqJskPODgPmvTrdAudY+HRUBwiKzlEK5TVOCzrQ1wMnGsW7eOe+65h+uuuw7DsE7p2bMnBw4ccGdsbVfNGg5nV403QI0YB/4B6K0dfDMsD9IH90H3PvWOK6VkZpVwKV1SCF3DvB2G2ziVOIqKiujZs25/vlKq485AqZmK28KuKgAV2hk1bJRV+LDC7qLARGP08WPw048/L/w7iYpLkhaHcJ3iQpSPruEAJwfH+/Tpw5YtWxg//udd7LZu3UpycrJTN7Hb7cyfP5/KykqqqqoYNWoUF198MaWlpSxevJi8vDyio6O57bbb6Ny5MwDr169n06ZNGIbBjBkzSEtLA2Dfvn0sX74cu93OsGHDmDFjhscTmGPxXyu6qgDU2Ax01vvoHdtRw8e6IDLRqEMHAFDd+zb8eFwifPQu+nh53TUeQrREUSH07+BdVTNmzODf//438+fP58SJEzz44IO88MILXH311U7dJCAggPnz5/PYY4/x6KOPsmPHDnbv3k1mZiaDBw9m2bJlDB48mMzMTMAaiN+2bRuLFi3irrvuYvXq1ZimCcDKlSuZPXs2y5YtIycnhx07drTwpbdCfi4EhUCnzq27Tr8hEBGF3vq2a+ISjdLVpUZorMVRPUDOT0c8FJHwVbrCDuW+W24EnEwciYmJLFmyhHPPPZdLL72UCRMmsHDhQuLj4526iVKK4OBgAKqqqqiqqkIpRVZWlqMVM378eLKysgDIyspizJgxBAQEEBMTQ1xcHHv27KGgoIDy8nJSU1NRSjFu3DjHOZ6k8/MgIqrVLR1l+KFGT4Kvd1h7ewj3ObgPuoRZA+ENiauZWSXdVaKViousv3108R842VUFEBQURL9+/bDZbERERDgSgbNM0+SPf/wjOTk5nHvuuaSkpFBUVER4uPXmhoeHU1xcDFiLDFNSUhznRkREYLPZ8PPzq7MXSGRkZKMLEjdu3MjGjdbA84IFC4iKal5pkBr+/v71zs0vLsCITyK8hdesrfKCi8h/fR0hOz+m8++mt/p6NRqKuz1wV9z5R37ASO5PeHTD41I6rCu5hh+dimx0bsH95f32rLYcd4UtFxsQltSDoAZibMuxO8upxHH06FGWLVvG999/T2hoKGVlZSQnJzNnzhyiG/mPeDLDMHjssccoKyvjr3/9KwcPHmz0uY2tbWjOmoeMjAwyMjLqvIaWiIqKqnduVe4RVPfeLb5mHQHBkDqIsrf/S/mEC1w2XtNQ3O2BO+LWFXbMQ/tR/dOavnZULMf27eZ4C+4v77dnteW4dfV4WjEGqoEY23LsJ0tISGjwuFNdVcuXL6dPnz6sWbOGVatWsWbNGvr27cvy5cubHUhoaCgDBgxgx44dhIWFUVBgLc0vKCiga1er/EZkZCT5+fmOc2paOScfz8/PJyKika4HN9EnjkNpifMbODlBnZkBeTnw/Vcuu6ao5ccfoKoK1bORgfEa8TIlV7SeLqopN+K7XVVOJY59+/Yxbdo0R/dUcHAw06ZNY98+5xavFRcXU1ZWBlgzrL788ksSExNJT09n8+bNAGzevNlRDys9PZ1t27ZRUVFBbm4u2dnZJCcnEx4eTkhICLt370ZrzZYtW0hP93ChQJtrZlTVps4YA8Eh6K2yT4c7/LxivOnEoeIS4acjaFNK3otWcJQb8d11HE51VaWkpLBnzx769evnOLZ3715SU1OduklBQQHLly/HNE201owePZozzjiD1NRUFi9ezKZNm4iKimLu3LkAdO/endGjRzN37lwMw2DmzJmOhYezZs1ixYoV2O120tLSGDZsWHNfc+u0YAOnU1FBwajhZ1lrOi67FhXcyWXXFlibN4WEQlRs08+LS4LKCsjPg+g4z8QmfI+PlxuBJhLHCy+84Pg6NjaWhx9+mNNPP93RXfT5558zdqxzaw969uzJo48+Wu94ly5dmDdvXoPnTJ06lalTp9Y73rdvXxYuXOjUfd2hJRs4OUONnoR+/y30l5/Jmo5T0KaJMpzf9Vgf3Ac9+pxy/EjFJVnFDnMOS+IQLaaLC3x2r/Eajf7vy8/Pd/ypqKhg5MiRBAQEUFxcTEBAACNGjMBu74Arnm154OcH3Vzcf9krBQzDql8lGmVmvY95xzVOFyTUVVVw+ACqgVIj9cQlWufINrKiNYoLfXoNBzTR4pBS6o3Iz4VukSjDz6WXVQEBEBOPPtL4bLOOTttPoF9cA0U2zJfW4HfT3ac+KecwVNgbXfhXm+rc1VrrIQPkojWKi1Dde3s7Crdyeh3HiRMnyMnJ4fjx43WOn3baaS4Pqi1r6QZOTknoAYc7Zql6Z+hNr0HBUUgbBTs+Qn+zE9V/aNPnVA+MN1pq5GRxiTKzSrROcQF09fDYq4c5lTg2b97MU089hb+/P4GBdQd8nnjiCbcE1mbZclGpg91yaZXQA/35x+gKu08PrLWELitBv/ESDE7HuO7/MO+5AXPdUxj3LGq69XdwLwQGOrqhTkXFJaF3fOyiqEVHY5UbOdZxu6pqe+6557j99tsZMmSIu+Np03RVFRTYWlUVt0kJPUGb1k50J2821MHpN16C8mMYU69EBQSifns1+h+Pobe+gzrrF42fd3AfJPZC+TnZtRiXBCVvoUuLra4rIZrDx3f+q+HU1BR/f38GDBjg7ljavsJ86xe7m7qqVEIPABnnOIm25aHfeQ01aiIqyeo7VuljoW8/9Cv/tEqmN3SeacKhfade+FeLo9ih7AYoWqJ68Z8vl1QHJxPHJZdcwtq1ax21pDosF2zg1KTYeGvG1hEZ56hNv/I8AOrCKxzHlFIYF8+EogL0Gy83fOLRn6xuA2dmVNWQYoeiNTpIi8OprqqEhATWrVvHm2++We+x2us9fJ12wQZOTVH+ARCTgD5yyC3Xb4/04QPoDzehzrmw3qJL1ec01Ijx6Lcz0ePOrb8os7qUenNaHERGg3+AzKwSLaJrEoePr+NwKnE8/vjjjBs3jjFjxtQbHO9QHIv/3NTioHqAvGbvCIG5/lkI7oQ676IGH1dTr0J//iH65bWoa2+v85g+uM9qwSX0bPDcBq9n+EFsgtPrRISoo7i6TlUXSRyUlpZyySWXdNytYmvk50KXMFRgkPvukdADPtuGPnECFeTG+7QDevcu+CILNfXqRgeqVWQ06pwp6A3r0GdPRvX5eXq4PrgX4ntYa2SaQcUloQ/tb1XsooMqLoROoc3+mWtvnBrjmDBhAlu2bHF3LG2etYGT+1obACqxB2gNOR27u0prjfmfZ6zFlmdPbvK56rzfQlg45rrVjtL7Wms4uK/RPcabFJ8ER3PQFRUtCV10YLq40Ker4tZwqsWxZ88e/ve///Hyyy/TrVvdJth9993nlsDaJFue1SJwp+puFf3jQVRP5/Z090mffwj7vkNdddMpW3gqOAR14RXotX9Df/IBavhZUGiDkiJwduFfbXFJYJqQl+3+f2/hW4p8v9wIOJk4zj77bM4++2x3x9Kmaa2txX+Dz3DvjWLiwd8fOvCUXF1VhfnysxDfHTXGuZ87debZ6HdfR//nGXTaSGurWGhRi6NOsUNJHB2ePnECjh9DObMVbHEhqgOswXIqcUyYMMHNYbQDpcVgt7u/q8rPD2ITO/RaDr31bfjpR4wb73J64Z4y/DB+dw3monvQb78CVVWgFCS1oGZQrLXrmc4+TAcf1ROAfukp9PYtGPevOPX6jOICn95rvIZTiWPTpk2NPjZp0iSXBdOmVU/FVe6qU1WLSuiB3ved2+/TFunj5ej//huS+8PQEc06V/UfCkNHoDe8BEk9ITYBFRzS7BhUcAiER8kiwGpa6w47MUZrjf4iC46VWTP3pt/S+HPtJ+B4uVUo08c5lTjef//9Ot8XFhaSk5NDv379Ok7iqF7857ZyI7Ul9ICs99HHy1v0i689K3v1BSiyYfz+jhb9sjIumoF5702w91trrKOlpNghALowH3PBH1Hn/gZj4gXeDsfzfvoRbEchOg699R30uF/WmblXh2MNh7Q4AJg/f369Y5s2beLHHzvOJzLHBk6eanEAZB+C3s7tsugLdEkxx9Y/B2kjUcktK3Gj4hJRE85Hv/MqNGfhX73rJKE/3NShP20D6M8/gvxc9PN/xwwKwRjTQT4oVtNf7QDAuOFPmEvuw/zXPzD+9FjDG4k5yo3I4HijJkyYwMyZM7nyyitdGU/bZcuDoGDo1Nn996pVs0r5SOIwn1qM3vWZNdVYa6vml9Zg1v66CrTG+E3rfqbUry6DkiLUsNEtv0h8ktXtUGSDbpGtiqc90zu2WxM2ImPQTy9DB4egTm/F+9rO6K8/h+g4VFJv1EVXo1cvRm/d2HBhzZKOUW4EnEwcpmnW+d5ut7NlyxZCQ0PdElRbpPNzISLaM58+Y+Ksshc+MkCuD+5Df/guDEhDxSRYg9aGYf2tFKjqrw1F2LCRlLRyJpMK7Yy69v9ad42amVXZhzts4tDlx+C7L1FnT0b96jLMxfMwVz6GcfM9qAG+vd8EgK6sgO92oUZPAECNnIDe/Cb65bXo08egQut+iHSUG5F1HJbLLrus3rGIiAhmz57t8oDaLHdu4HQSZfhBfJLPzKzSG1+BoGCM2XegTtFiC4qKouToUQ9F1gRHscMfT7lZlM/6+nOoqkQNHYEKDsGYMx/zsT9hLn8IY+79qL79vB2he+37Dk6UO5KkUgrjsuswH5iL/u/zqMuuq/v8oprEIYPjAPztb3+r831QUBBdu3awvQpsuaheKR67nUrogd79lcfu5y660Ibe/j5q/C9PmTTalG4REBTSoYsd6p3bIbQL9O0PWC0547a/YD56J+ay+zD+7yGf3iJVf7XDahmf9vPGbapHH9T4X6Lf3YA+6xxHmX+gutxIZ6tYqY9zquRIdHR0nT8dLWnoE8ehtMQzM6pqJPSAgqPoY2Weu6cb6Hc3gFmFOvtX3g6lWZRSHXpmlTar0F9+ghp8Rp21NCosHGPu/RAUgrl4HvqnI16M0r30NzugdyqqU90ueTXlCggNxfzXPxwlbqC6q6oDzKiCU7Q4TlVORCnFvHnzXBpQm2Tz3IyqGnVmVrXTLgF94gR68xswdCQqJt7b4TSbik+yCi12RHu/g9ISVANraVRkjNXyeOxPmIvuwbhjQf2S9u2cLiuBA9+jJl9a7zEV2gU15Ur0cyvQWe+jRoyzHigu6BAD43CKxHHWWQ3Pg7fZbLzxxhucOHHCLUG1Oe7ewKkhtWdWtdfE8dG7UFaCcc6vvR1Ky8QlwUfvdcj1NHrnx+DnDwNPb/BxFZ+Eceu9mH+9C3PxPIw7Hvataajf7AStUQPSGnxYnXUOesub6BfXoIcMt34+igs7TH25JruqJk2aVOfP8OHDOXz4MK+++iojRoxg6dKlnorTq9y9gVODomIhMLDdzqzSpmkNivdMhpSB3g6nRVT1ADk+3B3TGL1zO5w2CBXSqdHnqB59MW6eBwV5mEvmo4+VejBC99Jf74CQTo2uo1KGH8bls6EwH71hnXWwuGMUOAQnxziOHTvGv//9b+bMmUNRURGPPPIIs2fPJjKyg0xTzM+zNgTqFuGxWyrDgPge7Xdm1VefQc6PqIxft98FdB10G1md86P1bzfk1CVfVMoAjOv/DEcOYS6515rC285pra3E0W9Ik7XSVN9+qNET0W+9Yu3fcrxcEgdY6zXWr1/PzTffzI8//shf/vIXbr75ZuLi4jwVX9uQn2vtC2E4V3DPVVRCd6+2OHRZCebr69Blzf8kab79ivWepZ/phsg8JCbeWmPS0RLHF9sBUEOHO/V8Neh0jNl3wMG9VsujvSePn45Afm6j3VS1qd9Oh4AAzKeWWAckccCNN97Ia6+9xq9//WvOPfdcioqK2LVrV50/HYHOz/NsN1WNhB5QaGvRL25X0P/6BzrzOcxVf0WbVc6fd3g/fLMTNemCdj01UQUEQHQs+oe96CrnX397p3dmQVIvVFSs0+eoYaMwrrsDftiDubR9tzz0N1aZEWcWOaqwcKtSweH9ju87giYHx2v2F3/rrbcafFwpVW+Nh0+y5aFSB3n8to6ZVUcOQkrLaje1lP7yU/THm60qtbs+Q2f+EzX1KufOffu/EBiEGneum6N0P3XaYPT7b2HeMQM1/CzUyPHQK6X9dr+dgi4rgT1fo37Z8B7vTVGnj8a47g+Y/3gMc+m9GLfeiwpufIykrdJffQ5RsU7PBFSTJqM/eNuaAdlBWhxNJo7ly5e75CZHjx5l+fLlFBYWopQiIyOD888/n9LSUhYvXkxeXh7R0dHcdtttdO5sLRJbv349mzZtwjAMZsyYQVqa1Wzct28fy5cvx263M2zYMGbMmOHW/8S6qhIK873X4qB6ZpUHE4c+Xo753AqI744x9wH0v/+BfuMldI8+qPSxTZ9bVIDevhk19heo0C4eith91GWzUYNOx/x4M3rzG1bxxJh41IjxqJHjfh5A9xH6y0/BNFFpzStpX0OdPgbj2j9g/uNRzCXtL3noykqrzMqI8U6fo/z9Ma66EXP9cxCb6Mbo2g6nBsdby8/PjyuvvJLFixfz4IMP8uabb3L48GEyMzMZPHgwy5YtY/DgwWRmZgJw+PBhtm3bxqJFi7jrrrtYvXq1o17WypUrmT17NsuWLSMnJ4cdO3a4NXYzP8/aRtSTU3FrRERbhRU9PM6hM5+DgqMYV92ECghAXXod9O2HuWYp+vCBps99bwNUtb8Ff41RAQGo08fgd/2fMBauRV19s1Xw7/UXMO+5gaoH5mK+lUlVTfXk9m7ndmsRWyumlaozxljdVvt3Yy69D328HXVb7d8Nx8tRA089vlGbSh6A3x8e6jDTtj2SOMLDw+nTx9pOMSQkhMTERGw2G1lZWYwfb2X28ePHk5WVBUBWVhZjxowhICCAmJgY4uLi2LNnDwUFBZSXl5OamopSinHjxjnOcZeqoz8BntnA6WTWzKruHp1Zpfd+i970GmrCeajk6lITAQEYv78TQkIxVzxkdWc0dK79BPq9N2DIcFSc733yUp06Y4w9B7+592M8+hTq4pkA6Bef4uisC6m6+3rMtX/D/PDdn8vwtyO6sgK961PUkOENlw1vBnXGGIxr/w/2fYe59C/tJnnorz+3JkT0G+LtUNq0FpdVb6nc3Fz2799PcnIyRUVFhIdbg0nh4eEUFxcD1gLDlJSf60JFRERgs9nw8/OrMwU4MjISm83W4H02btzIxo0bAViwYAFRUVEtitf+zedWfMmp+LfwGq1R1CcV+2cfNjt+f3//Zp+jKyrI/+cTGBHRRF57G0ZIrVILUVHY/7SAgrtvxP/ppXS7e2G9qYrH3nqFktJiwi+6isAWvlctidsroqIg+TS4bCaVP/5AxafbOL7rcyo++xD9/ltowIiOI3BgGgED0ggckIZfQvc2NzZS+/0+sTOLwuPlhJ2VQZAr/g1+OYXjXbpQtOhe/FY8TLd7FmI0sS6kOdz1c2LbvQtS+hPRo5fLr12j3fyMN8GjieP48eMsXLiQ6dOn06lT4z9Ateu/OHO8IRkZGWRkZDi+P9rCiqvB1duHFih/lBeqtpqRMehCG3kH9qE6O18jLCoqqtmv2Xzt3+hD+zFuugdbWTmUldd9QmQc6vLZ2Nf+jbyVizEumu54SGuNmfk8dO9NUWz3Fr9XLYnb64JCifr1ZZSNOQdlmqgjP6C/+wrz+10c//RDjr/3P+t5EVHWPuo9Wr7BlKvVfr/NLW9DQCDFCb1d97N+2lDUrLlUrFpI3vw5GHPmu6Q7xx0/J7qsFPP7b1AXXOzWn8H29DOekJDQ4HGPdFUBVFZWsnDhQs466yxGjhwJQFhYGAUF1q5ZBQUFjuKJkZGR5OfnO8612WxERETUO56fn09EhHsX5ZlHf4IuYajAILfepzGqZm8KN3dX6exD6NfXWTOHmpi/b5z1C6s66JsvY2bV2lL4q88g+xDqnClt7lO1JynDQCX1xjh7Mn6/vxNj4VqM+1egrrwRNJhPLGi0q8+btNbWavEBaagg1/6sG8PPQs26HfZ8i/m3B9AVFS69vst8+wVo06n1Gx2dRxKH1ponn3ySxMREJk+e7Dienp7O5s2bAdi8eTPDhw93HN+2bRsVFRXk5uaSnZ1NcnIy4eHhhISEsHv3brTWbNmyhfT0dLfGXpWX452B8Ro1M6t+dF/i0KaJufZvEBSCuvTaUz5fXXotJPe3doQ7ZM1fN99+BcIiUMObnnXV0SilUHFJGOPOxbj+TijMx1y1sFnrYjzixx+sRW9DnFv011zG8LNQM26B7760fm6a0XvgKfrrHRAc0qG2a24pj3RVfffdd2zZsoUePXrwhz/8AbA2h5oyZQqLFy9m06ZNREVFMXfuXAC6d+/O6NGjmTt3LoZhMHPmTIzqwbpZs2axYsUK7HY7aWlpDBvm3p3IqnJzvDvFLjzKqpnjxhaH3vI/2PMNasYtThWqU/7WYLn5wFzMFQ9hTL8Fvt6BmjKtXS/4czfVOxV16XVWVdVXX0BdeLm3Q3LQO6tXi7spcQAYoydi2vKsWXtRMahWbhHsSlpr9FefwWmDUf4eH/ptdzzyDvXr149169Y1+FhjZdmnTp3K1KlT6x3v27cvCxcudGl8jdFaY+bloPp7r+mqlHLrzCptO4r+zzPQfyhq9CTn4woLx7j+Tqu09pJ5EBiIGv9Lt8ToS9S4c2H/d+jX/o3uleJ0WQ930zu3Wwsb3VyPTZ3/O8jPRW94ETMyGmNcG/mZycu2Wlzn/sbbkbQLHhvjaJdKS8B+wjuL/2pRCT3c0uLQWmM+/ySYJsaVNzZ7bEL1OQ11xfVQWYkaNalZg/cdlVIKdfnvoUdfzNWL0Lner7yriwpg/+4G995wNcfrH3Q6+p9PWgsO2wD9tfNlRoQkjqbZqvfh8MIajjoSekBpsbXDmAvpT7bCzu2oC69ARbescKUx9hyMPzzkWNMgTk0FBlnjHYaBueJhtJf3tdFfWGuhWrpavLmUv79VFDGxJ+bfH0H/sNcj922K/mqHtVFbO9xwzBskcTSlegMnrw6OAyrR9TOrdFkJ+l9/h57JrV7lrVIHuXwmjq9TUbHWArkjB9HP/s2rg8V653brZzyxl8fuqYI7YcyZB6GdMR//C7rm/5oX6Koq+O4L1IC0Dj0jsDkkcTTBKxs4NaRWzSpX0euftXbnu/rmJvccEO6jBg5DXXgF+uPN6E2veyUGfeIEfLMDNXSEx39pqm6RGHPuBbvdKk3irY2g9u+G8mOogdJN5SxJHE3Jz7MWK3m7WF9YBHQKBRdNydU/HkRveQs14XxU994uuaZoGXXeRTB0BPrF1ejvv/b4/e1ffAJ2u0fGNxqiEntY3Xa52Va3XaXn13hYZUaUlBlpBkkcTdD5uRjRcV5vviqlIMF1uwGaLz0FISGoX13qkuuJllOGgXHNrRAZY/X3FzZcQsddTmS9b61d8MK2ATVU/6FW8cjvvkQ/83iD3Xb6xAlrkeqXn2K+9wal69agUcY9AAAADvNJREFUv/zEJTWw9Nc7rBll3v6A2I7IhOWm5OfhFxVLW1iqpRJ6oD/Zita6VYlM7/oUdn2G+t01MguqjVCdOmNc/yfMh61y5MbcBzyylkCbJic+2YYaeLq1aZUXGaMnYubnol/5JygD3SXMmrabn2uNNZYU1Xl+meNEA3qnovoNQfUbAn37oQICnb6vPlZqzSg7r/n7j3RkkjiaoIaNIiixO22irmdCDzj2JhQVtHjvc11VhfniGoiOQ028wMUBitZQSb1QV92EXrUQ/co/Ub+92v03/f5rzIKjqKFtYyGeuuBiKMxHb/4fBARaY4sRMagefawZT5Ex1gzHqBiiknpwNGsb+tsvrD9vvIR+fZ11XnL/nxNJbCJ0Cm38w9a3X1r7j0iZkWaRxNEEY/IldIqK4lgbKEhWZzfAliaOD96GIwcxrr/T658wRX3GyPGY332JfvNl9OB0VOpAt91Lf/Yh5lNLMLpFgBtXizeHUgo17Qb01KsgpIlf9oAKDrG6uPoPBUAfK4Pvv0Z/u9NKJOufxdHh5ecHXcKq/3RDdf35a/3tFxAUAn1Oc/8L9CGSONqLxFq7Abbg05EuP2Z1A6QMgGGjXR2dcBF18Uz0t19gPrUYY/4ylIvKkNfQZhX6lefRG16E3qlE/PlRCtrYUKfq1LkF54TC0OGOlfi6pAh2f4UuyIPiIigpstZBlRShf/oRSgrBbrdOPmOMlMppJkkc7UWXbtC5S4vXcug3XoKSIow587w+2C8ap4JDMK65DfPRP6FfWIWaPsdl19alxZgrF8LXn6PO+gXqstn4RcVAG2hRu5rqEmYlhCaeo08ch+LCFrfgOzJJHO1Ea2ZW6fxc9NuvoEZNRPVKOeXzhXep5P6o8y5Cb1iHHjoCNWxUq6+pD+7FXPEwFNlQV96IMe5cF0TavqmgYGhhxYSOrm21UUWTampWNXeVsX55LRgK9ZtpbopMuJr61SXQow/m2r+hiwtadS3zo3cxF/zRqkl2xwJJGqLVJHG0Jwk9oPwYFOSf+rnV9N5v0du3oH7xG5SXS6cI5yn/AIyZc+F4OeYzLStJoisrMf/1D/TqxdA7FePuRSjZa0K4gCSOdqS5uwFqrTFffArCwlHn1i9RL9o2ldDDmpb7RZY1I64ZdGE+5sK70ZteQ51zIcbc+53aa0UIZ0jiaE9qalZ9u9OpHeT0J1th77fWBksu2OdZeJ6aNBn6D0W/sAqdm33K5+vKSsy3X8G85wY4uBd17f9hXDxT6pEJl5LE0Y6oLmHQOxX95nrMP12H+dq/0Y10W2n7CfR/noak3qgxzm/QJNoWZRgY0+eA4Yf51GKrkmsj9Dc7Mf9yC3rdakjujzFvKcaIcR6MVnQUMquqnTHueBh2fIy55U1rPv5//w1D0q0Bz0Gnowzrk+Wx11+E/Fyri8KQT5vtmYqIRl3xe2tV+f/+Y62wrkXn56FffAr96VaIisW48S7wQrVb0XFI4mhnlH8ApI/FL30sOjcb/cFb6A82Yu7cDhFRqLG/QA0dTtlLz1i/PKpX1or2TY0YBzu3o1/9F3rwGagefdEVdvRbmegN60CDuvByaxJEoOyNItxLEkc7pmLiUVOvRv/6ctiZhbnlf+j/Po/+7/Pg54dx0XRvhyhcRCkFV/we/f1XmKsWYUyZhvnSGsjLgdNHY/zuGlRUrLfDFB2EJA4foPwD4Iwx+J0xBp2Xg/5gI5179uZYXJK3QxMupEK7YEy/BXPJfMwnHoa4JIzb7pN9soXHSeLwMSo6DvWbaW2mOKNwLTVwGOqK30NlJWrCeVJjSXiFJA4h2hljwvneDkF0cDIdVwghRLNI4hBCCNEskjiEEEI0iyQOIYQQzSKJQwghRLNI4hBCCNEskjj+v727DW2r7OM4/s3DRtOn2LSxajsdsq5OrXTQOfFha8G9kSkImzAFbbuic7qCY84KpSvCQIXYITSs6vBFh/PFaMUXymCutA6ddLZQttm5pBNWlMY2aZfZnGXJue4XpYd7t0vvnT6cZPT/eXmSHH75c5H/Odc5OZcQQghTpHEIIYQwxabms7SYEEKIZUvOOP6PpqamdEeYF8ltLcltrTs1N9zZ2WdJ4xBCCGGKNA4hhBCmOFpbW1vTHSLTPfjgg+mOMC+S21qS21p3am64s7ODXBwXQghhkkxVCSGEMEUahxBCCFOW3UJOfr+fgYEB3G43Pp8PgD/++IPPP/8cTdPwer00NjaSnZ1NIpHg8OHDXL58GV3X2bRpEy+++CIAIyMjtLe3E4/HWb9+PXV1dTPrQmd47tbWViKRCCtXrgSgubkZt9u9ZLnnk/2zzz4jGAxit9upra3lkUceATK/5qlyW1nz8fFx2tvbmZycxGaz8eyzz/Lcc89x7do12tra+Pvvv/F6vbzzzjvk5uYC0N3dzalTp7Db7dTV1VFZWQlYW+/FzG31GDebPRqN8sknnxAIBKiurmbnzp3Gvqwe4/Omlpnz58+rYDCo9u7da2xrampS58+fV0op9cMPP6hjx44ppZT68ccfVVtbm1JKKU3T1O7du9XY2JjxmYsXLypd19XBgwfVwMDAHZH7wIEDKhAILGnWhWT//vvvVXt7u1JKqcnJSbV//36VTCaNz2RqzefKbWXNw+GwCgaDSimlpqenVWNjo7py5Yrq7OxU3d3dSimluru7VWdnp1JKqStXrqh9+/apeDyuxsbG1Ntvv52Wei9mbqvHuNnssVhM/fbbb+rEiRPqiy++uGlfVo/x+Vp2U1UPP/ywccQy688//2TdunUAPPbYY/zyyy/Ga5qmkUwmicfjOJ1OsrOziUQixGIx1q5di81mY9OmTfT392d87nQxk310dJRHH30UALfbTU5ODiMjIxlf81S5rVZQUGDcseNyuSgpKSEcDtPf38/mzZsB2Lx5s1G7/v5+nnzySVasWMHdd9/NPffcQyAQsLzei5U7Hcxmz8rK4qGHHjLOiGalY4zP17JrHLeyatUqzp49C8CZM2eYmJgA4IknniArK4vXX3+d3bt38/zzz5Obm0s4HKawsND4fGFhIeFwOONzz/L7/bz77rscP34claab6lJlX716NWfPniWZTBIKhRgZGWF8fDzja54q96x01DwUCnH58mXWrFnD1NQUBQUFwMwP3dWrVwH+VVePx0M4HE5rvReSe1a6xvjtZE8lU8b47Vh21zhu5c033+TLL7/k+PHjVFVV4XTOlCUQCGC32+no6OCff/6hpaWFioqKtP3Y/i+zuYuLi2lsbMTj8RCLxfD5fPT19RlHRZmQvaamhtHRUZqamvB6vZSXl+NwODK+5qlyA2mpuaZp+Hw+amtr5zzbTFXXdNV7obkhPfWG28+eSqaM8dshjQMoKSmhubkZmJmKGBgYAOD06dNUVlbidDpxu92Ul5cTDAZZt26dcaQJMDExgcfjyfjcxcXFRk6Xy8XTTz9NIBBIS+NIld3hcFBbW2u8r7m5mXvvvZecnJyMrnmq3IDlNU8kEvh8Pp555hk2btwIzEyfRSIRCgoKiEQi5OfnAzNHtf9d13A4jMfj+dd2K+q9GLnB+nqbzZ5KOmo+XzJVBUxNTQGg6zpdXV1s2bIFgKKiIs6dO4dSCk3TuHTpEiUlJRQUFOByufj9999RStHX10dVVVXG504mk8bpciKR4Ndff2XVqlWW554r+/Xr19E0DYChoSEcDgelpaUZX/NUua2uuVKKw4cPU1JSwtatW43tVVVV9Pb2AtDb28uGDRuM7T/99BM3btwgFArx119/sWbNGsvrvVi50zHGzWZPJVPG+O1Ydv8cP3ToEBcuXCAajeJ2u3nppZfQNI0TJ04A8Pjjj/Pyyy9js9nQNA2/38/o6ChKKWpqanjhhRcACAaD+P1+4vE4lZWV1NfXL+ltc4uRW9M0Dhw4QDKZRNd1KioqeO2117Dbl/b4wUz2UCjEwYMHsdvteDwedu3ahdfrBTK75qlyW13z4eFhWlpauP/++43a7Nixg7KyMtra2hgfH6eoqIi9e/ca1726urro6ekxbiNev349YG29Fyt3Osb4fLK/9dZbTE9Pk0gkyMnJobm5mdLSUsvH+Hwtu8YhhBBiYWSqSgghhCnSOIQQQpgijUMIIYQp0jiEEEKYIo1DCCGEKdI4hBBCmCKNQ4h5+vTTT/H7/Tdtu3DhAvX19UQikTSlEmLpSeMQYp7q6uoYHBxkaGgIgHg8TkdHB6+++qrxcLvFoOv6ou1LiMUgz6oSYp7y8vKor6+no6MDn89HV1cXxcXFVFdXo+s633zzDT09PUxPT1NRUUFDQwO5ubnouk5bWxvDw8PcuHGD1atX09DQQGlpKTBzJpOdnc3Y2BjDw8M0NTWhaRpHjx5lYmKC7Oxstm7detPjLYSwkvxzXIgF8vl8JBIJLl68yMcff0xRURHffvst/f39xqpvR44cIZFIsGfPHnRdp6+vj40bN+JwOOjs7OTSpUt8+OGHwEzjGBwc5P333zeev7Rr1y72799PeXk5165dIxQKGWtACGE1maoSYoF27tzJuXPn2LZtG0VFRQCcPHmSHTt24PF4WLlyJdu3b+fnn39G13XsdjvV1dW4XC7jtZGREeMhiQAbNmxg7dq12O12VqxYgdPpZHR0lFgsRm5urjQNkVYyVSXEAt11113k5+cbU00wsw71Rx99dNMD6mw2G1evXiU/P5+vvvqKM2fOEI1GjfdEo1GysrIAjAY0a9++fXR1dXH06FEeeOABXnnlFcrKyiz4dkL8mzQOIZZAYWEhjY2Nt/xx7+npYXBwkJaWFrxeL9FolIaGhjkX8ikrK+O9994jkUjw3XffcejQIdrb25fyKwiRkkxVCbEEtmzZwrFjx4zlY6empowlZ2OxGE6nk7y8PK5fv87XX389577i8TinT59menoap9OJy+Va8kfhCzEXOeMQYgnM3vH0wQcfMDk5idvt5qmnnqKqqoqamhqGhoZ44403yMvLY/v27Zw8eXLO/fX29nLkyBF0Xee+++5jz549VnwNIW5J7qoSQghhipzvCiGEMEUahxBCCFOkcQghhDBFGocQQghTpHEIIYQwRRqHEEIIU6RxCCGEMEUahxBCCFP+A1gQTngz8NZBAAAAAElFTkSuQmCC\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": 119,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
" \n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
" \n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" We will cover advanced annotation methods in later modules."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"india_china = df_can.loc[['India','China'],years]\n",
"\n",
"india_china.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc5b69637f0>"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"india_china.plot(kind='line')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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": 129,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Number of Immigrants')"
]
},
"execution_count": 129,
"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",
"\n",
"india_china.T.plot(kind='line')\n",
"plt.title('India_china immigration to canada data')\n",
"plt.xlabel('years of immigraion')\n",
"plt.ylabel('Number of Immigrants')\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.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": 151,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fc5b6aa6cc0>"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
},
{
"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',inplace=True,ascending=False,axis=0)\n",
"\n",
"top5 = df_can.head()\n",
"top_5 = top5[years].transpose()\n",
"top_5.index = top_5.index.map(int)\n",
"\n",
"\n",
"top_5.plot(kind='line', figsize=(14,8))\n",
"plt.xlabel('years of immigration into canada')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.legend()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # 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 the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course online by clicking [here](https://cocl.us/DV0101EN_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",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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