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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots.ipynb
{
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"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Data Visualization\n",
"\n",
"Estimated time needed: **30** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Create Data Visualization with Python\n",
"- Use various Python libraries for visualization\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no _best_ data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in _pandas_ dataframes. Therefore, in this lab, we provide a brief crash course on _pandas_. However, if you are interested in learning more about the _pandas_ library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a _pandas_ dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"* * *\n"
]
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with _pandas_](#0)<br>\n",
" 1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
" 1.2 [_pandas_ Basics](#4) <br>\n",
" 1.3 [_pandas_ Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
" 2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
" </div>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
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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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ):\n",
"\n",
"> _pandas_ is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on _pandas_ for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the _pandas_ API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
]
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"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png\" align=\"center\" width=900>\n",
"\n",
" The Canada Immigration dataset can be fetched from <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx\">here</a>.\n",
"\n",
"* * *\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## _pandas_ Basics<a id=\"4\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The first thing we'll do is import two key data analysis modules: _pandas_ and **Numpy**.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using _pandas_ `read_excel()` method. Normally, before we can do that, we would need to download a module which _pandas_ requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"new_sheet": false,
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"outputs": [
{
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" <th></th>\n",
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" <th>Coverage</th>\n",
" <th>OdName</th>\n",
" <th>AREA</th>\n",
" <th>AreaName</th>\n",
" <th>REG</th>\n",
" <th>RegName</th>\n",
" <th>DEV</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>190</th>\n",
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" <td>Viet Nam</td>\n",
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" <td>Asia</td>\n",
" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
" <td>1816</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Western Sahara</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <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>192</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Yemen</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
" <td>434</td>\n",
" <td>437</td>\n",
" <td>407</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method.\n",
"\n",
"This method can be used to get a short summary of the dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Columns: 43 entries, Type to 2013\n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info(verbose=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in _pandas_ objects are _float_, _int_, _bool_, _datetime64[ns]_ and _datetime64[ns, tz] (in >= 0.17.0)_, _timedelta[ns]_, _category (in >= 0.15.0)_, and _object_ (string). In addition these dtypes have item sizes, e.g. int64 and int32. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use _pandas_ `drop()` method as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"## _pandas_ Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python\n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"\n",
"* * *\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country').\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
"\n",
"```\n",
"1. The full row data (all columns)\n",
"2. For year 2013\n",
"3. For years 1980 to 1985\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n"
]
},
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"[print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\n",
"\n",
"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
"\n",
"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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"<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",
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" <th>2010</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",
" <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>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>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": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <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",
" </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": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
"\n",
"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). As mentioned on their website: \n",
"\n",
"> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
"\n",
"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Matplotlib.Pyplot\n",
"\n",
"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded.\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.3.3\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in _pandas_\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in _pandas_ is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti.\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 37,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type _string_. Therefore, let's change the type of the index values to _integer_ for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
"\n",
"```\n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
"```\n",
"\n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```\n",
"\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"We will cover advanced annotation methods in later modules.\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI=df_can.loc[[\"India\", \"China\"], years]\n",
"df_CI.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" df_CI = df_can.loc[['India', 'China'], years]\n",
" df_CI.head()\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.plot(kind='line')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" df_CI.plot(kind='line')\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that _pandas_ plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>India</th>\n",
" <th>China</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8880</td>\n",
" <td>5123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>8670</td>\n",
" <td>6682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>8147</td>\n",
" <td>3308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>7338</td>\n",
" <td>1863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>5704</td>\n",
" <td>1527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" India China\n",
"1980 8880 5123\n",
"1981 8670 6682\n",
"1982 8147 3308\n",
"1983 7338 1863\n",
"1984 5704 1527"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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IevNadGmJhwKrShKHEEI0BFnpEBSCauFXabM6LxEKC2BHsocCq0oShxBCNAA6K6PS3YZNr34QHIalAXWSS+IQQoiGIDPdNhS3ImV4oYaNhV3b0DnZHgisKkkcQgjhYdp8CnJNUGFEVUVq2PmgLehNa9wcWfW83XWiEydO8Morr3D06FGUUtxxxx1ER0czb948srKyCA8P57777iMwMBCAlStXsmbNGgzDYPLkycTHxwOQmprKokWLMJvN9O/fn8mTJ6OUctfLEEII58s6XUK9uqYqTg/R7XYOesNq9F+u8vhnntvuON58803i4+OZP38+L7zwAjExMaxatYo+ffqwcOFC+vTpw6pVqwBIS0tj48aNzJ07l5kzZ7J06VIsp6tEvv7660ybNo2FCxeSkZFBSkqKu16CEEK4RtYxoPIcjjOp8xKtI69+3eOuqGrklsRx8uRJfvnlF8aOHQuAt7c3AQEBJCcnM2rUKABGjRpFcrJ11EBycjLDhg3Dx8eHiIgIoqKiOHDgADk5ORQVFdGtWzeUUowcOdK2jxBCNFY68/QcjtoSx7nDwM+/Qcwkd0tTVWZmJq1bt2bx4sX89ttvdO7cmUmTJpGXl0dwcDAAwcHB5OfnA2Aymejatatt/5CQEEwmE15eXoSGhtq2h4aGYjKZqj3n6tWrWb3aeoGfffZZwsLC6hS7t7d3nff1JInbvSRu92qscUP1secX5FAc0IrwDp1q3Td/xAUUrf+akLsfwvAPcGWYtXJL4igrK+PQoUPccsstdO3alTfffNPWLFUdrbVD26uTmJhIYmKi7ffjx4/bvW9FYWFhdd7XkyRu95K43auxxg3Vx1525DCERZ71Nelzh8M3H3P8y48wRlzowiitoqOjq93ulqaq0NBQQkNDbXcRQ4YM4dChQwQFBZGTkwNATk4OrVu3tj0/O/vPYWcmk4mQkJAq27OzswkJCXHHSxBCCNfJSq+1f8Omc3do287jzVVuSRxt2rQhNDSUY8esHUA7d+4kNjaWhIQE1q1bB8C6desYOHAgAAkJCWzcuJGSkhIyMzNJT08nLi6O4OBg/P392b9/P1pr1q9fT0JCgjteghBCuIQuLYXszBpHVFWklLJ2kh/ci05Pc0N01XPbcNxbbrmFhQsXUlpaSkREBHfeeSdaa+bNm8eaNWsICwtjxowZALRr146hQ4cyY8YMDMNgypQpGIY1x916660sXrwYs9lMfHw8/fv3d9dLEEII5zNlgcVSa8d4RWroaPSH/0ZvWI26apJrY6spBu1Ix0EjVn6346jG2pYqcbuXxO1ejTVuqBq73r0dy/w5GPc/jep2jl3HKFvwKGRn4fX4IhdFaeXRPg4hhBDV07aquPbdcQComA6QlY62lLkoqtpJ4hBCCE/KSgdfXwgKtn+fiGgoLYWc6qcjuJokDiGE8CCdlQFhUSjD/o9j2wiszLo1wdeXJA4hhPCkzHS7RlRVcjpx2Jq53EwShxBCeIi2WOB4hn1zOCpqEwo+vn+uGuhmkjiEEMJT8nLAbHaoYxywNmuFR8kdhxBCNDtZ1g9+h+84wNpcJX0cQgjRvOis01VxHe3j4HSyycqwNne5mV2J49NPP+Xw4cMA7N+/nzvuuIO7776b/fv3uzI2IYRo2jLTwTAgJNzxfcPbQonZunKgm9mVOD777DMiIiIAeOeddxg/fjwTJkzgrbfecmVsQgjRtGVlQGgEytvx6k8q8vSs7iz393PYlThOnjxJy5YtKSoq4vDhw/zlL39h7NixdS7jIYQQ4vRwWgc7xm3Kh+T+4f7PYbvSXGhoKPv27ePo0aP07NkTwzA4efKkrfCgEEKIOshKR3XqevbnVSc4FLy9PTIk167EccMNNzB37ly8vb35xz/+AcC2bduIi4tzaXBCCNFU6RMFcPJEne84lOEFYVFoDzRV2ZU4BgwYwKuvvlpp25AhQxg6dKhLghJCiCYvs3woruMjqmwio8EDTVV2tTVNnjy5yjZvb2+mTZvm9ICEEKI5qEtV3DOp8LbWKrluXh3DrsRRVla1dG9paSkWD4wfFkKIJqF8DkdYPe44ItpaZ57nuXdIbq1NVbNnz0YpRUlJCXPmzKn0WHZ2Nt26dXNpcEII0WRlpkObEFSLFnU+hIpoi7YdK9RpoZ1NrYlj7NixABw4cIAxY8bYtiulCAoK4pxz7FutSgghRGU6K8Pu5WJrVGFIrr2rBzpDrYlj9OjRAHTt2pWYmBh3xCOEEM1DVgbqnP71O0ZIOHh5u30SoF2jqmJiYtixYweHDx+muLi40mMTJ050SWBCCNFU6VPF1n6JenSMAygvLwiLdHuVXLsSx9KlS9m0aRO9e/emRT3a44QQQvBnx3h9m6rKj9EQE8eGDRt4/vnnCQsLc3U8QgjR9JXP4ahDVdwzqYi26P270FqjlKr38exh13DcVq1aERAQ4OpYhBCiWfiznLqT7jhOFUN+bv2PZSe7Esf48eNZuHAh+/fv548//qj0RwghhIOy0iGgFSogsN6Hsi0C5cbmKruaqpYsWQJY61Od6b333nNuREII0cRZq+LWv5kK+HNIbmY6qmsv5xzzLOxKHJIchBDCibIyUJ2cNIE6NBK8vNy6jKzURRdCCDfSJSWQneWcEVWcHpIbGtHwmqrKysr46quv2LNnDwUFBZUee+yxx1wSmBBCNEVlWRmgLc7pGC8X0datcznsuuP497//zerVq+nVqxepqakMHjyYvLw8evfu7er4hBCiSSnL+B1wzlDccu6ukmvXHcePP/7IU089RVhYGO+//z4XX3wx/fr147XXXrP7RHfddRd+fn4YhoGXlxfPPvsshYWFzJs3j6ysLMLDw7nvvvsIDLSOMli5ciVr1qzBMAwmT55MfHw8AKmpqSxatAiz2Uz//v2ZPHmy28YuCyFEfZUnDmc1VQHWdTmKTkJBHrRu47zj1sCuxGE2mwkNtVZe9PX15dSpU8TExHD48GGHTjZnzhxat25t+33VqlX06dOHK664glWrVrFq1SpuuOEG0tLS2LhxI3PnziUnJ4cnnniCBQsWYBgGr7/+OtOmTaNr164888wzpKSk0L9/Peu9CCGEm5RlpIFvCwgKdtoxK1XJdUPisKupKiYmhoMHDwLQuXNnPvjgA/73v/8REhJSr5MnJyczatQoAEaNGkVycrJt+7Bhw/Dx8SEiIoKoqCgOHDhATk4ORUVFdOvWDaUUI0eOtO0jhBCNQWnG7xAe5dyWkvA/h+S6g113HJMmTcLLywuAm2++mSVLllBUVMTUqVMdOtlTTz0FwAUXXEBiYiJ5eXkEB1uzbnBwMPn5+QCYTCa6dv1zAfeQkBBMJhNeXl62Ox+A0NBQTKbqFzBZvXo1q1evBuDZZ5+tc7kUb2/vRllqReJ2L4nbvRpr3ADZGb/TIrYDbZwYvw4KItMwaFmYR6AbrstZE4fFYuHIkSOMGDECgLZt2/LII484fKInnniCkJAQ8vLyePLJJ4mOjq7xuTV18DjS8ZOYmEhiYqLt9+PHj9sfbAVhYWF13teTJG73krjdq7HGrS0WLBm/U9azn/PjD43g5OEDFDvxuDV9Tp+1qcowDJYtW4aPj0+9Aihv1goKCmLgwIEcOHCAoKAgcnJyAMjJybH1f4SGhpKdnW3b12QyERISUmV7dnZ2vZvLhBDCbXJNUGJ27lDccuHuG5JrVx/Hueeey08//VTnkxQXF1NUVGT7+eeff6Z9+/YkJCSwbt06ANatW8fAgQMBSEhIYOPGjZSUlJCZmUl6ejpxcXEEBwfj7+/P/v370Vqzfv16EhIS6hyXEEK41ekFl1SE84billOny6u7Y0iuXX0cJSUlzJ07l27duhEaGlqpU+fuu+8+6/55eXm8+OKLgHUy4fDhw4mPj6dLly7MmzePNWvWEBYWxowZMwBo164dQ4cOZcaMGRiGwZQpUzAMa4679dZbWbx4MWazmfj4eBlRJYRoNGx3BK6444hoC0Un4EQBBLY++/Prwa7E0a5dO9q1a1fnk0RGRvLCCy9U2d6qVStmz55d7T4TJkxgwoQJVbZ36dKFpKSkOscihBAek5VhrSsVEu70Q6uIaOuQ3D+ONYzE8be//c2lQQghRLOQmY5XRFtr8nC28iq5WemoLj2cf/wK7Eocu3btqn5nb29CQ0MJD3d+9hRCiKZGZ2XgFRVDmSsOHhYJSrml2KFdiePll1+2jX5q1aqVrdBhUFAQubm5tG/fnnvvvZe2bV3QbieEEE2A1hqy0vHqHe+SxKF8fKxNYA0lcYwdO5aTJ08yceJEfH19MZvNvP/++7Rs2ZKLL76YZcuWsWTJkjrN7xBCiGahsACKTuIVFeO6c7ipSq5dw3E///xzrrvuOnx9fQFrvaprrrmGzz77DD8/P2666SZSU1NdGqgQQjRqOdaJeV7hkS47RfmQXFezK3H4+fnZalWVS01NpUWLFtaDGLIelBBC1Crf2txvtAk9yxPrIaItnChAnyg4+3Prwa6mqquvvponn3yShIQE2+ztrVu3cssttwCwc+dOBg8e7NJAhRCiMdP5uQAYbVxX7eLPKrkZ0KmVy85jV+IYNWoUXbp0YfPmzeTk5BAdHc2ECROIjY0FrDPLzz33XJcFKYQQjV7FxHHipGvOEWGtLaUzj6E6dT3Lk+vOrsQBEBsby1VXXeWyQIQQoknLywXfFhj+LV2XOMKj3DIkt8bE8eqrrzJt2jQAXnrppRprx9tTckQIIZq9/FyXL7KkfHwhONRziSMiIsL2c1SU8wtyCSFEc6ILcp266l+NwtuiszyUOK688krbz1JyRAgh6ikvx9YH4UoqMhq9bZNLz2F3H0dmZiZHjhyhuLi40vbhw4c7PSghhGhy8nNRXXu5/jwRbaEwH32yENUy0CWnsCtxrFy5khUrVtCuXTvbJEAApZQkDiGEOAtdWgqF+S7v4wBQ4aeH5GZlQIc4l5zDrsTx6aef8txzz9mG3wohhHBAYZ71bzckDluV3Mx0lIsSh11TvgMDA6UCrhBC1NXpORyqtXs6xwHruhwuYtcdx6RJk3j11Ve55JJLCAoKqvRYWFiYSwITQogmIy/X+rc7mqpatIA2rh2Sa1fiKC0t5eeff2bDhg1VHnvvvfecHpQQQjQl5eVG3NJUBdYquS4ckmtX4liyZAnXXnst5513XqXOcSGEEHZwc+JQEW3RPye77Ph2JQ6LxcKYMWOkCq4QQtRFfg608EP5+bvnfBHRkJ+LLjqJ8m/p9MPblQkuvfRSVq1aZV3BSgghhGPcUG6kInV6ZBUuaq6y647jiy++IDc3l5UrVxIYWHlCycsvv+ySwIQQoqnQbk4c5UNyyUyH9l2cfni7Esff//53p59YCCGajbwccOWSsWcKt9YX1JnpVF+etn7sShy9erlhmrwQQjRVBbmobr3ddjrl5w9BIZDpmrkcdiWOsrIyNmzYwKFDh6rUqiovvS6EEKIqa7mRAnDH5L+KIqLQLprLYVfieOmllzhy5Ajx8fFVJgAKIYSoRYEby41UoCLaondtd8mx7UocKSkpvPzyy/j7u2komRBCNBW2ciNt3Hve8LaQ9y36VDGqhZ9TD21X4oiNjaWwsFAShxBCOCo/x/q3OxZxqkANGYPq3R+8fZx+bLtHVb3yyiv069evSlPVqFGjnB6UEEI0FW4vN3KaCg2HUNcUp7UrcXz33Xfs3buXEydOVFmPw5HEYbFYeOihhwgJCeGhhx6isLCQefPmkZWVRXh4OPfdd59tnsjKlStZs2YNhmEwefJk4uPjAUhNTWXRokWYzWb69+/P5MmTa1wPXQghPM5DicOV7Eocn3/+uVPW4/j888+JiYmhqKgIgFWrVtGnTx+uuOIKVq1axapVq7jhhhtIS0tj48aNzJ07l5ycHJ544gkWLFiAYRi8/vrrTJs2ja5du/LMM8+QkpJC//796xWXEEK4TF4OtPB3ej+DJ9lVcqRNmzb1Lp+enZ3Ntm3bOP/8823bkpOTbXcso0aNIjk52bZ92LBh+Pj4EBERQVRUFAcOHCAnJ4eioiK6deuGUoqRI0fa9hFCiAYpPxdaN63RqHbdcVxyySUsXLiQK664okofR2RkpF0neuutt7jhhhtsdxsAeXl5BAdbO4yCg4PJz88HwGQy0bVrV9vzQkJCMJlMeHl5ERoaatseGhqKyWSq9nyrV69m9erVADz77LN1Tnze3t6Ncs0Ridu9JG73akxxm4pOQGgEIafjbUyx18SuxLF06VIAtm7dWuUxe9bj2Lp1K0FBQXTu3Jndu3ef9fk1FVN0pMhiYmIiiYmJtt+PHz9u974VhYWF1XlfT5K43Uvidq/GFHdZdha0jbXF25hij46Orna7XYmjvos17du3j59++ont27djNpspKipi4cKFBAUFkZOTQ3BwMDk5ObRu3Rqw3klkZ2fb9jeZTISEhFTZnp2dTUhISL1iE0IIl8rPRXU/x9NROJVbFti47rrreOWVV1i0aBH33nsv55xzDtOnTychIYF169YBsG7dOgYOHAhAQkICGzdupKSkhMzMTNLT04mLiyM4OBh/f3/279+P1pr169eTkJDgjpcghBAO06UlcMID5UZcrMY7jqeeeoqZM2cCMHv27BqHvD722GN1PvkVV1zBvHnzWLNmDWFhYcyYMQOAdu3aMXToUGbMmIFhGEyZMsW2iNStt97K4sWLMZvNxMfHy4gqIUTDle+ZciOuVmPiqDg/Y+zYsU47Ye/evend21olslWrVsyePbva502YMIEJEyZU2d6lSxeSkpKcFo8QQrhMQS7ggXIjLlZj4hg+fLjt59GjR7sjFiGEaFqa4OQ/cFMfhxBCNEc673SdKkkcQggh7GK742haneOSOIQQwlXyc8HPH9WihacjcaoaE0f5iCqADz74wC3BCCFEk5Kf2+SaqaCWxHHs2DHMZjMAn376qdsCamz0to1Y3lqAPlHg6VCEEA2MbqKJo8ZRVQMHDuSee+4hIiICs9nMnDlzqn1efeZxNAWWTd9Bymb0vl0Yd/4fql0nT4ck7KRT90FoBMrNC+yIZiQvB6LbezoKp6sxcdx5553s3buXzMxMDhw4wJgxY9wZV+NhyoS27aDoJJZn70fdPB1j0EhPRyXOQpeVYZn7CGroGNT1d3g6HNFU5eeievT1dBROV2utqh49etCjRw9KS0tlLkdNTFmoc89DXXotlleeQ7/+IpbfDqIm3ITy8vJ0dKImGWlwqhh97IinIxFNlC4pgZOFENTG06E4nV1FDseOHcuuXbtYv369rSjhyJEjOeecplW4y1H6VDEUFtiaO4x/PIF+fyn665Xoo6kYU+9HBbb2dJiiGvq3g9Yf0tM8G4houk7PGm+KfRx2Dcf99ttvmT9/Pm3atGHQoEEEBwezYMEC23oXzVZ2pvXvEOu6vsrbB+O621GTpsOve7A8OQN9JNWDAYoaHT3971KQJwMbhGucnsPR1MqNgJ13HB9//DGzZs2iY8eOtm3Dhg0jKSmp0poXzY4pCzi9KHwFxnmJ6OgOWF5+BstzD6Bu+jvGYPvXZheup48cBMMAi8V61xHX09MhiaamiU7+AzvvOAoKCqqsNx4dHU1hYaFLgmosdLY1cRASUeUx1akrxqwk6NgVvSQJywdvOrQQlXAdbbHA0UPQs5/19wxprhLO11TLjYCdiaNHjx4sW7aMU6dOAVBcXMzy5cvp1q2bS4Nr8LIzwcsL2lT/jUK1Dsa47wnUyIvQX6+E/Wdf/VC4wfEMKDqJ6j8UvH2sHeVCOFsTLXAIdjZV3XbbbcyfP59JkyYRGBhIYWEh3bp145577nF1fA1bdha0CUUZNY+eUt7ecPUU9JZ16A2rm9xKYI3S6X4n1TEOHRmNzvjdwwGJJqkgD/xbonybVrkRsDNxBAcH89hjj5GdnW0bVRUaGurq2Bo8bcqC0KrNVGdSLVqgBo5A/7gOfd1UlF9LN0QnaqKPHLTeKUZ3QEXFoo8e8nRIoinKy4FWbTwdhUs4VOQwNDSUuLg4SRrlTJlVOsZros5LBPMpdPIPLg5KnI0+kgpt26N8fKBtLBzPsI65F8KJmmq5EZDquHWmS0shx2QbintWnbtDVCx647euDUzUSmsNR1JRHTpbN0TFWkdWZaV7NjDR9OTnNsnJfyCJo+7yTKAtdjVVASilUOedDwd+kVE8npRrsrY9t+sCgIo6PVpQ/k2Es+XnNMk5HGBH4rBYLOzatYvS0lJ3xNN4nJ78Z29TFYAaMgYMQ+46POmIdca47Y4jMhoALTPIhRNZy42caL5NVYZh8Pzzz+PtbVc/erNR2xyOmqg2IXDOueiNa9FlZS6KTNRGH0kFpSDWWsVY+flDSBjIyCrhTE148h/Y2VTVs2dP9u/f7+pYGhdTeeIIc2g347xEazPXnu0uCEqcjT5yECKjrQmjXFSsNB8K52rC5UbAzuG44eHhPPPMMyQkJBAaGopSyvbYxIkTXRZcg5adCa2CHB+j3TcBAltj+WE1Xn0SXBObqNmRg6i4XpU2qdODFrTWld7bQtRZ+R1HE13rxa7EYTabGThwIAAmk8mlATUWOtu+ORxnUt4+qCFj0Gs/Qxfko1pJ9Vx30QX5YDoO7TtXfiAqFoqLrHeCbWSouag/nd90y42AnYnjzjvvdHUcjY8pE6I71GlXdd756NUfoX/8DpV4mZMDEzU6erpjvH2XSptVVAwarMUOJXEIZ2jC5UbAgeG4aWlprFixgqVLlwLWNcl/++03lwXWkGmtrQs4OTCiqiIV2xE6xKE3rJbCh25kK3F/5h1HW+uQXCk9IpwmPxf8A1A+vp6OxCXsShybNm1izpw5mEwm1q9fD0BRURHLli1zaXANVmE+mM11aqoqp85LhLTDtrpJwg2OpFoX3QpoVXl7UAj4+ctcDuE8eTlN9m4D7Ewc77//Po888ghTp07FMKy7dOjQgcOHD7sytoarfA6HvbPGq6EGjQRvH/SGZr4YlhvpI6nQrnOV7UopGVklnEoX5ELrIE+H4TJ2JY68vDw6dKjcnq+Uar4jUMqH4taxqQpABQSi+g+xFj4sMTspMFETXXwS/vj9z4l/Z1BRsXLHIZwnPxfVROdwgJ2d4507d2b9+vWMGvXnKnYbNmwgLi7OrpOYzWbmzJlDaWkpZWVlDBkyhKuvvprCwkLmzZtHVlYW4eHh3HfffQQGBgKwcuVK1qxZg2EYTJ48mfj4eABSU1NZtGgRZrOZ/v37M3nyZLcnMNvkv3o0VQGo4Yno5O/RKVtQA4c7ITJRo6OHAVDtulT/eFQMbF6LLi6qPMdDiLrIy4WebTwdhcvYdccxefJk3n33XebMmcOpU6d46qmneO+997j55pvtOomPjw9z5szhhRde4PnnnyclJYX9+/ezatUq+vTpw8KFC+nTpw+rVq0CrB3xGzduZO7cucycOZOlS5disVgAeP3115k2bRoLFy4kIyODlJSUOr3wesnOhBb+0DKwfsfp0RdCwtAbvnFOXKJG+nSpEWq64zjdQc4fx9wUkWiqdIkZippuuRGwM3HExMQwf/58xo0bxzXXXMPo0aNJSkqibdu2dp1EKYWfnx8AZWVllJWVoZQiOTnZdhczatQokpOTAUhOTmbYsGH4+PgQERFBVFQUBw4cICcnh6KiIrp164ZSipEjR9r2cSednQUhYfW+01GGF2roWNiTYl3bQ7jOkVRoFWTtCK9OVPnIKmmuEvWUn2f9u4lO/gM7m6oAWrRoQY8ePTCZTISEhNgSgb0sFgsPPvggGRkZjBs3jq5du5KXl0dwsPXiBgcHk5+fD1gnGXbt2tW2b0hICCaTCS8vr0prgYSGhtY4IXH16tWsXm3teH722WcJC3OsNEg5b2/vKvtm5+dgtI0luI7HrKj0kqvI/ux9/Hf8SODfJtX7eOWqi7sxcFXc2cd+w4jrSXB49f1SOqg1mYYXLfNMBNbh/HK93ashx11iysQEBMW2p0U1MTbk2O1lV+I4fvw4Cxcu5NdffyUgIIATJ04QFxfH9OnTCa/hP+KZDMPghRde4MSJE7z44oscOXKkxufWNLfBkTkPiYmJJCYmVnoNdREWFlZl37LMY6h2nep8zEp8/KDbOZz45mOKRl/itP6a6uJuDFwRty4xYzl6CNUzvvZjh0VyMnU/xXU4v1xv92rIcevT/Wn5GKhqYmzIsZ8pOjq62u12NVUtWrSIzp078+abb7JkyRLefPNNunTpwqJFixwOJCAggF69epGSkkJQUBA5Odap+Tk5ObRubS2/ERoaSnZ2tm2f8rucM7dnZ2cTElJD04OL6FPFUFhg/wJOdlDnJUJWBvy622nHFBX8/huUlaE61NAxXq6tDMkV9afzysuNNN2mKrsSR2pqKjfccIOtecrPz48bbriB1FT7Jq/l5+dz4sQJwDrCaufOncTExJCQkMC6desAWLduna0eVkJCAhs3bqSkpITMzEzS09OJi4sjODgYf39/9u/fj9aa9evXk5Dg5kKBJueMqKpInTsM/PzRG2SdDlf4c8Z47YlDRcXAH8fQFil5L+rBVm6k6c7jsKupqmvXrhw4cIAePXrYth08eJBu3brZdZKcnBwWLVqExWJBa83QoUM599xz6datG/PmzWPNmjWEhYUxY8YMANq1a8fQoUOZMWMGhmEwZcoU28TDW2+9lcWLF2M2m4mPj6d///6Ovub6qcMCTmejWvihBo6wzum49jaUX0unHVtgXbzJPwDCImt/XlQslJZAdhaER7knNtH0NPFyI1BL4njvvfdsP0dGRvLMM88wYMAAW3PR9u3bGT7cvrkHHTp04Pnnn6+yvVWrVsyePbvafSZMmMCECROqbO/SpQtJSUl2ndcV6rKAkz3U0LHo779G79wmczrOQlssKMP+VY/1kVRo3/ms/UcqKtZa7DAjTRKHqDOdn9Nk1xovV+P/vuzsbNufkpISBg8ejI+PD/n5+fj4+DBo0CDM5mY449mUBV5e0MbJ7Zcdu4JhWOtXiRpZkr/H8sAtdhck1GVlkHYYVU2pkSqiYqz7yDKyoj7yc5v0HA6o5Y5DSqnXIDsT2oSiDC+nHlb5+EBEW/SxmkebNXfafAr9wZuQZ8Ky4k287p519p0y0qDEXOPEv4pUYGvrXA/pIBf1kZ+HatfJ01G4lN3zOE6dOkVGRgbFxcWVtnfv3t3pQTVkdV3AyS7R7SGteZaqt4de8ynkHIf4IZCyGf3LDlTPfrXvc7pjvMZSI2eKipGRVaJ+8nOgtZv7Xt3MrsSxbt063njjDby9vfH1rdzh8/LLL7sksAbLlInq1sclh1bR7dHbf0SXmJt0x1pd6BMF6C9WQJ8EjKn/xPLInVjefwPjkbm13/0dOQi+vrZmqLNRUbHolB+dFLVobqzlRk4236aqit5++23+8Y9/0LdvX1fH06DpsjLIMdWrKm6tojuAtlhXojtzsaFmTn+xAopOYky4EeXji/rrzejXXkBv+BY14sKa9zuSCjEdUV52Ni1GxULB1+jCfGvTlRCOaOIr/5Wza2iKt7c3vXr1cnUsDV9utvWD3UVNVSq6PYD0c5xBm7LQ336KGjIGFWttO1YJw6FLD/RH/7GWTK9uP4sFjqaefeJfBbZih7IaoKiL05P/mnJJdbAzcUycOJFly5bZakk1W05YwKlWkW2tI7aOST9HRfqj/wKgLr/etk0phXH1FMjLQX/xYfU7Hv/D2mxgz4iqclLsUNRHM7njsKupKjo6mvfff5+vvvqqymMV53s0ddoJCzjVRnn7QEQ0+thRlxy/MdJph9Gb1qAuuLzKpEvVuTtq0Cj0N6vQI8dVnZR5upS6I3cchIaDt4+MrBJ1ossTRxOfx2FX4njppZcYOXIkw4YNq9I53qzYJv+56I6D0x3k5WtHCCwrl4NfS9Rfrqr2cTXhJvT2TegPl6Fu+0elx/SRVOsdXHSHavet9niGF0RG2z1PRIhK8k/XqWrVxqNhuJpdiaOwsJCJEyc236Viy2VnQqsglG8L150juj1s24g+dQrVwoXnaQT0/l3wczJqws01dlSr0HDUBVegP38fff54VOc/h4frIwehbXvrHBkHqKhY9NFD9YpdNFP5udAywOH3XGNjVx/H6NGjWb9+vatjafCsCzi57m4DQMW0B60ho3k3V2mtsfzv39bJluePr/W56i9/haBgLO8vtZXe11rDkdQa1xivVdtYOJ6BLimpS+iiGdP5uU26Km45u+44Dhw4wJdffsmHH35ImzZtKj322GOPuSKuhsmUZb0jcKXTzSr69yOoDvat6d4kbd8EqftQN9191js85eePuvx69LJ/oX/6ATVwBOSaoCAP7J34V1FULFgskJXu+n9v0bTk5Tb5jnGwM3Gcf/75nH/++a6OpUHTWlsn//U517UnimgL3t7QjIfk6rIyLB8uh7btUMPse9+p885Hr/0M/b9/o+MHW5eKhTrdcVQqdiiJo9nTp05B8UmUPUvB5ueimsEcLLsSx+jRo10cRiNQmA9ms+ubqry8IDKmWc/l0Bu+gT9+x7hrpt0T95ThhfG3W7DMfQT9zUdQVgZKQWwdagZFWlc90+lpNPNePQHoFW+gt6zHeGLx2edn5Oc06bXGy9mVONasWVPjY2PHjnVaMA3a6aG4ylV1qipQ0e3Rqftcfp6GSBcXoT9+F+J6Qr9BDu2revaDfoPQn6+A2A4QGY3y83c4BuXnD8FhMgnwNK11sx0Yo7VG/5wMJ09YR+5Nuqfm55pPQXGRtVBmE2dX4vj+++8r/Z6bm0tGRgY9evRoPonj9OQ/l5UbqSi6PSR/jy4uqtMHX2N24pP3IM+EcfsDdfqwMq6ajOXRu+HgXmtfR11JsUMAdG42lmcfRI27EmPMJZ4Ox/3++B1MxyE8Cr3hW/TIiyqN3KvENodD7jgAmDNnTpVta9as4fffm883MtsCTu664wBIPwqd7FtlsSnQBfmcXPk2xA9GxdWtxI2KikGNvhj97SfgyMS/KseJRW9a06y/bQPo7ZshOxP931extPDHGNZMviiepnenAGDc+TCW+Y9heec1jIdfqH4hMVu5kTbuC9BD7C6rfqbRo0czZcoUbrzxRmfG03CZsqCFH7QMdP25KtSsUk0kcVjemIfetc061Fhra80vrcFS8ecy0Brjyvq9p9Sl10JBHqr/0LofpG2stdkhzwRtQusVT2OmU7ZYB2yERqDfWoj280cNqMd1bWT0nu0QHoWK7YS66mb00nnoDaurL6xZkGv9WxKHlcViqfS72Wxm/fr1BAQEuCSohkhnZ0JIuHu+fUZEWcteNJEOcn0kFb1pLfSKR0VEWzutDcP6t1KgTv9sKIL6D6agniOZVEAg6rZ/1u8Y5SOr0tOabeLQRSdh307U+eNRl16LZd5sLK+/gPH3R1C9mvZ6EwC6tAT27UINHQ2AGjwave4r9IfL0AOGoQIqf4m0lRuReRxW1157bZVtISEhTJs2zekBNViuXMDpDMrwgraxTWZklV79EbTww5j2AOosd2wtwsIoOH7cTZHVwlbs8PezLhbVZO3ZDmWlqH6DUH7+GNPnYHnhYSyLnsaY8QSqSw9PR+haqfvgVJEtSSqlMK6diuXJGeiP/4u6dmrl5+flWv9uLZ3jAPzrX/+q9HuLFi1o3bqZrVVgykR17Oq206no9uj9u912PlfRuSb0lu9Roy46a9JoUNqEQAv/Zl3sUO/YAgGtoEtPwHonZ9z3OJbnH8Ky8DGMfz7dpJdI1btTrHfG3f9cuE2174wadRF67efoERfYyvwDp8uNBFqLlTZxdpUcCQ8Pr/SnuSUNfaoYCgvcM6KqXHR7yDmOPnnCfed0Ab32c7CUoc6/1NOhOEQp1axHVmlLGXrnT6g+51aaS6OCgjFmPAEt/LHMm43+45gHo3Qt/UsKdOqGalm5SV5dcT0EBGB55zVbiRs43VTVDEZUwVnuOM5WTkQpxezZs50aUINkct+IqnKVRlY10iYBfeoUet0X0G8wKqKtp8NxmGobay202Bwd3AeFBahq5tKo0AjrnccLD2OZ+wjGA89WLWnfyOkTBXD4V9T4a6o8pgJaoa64Ef32YnTy96hBI60P5Oc0i45xOEviGDGi+nHwJpOJL774glOnTrkkqAbH1Qs4VafiyKrGmjg2r4UTBRgXXObpUOomKhY2f9cs59PoHT+Clzf0HlDt46ptLMa9j2J5cSaWebMxHnimaQ1D/WUHaI3qFV/tw2rEBej1X6E/eBPdd6D1/ZGf22zqy9XaVDV27NhKfwYOHEhaWhqffPIJgwYNYsGCBe6K06NcvYBTtcIiwde30Y6s0haLtVO8Qxx07e3pcOpEne4gpwk3x9RE79gC3c9B+bes8TmqfReMv8+GnCws8+egTxa6MULX0ntSwL9ljfOolOGFcd00yM1Gf/6+dWN+brO547Crj+PkyZO8++67TJ8+nby8PJ577jmmTZtGaGgzGaaYnWVdEKhNiNtOqQwD2rZvvCOrdm+DjN9RiZc13gl0zXQZWZ3xu/Xfru/ZS76orr0w7vg/OHYUy/xHrUN4GzmttTVx9Ohba6001aUHaugY9NcfWddvKS6SxAHW+RorV67k73//O7///juPP/44f//734mKinJXfA1DdqZ1XQjDvoJ7zqKi23n0jkOfKMDy2fvoE45/k7R885H1miWc54LI3CSirXWOSXNLHD9vAUD1G2jX89U5AzCmPQBHDlrvPBp78vjjGGRn1thMVZH66yTw8cHyxnzrBkkccNddd/Hpp59y2WWXMW7cOPLy8ti1a1elP82Bzs5ybzNVuej2kGuq0we3M+h3XkOvehvLkhfRljL790s7BL/sQI29pFEPTVQ+PhAeif7tILrM/tff2OkdyRDbERUWafc+qv8QjKkPwG8HsCxo3Hce+pcUALsmOaqgYGulgrRDtt+bg1o7x8vXF//666+rfVwpVWWOR5NkykJ1O8ftp7WNrDp2BLrWrXZTXemdW9E/rrNWqd21Db3qP6gJN9m37zcfg28L1MhxLo7S9VT3Pujvv8bywGTUwBGowaOgY9fG2/x2FvpEARzYg7qo+jXea6MGDMWYej+W117AsuBRjHsfRfnV3EfSUOnd2yEs0u6RgGrsePQP31hHQDaTO45aE8eiRYuccpLjx4+zaNEicnNzUUqRmJjIxRdfTGFhIfPmzSMrK4vw8HDuu+8+AgOtk8RWrlzJmjVrMAyDyZMnEx8fD0BqaiqLFi3CbDbTv39/Jk+e7NL/xLqsFHKzPXfHwemRVW5MHLq4CMvbi6FtO4wZT6LffQ39xQp0+86ohOG175uXg96yDjX8QlRAKzdF7Drq2mmocwZg+XEdet0X1uKJEW1Rg0ahBo/8swO9idA7t4LFgop3rKR9OTVgGMZt92N57Xks8xtf8tClpdYyK4NG2b2P8vbGuOkuLCvfhsgYF0bXcNjVOV5fXl5e3HjjjcybN4+nnnqKr776irS0NFatWkWfPn1YuHAhffr0YdWqVQCkpaWxceNG5s6dy8yZM1m6dKmtXtbrr7/OtGnTWLhwIRkZGaSkpLg0dkt2lnUZUXcOxS0XEm4trOjmfg696m3IOY5x090oHx/UNVOhSw8sby5Apx2ufd/vPoeyxjfhrybKxwc1YBhedzyMkbQMdfPfrQX/PnsPyyN3UvbkDCxfr6KsvHpyY7dji3USWz2Glapzh1mbrQ7tx7LgMXRxI2q2OrQfiotQveMd2k3F9cLr/qebzbBttySO4OBgOne2Lqfo7+9PTEwMJpOJ5ORkRo2yZvZRo0aRnJwMQHJyMsOGDcPHx4eIiAiioqI4cOAAOTk5FBUV0a1bN5RSjBw50raPq5Qd/wNwzwJOZ7KOrGrn1pFV+uBe9JpPUaP/goo7XWrCxwfj9ofAPwDL4qetzRnV7Ws+hf7uC+g7EBXV9L55qZaBGMMvwGvGExjPv4G6egoA+oM3OH7r5ZTNugPLsn9h2bT2zzL8jYguLUHv2orqO7D6suEOUOcOw7jtn5C6D8uCxxtN8tB7tlsHRPTo6+lQGrQ6l1Wvq8zMTA4dOkRcXBx5eXkEB1s7k4KDg8nPzwesEwy7dv2zLlRISAgmkwkvL69KQ4BDQ0MxmUzVnmf16tWsXr0agGeffZawsLA6xWv+Zbs1vrhueNfxGPWR17kb5m2bHI7f29vb4X10SQnZ/3kZIySc0Nvuw/CvUGohLAzzw8+SM+suvN9aQJtZSVWGKp78+iMKCvMJvuomfOt4reoSt0eEhUFcd7h2CqW//0bJ1o0U79pOybZN6O+/RgNGeBS+vePx6RWPb694vKLbNbi+kYrX+9SOZHKLiwgakUgLZ/wbXHQFxa1akTf3UbwWP0ObR5IwapkX4ghXvU9M+3dB156EtO/o9GOXazTv8Vq4NXEUFxeTlJTEpEmTaNmy5jdQxfov9myvTmJiIomJibbfj9ex4qrf6eVDc5Q3ygNVWy2hEehcE1mHU1GB9tcICwsLc/g1Wz59F330EMbdj2A6UQQniio/ITQKdd00zMv+Rdbr8zCummR7SGuNZdV/oV0n8iLb1fla1SVuj2sRQNhl13Ji2AUoiwV17Df0vt1Yft1F8dZNFH/3pfV5IWHWddTb132BKWereL0t678BH1/yozs5773evR/q1hmULEkia850jOlznNKc44r3iT5RiOXXX1CXXO3S92Bjeo9HR0dXu90tTVUApaWlJCUlMWLECAYPHgxAUFAQOTnWVbNycnJsxRNDQ0PJzs627WsymQgJCamyPTs7m5AQ107Ksxz/A1oFoXxbuPQ8NVHla1O4uLlKpx9Ff/a+deRQLeP3jREXWquDfvUhluQKSwrv3gbpR1EXXNHgvlW7kzIMVGwnjPPH43X7QxhJyzCeWIy68S7QYHn52Rqb+jxJa22dLd4rHtXCue91Y+AI1K3/gAN7sfzrSXRJiVOP7zR7fwZtsWv+RnPnlsShteaVV14hJiaG8ePH27YnJCSwbt06ANatW8fAgQNt2zdu3EhJSQmZmZmkp6cTFxdHcHAw/v7+7N+/H60169evJyEhwaWxl2VleKZjvFz5yKrfXZc4tMWCZdm/oIU/6prbzvp8dc1tENfTuiLcUev4dcs3H0FQCGpg7aOumhulFCoqFmPkOIw7HoLcbCxLkhyaF+MWv/9mnfTW175Jf44yBo5ATb4H9u20vm8caD1wF70nBfz8m9VyzXXllqaqffv2sX79etq3b8/9998PWBeHuuKKK5g3bx5r1qwhLCyMGTNmANCuXTuGDh3KjBkzMAyDKVOmYJzurLv11ltZvHgxZrOZ+Ph4+vd37UpkZZkZnh1iFxxmrZnjwjsOvf5LOPALavI9dhWqU97WznLLkzOwLH4aY9I9sCcFdcUNjXrCn6upTt1Q10y1VlX95D3U5dd5OiQbveP0bHEXJQ4AY+gYLKYs66i9sAhUPZcIdiatNXr3NujeB+Xt9q7fRsctV6hHjx68//771T5WU1n2CRMmMGHChCrbu3TpQlJSklPjq4nWGktWBqpnvFvOVx2llEtHVmnTcfT//g09+6GGjrU/rqBgjDsespbWnj8bfH1Roy5ySYxNiRo5Dg7tQ3/6LrpjV7vLeria3rHFOrHRxfXY1MV/g+xM9OcfYAkNxxjZQN4zWenWO65xV3o6kkbBbX0cjVJhAZhPeWbyXwUqur1L7ji01lj++wpYLBg33uVw34Tq3B11/R1QWooaMtahzvvmSimFuu52aN8Fy9K56EzPV97VeTlwaH+1a284m+31nzMA/Z9XrBMOGwC9JwWwr8yIkMRRO9PpdTg8MIejkuj2UJhvXWHMifRPG2DHFtTl16PC61a40hh+Acb9T9vmNIizU74trP0dhoFl8TNoD69ro3+2zoWq62xxRylvb2tRxJgOWF59Dv3bQbectzZ6d4p1obZGuOCYJ0jiqM3pBZw82jkOqBjnj6zSJwrQ77wKHeLqPctbdTvH6SNxmjoVFmmdIHfsCHr5vzzaWax3bLG+x2M6uu2cyq8lxvTZEBCI5aXH0eX/1zxAl5XBvp9RveKb9YhAR0jiqIVHFnCqToWaVc6iVy63rs53899rXXNAuI7q3R91+fXoH9eh13zmkRj0qVPwSwqq3yC3f2iqNqEY0x8Fs9lamsRTC0Ed2g9FJ1G9pZnKXpI4apOdZZ2s5OlifUEh0DIAnDQkV/9+BL3+a9Toi1HtOjnlmKJu1F+ugn6D0B8sRf+6x+3nN//8E5jNbunfqI6KaW9ttstMtzbblbp/joe1zIiSMiMOkMRRC52diREe5fHbV6UURDtvNUDLijfA3x916TVOOZ6oO2UYGLfcC6ER1vb+3OpL6LjKqeTvrXMXPLBsQDnVs5+1eOS+neh/v1Rts50+dco6SXXnVizffUHh+2+id/7klBpYek+KdUSZp78gNiIyYLk22Vl4hUXSEKZqqej26J82oLWuVyLTu7bCrm2ov90io6AaCNUyEOOOh7E8Yy1Hbsx40i1zCbTFwqmfNqJ6D7AuWuVBxtAxWLIz0R/9B5SBbhVkHbabnWntayzIq/T8E7YdDejUDdWjL6pHX+jSA+Xja/d59clC64iyvzi+/khzJomjFqr/EFrEtKNB1PWMbg8nv4K8nDqvfa7LyrB88CaER6HGXOLkAEV9qNiOqJvuRi9JQn/0H9Rfb3b9SX/dgyXnOKpfw5iIpy65GnKz0eu+BB9fa99iSASqfWfriKfQCOsIx7AIwmLbczx5I3rvz9Y/X6xAf/a+db+4nn8mksgYaBlQ85etvTut649ImRGHSOKohTF+Ii3DwjjZAAqSVVoNsK6J44dv4NgRjDse8vg3TFGVMXgUln070V99iO6TgOrW22Xn0ts2YXljPkabEHDhbHFHKKVQN9yJnnAT+NfyYQ8oP39rE1fPfgDokyfg1z3ovTusiWTlcmwNXl5e0Cro9J82qNZ//qz3/gwt/KFzd9e/wCZEEkdjEVNhNcA6fDvSRSetzQBde0H/oU4OTjiLunoKeu/PWN6YhzFnIcpJZcjLaUsZ+qP/oj//ADp1I+T/niengXV1qpaBddgnAPoNtM3E1wV5sH83OicL8vOgIM86D6ogD/3H71CQC2azdedzh0mpHAdJ4mgsWrWBwFZ1nsuhv1gBBXkY02d7vLNf1Ez5+WPcch+W5x9Gv7cENWm6046tC/OxvJ4Ee7ajRlyIunYaXmER0ADuqJ1NtQqyJoRanqNPFUN+bp3v4JszSRyNRH1GVunsTPQ3H6GGjEF17Hr2HYRHqbieqL9chf78fXS/Qaj+Q+p9TH3kIJbFz0CeCXXjXRgjxzkh0sZNtfCDOlZMaO4a1j2qqFV5zSpHZxnrD5eBoVBX3uCiyISzqUsnQvvOWJb9C52fU69jWTavxfLsg9aaZA88K0lD1JskjsYkuj0UnYSc7LM/9zR9cC96y3rUhVeiPFw6RdhPeftgTJkBxUVY/l23kiS6tBTLO6+hl86DTt0wZs1FyVoTwgkkcTQijq4GqLXG8sEbEBSMGle1RL1o2FR0e+uw3J+TrSPiHKBzs7EkzUKv+RR1weUYM56wa60VIewhiaMxKa9ZtXeHXSvI6Z82wMG91gWWnLDOs3A/NXY89OyHfm8JOjP9rM/XpaVYvvkIyyN3wpGDqNv+iXH1FKlHJpxKEkcjoloFQadu6K9WYnl4KpZP30XX0GylzafQ/3sLYjuhhtm/QJNoWJRhYEyaDoYXljfmWSu51kD/sgPL4/eg318KcT0xZi/AGDTSjdGK5kJGVTUyxgPPQMqPWNZ/ZR2P//G70DfB2uF5zgCUYf1mefKzDyA709pEYci3zcZMhYSjrr/dOqv8y/9ZZ1hXoLOz0B+8gd66AcIiMe6aCR6odiuaD0kcjYzy9oGE4XglDEdnpqN/+Br9w2osO7ZASBhq+IWofgM5seLf1g+P0zNrReOmBo2EHVvQn7yD7nMuqn0XdIkZ/fUq9OfvgwZ1+XXWQRC+sjaKcC1JHI2YimiLmnAz+rLrYEcylvVfoj/+L/rj/4KXF8ZVkzwdonASpRRcfzv6191YlszFuOIGLCvehKwMGDAU42+3oMIiPR2maCYkcTQBytsHzh2G17nD0FkZ6B9WE9ihEyejYj0dmnAiFdAKY9I9WObPwfLyMxAVi3HfY7JOtnA7SRxNjAqPQl15Q4MpziicS/Xuj7r+digtRY3+i9RYEh4hiUOIRsYYfbGnQxDNnAzHFUII4RBJHEIIIRwiiUMIIYRDJHEIIYRwiCQOIYQQDpHEIYQQwiGSOIQQQjhEEocQQgiHKF2XpcWEEEI0W3LHcRYPPfSQp0OoE4nbvSRu92qscUPjjr2cJA4hhBAOkcQhhBDCIZI4ziIxMdHTIdSJxO1eErd7Nda4oXHHXk46x4UQQjhE7jiEEEI4RBKHEEIIhzS7hZwWL17Mtm3bCAoKIikpCYDDhw/z+uuvU1xcTHh4ONOnT6dly5aUlpbyyiuvcOjQISwWCyNHjuTKK68EIDU1lUWLFmE2m+nfvz+TJ0+2rgvdwON+9NFHycnJwdfXF4BZs2YRFBTksrjrEvtrr73GwYMHMQyDSZMm0bt3b6DhX/Oa4nbnNT9+/DiLFi0iNzcXpRSJiYlcfPHFFBYWMm/ePLKysggPD+e+++4jMDAQgJUrV7JmzRoMw2Dy5MnEx8cD7r3ezozb3e9xR2MvKChg7ty5HDhwgNGjRzNlyhTbsdz9Hq8z3czs3r1bHzx4UM+YMcO27aGHHtK7d+/WWmv97bff6nfeeUdrrfX333+v582bp7XWuri4WN955536jz/+sO2zb98+bbFY9FNPPaW3bdvWKOKeM2eOPnDggEtjrU/sX3zxhV60aJHWWuvc3Fz9wAMP6LKyMts+DfWa1xa3O6+5yWTSBw8e1FprffLkST19+nR99OhRvXz5cr1y5UqttdYrV67Uy5cv11prffToUf3Pf/5Tm81m/ccff+i7777bI9fbmXG7+z3uaOxFRUX6l19+0V999ZVesmRJpWO5+z1eV82uqapXr162byzljh07Rs+ePQHo27cvP/74o+2x4uJiysrKMJvNeHt707JlS3JycigqKqJbt24opRg5ciTJyckNPm5PcST2tLQ0zjnnHACCgoIICAggNTW1wV/zmuJ2t+DgYDp37gyAv78/MTExmEwmkpOTGTVqFACjRo2yXbvk5GSGDRuGj48PERERREVFceDAAbdfb2fF7QmOxu7n50ePHj1sd0TlPPEer6tmlziq065dO3766ScANm/eTHZ2NgBDhgzBz8+PqVOncuedd3LppZcSGBiIyWQiNDTUtn9oaCgmk6nBx11u8eLF3H///axYsQLtoUF1NcXesWNHfvrpJ8rKysjMzCQ1NZXjx483+GteU9zlPHHNMzMzOXToEHFxceTl5REcHAxYP+jy8/MBqlzXkJAQTCaTR693feIu56n3uD2x16ShvMft0ez6OKpzxx138Oabb7JixQoSEhLw9rZelgMHDmAYBq+++ionTpxg9uzZ9OnTx2MftmdyNO7IyEimT59OSEgIRUVFJCUlsX79etu3ooYQ+5gxY0hLS+Ohhx4iPDyc7t274+Xl1eCveU1xAx655sXFxSQlJTFp0qRa7zZruq6eut71jRs8c73B/thr0lDe4/aQxAHExMQwa9YswNoUsW3bNgB++OEH4uPj8fb2JigoiO7du3Pw4EF69uxp+6YJkJ2dTUhISIOPOzIy0hanv78/w4cP58CBAx5JHDXF7uXlxaRJk2zPmzVrFm3btiUgIKBBX/Oa4gbcfs1LS0tJSkpixIgRDB48GLA2n+Xk5BAcHExOTg6tW7cGrN9qK15Xk8lESEhIle3uuN7OiBvcf70djb0mnrjmdSVNVUBeXh4AFouFDz/8kAsuuACAsLAwdu3ahdaa4uJifv31V2JiYggODsbf35/9+/ejtWb9+vUkJCQ0+LjLyspst8ulpaVs3bqVdu3auT3u2mI/deoUxcXFAPz88894eXkRGxvb4K95TXG7+5prrXnllVeIiYlh/Pjxtu0JCQmsW7cOgHXr1jFw4EDb9o0bN1JSUkJmZibp6enExcW5/Xo7K25PvMcdjb0mDeU9bo9mN3N8/vz57Nmzh4KCAoKCgrj66qspLi7mq6++AmDQoEFcd911KKUoLi5m8eLFpKWlobVmzJgxXHbZZQAcPHiQxYsXYzabiY+P55ZbbnHpsDlnxF1cXMycOXMoKyvDYrHQp08fbr75ZgzDtd8fHIk9MzOTp556CsMwCAkJ4fbbbyc8PBxo2Ne8prjdfc337t3L7Nmzad++ve3aXHvttXTt2pV58+Zx/PhxwsLCmDFjhq3f68MPP2Tt2rW2YcT9+/cH3Hu9nRW3J97jdYn9rrvu4uTJk5SWlhIQEMCsWbOIjY11+3u8rppd4hBCCFE/0lQlhBDCIZI4hBBCOEQShxBCCIdI4hBCCOEQSRxCCCEcIolDCCGEQyRxCFEPCxcuZPHixZW27dmzh1tuuYWcnBwPRSWEa0niEKIeJk+ezPbt2/n5558BMJvNvPrqq9x00022Anf1UVZWVu9jCOFsMgFQiHratGkTb7/9NklJSXz44YccPnyYq666imXLlpGWlkZ4eHilhZ3Wrl3Lxx9/THZ2Nq1bt+byyy+3lS7ZvXs3L730EhdddBGfffYZffv25eabb2bx4sXs3bsXpRTt2rXj0UcfdfmMfyFqIkUOhainoUOHsnHjRhYsWMC+fft47rnnePDBB7n77ruJj49n165dJCUlMX/+fFq3bk1QUBAPPvggkZGR/PLLLzz99NN06dLFtqZDbm4uhYWFLF68GK01K1asICQkhCVLlgDw66+/NsgyFKL5kK8sQjjBlClT2LVrF1dddRUbNmygf//+DBgwAMMw6Nu3L126dLFV0h0wYABRUVEopejVqxd9+/Zl7969tmMppbj66qvx8fHB19cXLy8vcnNzOX78ON7e3vTs2VMSh/AoueMQwgnatGlD69atiY2NZcuWLWzevJmtW7faHi8rK7M1VW3fvp0VK1Zw7NgxtNacOnWK9u3b257bunXrSqvDXXbZZXzwwQc8+eSTACQmJnLFFVe454UJUQ1JHEI4WWhoKCNGjOD222+v8lhJSQlJSUncfffdtoWgnn/++UrPOfNuwt/fn5tuuombbrqJo0eP8thjj9GlSxf69Onj0tchRE2kqUoIJxsxYgRbt24lJSUFi8WC2Wxm9+7dZGdnU1paSklJCa1bt8bLy6vSiKyabN26lYyMDLTW+Pv7YxiGdIwLj5I7DiGcLCwsjAceeIC3336bBQsWYBgGcXFx3Hbbbfj7+zN58mTmzZtHSUkJ55577lkX60lPT+eNN94gPz+fgIAALrzwQluzlxCeIMNxhRBCOETud4UQQjhEEocQQgiHSOIQQgjhEEkcQgghHCKJQwghhEMkcQghhHCIJA4hhBAOkcQhhBDCIf8PNFibEA3YzDoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.index = df_CI.index.map(int) # let's change the index values of China and India to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from China and India')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is:\n",
" df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
" df_CI.plot(kind='line')\n",
"\n",
" plt.title('Immigrants from China and India')\n",
" plt.ylabel('Number of Immigrants')\n",
" plt.xlabel('Years')\n",
"\n",
" plt.show()\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<br>From the above plot, we can observe that the China and India have very similar immigration trends through the years. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_Note_: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"\n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
"\n",
"> class 'pandas.core.series.Series' <br>\n",
"> 1980 1666 <br>\n",
"> 1981 3692 <br>\n",
"> 1982 3498 <br>\n",
"> 1983 2860 <br>\n",
"> 1984 1418 <br>\n",
"> Name: Haiti, dtype: int64 <br>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
" Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"#Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n",
" #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\n",
"inplace = True #paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"\n",
"print(df_top5)\n",
"\n",
"#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",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<details><summary>Click here for a sample python solution</summary>\n",
"\n",
"```python\n",
" #The correct answer is: \n",
" #Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n",
" #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
" \n",
" inplace = True paramemter saves the changes to the original df_can dataframe\n",
" df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"\n",
" # get the top 5 entries\n",
" df_top5 = df_can.head(5)\n",
"\n",
" # transpose the dataframe\n",
" df_top5 = df_top5[years].transpose() \n",
"\n",
" print(df_top5)\n",
"\n",
"\n",
" #Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
" df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
" df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"\n",
"\n",
"\n",
" plt.title('Immigration Trend of Top 5 Countries')\n",
" plt.ylabel('Number of Immigrants')\n",
" plt.xlabel('Years')\n",
"\n",
"\n",
" plt.show()\n",
"\n",
"```\n",
"\n",
"</details>\n"
]
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"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"- `bar` for vertical bar plots\n",
"- `barh` for horizontal bar plots\n",
"- `hist` for histogram\n",
"- `box` for boxplot\n",
"- `kde` or `density` for density plots\n",
"- `area` for area plots\n",
"- `pie` for pie plots\n",
"- `scatter` for scatter plots\n",
"- `hexbin` for hexbin plot\n"
]
},
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"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
"\n",
"### Other Contributors\n",
"\n",
"[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
"[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
"[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ------------- | ---------------------------------- |\n",
"| 2021-01-20 | 2.3 | Lakshmi Holla | Changed TOC cell markdown |\n",
"| 2020-11-20 | 2.2 | Lakshmi Holla | Changed IBM box URL |\n",
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL and info method |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved Lab to course repo in GitLab |\n",
"| | | | |\n",
"| | | | |\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
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