Week 1.6 - DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "# Data Visualization\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Create Data Visualization with Python\n",
    "*   Use various Python libraries for visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Introduction\n",
    "\n",
    "The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible.\n",
    "Speaking of consistency, because there is no *best* data visualization library available 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 visualization technique and tool for a given problem *and* audience.\n",
    "\n",
    "Please make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01).\n",
    "\n",
    "**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01).\n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1.  [Exploring Datasets with *pandas*](#0)<br>\n",
    "\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",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
    "\n",
    "*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01):\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 familiarize yourself with the *pandas* API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01).\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",
    "![Data Preview](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png)\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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01\">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": {},
   "source": [
    "The first thing we'll do is install **openpyxl** (formerly **xlrd**), a module that *pandas* requires to read Excel files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mamba install openpyxl==3.0.9 -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Next, we'll do is import two key data analysis modules: *pandas* and *numpy*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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*'s `read_excel()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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(\n",
    "    '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": 4,
   "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>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "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 view the bottom 5 rows of the dataset using the `tail()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>920</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>...</td>\n",
       "      <td>1816</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <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": 5,
     "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": 6,
   "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 data frame's `columns` instance variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([    'Type', 'Coverage',   'OdName',     'AREA', 'AreaName',      'REG',\n",
       "        'RegName',      'DEV',  'DevName',       1980,       1981,       1982,\n",
       "             1983,       1984,       1985,       1986,       1987,       1988,\n",
       "             1989,       1990,       1991,       1992,       1993,       1994,\n",
       "             1995,       1996,       1997,       1998,       1999,       2000,\n",
       "             2001,       2002,       2003,       2004,       2005,       2006,\n",
       "             2007,       2008,       2009,       2010,       2011,       2012,\n",
       "             2013],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Similarly, to get the list of indices we use the `.index` instance variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=195, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note: The default type of intance variables `index` and `columns` are **NOT** `list`.\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 '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": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Type',\n",
       " 'Coverage',\n",
       " 'OdName',\n",
       " 'AREA',\n",
       " 'AreaName',\n",
       " 'REG',\n",
       " 'RegName',\n",
       " 'DEV',\n",
       " 'DevName',\n",
       " 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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 1,\n",
       " 2,\n",
       " 3,\n",
       " 4,\n",
       " 5,\n",
       " 6,\n",
       " 7,\n",
       " 8,\n",
       " 9,\n",
       " 10,\n",
       " 11,\n",
       " 12,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 16,\n",
       " 17,\n",
       " 18,\n",
       " 19,\n",
       " 20,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 24,\n",
       " 25,\n",
       " 26,\n",
       " 27,\n",
       " 28,\n",
       " 29,\n",
       " 30,\n",
       " 31,\n",
       " 32,\n",
       " 33,\n",
       " 34,\n",
       " 35,\n",
       " 36,\n",
       " 37,\n",
       " 38,\n",
       " 39,\n",
       " 40,\n",
       " 41,\n",
       " 42,\n",
       " 43,\n",
       " 44,\n",
       " 45,\n",
       " 46,\n",
       " 47,\n",
       " 48,\n",
       " 49,\n",
       " 50,\n",
       " 51,\n",
       " 52,\n",
       " 53,\n",
       " 54,\n",
       " 55,\n",
       " 56,\n",
       " 57,\n",
       " 58,\n",
       " 59,\n",
       " 60,\n",
       " 61,\n",
       " 62,\n",
       " 63,\n",
       " 64,\n",
       " 65,\n",
       " 66,\n",
       " 67,\n",
       " 68,\n",
       " 69,\n",
       " 70,\n",
       " 71,\n",
       " 72,\n",
       " 73,\n",
       " 74,\n",
       " 75,\n",
       " 76,\n",
       " 77,\n",
       " 78,\n",
       " 79,\n",
       " 80,\n",
       " 81,\n",
       " 82,\n",
       " 83,\n",
       " 84,\n",
       " 85,\n",
       " 86,\n",
       " 87,\n",
       " 88,\n",
       " 89,\n",
       " 90,\n",
       " 91,\n",
       " 92,\n",
       " 93,\n",
       " 94,\n",
       " 95,\n",
       " 96,\n",
       " 97,\n",
       " 98,\n",
       " 99,\n",
       " 100,\n",
       " 101,\n",
       " 102,\n",
       " 103,\n",
       " 104,\n",
       " 105,\n",
       " 106,\n",
       " 107,\n",
       " 108,\n",
       " 109,\n",
       " 110,\n",
       " 111,\n",
       " 112,\n",
       " 113,\n",
       " 114,\n",
       " 115,\n",
       " 116,\n",
       " 117,\n",
       " 118,\n",
       " 119,\n",
       " 120,\n",
       " 121,\n",
       " 122,\n",
       " 123,\n",
       " 124,\n",
       " 125,\n",
       " 126,\n",
       " 127,\n",
       " 128,\n",
       " 129,\n",
       " 130,\n",
       " 131,\n",
       " 132,\n",
       " 133,\n",
       " 134,\n",
       " 135,\n",
       " 136,\n",
       " 137,\n",
       " 138,\n",
       " 139,\n",
       " 140,\n",
       " 141,\n",
       " 142,\n",
       " 143,\n",
       " 144,\n",
       " 145,\n",
       " 146,\n",
       " 147,\n",
       " 148,\n",
       " 149,\n",
       " 150,\n",
       " 151,\n",
       " 152,\n",
       " 153,\n",
       " 154,\n",
       " 155,\n",
       " 156,\n",
       " 157,\n",
       " 158,\n",
       " 159,\n",
       " 160,\n",
       " 161,\n",
       " 162,\n",
       " 163,\n",
       " 164,\n",
       " 165,\n",
       " 166,\n",
       " 167,\n",
       " 168,\n",
       " 169,\n",
       " 170,\n",
       " 171,\n",
       " 172,\n",
       " 173,\n",
       " 174,\n",
       " 175,\n",
       " 176,\n",
       " 177,\n",
       " 178,\n",
       " 179,\n",
       " 180,\n",
       " 181,\n",
       " 182,\n",
       " 183,\n",
       " 184,\n",
       " 185,\n",
       " 186,\n",
       " 187,\n",
       " 188,\n",
       " 189,\n",
       " 190,\n",
       " 191,\n",
       " 192,\n",
       " 193,\n",
       " 194]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "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` instance variable of it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 13,
     "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]`, `datetime64[ns, tz]`, `timedelta[ns]`, `category`, 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": 14,
   "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": 14,
     "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": 15,
   "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": 15,
     "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": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "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": 17,
   "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": 17,
     "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": 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>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": 18,
     "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               # returns series\n",
    "```\n",
    "\n",
    "Method 2: More robust, and can filter on multiple columns.\n",
    "\n",
    "```python\n",
    "    df['column']                  # returns series\n",
    "```\n",
    "\n",
    "```python\n",
    "    df[['column 1', 'column 2']]  # 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": 19,
   "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": 19,
     "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 ('Country') and the data for years: 1980 - 1985.\n"
   ]
  },
  {
   "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>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": 20,
     "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 2 ways to select rows:\n",
    "\n",
    "```python\n",
    "    df.loc[label]    # filters by the labels of the index/column\n",
    "    df.iloc[index]   # 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 default 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 corresponding 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": 21,
   "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": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
    "1\\. The full row data (all columns)\n",
    "2\\. For year 2013\n",
    "3\\. For years 1980 to 1985\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. the full row data (all columns)\n",
    "df_can.loc['Japan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternate methods\n",
    "df_can.iloc[87]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Continent        Region            DevName  1980  1981  1982  1983  \\\n",
       "Japan      Asia  Eastern Asia  Developed regions   701   756   598   309   \n",
       "\n",
       "       1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "Japan   246   198   248  ...  1067  1212  1250  1284  1194  1168  1265  1214   \n",
       "\n",
       "       2013  Total  \n",
       "Japan   982  27707  \n",
       "\n",
       "[1 rows x 38 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can[df_can.index == 'Japan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "982"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. for year 2013\n",
    "df_can.loc['Japan', 2013]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "982"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternate method\n",
    "# year 2013 is the last column, with a positional index of 36\n",
    "df_can.iloc[87, 36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    701\n",
       "1981    756\n",
       "1982    598\n",
       "1983    309\n",
       "1984    246\n",
       "1985    198\n",
       "Name: Japan, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. for years 1980 to 1985\n",
    "df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1985]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    701\n",
       "1981    756\n",
       "1982    598\n",
       "1983    309\n",
       "1984    246\n",
       "1985    198\n",
       "Name: Japan, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative Method\n",
    "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": 31,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df_can.columns = list(map(str, df_can.columns))\n",
    "# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "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": 33,
   "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": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. pass this condition into the dataFrame\n",
    "df_can[condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can pass multiple 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": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('data dimensions:', df_can.shape)\n",
    "print(df_can.columns)\n",
    "df_can.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "***\n",
    "\n",
    "# Visualizing Data using Matplotlib<a id=\"8\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
    "\n",
    "The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01).  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": 37,
   "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": 38,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.5.3\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": 39,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n",
    "mpl.style.use(['ggplot']) # optional: for ggplot-like style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Plotting in *pandas*\n",
    "\n",
    "Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
    "\n",
    "Documentation:\n",
    "\n",
    "*   [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01#plotting)<br>\n",
    "*   [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01#api-dataframe-plotting)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**What is a line plot and why use it?**\n",
    "\n",
    "A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
    "Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Let's start with a case study:**\n",
    "\n",
    "In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
    "\n",
    "**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "First, we will extract the data series for Haiti.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "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": 41,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGdCAYAAADjWSL8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABgkUlEQVR4nO3deXzV1Z3/8de52chGFkhIEIJBCIrIbrWWamu1VqVlaIXWLlqDVqsjdmbs0Cltf6MjVbpoq+O0uIFFrVU0ta61oq0FV0BERQkIGGULgYSQhKz3/P743vtNLgmQ5W7f5P18PHwkN/nme0+ON+STcz7n8zHWWouIiIiIx/hiPQARERGR3lAQIyIiIp6kIEZEREQ8SUGMiIiIeJKCGBEREfEkBTEiIiLiSQpiRERExJMUxIiIiIgnKYgRERERT1IQIyIiIp6UGOsBRFp1dTWtra1hv29eXh579+4N+329SvMRSvPRmeYklOYjlOajs4E6J4mJieTk5HTv2giPJeZaW1tpaWkJ6z2NMe691XpK83E4zUdnmpNQmo9Qmo/ONCfdo+0kERER8SQFMSIiIuJJCmJERETEkxTEiIiIiCcpiBERERFPUhAjIiIinqQgRkRERDxJQYyIiIh4koIYERER8SQFMSIiIuJJCmJERETEkxTEiIiIiCcpiBEREekHbOUu/C/8BdvaGuuhRE2/72ItIiIyEPgfux/WvQJ+P+aL/xLr4USFVmJERET6gz07ALBrVsV4INGjIEZERMTjrLWwr9J5sK0cW7UntgOKEgUxIiIiXtdQD42H3Id27SsxHEz0KIgRERHxuuAqTIBduzpGA4kuBTEiIiJetz8QxAzJB+MbMFtKCmJEREQ8zu6rct4ZNQZKTnY+NgC2lBTEiIiIeF1gJcYMycNM/wwwME4pKYgRERHxOLuvfTvJTP20s6W0fXO/31JSECMiIuJ1+/YCYHLzMINzBsyWkoIYERERr3NXYvIABsyWkoIYERERD7PNTXDwgPNgSD7AgNlSUhAjIiLiZfsDJ5NSUiEtA2DAbCkpiBEREfGy/e1bScYY98MDYUtJQYyIiIiH2UBSL7l5IR8fCFtKCmJERES8bF97jZiOQreU+mcbAgUxIiIiXhZciQkk9XbUvqWkIEZERETijA3mxBy2nQT9f0tJQYyIiIiXBQvddbUS08+3lBTEiIiIeJT1t0HNPudBF0EMgJk+w7m2H24pKYgRERHxqpr90NYGCQmQld3lJf15S0lBjIiIiFcFk3pzhmJ8CV1eYgZn99stJQUxIiIiHtWxe/XR9NctJQUxIiIiXrW/vXv10YRsKe3dHY2RRYWCGBEREa86So2YjszgbBg3AQC7rv/0UlIQIyIi4lG2Q9+kYzHT+l/hOwUxIiIiXrWve9tJ0D+3lBTEiIiIeJC11u2bdKztJOifW0oKYkRERLyo7iA0Nznv5w7t1pe4W0pvrorUqKIqsadfsH//fh544AHWr19Pc3MzhYWFfP/732f06NGAExk++uijrFy5krq6OsaOHcu8efMYOXKke4+WlhaWL1/O6tWraW5uZsKECVx++eUMGTLEvaauro6lS5eyZs0aAKZPn05paSnp6el9/Z5FRES8L3AyiawcTFJyt77ETP009qEl8NEW7N7dmLyCCA4w8nq0ElNXV8dPf/pTEhMT+fGPf8ytt97KJZdcQlpamnvNE088wdNPP01paSk333wz2dnZ3HTTTRw6dMi9ZtmyZbzxxhtcd9113HjjjTQ2NnLLLbfg9/vda26//Xa2b9/OwoULWbhwIdu3b+eOO+4Iw7csIiLSD+w7cuPHI+lvW0o9CmKeeOIJhgwZwtVXX82YMWPIz8/nlFNOoaDAieSstTzzzDPMnj2b0047jaKiIq655hqamppYtcpZumpoaODFF1/kkksuYeLEiRQXF3PttddSUVHBhg0bAPjkk09Yv349V111FSUlJZSUlHDllVeybt06du7cGeYpEBER8Z7gyaTuJPV21J+2lHq0nbRmzRomTZrErbfeysaNG8nNzeWLX/wi55xzDgCVlZXU1NQwadIk92uSkpIYP348mzZt4txzz2Xr1q20tbUxceJE95rc3FyKioooLy9n8uTJlJeXk5aWxtixY91rSkpKSEtLY9OmTQwfPrzT2FpaWmhpaXEfG2NITU113w+n4P3CfV+v0nyE0nx0pjkJpfkIpfnorFtzEqwRMzS/R3Pnm3YGbYEtJar2eHpLqUdBTGVlJX/729+48MILmT17Nlu2bGHp0qUkJSVx1llnUVNTA0BWVlbI12VlZVFVVQVATU0NiYmJZGRkdLom+PU1NTWd7nH4NYcrKytjxYoV7uPi4mIWL15MXl7PItSeCK5AiUPzEUrz0ZnmJJTmI5Tmo7OjzUlV/UEOAVnFY8gsLOz+TQsLqZw4jaa33yRj8zsMnjil7wONkR4FMX6/nxNOOIFvfvObgBMofPzxxzz//POcddZZ7nWHR4TW2mPeu7vXHCnanD17NjNnzuw0hr1799La2nrMe/eEMYaCggJ2797drXH3d5qPUJqPzjQnoTQfoTQfnXVnTlp3VABQmziIul27enR//ymnwttvcuDFZ6g/49w+jzecEhMTu70A0aMgJicnhxEjRoR8bMSIEbz++usAZGdnA85KSk5OjntNbW2tu7KSnZ1Na2srdXV1IasxtbW1jBs3zr3mwIEDnZ6/430Ol5SURFJSUpefi9QPhbVWP3AdaD5CaT4605yE0nyE0nx0dtQ5CZ5OGjK05/M25XR48PewfQv+yl2e3VLqUWLvuHHjOiXW7ty5042Y8vPzyc7OdhN0AVpbW9m4caMboIwePZqEhISQa6qrq6moqKCkpARw8l8aGhrYsmWLe83mzZtpaGhw7yMiIjJQ2aZGqKt1HuQeu9Dd4UJOKa31bhuCHgUxF154IZs3b+bxxx9n9+7drFq1ipUrV3LeeecBzvLXBRdcQFlZGW+88QYVFRXceeedpKSkMGOG0wY8LS2Ns88+m+XLl/POO++wbds27rjjDoqKitxk3xEjRjB58mSWLFlCeXk55eXlLFmyhKlTp3aZ1CsiIjKgBFdhUtMwab2rn2amO7+XvdxLqUfbSWPGjOH666/noYce4rHHHiM/P59LL72Uz372s+41s2bNorm5mXvuuYf6+nrGjBnDwoUL3ZNCAJdeeikJCQncdtttbrG7BQsW4PO1x1Tz58/nvvvuY9GiRQBMmzaNefPm9fX7FRER8b5e1Ig5nJlyOvbB33u68F2PK/ZOmzaNadOmHfHzxhjmzp3L3Llzj3hNcnIypaWllJaWHvGajIwM5s+f39PhiYiI9Hs2eLy6Gz2TjsTdUvpgA3btasyXvhaewUWReieJiIh4TWAlxgzpWxkRr28pKYgRERHxmv19X4kBZ0sJcLaU6uv6OKjoUxAjIiLiMe52Uh9yYiCwpZQZKF2yb0/fBhUDCmJERES8ppd9k7o0dJjztqqy7/eKMgUxIiIiHmJbW6F6v/Ogj9tJACZwj2BDSS9RECMiIuIlNfvA+iExEQZn9/1+weRgrcSIiIhIRO1vz4cxvjD8Gh/ibCe5eTYeoiBGRETEQ8JRI6Yj95i2EntFREQkooI1YnKHhud+gZUYtwqwhyiIERER8RJ3Oyk8KzFuTkxDPbahPjz3jBIFMSIiIh5igysm4dpOGpQKGZnOA4+dUFIQIyIi4iWBnJi+thwI4W4peSu5V0GMiIiIR1hrw9ZyIEQgILIeO2atIEZERMQrDh6AlmYwBnKGhO22wYJ3XjuhpCBGRETEK4LbPVk5mMSk8N03WLVX20kiIiISEfvDm9Qb1L4So+0kERERiQC7L4yNHzsaqiBGREREIinM1XpdwZozdbXYxkPhvXcEKYgRERHxCOueTArvSoxJS4e0dOeBh/JiFMSIiIh4RaS2k6B9NcZDBe8UxIiIiHhFpLaTwM2L8VKtGAUxIiIiHmAbG6ChznkQ5u0k8OYJJQUxIiIiXhBchUnLwAxKC//9FcSIiIhIRLiNHyOQD0P7SoxVECMiIiLh5J5MikRSL2glRkRERCLE7V4dgaReaC94V1uDbW6KzHOEmYIYERERL4jwdhJpGTAo1Xl/vzdqxSiIERER8YDgdlKkVmKMMe1bSh45Zq0gRkRExAuCKzG5EdpOgg7drBXEiIiISBjY1hY4UO08iNR2EmCC91YQIyIiImFRvQ+shaRkyMyK3PMMGea8VRAjIiIiYeFuJeU5uSsRYoZqO0lERETCyO6LTPfqTnK9VStGQYyIiEi8C3avjlSNmKBgrZia/diWlsg+VxgoiBEREYl3+9u3kyIqYzAkJzvvV8d/rRgFMSIiInHO7q9y3olwEOPUigkk93qgVoyCGBERkXgXre0kcPNuvJDcqyBGREQkjlm/v70NQKQTe+kQKCmIERERkT6prYHWVjA+yB4S+efzUK0YBTEiIiLxLBhM5ORiEhMj/3zaThIREZFwCDZ+jGjPpA60nSQiIiLhEexeHenj1UHBIKZ6P7a1NTrP2UsKYkREROJZcEUkCkm9AAzOhsQksH6ororOc/aSghgREZE41t5yIErbST5f+3PF+ZaSghgREZF45taIidJKDHRI7o3vqr0KYkREROLZ/uiuxEDH5N49UXvO3lAQIyIiEqdsQx0canAeRCuxFzpsJ2klRkRERHojGERkZGJSBkXveQNBTLzXilEQIyIiEqfcnJQo1YgJMkMDz1el7SQRERHpjf1RPl4dFAyaavZh29qi+9w9oCBGREQkTtlodq/uKDsHEhKhrQ1q9kf3uXtAQYyIiEi8inahuwDjS4DcoaFjiEMKYkREROKU3e9UzI1ay4GOPJDc26N2mI888ggrVqwI+VhWVhZ33303ANZaHn30UVauXEldXR1jx45l3rx5jBw50r2+paWF5cuXs3r1apqbm5kwYQKXX345Q4a0txevq6tj6dKlrFmzBoDp06dTWlpKenp6r79RERERz3FXYqK8nYSzhWUhrmvF9Lin98iRI/npT3/qPvb52hdznnjiCZ5++mmuvvpqCgsLefzxx7npppv4zW9+Q2pqKgDLli1j7dq1XHfddWRmZvKHP/yBW265hcWLF7v3uv3229m3bx8LFy4EYMmSJdxxxx386Ec/6tM3KyIi4hW2uQkOVDsPonw6CfBErZgebyf5fD6ys7Pd/wYPHgw4qzDPPPMMs2fP5rTTTqOoqIhrrrmGpqYmVq1aBUBDQwMvvvgil1xyCRMnTqS4uJhrr72WiooKNmzYAMAnn3zC+vXrueqqqygpKaGkpIQrr7ySdevWsXPnzjB+6yIiIvGrdW9gBSQ5BTIyoz+A/radBLB7926uvPJKEhMTGTt2LBdffDHDhg2jsrKSmpoaJk2a5F6blJTE+PHj2bRpE+eeey5bt26lra2NiRMnutfk5uZSVFREeXk5kydPpry8nLS0NMaOHeteU1JSQlpaGps2bWL48OFdjqulpYWWlhb3sTHGXf0xxvT02zyq4P3CfV+v0nyE0nx0pjkJpfkIpfnozBhDW+Uu58GQ/JBdj2jxDc2nDWBfZdz+v+lREDN27FiuueYahg8fTk1NDY8//jg/+clPuPXWW6mpqQGcHJmOsrKyqKpyEpNqampITEwkIyOj0zXBr6+pqel0j8Ov6UpZWVlIvk5xcTGLFy8mLy9yyVAFBQURu7cXaT5CaT4605yE0nyE0nyEqnv7NQAGDR9JXmFh1J+/NQF2AezfS8GwYU536zjToyBmypQp7vtFRUWUlJRw7bXX8o9//MNdOTk8WrPWHvO+3b3maJHg7NmzmTlzpvs4eO3evXtpbW095v17whhDQUEBu3fv7tbY+zvNRyjNR2eak1Caj1Caj86MMaQGtpOa0jPZtWtX1Mdg29rA54PWVnZ9sBGTM+TYXxQGiYmJ3V6A6PF2UkeDBg2iqKiIXbt2ceqppwLOSkpOTo57TW1trbuykp2dTWtrK3V1dSGrMbW1tYwbN8695sCBA52eq+N9upKUlERSUlKXn4vUD4W1Vj9wHWg+Qmk+OtOchNJ8hNJ8hGoNbifl5sVmXnw+yBkK+yqxVXsgOzf6YziGPq0NtbS0sGPHDnJycsjPzyc7O9tN0AVobW1l48aNboAyevRoEhISQq6prq6moqKCkpISwMl/aWhoYMuWLe41mzdvpqGhwb2PiIhIf9cxJyZm4jy5t0crMX/4wx+YPn06Q4cO5cCBAzz22GMcOnSIs846C2MMF1xwAWVlZRQWFlJQUEBZWRkpKSnMmDEDgLS0NM4++2yWL19OZmYmGRkZLF++nKKiIjfZd8SIEUyePJklS5ZwxRVXAHDXXXcxderUIyb1ioiI9DetlbuBGLQc6MAMyQvUiukHQcz+/fv57W9/S21tLYMHD2bs2LEsWrTI3buaNWsWzc3N3HPPPdTX1zNmzBgWLlzonhICuPTSS0lISOC2225zi90tWLAgJPN6/vz53HfffSxatAiAadOmMW/evHB8vyIiInHP+ttoq3KCmKg3f+xoyDDnbX8IYn7wgx8c9fPGGObOncvcuXOPeE1ycjKlpaWUlpYe8ZqMjAzmz5/fk6GJiIj0HzXVTvNFnw+yYpiLEgigbFV8BjHxd15KRERkoNsfqJKbMxSTkBCzYbhbWfsVxIiIiEg32Bh1r+5kaHA7aW9cnhxTECMiIhJvAkFMLJN6AcgZAsZASzMcrIntWLqgIEZERCTO2GDTxRgHMSYxCbIDRe7iMC9GQYyIiEi8Ca7E5MZ4Ownak3vj8ISSghgREZE4Y/fHSU4MHba0FMSIiIjI0VhrYZ/TODnmOTEQ17ViFMSIiIjEk4Y6aDrkvB9X20l7YzyQzhTEiIiIxJPAiocvOxeTnBLjwYAZGlgNqtoT24F0QUGMiIhIPAmseCTmF8R4IAG57Tkx8VYrRkGMiIhIHLEHqgFIiIetJGhPLm5ugrqDsR3LYRTEiIiIxJO6WgB8WTkxHojDJCW392/aF19bSgpiRERE4kkwiBmcHdtxdBRcjYmz5F4FMSIiIvHkYPwFMcGj3lYrMSIiInIkNrASk5CVHduBdBSsVxNnrQcUxIiIiMSTuNxOCqzE7Nd2koiIiBxJ3QEgvoKYeK0VoyBGREQknrjbSfFxOglo307avzeuasUoiBEREYkTtqkRmpuB+FqJcQveHWqAhvrYjqUDBTEiIiLxIrAKQ2IiJjUttmPpwKSkQGaW8yCOTigpiBEREYkXwSAmMwtjTGzHcrjgllIc1YpRECMiIhIvDjpJvWQMju04uhCPtWIUxIiIiMSJYI0YE4dBjFZiRERE5Mg6bCfFncAxaxtHBe8UxIiIiMSLQMuBuNxOCp5Q0naSiIiIdBLP20lDtZ0kIiIiR2Dd7aQ4DGKCnawb6rCHGmI7lgAFMSIiIvEink8nDUqD9Eznwb74yItRECMiIhIv6g46b+MxsRc6nFBSECMiIiIdxXNODLhbSvFyQklBjIiISBywfn/7SkycBjFmyDDnnf0KYkRERCSooQ6s33k/ToMYrcSIiIhIZ8GTSanpmMTE2I7lCMxQ5cSIiIjI4dxCd5mxHcfRBLeTFMSIiIiIK55bDgQFa8XU1WKbGmM7FhTEiIiIxAW30F285sMAJi0DUtOdB3GwGqMgRkREJB4ECt3F7fHqoOBqjIIYERERATpsJ8V7EBPoZq0gRkRERIC47mDdkRkaSO6Ng2PWCmJERETigBdyYgDI1XaSiIiIdBRsORDPp5NorxWj7SQRERFxxHEH6xBx1ARSQYyIiEg8cDtYeySIqa3BNjfFdCgKYkRERGLMtjRD0yHnQbyvxKRnQkqq8/7+vTEdioIYERGRWAueTEpIaC8mF6eMMe21YmJ8QklBjIiISKx1OJlkjIntWLojWCtmv4IYERGRgc0rx6sDTDAvRisxIiIiA5v1ysmkoKHBE0rKiRERERnYAieT4r5vUoBxWw/siek4EmP67CIiIuKdvklB4ybiW7AYgi0IYkRBjIiISKzVeWs7yWQOjouAS9tJIiIiseY2f4zvlgPxRkGMiIhIjLU3f8yM7UA8RkGMiIhIrLnNH2O/ReMlfcqJKSsr449//CMXXHAB3/3udwGw1vLoo4+ycuVK6urqGDt2LPPmzWPkyJHu17W0tLB8+XJWr15Nc3MzEyZM4PLLL2fIkCHuNXV1dSxdupQ1a9YAMH36dEpLS0lPj+9KhiIiIj1Wp+2k3uj1SsyWLVt44YUXGDVqVMjHn3jiCZ5++mlKS0u5+eabyc7O5qabbuLQoUPuNcuWLeONN97guuuu48Ybb6SxsZFbbrkFv9/vXnP77bezfft2Fi5cyMKFC9m+fTt33HFHb4crIiISl6y1nit2Fy96FcQ0NjZyxx13cOWVV4asjFhreeaZZ5g9ezannXYaRUVFXHPNNTQ1NbFq1SoAGhoaePHFF7nkkkuYOHEixcXFXHvttVRUVLBhwwYAPvnkE9avX89VV11FSUkJJSUlXHnllaxbt46dO3eG4dsWERGJE4fqoa3NeV/bST3Sq+2ke+65hylTpjBx4kQef/xx9+OVlZXU1NQwadIk92NJSUmMHz+eTZs2ce6557J161ba2tqYOHGie01ubi5FRUWUl5czefJkysvLSUtLY+zYse41JSUlpKWlsWnTJoYPH95pTC0tLbS0tLiPjTGkpqa674dT8H6e6G8RBZqPUJqPzjQnoTQfoQb8fAQK3ZGSii85BdCcdFePg5jVq1ezbds2br755k6fq6mpASArK3RPLysri6qqKveaxMREMjIyOl0T/PqamppO9zj8msOVlZWxYsUK93FxcTGLFy8mLy+vu99ajxUUFETs3l6k+Qil+ehMcxJK8xFqoM5HU81eKoGE7BwKCwtDPjdQ56S7ehTEVFVVsWzZMhYuXEhycvIRrzs8crTWHvPe3b3mSFHp7NmzmTlzZqcx7N27l9bW1mPeuyeMMRQUFLB79+5ujbu/03yE0nx0pjkJpfkINdDnw799GwBtqens2rULGNhzkpiY2O0FiB4FMVu3buXAgQP86Ec/cj/m9/t5//33ee655/jNb34DOCspOTk57jW1tbXuykp2djatra3U1dWFrMbU1tYybtw495oDBw50ev6O9zlcUlISSUlJXX4uUi8Aa+2Ae3EdjeYjlOajM81JKM1HqIE6H7ZDtd7Dv/+BOifd1aMg5pRTTuFXv/pVyMd+97vfMXz4cGbNmsWwYcPIzs5mw4YNFBcXA9Da2srGjRv51re+BcDo0aNJSEhgw4YNnHHGGQBUV1dTUVHhXlNSUkJDQwNbtmxhzJgxAGzevJmGhgY30BEREekXAh2svdL8MZ70KIhJTU2lqKgo5GMpKSlkZma6H7/gggsoKyujsLCQgoICysrKSElJYcaMGQCkpaVx9tlns3z5cjIzM8nIyGD58uUUFRW5yb4jRoxg8uTJLFmyhCuuuAKAu+66i6lTp3aZ1CsiIuJZXmv+GEfC3gBy1qxZNDc3c88991BfX8+YMWNYuHChe1II4NJLLyUhIYHbbrvNLXa3YMECfL72E9/z58/nvvvuY9GiRQBMmzaNefPmhXu4IiIisaUaMb1mbD/fbNu7d2/I0etwMMZQWFjIrl27tFeJ5uNwmo/ONCehNB+hBvp8tN3xP7DhTcx3rsF35nnAwJ6TpKSkbif2qneSiIhILLl9k9RyoKcUxIiIiMSStpN6TUGMiIhILB1UENNbCmJERERixLa2Or2TQKeTekFBjIiISKzUB/omGR+kpR/9WulEQYyIiEisBArdkZ6B8SXEdiwepCBGREQkVtxCdzqZ1BsKYkRERGLFPZmUGdtxeJSCGBERkRixB7US0xcKYkRERGIlWOhOx6t7RUGMiIhIrAQTexXE9IqCmCix1mL37431MEREJJ6og3WfKIiJEvvsCvwL5uFffifW3xbr4YiISBywajnQJwpiosRu3eS8ffmv+Jf8AtvSHOMRiYhIzB1UTkxfKIiJlpr97e+vexX/b2/AHmqI3XhkQLMb36Lttp9hK3fFeigiA5vqxPSJgphoCQQx5qLLYFAqbHoH/68WYmtrYjsuGZD8LzwJG9dj3/hHrIciMmBZa9XBuo8UxESBbWuDQLBiTjsL3/U/d6Luig/xL16ArdoT2wHKwLPjI+dt5e7YjkNkIGs6BK0tzvsKYnpFQUw0HKgG6wefDwZnYUadgG/BYhiSD5W78N+yAPvJ9liPUgYI29gAgZNytkpBjEjMBAvdJSdjUgbFdiwepSAmGg4E8mGyct0GX2bYcHw/WgzHjYID+/H/8r+wWzbGcJAyYOz8uP39vQpiRGKmLtDBWqswvaYgJhqq9zlvs3NDPmyyh+D74c0w5iRoqMd/28+wG96MwQBlILE7K9of1OzHNjfFbjAiA1mdCt31lYKYKLA1XQcxACY9A98PboRTpkNzM/47F+F/9aUoj1AGlI5BDMBe5WSJxILbNylDJ5N6S0FMNASCGJM9pMtPm5QUfFf/GHP658Hvx953G/6/PRHNEcoAYnccHsTomLVITKhvUp8piImGYI2YnK6DGACTmIi57DrMObMAsI/ci//x+50jeCLhFFyJyRkKgFVejEhsBLeT1HKg1xTERIENBjFHWIkJMj4fZm4p5quXOF/37GPYP/yvc0RbJAxsQ137yuCkU50PaiVGJDaU2NtnCmKioTq4ndQ5J+Zwxhh851+EueRfwfiwq/6G/65fakVGwiN4Mil7CBSdAIBVToxITLTnxCiI6S0FMdHgJvYefSWmI99nv4jvqgWQmAjrXoHy9yI0OBlI3JNJw4swQ4c572s7SSQ2AttJRi0Hek1BTITZxgZoPOQ8yDn2SkxHZuqnMaee6dxn7apwD00GokAQY44rgvxC52NVe9RZXSQW1HKgzxTERFowH2ZQKmZQWo+/3Ez/DAB23av6RSN91nElhpwhkJAIba3ttYxEJHq0ndRnCmIirbrnW0khxk+G1HSndcGW98M2LBmggisxw4uc6tHBLSV1sxaJKtvWBg11zgOdTuo1BTER1n4yqWdbSUEmMQkz+VPOvdasDtewZACy9QedYBhg+EjnbV6B8znlxYhEV0MdBA9spGfGdiwepiAm0o5R6K47zLQZQHBLyR+WYckAFCxyl5vnbm2aQBCj5F6RKDsYqBGTloFJSIjtWDxMQUykuYXuercSAwS2lNKcRpLaUpJeCsmHCcpXECMSE8GkXp1M6hMFMRFme3G8+nAmKQkz6TTnfuteCcewZCDqeDIpwOQ5J5S0nSQSZe7JJG0l9YWCmEjrQaG7o3FPKa1drS0l6ZUuV2KGtq/EqKCiSPSo0F14KIiJtG62HDim8VOcLaWa/bD1g76PSwaeDieTXHmB00mH6qH+YAwGJTJABZs/ajupTxTERJD1+508FuhzEONsKemUkvSOPXigPZGwcKT7cZOc0n5yTltKItGjQndhoSAmkg4eAL8fjIGsnD7fzkwLbim9oi0l6ZngVtLQYZiUQaGfCx6zVq0YkegJ/lGhIKZPFMREUjCpd3B2eI7QnTwFBqU69926qe/3kwGjy3yYgGByr1ZiRKLHuqeTFMT0hYKYSOprtd7DmKTk9i2ltdpSkh7o4mSSS7ViRKKvzslBM1qJ6RMFMRHU12q9XWk/paQtJek+u+Mj550uVmLc7aQqBTEiURPcTlJib58oiImkYLXenPCsxABw8lRnS6m6CraVh+++0m9Za2Hnx8BhJ5MCTLCbdaWCGJGoUWJvWCiIiaRgTkxWGFdikpIxE3VKSXqgtsY5Pm18UDCi8+eDtWJq9mGbm6I6NJGByDY1QfBnTUFMnyiIiSBbHWw5EMaVGDpsKa1T4TvphmBSb94w50j14TIynRpEAFV7ojcukYEquAqTmOisrEuvKYiJpECNmL40f+zSyVMgJRX2a0tJju1oJ5MAjDFK7hWJpg5bScaY2I7F4xTERFKYTycFmeQUzKRTAZ1Skm5wK/WOOvI1weTevaoVIxJxyocJGwUxEWKbm6ChznnQlw7WRxBS+E49b+Qo2k8mjTziNW6tGCX3ikSc1cmksFEQEynBpN7kZEhND//9J0wNbCnt1ZaSHFHIyaSuasQEuceslRMjEnHBvklaiekzBTGR0qHxYyT2PE1yCmbidEBbSnIUNfud5o4+Hwzr4mRSgHFzYrSdJBJxbgfrzNiOox9QEBMhNkL5MB2FFL7TlpJ0JZjUm1+ISUo68nXBIKZqD9bfFvlxiQxkbk6MtpP6SkFMpNRE6GRSRxOmQcog2FcJ2zdH7nnEs451MsmVOxQSEqG1FYKlAUQkItQ3KXwUxERKMCcmjC0HDudsKQVOKanwnXSlOyeTAONLgCH5zgNtKYlEVp06WIeLgphICebEROBkUkftp5RWa0tJOjlqz6TD5QePWeuEkkhEHVRib7goiIkQG+ybFMntJHC2lJJTAltKWyL7XOIp1lrYdeSeSYdTcq9IlGg7KWwSe3Lx888/z/PPP8/evXsBGDFiBBdddBFTpkwBnH80H330UVauXEldXR1jx45l3rx5jBzZXp+ipaWF5cuXs3r1apqbm5kwYQKXX345Q4a0/7Kvq6tj6dKlrFmzBoDp06dTWlpKenoEjipHShQSewFMirOlZNeswq5djSkeG9HnEw/ZXwWNhyAhAYYVHvt61YoRiTjr9zu9zECJvWHQo5WY3NxcvvnNb3LzzTdz8803M2HCBH7xi1/w8cfOX3tPPPEETz/9NKWlpdx8881kZ2dz0003cejQIfcey5Yt44033uC6667jxhtvpLGxkVtuuQV/hx5At99+O9u3b2fhwoUsXLiQ7du3c8cdd4TpW448a63bciCSOTFB7aeUtKUkHbgnk4ZjEo9yMinAqFaMSOQdqofg7zsdse6zHgUx06dPZ+rUqQwfPpzhw4dz8cUXM2jQIDZv3oy1lmeeeYbZs2dz2mmnUVRUxDXXXENTUxOrVq0CoKGhgRdffJFLLrmEiRMnUlxczLXXXktFRQUbNmwA4JNPPmH9+vVcddVVlJSUUFJSwpVXXsm6devYuXNn+GcgEuoOOqc8ICpBDBOmO1tKVXug4sPIP594gnWTeruRDwPtKzHaThKJnGCNmNS0bv1xIUfXo+2kjvx+P6+++ipNTU2UlJRQWVlJTU0NkyZNcq9JSkpi/PjxbNq0iXPPPZetW7fS1tbGxIkT3Wtyc3MpKiqivLycyZMnU15eTlpaGmPHtm+LlJSUkJaWxqZNmxg+fHiX42lpaaGlpcV9bIwhNTXVfT+cgvc74n2DqzCZWfiSksP63F2OZ9Ag7MTp2DWrsWtW4zs+ultKx5yPASZu5mOnk9RrRozq3ljyhjlvG+qhvg4Txr8S42ZO4oTmI9SAmo8ONWKO9v0OqDnpgx4HMRUVFSxcuJCWlhYGDRrE9ddfz4gRI9i0aRMAWVmhe3xZWVlUVVUBUFNTQ2JiIhkZGZ2uqampca85/B6HX9OVsrIyVqxY4T4uLi5m8eLF5OXl9fRb7LaCgoIuP37okw+pApLyhlFQ2I1chDBoOOfL7FuzGt/61yj41x/F5IV/pPkYqGI9H7v37qYFyDl5EmndfB3uyB2Kf38VQ2wLKRF47cZ6TuKN5iPUQJiPQ9s3UQUk5w5hWDd+xgbCnPRFj4OY4cOH88tf/pL6+npef/117rzzTm644Qb384f/8uxOjkZ3rznaL+bZs2czc+bMTuPYu3cvrcGtnTAxxlBQUMDu3bu7HLt/q3NKqDV9MLt2RWdp3o48AZKTadu9g12vr8KMGhOV54Vjz8dAEw/zYf1+2j5ythZrUjM50M3XoX9IPuyvour9d/Flhm8rNB7mJJ5oPkINpPnwf+yskLakDDrq74eBNCeHS0xM7PYCRI+DmMTERDcyPOGEE/jwww955plnmDVrFuCspOTk5LjX19bWuisr2dnZtLa2UldXF7IaU1tby7hx49xrDhw40Ol5O96nK0lJSSQdoax6pF4A1tou793eciA3ei++5BQ4ZTqsfQX/mlX4ik6IzvN2cKT5GKhiOR+2ag80N0FiIjavELo5DjO0ALt5I7ZyV0TGrtdIKM1HqIEwH24H64ysbv8B39/npC/6XCfGWktLSwv5+flkZ2e7CboAra2tbNy40Q1QRo8eTUJCQsg11dXVVFRUUFJSAjj5Lw0NDWzZ0l7zZPPmzTQ0NLj3iXs10TlefTgzbQbgVO/Vi36AC3SuZthxmISE7n9dvmrFiESUmxOjGjHh0KOVmIceeogpU6YwZMgQGhsbWb16Ne+99x4LFy7EGMMFF1xAWVkZhYWFFBQUUFZWRkpKCjNmOL9c09LSOPvss1m+fDmZmZlkZGSwfPlyioqK3GTfESNGMHnyZJYsWcIVV1wBwF133eWeivIC61brjXIQM3E6NjkZ9u6Gj7dCDFZjJD70+GRSUOCEkt2rY9YiEXFQQUw49SiIOXDgAP/7v/9LdXU1aWlpjBo1ioULF7oByKxZs2hubuaee+6hvr6eMWPGsHDhQveUEMCll15KQkICt912m1vsbsGCBfh87YtC8+fP57777mPRokUATJs2jXnz5oXj+42O6mC13igcr+7ApAxyjluvewW7ZjVGQczAFTiZxHFH75l0OJNXgAUnEBaRsFPzx/DqURDz/e9//6ifN8Ywd+5c5s6de8RrkpOTKS0tpbS09IjXZGRkMH/+/J4MLb64he6iuxIDTuE7u+4Vp/Dd7O/oeN4A1fuVmMB2Us0+bEszJgolAkQGlDr1TQon9U4KM9vSAsHErVgEMadMh6RkqNwFH2+L+vNL7Fl/G+z6xHnQ0yAmYzAMSnUSgVW5VyT8lBMTVgpiwi24CpOYFJOS0mZQKpwyDXDaEMgAVLUHWpqdYDZYwK6bjDHtqzHqoSQSfsE/cjPVNykcFMSEW0378epYbeWYqWcAYN9dG5PnlxgL9kwqOA7j68HJpCA3uVcnlETCyba0OE1ZQSsxYaIgJsxsdfQaPx6JW+huz04dtfYQ+/7b2LWv9P0+O3qZDxMQbASp5F6RMAtuJfl8kJoW27H0Ewpiwu1A8GRS9PNhXEOHOT8kTY3t21sS12z1Pvy334D/97dgt7zft5sFV2J6eDLJFagVYxXEiIRXh3wY49Ov33DQLIZbdexOJgWZxEQnkAHY45HO3wOcfeEJt/O5/+k/9e1evT2ZFGDcbtYKYkTCSkm9YacgJtyCOTE5sdtOAiDfKQxoFcTEPVt/EPuP59o/8O467Lby3t2rrQ129/JkUlBwO6lqD9bv7909RKQTq6TesFMQE2Y2GMRkxTaIMcMC1Y0VxMQ9+9LTztbfiGLMGV8AwP9UL1dj9u5yVnSSU2BIfu/ukTMUEhKgtaU9KBeRvnNXYqJ/crW/UhATboGWAybKLQc6GXYcAHbPjtiOQ47KNjViVz4JgDn/a5gL54DxwYY3sR9tOcZXdyGYD1M4std77iYhoT0A0paSSPio0F3YKYgJI2ttzJo/Hs4MC+Q1VOqYbDyzq/4GdQchrwAz7TOY/OGY084Cerca054PM7JvAwtsKVm9fkTCJ9g3SdtJYaMgJpwa6qG52Xk/hkesAXclhr27nAquEndsawv2+TIAzHlfdbtNO6sxBta/jq3Y2rOb7ujjyaQAJfeKRIASe8NOQUw4BVdh0jIwySmxHUvOUKdqcGsr7Nsb27FIl+wbL8P+KsjKwZxxtvtxUzACc+pnAfA//UjP7tnHk0ku1YoRCTurICbsFMSEUyAfhljnw4CTD5Ef+Gtayb1xx/r92OceB8Cc85VOjRbNBXOd1Zh1r2B3fNS9e7a2tv+/7mMQY1QrRiT8AqeTjDpYh42CmDCyHVoOxIVhOmYdt95+A3Z9DKnpmLPO7/Rpc1xRe/uI7q7GVO6EtlZISYXcvL6NT9tJIuFXd9B5q5WYsFEQE07VcVCttwMTqBVDpYKYeGKtxf/sCgDM5y/AHKH8uJk517l+zSrsro+PfePgyaThI/vet2toYDupoQ5bX9e3e4mIc/BD20lhpyAmnOJ2JUbHrOPKpndgWzkkJWO+8OUjXmZGFMOU08Habq3GhO1kEmBSUiArx3mgRpAifXeowVkpBQUxYaQgJoxsTexbDnTkFrzTMdm44q7CzDgHMzj7qNf6Zn4DAPvGP7G7jx6MBhs/MrxvJ5NcecqLEQmb4CpMyqDYH/zoRxTEhFO8FLoLCh6zrqp0WsBLzNmPtsDG9eDzYb44+5jXm6LRMOlTYP3YZx49+sXhOpkUfO7gCSUFwSJ9p62kiFAQE05xUujONTgbBqWC9UOV/pqOB+4qzKfOxASbdB6Db+bXAbCv//2IxedsS0t77lOYghgl94qE0UEFMZGgICZMbGsr1NY4D2Ld/DHAGOM2gtQx69izu3fAulcBMF/6Wre/zhw/Fk6ZDv6jrMbs2QF+P6Smhe+If3A7qWpPeO4nMoC5NWJ0vDqsFMSES20NWOs0zsuIn5LSRses44b96+POa2TSpzA9rKjruzBwUum1l7rMUbHuyaSivp9MCnC3k5TYK9J3dYEaMXH0+6E/UBATLm736pxeN96LiGE6Zh0PbPU+7KsvAeA7/6Ief7054UQYPwXa2rDPPdb5gh3hzYcB2oslVu9TTpVIX2k7KSLi6Letx8VbPkyQVmLigv3bn53jlSUnOwFJL/i+HMiNWb0Se1griY4rMWGTMdgpnGctaEtJpG/cxN7M2I6jn1EQEya2Or6OVweZ4Akl1YqJGVt/EPvyX4HercIEmTHj4cSJ0NbaeTUmzCeTIJBTpS0lkbBoz4nRdlI4KYgJlwPBar3xkdTrCib21uzHNh6K7VgGKPvi09DUCCOL4eSpfbqXWzdm1fPYQIVo29LcfoIonCsxAOqhJBIegSDGaDsprBTEhEu8rsSkZ7TvwareR9TZpkbsi08CYM6/qM9Jt2bcBCg5GVpbnURhgF2fOMfo0zLaq+yGiVE3axEA7Jb38d9/BzZ4CrWnlBMTEQpiwsRt/hgnx6tDKLk3Zuw/n3eavuUVuA0d+8pdjXn5r9ia/RE5meQK1Io5Un0akYHC//j92FV/wz52f+9uEDidpO2k8FIQEy418dX8saNgI0gl90aXbW1xEnoB86WvYhISwnPjEyfCCSdCSzP2+TLY+ZHzHOHeSqLDSowSe2UAsy3NTr8zwL76Enb3Jz37+tZWaKh3HmglJqwUxISL2zcpjldilNwbVfb1f8D+KufY/afPDtt9jTH4vnyx8xz/eBa76V3nE8eFP4ihw3aS9fvDf38RL9i+BVoDzRutH/uXP/bs6xsOOm+NgfT08I5tgFMQEwa2sQGCSbPxuBITPGatLYGosX4//medE0Tm3FmYpOTwPsH4yVBcAs3NsHWT8zwRWIkhN88p4Nja0h6oS0zYvbtp+9Hl+Fcsw1ob6+EMKHbze847gSKVds0q7Cfbu3+DYD5MeibGF6YVWQEUxIRHMKk3NQ0zKDW2Y+mKjllH3aHX/gG7P4G0dMyZXwr7/Z3VmG+EfjAS20kJCU4gA0rujTH7+j9gXyX2r49j//5srIczoNjNG4FA5/npM8Ba/H95qPs3UPPHiFEQEw7xWuguKFh5te4gtv5gbMcyAFhrOfjoMgDM5y7EpKZF5okmTINRY5z3MwZjBmdH5nmCyb2qFRNT9v23299/+C7sBxtiOJqBw/rb4MMPADBjT8Z85WIwPnjrNacrfXcoiImYxFgPoD+w8ZwPA5iUQU6AVbPPaQQ5elysh+QZ9t11zskza51jzH6/877fgm0LvPV3eOuH+oO0lb8HScmYL8yM2NiMMfhmfQv/HTfCuAmRe578AuxGtBITQ7ap0f1FykmT4P238S9ZjO/Hv25PvpbI2FEBh+qd6tUjijEJCZjTz8K++hL+Pz9IwnX/75i3sAeDJ5MUxISbgphwqInTQncdDRsONfuwe3ZiFMR0i93wJv47/qfXX29mnBu51ZHgc5wyDd8Nd0b2aL9qxcTe5o1O24oh+fj+9Sf4f/Ff8NEW/HcuwvejxZhBEVrtk/Z8mBPGuScMzcxvYN94Gd5di92y0ammfTQqdBcxCmLCoTrOt5NwknvtpndUK6YH/P94znnnuFEwdJhzssDnA2Oc5LwOj523PvdtxtA8Gs48PyrjNIUjInv/vEIsqtobS8GtJHPiRExyCr5rFuJf9O+w4yP89/4G3/d/FF+NZ/uTLe8DYMa2ByomvxDzmXOwL//VWY25ftHR71EX2MZXEBN2CmLCoL3QXfwGMe3HrBXEdIet2QfvrAXAd+WCHgUKxhiyCws5tGtX/zhFopWYmLPvr3feOWkSACZnCL7v/xf+X/0Y1r+GffJhzKxvxm6A/ZS11l2JMWNPDvmcuXAu9pWVsOkd7PtvYwL/b7p0UIXuIkWhezgEcmLisdBdkAre9Yx95UUnx+WEEyO+0hH3gkFM/UFsQ11sxzIA2YO18PE2AMxJE92PmxNOxHznGueapx7GrlkVk/H1a1V7nH/fExLh+JKQT5ncPPfkof+JB4/6B4tVYm/EKIgJhzhP7AU6HLPe2T9WByLIWotd/QLg5LUMdCZlUHtPJq3GRJ17Cum4UZjBob2xfGd8AXPuLAD8S3+Lrdga7eH1a8Gj1Yw6AZOS0unz5vyLIDnZSbp+d+2RbxTMiVFib9gpiOkj62+DA/HZ/DFE3jAnZ6PpEByojvVo4lv5e06zzJRUpyaEODlBgK1UEBN1HwTyYU6a3OWnzde+C+OnQHMT/jsX9b5BoXS2JVAfZmzXibsmOxfz+QsB8P/5gSP/gajmjxGjIKavag84x2qNDyJ8EqUvTGISDM13Hii596jsqr8BYE6dEZ/FC2PABGrFoFoxUecm9XbYSurIJCTg+94PIX847N+L//e3YFtbojnEfutI+TAdmfO+5hy/rtgKb73a+R7Wqk5MBCmI6avgKkxWdvga/EXKMOXFHIttqMeuWw1oKymEkntjwu7d7cx5QgKUHOUXaXoGvn9dCKlpsHkj9o93R3GU/ZOtrYHdgSrnJ5x4xOtM5mDMuV8BwP/EQ87qfEdNjdDS7LyvxN6wUxDTV8Hj1VlxnA8TEEzu1QmlI7Nv/tPpR1Q4UkUBO8p3ghgds44uNx+muOSYtWBM4Uh8V1wPxmBffg7/35+Jwgj7scDRaoYXHbO+izl3FqSlw84K7JuHJVgHV2GSkiG5c16N9I2CmD7yxPHqIK3EHJO7lTTjHIwxMR5N/GjfTlIQE1XuVtJRju92YE6Zjpl9CQD24bud2lDSK26/pCPkw3Rk0jIwX5ztfN1fHsK2dViN6bCVpH9Twk9BTF9Vx//x6iCjRpBHZT/ZDts3Q0IC5vTPx3o48SW4nVRdhW1RvkU0WL/fXYk5UlJvV8yXvor51FnQ1ubkxyjw7BW3Uu9R8mE6Ml/4spPzUrkL++qL7Z8IJvXqZFJEKIjpqwPBar3xv53kNoLcu7vzvq24qzBM+lTE2wV4TmaWk7xoLezbE+vRDAw7PnKKpKUMguKx3f4yYwzm0n91moPWHXROLDUeiuBA+x/beAg+do6rH7OlQIAZlOocuQbskw+7wb5qxESWgpg+stUeOF4dNCQPEhOhtQX2V8V6NHHFtrRgX/s7AD4l9HZijHGO6YO2lKLErdJbMsE5XdgDJjkF39U/dur77PgI/323Yf3+8A+yv9q6yTl1mpuHGZLX7S8znzvfyY/cv7f9jyL1TYooBTF9FWz+GMkGfGFifAkQzG3QMesQdv3rUH/QCUZPnhLr4cSnwJaSasVEh30/sJV0YtdHq4/F5A7F9/3/cv5wees17J8fCOfw+rWe5MN0ZJJTMBfOce7x9CPY5ia1HIgwBTF9VRP/zR9DKLm3S25C7xlfcII96US1YqLHtrZA+bsAmPHdS+rtitOa4F+dez67Av8/nw/L+Pq7nubDdGRmfBFy8+DAfuzfn+2Q2JsZxhFKkIKYPrBNjdBQ7zzwSBBjPN4I0lbtwf/0I2Ht4WP3VUJg6d7MOCds9+138nTMOmq2lkNzk/PX+/BRfbqV74yzMTO/AYB94P+w770VjhH2W7a1FbZtArqfD9ORSUrCzPy6c69nVzj/voByYiJEQUxfBHsmJac4Raa8wMONIK3fj//3i7F/fgD/vbeFrQeUXb3SSVgddwomeApHOjH5KngXLW6V3hMnYnx9/2fafOVi58Sd3++cWAo0lJQuVHzo1IpKz4ReNn81nz7bOUhRVwsb1zsf03ZSRCiI6YsOW0leOf/v5WPW9o2X4aMtzoMNb2L/+de+39Pvx76yElCF3mMKbidV7cE2NsR2LP2cm9Tbzfowx+KeWBp3CjQewn/7jdhgoU4J4TZ9HHNSrwNIk5iI+fLFoR/USkxEKIjpA08VugsaFvhFtK/SU/1VbHMTtuwPzoOi0c7H/nRv31eUPngb9lVCajpm6qf7OMp+LjcPEpOgpRn/9ZfhX/5/6pocAfZQA2wrB7pf5K47TGKSk+hbOBJq9jmBjILRTuwxmj52l/nUZ525DsrQSkwkKIjpC7fQXfyfTHJl5Tp1J/x+qPJOvQ/7wl+cY+G5Q/H95y3OX5TNTfjvvTW0OmZP77vqBQDMaWdhVBL8qExCAua786HgOGg65JS2/58f0Pbz6/GvXoltaor1EPuH8vecn8+8Akyge3i4mPQMfPN/5uTafLIN/5Jf9Onnp7+xfn975+pe5MN0ZHwJ+GZ9q/0DmUrsjQQFMX3grsR4KIgxxrgnlLyS3Gtra7DPrgDAzP4OJmUQvtIfQGo6bCvHPv1I7+5bV4sNdJ3VVlL3+E47C9+N/4fv+kWYUz8LCYnO/4Nlv8X/n9/F//Dd2F0fx3qYnhbcSgrnKkxHZugwfNf+zMnle3cd9qHfhy2/zPN2fwJ1ByE5GUad0Pf7TTkd89kvYs78EmRm9/1+0kliTy4uKyvjjTfeYMeOHSQnJ1NSUsK3v/1thg8f7l5jreXRRx9l5cqV1NXVMXbsWObNm8fIke3Lai0tLSxfvpzVq1fT3NzMhAkTuPzyyxkypH1bpq6ujqVLl7JmzRoApk+fTmlpKenp6X39nsPHa8erA0z+cGzFVuyenXghk8f+5SFoPASjxjjl1AGTm4f51lXYe36NffpP2AlTMT1s2GhffxlaW2FEsbtFJcdmjHGSoMed4gSYq1diX37OyZVZ+SR25ZNOgbYzz8NMPQOT1LNCbQNde6uByAQxAKZ4LL4rrsf/fz/HvvxXGFqAOf9rEXs+r3DzYYrH9bjAYFeMz4e55F/7fB85sh6txGzcuJHzzjuPRYsW8ZOf/AS/389NN91EY2Oje80TTzzB008/TWlpKTfffDPZ2dncdNNNHDrUXvZ62bJlvPHGG1x33XXceOONNDY2csstt+DvUFHy9ttvZ/v27SxcuJCFCxeyfft27rjjjjB8y+ETrNZrvJQTA55aibE7K7AvO7UtfHNLQxLtfKed5awG+P3OaaWmxiPdpvN9re3Q7PFczyRmxxszOBvf+V/Dt2gJvuv+GyafDsYH5e9i7/k1/v+8DP+KZdhK1ZbpDnug2mk3YAyM612Ru+4yk0/DfP0K53kfvx//m/+M6PN5QpjyYSR6ehTELFy4kM997nOMHDmS448/nquvvpqqqiq2bnWS+6y1PPPMM8yePZvTTjuNoqIirrnmGpqamli1ymlP3tDQwIsvvsgll1zCxIkTKS4u5tprr6WiooING5y/QD755BPWr1/PVVddRUlJCSUlJVx55ZWsW7eOnTvj6BevR1di3GPWHqja61+xDKwfJp+OKZnQ6fPmW9+HnKFQuRP7yH3dv3HFh/DJNkhMwpx+VvgGPEAZnw8zYSoJ1/wY3y33OCczsodAXS32r4/T9uPvseffv0vbEw9hP/xAvbuOILgKw8hiTBQaBvq+MBNzzlec577vN+0rEQNUbyv1Suz0aDvpcA0NTmZ7RkYGAJWVldTU1DBpUvsyaFJSEuPHj2fTpk2ce+65bN26lba2NiZObP8rIzc3l6KiIsrLy5k8eTLl5eWkpaUxdmx707OSkhLS0tLYtGlTyPZVUEtLCy0duusaY0hNTXXfDydjjLOHXNO+EuOlv+R9BcfRBrBnZ1jGHbxHuOfAv3E9vLMGEhJIuOi7Xd7fZGRC6Q/w//on2Jefw046Fd+kTx373sFVmKmfxhfmo4+Rmg+vMEPyYNY3sTO/jt3wJvYfz2Lfe4vmTe/CpnexT/4R0jIw4ydhTp7q/Jc7NNbDjqojvkaC9WFOmhy1149vbin+fZXYt17Df+ciEv7rl5iC46Ly3EHx8DNj9+91Tir6fE6l4xj//MbDnHhBr4MYay33338/J554IkVFRQDU1NQAkJUVepQsKyuLqqoq95rExEQ38Ol4TfDra2pqOt3j8GsOV1ZWxooVK9zHxcXFLF68mLy87jfv6om2A9XQ1gpA4bjxntr3b8tIYydAdRXDcnLwDRoUlvsWFISvUJxta2PPoj/gBzIunEPOlOlHvriwkOoPN1L354dg+Z3kn/5ZEo6SbO1vamRnYOl86Fe+zqDCwrCNu6NwzodnjRgBF8ymtWoPjWtfpXHdazS+9Tq2/iB2zWrsmtUAJI4azaCpnyZ12qdJOXnKgDkp1vE1Yq1lV/k7tAFDPvN5UiP0uuyK/ye/Yu9/fY/m8o2YO28i/9dLScjKidrzB8XyZ6Z+09vsB5JGj6OgOAxJvWGif0eOrtdBzL333ktFRQU33nhjp88dHjl2J/O9u9ccKSqdPXs2M2fO7DSGvXv30traesx794Qxhtz6YFOvbHZXebAjdHom1B9k94Z1mJHFfbqVMYaCggJ2794dtlMO/lV/w79tM6Slc+jsL9O46+g5Ffa8r8Gbq/Hv+Ihdv/wpvmsWHvG14n/1JWx9HQzNZ3/+CMwx7t1TkZgPrzPGUHDev1A36XR832mF7Zvxv7sO+9462LaZ1o+2UvfRVurKHoTkZEzJKZgJUzGnTG9vldGPdPUasXt20rZ3DyQmUj2kgJowvy6PxV75I7j5h7Tu+oSdP72WhP+4KWrBZDz8zLS96QTUrcePZVeU574r8TAnsZKYmNjtBYheBTH33Xcfa9eu5YYbbgg5UZSdnQ04Kyk5Oe1RfG1trbuykp2dTWtrK3V1dSGrMbW1tYwbN8695sCBA52et+N9DpeUlETSEVZDIvECaNu313knJ9ebL7Bhw2HrJuyeHTDi+LDc0loblrmwjYfwlzkdd82FcyE989j3TUzCN+/f8f/8P7DrX8f/z+fxffaLXV7qbiWdcQ4EtwYjIFzz0Z9Ya8Hng9Hj8I0eB1+52FmV2fg2vBcIamr2Y99di313LfzpXkzpdfhO/3yshx4RHV8j/o2BnkYnnATJKdF/7QzOxjf//+G/5Yfw4Qe03Xsrvu/9Z1jaHnRXLH9mOubDxNPPrf4dOboevTqttdx77728/vrr/OxnPyM/Pz/k8/n5+WRnZ7sJugCtra1s3LjRDVBGjx5NQkJCyDXV1dVUVFRQUlICOPkvDQ0NbNmyxb1m8+bNNDQ0uPeJtbb9gSDGa0m9ASaOeyjZ58vgwH6n2NfnZx77CwLMyGLMv3zbucef7ukycdlW7oJN74AxmDO+ELYxS++Z9Ex8p87A9935+H6xFN9/34GZcxmMHQ/Wj33w9+1N9Pox+37gaPWJkT2VdDSmcAS+qxc69X/WvoJ97P6YjSWabH2dcyoMoI9F7iS6ehTE3Hvvvfzzn//kuuuuIzU1lZqaGmpqamhubgac5a8LLrjArSdTUVHBnXfeSUpKCjNmzAAgLS2Ns88+m+XLl/POO++wbds27rjjDoqKitxk3xEjRjB58mSWLFlCeXk55eXlLFmyhKlTp3aZ1BsLbYF/VD1VrbejOD1mbWv2Yf9aBoDvq5f0ONfInDsLSiZAU6Nz7PqwaqR2tVOhl/GTnQRUiSvGGMxxo/B9cTa+6xfBmJOcXj9Lf+tUU+2nrL8NolAfpjvMuAlOZWacPygGxNHrLe87b4cdhxmcHdOhSM/0aDvp+eedeh3//d//HfLxq6++ms997nMAzJo1i+bmZu655x7q6+sZM2YMCxcudE8KAVx66aUkJCRw2223ucXuFixYgK/DsuX8+fO57777WLRoEQDTpk1j3rx5vfkeI8LdTvLoSgyBRpA2zhpB2j8/AM1NcMKJMO0zPf5640vAV/oD/DfMd7bLnl2Bmfl1595tbW6zR58q9MY940vAd9l1+G+4Dja9g33pacwXvhzrYUVGxVZoqIPUNDh+7LGvjzDf6Z/Dv+tj7DOPYu//X+zIYkxB7zo6e4Hd/B6go9Ve1KMg5pFHjl3e3RjD3LlzmTt37hGvSU5OprS0lNLS0iNek5GRwfz583syvKgKrsR4qeVAR2ZYIRYgjoqQ2Yqt2FdeBMA3p7TXRwvNkHzMN6/E3nsb9qmHnWq+x4+FQL4FGZkw6bRwDl0ixOQPx8y5zNlSeux+7MlT+uUv0+BWEiUTMAkJsR1MgPnKN7EffgCb3sH/+8X4/utXmJT+eWos2PQRBTGeo95JvdS2zzmR5LlqvUGBnBgOHsA21MV2LDj5Vv5H7wNrMad+FnPCiX26nzntc5jpM6CtzWkS2dTUntB7+uc9dSR+oDNnnQ/jpzjds+/7Tb9sWNjeL2lyTMfRkUlIwHfF9ZCVAzs+wj74u36ZYGqbm2C7k39pxp4c49FITymI6SXPr8QMSnU6WgPsiYPVmA1rnJyAxETM7O/0+XbGGMy3v+9s9+3egb3/dtjwpvM5bSV5ijEG36XXtjf8fO6xWA8prGxLs5uTYU6KXVJvV0xWjhPIGB/21RfdVh39yrbNTs2vrFwIc9dwiTwFMb1gW1rw19Y4D7yaEwNucm+s82Jsayv+FUsBMF/4MiYvPMWdTHomvsuuc57jzX9CWxsUl2COGxWW+0v0mNyhmIu/B4B98mFsxdYYjyiMtrwPLc3OL9HCkce+PsrMuFMw//ItAOwf78J+vC3GIwqvjvkwqo7rPQpieiPYMykxySka51EmTk4o2X8+D7s/gYxMzAVzwnpvM35ySDKomXFOWO8v0WNO/xxMOR3aWvHfdxu2Q5sRL7Nuq4GJcftL1Hzpa3DKdGdL7/e3YBvqYz2ksFE+jLcpiOmNQM8kPNYzqZNgEBPDRpC2oR77l4cAMF++GJOWcYyv6Dnz1Utg9Din7sypZ4b9/hIdxhh837kGMrOcHI0nH4r1kMLCbfoY46PVR2N8PnylP4DcPKjchf/+O/pFfoz1t8GHHwBgVB/GkxTE9IJ1u1d7Mx8myAyLfcE7++wKqKuFguMwZ34pIs9hklPwLViMb9ESTGpaRJ5DosNkZjmBDGCfK8MG63t4lK2va08qPTF+gxgAkzEY31ULnEJ4617Brnwy1kPqu4+3Q+Mh52j7CG0ze5GCmN6odoIY4+V8GGg/oVS5MyZ/VdmqPdgX/gKA72vfxST2qan6URmfz9urZuIyU07HfPrzYP34l/4G29QY6yH1mt30Dli/E8R7oJO3KS7BzHFKY9gVS50j2B4WzIfhhJMwvvg42i49oyCmF9pXYjwexOQVgjFwqAEO1kT96W3ZcmhtgXGnwKRPRf35xbvMN66AnKFQuQv72LJYD6fX2vNh4nsVpiNz9oWYaZ9xyhfc9QtsXW2sh9RrwXwYFbnzLgUxvRHIifFsjZgAk5QEQwL9r3ZHd0vJfrQF+8bLYEyfCtvJwGTSMvAFS+O/9Ax24/rYDqiX3Powcb6V1JExBnPptc5K7v4qp72HB1tCWGsh2PRR+TCepSCmF/rNSgy4W0pdNUuMJP+TDwNgPnUmZtQJUX1u6R/M+MmYz18AgH/Z7XFRtLEnWqv2wO4dYHzOaqSHmNQ0fN9fAEnJ8O5aJ7fNayp3QW0NJCZCcexbPUjvKIjpjerASozHE3shNsesbcVWePsNp5N0oK+RSG+Yr30X8guhugr78N2xHk6PNK13ii8y6gRMevhP5UWaGVGM+eaVANgnHnK3xrzCPVp9fAkmKTm2g5FeUxDTQ9ba9joxHt9OAtobQUZxJcb/tNODy0yf0S/74Ej0mJRB+C77QaCi7EvYt16L9ZC6rXH964C38mEO55txLuaMLzhJ1nf/qn2V2gvU9LFfUBDTUw31TnVN8PwRa3AaQQJRW4mxOz6Cda84z32hVmGk78yYkzDnzQbAv/xObLCadhyz1tL4dqANhoeDGADzzavguFFw8IATyHSzt5Vta8NW7sK+uw7/i09x4OF7sOXvRq03lt2spN7+IHJnWvurwF8avsFZmKRk7xd8CqzEULkL6/djfJGNa21gFYapZ2COK4roc8nAYb7yTew7a2DHR/gf+D983/+v+E4W3/Ux/v1VTk7JmJNiPZo+MSkp+K5agP+m/4Dy97B/fgDztUsBp6UIVXtg7y5s5S7n35nKXU6BzX2VTiuQAPeMU1oGZsJUmHgqZsK0iGy12QPVTk6MMdDHZrMSWwpieipQIyYhNw+Phy+O3DyneFVLs/O9DcmL2FPZXZ9g16wCwHfh3Ig9jww8JikJX+m/4f/59fDWa9jX/445/fOxHtYRuUerx4zvF/kYpmAE5tJrsXf9AvvcY7RtK4f9e51A5Wgnl5KSIb8Qk1/IoMxMDq19DeoPOicX33gZ6/PBmJMwE0/FTDwVCkaEJzgN5sMcd3xEqoRL9CiI6ansXMw5XyFt+Aj6Q/cQk5AAeQVO76I9OyIbxDzzKFgLkz6FKRodseeRgckUjcZ8+RvYPz+Afegu7EmTMVk5sR5Wlzr2S+ovfKfOwL/5PexLT8Omd9o/kZziJF/nF2Lyh7tBC3mFzr+ngUKUQwsL2bnjE+yHH2DffhO74U3YWeGs7pS/h12xzGkdEgxoSk7GJCb1aqztW0neXgUTBTE9ZkYcj+8bVzC4sJD6XbtiPZzwGDYcdn+CrdyJGT85Ik9hK3diX/8HAD6dSJIIMV/6mpPc+9EW7IqlmHn/HushdWLL38VufAsgYj9vsWLmznPyYxISnEAlfzhk5XR79cT4EmDMeKduy9cuxe7djX1njRPQbHoH9u7GrnzSaXkwKBXGT3G6T+cMgawcpxN4Vg4mOeWoz+NW6h17cl+/ZYkxBTGCGTbc2RqLYHKvfWaFU159wjTM8arJIJFhEhLwffv7+H9+Pfa1v2NnfBEzbkKsh+Wy617Ff/evoLWFlEmn0lrUv2okmcREzFnh64Fm8gowZ8+Es2diGw/BxvXYDW86+U+1NU4Pp3WvdN7aT0t3AprsXEwgsCE7B7KGYDIynZ5JKKm3P1AQI24360g1grRVe7CvvQRoFUYizxw/FnPmedh/PIf/od/j++lvItqXq7v8Lz+HfeD3YP2Yyacx9Ge/Zs/+au8fDogSMygVpn4aM/XTToXgjz50Vmh2fYw9sB8OVDvV1FuanVOkDfXO5w67j/s4r8D7/e9EQYyAyY/sSox99jHnFMJJkzA6CSBRYGZ/B7v2FdhZgX3xScwXZ8dsLNZa7FN/wv7lIWdsn/0ivm9fjS9lUMzG5HXG54PisZjDKu1aa+FQvRvQ2AP7ncDmQDUcqHbq2Byohvo6zOcvjNHoJZwUxEj7Meuq3djW1rD+1Wr378WufgEA38xvhO2+Ikdj0jMxF30Xu+x27F8exp56Zkx6nVl/G/aPd2P//owzrgvnYmZ9K+KlDAYqYwykZTj/FY4kjg/ZS5joJ0mcon3JKc5RyH2VYb21fe5xaGuFkgmYEiXRSfSYT5/t1ABpOoR95N6oP79tacZ/1y+dAMYYzMXfw/cv347v+jUiHqMgRpx/VPODPZR2hO2+tmY/9p/PA8qFkegzPh++b33faUmwZlVUO13bhnr8v70B1r4CCYmYK36I7+yZUXt+kYFCQYwA7Y0gw5nca/9aBq0tzl/DJ/afehjiHWZkMeZsJ/fB/9ASbEtLxJ/THqjG/6sfO0eCB6Xiu+7/4Tt1RsSfV2QgUhAjjmA36zA1grS1NdiXnwWcXBgtoUusmK980zliu2cH9vmyiD6XrdyJ/5b/hI+3QWYWvut/7vneSCLxTEGMOMK8EmOf/zM0N8PxY+HkKWG5p0hvmLR0zEWXAWCfeQRbtSciz2M/2oL/lgVOr6C8Anw/WowZ1b/qwIjEGwUxAoAJnlAKQxBjD9a6pzG0CiPxwJx2Fow7BZqb8f/pnrDf325cj/+XC+HgARhZjG/BYqfEvohElIIYcQT/wd2/F9vc1Kdb2Rf+Ak2NUDQaJk4Pw+BE+sYYg++bV0JCAqx/3SmSFib+N/+J//YboekQnDgR3w9vjtueTSL9jYIYcWRkOrUVwGlR30u2vg774pMA+C78ulZhJG6Y4UWYc74CgP+Pd/U9WG9rw//Un7B3/8opIzDtDHzz/x8mNS0cwxWRblAQI0DgmHUwL2bHR72+j135JDQecprATT4tXMMTCQsz8xuQMxSq9mCfe6zX97G7PsG/eAH2iQfBWsznLsD3vR9iknrXVVlEekdBjLhMwQgA7H23OUW6PvqwR19vDzVgV/7FudeFX1dVUok7ZlAqvq/PA5x2GLaHq47W78f/whP4/+cHsK0cUtMxl12H+eaVTgdmEYkqtR0Ql7lwLvZANWx8C/vmP7Fv/tPZ4z9vNpw89ZhbQ/bFp5yma4UjMdM+HaVRi/TQ1DNg/BTY+Bb+P96Fb/7PurXtaffuxr/sdih/1/nA+Mn4Lr0Wk5sX4QGLyJEoiBGXGTachH+7AfvxNuzzZdg3XoYPNuD/YAMcNwpz3lcxp362y95K/oZ6/M//2bnPBXP0V6nErWCSr/+//xXeXQtvvQZTjxx0W2ux//wr9pGlTvJuyiDMRZdhzvqScr5EYkzr/dKJGVmMb96/4/v53ZhzZkFKKuz4yNlm+vH38D9fhj3UEPI1dc+sgPqDkD8cc+pnYzRyke4xw4ZjzvsqAP4/3Y1tauzyOlu9D//tN2CX/58TwIwdj+9nv8X3ufMVwIjEAQUxckRmSB6+r8/Dt/hezFcvcaqeVldhH12Kf8E8/I/dj63Zh21q5ODjDzhfc8EcTIJWYST+mfPnwJB82F+FffpPIZ+z1uJ/7aXAas06SEzCzCnFd/0iTH5hjEYsIofTdpIck0nPwJx/EfacWdjXXnKq8e7+BPvcY9i/PQGjToAD1TB0mFNUTMQDTEoKvm9cgf/ORdjnn8B++guYwhHY2hr8D/4O1r3qXDhqDL55/4YpHBnbAYtIJwpipNtMUhLms1/EfuYc2PAm/r+WwZaNsHUTAL4L5nSZLyMSr8zk02Diqc7r+aHf4/v8Bfgf+J1TeTchATPzG5jzL9Lqokic0m8c6THj88Hk00iYfBr2ww+wLz5Fano6TWecHeuhifSY7xtX4H//7fYkdoDjRuEr/QGmSL2PROKZghjpE3PCifjGnMSQwkJ27dqFtTbWQxLpEZNXgLngIuwTD4HxYb70VcyXL1bhOhEPUBAjIgOeOX8OZA/BjCzGjBoT6+GISDcpiBGRAc8kJGBmnBvrYYhID+mItYiIiHiSghgRERHxJAUxIiIi4kkKYkRERMSTFMSIiIiIJymIEREREU9SECMiIiKepCBGREREPElBjIiIiHiSghgRERHxJAUxIiIi4kkKYkRERMSTFMSIiIiIJ/X7LtaJiZH7FiN5by/SfITSfHSmOQml+Qil+ehsIM5JT75nY621ERyLiIiISERoO6kXDh06xIIFCzh06FCshxIXNB+hNB+daU5CaT5CaT4605x0j4KYXrDWsm3bNrSI5dB8hNJ8dKY5CaX5CKX56Exz0j0KYkRERMSTFMSIiIiIJymI6YWkpCQuuugikpKSYj2UuKD5CKX56ExzEkrzEUrz0ZnmpHt0OklEREQ8SSsxIiIi4kkKYkRERMSTFMSIiIiIJymIEREREU8aeE0ZAjZu3Mhf/vIXtm3bRnV1Nddffz2f+tSn3M/X1NTw4IMPsmHDBurr6znppJMoLS2lsLAw5Jrly5ezYcMGGhsbGT58OLNnz+b00093r6mrq2Pp0qWsWbMGgOnTp1NaWkp6enr0vtluiNZ8XHPNNezduzfkuWfNmsW3vvWtyH+TPRCO+di9ezfLly/ngw8+oLW1lUmTJlFaWkp2drZ7jVdeHxC9OfHCa6SsrIw33niDHTt2kJycTElJCd/+9rcZPny4e421lkcffZSVK1dSV1fH2LFjmTdvHiNHjnSvaWlpYfny5axevZrm5mYmTJjA5ZdfzpAhQ9xrvPIaieacDKTXyAsvvMCqVavYtm0bhw4dYunSpZ3+33vlNRIJA/Z00ltvvcWmTZsoLi7m17/+dcg/yNZafvKTn5CYmMh3vvMd0tLSeOqpp1i/fj233norgwYNAuB//ud/aGhoYN68eWRmZrJq1SoeeeQRbrnlFoqLiwH4+c9/zr59+7jyyisBWLJkCXl5efzoRz+KzTd+BNGaj2uuuYbPf/7znHPOOe5zDxo0yL1HvOjrfDQ2NvLDH/6QUaNGMXfuXAAefvhhqqurWbRoET6fswjqldcHRG9OvPAaWbRoEZ/5zGc44YQTaGtr4+GHH6aioiLk5+HPf/4zZWVlXH311RQWFvL444/z/vvv85vf/IbU1FQA7r77btauXcvVV19NZmYmf/jDH6irq2Px4sWee41Ec04G0mvk6aefpqWlBYCHHnqoyyDGK6+RiLBi58yZY19//XX38Y4dO+ycOXNsRUWF+7G2tjZ72WWX2RdeeMH92Le//W37j3/8I+Rel112mV25cqW11tqPP/7Yzpkzx5aXl7uf37Rpk50zZ47dsWNHpL6dPovUfFhr7dVXX22feuqpCI4+/HozH+vXr7dz58619fX17jUHDx60c+bMsW+//ba11ruvD2sjNyfWevM1cuDAATtnzhz73nvvWWut9fv99oorrrBlZWXuNc3NzfbSSy+1zz//vLXW2vr6evuNb3zDrl692r1m3759du7cufatt96y1nr7NRKpObF24LxGOnr33XftnDlzbF1dXcjHvfwaCQflxHShtbUVIKTIkM/nIzExkQ8++MD92Iknnsgrr7xCXV0dfr+f1atX09LSwvjx4wEoLy8nLS2NsWPHul9TUlJCWloamzZtitJ303fhmo+gJ554gtLSUn74wx/y+OOPu/f3iu7MR0tLC8aYkGuSk5MxxrjX9JfXB4RvToK89hppaGgAICMjA4DKykpqamqYNGmSe01SUhLjx493/99u3bqVtrY2Jk6c6F6Tm5tLUVER5eXlgLdfI5Gak6CB8BrpDi+/RsJhwObEHM3w4cPJy8vjoYce4nvf+x6DBg3iqaeeoqamhpqaGve6f/u3f+O2226jtLSUhIQEkpOT+eEPf0hBQQHg5AhkZWV1un9WVlbIfeJduOYD4Pzzz2f06NGkp6ezZcsWHnroISorK7nqqqti8J31Tnfmo6SkhJSUFB588EEuvvhirLU8+OCDWGvda/rL6wPCNyfgvdeItZb777+fE088kaKiIgD3+zn8/29WVhZVVVXuNYmJie4vtY7XeP01Esk5gYHzGukOr75GwkVBTBcSExP5j//4D373u99RWlqKz+fjlFNOYcqUKSHXPfzww9TX1/PTn/6UzMxM3nzzTW699VZuvPFG94XaFWstxphIfxthE875mDlzpnv9qFGjSE9P59Zbb+Vb3/oWmZmZUf2+eqs78zF48GD+/d//nXvuuYdnn30WYwyf+cxnKC4udvf1j8Rrrw8I75x47TVy7733UlFRwY033tjpc4f/f7TdSEHs7jXx/BqJ9JwM9NdId8T7ayRcFMQcwejRo/nlL39JQ0MDra2tDB48mB//+MeMHj0acE5ZPPfcc/z61792M8mPP/54PvjgA5577jm+973vkZ2dzYEDBzrdu7a2tsvIOZ6FYz66UlJS4n59PP7jcyTHmg+ASZMmcccdd1BbW0tCQgLp6elcccUV5OfnA/Sr1weEZ066Es+vkfvuu4+1a9dyww03hJyeCZ62qqmpIScnx/14x/+32dnZtLa2UldXF7LyUFtby7hx49xrvPYaifScdKW/vka6w4uvkXBSTswxpKWlMXjwYHbt2sWHH37IqaeeCkBzczPQOYr2+XxuJF1SUkJDQwNbtmxxP79582YaGhqO+gMZz/oyH13Ztm0bQMgPsZccaT46Gjx4MOnp6bz77rvU1tYyffp0oH++PqBvc9KVeHyNWGu59957ef311/nZz37WKQjLz88nOzubDRs2uB9rbW1l48aN7v/b0aNHk5CQEHJNdXU1FRUV7i9lL71GojUnXemvr5Hu8NJrJBIG7EpMY2Mju3fvdh9XVlayfft2MjIyGDp0KK+++iqDBw9m6NChVFRUsGzZMk499VQ3CWv48OEUFBRw9913853vfIeMjAzefPNNNmzYwIIFCwAYMWIEkydPZsmSJVxxxRUA3HXXXUydOjWkVkA8iMZ8lJeXU15ezoQJE0hLS2PLli3cf//9TJ8+naFDh8bk+z6Svs4HwEsvvcRxxx3H4MGDKS8vZ9myZVx44YXu/3svvT4gOnPildfIvffey6pVq/jP//xPUlNT3dyDtLQ0N1n5ggsuoKysjMLCQgoKCigrKyMlJYUZM2a415599tksX76czMxMMjIyWL58OUVFRW5iq5deI9Gak4H0GgHcvLLgz15FRQWpqakMHTqUjIwMT71GImHA1ol57733uOGGGzp9/KyzzuKaa67hmWee4cknn3SX+s4880wuuugiEhPb475du3bx4IMPsmnTJhobGykoKODLX/4yZ555pntNXV2du5wIMG3aNObNmxd3RYiiMR9bt27l3nvvZceOHbS0tJCXl8cZZ5zBrFmzSElJidr32h3hmI8HH3yQv//979TV1ZGfn8+5557LhRdeGLJa5ZXXB0RnTrzyGgnWuTnc1Vdfzec+9zmgvZDZCy+8QH19PWPGjGHevHkh+XLNzc088MADrFq1KqSwW8dfxl55jURrTgbaa+SRRx5hxYoVR72PV14jkTBggxgRERHxNuXEiIiIiCcpiBERERFPUhAjIiIinqQgRkRERDxJQYyIiIh4koIYERER8SQFMSIiIuJJCmJERETEkxTEiIiIiCcpiBERERFPUhAjIiIinqQgRkRERDzp/wPAVgxbQUkuTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 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": 42,
   "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 640x480 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": 43,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 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 the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using 'df.loc' select row years from columns 'India' and 'China' from dataframe 'df_can'\n",
    "df_CI = df_can.loc[['India', 'China'], years]\n",
    "df_CI"
   ]
  },
  {
   "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\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": 45,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 46,
   "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": 46,
     "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": 47,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start by converting the index type of 'df_CI' to type 'int'\n",
    "df_CI.index = df_CI.index.map(int)\n",
    "\n",
    "#Plot the data as line graph\n",
    "df_CI.plot(kind='line')\n",
    "\n",
    "#Assign a graph title, X and Y labels \n",
    "plt.title(\"Immigration from India and China to Canada from 1980 to 2013\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    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": 50,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using the function 'sort_values', order the column 'total' in dataframe 'df_can' by ascending\n",
    "df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
    "\n",
    "#Return the top 5 rows into variable 'df5' \n",
    "dft5 = df_can.head(5)\n",
    "\n",
    "#Transpose the dataframe\n",
    "dft5 = dft5[years].transpose()\n",
    "\n",
    "#Standardize the data type of the first 5 rows to type 'int' \n",
    "dft5.index = dft5.index.map(int)\n",
    "\n",
    "#Plot the data as a line graph and set the draw window size by calling figsize('width' ,'height')\n",
    "dft5.plot(kind='line', figsize=(14,8))\n",
    "\n",
    "#Assign a graph title, X and Y labels \n",
    "plt.title('Immigration Trend of the Top 5 Countries')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "#Plot the graph\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Other Plots\n",
    "\n",
    "Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
    "\n",
    "*   `bar` for vertical bar plots\n",
    "*   `barh` for horizontal bar plots\n",
    "*   `hist` for histogram\n",
    "*   `box` for boxplot\n",
    "*   `kde` or `density` for density plots\n",
    "*   `area` for area plots\n",
    "*   `pie` for pie plots\n",
    "*   `scatter` for scatter plots\n",
    "*   `hexbin` for hexbin plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01),\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01),\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01),\n",
    "[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2022-01-01)\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n",
    "| ----------------- | ------- | ------------- | ---------------------------------- |\n",
    "| 2021-05-29        | 2.4     | Weiqing Wang  | Fixed typos and code smells.       |\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",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}