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FuzelahamedShaik/DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb

Created June 8, 2020 13:27
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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
},
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely <a href='http://cocl.us/PY0101EN_DV0101EN_LAB1_Coursera'>**Python for Data Science**</a> and <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which are part of this specialization. \n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which is also part of this specialization. \n",
"\n",
"------------"
]
},
{
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
"1.2 [*pandas* Basics](#4) <br>\n",
"1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
"2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
"\n",
"*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
]
},
{
"cell_type": "markdown",
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod1Fig1-Dataset.png\" align=\"center\" width=900>\n",
"\n",
"For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
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"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
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"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 1,
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"button": false,
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},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"run_control": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
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" <td>2978</td>\n",
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" <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",
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" <td>1450</td>\n",
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" <td>Immigrants</td>\n",
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" <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",
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" <td>3623</td>\n",
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" <td>5393</td>\n",
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" <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",
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" <td>0</td>\n",
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"<p>5 rows × 43 columns</p>\n",
"</div>"
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"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"outputs": [
{
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" }\n",
"\n",
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" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
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" <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",
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" <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",
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],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Type 195 non-null object\n",
" 1 Coverage 195 non-null object\n",
" 2 OdName 195 non-null object\n",
" 3 AREA 195 non-null int64 \n",
" 4 AreaName 195 non-null object\n",
" 5 REG 195 non-null int64 \n",
" 6 RegName 195 non-null object\n",
" 7 DEV 195 non-null int64 \n",
" 8 DevName 195 non-null object\n",
" 9 1980 195 non-null int64 \n",
" 10 1981 195 non-null int64 \n",
" 11 1982 195 non-null int64 \n",
" 12 1983 195 non-null int64 \n",
" 13 1984 195 non-null int64 \n",
" 14 1985 195 non-null int64 \n",
" 15 1986 195 non-null int64 \n",
" 16 1987 195 non-null int64 \n",
" 17 1988 195 non-null int64 \n",
" 18 1989 195 non-null int64 \n",
" 19 1990 195 non-null int64 \n",
" 20 1991 195 non-null int64 \n",
" 21 1992 195 non-null int64 \n",
" 22 1993 195 non-null int64 \n",
" 23 1994 195 non-null int64 \n",
" 24 1995 195 non-null int64 \n",
" 25 1996 195 non-null int64 \n",
" 26 1997 195 non-null int64 \n",
" 27 1998 195 non-null int64 \n",
" 28 1999 195 non-null int64 \n",
" 29 2000 195 non-null int64 \n",
" 30 2001 195 non-null int64 \n",
" 31 2002 195 non-null int64 \n",
" 32 2003 195 non-null int64 \n",
" 33 2004 195 non-null int64 \n",
" 34 2005 195 non-null int64 \n",
" 35 2006 195 non-null int64 \n",
" 36 2007 195 non-null int64 \n",
" 37 2008 195 non-null int64 \n",
" 38 2009 195 non-null int64 \n",
" 39 2010 195 non-null int64 \n",
" 40 2011 195 non-null int64 \n",
" 41 2012 195 non-null int64 \n",
" 42 2013 195 non-null int64 \n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in *pandas* objects are *float*, *int*, *bool*, *datetime64[ns]* and *datetime64[ns, tz] (in >= 0.17.0)*, *timedelta[ns]*, *category (in >= 0.15.0)*, and *object* (string). In addition these dtypes have item sizes, e.g. int64 and int32. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python \n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country')."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
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"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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"run_control": {
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},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
" 1. The full row data (all columns)\n",
" 2. For year 2013\n",
" 3. For years 1980 to 1985"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
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"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
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"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\n",
"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
"\n",
"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
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"run_control": {
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}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>16</td>\n",
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" <td>58639</td>\n",
" </tr>\n",
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" <td>Asia</td>\n",
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" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
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" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 \n",
"Bangladesh Asia Southern Asia Developing regions 83 \n",
"Bhutan Asia Southern Asia Developing regions 0 \n",
"India Asia Southern Asia Developing regions 8880 \n",
"Iran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \n",
"Maldives Asia Southern Asia Developing regions 0 \n",
"Nepal Asia Southern Asia Developing regions 1 \n",
"Pakistan Asia Southern Asia Developing regions 978 \n",
"Sri Lanka Asia Southern Asia Developing regions 185 \n",
"\n",
" 1981 1982 1983 1984 1985 1986 ... 2005 \\\n",
"Afghanistan 39 39 47 71 340 496 ... 3436 \n",
"Bangladesh 84 86 81 98 92 486 ... 4171 \n",
"Bhutan 0 0 0 1 0 0 ... 5 \n",
"India 8670 8147 7338 5704 4211 7150 ... 36210 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \n",
"Maldives 0 0 1 0 0 0 ... 0 \n",
"Nepal 1 6 1 2 4 13 ... 607 \n",
"Pakistan 972 1201 900 668 514 691 ... 14314 \n",
"Sri Lanka 371 290 197 1086 845 1838 ... 4930 \n",
"\n",
" 2006 2007 2008 2009 2010 2011 2012 \\\n",
"Afghanistan 3009 2652 2111 1746 1758 2203 2635 \n",
"Bangladesh 4014 2897 2939 2104 4721 2694 2640 \n",
"Bhutan 10 7 36 865 1464 1879 1075 \n",
"India 33848 28742 28261 29456 34235 27509 30933 \n",
"Iran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \n",
"Maldives 0 2 1 7 4 3 1 \n",
"Nepal 540 511 581 561 1392 1129 1185 \n",
"Pakistan 13127 10124 8994 7217 6811 7468 11227 \n",
"Sri Lanka 4714 4123 4756 4547 4422 3309 3338 \n",
"\n",
" 2013 Total \n",
"Afghanistan 2004 58639 \n",
"Bangladesh 3789 65568 \n",
"Bhutan 487 5876 \n",
"India 33087 691904 \n",
"Iran (Islamic Republic of) 11291 175923 \n",
"Maldives 1 30 \n",
"Nepal 1308 10222 \n",
"Pakistan 12603 241600 \n",
"Sri Lanka 2394 148358 \n",
"\n",
"[9 rows x 38 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <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",
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" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
"\n",
"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/). As mentioned on their website: \n",
">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
"\n",
"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Matplotlib.Pyplot\n",
"\n",
"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['fast', 'seaborn-deep', 'seaborn-white', 'grayscale', 'classic', 'seaborn-colorblind', 'seaborn-darkgrid', 'seaborn-pastel', 'tableau-colorblind10', 'seaborn-poster', 'seaborn-bright', 'seaborn-dark', 'bmh', 'seaborn-talk', 'dark_background', 'seaborn-dark-palette', 'seaborn-notebook', 'fivethirtyeight', 'ggplot', 'Solarize_Light2', 'seaborn-whitegrid', '_classic_test', 'seaborn-ticks', 'seaborn-muted', 'seaborn', 'seaborn-paper']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in *pandas*\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd4b4018240>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind=\"line\",c=\"blue\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line',c=\"blue\")\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
" \n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
" \n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" We will cover advanced annotation methods in later modules."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[[\"China\",\"India\"],years]\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd4af96ac50>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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QhPOBHECf9z5uy3BkdXxYRQOG9rgtIzDWYAhqaKgpOFvVfagpgN8fGnJ6On/wewkJQf7zHxtFRRI3Lfo3FH0f2lKzETlxQkEtt2apHTkYtVOLgCAI5wFN8TaU3lxctezGKbtoOkpvHvr8jys/KODCmLMGt/VqArqKd/YK1/ffqykrU5yVP/i97t39LF5czDf7OjN71XyUrqMRqTsSZBkmTUpg0qT6meOrKdpC4o5RaIq+isr5RUAQhPOAPi+NoMKAx3plrcp74ofg17fHmL2q0mMMeWmnhpr+qbbNPMfp/EFlbwinjR3rZuaff+aljDtZ80rtJtJFQ2amhh9/VJOZKWGzRe92qnQdI37/HVj33ITkdyBF6S1BBARBaOqCXvT5H+O2Xo2srOWWkZKC0lanh6BWMAFMljFmnx5qGrnlI7Zt09C2rZ8WLaq/wd3/sJKRyenMf74/LlfEmlAnq1cbUKlkZFli0yZtxM8v+cswZy0k6dthaG1fYm/3AHn9NuKxDI14XSACgiA0eVrbRhT+YlzN6jbqx9V8QmgI6vFzJ6ppSrahLjtAWas/RWSoKYS6W7Ztqzx/8HtKtYo5E9MoKTOwYUN4yzpHg82m4JNP9NxyixOrVSYjI4IBQQ6iz32HpG8HYz62DFfSaPL6fxVaMypCyfyKiIAgCE2cPi+NgCoeT/yQOp2nfAhq3rlDUI3HQ0NNXc1qPqS1Oj/+qKKoSFll/uD3Bg7V0zrhGOvebvh1Odet0+P1SkyZUsaVVwbZtElLMAI9OeqSnVi/u574H2YT0LYkv9cHFF+6jKC2BUD57O1oEAFBEJowyV+GruAz3EljwlpqujIVDUFVuI+jy/8fZS0mIUfw6bSm+YMzeZOuYerg19j8tYmcnIa7fckyrFljpG9fD507+7nqKpnCQiV799b+70AKOIk7OJPEXdej9JygqMtzFPT+AF/sb7vMFRYquPrqRNLTI989BSIgCEKTpiv8DEXQHdZktKpUNATVeOJ1QMbZampE6jht2zYNSUkB2rWr+SOv39iZyVd+STCo4N13a5kviYBvvtGQlaVi8mQnAFdeGUSSZL78svY3amP2yxhOvoejzd3k9duMq/mEs3aYczolpk61kJWlIi5OJJUFQfgd/cn1+LUt8cb2jdg5y1r96bchqAEXhhNrTq1qGpmhphB6ws7M1NKvnze8lIQk0apHdwZ3/op1a7UNNiVh9WoDsbFBxowJZbetVkhJ8ZGRUcvcRtCH8fjruOOH4Gj/ELLKdNbXfj/85S/x7NmjZuXKIlJTfXW9hAqJgCAITZTCa0NbtDk09yDMvYqr4rEMxa9vhzF7Ffq8D1D6iyhrNS1i5wfIzlaSkxNe/uA0d+JobhvyCj9lafnuu7p3k4WrsDCUTP7DH5ycuUPn8OEedu1SY7OFn3TXFXyG0ptb4c9ZluHhh2NJT9exYEEJV13lrkvzqyQCgiA0Ubr8j5Bkf51HF51DUlB2aghqzM8L8Rm74I0bFNEqapM/OM1n6s64oZkYtC7Wrav/bqN33tHj80nccovzrM+HD3cjyxKbN4f/lmA8/ip+XWs8CVec891zz5lYs8bIrFkOpkxxnls4gkRAEIQmSp+Xhs/QEb+xa8TP7Tw1BFXpzYvoUNPTvv1WQ2xskC5d/OEXliTUbYYyvu+7fPC+rl7nJMgyrF5tpF8/D506nd32nj19xMcHwh5+qio9gLbkG8paTj1rAyOAt9/W8+yzMUyY4OSBBxx1bn91REAQhCZI6T6OtmTbqe6iyN6sjx1TsuzFFvR85CDJcw9gt9wY0fNDaIZvaqoXpbL6YyviShzNbUP+g92hrNc5CVu2aPj559+SyWdSKmHoUE/Yw0+Nx19FVuhwtph41udffKHlgQfiGDbMzTPPFEf6r7lCIiAIQhOkz3sfIGLdRfn5Cv7zHyPXXWdl4MBmPPVUDB5FM/b/eikbMsJbLK86BQUKfvpJzYABtV+CwmdOYXDKIVonnazXbqM1a4zExQUZPbri15Lhwz0UFCjZv79muQ3JV4z+5Hs4k8Yhqy3ln+/erWbGjHi6dvXx0ktFqOspVSICgiA0Qfq8NLzmXgT0F9f6HCUlEm+/refmmxPo3bsZjz4ai8sl8dBDdjIzT5Kenk+LFgHeeCOyN9zf8gfhJ5TLSQo8Sdcw9bJVbN6srZc5CQUFCj79VMf48Wcnk880bFjommrabWTIXYsi6KKs1W3lnx09qmTKFAtWa5DXX7dhMtXfUCoREAShiVGVHUZd+n2dZg0vWGAmJaU5994bz6+/Kpk5s5Qvv8wjPT2fu+8upXXrACoVTJ5cxubNOo4erWXfTgW2bdOg0wXp0aNuQyfdiaO5bfAqgkGpXuYknE4m33pr5YldqzVIz55evvyyBt1YcgDj8dfwxPbDb+4OhEYwTZ6cQDAIq1cXkpQUvaWuKyICgiA0Mfq8NGQUuBKvq1X59ev1rFxp5tprXXz8cT5ff53HAw84zkmSAtx8sxOlUmbNmsjdcLdt09C7tw+Npm7n8camcnHrUi7rvp+1aw1RnZMQukEb6d/fQ8eOVSfChw/38N13aoqKqu7019q+ROX+pfztwOUKTTzLzVXw2ms2OnSI4hoVlRABQRCaEllGfzINb/xlBLVJYRf/5Rclc+fG0revh+efLyYlxVdlsrJFiyBXXulm7VoDnjr08Jxmt0scOFC3/EE5SYnbejW3DVpOVpaKnTuj19G+ZYuGo0crTib/3vDhboJBic2bq+42Mma/QkDTDLc1tMPd88+b2LVLw8qVxfTpE52JZ9URAUEQmhC1Yxcq9y84a7FUhc8Hd90Vj0IBy5cXo6rh+nC33uqksFDJ//5X99E8O3ZoCAaluuUPzuBKHM3Efmsw6HxRTS5Xl0w+U69ePuLiglV2GymdP6Er2khZy1tBoeb4cSUvv2zixhudUZ14Vp1qfyW8Xi+PPfYYfr+fQCDAgAEDuOmmmygtLWXJkiXk5+eTmJjIPffcg8kUmm69fv16MjIyUCgUTJs2jZSUFACysrJYsWIFXq+XXr16MW3aNCRJwufzsXz5crKysjCbzcyePZukpPCffgThfKc/mYYsaXEnXhN22UWLzOzapeGFF2xcdFHNuyOGDPHQpo2fN94wMnZs3W5W27ZpUKnkiD0Be2MHEG9WM/byLXzwwRD+8Y+SShO+tVVQoOB//9MxdWoZujPv8XIQbdHXGHLeQhnfAVrcA5Li1PBTNxs3hoafKip47DYefw1ZUuNsMRmAhQvNAMydG/25BlWp9g1BrVbz2GOP8cwzz/D000+ze/duDh8+TFpaGsnJySxdupTk5GTS0tIAyM7OZuvWrSxevJhHHnmEVatWETw1KPfll19mxowZLF26lNzcXHbvDm2YnZGRgdFoZNmyZYwePZo1a9ZE8ZIFoYmSA+jzPsCdcAWyKiasolu2aFi+3MTNN5dx/fXh3dQVCpg82ck332g5cqRuy05v26YhOdmHwRChDn+FCrf1av404BkcDgWffVbzaCD5y2q0P/PatYazZiYrvPmYjq0gadvlJOz9I1rbFygPLyb20APl+x0PH+4hP1/J99+f240l+Usx5K7DlTiGoDaJ3bvVvPeegdtvL6VVq/rPG5yp2oAgSRK6U2ExEAgQCASQJInt27czdGho156hQ4eyfft2ALZv386gQYNQq9UkJSXRvHlzjhw5QlFRES6Xi06dOiFJEkOGDCkvs2PHDoYNGwbAgAED2L9/P3Ij20hbEBqaxv4dSl8+rqTwksk2m8SsWfG0axfg8cfttap74kQnarVcpyGoLhfs2aOJTP7gDO7E0Yzo/CkXtShl3bqaBQRtwQaaf30pzbamYNl3G6ZflqIp+grJf/YTejAIb75pYEB/D92sXxL//QyafdOXmKwnCWhbUHTpMnIH7SVw6cMYc98i7tC9IAeqHH6qP/lfFAEHZa1uQ5Zh/vwYrNYAd99dWvcfRh3VKNwHg0EefPBBcnNzueqqq+jYsSMlJSXEx4cmrMTHx2O3h37RbDYbHTt2LC9rsViw2WwolUoSEhLKP09ISMBms5WXOf2dUqnEYDDgcDiIiTn7KSg9PZ309HQAFi5ciNVqrd1Fq1S1LtuQmmq7oem2vTG1W2H7AQBT+9GYdFW36XS7ZRnuvFNFYaFEWpqfNm0SqixXGasVxo4N8u67Rp59VlOrbpnNmyW8XomRI7WV/kxr9fO2jEX6IYapV6Xz5GtjcbutXFTVwqz2H1D/MAs5thvE9UBr+xbdz58DICMhx1yKbOmHbOlH+t4hHD3aksfHPYB1zxJkdTzBS+4k0H46UsylGAEjILV6HL+kxHDgcbQaNda+L9O7d5Cvvzbx+ONn9DPJMuqdbxCM701s+6t4/wMF27apWb7cT7t2tfu7iaQaBQSFQsEzzzxDWVkZzz77LMeOHav02Mqe7Kt64q/oO6mCoQ8jR45k5MiR5X8uKCioqtmVslqttS7bkJpqu6Hptr0xtTs+ZwtoL6KgVAmlVbfpdLtfe83Ahx/GMW9eCa1bl1GXS7npJg3//a+VV18tY8KE8BcQ2rDBhCSp6NKlgIKCiu8Htf15x1muZGqvv7Pg1Rt4+WU3M2dW/LQt+e1Yd95IUNKSf+m/CepaQbvQjGGNYzdq+y409u/QZKehPPoqq55fR4KpGdcN3k5R26W4EkeDUgdeOPOHabVaKUi6E5PTTczRZ/C4nVx+2SssXxHDkSOFxMWFrldT9DVWxw8Ud15MSU4hDzyQRKdOPq67Lr9OfzfhaNmyZaXfhTXKyGg00rVrV3bv3k1sbCxFRUUAFBUVlT/NJyQkUFhYWF7GZrNhsVjO+bywsBCLxXJOmUAggNPpLE9QC4IQonbsxhfTq8bH//CDivnzYxk2zM0dd5TVuf5Bg7y0b+9n9Wpjrcpv26ahSxd/+c0xklyJo+lo3cPAPqGlLCp8/pSDxB+chcr9C0XdXgoFg9NfqePwWIZRevE92Hq8wd6OBxj3Zh7//XYCN9/spKz/WlzNx4eCQRVKL56Nvd1DGPLWM7b9k+cMPzUef4WAKh5X0lhee83I0aMqHn3UXuMRX9FWbUCw2+2UlYV+mbxeL/v27aNVq1akpqayadMmADZt2kTfvqENOlJTU9m6dSs+n4+8vDxycnLo0KED8fHx6PV6Dh8+jCzLbN68mdTUVAD69OnDxo0bAcjMzKRbt24VviEIwoVK4S1A5f4Vb0xKjY53uUJDTE2mIM89V1zhSJdwSRLccksZO3ZoOHAgvDuY3x8actq/f2TzB6d54ocQVBqZMuL9SuckmI8uQVf4OSWX/B1v3IAKzxMMwhtvGBg6rBmffG7l3nvt3PtweNda2vZuStr/jSEJi4gzlfJlRmgGntKdja5gA86Wkymy63nuOTNDhrgZPjwyQ3AjodorLSoqYsWKFQSDQWRZZuDAgfTp04dOnTqxZMkSMjIysFqtzJkzB4DWrVszcOBA5syZg0KhYPr06ShO/TbefvvtrFy5Eq/XS0pKCr16hZ52RowYwfLly5k5cyYmk4nZs2dH8ZIFoelR23cB4DPX7A1h7lwlP/ygZPXqQhITI7f8wYQJTv75zxhWrzby5JMlNS63f78ap1MRsfkH51DqcCeM5ObuC5ijv5116wykpv7WPl3BZ5h/WYyz2QScZ6wbdKaDB1U8+GAcO3dqGDTIw1NPFdd6tnBZm7+ApGRUt4/Z9MWVyAEvhuOvA+BsOYXnF5opKZF49FF7vaxiWlOS3ISH85w4caJW5RpTv3A4mmq7oem2vbG02/zzM5h+WUbu4B+QlVWP9NmwQcu0aQnccUcpf/977UYVVWXmzDg2bNDx3XcnMRprdvt46SUj8+fHsnNnLs2bVx6g6vLz1uV/jOX7PzNx7c98+Hkbxo93MWiQh8E9D9H111H4DZdQkPLeOd0+LpfEkiUmXnrJhNkc5LHH7PzhD66wbtSVtTvtxe3c9fhYMlfcSap1Hd64AXxnfIXhw5OYMMHJM8/UPKhGSsRyCIIgNAy1fRd+Y+dqg4HfD/ffH0dKSpCHHop8MACYMsVJaamC99+v+VCjbds0XHyxv8pgUFcey3CCCh3zbl7OZZd5SUvT83//ZyH5svoute4AACAASURBVIF0u3cnf167gQ8+jiM//7fbXkaGluHDE1mxwswf/uBk8+Y8JkwILxhUZdCN/UP1bIov34r0ySdjUKtl7r+/YSehVaSRpDIEQaiULKNx7MaVOKbaQ48cUVFQoOTpp/1ow9u4q8ZSU7106eJj9WoDkyZVv7aPxxPaIW3UqOj2lctKAx7LCLrY3+C1V2fj98Ox9xeyNdPI58fv4d33rby2JhQMOnXykZQU5OuvtXTo4OO//y1g4MDI5zeSkoJ07+7lwx/vYlbLE3x1aBiffKLnvvvs9b6SaU2INwRBaOSUriwU/pIajTDauzeUTO3dO3o3m9PJ5T17NOX1VUSW4eOPdQwfnkRRkZIrr4z+Gj3uxNEovXloSnYQn72Ey60r+PPsGF57K8j33+fy0Uf5PPKInYsuCpCVpeS+++xs2JAflWBw2vDhHr7d24pfkhYy//FYmjcPcOeddR/1FQ0iIAhCI6exh5Z48ZqrH2G0b58agyFIp07RbdP48S70+iCrV1fchbVnj5rx4xP4858t6PUyb75ZyDXX1ENASLgCWdIQ89P8c5LIKlVo4bn/+79S3njDxvbtedxzT2nU3qROGzHCQyAgcf/9cezerWHuXDt6feNM3YqAIAiNnNqxm6DCgN9Y/V1+714N3br5ar1XcU3FxMiMHeti/Xo9dvtvHe4nTiiYNSuOa69N5KefVDz9dDGffZbP0KH1M7RSVpnxWIaicezCa+5JcaeFEd9zOly9e3uJjQ3yySd6kpO9jB8f/qS++iICgiA0chr7LnzmniBVfZcPBOD771V13omspm691YnTqeC99/SUlUk8+6yZwYOT+OgjPXff7eDrr/OYPNlZ75OuSi+ajtfcC1u3l6udSFYfVCoYPDgUEOfNs0dkTki0iKSyIDRmQQ/q0u8pu2h6tYceOaLC5VKQnOwD6rgdWQ307OkjOdnLypUmli41c/KkkrFjnTz8sCOs5bUjzRs/mII+gxus/orMmeNgyBAPgwZFL1cRCSIgCEIjpi49gCR78dYgobxnTyjB27Nn/bwhSBJMnerkvvvi6N3by7/+ZSM1tWF2+mrsOnf207lz1VtvNgYiIAhCI6auRUL5kkvq78Zz881Oevb0cuml/obuqhciQAQEQWjENI5dBDTNCGorn116Wn0llM8kSdC1a+N/8hVqphGnNwRB0Nh3hd4Oqnn8ru+EsnB+EgFBEBopyVeMypWFrwYrnJ6dUBaE2hEBQRAaKY1jL0CNEsqnZwyLNwShLkRAEIRGSm3/DiA0B6Ea+/ap0euDdOgg+vOF2hMBQRAaKY1jNz5DB2RVTLXH7t2rpls3f70mlIXzjwgIgtAYyTJq+64abYgTCIQ2oOnRo3FPehIaPxEQBKERUnqOo/QV1GjLzJ9+EgllITJEQBCERqh8y8yY3tUeezqhXF8zlIXzlwgIgtAIaey7kCUtPmOXao/du1cklIXIEAFBEBohtWM3PnM3UFS/SN2+fSKhLESGCAiC0NgE/agde/HWMKG8b59IKAuRIQKCIDQyKuchFEFXjbbMFAllIZJEQBCEOvjxRxUnT0b2n1E4W2aKGcpCJImAIAi15HLB2LFWrrgikW++idyGNGr7LoKqOAL6i6s9du9eNTqdSCgLkSECgiDUUnq6jpISBUol3HxzAm++WfGG8+HSOHaH1i+qwQYDpxPK9b1NpXB+EgFBEGpp/Xo9zZoF+PLLPC6/3MP998fx2GMx+OvwsC75y1CVHRIzlIUGIQKCINRCUZFERoaO6693YbHIvPaajenTS/n3v01MnWrBbq/d9mHq0r1IBGs0QzkrS4XTKRLKQuSIgCAItfDRR3p8Ponx410AqFQwf76dp58u5uuvtVx3nZWffw5/YsDpLTN9IqEsNAAREAShFtav19Ohg4/u3c++GU+e7OTttwspLFQwZkwiX38dXrJZY/8Ov64tQU1CtceeTih37CgSykJkiIAgCGHKzlaybZuWceNcFeZ9Bw708vHHBSQlBZg8OYHXX695slnt2F2j7iIQCWUh8kRAEIQwpaXpARg3zlXpMW3bBvjggwKGDPHw0ENxLFxorva8Cs9JVJ4TNeouCgZFQlmIPBEQBCEMsgzvvacnNdVL27aBKo81m2VefdXGuHFOXnjBRGFh1f/cNI5TE9JqMEM5K0tFWZlIKAuRJQKCIIThwAEVhw6pGTfOWaPjlUqYObMUv1/i3Xf1VR6rtu9CllT4TN2rPa9IKAvRIAKCIIRh/XoDKpXM9de7a1ymc2c/vXp5WbfOgCxXfpzGsRuf8VJQVh04APbsEQllIfJEQBDOK6++auDaa62UldVuHkBVAoHQ6KJhwzxYLMGwyt50k5ODB9XlT/bnkIOo7bvxhZFQ7tpVJJSFyBIBQTivvP++nj17NDz1VPVJ3HBlZmrIzVVy44016y4609ixLnQ6mbVrKx5xpHJmoQg4arTk9W8JZdFdJESWCAjCecPlgt27NcTGBnnlFRNbt0ZuwTkIvR0YjUFGjfKEXTY2Vuaaa1ykpelxn9HbJPnL0OV/SkzW4wA1WvL6dEJZjDASIk0EBOG8sWuXBq9XYuHCYi6+2M+998bhdEam68jtDs1OvuYaN3p9FYmAKkyc6KSkRMHnHzoxHH8Vy57JNN/SHcv3t6Mp/pbSVtPxGzpUe57T3U5ihJEQaSIgCOeNzEwNkiQzdKiHxYuL+fVXJU8+GZmuoy++0OFwKLjxxsrnHlRKDqIu2cnVLR6nbeJx3v13FnE/PoLKfYyyVrdR0PMdci/bi73jfJCq/ycZmqEs06mTSCgLkVVtSqqgoIAVK1ZQXFyMJEmMHDmSa6+9ltLSUpYsWUJ+fj6JiYncc889mEwmANavX09GRgYKhYJp06aRkhJKlGVlZbFixQq8Xi+9evVi2rRpSJKEz+dj+fLlZGVlYTabmT17NklJSdG9cuG8k5mppWtXP7GxMv37e/nTn8pYtcrE6NFuBg6sW/fK+vV6kpICXHZZGN1FsozW9gUxPy1A7TyMjJJbruzBk29NY+dF22jR4aJatSWUUPaJhLIQcdU+jiiVSm699VaWLFnCggUL+Oyzz8jOziYtLY3k5GSWLl1KcnIyaWlpAGRnZ7N161YWL17MI488wqpVqwgGQyMyXn75ZWbMmMHSpUvJzc1l9+7QRJyMjAyMRiPLli1j9OjRrFmzJoqXLJyPvF7YuVPNgAG/3bAfesgRka6j4mKJL74IrWxa05uw2rGPhD0TSdg3FUn2UdTlOXIv28uYmWOQZQVvf9ilVm0RCWUhmqoNCPHx8bRv3x4AvV5Pq1atsNlsbN++naFDhwIwdOhQtm/fDsD27dsZNGgQarWapKQkmjdvzpEjRygqKsLlctGpUyckSWLIkCHlZXbs2MGwYcMAGDBgAPv370euasC2IPzOnj0a3G7FWW8Cer3M4sXFHDtWt66jjz/W4/VKNeouUrqPE3dwFok7r0ZVdpDiDk+Q1/dLXM0nIKvjaNMm9JbxzjsGguGNXAUgK0tJaalIKAvREdZLZ15eHj///DMdOnSgpKSE+Ph4IBQ07HY7ADabjY4dO5aXsVgs2Gw2lEolCQm/reCYkJCAzWYrL3P6O6VSicFgwOFwEBMTc1b96enppKenA7Bw4UKsVmu41wuASqWqddmG1FTbDdFv+759oWeba64xYbWayj8fPRruuivI8uUmJk3SMmRIeA8aKpWKjz6KoWNHmREjYivfxMxXgvKHZ1D8uBSAQOf7CXS5H4M6lt8PNL3jDgW33abi4MFEhg4Nrz3p6aHrHDLEhNVqrLLdTfF3RbS7YdU4ILjdbhYtWsRtt92GwVD56o2VPdlX9cRf0XdSBf/yRo4cyciRI8v/XFBQUFWTK2W1WmtdtiE11XZD9Nv+xRcWOneWgQJ+X81f/yrx0UeJ3H67RHp6AQZDzW/CTqeVzZs13HefncLC0nMPCPow5KzBfHQRSp8NZ7PxONo9SEDXCkp8wLnXfPnlEmZzM156yUe3bsVhXeeWLTHodEYSE/PPuc4zNdXfFdHu6GvZsmWl39VolJHf72fRokUMHjyY/v37AxAbG0tRUREARUVF5U/zCQkJFBYWlpe12WxYLJZzPi8sLMRisZxTJhAI4HQ6yxPUglAdvx+2b9cwYEDF3SgGw29dR+FOWFu3LvRPpMKVTWWZhD03E/fjI/iNXcjv8z+KL10aCgZV0Otlxo518fHHOhyO8HIb+/apufRSkVAWoqPagCDLMi+++CKtWrVizJgx5Z+npqayadMmADZt2kTfvn3LP9+6dSs+n4+8vDxycnLo0KED8fHx6PV6Dh8+jCzLbN68mdTUVAD69OnDxo0bAcjMzKRbt24VviEIQkX27VNTVqagf//KRwCdHnX0n/+Y+Oabmk9Ye+stBb17e7n44nNXNlU7dqMtycTe/mEKe67DZ06u8XknTnTidiv44IPq1y06LRgMXatIKAvRUu1zxqFDh9i8eTNt2rTh/vvvB+CPf/wjN9xwA0uWLCEjIwOr1cqcOXMAaN26NQMHDmTOnDkoFAqmT5+OQhGKO7fffjsrV67E6/WSkpJCr16hWZkjRoxg+fLlzJw5E5PJxOzZs6N1vcJ5aNu20A2+uqGlc+c6+OILHffeG0d6en61XUcHDqjYv1/BggUVL1VhyF1LUKGjrOUUKk8uVKxXLx+dOvlYu9bA5Mk1WwojM1MjEspCVElyEx7Oc+LEiVqVa0r9fWdqqu2G6LZ96lQLWVkqvvoqr9pjMzM1jB9vZerUMubOtWM2y5XeyxcsMPPSSyZ27TpJQsLZQ4KkgItmW3vhtl5F8aXP16rdL75o5PHHY9m4Ma/aVUs3btRyxx3xWK1BPvqo4Jz2/F5T/V0R7Y6+qnIIoidSaNICAfj2Ww1jxtRsBvGAAV6mTy9l1SoTr71mRKGQiY0NEhsrEx8fJDY2SFxc6M+ffKLjyivlCm++uoJPUQQcOFvcXOu2jx/v4sknY1i3Ts8jjzgqPe7dd/XMmRNH585+Vq8urDYYCEJtiYAgNGkHD6qw2xWVJpQr8re/2enTx0turpKSEgXFxQpKSqRT/1Vw9KiK4mIFLpfEn/9c8ZO7Iedt/Lq2eGMH1LrtiYlBrrjCzX//a+DBBx0VJopPv0VcdpmHVatsmM1N9oVeaAJEQBCatMxMLcBZM5Sro9HA2LE12+Am1BVw9mdK169oi7dgv/j+sHMHv3fzzS42bNDz5Zdarrzyt2sIBmHBghhefNHEdde5eP75IrTaOlUlCNUSi9sJTVpmpoY2bfy0alV/3SiG3HXISDibT6jzuUaMcGO1Bs7aJ8Hng7/+NY4XXzQxbVopK1eKYCDUDxEQhCZLlkMBoX//ehx1IwfR567FEz+UYDXzDWpCrQ7lEj7/XEdhoYKyMolp0yy8956BBx+08/jjdhTiX6lQT8SvmtBkHT6soqhIycCB4W9YU1uaoq9ReY7jbHFTxM45caITv1/i5ZeNTJyYwKZNWp59tphZs0rr2iMlCGEROQShyTo9wSychHJdGXLXEVTF4U64KmLn7NzZT69eXpYtM6PTyaxaZavVrmyCUFfiDUFosrZt09K8eYA2bc6dRRwNkq8Yff4nOJuNA6Uuoue+885SLrrIz9tvF4pgIDQY8YYgNEmn8weXXeapt24Vfd77SLIHZ/Pazz2ozJgxbsaMqdnIJ0GIFvGGIDRJWVlK8vKU9dxdtBafsSt+c/d6q1MQ6pMICEKTtG3b6fkH9RMQVKUH0Tj21GlmsiA0diIgCE3SN99oSEwMcMkl9bPRvCF3LbKkCeUPBOE8JQKC0OScOf+gXvIHQS/6k+/ito5CVlvqoUJBaBgiIAhNTna2khMnVGEtV1EXusL00G5ozSfWS32C0FBEQBCanPqef2DIXUtA0xyPZWi91CcIDUUEBKHJyczUEhcXpHPnesgfuE6gLcwIrVskKaNfnyA0IBEQhCYnlD/w1MsaP4pf3kQiKLqLhAuCCAhCk3LihIJfflHVT3eRLKM8+iqe2P4EDO2iX58gNDAREIQm5fT8g+r2T44EtX0HUumP4u1AuGCIgCA0Kd98o8FsDtK1qy/qdRly1iKrTLgTx0S9LkFoDERAEBqHgCs0waAa27Zp6NvXizLK+V21fRf6/A8IXvQHZJUxupUJQiMhAoLQ4KSAi2aZ/TAffbbK4/LzFRw5oo5ud1HAjfmnBVi/ux5ZFUOg0z3Rq0sQGhkREIQGpy3ajNJnw3RsJUrXr5Uel5kZmn/Qv390JqSpS3aQuPMqzL+uxNnij+T1zYCYLlGpSxAaI7H8tdDgFDnpPP7+Pzhha4bq3Vykll3RamV0OhmtFrRaGa1W5uOPdRgMQXr0iHD+IOAi5udnMGb/i4C2JYU93sJjGRLZOgShCRABQWhQ9pIgt9w3gy/3DyEhrhSPW8btN+L3V/zyOnKkG7U6cvVrSrYT98McVK4sylreir3935BVpshVIAhNiAgIQoM5cULB1MlGDv80kJWPf8ENU9qQ9O1gArpW5CZ/gNenwO0Gt1vC45Fwu6WI7Y4mBVyYf16IMXsVAd1FFPRcizf+8oicWxCaKhEQhAbxww8qbrklgdISLx8/cD3JU55DVhmxt3uA+EP3Yir6AHfSWAwGgOpHH4VD6fqFhL2TULmOUtbyNuztHxYjiQQBkVQWGsDWrRrGjbMSDELG/PEMGeJBVpkBcDWfgM/YlZifnoRAdLaUjP3xURTeQgp6vkNJpwUiGAjCKSIgCPUqLU3P5MkJNG8e4JO120ht8SnuhFG/HSApKekwD5UnG9PxVRGvX1uYgc72BY6LZ+ONHxTx8wtCUyYCglAvZBleeMHIXXfF07u3l/XrC+igeR8At3XUWcd64wfjTrgS0y9LUXgLIteIoI+YI3/Hr29HWas/Re68gnCeEAFBiLpAAObMUfLEE7Fcd52LN98sJC5ORlewAa+5F0Ft83PKlFzyN6Sgu9rJauEwHn8VtesnSjr8HRSaiJ1XEM4XIiAIUeVywZ13xrNypZIZM0pZubIIrRYUnhw0jt3nvB2cFjB0oKzlFAwn1qAqO1Tndii8hZiPLsZtGY7HckWdzycI5yMREISosdkkbr7Zyqef6nj2WT/z5tnL9zDQFWwAwG29utLypW3vQVaaiPnpiTq3xfzz00hBJ/ZL/k79bMQsCE2PCAhCVBw7puSGG6zs26fmxReLmDkzeNb3uoIN+PUX4zd0rPQcQY0Fx8V/RWfLQGvbWOu2qBz7MeSsoazVNPzGDrU+jyCc70RAEM6Rm6vgttssjB5tZefO8KcF79un5vrrrRQWKnnrrULGjDl7+Kjkd6At3oI74apqn9bLWk3Dr2tLzJH5EKzFlpmyTOyRxwiq43G0FQvVCUJVREAQzvLRRzquuCKJr77SkJOjZOxYKw88EEtRUc26WTZu1DJ+fAIajUxaWgH9+5+7MqnWloEk+6rsLiqn0GK/5BHUzkMYct8O93LQ5X+EtiQTR7sHkdWxYZcXhAuJCAgCAHa7xKxZccyYYeHii/189lk+mzfncccdZbz9toGhQ5N45x19lVsWrF2rZ8oUC23bBvjggwI6dqz4iV5XsIGAOgFvbJ8atc1tvRZPbD/MPz+D5HfU/KICLmJ+ehyfsSvOFn+seTlBuECJgCCwdauGkSMTSUvTM2eOg7S0Ajp0CGAyyTz2mJ1PP82nbdsAs2fHM2FCAj/+ePaKJ7IMS5aYmDMnnkGDvLz3XgHNmwcrrizoRVeYgSdhJEg13OVGkrBf8hhKXwGxPz6M5LfXqJjp1xdReY5T0nF+zesShAuYCAgXMLcb5s+P4aabEtBoIC2tgHvvdZyzmmi3bn7ef7+Af/6zmIMH1Vx5ZSILF5pxuST8fnjwwViefTaG8eOdvP56IWZz5a8R2uJMFAE7rpp0F53BF5OCo/VdGE6+R7PMgRh/fanKpS0U7uOYji3HlTgGb9zAsOoShAtVtYvbrVy5ku+++47Y2FgWLVoEQGlpKUuWLCE/P5/ExETuueceTKbQksHr168nIyMDhULBtGnTSElJASArK4sVK1bg9Xrp1asX06ZNQ5IkfD4fy5cvJysrC7PZzOzZs0lKSoriJQsABw6omDUrnoMH1dx6axnz5tkxGCq/kSsUcMstTq6+2s0TT8SwbJmZtDQ9bdsG+PprLXff7WDuXEe1Izp1BZ8RVOjwxg8Ou82OSx7GnXQd5qyniP1pPsbsVTja3Yer2fhz3gBisp5CAuzt/xZ2PYJwoar2DWHYsGE8/PDDZ32WlpZGcnIyS5cuJTk5mbS0NACys7PZunUrixcv5pFHHmHVqlUEg6Gug5dffpkZM2awdOlScnNz2b17NwAZGRkYjUaWLVvG6NGjWbNmTaSvUfidV14xMHp0IgUFCl5/vZCFC0uqDAZnslqDPPdcMf/9bwE6nczWrRqefLKYhx6qPhggy+gKP8NjGYas1Neq7T5zMraeb1LQ822CGivxP9xD4o5RaAs+L9+TWVOyHUPeekpb30lA37pW9QjChajagNC1a9fyp//Ttm/fztChQwEYOnQo27dvL/980KBBqNVqkpKSaN68OUeOHKGoqAiXy0WnTp2QJIkhQ4aUl9mxYwfDhg0DYMCAAezfvx+5BputC7Wzfbuav/0tjssv95CRkc8VV9RuO8qBA71s2JDPN9/kMXWqs0Zl1KX7UHpyzl7Mrpa88YMp6P0xtq4vIgXdJOy/jYTdN6Ip/paYH+cR0DSntM1dda5HEC4ktdoPoaSkhPj4eADi4+Ox20NJPpvNRseOv000slgs2Gw2lEolCQkJ5Z8nJCRgs9nKy5z+TqlUYjAYcDgcxMTEnFNveno66enpACxcuBCr1Vqb5qNSqWpdtiHVtd1eLzz8sIrWrWXWrVNgNlvq3KaWLWt2nEqlwuL8ChkFxk4TMWoj9PNPnEawyy34f34FzYEnsO4eB4C/32skNGtT59NfqL8rDUW0u2FFdIOcyp7sq3rir+g7qZK+h5EjRzJy5MjyPxcU1G4lTKvVWuuyDamu7V661MSBAzG88kohHo8HT3T2qq+Q1WpFOrYef2w/Ch2AI8I//9gbkfpdjTH73yi8Bdj1V0AE/o4v1N+VhiLaHX0tq3iKq9Uoo9jYWIqKigAoKioqf5pPSEigsLCw/DibzYbFYjnn88LCQiwWyzllAoEATqfznC4qoe6yspQ895yZa691MWpUPUaC08p+Rl12sNLF7CJBVhoobTsLe8f5Yr0iQaiFWgWE1NRUNm3aBMCmTZvo27dv+edbt27F5/ORl5dHTk4OHTp0ID4+Hr1ez+HDh5Flmc2bN5OamgpAnz592LhxIwCZmZl069at0jcEoXZkGebOjUOjkXn88ZIGaYPixIcAuK1XNUj9giBUr9ouo+eee44DBw7gcDi48847uemmm7jhhhtYsmQJGRkZWK1W5syZA0Dr1q0ZOHAgc+bMQaFQMH36dBSnlre8/fbbWblyJV6vl5SUFHr16gXAiBEjWL58OTNnzsRkMjF79uwoXi4cP67kp58k9HoFSUlBVBfArtLvvKNnyxYtTz5ZXPmEsShTHP8Qn7ELAf3FDVK/IAjVk+QmPKTnxIkTYZdZudLEggWhLi6FQiYpKUiLFoHy/7VsGfpvq1YBkpN9aLWRbnXt1aaf0mZTMGRIIpdcEmD9+oLy5afrk+Sz0XxLT0rb3I2j/YP134A6aEp9w2cS7a5fTandVeUQLoDn47ONHeskNVXPoUNl5OQoy/93+LCKjRu1OJ2/3TF1uiCDBnkZPNjD0KEeOnXyN7mu6X/8IwaHQ8E//1nYIMEAQFf4BRJB0V0kCI3cBRcQWrUK0rOnTL9+546dl2VwOCRycpT8/LOKLVs0bNqkJSMjtEpm8+YBhgwJBYfBgz0kJDRM90tNbd6s4b//NTBrloMuXWqxdHSE6Ao+Q9a3wmfu0WBtEAShehdcQKiKJEFMjExMjJ/Onf1cfXVorZzsbCWbN2vZtEnLhg061q0zAJCc7OWGG1xMnOgkPr5x9by5XPDQQ3FcfLGfWbPCWCE00gIutLaNBC++FSSxdJYgNGYiINTARRcFmDTJyaRJTgIB2LtXzebNWtLTdTz+eCzPPBPDuHFObrutjO7dG+5J/EzPP2/m6FEVb79dgL52q0REhD7vAxRBF75WNzRcIwRBqBHxyBYmpRJ69fLx17+W8uGHBWzYkMf48U7Wr9dz1VVJjBuXwPvv6/D5Gq6NBw+qeOEFExMmOBk8+NwNaupN0Iv5lyV4zT2Rk0Y0XDsEQagRERDqqFs3P08/XcLOnSeZN6+EkyeV/N//WejfvxmLF5s4ebJ+f8TBIDzwQBxmc5B582q2b0C0GHLXoXL/iuPi+8REMUFoAkRAiJC4OJkZM8r4+us8XnutkK5dfSxaFEO/fs2YNy8Gfz31JL3+uoHvvtPw97/bsVgaMOkd9GD65Xm8MX3wWIY3XDsEQagxERAiTKGAkSM9rF5t46uvTjJxopNVq0xMmWLBbo/uU3JOjoKFC2O4/HIP48e7olpXdQwn3kTlOYFdvB0IQpMhAkIUtW8f4OmnS3jmmWK2bNFyww1Wfv01Ols5ynJoVJHPBwsXFjfsPTjgwnxsGZ7YAbXaCEcQhIYhAkIFlK5jxByZj8Jri8j5Jk1ysnp1ITk5SsaMsfLdd+rqC4Xp/ff1fP65jgcecNCuXSDi5w+H8cQbKL0ncbQTbweC0JSIgPA7mqItWHdeiyn7Jcw/PxWx8w4e7OWDDwowGGQmTLDy4Ye6iJ27sFDBo4/G0KuXl9tvL4vYeWtDCjgxHVuBJ+5ysZexIDQxIiCcJssYjr9Kwp4/EtRYcSaNw5DzFirH/ohV0bGjn48+KqB7dx933mlh2TITkVhJ6tFHQ8tTLFpUjDI6PVI1Zjz+CkpfAfZ29zVsQwRBCJsII13weQAADrxJREFUCABBL7GHHyDux0fwJIygoPeHlHR8gqAqjtif/kFE7tqnJCQEWbu2gBtucLJwYQz33huHtw5TBTZs0PL++wb++lcHnTs37KQ4ye/AdGwlbstwfLF9G7QtgiCE74IPCApvPgm7b8KY8yaONrOwdf8PssqMrI7D0e4+tMVb0RVuiGidOh0sX17MPfc4WLvWwKRJCRQVhd/XXlIi8dBDcVx6qY+77iqNaBtrw5i9CoW/ODTvQBCEJueCDghqx14Sd16DunQftq4vhJZmPmO9HWeLW/AZOhLz03wIRnbGryTBffc5WLq0iJ07NVx3XSI//BDeSiJPPBFDXl6oq0ijiWjzwib5SjBl/wtXwih8MSkN2xhBEGrlgg0I+pPrse4ah4yCgl7v4066/tyDFCrslzyGynUU4/FXotKO8eNdrF1bSGmpxJgxVtLSarbw0FdfaXjzTSN33llKz54NuE7GKabsf6Hwl+C4+N6GboogCLV04QUEOYBy78PEH7wbrzmFgj6f4jd3r/RwT8Jw3JbhmI8+h8JbWOlxddGvn5f//S+f7t193HVXPPPmxVS5FpLTKfHAA3G0a+dnzpwGXMn0FMlnw5j9b1zWa6v8WQqC0LhdcAHBfHQRysOLKGs5hcKebxHUJFRbxn7JPKRAGeaji6LWrubNg7zzTiHTp5eyapWJCRMSyM2t+K/nn/80c+yYimefLW7QlUxPM/36ElKgTLwdCEITd8EFhLJWt+NP/TclnZ4CRc063v3GTjhb3orhxGpUZf/f3v3HRH3neRx/zg9+zAwwzgyICrYs0iJaushitW7rjyvd2zT9ndRsu0380aRbbUMqa13qcWpMSPrHUprdg6itcS9e0/3D1eR6bc+LtdG2nl2R63r+ZsC2shpgmAFGmGH4znz2D3DujODKj5nvoO9HYiYz8J3vi3e+8c33w3fe3wsxy5aUBNu399LQ4OX06SR+/vMsjh+/MWNjYxK7d9tYtaqPxYt1nGQ6zBjyYGvbTWD602hpc/WOI4SYgLuuIUSSnUM3axkjf96vUSYbGe7tMUh1o2eeCfLJJx7S0xUrV7rYudOGUjAwABs3TmPWrDCbN+s7yfS6tB8aMEQG8OdV6h1FCDFBcoOc2xRJduLPexN7y3ZSug4z4IrtfP/CQo1PP+2ksnIa27fbaWpKJj/fRHOziQ8/7CItTf87tBkH2rFd+VcC2c8TthboHUcIMUF33RnCRPTlrEGz5A1fhhr7K3vS0xW7dvmoru7h009T+d3vTLzwQj/Llw/EfN9/jynwA65TvwQVxn/vm3rHEUJMAmkIY2FMpnfOFpL6m7Fe+be47NJggHXr+vjjH7t4+eUwW7f2xGW/t3J93pNp4Are4j8Qtv5I70hCiEkgDWGMgq6fMTBtCRnf/RbDYHfc9vvTn4bYvTuMw6HjUtEN855cdJb+BwPO5frlEUJMKmkIY2Uw0FOwDYPWQ/r3dXqniZ9ICPvF3wzNe3Iux1P6MWFrvt6phBCTSBrCOGhp8+mf+SK2v/4Bc1+z3nFizhjy4PrLL7Bd/RD/PW/gLd6DMmfoHUsIMcmkIYyT/0ebUCYbrm+fJ6XrC73jxIzZf5rMk0+Q7P8LvqJ6/Plvg0HnGdtCiJiQhjBOkeQsOkv/nUhyNq7/fZn01ncgou/46cmW2vExmf/zDAYVxlOyn0D2s3pHEkLEkHwOYQLC1gI8pR+T0fzPpP/we5J7GvHN+xciKTP0jnZLpuBfMQW+x6j1Ygj3Dj1q/uHHoedGzUdK938TyvgJ3vkfEEmZrndsIUSMSUOYIGWy0DP3t4SmLcJ+8W2yGv8RX9HvCTmX6h3tBsZQF6mdH2Nt309y78kRvyditKLMGUTM6ShzBtdyf0Vv/m/AmBLntEIIPUhDmCSBGS8wmP5jHGd+hevUS1y7dwP+vDd1XW83hPtJ9fwXlvY/keI7ikFpDNqK6M3fTCj9xyiznYg5nYg5A2VKB2OSblmFEPqThjCJNNv9eH7yCfaLb5P+/bsk9/x5aAkpOSt+ISIaKd1fYWn/E6md/4kx0k84ZSbXcl8lkP0cWtq8+GURQkwp0hAmmTJZ6Z77HgPTHmZa8z+R1fgz/HkbGUwvZtB6H5jGMa9aKYyhdgyeC6R2XsI46MWo+TAO+jAM+oaeD/owaj5MA+0Yw34iZjuB7GcJZD9PyL7ohjvBCSHESKQhxILBQGDmL4aWkM6+xrSLmwBQGAlb8hi0zWUwrQjNNpdB21zClnvBYMKg9WEOtGLub8E0/Gjub8EcaMUY7gPA+f92owwpRJIc0X+arZDQtCUMOJYSdP2DrP0LIcZEGkIMaWlFdC48jLn/Eua+cyT1ncfcd56ka2dJ9XyGgaExFBFjKspsxxRqj26rMBBOzUWzzKHfvhDNOgdbdjG+QApquAEoo2Vo2JEQQkwCaQixZjCh2QrQbAUEeer/Xg73Y+67ONQg+s5h1HrRLPlo1nw0yxw0y703LS9ZMzPRPJ54/wRCiLuENASdKJOVwYwSBjNKCOgdRgghkE8qCyGEGCYNQQghBJBAS0bffvste/bsIRKJ8Nhjj/HsszI3Rwgh4ikhzhAikQi7d+9m8+bN1NXV8fXXX9PW1qZ3LCGEuKskRENwu93MmDGD7OxszGYzS5Ys4cSJE3rHEkKIu0pCLBl5vV5cLlf0ucvlorn55hvPHDp0iEOHDgHwzjvvkJmZOa79mc3mcW+rp6maG6ZudskdX5JbXwnREJS6+T7BhhE+cFVeXk55eXn0uWec1+RnZmaOe1s9TdXcMHWzS+74ktyxN2vWrFG/lhBLRi6Xi66urujzrq4uHA6HjomEEOLukxBnCHPmzOHq1at0dHTgdDo5duwYFRUVf3e7W3W6WG6rp6maG6ZudskdX5JbPwlxhmAymVi7di01NTVs2LCBhx9+mNmzZ8dsf1VVVTF771iaqrlh6maX3PElufWVEGcIAKWlpZSWluodQwgh7loJcYYghBBCf6Zt27Zt0zuEHvLz8/WOMC5TNTdM3eySO74kt34MaqRrPoUQQtx1ZMlICCEEIA1BCCHEsIS5ymiiGhoaaGpqwm63U1tbC8B3333H+++/TzAYJCsri4qKCqxWK5qmsWPHDi5dukQkEmHp0qU899xzALS2tlJfX08oFGLBggWsWbNmxE9NJ1rubdu24fP5SE5OBqC6uhq73Z4wuXft2kVLSwtGo5HVq1czf/58IPHrPVrueNfb4/FQX19Pd3c3BoOB8vJynnjiCa5du0ZdXR2dnZ1kZWWxYcMG0tLSADhw4ACHDx/GaDSyZs0aSkpKgPjWfDJzx7PmY83t9/t59913cbvdLF++nFdeeSX6XvE+xidE3SHOnDmjWlpaVGVlZfS1qqoqdebMGaWUUp9//rn66KOPlFJKffnll6qurk4ppVQwGFTr169X7e3t0W0uXLigIpGIqqmpUU1NTVMi99atW5Xb7Y5p1vHm/uyzz1R9fb1SSqnu7m61adMmFQ6Ho9skar1vlTve9fZ6vaqlpUUppVR/f7+qqKhQly9fVnv37lUHDhxQSil14MABtXfvXqWUUpcvX1YbN25UoVBItbe3qzfeeEOXmk9m7njWfKy5A4GAOnfunDp48KD64IMPbniveB/jE3HHLBnNmzcv+hvGdVeuXKGoqAiABx98kG+++Sb6tWAwSDgcJhQKYTabsVqt+Hw+AoEA999/PwaDgaVLl8Z86upk5NbDWHK3tbXxwAMPAGC327HZbLS2tiZ8vUfLrQeHwxG9isVisZCTk4PX6+XEiRMsW7YMgGXLlkXrd+LECZYsWUJSUhLTp09nxowZuN3uuNd8snLH21hzp6amMnfu3OjZy3V6HOMTccc0hJHMnj2bxsZGAI4fPx6dl7R48WJSU1N59dVXWb9+PU899RRpaWkjTl31er0Jn/u6hoYG3nrrLfbt2zfiwEC9cufl5dHY2Eg4HKajo4PW1lY8Hk/C13u03NfpVe+Ojg4uXbpEQUEBPT090blfDoeD3t5e4OYJwk6nE6/Xq2vNJ5L7Oj1qfju5R5Mox/jtumP+hjCSdevWsWfPHvbt20dZWRlm89CP63a7MRqN7Ny5k76+PrZs2UJxcbEu/4mOZKy5s7OzqaiowOl0EggEqK2t5ejRo9HfZPTOvWLFCtra2qiqqiIrK4vCwkJMJlPC13u03IBu9Q4Gg9TW1rJ69epbnh2OVlu9aj7R3KBPzW8392gS5Ri/XXd0Q8jJyaG6uhoYWhZoamoC4KuvvqKkpASz2YzdbqewsJCWlhaKiopumrrqdDoTPnd2dnY0p8Vi4ZFHHsHtdse9IYyW22QysXr16uj3VVdXM3PmTGw2W0LXe7TcgC711jSN2tpaHn30URYtWgQMLWX5fD4cDgc+n4+MjAzg5gnCXq8Xp9M54mThWNd8MnJD/Gs+ltyj0aPeE3FHLxn19PQAQ7fo3L9/P48//jgwNLv89OnTKKUIBoM0NzeTk5ODw+HAYrFw8eJFlFIcPXqUsrKyhM8dDoejp66apnHy5MmYDgcca+6BgQGCwSAAp06dwmQykZubm/D1Hi23HvVWSrFjxw5ycnJ48skno6+XlZVx5MgRAI4cOcLChQujrx87dozBwUE6Ojq4evUqBQUFca/5ZOWOd83Hmns0iXKM36475pPK7733HmfPnsXv92O321m5ciXBYJCDBw8C8NBDD/HSSy9hMBgIBoM0NDTQ1taGUooVK1bw9NNPA9DS0kJDQwOhUIiSkhLWrl0b00vEJiN3MBhk69athMNhIpEIxcXFrFq1CqMxdv1+LLk7OjqoqanBaDTidDp57bXXyMrKAhK73qPl1qPe58+fZ8uWLdxzzz3R+rz44ovcd9991NXV4fF4yMzMpLKyMvp3pf379/PFF19EL5ldsGABEN+aT1bueNd8PLlff/11+vv70TQNm81GdXU1ubm5cT/GJ+KOaQhCCCEm5o5eMhJCCHH7pCEIIYQApCEIIYQYJg1BCCEEIA1BCCHEMGkIQgghAGkIQgghhv0Neux26zSpV/IAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_china.plot(kind=\"line\",c=\"orange\",label=\"China\",legend=\"China\")\n",
"df_india.plot(kind=\"line\",c=\"blue\",label=\"India\",legend=\"India\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd4af7530f0>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_CI.plot(kind=\"line\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.plot(kind=\"line\")\n",
"plt.title(\"China and India Imigrations\")\n",
"plt.xlabel(\"Years\")\n",
"plt.ylabel(\"No of imigrants\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
"df_CI.plot(kind='line')\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"--> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"From the above plot, we can observe that the China and India have very similar immigration trends through the years. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
">class 'pandas.core.series.Series' <br>\n",
">1980 1666 <br>\n",
">1981 3692 <br>\n",
">1982 3498 <br>\n",
">1983 2860 <br>\n",
">1984 1418 <br>\n",
">Name: Haiti, dtype: int64 <br>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"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",
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"</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>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>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>United Kingdom of Great Britain and Northern Ireland</th>\n",
" <td>Europe</td>\n",
" <td>Northern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>22045</td>\n",
" <td>24796</td>\n",
" <td>20620</td>\n",
" <td>10015</td>\n",
" <td>10170</td>\n",
" <td>9564</td>\n",
" <td>9470</td>\n",
" <td>...</td>\n",
" <td>7258</td>\n",
" <td>7140</td>\n",
" <td>8216</td>\n",
" <td>8979</td>\n",
" <td>8876</td>\n",
" <td>8724</td>\n",
" <td>6204</td>\n",
" <td>6195</td>\n",
" <td>5827</td>\n",
" <td>551500</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>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>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>San Marino</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>0</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>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Caledonia</th>\n",
" <td>Oceania</td>\n",
" <td>Melanesia</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>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>0</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Marshall Islands</th>\n",
" <td>Oceania</td>\n",
" <td>Micronesia</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>0</td>\n",
" <td>0</td>\n",
" <td>2</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>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Western Sahara</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</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>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",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Palau</th>\n",
" <td>Oceania</td>\n",
" <td>Micronesia</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>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>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent \\\n",
"India Asia \n",
"China Asia \n",
"United Kingdom of Great Britain and Northern Ir... Europe \n",
"Philippines Asia \n",
"Pakistan Asia \n",
"... ... \n",
"San Marino Europe \n",
"New Caledonia Oceania \n",
"Marshall Islands Oceania \n",
"Western Sahara Africa \n",
"Palau Oceania \n",
"\n",
" Region \\\n",
"India Southern Asia \n",
"China Eastern Asia \n",
"United Kingdom of Great Britain and Northern Ir... Northern Europe \n",
"Philippines South-Eastern Asia \n",
"Pakistan Southern Asia \n",
"... ... \n",
"San Marino Southern Europe \n",
"New Caledonia Melanesia \n",
"Marshall Islands Micronesia \n",
"Western Sahara Northern Africa \n",
"Palau Micronesia \n",
"\n",
" DevName 1980 \\\n",
"India Developing regions 8880 \n",
"China Developing regions 5123 \n",
"United Kingdom of Great Britain and Northern Ir... Developed regions 22045 \n",
"Philippines Developing regions 6051 \n",
"Pakistan Developing regions 978 \n",
"... ... ... \n",
"San Marino Developed regions 1 \n",
"New Caledonia Developing regions 0 \n",
"Marshall Islands Developing regions 0 \n",
"Western Sahara Developing regions 0 \n",
"Palau Developing regions 0 \n",
"\n",
" 1981 1982 1983 \\\n",
"India 8670 8147 7338 \n",
"China 6682 3308 1863 \n",
"United Kingdom of Great Britain and Northern Ir... 24796 20620 10015 \n",
"Philippines 5921 5249 4562 \n",
"Pakistan 972 1201 900 \n",
"... ... ... ... \n",
"San Marino 0 0 0 \n",
"New Caledonia 0 0 0 \n",
"Marshall Islands 0 0 0 \n",
"Western Sahara 0 0 0 \n",
"Palau 0 0 0 \n",
"\n",
" 1984 1985 1986 ... \\\n",
"India 5704 4211 7150 ... \n",
"China 1527 1816 1960 ... \n",
"United Kingdom of Great Britain and Northern Ir... 10170 9564 9470 ... \n",
"Philippines 3801 3150 4166 ... \n",
"Pakistan 668 514 691 ... \n",
"... ... ... ... ... \n",
"San Marino 0 0 1 ... \n",
"New Caledonia 0 0 0 ... \n",
"Marshall Islands 0 0 0 ... \n",
"Western Sahara 0 0 0 ... \n",
"Palau 0 0 0 ... \n",
"\n",
" 2005 2006 2007 \\\n",
"India 36210 33848 28742 \n",
"China 42584 33518 27642 \n",
"United Kingdom of Great Britain and Northern Ir... 7258 7140 8216 \n",
"Philippines 18139 18400 19837 \n",
"Pakistan 14314 13127 10124 \n",
"... ... ... ... \n",
"San Marino 0 0 0 \n",
"New Caledonia 0 0 0 \n",
"Marshall Islands 0 0 2 \n",
"Western Sahara 0 1 0 \n",
"Palau 0 0 1 \n",
"\n",
" 2008 2009 2010 \\\n",
"India 28261 29456 34235 \n",
"China 30037 29622 30391 \n",
"United Kingdom of Great Britain and Northern Ir... 8979 8876 8724 \n",
"Philippines 24887 28573 38617 \n",
"Pakistan 8994 7217 6811 \n",
"... ... ... ... \n",
"San Marino 0 0 1 \n",
"New Caledonia 1 0 0 \n",
"Marshall Islands 0 0 0 \n",
"Western Sahara 0 0 0 \n",
"Palau 0 0 0 \n",
"\n",
" 2011 2012 2013 \\\n",
"India 27509 30933 33087 \n",
"China 28502 33024 34129 \n",
"United Kingdom of Great Britain and Northern Ir... 6204 6195 5827 \n",
"Philippines 36765 34315 29544 \n",
"Pakistan 7468 11227 12603 \n",
"... ... ... ... \n",
"San Marino 0 0 0 \n",
"New Caledonia 0 0 2 \n",
"Marshall Islands 0 0 0 \n",
"Western Sahara 0 0 0 \n",
"Palau 0 0 0 \n",
"\n",
" Total \n",
"India 691904 \n",
"China 659962 \n",
"United Kingdom of Great Britain and Northern Ir... 551500 \n",
"Philippines 511391 \n",
"Pakistan 241600 \n",
"... ... \n",
"San Marino 5 \n",
"New Caledonia 5 \n",
"Marshall Islands 2 \n",
"Western Sahara 2 \n",
"Palau 1 \n",
"\n",
"[195 rows x 38 columns]"
]
},
"execution_count": 53,
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}
],
"source": [
"df_can.sort_values(by=[\"Total\"],ascending=False)"
]
},
{
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"execution_count": 58,
"metadata": {
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"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_can.loc[[\"India\",\"China\",\"United Kingdom of Great Britain and Northern Ireland\",\"Philippines\",\"Pakistan\"],years].transpose().plot(kind=\"line\")\n",
"plt.title(\"Imigrants trend of top 5 countries\")\n",
"plt.xlabel(\"Years\")\n",
"plt.ylabel(\"No of imgrants\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\\\\ inplace = True paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"* `bar` for vertical bar plots\n",
"* `barh` for horizontal bar plots\n",
"* `hist` for histogram\n",
"* `box` for boxplot\n",
"* `kde` or `density` for density plots\n",
"* `area` for area plots\n",
"* `pie` for pie plots\n",
"* `scatter` for scatter plots\n",
"* `hexbin` for hexbin plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
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"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week1_LAB1)."
]
},
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"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
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