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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb
{
"cells": [
{
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
},
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely <a href='http://cocl.us/PY0101EN_DV0101EN_LAB1_Coursera'>**Python for Data Science**</a> and <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which are part of this specialization. \n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which is also part of this specialization. \n",
"\n",
"------------"
]
},
{
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
"1.2 [*pandas* Basics](#4) <br>\n",
"1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
"2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
"\n",
"*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
]
},
{
"cell_type": "markdown",
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod1Fig1-Dataset.png\" align=\"center\" width=900>\n",
"\n",
"For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
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"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
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"scrolled": false
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"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"run_control": {
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"scrolled": false
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
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" <th>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",
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" <td>902</td>\n",
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" <td>2978</td>\n",
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" <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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" </tr>\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",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" </tr>\n",
" </tbody>\n",
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"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [
{
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" }\n",
"\n",
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" }\n",
"</style>\n",
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" <thead>\n",
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" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
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" <td>Africa</td>\n",
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" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
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" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
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" <td>611</td>\n",
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" <td>494</td>\n",
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"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
"Type 195 non-null object\n",
"Coverage 195 non-null object\n",
"OdName 195 non-null object\n",
"AREA 195 non-null int64\n",
"AreaName 195 non-null object\n",
"REG 195 non-null int64\n",
"RegName 195 non-null object\n",
"DEV 195 non-null int64\n",
"DevName 195 non-null object\n",
"1980 195 non-null int64\n",
"1981 195 non-null int64\n",
"1982 195 non-null int64\n",
"1983 195 non-null int64\n",
"1984 195 non-null int64\n",
"1985 195 non-null int64\n",
"1986 195 non-null int64\n",
"1987 195 non-null int64\n",
"1988 195 non-null int64\n",
"1989 195 non-null int64\n",
"1990 195 non-null int64\n",
"1991 195 non-null int64\n",
"1992 195 non-null int64\n",
"1993 195 non-null int64\n",
"1994 195 non-null int64\n",
"1995 195 non-null int64\n",
"1996 195 non-null int64\n",
"1997 195 non-null int64\n",
"1998 195 non-null int64\n",
"1999 195 non-null int64\n",
"2000 195 non-null int64\n",
"2001 195 non-null int64\n",
"2002 195 non-null int64\n",
"2003 195 non-null int64\n",
"2004 195 non-null int64\n",
"2005 195 non-null int64\n",
"2006 195 non-null int64\n",
"2007 195 non-null int64\n",
"2008 195 non-null int64\n",
"2009 195 non-null int64\n",
"2010 195 non-null int64\n",
"2011 195 non-null int64\n",
"2012 195 non-null int64\n",
"2013 195 non-null int64\n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in *pandas* objects are *float*, *int*, *bool*, *datetime64[ns]* and *datetime64[ns, tz] (in >= 0.17.0)*, *timedelta[ns]*, *category (in >= 0.15.0)*, and *object* (string). In addition these dtypes have item sizes, e.g. int64 and int32. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"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,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python \n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country')."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
"5 Angola\n",
"6 Antigua and Barbuda\n",
"7 Argentina\n",
"8 Armenia\n",
"9 Australia\n",
"10 Austria\n",
"11 Azerbaijan\n",
"12 Bahamas\n",
"13 Bahrain\n",
"14 Bangladesh\n",
"15 Barbados\n",
"16 Belarus\n",
"17 Belgium\n",
"18 Belize\n",
"19 Benin\n",
"20 Bhutan\n",
"21 Bolivia (Plurinational State of)\n",
"22 Bosnia and Herzegovina\n",
"23 Botswana\n",
"24 Brazil\n",
"25 Brunei Darussalam\n",
"26 Bulgaria\n",
"27 Burkina Faso\n",
"28 Burundi\n",
"29 Cabo Verde\n",
" ... \n",
"165 Suriname\n",
"166 Swaziland\n",
"167 Sweden\n",
"168 Switzerland\n",
"169 Syrian Arab Republic\n",
"170 Tajikistan\n",
"171 Thailand\n",
"172 The former Yugoslav Republic of Macedonia\n",
"173 Togo\n",
"174 Tonga\n",
"175 Trinidad and Tobago\n",
"176 Tunisia\n",
"177 Turkey\n",
"178 Turkmenistan\n",
"179 Tuvalu\n",
"180 Uganda\n",
"181 Ukraine\n",
"182 United Arab Emirates\n",
"183 United Kingdom of Great Britain and Northern I...\n",
"184 United Republic of Tanzania\n",
"185 United States of America\n",
"186 Uruguay\n",
"187 Uzbekistan\n",
"188 Vanuatu\n",
"189 Venezuela (Bolivarian Republic of)\n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Angola</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Argentina</td>\n",
" <td>368</td>\n",
" <td>426</td>\n",
" <td>626</td>\n",
" <td>241</td>\n",
" <td>237</td>\n",
" <td>196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Armenia</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Australia</td>\n",
" <td>702</td>\n",
" <td>639</td>\n",
" <td>484</td>\n",
" <td>317</td>\n",
" <td>317</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Austria</td>\n",
" <td>234</td>\n",
" <td>238</td>\n",
" <td>201</td>\n",
" <td>117</td>\n",
" <td>127</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Azerbaijan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Bahamas</td>\n",
" <td>26</td>\n",
" <td>23</td>\n",
" <td>38</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Bahrain</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Bangladesh</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Barbados</td>\n",
" <td>372</td>\n",
" <td>376</td>\n",
" <td>299</td>\n",
" <td>244</td>\n",
" <td>265</td>\n",
" <td>285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Belarus</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Belgium</td>\n",
" <td>511</td>\n",
" <td>540</td>\n",
" <td>519</td>\n",
" <td>297</td>\n",
" <td>183</td>\n",
" <td>181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Belize</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>13</td>\n",
" <td>21</td>\n",
" <td>37</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Benin</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bhutan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Bolivia (Plurinational State of)</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>42</td>\n",
" <td>49</td>\n",
" <td>38</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Bosnia and Herzegovina</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Botswana</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Brazil</td>\n",
" <td>211</td>\n",
" <td>220</td>\n",
" <td>192</td>\n",
" <td>139</td>\n",
" <td>145</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Brunei Darussalam</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Bulgaria</td>\n",
" <td>24</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>33</td>\n",
" <td>11</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Burkina Faso</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Burundi</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Cabo Verde</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>Suriname</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Swaziland</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>Sweden</td>\n",
" <td>281</td>\n",
" <td>308</td>\n",
" <td>222</td>\n",
" <td>176</td>\n",
" <td>128</td>\n",
" <td>158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Switzerland</td>\n",
" <td>806</td>\n",
" <td>811</td>\n",
" <td>634</td>\n",
" <td>370</td>\n",
" <td>326</td>\n",
" <td>314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>Syrian Arab Republic</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Tajikistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>Thailand</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>The former Yugoslav Republic of Macedonia</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>Togo</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>Tonga</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>Trinidad and Tobago</td>\n",
" <td>958</td>\n",
" <td>947</td>\n",
" <td>972</td>\n",
" <td>766</td>\n",
" <td>606</td>\n",
" <td>699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Tunisia</td>\n",
" <td>58</td>\n",
" <td>51</td>\n",
" <td>55</td>\n",
" <td>46</td>\n",
" <td>51</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>Turkey</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>Turkmenistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>Tuvalu</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>Uganda</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>38</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>Ukraine</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>United Kingdom of Great Britain and Northern I...</td>\n",
" <td>22045</td>\n",
" <td>24796</td>\n",
" <td>20620</td>\n",
" <td>10015</td>\n",
" <td>10170</td>\n",
" <td>9564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>United Republic of Tanzania</td>\n",
" <td>635</td>\n",
" <td>832</td>\n",
" <td>621</td>\n",
" <td>474</td>\n",
" <td>473</td>\n",
" <td>460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>United States of America</td>\n",
" <td>9378</td>\n",
" <td>10030</td>\n",
" <td>9074</td>\n",
" <td>7100</td>\n",
" <td>6661</td>\n",
" <td>6543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>Uruguay</td>\n",
" <td>128</td>\n",
" <td>132</td>\n",
" <td>146</td>\n",
" <td>105</td>\n",
" <td>90</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>Uzbekistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>Vanuatu</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>Venezuela (Bolivarian Republic of)</td>\n",
" <td>103</td>\n",
" <td>117</td>\n",
" <td>174</td>\n",
" <td>124</td>\n",
" <td>142</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 \\\n",
"0 Afghanistan 16 39 39 \n",
"1 Albania 1 0 0 \n",
"2 Algeria 80 67 71 \n",
"3 American Samoa 0 1 0 \n",
"4 Andorra 0 0 0 \n",
"5 Angola 1 3 6 \n",
"6 Antigua and Barbuda 0 0 0 \n",
"7 Argentina 368 426 626 \n",
"8 Armenia 0 0 0 \n",
"9 Australia 702 639 484 \n",
"10 Austria 234 238 201 \n",
"11 Azerbaijan 0 0 0 \n",
"12 Bahamas 26 23 38 \n",
"13 Bahrain 0 2 1 \n",
"14 Bangladesh 83 84 86 \n",
"15 Barbados 372 376 299 \n",
"16 Belarus 0 0 0 \n",
"17 Belgium 511 540 519 \n",
"18 Belize 16 27 13 \n",
"19 Benin 2 5 4 \n",
"20 Bhutan 0 0 0 \n",
"21 Bolivia (Plurinational State of) 44 52 42 \n",
"22 Bosnia and Herzegovina 0 0 0 \n",
"23 Botswana 10 1 3 \n",
"24 Brazil 211 220 192 \n",
"25 Brunei Darussalam 79 6 8 \n",
"26 Bulgaria 24 20 12 \n",
"27 Burkina Faso 2 1 3 \n",
"28 Burundi 0 0 0 \n",
"29 Cabo Verde 1 1 2 \n",
".. ... ... ... ... \n",
"165 Suriname 15 10 21 \n",
"166 Swaziland 4 1 1 \n",
"167 Sweden 281 308 222 \n",
"168 Switzerland 806 811 634 \n",
"169 Syrian Arab Republic 315 419 409 \n",
"170 Tajikistan 0 0 0 \n",
"171 Thailand 56 53 113 \n",
"172 The former Yugoslav Republic of Macedonia 0 0 0 \n",
"173 Togo 5 5 2 \n",
"174 Tonga 2 4 7 \n",
"175 Trinidad and Tobago 958 947 972 \n",
"176 Tunisia 58 51 55 \n",
"177 Turkey 481 874 706 \n",
"178 Turkmenistan 0 0 0 \n",
"179 Tuvalu 0 1 0 \n",
"180 Uganda 13 16 17 \n",
"181 Ukraine 0 0 0 \n",
"182 United Arab Emirates 0 2 2 \n",
"183 United Kingdom of Great Britain and Northern I... 22045 24796 20620 \n",
"184 United Republic of Tanzania 635 832 621 \n",
"185 United States of America 9378 10030 9074 \n",
"186 Uruguay 128 132 146 \n",
"187 Uzbekistan 0 0 0 \n",
"188 Vanuatu 0 0 0 \n",
"189 Venezuela (Bolivarian Republic of) 103 117 174 \n",
"190 Viet Nam 1191 1829 2162 \n",
"191 Western Sahara 0 0 0 \n",
"192 Yemen 1 2 1 \n",
"193 Zambia 11 17 11 \n",
"194 Zimbabwe 72 114 102 \n",
"\n",
" 1983 1984 1985 \n",
"0 47 71 340 \n",
"1 0 0 0 \n",
"2 69 63 44 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 6 4 3 \n",
"6 0 42 52 \n",
"7 241 237 196 \n",
"8 0 0 0 \n",
"9 317 317 319 \n",
"10 117 127 165 \n",
"11 0 0 0 \n",
"12 12 21 28 \n",
"13 1 1 3 \n",
"14 81 98 92 \n",
"15 244 265 285 \n",
"16 0 0 0 \n",
"17 297 183 181 \n",
"18 21 37 26 \n",
"19 3 4 3 \n",
"20 0 1 0 \n",
"21 49 38 44 \n",
"22 0 0 0 \n",
"23 3 7 4 \n",
"24 139 145 130 \n",
"25 2 2 4 \n",
"26 33 11 24 \n",
"27 2 3 2 \n",
"28 0 1 2 \n",
"29 0 11 1 \n",
".. ... ... ... \n",
"165 12 5 16 \n",
"166 0 10 7 \n",
"167 176 128 158 \n",
"168 370 326 314 \n",
"169 269 264 385 \n",
"170 0 0 0 \n",
"171 65 82 66 \n",
"172 0 0 0 \n",
"173 3 6 5 \n",
"174 1 2 5 \n",
"175 766 606 699 \n",
"176 46 51 57 \n",
"177 280 338 202 \n",
"178 0 0 0 \n",
"179 0 1 0 \n",
"180 38 32 29 \n",
"181 0 0 0 \n",
"182 1 2 0 \n",
"183 10015 10170 9564 \n",
"184 474 473 460 \n",
"185 7100 6661 6543 \n",
"186 105 90 92 \n",
"187 0 0 0 \n",
"188 0 0 0 \n",
"189 124 142 165 \n",
"190 3404 7583 5907 \n",
"191 0 0 0 \n",
"192 6 0 18 \n",
"193 7 16 9 \n",
"194 44 32 29 \n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": true,
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"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
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"scrolled": false
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"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
" 1. The full row data (all columns)\n",
" 2. For year 2013\n",
" 3. For years 1980 to 1985"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"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": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"# print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n"
]
}
],
"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": 25,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 26,
"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": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Country\n",
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
"Angola False\n",
"Antigua and Barbuda False\n",
"Argentina False\n",
"Armenia True\n",
"Australia False\n",
"Austria False\n",
"Azerbaijan True\n",
"Bahamas False\n",
"Bahrain True\n",
"Bangladesh True\n",
"Barbados False\n",
"Belarus False\n",
"Belgium False\n",
"Belize False\n",
"Benin False\n",
"Bhutan True\n",
"Bolivia (Plurinational State of) False\n",
"Bosnia and Herzegovina False\n",
"Botswana False\n",
"Brazil False\n",
"Brunei Darussalam True\n",
"Bulgaria False\n",
"Burkina Faso False\n",
"Burundi False\n",
"Cabo Verde False\n",
" ... \n",
"Suriname False\n",
"Swaziland False\n",
"Sweden False\n",
"Switzerland False\n",
"Syrian Arab Republic True\n",
"Tajikistan True\n",
"Thailand True\n",
"The former Yugoslav Republic of Macedonia False\n",
"Togo False\n",
"Tonga False\n",
"Trinidad and Tobago False\n",
"Tunisia False\n",
"Turkey True\n",
"Turkmenistan True\n",
"Tuvalu False\n",
"Uganda False\n",
"Ukraine False\n",
"United Arab Emirates True\n",
"United Kingdom of Great Britain and Northern Ireland False\n",
"United Republic of Tanzania False\n",
"United States of America False\n",
"Uruguay False\n",
"Uzbekistan True\n",
"Vanuatu False\n",
"Venezuela (Bolivarian Republic of) False\n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>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",
"Country \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",
"Country \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",
"Country \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",
"Country ... \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",
"Country \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",
"Country \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": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
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" <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",
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" <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",
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" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>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",
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" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
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" <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",
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" <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",
"Country \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",
"Country ... \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",
"Country \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",
"Country \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": 29,
"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": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <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",
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" <th>...</th>\n",
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" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
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" <th>Afghanistan</th>\n",
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" <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",
" <td>1</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",
"Country \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",
"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",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 30,
"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": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
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"scrolled": 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": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.0.3\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"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 about three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4ee62b7ef0>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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lVBDZG4M/B2CwvF804rN+ADwjIr8ANgCPWumPAk+JSAnub/5XARhjtonIc8B2oBO42Rjj/bqlSikVwoK1FeRwDCoAGGPeA96z7pfSyygeY4wDuLyP8+8G7h5sJpVSKtR1zQIegaOAlFJK9cPe2EZUhJAWxFnAg6UBQCmlfKCywUF2UmzYzAIGDQBKKeUTdmsz+HCiAUAppXwgmFtBDpUGAKWU8oHKhrawGgEEIyAA2Bsc3LhiLR/trQ52VpRSo5Sjw0l9a0dYjQCCERAAPiyp5u0ddq790xp+89ZOOpyugU9SYedoUxvrD9QEOxtK9aoqyFtBDlXYB4CymhZE4NK54/jdu3u54o+rKa9pCXa2lI/dv2oPVz+yhvZODfAq9HjmAGgTUICV17SSkxTL/1w+mwevPpGSyiYuvP8DXt2kywyNJJ+W1dLe6WJfdXOws6LUZxybBaxNQAFVXtPC+PR4AL4weyyv33oak3IS+dZfN/C95zfR0t45wBVUqHN0ONl5uBGAXZWNQc6NUp91bBaw1gACqqymhXwrAADkp8fz3NcW880zJ/HCpxV8/oEP2XqwPog5VMO17VA9nS73yuG7j2gAUKHn2CzgqGBnZVDCOgA4OpwcaXB01QA8oiJsfPe8qTx940Ka2zv50u8/4tEP9+FelFSFmw1ldQBkJkZrDUCFJLs1C9i9+WH4COsAUFHbCsD4jLhenz+5KJM3bj2d06dk8fPXtnP9E2tpcHQEMovKBzZV1DM2JZaFEzPYrQFAhaDKRkdYLQPtEdYBoLzWPdonPy2+z2PSE6J55Lp5/GzpDP61u4pH3i8NVPaUj2wsr2V2fipTc5Ioq2nRfh0Vctx7AYdX+z+EewCwhnv2bALqSUS4bnEhZ03N5q+flNHWqdsQhIujTW2U17QyJz+VKTlJGAMl9qZgZ0up41Q2hN8yEBDmAaDsaAsxkTavF2BatriA6qZ23tx6xM85U76yqcLd/j87P5WpY5IA2KUdwSqEODqcNDg6w24SGIR7ALCGgHrb8XL65CwKM+J5cvUBP+dM+crG8npsArPyUhifHk9MpE37AVRIsTd49gLWGkBAlde2Dtj8053NJnx5UQHrD9Sy7ZAODQ0HG8vrmJKTREJMJBE2YXJOIrsqtQlIhY7KxvCcBQxhHACMMZT3mAPgjcvn5RMbZeMprQWEPGMMm8rrmJOf2pU2JSdJ5wKokNI1CUz7AAKntqWDprbOQQeAlPgoLp6Tx8sbD1LfokNCQ9n+oy3Ut3Ywu1sAmJqTxJEGh753KmR4moB0FFAAlXk5Aqg3yxYX4Ohw8fz6cl9nS/nQpnJ3B/BxNQCrI3i3XWsBKjRUNjqIjrCRGmazgGGUBoAZY1OYX5DGUx8fwOXS2cGhamN5HXFREUzOTuxKm5qjI4FUaPFsBRlus4AhjAOAZw7AuLTeZwEPZNniAg4cbeH9PVW+zJbyoY3ldcwal0JkxLE/09yUWJJiInUkkAoZ4ToHAMI8AGQmRpMQEzmk8y+YmUtmYowOCQ1RbZ1Oth9qOK75B9yT+qaMSdIagAoZ9sbw2wrSI2wDQM9VQAcrOtLGNQvyeXeXXTeQCUE7DzfS7nR9JgCANRKoslEX91MhwV0D0AAQUGXd9gEYqmsWFmAT4c8fay0g1GwsPzYDuKepOYnUtnRQ1dQW6GwpdZzWdieNjk6vVyMINWEZADqcLg7Xf3YZ6MEakxLLkuIcnl1XjqND1wcKJZvK68hKimFsyme/WXWNBDqiE8JUcNnDeBIYhGkAOFznwOky/a4C6q3rFhdS19KhW0iGmI3ldcwel9rryIqukUDaEayCLFy3gvQIywDgGQI6nD4Aj0UT05mSk8hTqw9om3KIqG/poLS6mRPHf7b5ByAjMYbMxGidEayCLly3gvQYMACISKyIfCIim0Rkm4j81EqfICJrRGSPiDwrItFWeoz1uMR6vrDbtX5ope8SkfOGmumuOQAZww8AIsKyRQVsOVjf1e6sgqtrBdBxvQcAcHcEaw1ABZu9ceTXANqAs4wxs4E5wPkisgj4NXCvMWYyUAvcYB1/A1BrjJkE3Gsdh4gUA1cBM4Dzgd+LSMRQMl1W00JUhDDGR+1ul8wdR2JMpK4PFCI8M4BnjUvp85gpOUnsqWzUiXwqqOwNDqIjbaTEhd8sYPAiABg3T29blPVjgLOAF6z0FcDF1v2l1mOs588Wd0PuUuAZY0ybMWYfUAIsGEqmy2tbGJcWT4TNNzPvEmMiuXRuHq9tPky1jiwJuo3ldRRlJfT7TzV1TBLN7U4O1rUGMGdKHa+ywUF2mM4CBi/7AEQkQkQ2AnZgJbAXqDPGePbmqwDyrPt5QDmA9Xw9kNE9vZdzur/WTSKyTkTWVVX1Pku3vKZlyDOA+7JscQHtThfPrtX1gYLJGMOmirpeh392N8XqCNYZwSqYKhvCdxIYeBkAjDFOY8wcYBzub+3TezvMuu0tFJp+0nu+1sPGmPnGmPlZWVm95scXcwB6mpSdxMlFGfxlTRlObVYImoN1rVQ3tXPigAHAvT6Q9gOoYLI3hu8yEDDIUUDGmDrgPWARkCoinnUYxgGecZQVQD6A9XwKUNM9vZdzvNbg6KCupcPnAQDcQ0IP1rWyakelz6+tvNPfBLDukmKjyEuN05FAKqjsDW1hOwIIvBsFlCUiqdb9OOAcYAfwLnCZddhy4BXr/qvWY6zn3zHu8ZWvAldZo4QmAJOBTwabYW83gh+Kc6ZnMzYlVtcHCqJN5XVER9qYNiZ5wGOn6O5gKoha2jtpbOsMy41gPLypAeQC74rIZmAtsNIY8xrwA+A2ESnB3cb/qHX8o0CGlX4bcAeAMWYb8BywHXgTuNkYM+jpt+U+nAPQU2SEjWsWjufDkmr2VukHSzBsLK9jxthkoiMH/tOcMiaJvfYmOp2uAORMqeOF80YwHt6MAtpsjDnRGHOCMWamMeZnVnqpMWaBMWaSMeZyY0yble6wHk+yni/tdq27jTFFxpipxpg3hpJhX04C681VC8YTYRNe3nDQL9cfLVwuM+iJdZ1OF1sO1ve6AFxvpuYk0e50sf+oLuanAs8zCWzEdwKHkrKaFlLiovw27jYzMYbJ2YlsPaibxg9Vp9PFFX9czW3PbRrUebsqG3F09L4CaG90JJAKpkprEthIbwIKKeU1rX5p/+9uem4yOw7rh8pQPfXxAdYdqOWlDQf557YjXp+3qdwddL0NAJOyE7GJ7g6mgsPuqQGM5CagUFPuhyGgPRXnJnOkwUFNc7tfX2ckqmps47f/3M2pkzKZNiaJu17dRlNb58AnAhvLa0mLj/L6/Y2NiqAwI0FrACoo7I1txETaSI4b2qZUoSCsAoDTZaiobWVcum8ngfU0Pdc9AmXH4Qa/vs5IdM+bO3F0Ovnp0hncfcksjjQ4uHflbq/O3VRez+z83lcA7YuuCaSCpbLBQXZy+M4ChjALAJUNDtqdrgA0Abnblrcf0gAwGJ+W1fL8+gq+euoEirISmVeQxtULxvP4v/cN2KfS1NbJbntjvwvA9WbKmCT2Vzfrfg4q4OwNbWHd/ANhFgDK/DgHoLuMxBhykmO0BjAITpfhrle2kZMcwy1nTe5K/8F500hPiObHL23pd4b1lop6jIE5fSwB3ZepOUm4DJTYddiuCqzKxvDdCtIjrAKAPyeB9TQ9N5ntGgC89uzacrYcrOdHF04nMeZYm2hKfBR3fr6YTRX1PL2m7wl2XTOAB1kDmDrGvSSE9gOoQLM3tIXtVpAeYRcAbAJjU/3bBwDujuASexNtndq0MJDa5nbueWsnCyak88XZYz/z/Bdnj+XUSZn85s1dXWOne9pUXkdBRjzpCdGDeu2CjASiI2zaD6ACqrmtk6a2Tq0BBFJZTQu5KXFERfg/29Nzk+l0GfboUgMD+t+Vu2h0dPLTL87otUNMRPjFxTNpc7r42Wvbe72GZwvIwYqKsDExK0HXBFIBFe4bwXiEXQAIRPMPQPFYHQnkja0H63l6TRnLFhV0jZ7qTWFmArecOYl/bD7Me7vsxz13pN7BkQaH1+P/e5o6JondGqhVAIX7VpAeYRUAymv9PwnMozAjgdgom04I64fLZfh/r2wlPT6a75w7ZcDjb/rcRIqyErjzla20th9rWvN2BdC+TMlJ4mBdK42OjiGdr9RgHVsGQmsAAdHa7qSqsc0n+wB7I8ImTBuTzPbDuiREX17acJBPy+r4wQXTvFqaIyYygrsvmUV5TSsPvrOnK31TRR2RNmHG2IFXAO3N1K4lIbQWoAKjqmsZCK0BBER5rXsEkK93AuuPZ0mIwS5qNho0ODr47zd2Mic/lcvmjvP6vEUTM7hs3jgefr+0awmHjWV1TM9NJjZqSFtEM3WMrgmkhsflMl2jDL1R2eBwzwKODd9ZwBBGAaDsaOCGgHoUj02mvrWDQ/W9j1wZze5/ew9Hm9v42dIZ2Aa5N/OPLpxOUmwkP35pS9cKoLPz+94AfiB5qXHER0fomkBqyF7ZdJDT7nn3M/1TffFsBRnOs4AhnAJAAOcAeBRbM4J36Izg4+yubOSJj/Zz1UnjOWEII3fSE6L54YXTWXeglv9+YydNbZ3MyU8bcn5sNmFyTpLWALygtdnevbnVvWjhj1/a6tXaVeG+FaRH2ASA8toWEqIjBj1OfDimWrtS6YSwY4xxz/hNio3k++dNHfJ1Lp83jgUT0nn0w30AzBlGDQBgak6iBoABfLCnioW/XMUHe6qCnZWQ4uhw8sGeauYVpHGovpV73tw54Dn2hrawb/+HcAoANS3kp8cHtMqVGBNJYUa8DgXt5h9bDrO69CjfXTKVtGEEYxHhl5fMJCpCSIqJZGJm4rDyNSUnieqmdqqb2oZ1nZHsL2vKsDe2ccOKdbyzU/e99vi49Cgt7U6+eeYkvnJyIU+uPsAn+2r6PaeywUF2mM8CBgibHoyymhYKMhIC/rqjYUmImuZ2rn74Y442t+Ey4DLG2tELDNZjY3AZ6HC6mDE2masXjB/2607KTuKnX5xJfWvHoPsReureEZyZGP7/mL7W3NbJOzvtLJ0zln3VzXztqfU8ePVczp85JthZC7p3dtqJi4pgcVEGCyaks3J7JXe8uJnXbz2t14EJTW2dNLc7w34WMIRJADDGUF7TymmTswL+2sW5ybyx9QhNbZ3HrXEzkjz6YSm77Y1cOT+fqAgbNnF/Q7eJIAI2wbovREUIV8zPJ2KYH9ge1ywcfiCBbkNBjzRyclGmT645kryz005bp4urF4yneGwyX3nsE27+y6fce+WcXpfvGC2MMazaYeeUSZldH/a/+tIJfPnRNdy/ag8/OH/aZ86xj5A5ABAmAaC6qZ3WDmdAO4A9PLNbdx5uYH5hesBf39/qWzpY8dEBLpyZy68uPSHY2RmyrKQYUuOj2KVzAXr1+pbDZCXFcFJhOhE24ckbFvLVJ9by7Wc20N7p4rJ53g/lHUl2HmnkYF0rt5w1qSvt1MmZXDHfPVT5olm5zMw7vn+q0toMPtxnAUOY9AEEYwSQx0hfEuLxj/bR1NbJN7v9A4QjEWGKjgTqlaf554KZY7pqbokxkay4fgEnF2XyvRc28Zc1ZUHOZXC8s9M97POsadnHpf/4omIyEqL53gub6XC6jnvO3jhyagBhEQA8EzTygxAAclNiSYmLYvsIXBKi0dHBYx/u49zinH7X8QkXU3OS2H1EJ+715Gn+uXBW7nHpcdER/Gn5fM6YksWPXtrCE//eF6QcBs/bOyo5YVzKZ0b0pMRF8fOLZ7LjcAN//Nfe456zN4yMWcAQJgHAUwMI5CxgDxGheIR2BD+5+gANjk6+1W0Dl3A2ZUwSjW2dHNaJe8fp3vzTU2xUBH9YNo8lxTn85O/befj9vb1cYWSqbmpjY3kdZ0/L6fX582aM4aITcnlgVQkl9mNfACsbHMRG2UgaAX2CYRMAcpJjhrxUwHBNz01m15GGfne0Cjct7Z08+uE+zpiaxaxxwxuDHyo8HcG6N8AxvTX/9BQTGcHvrp3LRSfk8svXd/Lgqj29HjfSvLvTjjFw9vTsPo/5yRdmEB8Twfdf2Nz1/1/ZODJmAUOYBIDyAC4D3ZvpuUk4Olzsq24OWh587emPy6hpbj9QaxE6AAAXFUlEQVRu+8ZwNyXH2h1Ml4To0lfzT09RETbuv3IOXzoxj/9duZv/eWvXiG9KW7XDTk5yTL+LEGYlxXDXF4r5tKyOJ1fvB9yjgMJ9L2CPsAkAwWj/9xhpHcGODicPf1DKyUUZzCsY+hIMoSY1Ppqc5BitAXTTX/NPT5ERNv7n8tlcdVI+//duCT95dRuuEVTr7a6t08kHe6o4a1rOgN/kL56TxxlTs7jnzV2U17Rgb2wjawR0AEMYBIC2TieHGxxBrQFMyk4k0iYh0w9QXtPC794tGfJ2lc+uLaeqsW1Effv30JFAx3jT/NOTzSb88pJZ3HjqBFasPsCtz26kvdM18IlhZk1pDc3tTs7pp/nHwz1rfRY2gR/+bQuVWgMInIO1rRgD+WnBCwAxkRFMyk4MiRpAe6eL//zzen7z1i6++/zmQX9Da+t08od/7eWkwjQWTRx58xqm5iSxp7KJmub2YGcl6Lxt/unJZhP+6/PF/PCCafx90yFuWLGWZi8WSAsnq3ZUEhtl45RJ3k0aHJsaxx0XTufDkmpa2p0jYggoeBEARCRfRN4VkR0isk1EbrXS00VkpYjssW7TrHQRkQdEpERENovI3G7XWm4dv0dElnuTwfLaVoCAbQTTl+LcZLaHwKqgv125m22HGrhg5hj+vukQv35r4IWruntx/UEO1zu45azJI6ITq6e5BWm0dbo46e63ueaRj3lq9f4+N6If6QbT/NObr32uiHsuO4GP9h7lmkc+HjFB1RjDqp12TinKHNTAkmsXjGfBBPfvMnu0BACgE7jdGDMdWATcLCLFwB3AKmPMZGCV9RjgAmCy9XMT8BC4AwZwF7AQWADc5Qka/QnmJLDuiscmY29sC+piY6v3HuWP7+/l6gX5/P7auSxbVMAf/1XKio/2e3V+h9PF798rYXZ+KqdNHpnLJVw4K5fXbjmVb5xRhL2xjTtf2cbCX67iS7//N4+8X9q1r8RIN5Tmn95cMT+fP3x5HjuPNHLZHz6iojb8f3+7K5uoqG3l7Om9D//si80m3HPpCZxUmMbc8SOj72zAAGCMOWyM+dS63wjsAPKApcAK67AVwMXW/aXAk8btYyBVRHKB84CVxpgaY0wtsBI4f6DXL69pISbSRlaQF/jyTJQKVjNQfWsHtz+3kYL0eP7romJEhJ98cQbnFufwk79v61rPvD8vbzhIRW0r3zpr0oj89u8xMy+F25dM5e3bPsfbt53Od5dMod3p4u7Xd3D6b97lwvs/4IFVe9g1gieNDbX5pzfnFufw1A0LqWps47KHVod9H8sqayXUnrN/vVGYmcDz/3lyUBam9IdB9QGISCFwIrAGyDHGHAZ3kAA8v808oLzbaRVWWl/p/So72sK4tLhhrxY5XMEOAHe+vJXKxjbuu+pEEqwJKBE24YGrTmT2uFRufWYD6w/U9nm+02X4/Xt7Kc5NHtIffrialJ3EN8+azGu3nMYH3z+T/7poOvHREdz79m7Ou+995v58JV99Yi0PrtrDv0uqR8zG8sNt/ulpwYR0nvvaYlzGcPkfVrP+QP/LJYeyVTvszMxLZkzKyOjIHQ6vp7KJSCLwIvBtY0xDP98ge3vC9JPe83Vuwt10xPjx4ykL8hwAj/SEaMYkx7IjCEtCvLzhIK9uOsTt505hTv7xO3DFRUfw6PL5XPrQR9y4Yi0vfv1kJmZ9dm391zYfYl91Mw9dO3dEf/vvT356PDeeNpEbT5uIvcHBOzvtfFpWy6dldV1rwoi4O5JPHJ/KiePTmDs+lYmZiUH/AjIYnuafK0/y3aqt4P4S9OLXT+a6xz7h2j+t4aFr53FmmH2ZONrUxqdltSNm9vtweVUDEJEo3B/+Txtj/mYlV1pNO1i3ns00K4D8bqePAw71k34cY8zDxpj5xpj5WVlZQZ8E1t303KSAdwRX1LZw58tbmVeQxtfPKOr1mIzEGJ64fgEiwlceX/uZfgqXy/C7d0uYkpPIeTN0/Xdwr+Ny1YLx3HPZbN6+7XNsumsJK766gFvPnkxOciz/2HyY77+wmXN++z5zf7GSN7ceDnaWvebL5p+e8tPjef4/FzMpO5Ebn1zH3z6t8Plr+NN7u6oGnP07mngzCkiAR4EdxpjfdnvqVcAzkmc58Eq39Ous0UCLgHqriegtYImIpFmdv0ustD45XYbGts6gTgLrrnhsMnurmnB0DG38/WA5XYbbnt2EAe67cg6REX2/XYWZCTy6fD72Rgc3PLGWlvZjw/be2naE3ZVN3HzmpLD6JhtIKXFRfG5KFt8+ZworvrqAjf9vCW/f9jnuuewExqfH861nNvbbxBZKfN3801NmYgx//Y9FLJyQzm3PbeKpjw/45XX8YdXOSrKTYpg5dmQsfzJc3tQATgGWAWeJyEbr50LgV8C5IrIHONd6DPA6UAqUAI8A3wAwxtQAPwfWWj8/s9L65JmAEjo1gGQ6XYYSe2DWnP/j+3v5ZH8NP/niDK+C4Inj03jw6rlsOVjPLX/ZQKfThTGGB98pYWJmAp8/YfRu/DFYNpswKTuRK+bn88T1CxibEst/PLmOA0dDezkQX43+GUhSbBSPX38S50zP5s6Xt/LcuvKBTwqy9k4X7++u5uzp2fpFyOLNKKAPjTFijDnBGDPH+nndGHPUGHO2MWaydVtjHW+MMTcbY4qMMbOMMeu6XesxY8wk6+fxgV673VqHO2RqALmB2yR+S0U9v/3nbi6alculcwfsK+9ybnEOP106k1U77dz5yjZW7bCz/XAD3zhzkl8/EEay9IRoHr9+AS5juP6JtdS1hO54eH82//QUExnB/10zl9MmZ/KDFzfzysaDfn/N4fhkXw1NbZ2c1cfqn6NRSM8E9tQAQiUAFGQkEBcV4fd+gNZ2J7c+u4HMxBjuvmTmoDttly0q4OtnFPHXT8r4znMbyU+PY+kc/fY/HBMyE3jkuvlU1LRy01Prh7wMh7/5u/mnp9ioCB5eNp+TCt3NQd4MRw6Wt3dUEhNp41QvZ/+OBqEdAJwuMhKiQ2Yv3gibMC03ye9DQe9+fTulVc387xWzSY2PHtI1vrdkKhfPGUujo5NvnDGJqH76D5R3TipM5zeXn8An+2r44YtbQm4OQaCaf3qKi47gsa+cxKy8FG7566e8t8s+8EkB5p79W8nJRRnERQdnWflQFNKfCu2drpD59u8x3docxl///Kt2VPLnj8v4j9MmeL1OSW9sNuGey2bz5xsWcuX8/IFPUF5ZOieP28+dwt82HOS+t0Nr3fxANv/0lBgTyYqvLmBKThJfe2o9H+2tDnge+lNib6K8ZvCzf0e6kA8AodIB7FGcm0yjo5ODda0+v3ZVYxvff2Ez08Yk8d3zpg77etGRNk6dnKkdXj72zbMmcdm8cdy/ag8vrg+dYZCBbv7pKSUuiqduWMj49HhuXLGOdftDZ7LY2zvctRId/nm8kA4AHU4X+emB3wayP8dmBPt+Qthdr26lsa2T+686kZhIraaGKs/ywCcXZXDH3zazeu/RYGcpaM0/PaUnRPP0jQvJSY7l+sfXsrmiLmh56e6dnZUU5yaTmxJanyfBFtIBwBA6Q0A9po1JQgSfdwS/u9PO61uO8K2zJjF1TJJPr618LzrSxkNfnkdBRgJfe2pdwIYG9yWYzT89ZSfH8vSNC0mOi2LZo58EfRn12uZ21h+o9Wrt/9EmpAMAhM4III+EmEgKMxJ8+kfd2u7kzle2Mik7kZtO7322rwo9KXFRPP6Vk4iOtHH9E59wNIgrxQa7+aensalx/PU/FhEbZWPZo2v6DJCODif7qpv5qKSaF9dX8Lt3S3hy9X62H/LdHtzv7rLjMmj7fy9CY3hNP0KtBgDuJSG2HvRdAHjwnT1U1LbyzE2LiI4M+ZisuslPj+dPy0/iqodXc+OT66wPvcA23/lr7Z/hGp8Rz9M3LuKqh1dz7Z8+5rrFhRypd3C4vpVDdQ6ONDj63WMgKTaSeQVpnFSYzvyCNGbnpw7pd7tqp52spBhm5ens355COgCkxbsXYAs1xbnJvL7lCI2ODpJio4Z1rd2VjTz8fimXzh3HookZPsqhCqQ5+ancd+WJfP3p9dzx4mbuvXJOQBfc+/umQyHT/NPTpOxE/nzjQq59ZA2/eWsXybGRjE2NIzclltn5qYxNiSU3NY6xKbGMSYklNyWO6qY21h2o4ZN9tazbX8N7u3YBEB1hY9a4FOYXprGgMJ3iscmkJ0T321/W3uni/V1VXDgrVwdD9CKkA8C4tLh+178JFk9H8M4jjcOqcrtchh+/tIXE2Eh+dOE0X2VPBcH5M8fwnXOm8NuVuzlpQjrXLizw+2t2OF3c9/Zufv/eXqaNSQqZ5p+epo1J5t93nIXTZbqWMu9Pfno8+enxXHLiOMDdhr/ugDsYrN1fw2Mf7uOP/yrtOj4hOoK0hGgyEqJJS4gmPd66TYimqa2TxrZOztL2/16FdAAIVcVjj+0NMJx/uhfWV7B2fy2/vnQWGUHe8EYN3zfPnMS6A7X89NXtnJCXyqxx/mtyOHC0mW89s5FN5XVcMX8cd31hRkg1//Q0nGaxtIRozi3O4dxidxu+o8PJxvI69lY1UdvcTk1zB7Ut7dQ0u39K7O705nb3bO2kmEid/dsHDQBDMCY5ltT4qGGNBKppbueXb+xgfkEal8/TiVojgc0m3HflHD7/wAd84y/ree2bp5ESP7wmwp6MMby04SB3vryVCJvwu2vmctEJodf040+xUREsmpgxYJOpo8NJbUs70RE2r2oeo1Hota+EARGhODd5WCOB/vv1HTQ5Orn7klnaNjmCpCdE83/XzuVIvYPbn9/k0xnjDY4Ovv3sRm57bhMzxqbwxrdPH3Uf/oMRGxVBbkqc1q77oQFgiKbnJrPzSCOd1oqlg7Gm9CjPr6/gxtMm6pj/EWju+DR+dOF03t5RycPvlw58ghfWH6jhwvs/4LXNh7n93Cn89aZF5KXqpCY1PBoAhmh6bjJtnS72D3J9+PZOFz9+eSvj0uK49Wzdlm6k+srJhVw0K5d73trFmtKhzxTudLq4/+09XPHHjxGB5762mFvOnhzS7f0qfGgAGKJjewMMbkmIRz4opcTexM+WztBVCUcwEeFXl85ifHo8t/x1A1WNg58kVl7TwtWPfMy9b+/mCyfk8vq3TmNeQZofcqtGKw0AQzQpO5GoCOFfu6q83iKy7GgLD6zaw/kzxuimFKNAUmwUv792LvWtHdz6zAavZ7bWNrfzi9e2c/b//osdhxu598rZ3HfVicOec6JUT9o1PkTRkTbOnJrNi59W8M9tR7hg1hguOXEcCyek99qpa4zhzle2EmkT7vpicRByrIJhem4yv7h4Jt97YTP3vb2b25f0vcpra7uTx/69jz+8t5fm9k4unTuO75w7hbHa1q/8RAPAMDz05Xl8XHqUv316kH9sPsxz6yrIS3XvvvWluXlMyj7Wwfv6liP8a3cVd36+WFckHGUun5/P2v01PPhOCXML0jhz6vGTkjqdLp5fX8F9b++msqGNc6Zn873zpukAAeV3Emq7GnU3f/58s27duoEPDAGt7U7+uf0IL204yAd7qnG6DLPyUrjkxDzOmpbNFX9cTWZiDK9+85SQnN2s/MvR4eTi3/2bIw0O/vGt08hLjcMYw1vbKrnnrZ2UVjUzd3wqd1wwnQUTQnNGrwofIrLeGDN/wOM0APheVWMbr246xEsbKroWjROBl75xCnPyU4OcOxUs+6qb+cKDH1KUncgPzpvKb/65iw1ldRRlJfD986expDgnoGsIqZFLA0CI2FPZyMsbDzImOZZliwuDnR0VZG9sOczXn/4UgJzkGL5zzhQumzdOa4XKpzQAKBWinlq9n9YOJ8sWFepQYOUX3gYA7QRWKsC0JqhChdY7lVJqlNIAoJRSo5QGAKWUGqU0ACil1CilAUAppUYpDQBKKTVKaQBQSqlRSgOAUkqNUiE9E1hEGoFdwc6Hj2QC1cHOhI9oWULPSCkHaFl8ocAYkzXQQaE+E3iXN9OZw4GIrNOyhJ6RUpaRUg7QsgSSNgEppdQopQFAKaVGqVAPAA8HOwM+pGUJTSOlLCOlHKBlCZiQ7gRWSinlP6FeA1BKKeUnAQ8AIvKYiNhFZGu3tNkislpEtojI30Uk2UqPEpEVVvoOEflht3POF5FdIlIiIneEcTn2W+kbRSQou98MsizRIvK4lb5JRM7ods48K71ERB6QIOxv6MOyvGf9fW20frJ7eTl/liNfRN61/l62icitVnq6iKwUkT3WbZqVLtbvvERENovI3G7XWm4dv0dElgeyHH4oi7Pbe/JqGJRlmvW31yYi3+1xraB+hgFgjAnoD3A6MBfY2i1tLfA56/5XgZ9b968BnrHuxwP7gUIgAtgLTASigU1AcbiVw3q8H8gM9PswjLLcDDxu3c8G1gM26/EnwGJAgDeAC8K4LO8B84P4nuQCc637ScBuoBi4B7jDSr8D+LV1/0Lrdy7AImCNlZ4OlFq3adb9tHAsi/VcU7DekyGWJRs4Cbgb+G636wT9M8wYE/gagDHmfaCmR/JU4H3r/krgUs/hQIKIRAJxQDvQACwASowxpcaYduAZYKm/896dj8oREgZZlmJglXWeHagD5otILpBsjFlt3H/hTwIX+zvvPfmiLAHI5oCMMYeNMZ9a9xuBHUAe7r/zFdZhKzj2O14KPGncPgZSrffkPGClMabGGFOLu/znB7AovixL0A22LMYYuzFmLdDR41JB/wyD0OkD2Ap80bp/OZBv3X8BaAYOA2XA/xhjanD/wsu7nV9hpQXbYMsB7uDwTxFZLyI3BTKzA+irLJuApSISKSITgHnWc3m43wePUHlPYPBl8Xjcamq4MxjNWR4iUgicCKwBcowxh8H9YYT7Gyb0/T8RUv8rwywLQKyIrBORj0Uk4F8wuvOyLH0JifclVALAV4GbRWQ97mpVu5W+AHACY4EJwO0iMhF31bCnUBjONNhyAJxijJkLXGCde3qA89yXvsryGO4/1nXAfcBHQCeh+57A4MsCcK0xZhZwmvWzLKA5tohIIvAi8G1jTH+1xr5+/yHzvvigLADjjXtm7TXAfSJS5ONsemUQZenzEr2kBfx9CYmlIIwxO4ElACIyBbjIeuoa4E1jTAdgF5F/466il3P8N7VxwKHA5bh3QyhHqTHmkHWuXURewh0s3v/MxQOsr7IYYzqB73iOE5GPgD1ALe73wSMk3hMYUlkwxhy0bhtF5C+435cnA5lvEYnC/SHztDHmb1ZypYjkGmMOW80idiu9gt7/JyqAM3qkv+fPfPfGR2Wh2/9LqYi8h/sb+N4AFKHLIMvSlz7LGEghUQPwjLAQERvwX8AfrKfKgLOsUQEJuDuEduLu1JssIhNEJBq4Cgj4iICeBlsOEUkQkSTrnATcH1JbP3vlwOurLCISb+UVETkX6DTGbLeqvY0isshqLrkOeCU4uT/eYMtiNQllWulRwOcJ8Pti/Q4fBXYYY37b7alXAc9InuUc+x2/Clxn/Y0tAuqt9+QtYImIpFkjU5ZYaQHjq7JYZYixrpkJnAJsD0ghLEMoS19C4zMs0L3OwF9xt4V34I6CNwC34u5N3w38imMT1BKB54FtuN/o73W7zoXW8XuBH4djOXCPANhk/WwLRjmGUJZC3Cu07gDexr3qoOc683F/UO4F/s9zTriVBUjAPSJos/W+3A9EBLgcp+JuEtgMbLR+LgQycHdc77Fu063jBfid9bvfQrcRTLibwEqsn+uD8J74pCzAydbjTdbtDWFQljHW32ED7kEGFbgHS0CQP8OMMToTWCmlRquQaAJSSikVeBoAlFJqlNIAoJRSo5QGAKWUGqU0ACil1CilAUAppUYpDQBKKTVKaQBQSqlR6v8DKgtMvU3gYM0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": {
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"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,
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},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"run_control": {
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"scrolled": true
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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>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",
" <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>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",
"Country ... \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",
"Country \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": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[['China','India'], years]\n",
"df_CI.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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},
"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": {
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}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4ee5ea2940>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.plot(kind='line')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country</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": [
"Country China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 54,
"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": 55,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.index = df_CI.index.map(int)\n",
"\n",
"df_CI.plot(kind='line')\n",
"\n",
"plt.title('Immigration from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.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": "code",
"execution_count": 56,
"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",
" }\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>Country</th>\n",
" <th>Sweden</th>\n",
" <th>India</th>\n",
" <th>Sudan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>281</td>\n",
" <td>8880</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>308</td>\n",
" <td>8670</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>222</td>\n",
" <td>8147</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>176</td>\n",
" <td>7338</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>128</td>\n",
" <td>5704</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Sweden India Sudan\n",
"1980 281 8880 20\n",
"1981 308 8670 12\n",
"1982 222 8147 11\n",
"1983 176 7338 7\n",
"1984 128 5704 23"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# own example Sweden, India and Sudan\n",
"df_SIS = df_can.loc[['Sweden','India', 'Sudan'], years]\n",
"df_SIS = df_SIS.transpose()\n",
"df_SIS.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_SIS.index = df_SIS.index.map(int)\n",
"df_SIS.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Sweden, India and Sudan')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()"
]
},
{
"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": 60,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Country India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
"Country Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"# Sort by most immigrants\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"# get the top 5\n",
"df_top5 = df_can.head(5)\n",
"\n",
"# Transpose dataframe\n",
"df_top5 = df_top5[years].transpose()\n",
"print(df_top5)\n",
"\n",
"# Map index to integer\n",
"df_top5.index = df_top5.index.map(int)\n",
"\n",
"# Plot\n",
"df_top5.plot(kind='line', figsize=(14, 8))\n",
"plt.title('Immigration trend from top 5 countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\\\\ inplace = True paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"* `bar` for vertical bar plots\n",
"* `barh` for horizontal bar plots\n",
"* `hist` for histogram\n",
"* `box` for boxplot\n",
"* `kde` or `density` for density plots\n",
"* `area` for area plots\n",
"* `pie` for pie plots\n",
"* `scatter` for scatter plots\n",
"* `hexbin` for hexbin plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week1_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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