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Created October 25, 2019 19:22
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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": {
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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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"new_sheet": false,
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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,
"new_sheet": false,
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},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/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,
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"button": false,
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},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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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": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.7.12\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda/envs/python\n",
"\n",
" added / updated specs: \n",
" - xlrd\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" openssl-1.1.1 | h7b6447c_0 5.0 MB anaconda\n",
" certifi-2019.9.11 | py36_0 154 KB anaconda\n",
" xlrd-1.2.0 | py36_0 188 KB anaconda\n",
" ------------------------------------------------------------\n",
" Total: 5.4 MB\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" certifi: 2019.9.11-py36_0 conda-forge --> 2019.9.11-py36_0 anaconda\n",
" openssl: 1.1.1c-h516909a_0 conda-forge --> 1.1.1-h7b6447c_0 anaconda\n",
" xlrd: 1.1.0-py37_1 --> 1.2.0-py36_0 anaconda\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"openssl-1.1.1 | 5.0 MB | ##################################### | 100% \n",
"certifi-2019.9.11 | 154 KB | ##################################### | 100% \n",
"xlrd-1.2.0 | 188 KB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n"
]
}
],
"source": [
"!conda install -c anaconda xlrd --yes"
]
},
{
"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": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
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" <th>1980</th>\n",
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" <th>0</th>\n",
" <td>Immigrants</td>\n",
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" <td>903</td>\n",
" <td>Africa</td>\n",
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" <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",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" </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",
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"<p>5 rows × 43 columns</p>\n",
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"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 6,
"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": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
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" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
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" <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",
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" <td>59</td>\n",
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" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
" <td>434</td>\n",
" <td>437</td>\n",
" <td>407</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 7,
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
"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": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 9,
"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": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 10,
"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": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 13,
"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": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 16,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"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": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 18,
"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": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 19,
"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": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
" 1. The full row data (all columns)\n",
" 2. For year 2013\n",
" 3. For years 1980 to 1985"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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>2013</th>\n",
" <th>Total</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>16</td>\n",
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" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 \n",
"Bangladesh Asia Southern Asia Developing regions 83 \n",
"Bhutan Asia Southern Asia Developing regions 0 \n",
"India Asia Southern Asia Developing regions 8880 \n",
"Iran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \n",
"Maldives Asia Southern Asia Developing regions 0 \n",
"Nepal Asia Southern Asia Developing regions 1 \n",
"Pakistan Asia Southern Asia Developing regions 978 \n",
"Sri Lanka Asia Southern Asia Developing regions 185 \n",
"\n",
" 1981 1982 1983 1984 1985 1986 ... 2005 \\\n",
"Afghanistan 39 39 47 71 340 496 ... 3436 \n",
"Bangladesh 84 86 81 98 92 486 ... 4171 \n",
"Bhutan 0 0 0 1 0 0 ... 5 \n",
"India 8670 8147 7338 5704 4211 7150 ... 36210 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \n",
"Maldives 0 0 1 0 0 0 ... 0 \n",
"Nepal 1 6 1 2 4 13 ... 607 \n",
"Pakistan 972 1201 900 668 514 691 ... 14314 \n",
"Sri Lanka 371 290 197 1086 845 1838 ... 4930 \n",
"\n",
" 2006 2007 2008 2009 2010 2011 2012 \\\n",
"Afghanistan 3009 2652 2111 1746 1758 2203 2635 \n",
"Bangladesh 4014 2897 2939 2104 4721 2694 2640 \n",
"Bhutan 10 7 36 865 1464 1879 1075 \n",
"India 33848 28742 28261 29456 34235 27509 30933 \n",
"Iran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \n",
"Maldives 0 2 1 7 4 3 1 \n",
"Nepal 540 511 581 561 1392 1129 1185 \n",
"Pakistan 13127 10124 8994 7217 6811 7468 11227 \n",
"Sri Lanka 4714 4123 4756 4547 4422 3309 3338 \n",
"\n",
" 2013 Total \n",
"Afghanistan 2004 58639 \n",
"Bangladesh 3789 65568 \n",
"Bhutan 487 5876 \n",
"India 33087 691904 \n",
"Iran (Islamic Republic of) 11291 175923 \n",
"Maldives 1 30 \n",
"Nepal 1308 10222 \n",
"Pakistan 12603 241600 \n",
"Sri Lanka 2394 148358 \n",
"\n",
"[9 rows x 38 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
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" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
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" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1223</td>\n",
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" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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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": 34,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'tableau-colorblind10']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in *pandas*\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6900086780>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\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": 39,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
" \n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
" \n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" We will cover advanced annotation methods in later modules."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI = df_can.loc[[\"China\", \"India\"], years]\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f68fa64dcc0>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI.plot(kind=\"line\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 47,
"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": 48,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f68fab82a90>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI.plot(kind=\"line\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
"df_CI.plot(kind='line')\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"--> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"From the above plot, we can observe that the China and India have very similar immigration trends through the years. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
">class 'pandas.core.series.Series' <br>\n",
">1980 1666 <br>\n",
">1981 3692 <br>\n",
">1982 3498 <br>\n",
">1983 2860 <br>\n",
">1984 1418 <br>\n",
">Name: Haiti, dtype: int64 <br>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_can.sort_values(by=\"Total\", ascending=False, axis=0, inplace=True)\n",
"\n",
"df_top5 = df_can.head(5)\n",
"\n",
"df_top5 = df_top5[years].transpose() \n",
"\n",
"df_top5.index = df_top5.index.map(int)\n",
"\n",
"df_top5.plot(kind='line', figsize=(14, 8))\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\\\\ inplace = True paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"* `bar` for vertical bar plots\n",
"* `barh` for horizontal bar plots\n",
"* `hist` for histogram\n",
"* `box` for boxplot\n",
"* `kde` or `density` for density plots\n",
"* `area` for area plots\n",
"* `pie` for pie plots\n",
"* `scatter` for scatter plots\n",
"* `hexbin` for hexbin plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
{
"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",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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