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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots.ipynb
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"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Data Visualization\n",
"\n",
"Estaimted time needed: **30** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After complting this lab you will be able to:\n",
"\n",
"- Create Data Visualization with Python\n",
"- Use various Python libraries for visualization\n"
]
},
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no _best_ data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in _pandas_ dataframes. Therefore, in this lab, we provide a brief crash course on _pandas_. However, if you are interested in learning more about the _pandas_ library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a _pandas_ dataframe are provided in our course [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"* * *\n"
]
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"## 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>\n"
]
},
{
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"source": [
"# Exploring Datasets with _pandas_ <a id=\"0\"></a>\n",
"\n",
"_pandas_ is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ):\n",
"\n",
"> _pandas_ is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on _pandas_ for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the _pandas_ API Reference: [http://pandas.pydata.org/pandas-docs/stable/api.html](http://pandas.pydata.org/pandas-docs/stable/api.html?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
]
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"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n"
]
},
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"button": false,
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"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png\" align=\"center\" width=900>\n",
"\n",
"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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"* * *\n"
]
},
{
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"source": [
"## _pandas_ Basics<a id=\"4\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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**.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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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",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Now we are ready to read in our data.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
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" <td>935</td>\n",
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" <td>5501</td>\n",
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" <td>1758</td>\n",
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" <td>2635</td>\n",
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" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
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" <td>702</td>\n",
" <td>560</td>\n",
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" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
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" <td>3623</td>\n",
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" <td>5393</td>\n",
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" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
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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",
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" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
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" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"new_sheet": false,
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"outputs": [
{
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" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Viet Nam</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
" <td>1816</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Western Sahara</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Yemen</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
" <td>434</td>\n",
" <td>437</td>\n",
" <td>407</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method.\n",
"\n",
"This method can be used to get a short summary of the dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Columns: 43 entries, Type to 2013\n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info(verbose=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in _pandas_ objects are _float_, _int_, _bool_, _datetime64[ns]_ and _datetime64[ns, tz] \\(in >= 0.17.0)_, _timedelta[ns]_, _category (in >= 0.15.0)_, and _object_ (string). In addition these dtypes have item sizes, e.g. int64 and int32. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use _pandas_ `drop()` method as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"## _pandas_ Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python\n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"\n",
"* * *\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country').\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985.\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
"\n",
"```\n",
"1. The full row data (all columns)\n",
"2. For year 2013\n",
"3. For years 1980 to 1985\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\n",
"\n",
"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
"\n",
"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
" ... \n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print(condition)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
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" <th>Total</th>\n",
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" </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",
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" <td>58639</td>\n",
" </tr>\n",
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" <td>Asia</td>\n",
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" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <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": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
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" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"* * *\n",
"\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
"\n",
"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). As mentioned on their website: \n",
"\n",
"> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
"\n",
"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Matplotlib.Pyplot\n",
"\n",
"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"\\*optional: check if Matplotlib is loaded.\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.3.2\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"\\*optional: apply a style to Matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in _pandas_\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in _pandas_ is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti.\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type _string_. Therefore, let's change the type of the index values to _integer_ for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
"\n",
"```\n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
"```\n",
"\n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```\n",
"\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
"\n",
"```\n",
"We will cover advanced annotation methods in later modules.\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>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": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"ChinaIndia = df_can.loc[['China','India'], years] \n",
"ChinaIndia.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click **here** for the solution.\n",
"\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 60,
"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",
"ChinaIndia.plot(kind='line')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click **here** for the solution.\n",
"\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that _pandas_ plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"ChinaIndia = ChinaIndia.transpose()\n",
"ChinaIndia.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Years')"
]
},
"execution_count": 63,
"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",
"ChinaIndia.plot(kind='line')\n",
"plt.title('Immigration from China India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click **here** for the solution.\n",
"\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",
"--> \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_Note_: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"\n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
"\n",
"> class 'pandas.core.series.Series' <br>\n",
"> 1980 1666 <br>\n",
"> 1981 3692 <br>\n",
"> 1982 3498 <br>\n",
"> 1983 2860 <br>\n",
"> 1984 1418 <br>\n",
"> Name: Haiti, dtype: int64 <br>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
" Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"df_top5 = df_can.head(5)\n",
"df_top5 = df_top5[years].transpose() \n",
"print(df_top5)\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",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click **here** for the solution.\n",
"\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",
"-->\n"
]
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"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"- `bar` for vertical bar plots\n",
"- `barh` for horizontal bar plots\n",
"- `hist` for histogram\n",
"- `box` for boxplot\n",
"- `kde` or `density` for density plots\n",
"- `area` for area plots\n",
"- `pie` for pie plots\n",
"- `scatter` for scatter plots\n",
"- `hexbin` for hexbin plot\n"
]
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"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
"\n",
"### Other Contributors\n",
"\n",
"[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
"[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
"[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ------------- | ---------------------------------- |\n",
"| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL and info method |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved Lab to course repo in GitLab |\n",
"| | | | |\n",
"| | | | |\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
}
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