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DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots-py.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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"metadata": {
"button": false,
"new_sheet": false,
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},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?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"
]
},
{
"cell_type": "markdown",
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"button": false,
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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,
"metadata": {
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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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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,
"metadata": {
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{
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" <th>0</th>\n",
" <td>Immigrants</td>\n",
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" <td>935</td>\n",
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" <td>5501</td>\n",
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" <td>1758</td>\n",
" <td>2203</td>\n",
" <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",
" <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",
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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",
" <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",
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" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</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,
"run_control": {
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},
"outputs": [
{
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" <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"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 0",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-4c4e292b9439>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_can\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36minfo\u001b[0;34m(self, verbose, buf, max_cols, memory_usage, null_counts)\u001b[0m\n\u001b[1;32m 2588\u001b[0m ) -> None:\n\u001b[1;32m 2589\u001b[0m return DataFrameInfo(\n\u001b[0;32m-> 2590\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_usage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnull_counts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2591\u001b[0m ).info()\n\u001b[1;32m 2592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/io/formats/info.py\u001b[0m in \u001b[0;36minfo\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_non_verbose_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_verbose_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;31m# groupby dtype.name to collect e.g. Categorical columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/io/formats/info.py\u001b[0m in \u001b[0;36m_verbose_repr\u001b[0;34m(self, lines, ids, dtypes, show_counts)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 336\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpprint_thing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 881\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 882\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m if (\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 990\u001b[0m \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 992\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2891\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2892\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2893\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2895\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 0"
]
}
],
"source": [
"df_can.info()"
]
},
{
"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": 8,
"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": 8,
"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": 9,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"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": 11,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 11,
"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": 12,
"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": 12,
"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": 13,
"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": 13,
"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": 14,
"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": 15,
"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": 15,
"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": 16,
"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": 16,
"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": 17,
"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": 17,
"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": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 18,
"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": 19,
"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": 20,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 22,
"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": 23,
"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": 24,
"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": 25,
"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": 26,
"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": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 27,
"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": 29,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
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},
"outputs": [
{
"data": {
"text/html": [
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" 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",
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" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
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" <td>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",
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" <td>0</td>\n",
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" <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",
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" <td>0</td>\n",
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" <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",
" </tbody>\n",
"</table>\n",
"<p>5 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",
"Armenia Asia Western Asia Developing regions 0 0 0 \n",
"Azerbaijan Asia Western Asia Developing regions 0 0 0 \n",
"Bahrain Asia Western Asia Developing regions 0 2 1 \n",
"Bangladesh Asia Southern Asia Developing regions 83 84 86 \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",
"Armenia 0 0 0 0 ... 224 218 198 205 267 252 \n",
"Azerbaijan 0 0 0 0 ... 359 236 203 125 165 209 \n",
"Bahrain 1 1 3 0 ... 12 12 22 9 35 28 \n",
"Bangladesh 81 98 92 486 ... 4171 4014 2897 2939 2104 4721 \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",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition].head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
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"outputs": [
{
"data": {
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" <td>65568</td>\n",
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" <td>8670</td>\n",
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" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
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" <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",
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" <tr>\n",
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" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <td>Southern Asia</td>\n",
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" <td>1</td>\n",
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" <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",
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" <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": 30,
"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": 31,
"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": {
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 31,
"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": 32,
"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": 33,
"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": 34,
"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": 35,
"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": 35,
"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": 36,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"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": 37,
"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": 38,
"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": 40,
"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": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[['China', 'India'],years]\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 41,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI.plot(kind='line')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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": 42,
"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": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"_pandas_ will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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": [
"### type your answer here\n",
"\n",
"df_CI.index=df_CI.index.map(int)\n",
"df_CI.plot(kind='line')\n",
"\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"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": 51,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 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",
"\n",
"df_top5=df_can.head(5)\n",
"#df_top5\n",
"\n",
"df_top5=df_top5[years].transpose()\n",
"\n",
"# print(df_top5.head())\n",
"\n",
"df_top5.index=df_top5.index.map(int)\n",
"df_top5.plot(kind='line',figsize=(12,8))\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"- `bar` for vertical bar plots\n",
"- `barh` for horizontal bar plots\n",
"- `hist` for histogram\n",
"- `box` for boxplot\n",
"- `kde` or `density` for density plots\n",
"- `area` for area plots\n",
"- `pie` for pie plots\n",
"- `scatter` for scatter plots\n",
"- `hexbin` for hexbin plot\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/aklson/\" 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",
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"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | ---------------------------------- |\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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