Book Description
Understand, explore, and effectively present data using the powerful data visualization techniques of Python programming.
Key Features
 Study key visualization tools and techniques with realworld data
 Explore industrystandard plotting libraries, including Matplotlib and Seaborn
 Breathe life into your visuals with exciting widgets and animations using Bokeh
Book Description
Data Visualization with Python reviews the spectrum of data visualization and its importance. Designed for beginners, it'll help you learn about statistics by computing mean, median, and variance for certain numbers.
In the first few chapters, you'll be able to take a quick tour of key NumPy and Pandas techniques, which include indexing, slicing, iterating, filtering, and grouping. The book keeps pace with your learning needs, introducing you to various visualization libraries. As you work through chapters on Matplotlib and Seaborn, you'll discover how to create visualizations in an easier way. After a lesson on these concepts, you can then brush up on advanced visualization techniques like geoplots and interactive plots.
You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful visualizations. What's more? You'll study how to plot geospatial data on a map using Choropleth plot and understand the basics of Bokeh, extending plots by adding widgets and animating the display of information.
By the end of this book, you'll be able to put your learning into practice with an engaging activity, where you can work with a new dataset to create an insightful capstone visualization.
What you will learn
 Understand and use various plot types with Python
 Explore and work with different plotting libraries
 Learn to create effective visualizations
 Improve your Python data wrangling skills
 Hone your skill set by using tools like Matplotlib, Seaborn, and Bokeh
 Reinforce your knowledge of various data formats and representations
Who this book is for
Data Visualization with Python is designed for developers and scientists, who want to get into data science or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization, however, it'll help you to have some knowledge of Python and familiarity with high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will be able to improve their Python skills by working with realworld data.
Publisher Resources
Table of Contents
 Preface
 Chapter 1

The Importance of Data Visualization and Data Exploration
 Introduction
 Overview of Statistics

NumPy
 Exercise 1: Loading a Sample Dataset and Calculating the Mean
 Activity 1: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation for the Given Numbers
 Basic NumPy Operations
 Activity 2: Indexing, Slicing, Splitting, and Iterating
 Advanced NumPy Operations
 Activity 3: Filtering, Sorting, Combining, and Reshaping

pandas
 Advantages of pandas over NumPy
 Disadvantages of pandas
 Exercise 2: Loading a Sample Dataset and Calculating the Mean
 Activity 4: Using pandas to Compute the Mean, Median, and Variance for the Given Numbers
 Basic Operations of pandas
 Series
 Activity 5: Indexing, Slicing, and Iterating using pandas
 Advanced pandas Operations
 Activity 6: Filtering, Sorting, and Reshaping
 Summary
 Chapter 2
 All You Need to Know About Plots
 Chapter 3

A Deep Dive into Matplotlib
 Introduction
 Overview of Plots in Matplotlib
 Pyplot Basics
 Basic Text and Legend Functions

Basic Plots
 Bar Chart
 Activity 13: Creating a Bar Plot for Movie Comparison
 Pie Chart
 Exercise 4: Creating a Pie Chart for Water Usage
 Stacked Bar Chart
 Activity 14: Creating a Stacked Bar Plot to Visualize Restaurant Performance
 Stacked Area Chart
 Activity 15: Comparing Smartphone Sales Units Using a Stacked Area Chart
 Histogram
 Box Plot
 Activity 16: Using a Histogram and a Box Plot to Visualize the Intelligence Quotient
 Scatter Plot
 Activity 17: Using a Scatter Plot to Visualize Correlation Between Various Animals
 Bubble Plot
 Layouts
 Images
 Writing Mathematical Expressions
 Summary
 Chapter 4
 Simplifying Visualizations Using Seaborn
 Chapter 5
 Plotting Geospatial Data
 Chapter 6
 Making Things Interactive with Bokeh
 Chapter 7
 Combining What We Have Learned

Appendix

Chapter 1: The Importance of Data Visualization and Data Exploration
 Activity 1: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation for the Given Numbers
 Activity 2: Indexing, Slicing, Splitting, and Iterating
 Activity 3: Filtering, Sorting, Combining, and Reshaping
 Activity 4: Using pandas to Compute the Mean, Median, and Variance for the Given Numbers
 Activity 5: Indexing, Slicing, and Iterating Using pandas
 Activity 6: Filtering, Sorting, and Reshaping
 Chapter 2: All You Need to Know about Plots

Chapter 3: A Deep Dive into Matplotlib
 Activity 12: Visualizing Stock Trends by Using a Line Plot
 Activity 13: Creating a Bar Plot for Movie Comparison
 Activity 14: Creating a Stacked Bar Plot to Visualize Restaurant Performance
 Activity 15: Comparing Smartphone Sales Units Using a Stacked Area Chart
 Activity 16: Using a Histogram and a Box Plot to Visualize the Intelligence Quotient
 Activity 17: Using a Scatter Plot to Visualize Correlation between Various Animals
 Activity 18: Creating a Scatter Plot with Marginal Histograms
 Activity 19: Plotting Multiple Images in a Grid

Chapter 4: Simplifying Visualizations Using Seaborn
 Activity 20: Comparing IQ Scores for Different Test Groups by Using a Box Plot
 Activity 21: Using Heatmaps to Find Patterns in Flight Passengers' Data
 Activity 22: Movie Comparison Revisited
 Activity 23: Comparing IQ Scores for Different Test Groups by Using a Violin Plot
 Activity 24: Top 30 YouTube Channels
 Activity 25: Linear Regression
 Activity 26: Water Usage Revisited
 Chapter 5: Plotting Geospatial Data
 Chapter 6: Making Things Interactive with Bokeh
 Chapter 7: Combining What We Have Learned

Chapter 1: The Importance of Data Visualization and Data Exploration
Product Information
 Title: Data Visualization with Python
 Author(s):
 Release date: February 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789956467