Reimagining Data Visualization Using Python
ISBN: 9789354641336
For more information write to us at: acadmktg@wiley.com
Description
Reimagining Data Visualization Using Python is an extensive discourse on data visualization. It details how to perform data visualization on a variety of datasets using various data visualization libraries written in Python programming language. Understanding, visualizing, and presenting data is slowly and gradually becoming a must have skills for professionals in all disciplines. This book is designed for learners who are beginners in visualization using python. It is a guide with detailed out steps to write and execute command/code.
Preface
Acknowledgments
About the Authors
Chapter 1 Introduction to Data Visualization
1.1 What is Data Visualization?
1.2 Evolution of Data Visualization
1.3 Why do We Need Data Visualization?
1.4 Difference between Data Visualization and Infographics
1.5 Principles of Gestalt’s Theory of Visual Perception
1.6 Advantages of Data Visualization
1.7 Benefits of Data Visualization
Chapter 2 Types of Digital Data
2.1 What is in Store?
2.2 Classification of Digital Data
2.3 Structured versus Unstructured Data
Chapter 3 Reading Data from Varied Data Sources into Python DataFrame
3.1 Read from Excel Data Source
3.2 Read Data from .csv
3.3 Load a Python Dictionary into a DataFrame
3.4 Reading JSON data into a Pandas DataFrame
3.5 Reading Data from Microsoft Access Database
3.6 Reading Data from .txt File
3.7 Reading Data from XML File
Chapter 4 Pros and Cons of Charts
4.1 Pie Chart
4.2 Tree Map
4.3 Heat Map
4.4 Scatter Plot
4.5 Histogram
4.6 Word Cloud
4.7 Box Plot
Chapter 5 Good Chart Designs
5.1 Mistakes That Can Be Avoided
5.2 Less Is More
5.3 Tables versus Charts
Chapter 6 Data Wrangling in Python
6.1 Pandas Data Manipulation
6.2 Dealing with Missing Values
6.3 Date Reshaping
6.4 Filtering Data
6.5 Merging Data
6.6 Subsetting DataFrames in Pandas
6.7 Reshaping the Data and Pivot Tables
6.8 Backfill
6.9 Forward Fill
Chapter 7 Functions in Python Pandas
7.1 Pandas DataFrame Functions
Chapter 8 Matplotlib for Data Visualization
8.1 Exploratory Data Analysis using Python
8.2 Matplotlib
Chapter 9 Plotly for Data Visualization
9.1 Plotly Python Package
Chapter 10 Seaborn for Data Visualization
10.1 Seaborn Plots Using “iris” Dataset
10.2 Seaborn Plots Using “Superstore” Dataset
10.3 Seaborn Plots Using “OLYMPIC” Dataset
10.4 Seaborn Plots Using “Passengers Flights” Dataset
Chapter 11 Cases
11.1 Case Study 1
11.2 Case Study 2
Appendix Python Assignments