Name: Interactive Data Visualization with Python
Author: Abha Belorkar Sharath Chandra Guntuku Shubhangi Hora Anshu Kumar
ISBN: 9781800201064

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Interactive Data Visualization with PythonAbha Belorkar Sharath Chandra Guntuku Shubhangi Hora Anshu Kumar

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With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.

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Interactive Data Visualization with Python

Abha Belorkar Sharath Chandra Guntuku Shubhangi Hora Anshu Kumar

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Interactive Data Visualization with Python Second Edition
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Interactive Data Visualization with Python Second Edition
Preface
About
About the Book
About the Authors
Learning Objectives
Audience
Approach
Hardware Requirements
Software Requirements
Conventions
Installation and Setup
Additional Resources
1. Introduction to Visualization with Python – Basic and Customized Plotting
Learning Objectives
Introduction
Note
Handling Data with pandas DataFrame
Reading Data from Files
Note
Exercise 1: Reading Data from Files
Note
Observing and Describing Data
Exercise 2: Observing and Describing Data
Figure 1.1: Displaying the diamonds dataset
Figure 1.2: Using the describe function to show continuous variables
Figure 1.3: Use the describe function to show categorical variables
Figure 1.4: Information on the diamonds dataset
Selecting Columns from a DataFrame
Figure 1.5: Selecting specific columns from a DataFrame
Adding New Columns to a DataFrame
Exercise 3: Adding New Columns to the DataFrame
Figure 1.6: Simple addition of columns
Figure 1.7: Conditional addition of columns
Applying Functions on DataFrame Columns
Exercise 4: Applying Functions on DataFrame columns
Figure 1.8: Dataset after applying simple and complex functions
Figure 1.9: Dataset after applying the lambda function
Figure 1.10: Dataset after applying a user-defined function
Exercise 5: Applying Functions on Multiple Columns
Figure 1.11: Dataset after applying the function on multiple columns
Deleting Columns from a DataFrame
Exercise 6: Deleting Columns from a DataFrame
Figure 1.12: Dataset after deleting columns
Note
Writing a DataFrame to a File
Exercise 7: Writing a DataFrame to a File
Figure 1.13: The generated .csv file in the source folder
Plotting with pandas and seaborn
Creating Simple Plots to Visualize a Distribution of Variables
Exercise 8: Plotting and Analyzing a Histogram
Figure 1.14: Histogram plot
Figure 1.15: Histogram with bins = 50
Figure 1.16: Histogram plot using seaborn
Figure 1.17: Histogram plot with KDE = false
Figure 1.18: Histogram plot with increased bin size
Figure 1.19: Histogram using a log transformation
Bar Plots
Exercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution
Note
Figure 1.20: Table using the crosstab function
Figure 1.21: Bar plot using a pandas DataFrame
Figure 1.22: Bar plot using seaborn
Figure 1.23: Bar plot with the mean price distribution
Figure 1.24: Bar plot with proper order
Exercise 10: Creating Bar Plots Grouped by a Specific Feature
Figure 1.25: Grouped bar plot with legends
Tweaking Plot Parameters
Exercise 11: Tweaking the Plot Parameters of a Grouped Bar Plot
Figure 1.26: Nested bar plot with the hue parameter
Figure 1.27: Grouped bar plot with legends placed appropriately
Figure 1.28: Grouped bar plot with modified labels
Figure 1.29: Grouped bar plot with the rotation feature of the labels
Annotations
Exercise 12: Annotating a Bar Plot
Figure 1.30: Bar plot with seaborn's catplot function
Figure 1.31: Annotated bar plot
Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization
Figure 1.32: Olympics dataset
Figure 1.33: Filtered Olympics DataFrame
Figure 1.34: The number of medals awarded
Figure 1.35: Olympics DataFrame
Figure 1.36: Generated bar plot
Figure 1.37: Histogram plot with the Age feature
Figure 1.38: Bar plot with the number of medals won
Figure 1.39: Bar plot with the average weight of players
Note
Summary
2. Static Visualization – Global Patterns and Summary Statistics
Learning Objectives
3. From Static to Interactive Visualization
Learning Objectives
4. Interactive Visualization of Data across Strata
Learning Objectives
5. Interactive Visualization of Data across Time
Learning Objectives
6. Interactive Visualization of Geographical Data
Learning Objectives
7. Avoiding Common Pitfalls to Create Interactive Visualizations
Learning Objectives
Appendix
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Interactive Data Visualization with Python