Interactive Data Visualization With Python : present your data as an.

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Table of Contents
Preface
Chapter 1: Introduction to Visualization with Python – Basic and Customized Plotting
	Introduction
	Handling Data with pandas DataFrame
		Reading Data from Files
		Exercise 1: Reading Data from Files
		Observing and Describing Data
		Exercise 2: Observing and Describing Data
		Selecting Columns from a DataFrame
		Adding New Columns to a DataFrame
		Exercise 3: Adding New Columns to the DataFrame
		Applying Functions on DataFrame Columns
		Exercise 4: Applying Functions on DataFrame columns
		Exercise 5: Applying Functions on Multiple Columns
		Deleting Columns from a DataFrame
		Exercise 6: Deleting Columns from a DataFrame
		Writing a DataFrame to a File
		Exercise 7: Writing a DataFrame to a File
	Plotting with pandas and seaborn
		Creating Simple Plots to Visualize a Distribution of Variables
		Exercise 8: Plotting and Analyzing a Histogram
		Bar Plots
		Exercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution
		Exercise 10: Creating Bar Plots Grouped by a Specific Feature
	Tweaking Plot Parameters
		Exercise 11: Tweaking the Plot Parameters of a Grouped Bar Plot
		Annotations
		Exercise 12: Annotating a Bar Plot
		Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization
	Summary
Chapter 2: Static Visualization – Global Patterns and Summary Statistics
	Introduction
	Creating Plots that Present Global Patterns in Data
		Scatter Plots
		Exercise 13: Creating a Static Scatter Plot
		Hexagonal Binning Plots
		Exercise 14: Creating a Static Hexagonal Binning Plot
		Contour Plots
		Exercise 15: Creating a Static Contour Plot
		Line Plots
		Exercise 16: Creating a Static Line Plot
		Exercise 17: Presenting Data across Time with multiple Line Plots
		Heatmaps
		Exercise 18: Creating and Exploring a Static Heatmap
		The Concept of Linkage in Heatmaps
		Exercise 19: Creating Linkage in Static Heatmaps
	Creating Plots That Present Summary Statistics of Your Data
		Histogram Revisited
		Example 1: Histogram Revisited
		Box Plots
		Exercise 20: Creating and Exploring a Static Box Plot
		Violin Plots
		Exercise 21: Creating a Static Violin Plot
		Activity 2: Design Static Visualization to Present Global Patterns and Summary Statistics
	Summary
Chapter 3: From Static to Interactive Visualization
	Introduction
	Static versus Interactive Visualization
	Applications of Interactive Data Visualizations
	Getting Started with Interactive Data Visualizations
		Interactive Data Visualization with Bokeh
		Exercise 22: Preparing Our Dataset
		Exercise 23: Creating the Base Static Plot for an Interactive Data Visualization
		Exercise 24: Adding a Slider to the Static Plot
		Exercise 25: Adding a Hover Tool
		Interactive Data Visualization with Plotly Express
		Exercise 26: Creating an Interactive Scatter Plot
		Activity 3: Creating Different Interactive Visualizations Using Plotly Express
	Summary
Chapter 4: Interactive Visualization of Data across Strata
	Introduction
	Interactive Scatter Plots
		Exercise 27: Adding Zoom-In and Zoom-Out to a Static Scatter Plot
		Exercise 28: Adding Hover and Tooltip Functionality to a Scatter Plot
		Exercise 29: Exploring Select and Highlight Functionality on a Scatter Plot
		Exercise 30: Generating a Plot with Selection, Zoom, and Hover/Tooltip Functions
		Selection across Multiple Plots
		Exercise 31: Selection across Multiple Plots
		Selection Based on the Values of a Feature
		Exercise 32: Selection Based on the Values of a Feature
	Other Interactive Plots in altair
		Exercise 33: Adding a Zoom-In and Zoom-Out Feature and Calculating the Mean on a Static Bar Plot
		Exercise 34: An Alternative Shortcut for Representing the Mean on a Bar Plot
		Exercise 35: Adding a Zoom Feature on a Static Heatmap
		Exercise 36: Creating a Bar Plot and a Heatmap Next to Each Other
		Exercise 37: Dynamically Linking a Bar Plot and a Heatmap
		Activity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset
	Summary
Chapter 5: Interactive Visualization of Data across Time
	Introduction
	Temporal Data
	Types of Temporal Data
		Why Study Temporal Visualization?
	Understanding the Relation between Temporal Data and Time‑Series Data
	Examples of Domains That Use Temporal Data
	Visualization of Temporal Data
		How Time-Series Data Is Manipulated and Visualized
		Date/Time Manipulation in pandas
		Building a DateTime Index
	Choosing the Right Aggregation Level for Temporal Data
		Exercise 38: Creating a Static Bar Plot and Calculating the Mean and Standard Deviation in Temporal Data
		Exercise 39: Calculating zscore to Find Outliers in Temporal Data
	Resampling in Temporal Data
		Common Pitfalls of Upsampling and Downsampling
		Exercise 40: Upsampling and Downsampling in Temporal Data
		Using shift and tshift to Introduce a Lag in Time-Series Data
		Exercise 41: Using shift and tshift to Shift Time in Data
		Autocorrelation in Time Series
	Interactive Temporal Visualization
		Bokeh Basics
		Advantages of Using Bokeh
		Exercise 42: Adding Interactivity to Static Line Plots Using Bokeh
		Exercise 43: Changing the Line Color and Width on a Line Plot
		Exercise 44: Adding Box Annotations to Find Anomalies in a Dataset
		Interactivity in Bokeh
		Activity 5: Create an Interactive Temporal Visualization
	Summary
Chapter 6: Interactive Visualization of Geographical Data
	Introduction
	Choropleth Maps
		Worldwide Choropleth Maps
		Exercise 45: Creating a Worldwide Choropleth Map
		Exercise 46: Tweaking a Worldwide Choropleth Map
		Exercise 47: Adding Animation to a Choropleth Map
		USA State Maps
		Exercise 48: Creating a USA State Choropleth Map
	Plots on Geographical Maps
		Scatter Plots
		Exercise 49: Creating a Scatter Plot on a Geographical Map
		Bubble Plots
		Exercise 50: Creating a Bubble Plot on a Geographical Map
		Line Plots on Geographical Maps
		Exercise 51: Creating Line Plots on a Geographical Map
		Activity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the World
	Summary
Chapter 7: Avoiding Common Pitfalls to Create Interactive Visualizations
	Introduction
	Data Formatting and Interpretation
		Avoiding Common Pitfalls while Dealing with Dirty Data
		Outliers
		Exercise 52: Visualizing Outliers in a Dataset with a Box Plot
		Exercise 53: Dealing with Outliers
		Missing Data
		Exercise 54: Dealing with Missing Values
		Duplicate Instances and/or Features
		Bad Feature Selection
		Activity 7: Determining Which Features to Visualize on a Scatter Plot
	Data Visualization
		Choosing a Visualization
		Common Pitfalls While Visualizing Data
		Exercise 55: Creating a Confusing Visualization
		Activity 8: Creating a Bar Graph for Improving a Visualization
	Cheat Sheet for the Visualization Process
	Summary
Appendix
Index