book

Interactive Data Visualization with Python - Second Edition

by Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

April 2020

Beginner to intermediate

362 pages

5h 58m

English

Packt Publishing

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About the BookAbout the AuthorsLearning ObjectivesAudienceApproachHardware RequirementsSoftware RequirementsConventionsInstallation and SetupAdditional Resources
IntroductionHandling Data with pandas DataFrameReading Data from FilesExercise 1: Reading Data from FilesObserving and Describing DataExercise 2: Observing and Describing DataSelecting Columns from a DataFrameAdding New Columns to a DataFrameExercise 3: Adding New Columns to the DataFrameApplying Functions on DataFrame ColumnsExercise 4: Applying Functions on DataFrame columnsExercise 5: Applying Functions on Multiple ColumnsDeleting Columns from a DataFrameExercise 6: Deleting Columns from a DataFrameWriting a DataFrame to a FileExercise 7: Writing a DataFrame to a FilePlotting with pandas and seabornCreating Simple Plots to Visualize a Distribution of VariablesExercise 8: Plotting and Analyzing a Histogram Bar PlotsExercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution Exercise 10: Creating Bar Plots Grouped by a Specific FeatureTweaking Plot ParametersExercise 11: Tweaking the Plot Parameters of a Grouped Bar PlotAnnotationsExercise 12: Annotating a Bar PlotActivity 1: Analyzing Different Scenarios and Generating the Appropriate VisualizationSummary
IntroductionCreating Plots that Present Global Patterns in DataScatter PlotsExercise 13: Creating a Static Scatter PlotHexagonal Binning PlotsExercise 14: Creating a Static Hexagonal Binning PlotContour PlotsExercise 15: Creating a Static Contour PlotLine PlotsExercise 16: Creating a Static Line PlotExercise 17: Presenting Data across Time with multiple Line PlotsHeatmapsExercise 18: Creating and Exploring a Static HeatmapThe Concept of Linkage in HeatmapsExercise 19: Creating Linkage in Static HeatmapsCreating Plots That Present Summary Statistics of Your DataHistogram RevisitedExample 1: Histogram RevisitedBox PlotsExercise 20: Creating and Exploring a Static Box PlotViolin PlotsExercise 21: Creating a Static Violin PlotActivity 2: Design Static Visualization to Present Global Patterns and Summary StatisticsSummary
Introduction Static versus Interactive VisualizationApplications of Interactive Data Visualizations Getting Started with Interactive Data VisualizationsInteractive Data Visualization with BokehExercise 22: Preparing Our DatasetExercise 23: Creating the Base Static Plot for an Interactive Data VisualizationExercise 24: Adding a Slider to the Static PlotExercise 25: Adding a Hover ToolInteractive Data Visualization with Plotly ExpressExercise 26: Creating an Interactive Scatter PlotActivity 3: Creating Different Interactive Visualizations Using Plotly ExpressSummary
IntroductionInteractive Scatter PlotsExercise 27: Adding Zoom-In and Zoom-Out to a Static Scatter PlotExercise 28: Adding Hover and Tooltip Functionality to a Scatter PlotExercise 29: Exploring Select and Highlight Functionality on a Scatter PlotExercise 30: Generating a Plot with Selection, Zoom, and Hover/Tooltip FunctionsSelection across Multiple PlotsExercise 31: Selection across Multiple PlotsSelection Based on the Values of a FeatureExercise 32: Selection Based on the Values of a FeatureOther Interactive Plots in altairExercise 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 PlotExercise 35: Adding a Zoom Feature on a Static HeatmapExercise 36: Creating a Bar Plot and a Heatmap Next to Each OtherExercise 37: Dynamically Linking a Bar Plot and a HeatmapActivity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps DatasetSummary
IntroductionTemporal DataTypes of Temporal DataWhy Study Temporal Visualization?Understanding the Relation between Temporal Data and Time‑Series DataExamples of Domains That Use Temporal DataVisualization of Temporal DataHow Time-Series Data Is Manipulated and VisualizedDate/Time Manipulation in pandasBuilding a DateTime IndexChoosing the Right Aggregation Level for Temporal DataExercise 38: Creating a Static Bar Plot and Calculating the Mean and Standard Deviation in Temporal DataExercise 39: Calculating zscore to Find Outliers in Temporal DataResampling in Temporal DataCommon Pitfalls of Upsampling and DownsamplingExercise 40: Upsampling and Downsampling in Temporal DataUsing shift and tshift to Introduce a Lag in Time-Series DataExercise 41: Using shift and tshift to Shift Time in DataAutocorrelation in Time SeriesInteractive Temporal VisualizationBokeh BasicsAdvantages of Using BokehExercise 42: Adding Interactivity to Static Line Plots Using BokehExercise 43: Changing the Line Color and Width on a Line PlotExercise 44: Adding Box Annotations to Find Anomalies in a DatasetInteractivity in BokehActivity 5: Create an Interactive Temporal Visualization Summary
IntroductionChoropleth MapsWorldwide Choropleth Maps Exercise 45: Creating a Worldwide Choropleth MapExercise 46: Tweaking a Worldwide Choropleth MapExercise 47: Adding Animation to a Choropleth MapUSA State MapsExercise 48: Creating a USA State Choropleth MapPlots on Geographical MapsScatter PlotsExercise 49: Creating a Scatter Plot on a Geographical MapBubble PlotsExercise 50: Creating a Bubble Plot on a Geographical MapLine Plots on Geographical MapsExercise 51: Creating Line Plots on a Geographical MapActivity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the WorldSummary
IntroductionData Formatting and InterpretationAvoiding Common Pitfalls while Dealing with Dirty DataOutliersExercise 52: Visualizing Outliers in a Dataset with a Box PlotExercise 53: Dealing with OutliersMissing DataExercise 54: Dealing with Missing ValuesDuplicate Instances and/or FeaturesBad Feature SelectionActivity 7: Determining Which Features to Visualize on a Scatter PlotData Visualization Choosing a Visualization Common Pitfalls While Visualizing DataExercise 55: Creating a Confusing VisualizationActivity 8: Creating a Bar Graph for Improving a VisualizationCheat Sheet for the Visualization ProcessSummary
1. Introduction to Visualization with Python – Basic and Customized PlottingActivity 1: Analyzing Different Scenarios and Generating the Appropriate VisualizationSolution2. Static Visualization – Global Patterns and Summary StatisticsActivity 2: Design Static Visualization to Present Global Patterns and Summary StatisticsSolution3. From Static to Interactive Visualization Activity 3: Creating Different Interactive Visualizations Using Plotly ExpressSolution4. Interactive Visualization of Data across StrataActivity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset5. Interactive Visualization of Data across Time Activity 5: Create an Interactive Temporal Visualization using RangeTool and Aggregator6. Interactive Visualizations of Data across Geographical RegionsActivity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the WorldSolution7. Avoiding Common Pitfalls to Create Interactive VisualizationsActivity 7: Determining Which Features to Visualize on a Scatter PlotActivity 8: Creating a Bar Graph for Improving a Visualization

Overview

With Interactive Data Visualization with Python, you will learn to turn raw data into compelling, interactive visual stories. This book guides you through the practical uses of Python libraries such as Bokeh and Plotly, teaching you skills to create visualizations that captivate and inform.

What this Book will help me do

Understand and apply different principles and techniques of interactive data visualization to bring your data to life.
Master the use of libraries like Matplotlib, Seaborn, Altair, and Bokeh for creating a variety of data visualizations.
Learn how to customize data visualizations effectively to meet the needs of different audiences and use cases.
Gain proficiency in using advanced tools like Plotly for creating dynamic and engaging visual presentations.
Acquire the ability to identify common pitfalls in visualization and learn strategies to avoid them, ensuring clarity and impact.

Author(s)

Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, and Anshu Kumar are experts in Python programming and data visualization with years of experience in data science and software development. They have collaborated to blend their knowledge into this book-a clear and practical guide to mastering interactive visualization with Python.

Who is it for?

This book is perfect for Python developers, data analysts, and data scientists who want to enhance their skills in data presentation. If you are ready to transform complex data into digestible and interactive visuals, this book is for you. A basic familiarity with Python programming and libraries like pandas is recommended. By the end of the book, you'll feel confident in creating professional-grade data visualizations.