Book Image

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar
Book Image

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

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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Table of Contents (9 chapters)
Preface
Preface
About the Book
1
1. Introduction to Visualization with Python – Basic and Customized Plotting
1. Introduction to Visualization with Python – Basic and Customized Plotting
Introduction
Handling Data with pandas DataFrame
Plotting with pandas and seaborn
Tweaking Plot Parameters
Summary
Free Chapter
2
2. Static Visualization – Global Patterns and Summary Statistics
2. Static Visualization – Global Patterns and Summary Statistics
Introduction
Creating Plots that Present Global Patterns in Data
Creating Plots That Present Summary Statistics of Your Data
Summary
3
3. From Static to Interactive Visualization
3. From Static to Interactive Visualization
Introduction
Static versus Interactive Visualization
Applications of Interactive Data Visualizations
Getting Started with Interactive Data Visualizations
Summary
4
4. Interactive Visualization of Data across Strata
4. Interactive Visualization of Data across Strata
Introduction
Interactive Scatter Plots
Other Interactive Plots in altair
Summary
5
5. Interactive Visualization of Data across Time
5. Interactive Visualization of Data across Time
Introduction
Temporal Data
Types of Temporal Data
Understanding the Relation between Temporal Data and Time‑Series Data
Examples of Domains That Use Temporal Data
Visualization of Temporal Data
Choosing the Right Aggregation Level for Temporal Data
Resampling in Temporal Data
Interactive Temporal Visualization
Summary
6
6. Interactive Visualization of Geographical Data
6. Interactive Visualization of Geographical Data
Introduction
Choropleth Maps
Plots on Geographical Maps
Summary
7
7. Avoiding Common Pitfalls to Create Interactive Visualizations
7. Avoiding Common Pitfalls to Create Interactive Visualizations
Introduction
Data Formatting and Interpretation
Data Visualization
Cheat Sheet for the Visualization Process
Summary
Appendix
Appendix
1. Introduction to Visualization with Python – Basic and Customized Plotting
2. Static Visualization – Global Patterns and Summary Statistics
3. From Static to Interactive Visualization
4. Interactive Visualization of Data across Strata
5. Interactive Visualization of Data across Time
6. Interactive Visualizations of Data across Geographical Regions
7. Avoiding Common Pitfalls to Create Interactive Visualizations

Interactive Scatter Plots

As you know by now, scatter plots are one of the most essential types of plots for presenting global patterns within a dataset. Naturally, it is important to know how to introduce interactivity in these plots. We will first look at the zoom and reset actions on plots. Before that, though, let's have a look at the dataset.

We can view the HPI dataset using the following code:

import pandas as pd
#Download the data from Github repo 
hpi_url = "https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/hpi_data_countries.tsv"
# Once downloaded, read it into a DataFrame using pandas
hpi_df = pd.read_csv(hpi_url, sep='\t')
hpi_df.head()

The output is as follows:

Figure 4.1: HPI dataset

Note that there are 5 numerical/quantitative features in this dataset: Life Expectancy (years), Wellbeing (0-10), Inequality of outcomes, Ecological Footprint...

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