Home Data Interactive Data Visualization with Python - Second Edition

Interactive Data Visualization with Python - Second Edition

By Abha Belorkar , Sharath Chandra Guntuku , Shubhangi Hora and 1 more
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  1. Free Chapter
    2. Static Visualization – Global Patterns and Summary Statistics
About 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.
Publication date:
April 2020
Publisher
Packt
Pages
362
ISBN
9781800200944

 

2. Static Visualization – Global Patterns and Summary Statistics

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain various visualization techniques for different contexts
  • Identify global patterns of one or more features in a dataset
  • Create plots to represent global patterns in data: scatter plots, hexbin plots, contour plots, and heatmaps
  • Create plots that present summary statistics of data: histograms (revisited), box plots, and violin plots

In this chapter, we'll explore different visualization techniques for presenting global patterns and summary statistics of data.

 

Introduction

In the previous chapter, we learned how to handle pandas DataFrames as inputs for data visualization, how to plot with pandas and seaborn, and how to refine plots to increase their aesthetic appeal. The intent of this chapter is to acquire practical knowledge about the strengths and limitations of various visualization techniques. We'll practice creating plots for a variety of different contexts. However, you will notice that the variety in existing plot types and visualization techniques is huge, and choosing the appropriate visualization becomes confusing. There are times when a plot shows too much information for the reader to grasp or too little for the reader to get the necessary intuition regarding the data. There are times when a visualization is too esoteric for the reader to appreciate properly, and other times when an over-simplistic visualization just doesn't have the right impact. All these scenarios can be avoided by being armed with practical knowledge...

 

Creating Plots that Present Global Patterns in Data

In this section, we will study the context of plots that present global patterns in data, such as:

  • Plots that show the variance in individual features in data, such as histograms
  • Plots that show how different features present in data vary with respect to each other, such as scatter plots, line plots, and heatmaps

Most data scientists prefer to see such plots because they give an idea of the entire spectrum of values taken by the features of interest. Plots depicting global patterns are also useful because they make it easier to spot anomalies in data.

We will work with a dataset called mpg. It was published by the StatLib library, maintained at Carnegie Mellon University, and is available in the seaborn library. It was originally used to study the relationship of mileage – Miles Per Gallon (MPG) – with other features in the dataset; hence the name mpg. Since the dataset contains 3 discrete features...

 

Creating Plots That Present Summary Statistics of Your Data

It's now time for a switch to our next section. When datasets are huge, it is sometimes useful to look at the summary statistics of a range of different features and get a preliminary idea of the dataset. For example, the summary statistics for any numerical feature include measures of central tendency, such as the mean, and measures of dispersion, such as the standard deviation.

When a dataset is too small, plots presenting summary statistics may actually be misleading because summary statistics are meaningful only when the dataset is big enough to draw statistical conclusions. For example, if somebody reports the variance of a feature using five data points, we cannot make any concrete conclusions regarding the dispersion of the feature.

Histogram Revisited

Let's revisit histograms from Chapter 1, Introduction to Visualization with Python – Basic and Customized Plotting. Although histograms show...

 

Summary

In this chapter, we learned how choosing the most appropriate visualization(s) depends on four key elements:

  • The nature of the features in a dataset: categorical/discrete, numerical/continuous numerical
  • The size of the dataset: small/medium/large
  • The density of the data points in the chosen feature space: whether too many or too few data points are set to certain feature values
  • The context of the visualization: the source of the dataset and frequently used visualizations for the given application

For the purpose of explaining the concepts clearly and defining certain general guidelines, we classified visualizations into two categories:

  • Plots representing the global patterns of the chosen features (for example, histograms, scatter plots, hexbin plots, contour plots, line plots,and heatmaps)
  • Plots representing the summary statistics of the specific features (box plots and violin plots)

We are not implying that a single best visualization...

About the Authors
  • Abha Belorkar

    Abha Belorkar is an educator and researcher in computer science. She received her bachelor's degree in computer science from Birla Institute of Technology and Science Pilani, India and her Ph.D. from the National University of Singapore. Her current research work involves the development of methods powered by statistics, machine learning, and data visualization techniques to derive insights from heterogeneous genomics data on neurodegenerative diseases.

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  • Sharath Chandra Guntuku

    Sharath Chandra Guntuku is a researcher in natural language processing and multimedia computing. He received his bachelor's degree in computer science from Birla Institute of Technology and Science, Pilani, India and his Ph.D. from Nanyang Technological University, Singapore. His research aims to leverage large-scale social media image and text data to model social health outcomes and psychological traits. He uses machine learning, statistical analysis, natural language processing, and computer vision to answer questions pertaining to health and psychology in individuals and communities.

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  • Shubhangi Hora

    Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.

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  • Anshu Kumar

    Anshu Kumar is a data scientist with over 5 years of experience in solving complex problems in natural language processing and recommendation systems. He has an M.Tech. from IIT Madras in computer science. He is also a mentor at SpringBoard. His current interests are building semantic search, text summarization, and content recommendations for large-scale multilingual datasets.

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Have not read it yet so I can't say but all your product have been good.
Interactive Data Visualization with Python - Second Edition
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