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.
Table of Contents (9 chapters)

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...