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)

1. Introduction to Visualization with Python – Basic and Customized Plotting

Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization

Solution

  1. Download the dataset hosted on the book GitHub repository, and format it as a pandas DataFrame:
    # load necessary modules
    import pandas as pd
    import seaborn as sns
    from numpy import median, mean
  2. Read the dataset as a pandas DataFrame:
    # download file 'athlete_events.csv' from course GitHub repository: https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/datasets
    # read the dataset as a pandas DataFrame
    olympics_df = pd.read_csv('..../Interactive-Data-Visualization-with-Python/datasets/athlete_events.csv')
    # preview DataFrame
    olympics_df.head()

    The output is as follows:

    Figure 1.32: Olympics dataset
  3. Filter the DataFrame to contain only medal winners of the year 2016:
    # filter the DataFrame to contain medal winners only (for non-winners, the Medal feature...