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Interactive Data Visualization with Python

You're reading from   Interactive Data Visualization with Python Present your data as an effective and compelling story

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Product type Paperback
Published in Apr 2020
Publisher
ISBN-13 9781800200944
Length 362 pages
Edition 2nd Edition
Languages
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Authors (4):
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Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
Abha Belorkar Abha Belorkar
Author Profile Icon Abha Belorkar
Abha Belorkar
Anshu Kumar Anshu Kumar
Author Profile Icon Anshu Kumar
Anshu Kumar
Sharath Chandra Guntuku Sharath Chandra Guntuku
Author Profile Icon Sharath Chandra Guntuku
Sharath Chandra Guntuku
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Toc

Table of Contents (9) Chapters Close

Preface 1. Introduction to Visualization with Python – Basic and Customized Plotting 2. Static Visualization – Global Patterns and Summary Statistics FREE CHAPTER 3. From Static to Interactive Visualization 4. Interactive Visualization of Data across Strata 5. Interactive Visualization of Data across Time 6. Interactive Visualization of Geographical Data 7. Avoiding Common Pitfalls to Create Interactive Visualizations Appendix

4. Interactive Visualization of Data across Strata

Activity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset

Solution

  1. Load the necessary Python modules and download the dataset hosted in the book GitHub repository and format it as a pandas DataFrame:
    # load pandas library
    Import pandas as pd
    # download file 'googleplaystore.csv' from course GitHub repository
    # read the dataset as a pandas DataFrame
    gps_apps_df =pd.read_csv('https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/googleplaystore.csv')
    #worldrank_df = pd.read_csv('https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/googleplaystore.csv')
    # preview DataFrame
    gps_apps_df.head()

    The output is as follows:

    Figure 4.30: Google Play Store dataset apps
  2. Remove the entries in the DataFrame that have feature values of NA...
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