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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

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...
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