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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Reading HTML tables

You can use pandas to read HTML tables from websites. This makes it easy to ingest tables such as those found on Wikipedia or other websites.

In this recipe, we will scrape tables from the Wikipedia entry for The Beatles Discography. In particular, we want to scrape the table in the image that was in Wikipedia during 2019:

Wikipedia table for studio albums

Wikipedia table for studio albums

How to do it...

  1. Use the read_html function to load all of the tables from https://en.wikipedia.org/wiki/The_Beatles_discography:
    >>> url = https://en.wikipedia.org/wiki/The_Beatles_discography
    >>> dfs = pd.read_html(url)
    >>> len(dfs)
    51
    
  2. Inspect the first DataFrame:
    >>> dfs[0]
      The Beatles discography The Beatles discography.1
    0  The Beat...             The Beat...
    1  Studio a...                      23
    2  Live albums                       5
    3  Compilat...                      53
    4  Video al...                 ...
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