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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis FREE CHAPTER 2. Up and Running with pandas 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

Reading HTML data from the web

Pandas has support for reading data from HTML files (or HTML from URLs). Underneath the covers, pandas makes use of the LXML, Html5Lib, and BeautifulSoup4 packages. These packages provide some impressive capabilities for reading and writing HTML tables.

Your default installation of Anaconda may not include these packages. If you get errors using this function, install the appropriate library based on the error, using the Anaconda Navigator:

Else, you can use pip:

The pd.read_html() function will read HTML from a file (or URL) and parse all HTML tables found in the content into one or more pandas DataFrame objects. The function always returns a list of DataFrame objects (actually, zero or more, depending on the number of tables found in the HTML).

To demonstrate, we will read table data from the FDIC failed bank list, located at https://www.fdic...

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