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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 data from remote data services

Pandas prior to 0.19.0 had direct support for various web-based data source classes in the pandas.io.data namespace. This has changed, as the functionality has been refactored out of pandas and into the pandas-datareader package.

This package provides access to many useful data sources, including:

  • Daily historical stock prices from either Yahoo! or Google Finance
  • Yahoo! and Google Options
  • Enigma, a provider of structured data
  • The Federal Reserve Economic Data Library
  • Kenneth French's Data Library
  • The World Bank
  • OECD
  • Eurostat
  • EDGAR Index
  • TSP Fund Data
  • Oanda currency historical rates
  • Nasdaq Trader symbol definitions
Note that because this data is coming from an external data source and that the actual values can change over time, it may be possible that when you run the code, you get different values than those in the book.
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