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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

Accessing CSV data on the web

It is quite common to read data off the web and from the internet. Pandas makes it easy to read data from the web. All the pandas functions that we have examined can also be given an HTTP URL, FTP address, or S3 address, instead of a local file path, and all of them work just the same as they work with a local file.

The following demonstrates how easy it is to directly make HTTP requests using the existing pd.read_csv() function. The following retrieves the daily stock data for Microsoft in April 2017, directly from the Google Finance web service via its HTTP query string model:

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