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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Using REST web services and JSON


Representational State Transfer (REST) web services use the REST architectural style (for more information, refer to http://en.wikipedia.org/wiki/Representational_state_transfer). In the usual context of the HTTP(S) protocol, we have the GET, POST, PUT, and DELETE methods. These methods can be aligned with common operations on the data to create, request, update, or delete data items.

In a RESTful API, data items are identified by URIs such as http://example.com/resources or http://example.com/resources/item42. REST is not an official standard, but is so widespread that we need to know about it. Web services often use JavaScript Object Notation (JSON) (for more information refer to http://en.wikipedia.org/wiki/JSON) to exchange data. In this format, data is written using the JavaScript notation. The notation is similar to the syntax for Python lists and dicts. In JSON, we can define arbitrarily complex data consisting of a combination of lists and dicts...

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