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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

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. To...

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