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Learning NumPy Array

You're reading from   Learning NumPy Array Supercharge your scientific Python computations by understanding how to use the NumPy library effectively

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783983902
Length 164 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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Table of Contents (14) Chapters Close

Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with NumPy FREE CHAPTER 2. NumPy Basics 3. Basic Data Analysis with NumPy 4. Simple Predictive Analytics with NumPy 5. Signal Processing Techniques 6. Profiling, Debugging, and Testing 7. The Scientific Python Ecosystem Index

Comparing NumPy to Blaze


Since we are close to the end of the book, it seems appropriate to discuss the future of NumPy. The future of NumPy is Blaze, a new open source Python numerical library. Blaze is supposed to process Big Data better than NumPy ever can. Big Data can be defined in many ways. Here, we will define Big Data as data that cannot be stored in memory or even on a single machine. Usually, the data is distributed amongst several servers. Blaze should also be able to handle large quantities of streaming data that is never stored.

Note

Blaze can be found at http://blaze.pydata.org/.

Blaze, just like NumPy, allows scientists, analysts, and engineers to quickly write efficient code. Blaze, however, goes a step further and also takes care of the work related to distributing calculations as well as extracting and transforming data from a variety of data source types.

Blaze is centered around general multidimensional array and table abstractions. The classes in Blaze represent different...

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