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Mastering Python Scientific Computing

You're reading from   Mastering Python Scientific Computing A complete guide for Python programmers to master scientific computing using Python APIs and tools

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781783288823
Length 300 pages
Edition 1st Edition
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Author (1):
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 Kumar Mehta Kumar Mehta
Author Profile Icon Kumar Mehta
Kumar Mehta
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Table of Contents (12) Chapters Close

Preface 1. The Landscape of Scientific Computing – and Why Python? 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes FREE CHAPTER 3. Efficiently Fabricating and Managing Scientific Data 4. Scientific Computing APIs for Python 5. Performing Numerical Computing 6. Applying Python for Symbolic Computing 7. Data Analysis and Visualization 8. Parallel and Large-scale Scientific Computing 9. Revisiting Real-life Case Studies 10. Best Practices for Scientific Computing Index

Numerical scientific computing in Python


Scientific computing mainly demands the facility of performing calculations on algebraic equations, matrices, differentiations, integrations, differential equations, statistics, equation solvers, and more. By default, Python doesn't come with these functionalities. However, the development of NumPy and SciPy has enabled us to perform these operations and far more advanced functionalities beyond them. NumPy and SciPy are very powerful Python packages that enable users to efficiently perform the desired operations for all types of scientific applications.

The NumPy package

NumPy is the basic Python package for scientific computing. It provides the facilities of multidimensional arrays and basic mathematical operations, such as linear algebra. Python provides several data structures to store user data; the most popular data structures are lists and dictionaries. List objects may store any type of Python object as an element. These elements can be processed...

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