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Scientific Computing with Python

Scientific Computing with Python - Second Edition

By : Claus Führer, Claus Fuhrer, Jan Erik Solem, Olivier Verdier
4.5 (14)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (14)
By: Claus Führer, Claus Fuhrer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
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20
About Packt
22
References

The broadcasting problem

When NumPy is given two arrays with different shapes and is asked to perform an operation that would require the two shapes to be the same, both arrays are broadcast to a common shape.

Suppose the two arrays have shapes  and . Broadcasting consists of the two steps:

  1. If the shape  is shorter than the shape , that is, len(s1) < len(s2), then ones are added on the left of the shape . This is reshaping.
  2. When the shapes have the same length, the first array is extended to match the shape s2 (if possible).

Suppose we want to add a vector of shape  to a matrix of shape . The vector needs to be broadcast. The first operation is reshaping; the shape of the vector is converted from (3, ) to (1, 3). The second operation is an extension; the shape is converted from (1, 3) to (4, 3).

For instance, suppose a vector of size n is to be broadcast to the shape (m, n):

  1.  is automatically reshaped to (1,...
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Scientific Computing with Python
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