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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, 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

7.7 Anonymous functions the keyword lambda

The keyword lambda is used in Python to define anonymous functions, that is, functions without a name and described by a single expression. You might just want to perform an operation on a function that can be expressed by a simple expression without naming this function and without defining this function by a lengthy def block.

The name lambda originates from a special branch of calculus and mathematical logic, the -calculus.

We demonstrate the use of lambda-functions by numerically evaluating the following integral:

We use SciPy’s function quad, which requires as its first argument the function to be integrated and the integration bounds as the next two arguments. Here, the function to be integrated is just a simple one-liner and we use the keyword lambda to define it:

import scipy.integrate as si
si.quad(lambda x: x ** 2 + 5, 0, 1)

The syntax is as follows:

lambda parameter_list: expression

The definition of the function lambda...

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