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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

19.5 Exercises

Ex. 1: Implement a method __add__ that constructs a new polynomial  by adding two given polynomials  and . In monomial form, polynomials are added by just adding the coefficients, whereas in Newton form, the coefficients depend on the abscissas  of the interpolation points. Before adding the coefficients of both polynomials, the polynomial  has to get new interpolation points with the property that their abscissas  coincide with those of , and the method __changepoints__ has to be provided for that. It should change the interpolation points and return a new set of coefficients.

Ex. 2: Write conversion methods to convert a polynomial from Newton form into monomial form and vice versa.

Ex. 3: Write a method called add_point that takes a polynomial q and a tuple  as parameters and returns a new polynomial that interpolates self.points and .

Ex. 4: Write a class called LagrangePolynomial...

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