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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

Polynomial manipulation


The Polys module in SymPy allows users to perform polynomial manipulations. It has methods ranging from simple operations on polynomials, such as division, GCD, and LCM, to advanced concepts, such as Gröbner bases and multivariate factorization.

The following program shows polynomial division using the div method. This method performs polynomial division with the remainder. An argument domain may be used to specify the types of values of the argument. If the operation is to be performed only on integers, then pass domain='ZZ', domain='QQ' for rational and domain='RR' for real numbers. The expand method expands the expression into its normal representation:

from sympy import *
x, y, z = symbols('x,y,z')
init_printing(use_unicode=False, wrap_line=False, no_global=True)

f = 4*x**2 + 8*x + 5
g = 3*x + 1
q, r = div(f, g, domain='QQ')  ## QQ for rationals
q
r
(q*g + r).expand()
q, r = div(f, g, domain='ZZ')  ## ZZ for integers
q
r
g = 4*x + 2
q, r = div(f, g, domain='ZZ...
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