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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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Hemant Kumar Mehta Hemant Kumar Mehta
Author Profile Icon Hemant Kumar Mehta
Hemant Kumar Mehta
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Toc

Table of Contents (12) Chapters Close

Preface 1. The Landscape of Scientific Computing – and Why Python? FREE CHAPTER 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes 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

Calculus


Calculus involves operations that are performed to study the various properties of any function, including rates of change, the limit behavior of a function, and calculation of the area under a function graph. In this section, you will learn the concepts of limits, derivatives, summation of series, and integrals. The following program uses limit functions to solve simple limit problems:

from sympy import limit, oo, symbols,exp, cos

oo+5
67000 < oo
10/oo

x , n = symbols ('x n')
limit( ((x**n - 1)/ (x - 1) ), x, 1)

limit( 1/x**2, x, 0)
limit( 1/x, x, 0, dir="-")

limit(cos(x)/x, x, 0)
limit(sin(x)**2/x, x, 0)
limit(exp(x)/x,x,oo)

Any SymPy expression can be differentiated using the diff function with the diff(func_to_be_differentiated, variable) prototype. The following program uses the diff function to compute the differentiation of various SymPy expressions:

from sympy import diff, symbols, Symbol, exp, dsolve, subs, Function

diff(x**4, x)
diff( x**3*cos(x), x )
diff( cos(x...
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