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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with Jupyter and IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Jupyter Notebook 4. Profiling and Optimization 5. High-Performance Computing 6. Data Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Analyzing real-valued functions

SymPy contains a rich calculus toolbox to analyze real-valued functions: limits, power series, derivatives, integrals, Fourier transforms, and so on. In this recipe, we will show the very basics of these capabilities.

How to do it...

  1. Let's define a few symbols and a function (which is just an expression depending on x):
    >>> from sympy import *
        init_printing()
    >>> var('x z')
    How to do it...
    >>> f = 1 / (1 + x**2)
  2. Let's evaluate this function at 1:
    >>> f.subs(x, 1)
    How to do it...
  3. We can compute the derivative of this function:
    >>> diff(f, x)
    How to do it...
  4. What is How to do it...'s limit to infinity? (Note the double o (oo) for the infinity symbol):
    >>> limit(f, x, oo)
    How to do it...
  5. Here's how to compute a Taylor series (here, around 0, of order 9). The Big O can be removed with the removeO() method.
    >>> series(f, x0=0, n=9)
    How to do it...
  6. We can compute definite integrals (here, over the entire real line):
    >>> integrate(f, (x, -oo, oo))
    How to do it...
  7. SymPy can also compute...
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