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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

Computing exact probabilities and manipulating random variables

SymPy includes a module named stats that lets us create and manipulate random variables. This is useful when we work with probabilistic or statistical models; we can compute symbolic expectancies, variances, probabilities, and densities of random variables.

How to do it...

  1. Let's import SymPy and the stats module:
    >>> from sympy import *
        from sympy.stats import *
        init_printing()
  2. Let's roll two dice, X and Y, with six faces each:
    >>> X, Y = Die('X', 6), Die('Y', 6)
  3. We can compute probabilities defined by equalities (with the Eq operator) or inequalities:
    >>> P(Eq(X, 3))
    How to do it...
    >>> P(X > 3)
    How to do it...
  4. Conditions can also involve multiple random variables:
    >>> P(X > Y)
    How to do it...
  5. We can compute conditional probabilities:
    >>> P(X + Y > 6, X < 5)
    How to do it...
  6. We can also work with arbitrary discrete or continuous random variables:
    >>> Z = Normal('Z', 0, 1)  # Gaussian...
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