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NumPy Beginner's Guide

You're reading from   NumPy Beginner's Guide An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.

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Product type Paperback
Published in Apr 2013
Publisher Packt
ISBN-13 9781782166085
Length 310 pages
Edition 2nd Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (19) Chapters Close

Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. NumPy Quick Start FREE CHAPTER 2. Beginning with NumPy Fundamentals 3. Get in Terms with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Move Further with NumPy Modules 7. Peeking into Special Routines 8. Assure Quality with Testing 9. Plotting with Matplotlib 10. When NumPy is Not Enough – SciPy and Beyond 11. Playing with Pygame Pop Quiz Answers Index

Time for action – analyzing random values


We will generate random values that mimic a normal distribution and analyze the generated data with statistical functions from the scipy.stats package. Perform the following steps to do so:

  1. Generate random values from a normal distribution using the scipy.stats package.

    generated = stats.norm.rvs(size=900)
  2. Fit the generated values to a normal distribution. This basically gives us the mean and standard deviation of the data set.

    print “Mean”, “Std”, stats.norm.fit(generated)

    The mean and standard deviation would be shown as follows:

    Mean Std (0.0071293257063200707, 0.95537708218972528)
    
  3. Skewness tells us how skewed (asymmetric) a probability distribution is. Perform a skewness test. This test returns two values. The second value is the p-value; the probability that the skewness of the data set corresponds to a normal distribution. The pvalue instances range from 0 to 1.

    print “Skewtest”, “pvalue”, stats.skewtest(generated)

    The result of the skewness test...

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