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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
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14
C. NumPy Functions' References
15
Index

Time for action – sampling with numpy.random.choice()

We will use the numpy.random.choice() function to perform bootstrapping.

  1. Start the IPython or Python shell and import NumPy:
    $ ipython
    In [1]: import numpy as np
    
  2. Generate a data sample following the normal distribution:
    In [2]: N = 500
    
    In [3]: np.random.seed(52)
    
    In [4]: data = np.random.normal(size=N)
    
  3. Calculate the mean of the data:
    In [5]: data.mean()
    Out[5]: 0.07253250605445645
    

    Generate 100 samples from the original data and calculate their means (of course, more samples may lead to a more accurate result):

    In [6]: bootstrapped = np.random.choice(data, size=(N, 100))
    
    In [7]: means = bootstrapped.mean(axis=0)
    
    In [8]: means.shape
    Out[8]: (100,)
    
  4. Calculate the mean, variance, and standard deviation of the arithmetic means we obtained:
    In [9]: means.mean()
    Out[9]: 0.067866373318115278
    
    In [10]: means.var()
    Out[10]: 0.001762807104774598
    
    In [11]: means.std()
    Out[11]: 0.041985796464692651
    

    If we are assuming a normal distribution for...

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