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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 – computing the Simple Moving Average


The moving average is easy enough to compute with a few loops and the mean() function, but NumPy has a better alternative—the convolve() function. The SMA is, after all, nothing more than a convolution with equal weights or, if you like, unweighted.

Note

Convolution is a mathematical operation on two functions defined as the integral of the product of the two functions after one of the functions is reversed and shifted.

Convolution is described on Wikipedia at https://en.wikipedia.org/wiki/Convolution. Khan Academy also has a tutorial on convolution at https://www.khanacademy.org/math/differential-equations/laplace-transform/convolution-integral/v/introduction-to-the-convolution.

Use the following steps to compute the SMA:

  1. Use the ones() function to create an array of size N and elements initialized to 1, and then, divide the array by N to give us the weights:

    N = 5
    weights = np.ones(N) / N
    print("Weights", weights)

    For N = 5, this gives us...

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