Why Doesn\'t NumPy Have a Built-in Moving Average Function?

Simplified Moving Average Computation with Python and NumPy

Calculating the moving average or rolling average of a data series is essential for smoothing out noise and identifying trends. While NumPy/SciPy lacks a dedicated moving average function, implementing it manually is surprisingly simple.

Easiest Implementation with NumPy

Using NumPy's cumsum function, a straightforward non-weighted moving average can be implemented efficiently:

def moving_average(a, n=3):
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1:] / n

This implementation provides a quick and accurate way to calculate the moving average for any desired window size.

Inclusion in Batteries versus Implementation

The absence of a built-in moving average function in NumPy/SciPy may seem odd, considering its ubiquity. However, there are a few potential reasons for this:

• Simplicity of implementation: As demonstrated above, the moving average can be easily implemented using standard NumPy functions.
• Computational efficiency: The cumsum method is often faster than more complex FFT-based approaches.
• Potential bloat: Including specialized functions for every conceivable data analysis task could lead to bloat in the NumPy/SciPy libraries.

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