Why Is There No Built-in Moving Average Function in NumPy?

Approaching Moving Averages with Python and NumPy/SciPy

Despite the prevalence of moving averages in data analysis, implementing them in NumPy or SciPy has proved to be a challenge due to the absence of a dedicated function. This has given rise to intricate solutions and raised questions about the reasons for this omission.

Simplified Implementation with NumPy

For a basic, unweighted moving average, a straightforward implementation using NumPy's np.cumsum function emerges as a viable option. This approach surpasses even FFT-based methods in terms of efficiency:

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

This function smoothly calculates moving averages of specified window sizes.

The Question Remains: Why No Built-in Implementation?

Given the ease of implementation, the absence of a built-in moving average function in NumPy may raise eyebrows. However, the answer lies in NumPy's focus on providing core numerical operations while leaving specialized functionalities to external libraries. This allows NumPy to remain lean and efficient, leaving room for more tailored packages to address specific analytical needs.

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