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numpy.average() has a weights option, but numpy.std() does not. Does anyone have suggestions for a workaround?
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A weighted standard deviation allows you to apply a weight, or relative significance to each value in a set of values. Values with a higher value for their weight are considered as more significant to a sample as compared to the other values in a sample.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(x)) , where x = abs(a - a. mean())**2 . The average squared deviation is typically calculated as x. sum() / N , where N = len(x) .
std() in Python. numpy. std(arr, axis = None) : Compute the standard deviation of the given data (array elements) along the specified axis(if any).. Standard Deviation (SD) is measured as the spread of data distribution in the given data set.
How about the following short "manual calculation"?
def weighted_avg_and_std(values, weights): """ Return the weighted average and standard deviation. values, weights -- Numpy ndarrays with the same shape. """ average = numpy.average(values, weights=weights) # Fast and numerically precise: variance = numpy.average((values-average)**2, weights=weights) return (average, math.sqrt(variance)) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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