np.mean() vs np.average() in Python NumPy?

np.average takes an optional weight parameter. If it is not supplied they are equivalent. Take a look at the source code: Mean, Average

np.mean:

try:    mean = a.meanexcept AttributeError:    return _wrapit(a, 'mean', axis, dtype, out)return mean(axis, dtype, out)

np.average:

...if weights is None :    avg = a.mean(axis)    scl = avg.dtype.type(a.size/avg.size)else:    #code that does weighted mean hereif returned: #returned is another optional argument    scl = np.multiply(avg, 0) + scl    return avg, sclelse:    return avg...

np.mean always computes an arithmetic mean, and has some additional options for input and output (e.g. what datatypes to use, where to place the result).

np.average can compute a weighted average if the weights parameter is supplied.

In some version of numpy there is another imporant difference that you must be aware:

average do not take in account masks, so compute the average over the whole set of data.

mean takes in account masks, so compute the mean only over unmasked values.

g = [1,2,3,55,66,77]f = np.ma.masked_greater(g,5)np.average(f)Out: 34.0np.mean(f)Out: 2.0