np mean vs np average in Python NumPy

Question

I notice that   In  30   np mean  1  2  3   Out 30   2 0  In  31   np average  1  2  3   Out 31   2 0   However  there should be some differences  since after all they are two different functions   What are the differences between them

User · Answer

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 0  np mean f  Out  2 0

User · Answer

In your invocation  the two functions are the same   average can compute a weighted average though   Doc links  mean and average

User · Answer

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

User · Answer

In addition to the differences already noted  there s another extremely important difference that I just now discovered the hard way   unlike np mean  np average doesn t allow the dtype keyword  which is essential for getting correct results in some cases   I have a very large single-precision array that is accessed from an h5 file   If I take the mean along axes 0 and 1  I get wildly incorrect results unless I specify dtype  float64     gt T shape  4096  4096  720   gt T dtype dtype   lt f4    m1   np average T  axis  0 1                     garbage m2   np mean T  axis  0 1                        the same garbage m3   np mean T  axis  0 1   dtype  float64      correct results   Unfortunately  unless you know what to look for  you can t necessarily tell your results are wrong   I will never use np average again for this reason but will always use np mean     dtype  float64   on any large array   If I want a weighted average  I ll compute it explicitly using the product of the weight vector and the target array and then either np sum or np mean  as appropriate  with appropriate precision as well

User · Answer

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 mean except 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 here  if returned   returned is another optional argument     scl   np multiply avg  0    scl     return avg  scl else      return avg

[python] np.mean() vs np.average() in Python NumPy?

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