numpy max vs amax vs maximum

Question

numpy has three different functions which seem like they can be used for the same things --- except that numpy.maximum can only be used element-wise, w

Answer 1

For completeness, in Numpy there are four maximum related functions. They fall into two different categories:

• np.amax/np.max, np.nanmax: for single array order statistics
• and np.maximum, np.fmax: for element-wise comparison of two arrays

---

I. For single array order statistics

NaNs propagator np.amax/np.max and its NaN ignorant counterpart np.nanmax.

• np.max is just an alias of np.amax, so they are considered as one function.

  >>> np.max.__name__
  'amax'
  >>> np.max is np.amax
  True
  

• np.max propagates NaNs while np.nanmax ignores NaNs.

  >>> np.max([np.nan, 3.14, -1])
  nan
  >>> np.nanmax([np.nan, 3.14, -1])
  3.14
  

II. For element-wise comparison of two arrays

NaNs propagator np.maximum and its NaNs ignorant counterpart np.fmax.

• Both functions require two arrays as the first two positional args to compare with.

  # x1 and x2 must be the same shape or can be broadcast
  np.maximum(x1, x2, /, ...);
  np.fmax(x1, x2, /, ...)
  

• np.maximum propagates NaNs while np.fmax ignores NaNs.

  >>> np.maximum([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([ nan,  nan, 2.72])
  >>> np.fmax([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([-inf, 3.14, 2.72])
  

• The element-wise functions are np.ufunc(Universal Function), which means they have some special properties that normal Numpy function don't have.

  >>> type(np.maximum)
  
  >>> type(np.fmax)
  
  >>> #---------------#
  >>> type(np.max)
  
  >>> type(np.nanmax)
  
  

---

And finally, the same rules apply to the four minimum related functions:

• np.amin/np.min, np.nanmin;
• and np.minimum, np.fmin.