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  while numpy max and numpy amax can be used on particular axes  or all elements   Why is there more than just numpy max   Is there some subtlety to this in performance    Similarly for min vs  amin vs  minimum

User · Answer

np maximum not only compares elementwise but also compares array elementwise with single value   gt  gt  gt np maximum  23  14  16  20  25   18  array  23  18  18  20  25

User · Answer

np max is just an alias for np amax  This function only works on a single input array and finds the value of maximum element in that entire array  returning a scalar   Alternatively  it takes an axis argument and will find the maximum value along an axis of the input array  returning a new array     gt  gt  gt  a   np array   0  1  6                      2  4  1     gt  gt  gt  np max a  6  gt  gt  gt  np max a  axis 0    max of each column array  2  4  6     The default behaviour of np maximum is to take two arrays and compute their element-wise maximum  Here   compatible  means that one array can be broadcast to the other  For example    gt  gt  gt  b   np array  3  6  1    gt  gt  gt  c   np array  4  2  9    gt  gt  gt  np maximum b  c  array  4  6  9     But np maximum is also a universal function which means that it has other features and methods which come in useful when working with multidimensional arrays  For example you can compute the cumulative maximum over an array  or a particular axis of the array     gt  gt  gt  d   np array  2  0  3  -4  -2  7  9    gt  gt  gt  np maximum accumulate d  array  2  2  3  3  3  7  9     This is not possible with np max   You can make np maximum imitate np max to a certain extent when using np maximum reduce    gt  gt  gt  np maximum reduce d  9  gt  gt  gt  np max d  9   Basic testing suggests the two approaches are comparable in performance  and they should be  as np max   actually calls np maximum reduce to do the computation

User · Answer

You ve already stated why np maximum is different - it returns an array that is the element-wise maximum between two arrays   As for np amax and np max  they both call the same function - np max is just an alias for np amax  and they compute the maximum of all elements in an array  or along an axis of an array   In  1   import numpy as np  In  2   np amax Out 2    lt function numpy core fromnumeric amax gt   In  3   np max Out 3    lt function numpy core fromnumeric amax gt

User · Answer

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    gt  gt  gt  np max   name    amax   gt  gt  gt  np max is np amax True  np max propagates NaNs while np nanmax ignores NaNs    gt  gt  gt  np max  np nan  3 14  -1   nan  gt  gt  gt  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    gt  gt  gt  np maximum  np nan  3 14  0    np NINF  np nan  2 72   array   nan   nan  2 72    gt  gt  gt  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    gt  gt  gt  type np maximum   lt class  numpy ufunc  gt   gt  gt  gt  type np fmax   lt class  numpy ufunc  gt   gt  gt  gt   ---------------   gt  gt  gt  type np max   lt class  function  gt   gt  gt  gt  type np nanmax   lt class  function  gt       And finally  the same rules apply to the four minimum related functions    np amin np min  np nanmin   and np minimum  np fmin

[python] numpy max vs amax vs maximum

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