Iterating over a numpy array

Question

Is there a less verbose alternative to this   for x in xrange array shape 0        for y in xrange array shape 1            do stuff x  y    I came up with this   for x  y in itertools product map xrange  array shape        do stuff x  y    Which saves one indentation  but is still pretty ugly   I m hoping for something that looks like this pseudocode   for x  y in array indices      do stuff x  y    Does anything like that exist

User · Answer

If you only need the indices  you could try numpy ndindex    gt  gt  gt  a   numpy arange 9  reshape 3  3   gt  gt  gt    x  y  for x  y in numpy ndindex a shape     0  0    0  1    0  2    1  0    1  1    1  2    2  0    2  1    2  2

User · Answer

I think you re looking for the ndenumerate    gt  gt  gt  a  numpy array   1 2   3 4   5 6     gt  gt  gt  for  x y   value in numpy ndenumerate a        print x y      0 0 0 1 1 0 1 1 2 0 2 1   Regarding the performance  It is a bit slower than a list comprehension    X   np zeros  100  100  100     timeit list    i j k   X i j k   for i in range X shape 0   for j in range X shape 1   for k in range X shape 2     1 loop  best of 3  376 ms per loop   timeit list np ndenumerate X   1 loop  best of 3  570 ms per loop   If you are worried about the performance you could optimise a bit further by looking at the implementation of ndenumerate  which does 2 things  converting to an array and  looping  If you know you have an array  you can call the  coords attribute of the flat iterator    a   X flat  timeit list   a coords  x  for x in a flat   1 loop  best of 3  305 ms per loop

User · Answer

see nditer  import numpy as np Y   np array  3 4 5 6   for y in np nditer Y  op flags   readwrite         y    3  Y    np array  6  7  8  9        y   3 would not work  use y    0 and y    3 instead

User · Answer

I see that no good desciption for using numpy nditer   is here  So  I am gonna go with one  According to NumPy v1 21 dev0 manual  The iterator object nditer  introduced in NumPy 1 6  provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion  I have to calculate mean squared error and I have already calculate y predicted and I have y actual from the boston dataset  available with sklearn  def cal mse y actual  y predicted        quot  quot  quot  this function will return mean squared error        args             y actual  ndarray   np array containing target variable            y predicted  ndarray   np array containing predictions from DecisionTreeRegressor        returns             mse  integer       quot  quot  quot      sq error   0     for i in np nditer np arange y pred shape 0             sq error     y actual i  - y predicted i    2     mse   1 y actual shape 0    sq error          return mse  Hope this helps     for further explaination visit

[python] Iterating over a numpy array

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