python - Element-wise array maximum function in NumPy (more than two arrays)

Question

I'm trying to return maximum values of multiple array in an element-wise comparison. For example:

A = array([0, 1, 2])
B = array([1, 0, 3])
C = array([3, 0, 4])

I want the resulting array to be array([3,1,4]).

I wanted to use numpy.maximum, but it is only good for two arrays. Is there a simple function for more than two arrays?

Answer 1

With this setup:

>>> A = np.array([0,1,2])
>>> B = np.array([1,0,3])
>>> C = np.array([3,0,4])

You can either do:

>>> np.maximum.reduce([A,B,C])
array([3, 1, 4])

Or:

>>> np.vstack([A,B,C]).max(axis=0)
array([3, 1, 4])

I would go with the first option.

Answer 2

You can use reduce. It repeatedly applies a binary function to a list of values...

For A, B and C given in question...

np.maximum.reduce([A,B,C])

 array([3,1,4])

It first computes the np.maximum of A and B and then computes the np.maximum of (np.maximum of A and B) and C.

Answer 3

You can also use:

np.column_stack([A, B, C]).max(axis=1)

The result is the same as the solutions from the other answers.

I use Pandas more heavily than NumPy so for me it's easier to think of 1D arrays as something similar to Pandas Series. The above would be equivalent to:

pd.concat([A, B, C], axis=1).max(axis=1)