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?
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.
reduce in the end, I don't understand why it matters.
np.maximum, np.maximum.reduce won't work out of the box with masked array. Neither is np.ma.maximum.reduce(…, axis=0) . You can manually keep track of the masks however.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.
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)
