One explanation for this behavior is that doing otherwise would be slow.

Consider an array like

arr = np.array([1]*10**6 + [-1])
ret = np.log(arr)

Today, what happens is:

• The output array is allocated as np.double
• The input array is iterated over, and log evaluated on each element in turn

For what you describe to happen, the behavior would have to be either:

• The output array is allocated as np.double

• The input array is iterated over, and log evaluated on each element in turn

• If any negative element is encountered, allocate a new array as np.cdouble, copy all the data over, then continue. This results in the whole array being promoted.

or:

• The input array is iterated over, and checked to see if all the values are positive

• The output array is allocated as np.double or np.cdouble based on this result

• The input array is iterated over, and log evaluated on each element in turn

In either case, you’ve converted a 1-pass iteration to a 2-pass one.

There are static-typing-based explanations for this behavior too, but I’ll let someone else present one of those.

Eric