I"m trying to perform an element wise divide in python, but if a zero is encountered, I need the quotient to just be zero.
array1 = np.array([0, 1, 2]) array2 = np.array([0, 1, 1]) array1 / array2 # should be np.array([0, 1, 2])
I could always just use a for-loop through my data, but to really utilize numpy"s optimizations, I need the divide function to return 0 upon divide by zero errors instead of ignoring the error.
Unless I"m missing something, it doesn"t seem numpy.seterr() can return values upon errors. Does anyone have any other suggestions on how I could get the best out of numpy while setting my own divide by zero error handling?
In numpy v1.7+, you can take advantage of the "where" option for ufuncs. You can do things in one line and you don"t have to deal with the errstate context manager.
>>> a = np.array([-1, 0, 1, 2, 3], dtype=float) >>> b = np.array([ 0, 0, 0, 2, 2], dtype=float) # If you don"t pass 'out' the indices where (b == 0) will be uninitialized! >>> c = np.divide(a, b, out=np.zeros_like(a), where=b!=0) >>> print(c) [ 0. 0. 0. 1. 1.5]
In this case, it does the divide calculation anywhere "where" b does not equal zero. When b does equal zero, then it remains unchanged from whatever value you originally gave it in the "out" argument.