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I'd like to calculate element-wise average of numpy ndarray.
In [56]: a = np.array([10, 20, 30]) In [57]: b = np.array([30, 20, 20]) In [58]: c = np.array([50, 20, 40]) What I want:
[30, 20, 30] Is there any in-built function for this operation, other than vectorized sum and dividing?
983
elementwise (not comparable) (mathematics) Obtained by operating on one element (of a matrix etc) at a time.
Addition, subtraction, multiplication, and division of arguments(NumPy arrays) element-wise. First array elements raised to powers from second array, element-wise. Return element-wise remainder of division.
An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape , which is a tuple of N non-negative integers that specify the sizes of each dimension.
You can just use np.mean directly:
>>> np.mean([a, b, c], axis=0) array([ 30., 20., 30.]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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