How to conditionally combine two numpy arrays of the same shape

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We could use NumPy built-in np.maximum, made exactly for that purpose –

np.maximum(array1, array2)

Another way would be to use the NumPy ufunc np.max on a 2D stacked array and max-reduce along the first axis (axis=0) –

np.max([array1,array2],axis=0)

Timings on 1 million datasets –

In [271]: array1 = np.random.randint(0,9,(1000000))

In [272]: array2 = np.random.randint(0,9,(1000000))

In [274]: %timeit np.maximum(array1, array2)
1000 loops, best of 3: 1.25 ms per loop

In [275]: %timeit np.max([array1, array2],axis=0)
100 loops, best of 3: 3.31 ms per loop

# @Eric Duminil's soln1
In [276]: %timeit np.where( array1 > array2, array1, array2)
100 loops, best of 3: 5.15 ms per loop

# @Eric Duminil's soln2
In [277]: magic = lambda x,y : np.where(x > y , x, y)

In [278]: %timeit magic(array1, array2)
100 loops, best of 3: 5.13 ms per loop

Extending to other supporting ufuncs

Similarly, there’s np.minimum for finding element-wise minimum values between two arrays of same or broadcastable shapes. So, to find element-wise minimum between array1 and array2, we would have :

np.minimum(array1, array2)

For a complete list of ufuncs that support this feature, please refer to the docs and look for the keyword : element-wise. Grep-ing for those, I got the following ufuncs :

add, subtract, multiply, divide, logaddexp, logaddexp2, true_divide,
floor_divide, power, remainder, mod, fmod, divmod, heaviside, gcd,
lcm, arctan2, hypot, bitwise_and, bitwise_or, bitwise_xor, left_shift,
right_shift, greater, greater_equal, less, less_equal, not_equal,
equal, logical_and, logical_or, logical_xor, maximum, minimum, fmax,
fmin, copysign, nextafter, ldexp, fmod

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