Given a 2D numpy array, e.g. np.array([[1,1,0], [1,2,0], [0,0,4]]), I want to return the sub-array with the minimal sum.
In this case, this would be the array np.array([1,1,0]).
How can I achieve this, ideally only using built-in numpy functions? It seems numpy does not allow specifying a key function, unlike vanilla python.
First we can use the build in function numpy.sum and get sums allong axis 1. How a numpy array is indexed can be found here.
array = np.array([[1,1,0], [1,2,0], [0,0,4]])
internal_sums = np.sum(array, axis=1)
Now we have all the sums stored in the variable 'internal_sums'. Next we use the numpy function 'numpy.argmin' to get the index of the minimal sum.
minsum_index = np.argmin(internal_sums)
Now we simply get the array at the correct index
minsum_array = array[minsum_index]
To add on to the excellent answer above, you can also use the ndarray methods, which lets you squeeze it all into one line!
array = np.array([[1,1,0], [1,2,0], [0,0,4]])
min_array = array[array.sum(1).argmin()]
