Column-wise Aggregation
Easy
~10 min
code completion
Aggregation Along an Axis
NumPy's aggregation functions accept an axis argument to reduce along a specific dimension.
X = np.array([[1, 2, 3],
[4, 5, 6]])
np.sum(X, axis=0) # column sums: [5, 7, 9]
np.sum(X, axis=1) # row sums: [6, 15]
np.mean(X, axis=0) # column means: [2.5, 3.5, 4.5]axis=0 collapses rows (operates down the columns).
axis=1 collapses columns (operates across the rows).
Your task:
Implement column_sums(X) that returns the sum of each column.
Example Tests
2x3 matrix column sums
Input: {"X":[[1,2,3],[4,5,6]]}
Expected: [5,7,9]
3x2 matrix column sums
Input: {"X":[[10,20],[30,40],[50,60]]}
Expected: [90,120]
Cancelling values give zero
Input: {"X":[[1,0],[-1,0]]}
Expected: [0,0]