JoshAdel's solution uses np.newaxis to add a dimension. An alternative is to use reshape() to align the dimensions in preparation for broadcasting.
data = np.array([[1,1,1],[2,2,2],[3,3,3]])
vector = np.array([1,2,3])
data
# array([[1, 1, 1],
# [2, 2, 2],
# [3, 3, 3]])
vector
# array([1, 2, 3])
data.shape
# (3, 3)
vector.shape
# (3,)
data / vector.reshape((3,1))
# array([[1, 1, 1],
# [1, 1, 1],
# [1, 1, 1]])
Performing the reshape() allows the dimensions to line up for broadcasting:
data: 3 x 3
vector: 3
vector reshaped: 3 x 1
Note that data/vector is ok, but it doesn't get you the answer that you want. It divides each column of array (instead of each row) by each corresponding element of vector. It's what you would get if you explicitly reshaped vector to be 1x3 instead of 3x1.
data / vector
# array([[1, 0, 0],
# [2, 1, 0],
# [3, 1, 1]])
data / vector.reshape((1,3))
# array([[1, 0, 0],
# [2, 1, 0],
# [3, 1, 1]])