numpy Broadcasting array operations


Example

Arithmetic operations are performed elementwise on Numpy arrays. For arrays of identical shape, this means that the operation is executed between elements at corresponding indices.

# Create two arrays of the same size
a = np.arange(6).reshape(2, 3)
b = np.ones(6).reshape(2, 3)

a
# array([0, 1, 2],
#       [3, 4, 5])
b
# array([1, 1, 1],
#       [1, 1, 1])

# a + b: a and b are added elementwise
a + b
# array([1, 2, 3],
#       [4, 5, 6])

Arithmetic operations may also be executed on arrays of different shapes by means of Numpy broadcasting. In general, one array is "broadcast" over the other so that elementwise operations are performed on sub-arrays of congruent shape.

# Create arrays of shapes (1, 5) and (13, 1) respectively
a = np.arange(5).reshape(1, 5)
a
# array([[0, 1, 2, 3, 4]])
b = np.arange(4).reshape(4, 1)
b
# array([0],
#       [1],
#       [2],
#       [3])

# When multiplying a * b, slices with the same dimensions are multiplied
# elementwise. In the case of a * b, the one and only row of a is multiplied
# with each scalar down the one and only column of b.
a*b
# array([[ 0,  0,  0,  0,  0],
#        [ 0,  1,  2,  3,  4],
#        [ 0,  2,  4,  6,  8],
#        [ 0,  3,  6,  9, 12]])

To illustrate this further, consider the multiplication of 2D and 3D arrays with congruent sub-dimensions.

# Create arrays of shapes (2, 2, 3) and (2, 3) respectively
a = np.arange(12).reshape(2, 2, 3)
a
# array([[[ 0  1  2]
#         [ 3  4  5]]
#
#        [[ 6  7  8]
#         [ 9 10 11]]])
b = np.arange(6).reshape(2, 3)
# array([[0, 1, 2],
#        [3, 4, 5]])

# Executing a*b broadcasts b to each (2, 3) slice of a,
# multiplying elementwise.
a*b
# array([[[ 0,  1,  4],
#         [ 9, 16, 25]],
#
#        [[ 0,  7, 16],
#         [27, 40, 55]]])

# Executing b*a gives the same result, i.e. the smaller
# array is broadcast over the other.

When is array broadcasting applied?

Broadcasting takes place when two arrays have compatible shapes.

Shapes are compared component-wise starting from the trailing ones. Two dimensions are compatible if either they're the same or one of them is 1. If one shape has higher dimension than the other, the exceeding components are not compared.

Some examples of compatible shapes:

(7, 5, 3)    # compatible because dimensions are the same
(7, 5, 3)


(7, 5, 3)    # compatible because second dimension is 1  
(7, 1, 3)

(7, 5, 3, 5) # compatible because exceeding dimensions are not compared
      (3, 5)

(3, 4, 5)    # incompatible 
   (5, 5) 

(3, 4, 5)    # compatible 
   (1, 5) 

Here's the official documentation on array broadcasting.