General broadcasting rule
Two dimensions are compatible when
1. they are equal, or
2. one of them is 1.
| No. | Example Code | |
| 1 | import numpy as np a = np.array( [1.0, 2.0, 3.0] ) b = 2.0 a * b |
an array and a scalar value are combined in an operation:  |
| 2 |
import numpy as np a = np.array( [ [ 0.0, 0.0, 0.0], [10.0, 10.0, 10.0], [20.0, 20.0, 20.0], [30.0, 30.0, 30.0] ] ) b = np.array([1.0, 2.0, 3.0]) a + b b = np.array([1.0, 2.0, 3.0, 4.0]) a + b |
 |
| 3 | import numpy as np a = np.array( [ [ 0.0, 0.0, 0.0], [10.0, 10.0, 10.0], [20.0, 20.0, 20.0], [30.0, 30.0, 30.0] ] ) b = np.array( [1.0, 2.0, 3.0, 4.0] ) a + b |
 |
| 4 | import numpy as np a = np.array( [0.0, 10.0, 20.0, 30.0] ) b = np.array( [1.0, 2.0, 3.0] ) a[ :, np.newaxis ] + b |
 |
Source: https://numpy.org/doc/stable/user/basics.broadcasting.html
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