What are the rules of broadcasting in NumPy?

What are the rules of broadcasting in NumPy?

Broadcasting in NumPy allows arrays of different shapes to be used together in arithmetic operations. The rules are: 1. If the arrays have a different number of dimensions, prepend the shape of the smaller-dimensional array with ones until both shapes are the same. 2. The sizes of the dimensions must either be the same or one of them must be 1. 3. If the dimensions are not compatible, a ValueError will be raised.

Can you give me an example of broadcasting?

Sure! If you have a 2D array of shape (3, 4) and a 1D array of shape (4), NumPy will automatically expand the 1D array to match the 2D array's shape for operations. This means the 1D array is treated as if it were a (3, 4) array where each row is a copy of the 1D array.

What happens if the shapes are incompatible?

If the shapes are incompatible, NumPy will raise a ValueError. For example, trying to add an array of shape (3, 4) to an array of shape (2, 3) will result in an error because the dimensions do not align.

Are there any exceptions to the broadcasting rules?

Yes, the main exception is when one of the dimensions is zero. In that case, the result will also be an empty array, and broadcasting rules will not apply.