# numpy.dot¶

numpy.dot(a, b)

Dot product of two arrays.

For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of a and the second-to-last of b:

```dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
```
Parameters: a : array_like First argument. b : array_like Second argument. output : ndarray Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. ValueError : If the last dimension of a is not the same size as the second-to-last dimension of b.

vdot
Complex-conjugating dot product.
tensordot
Sum products over arbitrary axes.

Examples

```>>> np.dot(3, 4)
12
```

Neither argument is complex-conjugated:

```>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)
```

For 2-D arrays it’s the matrix product:

```>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
```
```>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128
```

#### Previous topic

numpy.linalg.LinAlgError

numpy.vdot