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# Matrix methods

Besides inheriting all the array methods, matrices enjoy four extra attributes – `T` for transpose, `H` for conjugate transpose, `I` for inverse, and `A` to cast as `ndarray`.

```>>> A = numpy.matrix("1+1j, 2-1j; 3-1j, 4+1j")
>>> print A.T; print A.H
[[ 1.+1.j  3.-1.j]
[ 2.-1.j  4.+1.j]]
[[ 1.-1.j  3.+1.j]
[ 2.+1.j  4.-1.j]]
```

## Operations between matrices

We have briefly covered the most basic operation between two matrices, the matrix product. For any other kind of product we resort to the basic utilities in the NumPy libraries – dot product for arrays or vectors (`dot`, `vdot`), inner and outer products of two arrays (`inner`, `outer`), tensor dot product along specified axes (`tensordot`), or the Kronecker product of two arrays (`kron`).

Let's see an example ...

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