Examples of how to get the type of a matrix using numpy in python:

### Get the type of a matrix using the attribute dtype

To get the type a matrix, a solution is to use dtype, example:

`import numpy as np`

`A = np.array([[1.2, 2.3, 3.4]])`

`print(A.dtype)`

returns

`float64`

### Change the type of a matrix

To indicate the type of a matrix, a solution is to do:

`A = np.array([[1.2, 2.3, 3.4]], dtype=int)`

`print(A)`

`print(A.dtype)`

returns respectively

`[[1 2 3]]`

and

`int64`

Or to use astype to change the type of an existing matrix::

`A = A.astype('float64')`

`print(A.dtype)`

returns

`float64`

### Combine matrix with different type

It is important to check the type of a matrix to avoid loosing information, an example let's consider the following matrix A:

`A = np.array([[10, 20, 30], [60, 20, 10], [50, 30, 90]])`

returns

`[[10 20 30]`

`[60 20 10]`

`[50 30 90]]`

and the matrix B:

`B= np.array([[2.1, 7.3, 4.5]])`

returns

`[[2.1 7.3 4.5]]`

Now, if the matrix A is updated using B, like:

`A[1,:] = B`

returns

`[[10 20 30]`

`[ 2 7 4]`

`[50 30 90]]`

but the elements of B have been modified. To avoid that a solution would have been to do first:

`A = A.astype('float64')`

`A[1,:] = B`

returns

`[[10. 20. 30. ]`

`[ 2.1 7.3 4.5]`

`[50. 30. 90. ]]`