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# numpy.ma.dstack

`numpy.ma.``dstack`(tup) = <numpy.ma.extras._fromnxfunction_seq instance>

  > Stack arrays in sequence depth wise (along third axis).

  > Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing.

  > This function continues to be supported for backward compatibility, but you should prefer `np.concatenate` or `np.stack`. The `np.stack` function was added in NumPy 1.10.

  Parameters: | tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis.
  Returns: | stacked : ndarray The array formed by stacking the given arrays.

  See also

  `stack`
    Join a sequence of arrays along a new axis.
  `vstack`
    Stack along first axis.
  `hstack`
    Stack along second axis.
  `concatenate`
    Join a sequence of arrays along an existing axis.
  `dsplit`
    Split array along third axis.

  Notes

  The function is applied to both the _data and the _mask, if any.

  Examples

      >>> a = np.array((1,2,3))
      >>> b = np.array((2,3,4))
      >>> np.dstack((a,b))
      array([[[1, 2],
              [2, 3],
              [3, 4]]])

      >>> a = np.array([[1],[2],[3]])
      >>> b = np.array([[2],[3],[4]])
      >>> np.dstack((a,b))
      array([[[1, 2]],
             [[2, 3]],
             [[3, 4]]])