# numpy.ma.vstack

ma.vstack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>

  Stack arrays in sequence vertically (row wise).

  This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by `vsplit`.

  This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions `concatenate`, `stack` and `block` provide more general stacking and concatenation operations.

  Parameters
    tup sequence of ndarrays

      The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.

  Returns
    stacked ndarray

      The array formed by stacking the given arrays, will be at least 2-D.

  See also

  `concatenate`

    Join a sequence of arrays along an existing axis.

  `stack`

    Join a sequence of arrays along a new axis.

  `block`

    Assemble an nd-array from nested lists of blocks.

  `hstack`

    Stack arrays in sequence horizontally (column wise).

  `dstack`

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

  `column_stack`

    Stack 1-D arrays as columns into a 2-D array.

  `vsplit`

    Split an array into multiple sub-arrays vertically (row-wise).

  Notes

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

  Examples

      >>> a = np.array([1, 2, 3])
      >>> b = np.array([4, 5, 6])
      >>> np.vstack((a,b))
      array([[1, 2, 3],
             [4, 5, 6]])

      >>> a = np.array([[1], [2], [3]])
      >>> b = np.array([[4], [5], [6]])
      >>> np.vstack((a,b))
      array([[1],
             [2],
             [3],
             [4],
             [5],
             [6]])

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