# numpy.ma.hstack

ma.hstack = <numpy.ma.extras._fromnxfunction_seq object>

  hstack

  Stack arrays in sequence horizontally (column wise).

  This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by `hsplit`.

  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 second axis, except 1-D arrays which can be any length.

    dtype str or dtype

      If provided, the destination array will have this dtype. Cannot be provided together with out.

    .. versionadded:: 1.24
    casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

      Controls what kind of data casting may occur. Defaults to ‘same_kind’.

    .. versionadded:: 1.24
  Returns:
    stacked ndarray

      The array formed by stacking the given arrays.

  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.

  `vstack`

    Stack arrays in sequence vertically (row wise).

  `dstack`

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

  `column_stack`

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

  `hsplit`

    Split an array into multiple sub-arrays horizontally (column-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.hstack((a,b))
      array([1, 2, 3, 4, 5, 6])
      >>> a = np.array([[1],[2],[3]])
      >>> b = np.array([[4],[5],[6]])
      >>> np.hstack((a,b))
      array([[1, 4],
             [2, 5],
             [3, 6]])