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# numpy.concatenate

`numpy.``concatenate`((a1, a2, ...), axis=0)

  Join a sequence of arrays along an existing axis.

  Parameters: | a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0.
  Returns: | res : ndarray The concatenated array.

  See also

  `ma.concatenate`
    Concatenate function that preserves input masks.
  `array_split`
    Split an array into multiple sub-arrays of equal or near-equal size.
  `split`
    Split array into a list of multiple sub-arrays of equal size.
  `hsplit`
    Split array into multiple sub-arrays horizontally (column wise)
  `vsplit`
    Split array into multiple sub-arrays vertically (row wise)
  `dsplit`
    Split array into multiple sub-arrays along the 3rd axis (depth).
  `stack`
    Stack a sequence of arrays along a new axis.
  `hstack`
    Stack arrays in sequence horizontally (column wise)
  `vstack`
    Stack arrays in sequence vertically (row wise)
  `dstack`
    Stack arrays in sequence depth wise (along third dimension)

  Notes

  When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are not preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead.

  Examples

      >>> a = np.array([[1, 2], [3, 4]])
      >>> b = np.array([[5, 6]])
      >>> np.concatenate((a, b), axis=0)
      array([[1, 2],
             [3, 4],
             [5, 6]])
      >>> np.concatenate((a, b.T), axis=1)
      array([[1, 2, 5],
             [3, 4, 6]])

  This function will not preserve masking of MaskedArray inputs.

      >>> a = np.ma.arange(3)
      >>> a[1] = np.ma.masked
      >>> b = np.arange(2, 5)
      >>> a
      masked_array(data = [0 -- 2],
                   mask = [False  True False],
             fill_value = 999999)
      >>> b
      array([2, 3, 4])
      >>> np.concatenate([a, b])
      masked_array(data = [0 1 2 2 3 4],
                   mask = False,
             fill_value = 999999)
      >>> np.ma.concatenate([a, b])
      masked_array(data = [0 -- 2 2 3 4],
                   mask = [False  True False False False False],
             fill_value = 999999)