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# numpy.concatenate

numpy.concatenate(arrays, /, axis=0, out=None, *, dtype=None, casting='same_kind')

  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. If axis is None, arrays are flattened before use. Default is 0.

    out ndarray, optional

      If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

    dtype str or dtype

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

      Added in version 1.20.0.

    casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

      Controls what kind of data casting may occur. Defaults to ‘same_kind’. For a description of the options, please see casting.

      Added in version 1.20.0.

  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.

  `block`

    Assemble arrays from blocks.

  `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).

  `column_stack`

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

  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

      >>> import numpy as np
      >>> 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]])
      >>> np.concatenate((a, b), axis=None)
      array([1, 2, 3, 4, 5, 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)