numpy.concatenate¶

numpy.concatenate((a1, a2, ...), axis=0, out=None)¶

Join a sequence of arrays along an existing axis.

Parameters:

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. 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.
Returns:	res : ndarray The concatenated array.

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.

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.

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)