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numpy.concatenate

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

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.

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)
block
Assemble arrays from blocks.

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]])
>>> 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)