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)