jax.numpy.concatenate

jax.numpy.concatenate(arrays, axis=0)[source]

Join a sequence of arrays along an existing axis.

LAX-backend implementation of concatenate(). Original docstring below.

concatenate((a1, a2, …), axis=0, out=None)

Parameters

axis (int, optional) – The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.

Returns

res – The concatenated array.

Return type

ndarray

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

>>> 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)