numpy.concatenate#

numpy.concatenate((a1, a2, ...), 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).
axisint, optional: The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.
outndarray, 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.
dtypestr or dtype: If provided, the destination array will have this dtype. Cannot be provided together with out.

New 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’.

New in version 1.20.0.

Returns:

resndarray: 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

>>> 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)