numpy.concatenate#

numpy.concatenate(arrays/axis=0out=None*dtype=Nonecasting='same_kind')#

Join a sequence of arrays along an existing axis.

Parameters:

a1a2…sequence of array_like: The arrays must have the same shapeexcept in the dimension corresponding to axis (the firstby default).
axisintoptional: The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.
outndarrayoptional: If providedthe destination to place the result. The shape must be correctmatching that of what concatenate would have returned if no out argument were specified.
dtypestr or dtype: If providedthe 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’. For a description of the optionsplease see casting.

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 inputuse the ma.concatenate function from the masked array module instead.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[12],
       [34],
       [56]])
>>> np.concatenate((a, b.T), axis=1)
array([[125],
       [346]])
>>> np.concatenate((a, b), axis=None)
array([123456])

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 TrueFalse],
       fill_value=999999)
>>> b
array([234])
>>> np.concatenate([a, b])
masked_array(data=[012234],
             mask=False,
       fill_value=999999)
>>> np.ma.concatenate([a, b])
masked_array(data=[0--2234],
             mask=[False TrueFalseFalseFalseFalse],
       fill_value=999999)