numpy.concat#

numpy.concat((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 dimensioncorresponding toaxis (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 becorrect, matching that of what concatenate would have returned if noout argument were specified.

dtypestr or dtype

If provided, the destination array will have this dtype. Cannot beprovided together without.

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 options, please seecasting.

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 arenot preserved. In cases where a MaskedArrayis expected as input, use the ma.concatenate function from the maskedarray module instead.

Examples

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>>>importnumpyasnp>>>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)>>>amasked_array(data=[0, --, 2],             mask=[False,  True, False],       fill_value=999999)>>>barray([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)

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