nums.numpy.concatenate

nums.numpy.concatenate(arrays, axis=0, out=None)

Join a sequence of arrays along an existing axis.

This docstring was copied from numpy.concatenate.

Some inconsistencies with the NumS version may exist.

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.

outBlockArray, 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.

resBlockArray

The concatenated array.

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.

When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an BlockArray, 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.

out is currently not supported for concatenate.

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, 2], [3, 4]])  
>>> b = nps.array([[5, 6]])  
>>> nps.concatenate((a, b), axis=0).get()  
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> nps.concatenate((a, b.T), axis=1).get()  
array([[1, 2, 5],
       [3, 4, 6]])