np.concatenate 拼接-白红宇

concatenate(...)

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

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).

axis : int, optional

The axis along which the arrays will be joined. Default is 0.

out : ndarray, 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.

Returns

-------

res : ndarray

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.

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)

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]])

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)