np.concatenate 拼接

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,

    >>> np.ma.concatenate([a, b])

    masked_array(data = [0 -- 2 2 3 4],

                 mask = [False  True False False False False],