Numpy concatenate 二维数组的拼接

所属的课程名称及链接

[AI基础课程--常用框架工具]

环境信息

• * ModelArts
  •   * Notebook - Multi-Engine 2.0 (python3)
    •     * JupyterLab - Notebook - Conda-python3
      •       * numpy 1.19.1

Numpy concatenate 二维数组的拼接

import numpy as np
a = np.arange(6).reshape(2,3)
b = np.arange(3,9).reshape(2,3)
c = np.concatenate((a,b),axis=0)  # 二维数组，按列拼接
d = np.concatenate((a,b),axis=1)  # 二维数组，按行拼接

# concatenate 与 shape 有关
# Join a sequence of arrays along an existing axis.
# 沿现有（已存在的）轴连接数组序列！

print("a",a)
print("shape_a",a.shape,"\n")
print("b",b)
print("shape_b",b.shape,"\n")
print("c",c)
print("shape_c",c.shape,"\n")
print("d",d)
print("shape_d",d.shape,"\n")

a [[0 1 2]
 [3 4 5]]
shape_a (2, 3) 

b [[3 4 5]
 [6 7 8]]
shape_b (2, 3) 

c [[0 1 2]
 [3 4 5]
 [3 4 5]
 [6 7 8]]
shape_c (4, 3) 

d [[0 1 2 3 4 5]
 [3 4 5 6 7 8]]
shape_d (2, 6) 

help

help(np.concatenate)
Help on function concatenate in module numpy:

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.  If axis is None,
        arrays are flattened before use.  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.
    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 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]])
    >>> 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)
    >>> 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)

备注