Numpy delete 删除数组的行和列

import numpy as np
arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

print("arr",arr,"\n")
print("arr.flatten()",arr.flatten(),"\n")
print("arr.flatten()[2]",arr.flatten()[2],"\n")
print("np.delete(arr,obj=2)",np.delete(arr,obj=2),"\n")  # axis为None。先 arr.flatten()，再移除arr[obj]元素)

print("np.delete(arr,obj=0,axis=0)",np.delete(arr,obj=0,axis=0),"\n") # axis=0 行
print("np.delete(arr,obj=[0,1],axis=0)",np.delete(arr,obj=[0,1],axis=0),"\n") # axis=0 行，一次删除多行
print("np.delete(arr,obj=0,axis=1)",np.delete(arr,obj=2,axis=1),"\n") # axis=1 列

arr [[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]] 

arr.flatten() [ 1  2  3  4  5  6  7  8  9 10 11 12] 

arr.flatten()[2] 3 

np.delete(arr,obj=2) [ 1  2  4  5  6  7  8  9 10 11 12] 

np.delete(arr,obj=0,axis=0) [[ 5  6  7  8]
 [ 9 10 11 12]] 

np.delete(arr,obj=[0,1],axis=0) [[ 9 10 11 12]] 

np.delete(arr,obj=0,axis=1) [[ 1  2  4]
 [ 5  6  8]
 [ 9 10 12]] 

help(np.delete)

Help on function delete in module numpy:

delete(arr, obj, axis=None)
    Return a new array with sub-arrays along an axis deleted. For a one
    dimensional array, this returns those entries not returned by
    `arr[obj]`.
    
    Parameters
    ----------
    arr : array_like
        Input array.
    obj : slice, int or array of ints
        Indicate indices of sub-arrays to remove along the specified axis.
    
        .. versionchanged:: 1.19.0
            Boolean indices are now treated as a mask of elements to remove,
            rather than being cast to the integers 0 and 1.
    
    axis : int, optional
        The axis along which to delete the subarray defined by `obj`.
        If `axis` is None, `obj` is applied to the flattened array.
    
    Returns
    -------
    out : ndarray
        A copy of `arr` with the elements specified by `obj` removed. Note
        that `delete` does not occur in-place. If `axis` is None, `out` is
        a flattened array.
    
    See Also
    --------
    insert : Insert elements into an array.
    append : Append elements at the end of an array.
    
    Notes
    -----
    Often it is preferable to use a boolean mask. For example:
    
    >>> arr = np.arange(12) + 1
    >>> mask = np.ones(len(arr), dtype=bool)
    >>> mask[[0,2,4]] = False
    >>> result = arr[mask,...]
    
    Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
    use of `mask`.
    
    Examples
    --------
    >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
    >>> arr
    array([[ 1,  2,  3,  4],
           [ 5,  6,  7,  8],
           [ 9, 10, 11, 12]])
    >>> np.delete(arr, 1, 0)
    array([[ 1,  2,  3,  4],
           [ 9, 10, 11, 12]])
    
    >>> np.delete(arr, np.s_[::2], 1)
    array([[ 2,  4],
           [ 6,  8],
           [10, 12]])
    >>> np.delete(arr, [1,3,5], None)
    array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])