numpy-numpy.sum()中‘keepdims‘参数的作用

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原创最新推荐文章于 2026-09-24 15:09:23 发布 2.5k 阅读 AI 写同款· GEO 优化 ›

第七周python作业 1、第4章-23 求矩阵的局部极大值给定M行N列的整数矩阵A，如果A的非边界元素A[i][j]大于相邻的上下左右4个元素，那么就称元素A[i][j]是矩阵的局部极大值。本题要求给定矩阵的全部局部极大值及其所在的位置。我的代码： # -*- coding: utf-8 -*- import numpy as np # 初始化 M, N = map(int, input().split()) matrix = [] count = 0 # 生成矩阵 for _ in range(M): temp 阅读详情

在numpy的许多函数中，会出现'keepdims'参数，以numpy.sum()为例：

官方文档中给出的解释：

numpy.sum(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

'''
keepdimsbool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the sum method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
'''

看的一脸懵，还是跑个代码来得实在：

a = np.array([[0, 0, 0],
       [0, 1, 0],
       [0, 2, 0],
       [1, 0, 0],
       [1, 1, 0]])
print(a)

'''
输出：
[[0 0 0]
 [0 1 0]
 [0 2 0]
 [1 0 0]
 [1 1 0]]
'''

a_sum_true = np.sum(a, keepdims=True)
print(a_sum_true)
print(a_sum_true.shape)
a_sum_false = np.sum(a, keepdims=False)
print(a_sum_false)
print(a_sum_false.shape)
'''
输出：
[[6]]
(1, 1)
6
()
'''

a_sum_axis1_true = np.sum(a, axis=1, keepdims=True)
print(a_sum_axis1_true)
print(a_sum_axis1_true.shape)
a_sum_axis1_false = np.sum(a, axis=1, keepdims=False)
print(a_sum_axis1_false)
print(a_sum_axis1_false.shape)
'''
输出：
[[0]
 [1]
 [2]
 [1]
 [2]]
(5, 1)
[0 1 2 1 2]
(5,)
'''

a_sum_axis0_true = np.sum(a, axis=0, keepdims=True)
print(a_sum_axis0_true)
print(a_sum_axis0_true.shape)
a_sum_axis0_false = np.sum(a, axis=0, keepdims=False)
print(a_sum_axis0_false)
print(a_sum_axis0_false.shape)
'''
输出：
[[2 4 0]]
(1, 3)
[2 4 0]
(3,)
'''

如果并不指定'axis'参数，输出的结果是相同的，区别在于当' keepdims = True'时，输出的是2D结果。

如果指定'axis'参数，输出的结果也是相同的，区别在于'keepdims = True'时，输出的是2D结果。

可以理解为'keepdims = True'参数是为了保持结果的维度与原始array相同。

参考：

https://stackoverflow.com/questions/39441517/in-numpy-sum-there-is-parameter-called-keepdims-what-does-it-do

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