numpy-numpy.sum()中‘keepdims‘参数的作用

最新推荐文章于 2025-09-22 07:22:48 发布

原创最新推荐文章于 2025-09-22 07:22:48 发布 · 2.4k 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#numpy #python #keepdims

numpy学习专栏收录该内容

6 篇文章

订阅专栏

本文通过实例解析了numpy.sum()函数中'keepdims'参数的作用。'keepdims'参数用于在进行数组运算时保留原来的维度，使得结果能够正确广播与原始数组。当不指定'axis'时，'keepdims=True'会保持二维结果；指定'axis'时，它会保留相应轴的维度。这对于处理多维数组和后续运算的兼容性非常重要。

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