Python 在numpy.sum（）中有一个名为；keepdims"；。它有什么作用？

在

numpy.sum（）中有一个名为
keepdims
的参数。它有什么作用

正如您在文档中看到的：

@内伊
@hpaulj是正确的，您需要进行实验，但我怀疑您没有意识到某些数组的求和可以沿轴进行。阅读文档时，请遵循以下步骤

>>> a
array([[0, 0, 0],
       [0, 1, 0],
       [0, 2, 0],
       [1, 0, 0],
       [1, 1, 0]])
>>> np.sum(a, keepdims=True)
array([[6]])
>>> np.sum(a, keepdims=False)
6
>>> np.sum(a, axis=1, keepdims=True)
array([[0],
       [1],
       [2],
       [1],
       [2]])
>>> np.sum(a, axis=1, keepdims=False)
array([0, 1, 2, 1, 2])
>>> np.sum(a, axis=0, keepdims=True)
array([[2, 4, 0]])
>>> np.sum(a, axis=0, keepdims=False)
array([2, 4, 0])

您会注意到，如果不指定轴（前两个示例），数值结果是相同的，但是
keepdims=True
返回了一个带有数字6的
2D
数组，而第二个化身返回了标量。
类似地，当沿
轴1（跨行）求和时，当
keepdims=True
时，将再次返回
2D
数组。
最后一个示例沿轴0（向下列）显示了类似的特征。。。当
keepdims=True
时，将保留维度

在处理多维数据时，研究轴及其属性对于充分理解NumPy的功能至关重要
 一个示例，显示使用高维数组时，
keepdims
起作用。让我们看看阵列的形状在进行不同的缩减时是如何变化的：

import numpy as np
a = np.random.rand(2,3,4)
a.shape
# => (2, 3, 4)
# Note: axis=0 refers to the first dimension of size 2
#       axis=1 refers to the second dimension of size 3
#       axis=2 refers to the third dimension of size 4

a.sum(axis=0).shape
# => (3, 4)
# Simple sum over the first dimension, we "lose" that dimension 
# because we did an aggregation (sum) over it

a.sum(axis=0, keepdims=True).shape
# => (1, 3, 4)
# Same sum over the first dimension, but instead of "loosing" that 
# dimension, it becomes 1.

a.sum(axis=(0,2)).shape
# => (3,)
# Here we "lose" two dimensions

a.sum(axis=(0,2), keepdims=True).shape
# => (1, 3, 1)
# Here the two dimensions become 1 respectively

您是否尝试过使用和不使用此参数的示例？在交互式会话中进行测试应该很容易。如果您知道
sum
在没有它的情况下可以做什么，则此参数最有意义。您熟悉结果的形状如何依赖于输入数组和轴选择吗？
import numpy as np
a = np.random.rand(2,3,4)
a.shape
# => (2, 3, 4)
# Note: axis=0 refers to the first dimension of size 2
#       axis=1 refers to the second dimension of size 3
#       axis=2 refers to the third dimension of size 4

a.sum(axis=0).shape
# => (3, 4)
# Simple sum over the first dimension, we "lose" that dimension 
# because we did an aggregation (sum) over it

a.sum(axis=0, keepdims=True).shape
# => (1, 3, 4)
# Same sum over the first dimension, but instead of "loosing" that 
# dimension, it becomes 1.

a.sum(axis=(0,2)).shape
# => (3,)
# Here we "lose" two dimensions

a.sum(axis=(0,2), keepdims=True).shape
# => (1, 3, 1)
# Here the two dimensions become 1 respectively