numpy.shape给出不一致的响应-为什么?

问题描述

为什么要程序

import numpy as np

c = np.array([1,2])
print(c.shape)
d = np.array([[1],[2]]).transpose()
print(d.shape)

给予

(2,)
(1,2)

作为其输出?不应该吧

(1,2)
(1,2)

而不是?我在python 2.7.3和python 3.2.3中都得到了这个

instead? I got this in both python 2.7.3 and python 3.2.3

推荐答案

当调用ndarray的.shape属性时，您将获得一个元组，其中的元素与数组的维数一样多.长度(即行数)是第一个维度(shape[0])

When you invoke the .shape attribute of a ndarray, you get a tuple with as many elements as dimensions of your array. The length, ie, the number of rows, is the first dimension (shape[0])

• 您从一个数组开始:c=np.array([1,2]).那是一个普通的一维数组，所以它的形状将是一个1元素的元组，而shape[0]是元素的数量，所以c.shape = (2,)
• 考虑c=np.array([[1,2]]).那是一个2D数组，只有1行.第一行也是唯一的行是[1,2]，这给了我们两列.因此，c.shape=(1,2)和len(c)=1
• 考虑c=np.array([[1,],[2,]]).另一个2行2列1列的2D数组:c.shape=(2,1)和len(c)=2.
• 考虑d=np.array([[1,],[2,]]).transpose():此数组与np.array([[1,2]])相同，因此其形状为(1,2).

• You start with an array : c=np.array([1,2]). That's a plain 1D array, so its shape will be a 1-element tuple, and shape[0] is the number of elements, so c.shape = (2,)
• Consider c=np.array([[1,2]]). That's a 2D array, with 1 row. The first and only row is [1,2], that gives us two columns. Therefore, c.shape=(1,2) and len(c)=1
• Consider c=np.array([[1,],[2,]]). Another 2D array, with 2 rows, 1 column: c.shape=(2,1) and len(c)=2.
• Consider d=np.array([[1,],[2,]]).transpose(): this array is the same as np.array([[1,2]]), therefore its shape is (1,2).

另一个有用的属性是.size:这是所有维度上的元素数，您需要数组c c.size = np.product(c.shape).

Another useful attribute is .size: that's the number of elements across all dimensions, and you have for an array c c.size = np.product(c.shape).

文档中有关形状的详细信息.

More information on the shape in the documentation.

这篇关于numpy.shape给出不一致的响应-为什么?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！