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python中常见的二维数组有list与numpy.array。在很多情况下我们需要获取数组的大小，阅读过一些python代码可以发现，常见的方法一般有len, size, shape这三种，那么这三种方法分别应用于那些场合？有什么区别？

import numpy as np

a = [[1,2,3,4], [5,6,7,8], [9, 10, 11, 12]]

b = np.array(a)

print type(a)

print a

print type(b)

print b

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

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

list

list---len

print len(a), len(a[0])

3 4

---------------------------------------------------------------------------

NameError Traceback (most recent call last)

in ()

1 print len(a), len(a[0])

----> 2 print size(a)

NameError: name 'size' is not defined

list---size

print size(a)

---------------------------------------------------------------------------

NameError Traceback (most recent call last)

in ()

----> 1 print size(a)

NameError: name 'size' is not defined

In [6]:

print a.size

---------------------------------------------------------------------------

AttributeError Traceback (most recent call last)

in ()

----> 1 print a.size

AttributeError: 'list' object has no attribute 'size'

list---shape

print shape(a)

---------------------------------------------------------------------------

NameError Traceback (most recent call last)

in ()

----> 1 print shape(a)

NameError: name 'shape' is not defined

In [8]:

print a.shape

---------------------------------------------------------------------------

AttributeError Traceback (most recent call last)

in ()

----> 1 print a.shape

AttributeError: 'list' object has no attribute 'shape'

由上可知，list只支持len(), 该方法实际是调用了对象的len(self)方法

numpy.array

对比之下，numpy.array同时支持len, size, shape, 注意看三者返回值的区别。

此外，numpy中还提供matrix的数据类型，具体请看：

c = np.mat(a)

print type(c)

print c

d = np.mat(b)

print type(d)

print d

print len(d)

print d.size

print d.shape

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

3

12

(3, 4)

从上面的例子可以看出，martix支持由list和numpy.array创建，同时支持len, size以及shape.