浅析Numpy中的深复制和浅复制

复制和视图

当运算和处理数组时，它们的数据有时被拷贝到新的数组有时不是。这通常是新手的困惑之源。这有三种情况:

• 完全不拷贝
  简单的赋值不拷贝数组对象或它们的数据。

In [68]:

a = arange(12)

b = a            # no new object is created

b is a           # a and b are two names for the same ndarray object

Out[68]:

True

In [69]:

b.shape = 3,4    # changes the shape of a

a.shape 

Out[69]:

(3, 4)

Python 传递不定对象作为参考，所以函数调用不拷贝数组。

In [46]:

def f(x):

    print (id(x))

In [49]:

id(a)                           # id is a unique identifier of an object

Out[49]:

139814515500016

In [48]:

f(a)

139814515500016

视图(view)和浅复制
不同的数组对象分享同一个数据。视图方法创造一个新的数组对象指向同一数据。

In [50]:

c = a.view()

c is a 

Out[50]:

False

In [51]:

c.base is a                        # c is a view of the data owned by a

Out[51]:

True

In [57]:

c.flags.owndata

Out[57]:

False

In [70]:

c.shape = 2,6                      # a's shape doesn't change

a.shape 

 

Out[70]:

(3, 4)

In [74]:

c[0,4] = 4321                    # a's data changesa

a

Out[74]:

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

切片数组返回它的一个视图：

In [77]:

s = a[ : , 0:3] # spaces added for clarity; could also be written "s = a[:,1:3]"

s[:] = 10           # s[:] is a view of s. Note the difference between s=10 and s[:]=10

a

Out[77]:

array([[10, 10, 10,  3],
       [10, 10, 10,  7],
       [10, 10, 10, 11]])

• 深复制
  这个方法完全复制数组和它的数据

In [78]:

d = a.copy()                          # a new array object with new data is created

d is a

Out[78]:

False

In [79]:

d.base is a                           # d doesn't share anything with a

Out[79]:

False