淺析Numpy中的深復制和淺復制

復制和視圖

當運算和處理數組時，它們的數據有時被拷貝到新的數組有時不是。這通常是新手的困惑之源。這有三種情況:

• 完全不拷貝
  簡單的賦值不拷貝數組對象或它們的數據。

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 傳遞不定對象作為參考，所以函數調用不拷貝數組。

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

視圖(view)和淺復制
不同的數組對象分享同一個數據。視圖方法創造一個新的數組對象指向同一數據。

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]])

切片數組返回它的一個視圖：

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]])

• 深復制
  這個方法完全復制數組和它的數據

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