Numpy 简明教程

NumPy - Copies & Views

在执行函数时，其中一些返回输入数组的副本，而另一些返回视图。当内容物理存储在另一个位置时，称为 Copy 。另一方面，如果提供了相同内存内容的不同视图，则我们将其称为 View 。

While executing the functions, some of them return a copy of the input array, while some return the view. When the contents are physically stored in another location, it is called Copy. If on the other hand, a different view of the same memory content is provided, we call it as View.

No Copy

简单赋值不会生成数组对象的副本。相反，它使用原始数组的相同id（）来访问它。 id() 返回Python对象的通用标识符，类似于C语言中的指针。

Simple assignments do not make the copy of array object. Instead, it uses the same id() of the original array to access it. The id() returns a universal identifier of Python object, similar to the pointer in C.

此外，一个中的任何更改都会反映在另一个中。例如，改变一个的形状也将改变另一个的形状。

Furthermore, any changes in either gets reflected in the other. For example, the changing shape of one will change the shape of the other too.

Example

import numpy as np
a = np.arange(6)

print 'Our array is:'
print a

print 'Applying id() function:'
print id(a)

print 'a is assigned to b:'
b = a
print b

print 'b has same id():'
print id(b)

print 'Change shape of b:'
b.shape = 3,2
print b

print 'Shape of a also gets changed:'
print a

它将生成如下输出：

It will produce the following output −

Our array is:
[0 1 2 3 4 5]

Applying id() function:
139747815479536

a is assigned to b:
[0 1 2 3 4 5]
b has same id():
139747815479536

Change shape of b:
[[0 1]
 [2 3]
 [4 5]]

Shape of a also gets changed:
[[0 1]
 [2 3]
 [4 5]]

View or Shallow Copy

NumPy具有 ndarray.view() 方法，该方法是一个新的数组对象，它查看原始数组的相同数据。与之前的案例不同，新数组尺寸的变化不会改变原始数组的尺寸。

NumPy has ndarray.view() method which is a new array object that looks at the same data of the original array. Unlike the earlier case, change in dimensions of the new array doesn’t change dimensions of the original.

Example

import numpy as np
# To begin with, a is 3X2 array
a = np.arange(6).reshape(3,2)

print 'Array a:'
print a

print 'Create view of a:'
b = a.view()
print b

print 'id() for both the arrays are different:'
print 'id() of a:'
print id(a)
print 'id() of b:'
print id(b)

# Change the shape of b. It does not change the shape of a
b.shape = 2,3

print 'Shape of b:'
print b

print 'Shape of a:'
print a

它将生成如下输出：

It will produce the following output −

Array a:
[[0 1]
 [2 3]
 [4 5]]

Create view of a:
[[0 1]
 [2 3]
 [4 5]]

id() for both the arrays are different:
id() of a:
140424307227264
id() of b:
140424151696288

Shape of b:
[[0 1 2]
 [3 4 5]]

Shape of a:
[[0 1]
 [2 3]
 [4 5]]

数组的分片创建视图。

Slice of an array creates a view.

Example

import numpy as np
a = np.array([[10,10], [2,3], [4,5]])

print 'Our array is:'
print a

print 'Create a slice:'
s = a[:, :2]
print s

它将生成如下输出：

It will produce the following output −

Our array is:
[[10 10]
 [ 2 3]
 [ 4 5]]

Create a slice:
[[10 10]
 [ 2 3]
 [ 4 5]]

Deep Copy

ndarray.copy() 函数创建深度副本。它是数组及其数据的完整副本，并且不与原始数组共享。

The ndarray.copy() function creates a deep copy. It is a complete copy of the array and its data, and doesn’t share with the original array.

Example

import numpy as np
a = np.array([[10,10], [2,3], [4,5]])

print 'Array a is:'
print a

print 'Create a deep copy of a:'
b = a.copy()
print 'Array b is:'
print b

#b does not share any memory of a
print 'Can we write b is a'
print b is a

print 'Change the contents of b:'
b[0,0] = 100

print 'Modified array b:'
print b

print 'a remains unchanged:'
print a

它将生成如下输出：

It will produce the following output −

Array a is:
[[10 10]
 [ 2 3]
 [ 4 5]]

Create a deep copy of a:
Array b is:
[[10 10]
 [ 2 3]
 [ 4 5]]
Can we write b is a
False

Change the contents of b:
Modified array b:
[[100 10]
 [ 2 3]
 [ 4 5]]

a remains unchanged:
[[10 10]
 [ 2 3]
 [ 4 5]]