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numpy中的flatten和ravel函数有什么区别？

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问题：numpy中的flatten和ravel函数有什么区别？
回答 0
回答 1
回答 2

问题：numpy中的flatten和ravel函数有什么区别？

import numpy as np
y = np.array(((1,2,3),(4,5,6),(7,8,9)))
OUTPUT:
print(y.flatten())
[1   2   3   4   5   6   7   8   9]
print(y.ravel())
[1   2   3   4   5   6   7   8   9]

这两个函数返回相同的列表。那么需要两个不同的功能来执行相同的工作。

点击查看英文原文 
import numpy as np
y = np.array(((1,2,3),(4,5,6),(7,8,9)))
OUTPUT:
print(y.flatten())
[1   2   3   4   5   6   7   8   9]
print(y.ravel())
[1   2   3   4   5   6   7   8   9]

Both function return the same list. Then what is the need of two different functions performing same job.

回答 0

当前的API是：

  • flatten 总是返回一个副本。
  • ravel尽可能返回原始数组的视图。这在打印输出中不可见，但是如果您修改ravel返回的数组，则可能会修改原始数组中的条目。如果您修改从flatten返回的数组中的条目，则将永远不会发生。ravel通常会更快，因为没有内存被复制，但是您在修改返回的数组时要格外小心。
  • reshape((-1,)) 只要数组的步幅允许，就可以得到一个视图，即使这意味着您并不总是可以获得连续的数组。
点击查看英文原文

The current API is that:

  • flatten always returns a copy.
  • ravel returns a view of the original array whenever possible. This isn’t visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array. If you modify the entries in an array returned from flatten this will never happen. ravel will often be faster since no memory is copied, but you have to be more careful about modifying the array it returns.
  • reshape((-1,)) gets a view whenever the strides of the array allow it even if that means you don’t always get a contiguous array.

回答 1

如此处所述，关键区别在于：

  • flatten 是ndarray对象的方法，因此只能用于真正的numpy数组。

  • ravel 是库级别的函数，因此可以在任何可以成功解析的对象上调用。

例如，ravel将对ndarray列表起作用，flatten而不适用于该类型的对象。

@IanH还在回答中指出了与内存处理的重要区别。

点击查看英文原文

As explained here a key difference is that:

  • flatten is a method of an ndarray object and hence can only be called for true numpy arrays.

  • ravel is a library-level function and hence can be called on any object that can successfully be parsed.

For example ravel will work on a list of ndarrays, while flatten is not available for that type of object.

@IanH also points out important differences with memory handling in his answer.

回答 2

这是函数的正确命名空间：

这两个函数均返回指向新存储器结构的展平一维数组。

import numpy
a = numpy.array([[1,2],[3,4]])

r = numpy.ravel(a)
f = numpy.ndarray.flatten(a)  

print(id(a))
print(id(r))
print(id(f))

print(r)
print(f)

print("\nbase r:", r.base)
print("\nbase f:", f.base)

---returns---
140541099429760
140541099471056
140541099473216

[1 2 3 4]
[1 2 3 4]

base r: [[1 2]
 [3 4]]

base f: None

在上例中：

  • 结果的存储位置不同，
  • 结果看起来一样
  • 展平将返回副本
  • ravel将返回一个视图。

我们如何检查某物是否是副本？使用的.base属性ndarray。如果是视图，则基础将是原始数组；如果是副本，则基数为None。

点击查看英文原文

Here is the correct namespace for the functions:

Both functions return flattened 1D arrays pointing to the new memory structures.

import numpy
a = numpy.array([[1,2],[3,4]])

r = numpy.ravel(a)
f = numpy.ndarray.flatten(a)  

print(id(a))
print(id(r))
print(id(f))

print(r)
print(f)

print("\nbase r:", r.base)
print("\nbase f:", f.base)

---returns---
140541099429760
140541099471056
140541099473216

[1 2 3 4]
[1 2 3 4]

base r: [[1 2]
 [3 4]]

base f: None

In the upper example:

  • the memory locations of the results are different,
  • the results look the same
  • flatten would return a copy
  • ravel would return a view.

How we check if something is a copy? Using the .base attribute of the ndarray. If it’s a view, the base will be the original array; if it is a copy, the base will be None.