Assert是检查- 1. 有效条件， 2. 有效的表述， 3.真正的逻辑; 源代码。它不会让整个项目失败，而是会发出警报，提示源文件中有些地方不合适。

在例1中，因为变量'str'不是空的。因此不会引发任何断言或异常。

示例1:

#!/usr/bin/python

str = 'hello Python!'
strNull = 'string is Null'

if __debug__:
    if not str: raise AssertionError(strNull)
print str

if __debug__:
    print 'FileName '.ljust(30,'.'),(__name__)
    print 'FilePath '.ljust(30,'.'),(__file__)


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

Output:
hello Python!
FileName ..................... hello
FilePath ..................... C:/Python\hello.py

在例2中，var 'str'为空。因此，我们可以通过assert语句来避免用户走在错误程序前面。

示例2:

#!/usr/bin/python

str = ''
strNull = 'NULL String'

if __debug__:
    if not str: raise AssertionError(strNull)
print str

if __debug__:
    print 'FileName '.ljust(30,'.'),(__name__)
    print 'FilePath '.ljust(30,'.'),(__file__)


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

Output:
AssertionError: NULL String

当我们不想调试并意识到源代码中的断言问题时。禁用优化标志

python -O assertStatement.py 没有东西会被打印出来

无论如何，如果你处理的代码依赖assert来正常工作，那么添加以下代码将确保assert被启用:

try:
    assert False
    raise Exception('Python assertions are not working. This tool relies on Python assertions to do its job. Possible causes are running with the "-O" flag or running a precompiled (".pyo" or ".pyc") module.')
except AssertionError:
    pass

是否存在性能问题?

Please remember to "make it work first before you make it work fast". Very few percent of any program are usually relevant for its speed. You can always kick out or simplify an assert if it ever proves to be a performance problem -- and most of them never will. Be pragmatic: Assume you have a method that processes a non-empty list of tuples and the program logic will break if those tuples are not immutable. You should write: def mymethod(listOfTuples): assert(all(type(tp)==tuple for tp in listOfTuples)) This is probably fine if your lists tend to be ten entries long, but it can become a problem if they have a million entries. But rather than discarding this valuable check entirely you could simply downgrade it to def mymethod(listOfTuples): assert(type(listOfTuples[0])==tuple) # in fact _all_ must be tuples! which is cheap but will likely catch most of the actual program errors anyway.

除了其他答案之外，断言本身也会抛出异常，但仅抛出AssertionErrors。从实用主义的角度来看，当您需要对捕获的异常进行精细控制时，断言并不适合。

我补充说，我经常使用断言来指定属性，比如循环不变量或我的代码应该具有的逻辑属性，就像我在正式验证的软件中指定它们一样。

它们有两个目的，告诉读者，帮助我推理，并检查我在推理中没有犯错误。例如 :

k = 0
for i in range(n):
    assert k == i * (i + 1) // 2
    k += i 
    #do some things      

或者在更复杂的情况下:

def sorted(l):
   return all(l1 <= l2 for l1, l2 in zip(l, l[1:]))
 
def mergesort(l):
   if len(l) < 2: #python 3.10 will have match - case for this instead of checking length
      return l
   k = len(l // 2)
   l1 = mergesort(l[:k])
   l2 = mergesort(l[k:])
   assert sorted(l1) # here the asserts allow me to explicit what properties my code should have
   assert sorted(l2) # I expect them to be disabled in a production build
   return merge(l1, l2)

因为当python在优化模式下运行时，断言是禁用的，所以不要犹豫在它们中编写代价高昂的条件，特别是当它使您的代码更清晰，更不容易出现错误时

"assert"语句在编译优化时被删除。所以，是的，它们在性能和功能上都有差异。

在编译时请求优化时，当前代码生成器不会为assert语句生成代码。Python 3 Docs

如果您使用assert来实现应用程序功能，然后优化部署到生产环境，那么您将受到“但它在开发中有效”缺陷的困扰。

参见PYTHONOPTIMIZE和-O -OO