Your SlideShare is downloading. ×
Python advanced 1.handle error, generator, decorator and decriptor
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Python advanced 1.handle error, generator, decorator and decriptor

255
views

Published on

Published in: Technology

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
255
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
24
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. HANDLE ERROR, GENERATOR AND DECORATOR John Saturday, December 21, 2013
  • 2. HANDLE ANY UNEXPECTED ERROR
  • 3. Brief introduction • Python provide 2 ways to handle unexpected error: exception and assert. • Exception handling: is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions. • The exceptions are defined in the built-in class exceptions • For example: If divided by 0, we want to raise an exception
  • 4. Built-in exceptions
  • 5. Warnings • It is defined on the warnings module
  • 6. Raising Exceptions • The raise statement allows the programmer to force a specified exception to occur raise NameError('HiThere') • Raise statement is to raise an exceptions, tryexception-finally clause is to catch an exceptions and decide how to do.
  • 7. Try …except…finally structure • • • • First, the try clause(print 100/0) is executed If no exception occurs, the except clause is skipped Otherwise, the rest of the try clause is skipped. Go to the line its type matches the exception name(ZeroDivisionError). Clean-up info in the finally sentence. It executed under all conditions
  • 8. Write User-defined Exceptions >>> class MyError(Exception): ... def __init__(self, value): ... self.value = value ... def __str__(self): ... return repr(self.value) • Define user-defined exception MyError. • Raise an exception when x == 0. Also write the try-except-finally clause • When call f(0,100), the exception is raised and catched.
  • 9. Brief introduction of assert • The assert clause is used on situation or condition that should never happen. For example: assert 1>0 • “assert” statement is removed when the compilation is optimized (-O and -OO option, it is because __debug__ change to False when -O or -OO option are added). • So It is a convenient way to insert debugging assertion into a program
  • 10. Quick example • We can see assert is ignored when add -O option
  • 11. GENERATOR
  • 12. Brief introduction • Generator s are a simple and powerful tool for create iterators. • Use yield statement instead of return to return data • the __iter__() and next() methods are created automatically. The local and execution state are saved automatically. • When generator terminate, it raise StopIteration
  • 13. Quick example • When you call the generator function, the co de does not run. It just return the generator object.
  • 14. The difference between generator and sequence type >>> mylist = [x*x for x in range(3)] >>> mygenerator = (x*x for x in range(3)) •Both mylist and mygenerator are iterable •But you can only read generator once. •Generator do not store all the values in memory, they generate the values on the fly.
  • 15. DECORATOR
  • 16. Brief introduction • Functions are objects in python. • We can define other function inside function definition. • We can pass a function as argument of other function.
  • 17. Quick example • benchmark function accept func as input argument. • We can see @benchmark equal to apply benchmark function on f f = benchmark(f) • This is the typical usage of decorator: Use func as input argument. define wrapper function inside function definition
  • 18. Official document • PEP - 318 Decorators for Functions and Methods
  • 19. DESCRIPTOR
  • 20. Brief introduction • A descriptor is an object attribute with “binding behavior”. • If any of __get__(), __set__() and __delete__() are defined for an object, it is said to be a descriptor.
  • 21. Descriptor protocol • If an object defines both __get__() and __set__(), it is considered a data descriptor. • Descriptors that only define __get__() are called non-data descriptors • descriptors are invoked by the __getattribute__() method • overriding __getattribute__() prevents automatic descriptor calls
  • 22. Descriptor example • Define __set__ and __get__ method.
  • 23. Implement the property() method • Calling property() is a succinct way of building a data descriptor
  • 24. Let us write the similar property() descriptor
  • 25. Function are non-data descriptor • All functions include __get__() method for binding methods.
  • 26. Non-data descriptor staticmethod • The pure python verson of static method should be like: Use static method
  • 27. Non-data descriptor classmethod • Pure python version of classmethod looks like: