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

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

  • HANDLE ERROR, GENERATOR AND DECORATOR John Saturday, December 21, 2013
  • HANDLE ANY UNEXPECTED ERROR
  • 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
  • Built-in exceptions
  • Warnings • It is defined on the warnings module
  • 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.
  • 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
  • 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.
  • 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
  • Quick example • We can see assert is ignored when add -O option
  • GENERATOR
  • 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
  • Quick example • When you call the generator function, the co de does not run. It just return the generator object.
  • 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.
  • DECORATOR
  • 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.
  • 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
  • Official document • PEP - 318 Decorators for Functions and Methods
  • DESCRIPTOR
  • 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.
  • 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
  • Descriptor example • Define __set__ and __get__ method.
  • Implement the property() method • Calling property() is a succinct way of building a data descriptor
  • Let us write the similar property() descriptor
  • Function are non-data descriptor • All functions include __get__() method for binding methods.
  • Non-data descriptor staticmethod • The pure python verson of static method should be like: Use static method
  • Non-data descriptor classmethod • Pure python version of classmethod looks like: