Python advanced 3.the python std lib by example – algorithm
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This document introduces various algorithms and functions from the Python standard library that can be used to implement algorithms elegantly and concisely. It discusses functions from the functools module for manipulating functions like partial objects to provide default arguments, and the update_wrapper() function to copy attributes from the original function. It also covers iterator functions from the itertools module like chain() to merge iterators, imap() similar to map(), and count() to produce consecutive integers. Functions like cycle() to indefinitely repeat an iterator and dropwhile()/takewhile() to filter iterators based on conditions are also summarized.
![Function starmap:
• First, let us review the star * syntax in Python.
• Star * means unpack the sequence reference as
argument list.
>>> def foo(bar,lee):
print bar,lee
>>> a = [1,2]
>>> foo(a) # it is wrong, need two arguments
>>> foo(*a) # it is right. The list is unpack
>>>foo(1,2) # it is the same thing](https://image.slidesharecdn.com/pythonadvanced3-131221115010-phpapp01/85/Python-advanced-3-the-python-std-lib-by-example-algorithm-15-320.jpg)
![Function repeat: repeat same
value several time
• This example mean repeat ‘a’ 5 times.
>>> itertools.repeat(‘a’, 5)
It is similar as list [‘a’,’a’.’a’,’a’,’a’]
The return is a iterator but not list. So it use the
memo only when it is called.](https://image.slidesharecdn.com/pythonadvanced3-131221115010-phpapp01/85/Python-advanced-3-the-python-std-lib-by-example-algorithm-19-320.jpg)
Python advanced 3.the python std lib by example – algorithm
- 1. THE PYTHON STD LIB BY EXAMPLE – ALGORITHM John Saturday, December 21, 2013
- 2. Brief introduction • Python includes several modules which can implement algorithm elegantly and concisely. • It support uprely procedural, OOP and functional styles. • It includes: functools, partial , itertools, operator, contextlib etc
- 3. FUNCTOOLS –TOOLS FOR MANIPULATING FUNCTIONS
- 4. Partial Objects: provide default argument • The partial objects can provide or change the default value of the argument. • Example code (assume we have define function myfunc(a,b=1) : >>> import functools >>> p1 = functools.partial(myfunc,b=4) >>> p1(‘passing a’) >>> p2 = functools.partial(myfunc,’default a’,b=99) >>> p2() >>> p2(b=‘override b’)
- 5. Function update_wrapper() • The partial object does not have __name__ and __doc__ attributes by default. • Using update_wrapper(0 copies or added attributes from the original function. Format: >>> functools.update_wrapper(p1,myfunc)
- 6. the “rich comparison” First let us learn which is “rich comparion” in python. •Rich comparison method API (__lt__, __le__, __eq__, __gt__, __ge__) (Here le means less than, le means “less or equal”, gt means “greater than”, ge means “greater than or equal”) •These method API can help perform a single comparison operation and return a Boolean value
- 7. Example of rich comparision We implement __eq__ and __gt__. Functools.total_ordering can implement other operator (<, <=, >= etc) base on eq and gt.
- 8. Function cmp_to_key: convert cmp to key for sorting • In Python 2.xx, cmp do comparion: cmp(2,1) -> 1 cmp(1,1) -> 0 cmp(1,2) -> -1 • In python 3, cmp in sort function no longer supported. • Functools.cmp_to_key convert cmp to key for sorting
- 9. Quick example of cmp_to_key • Built-in funtion cmp need two argument. • Sorted function can use other option key=func. Sorted by key (only support this on Python 3.X)
- 11. Brief introduction • The itertools module includes a set of functions for working with sequence data sets (list, tuple,set,dict etc). • Iterator based code offer better memory comsumption.
- 12. Function chain(): Merge iterators • Take serveral iterators as arguments and return a single iterator
- 13. Function imap: similar as map • Imap accept a function, and multiple sequences, return a tuple.
- 14. Other function merge and split iterators • Function izip: like zip, but combine iterator and return iterator of tuple instead of list • Function islice: similar as slice • Function imap: similar as map • Function ifilter: similar as filter, filter those items test functions return True • Function ifilterfalse: filter those items where the test function return False
- 15. Function starmap: • First, let us review the star * syntax in Python. • Star * means unpack the sequence reference as argument list. >>> def foo(bar,lee): print bar,lee >>> a = [1,2] >>> foo(a) # it is wrong, need two arguments >>> foo(*a) # it is right. The list is unpack >>>foo(1,2) # it is the same thing
- 16. Function starmap: unpack the input • Unpack the item as argument using the * syntax
- 17. Function count(): iterator produce consecutive integers • Function count(start=0,step=1): user can pass the start and step value.No upper bound argument. >>> a = itertools.count(start=10,step=10) >>> for i in a: print I if I >100: break Print list 10,20,30 … 110
- 18. Function cycle: iterator do indefinitely repeats • It need remember the whole input, so it may consume quite a bit memo if input iterator is long.
- 19. Function repeat: repeat same value several time • This example mean repeat ‘a’ 5 times. >>> itertools.repeat(‘a’, 5) It is similar as list [‘a’,’a’.’a’,’a’,’a’] The return is a iterator but not list. So it use the memo only when it is called.
- 20. Function dropwhile and takewhile • Func dropwhile start output while condition become false for the first time • Example, 3rd element do not met x<1. So it return 3 to end of this list
- 21. Function dropwhile and takewhile • The opposite of dropwhile: stop output while condition become false for the first time • So all output items meet the condition function.