python advanced 3.the python std lib by example – algorithm
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THE PYTHON STD LIB BY EXAMPLE – ALGORITHM
JohnWednesday, April 12, 2023
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
FUNCTOOLS –TOOLS FOR MANIPULATING FUNCTIONS
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’)
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)
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
Example of rich comparision
We implement __eq__ and __gt__. Functools.total_ordering can implement other operator (<, <=, >= etc) base on eq and gt.
Function cmp_to_key: convert cmp to key for sorting• In Python 2.xx, cmp do comparion:
cmp(2,1) -> 1cmp(1,1) -> 0cmp(1,2) -> -1
• In python 3, cmp in sort function no longer supported.
• Functools.cmp_to_key convert cmp to key for sorting
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)
ITERTOOLS-ITERATOR FUNCTIONS
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.
Function chain(): Merge iterators
• Take serveral iterators as arguments and return a single iterator
Function imap: similar as map
• Imap accept a function, and multiple sequences, return a tuple.
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
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
Function starmap: unpack the input• Unpack the item as argument using the *
syntax
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 Iif I >100:
break
Print list 10,20,30 … 110
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.
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.
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
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.