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THE PYTHON STD LIB BY
EXAMPLE – ALGORITHM
John
Saturday, December 21, 2013
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) -> 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
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)
ITERTOOLSITERATOR
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 I
if 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.

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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.