# Functools Module
partial function creates partial function application from another function. It is used to bind values to some of the function's arguments (or keyword arguments) and produce a callable without the already defined arguments.
>>> from functools import partial >>> unhex = partial(int, base=16) >>> unhex.__doc__ = 'Convert base16 string to int' >>> unhex('ca11ab1e') 3390155550
partial(), as the name suggests, allows a partial evaluation of a function.
Let's look at at following example:
In : from functools import partial In : def f(a, b, c, x): ...: return 1000*a + 100*b + 10*c + x ...: In : g = partial(f, 1, 1, 1) In : print g(2) 1112
g is created,
f, which takes four arguments(
a, b, c, x), is also partially evaluated for the first three arguments,
a, b, c,. Evaluation of
f is completed when
g is called,
g(2), which passes the fourth argument to
One way to think of
partial is a shift register; pushing in one argument at the time into some function.
partial comes handy for cases where data is coming in as stream and we cannot pass more than one argument.
@lru_cache decorator can be used wrap an expensive, computationally-intensive function with a Least Recently Used cache. This allows function calls to be memoized, so that future calls with the same parameters can return instantly instead of having to be recomputed.
@lru_cache(maxsize=None) # Boundless cache def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2) >>> fibonacci(15)
In the example above, the value of
fibonacci(3) is only calculated once, whereas if
fibonacci didn't have an LRU cache,
fibonacci(3) would have been computed upwards of 230 times. Hence,
@lru_cache is especially great for recursive functions or dynamic programming, where an expensive function could be called multiple times with the same exact parameters.
@lru_cache has two arguments
maxsize: Number of calls to save. When the number of unique calls exceeds
maxsize, the LRU cache will remove the least recently used calls.
typed(added in 3.3): Flag for determining if equivalent arguments of different types belong to different cache records (i.e. if
3count as different arguments)
We can see cache stats too:
>>> fib.cache_info() CacheInfo(hits=13, misses=16, maxsize=None, currsize=16)
@lru_cache uses dictionaries to cache results, all parameters for the function must be hashable for the cache to work.
Official Python docs for
@lru_cache was added in 3.2.
Python changed it's sorting methods to accept a key function. Those functions take a value and return a key which is used to sort the arrays.
Old comparison functions used to take two values and return -1, 0 or +1 if the first argument is small, equal or greater than the second argument respectively. This is incompatible to the new key-function.
functools.cmp_to_key comes in:
>>> import functools >>> import locale >>> sorted(["A", "S", "F", "D"], key=functools.cmp_to_key(locale.strcoll)) ['A', 'D', 'F', 'S']
Example taken and adapted from the Python Standard Library Documentation.
When we want to create an orderable class, normally we need to define the methods
total_ordering decorator, applied to a class, permits the definition of
__eq__() and only one between
__ge__(), and still allow all the ordering operations on the class.
@total_ordering class Employee: ... def __eq__(self, other): return ((self.surname, self.name) == (other.surname, other.name)) def __lt__(self, other): return ((self.surname, self.name) < (other.surname, other.name))
The decorator uses a composition of the provided methods and algebraic operations to derive the other comparison methods. For example if we defined
__eq()__ and we want to derive
__gt__(), we can simply check
not __lt__() and not __eq()__.
total_ordering function is only available since Python 2.7.
In Python 3.x, the
reduce function already explained here has been removed from the built-ins and must now be imported from
from functools import reduce def factorial(n): return reduce(lambda a, b: (a*b), range(1, n+1))