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.
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))
@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.