# Functools Module

# partial

The 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')

partial(), as the name suggests, allows a partial evaluation of a function. Let's look at at following example:

In [2]: from functools import partial

In [3]: def f(a, b, c, x):
   ...:     return 1000*a + 100*b + 10*c + x

In [4]: g = partial(f, 1, 1, 1)

In [5]: print g(2)

When 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 f.

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

The @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 3.0 and 3 count as different arguments)

We can see cache stats too:

>>> fib.cache_info()
CacheInfo(hits=13, misses=16, maxsize=None, currsize=16)

NOTE: Since @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. @lru_cache was added in 3.2.

# cmp_to_key

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.

That's where 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.

# total_ordering

When we want to create an orderable class, normally we need to define the methods __eq()__, __lt__(), __le__(), __gt__() and __ge__().

The total_ordering decorator, applied to a class, permits the definition of __eq__() and only one between __lt__(), __le__(), __gt__() and __ge__(), and still allow all the ordering operations on the class.

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 __lt__() and __eq()__ and we want to derive __gt__(), we can simply check not __lt__() and not __eq()__.

Note: The total_ordering function is only available since Python 2.7.

# reduce

In Python 3.x, the reduce function already explained here has been removed from the built-ins and must now be imported from functools.

from functools import reduce
def factorial(n):
    return reduce(lambda a, b: (a*b), range(1, n+1))