Functools Module help in managing the applications and different functions in Python language and you can learn it easily. Find out how.
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'
partial(), as the name suggests, allows a partial evaluation of a function. Let’s look at following example:
In : from functools import partial
In : def f(a, b, c, x):
…: return 1000a + 100b + 10*c + x
In : g = partial(f, 1, 1, 1)
In : 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.
Functools Module: cmp_to_key
Python changed its 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:
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.
Functools Module: 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.
GoalKicker.com – Python® Notes for Professionals 301
@lru_cache(maxsize=None) # Boundless cache
if n < 2:
return fibonacci(n-1) + fibonacci(n-2)
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:
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.
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.
…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.
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
return reduce(lambda a, b: (a*b), range(1, n+1))
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