Module 5
5.3 Anonymous Functions
Anonymous functions do not have a function name. They are not declared in the traditional way using (def) keyword but instead (lambda) keyword is used.
lambda arguments: expression
This small anonymous function is called a lambda function. It can take any number of arguments, but can have only one expression. Executing a lambda function evaluates its expression and then automatically returns its result. So there’s always an implicit return statement. It is also known as single expression functions.
Example
>>> sum = lambda arg1, arg2: arg1 + arg2
>>> sum(1, 2)
Some use cases of Lambda function
>>> (lambda x, y: x + y)(5, 3)
8
>>> sorted(range(-5, 6), key=lambda x: x ** 2)
[0, -1, 1, -2, 2, -3, 3, -4, 4, -5, 5]
Lambdas are also used as lexical closures, a function that remembers the values from the enclosing lexical scope even when the program flow is no longer in that scope.
>>> def make_adder(n):
... return lambda x: x + n
>>> plus_3 = make_adder(3)
>>> plus_5 = make_adder(5)
>>> plus_3(4)
7
>>> plus_5(4)
9
In the above example the x + n lambda can still access the value of n even though it was defined in the make_adder function (the enclosing scope).
Map, Filter and Reduce
Lambda functions can be used along with built-in functions like filter(), map() and reduce(). These are three functions which facilitate a functional approach to programming.
Map
Map applies a function to all the items in an input_list.
map(function_to_apply, list_of_inputs)
Example
(Without using map)
>>> items = [1, 2, 3, 4, 5]
>>> squared = []
>>> for i in items:
squared.append(i**2)
(Using map)
>>> items = [1, 2, 3, 4, 5]
>>> squared = list(map(lambda x: x**2, items))
Filter
Filter creates a list of elements for which a function returns true.
number_list = range(-5, 5)
less_than_zero = list(filter(lambda x: x < 0, number_list))
print(less_than_zero)
The filter resembles a for loop but it is a builtin function and faster.
Reduce
Reduce is a really useful function for performing some computation on a list and returning the result. It applies a rolling computation to sequential pairs of values in a list.
Example
(Without using Reduce)
>>> product = 1
>>> list = [1, 2, 3, 4]
>>> for num in list:
product = product * num
(Using Reduce)
>>> from functools import reduce
>>> product = reduce((lambda x, y: x * y), [1, 2, 3, 4])