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Lambda Expressions

Lambda Expressions

Lambda Expressions are used when an operation only has to be performed once, meaning that there is no need for defining a function as it will not be used again. Lambda expressions also known as anonymous functions, as they are not named (defined).

Lambda functions can contain only one expression, so they are not best suited for functions with control-flow statements.

Syntax of Lambda Function

lambda arguments: expression

Lambda functions can have any number of arguments but only one expression.

Example code

# Lambda function to calculate square of a number
square = lambda x: x ** 2
print(square(3)) # Output: 9

# Traditional function to calculate square of a number
def square1(num):
  return num ** 2
print(square(5)) # Output: 25

In the above lambda example lambda x: x ** 2 yields an anonymous function object which can be associated with any name. So, we associated the function object with square and hence from now on we can call the square object like any traditional function. e.g. square(10)

Examples

Beginner

lambda_func = lambda x: x**2 # Function that takes an integer and returns its square
lambda_func(3) # Returns 9

Intermediate

lambda_func = lambda x: True if x**2 >= 10 else False  # Function that returns True if the square of x >= 10
lambda_func(3) # Returns False (3**2 = 9, which is < 10)
lambda_func(4) # Returns True (4**2 = 16, which is >= 10)

Complex

my_dict = {"A": 1, "B": 2, "C": 3}
sorted(my_dict, key=lambda x: my_dict[x]%3) # Returns ['C', 'A', 'B']  # sort dict by the values % 3 (remainders from division by 3)

Passing lambda as fuction parameter

def apply(x, y, fun):
  return fun(x, y)
  
res = apply(3, 5, lambda x, y: x + y)
print(res) # Output: 8

Use-case

Let's say you want to filter out odd numbers from a list. You could use a for loop:

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
filtered = []

for num in my_list:
     if num % 2 != 0:
         filtered.append(num)

print(filtered)      # Python 2: print filtered
# [1, 3, 5, 7, 9]

You could write this as a one-liner with list-comprehensions

filtered = [x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] if x % 2 != 0]

But you might be tempted to use the built-in filter function. Why? The first example is a bit to verbose, the one-liner can be harder to understand, where as filter offers the best of both words. What is more, the built-in functions are usually faster.

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

filtered = filter(lambda x: x % 2 != 0, my_list)

list(filtered)
# [1, 3, 5, 7, 9]

NOTE: in Python 3 built in function return generator objects, so you have to call list, while in Python 2 they return a list, tupleor string.

What happened? You told filter to take each element in my_list and apply the lambda expressions. The values that return False are filtered out.

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