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Lambda expressions in Python

Lambda expressions are one of the most useful tools in Python. lambda expressions allow us to create "anonymous" functions. This basically means we can define functions without using def.

In Python, an anonymous function is a function that is defined without a name. The def keyword is used to define a normal function in Python. But the lambda keyword is used to define an anonymous function in Python.

Python Lambda Function Syntax:

lambda argument(s): expression

  • Lambda function can have any number of arguments but only one expression, which is evaluated and returned.

  • One is free to use lambda functions wherever function objects are required.

  • Function objects returned by running lambda expressions work exactly the same as those created and assigned by def.

The lambda's body is similar to what we would put in a def body's return statement. We simply type the result as an expression instead of explicitly returning it.

Let's explain a lambda expression by starting from a normal function:

def double_value(x):
    result = x * 2
    return result
double_value(2)
output: 4

Continuing the illustration:

def double_value(x):
    return x * 2
double_value(2)
output: 4

We can write this function in one line (although it is a bad style to do that)

def double_value(x): return x * 2
double_value(2)
output: 4

This is the form of a function that a lambda expression uses. A lambda expression can then be written as:

lambda x: x * 2
output: <function __main__.<lambda>(x)>

In the example above, the expression is composed of:

  • The keyword: lambda

  • A variable: x

  • A body: x * 2


Note how we get a function back. We can assign this function to a variable:

double_value_with_lambda= lambda x: x * 2

and we can call the lambda function using this variable

double_value(3)

double_value_with_lambda(3)
output: 4

Let's see other examples:

Example 1: Add two numbers

In this example, we have two arguments: x, y

add_numbers = lambda x, y: x+y
add_numbers(3, 5)
output: 8

Example 2 Lambda with lists:

list_num = [1,25,14,7,33,96,47,78,19]

The filter() function in Python takes in a function and a list as arguments.

The function is called with all the items in the list and a new list is returned which contains items for which the function evaluates to True.

list(filter(lambda x: x > 50, list_num))
output: [96, 78]

Example 3 Lambda with Series:


import pandas as pd
df = pd.DataFrame({
    'Company': ['Microsoft','Google','Youtube','Apple'],
    'Year': [1975, 1998, 2005, 1976],
})
df['duration'] = df['Year'].apply(lambda x: 2022-x)

df
output: Company   Year   duration
      0 Microsoft 1975      47 
      1 Google    1998      24
      2 Youtube   2005      17
      3 Apple     1976      46


lambda expressions really shine when used in conjunction with map(), filter(), and reduce(), which each function of them needs a separated article.




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