Lambda Functions and Map, Filter, Reduce

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Lambda Functions and Map, Filter, Reduce in Python

Python provides anonymous functions (lambda) and higher-order functions like map, filter, and reduce to write concise and functional-style code.

1. Lambda Functions

• Anonymous functions created using the lambda keyword.

• Can have any number of arguments but only one expression.

• Useful for short, simple functions.

Syntax:

lambda arguments: expression

Examples:

# Basic lambda
square = lambda x: x * x
print(square(5))  # 25

# Lambda with multiple arguments
add = lambda a, b: a + b
print(add(3, 4))  # 7

# Using lambda directly
print((lambda x, y: x * y)(2, 3))  # 6

2. Map Function

• map(function, iterable) applies a function to every element of an iterable.

• Returns a map object (can be converted to list, tuple, etc.).

Example with lambda:

numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x * x, numbers))
print(squared)  # [1, 4, 9, 16, 25]

Example with a regular function:

def double(x):
    return x * 2

result = list(map(double, numbers))
print(result)  # [2, 4, 6, 8, 10]

3. Filter Function

• filter(function, iterable) selects elements where the function returns True.

• Returns a filter object.

Example with lambda:

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # [2, 4, 6]

Example with a regular function:

def is_odd(x):
    return x % 2 != 0

odd_numbers = list(filter(is_odd, numbers))
print(odd_numbers)  # [1, 3, 5]

4. Reduce Function

• reduce(function, iterable) applies a function cumulatively to the items of an iterable.

• Returns a single value.

• Requires importing from functools.

Example with lambda:

from functools import reduce

numbers = [1, 2, 3, 4, 5]
sum_total = reduce(lambda a, b: a + b, numbers)
print(sum_total)  # 15

product = reduce(lambda a, b: a * b, numbers)
print(product)  # 120

Example with regular function:

def multiply(a, b):
    return a * b

product = reduce(multiply, numbers)
print(product)  # 120

5. Key Points

• Lambda functions are best for small, simple operations.

• Map transforms each element of a sequence.

• Filter selects elements based on a condition.

• Reduce aggregates elements into a single result.

• Combining these allows concise, functional-style programming in Python.