Python provides powerful built-in functions like
map(), filter(), and reduce() to work with data more efficiently. They are especially useful with lambda functions.
1) Introduction
These functions belong to the concept of functional programming.
- map() → Applies a function to every element of an iterable.
- filter() → Selects elements based on a condition.
- reduce() → Reduces a sequence to a single value by applying a function cumulatively.
2) The map() Function
# Syntax
map(function, iterable)
# Example: Double each number
nums = [1, 2, 3, 4, 5]
doubled = list(map(lambda x: x * 2, nums))
print(doubled) # [2, 4, 6, 8, 10]
Use case: Transforming data without writing explicit loops.
3) The filter() Function
# Syntax
filter(function, iterable)
# Example: Filter even numbers
nums = [1, 2, 3, 4, 5]
evens = list(filter(lambda x: x % 2 == 0, nums))
print(evens) # [2, 4]
Use case: Extracting only elements that satisfy a condition.
4) The reduce() Function
reduce() is not a built-in like map() and filter().
It is available in the functools module.
from functools import reduce
# Syntax
reduce(function, iterable)
# Example: Multiply all numbers
nums = [1, 2, 3, 4, 5]
product = reduce(lambda x, y: x * y, nums)
print(product) # 120
Use case: Cumulative computations like sum, product, max, etc.
5) Comparison with Loops
Everything done with map(), filter(), and reduce() can also be achieved with for loops.
However, these functions make the code more concise and functional.
# Using a for loop
nums = [1, 2, 3, 4, 5]
doubled = []
for n in nums:
doubled.append(n * 2)
print(doubled) # [2, 4, 6, 8, 10]
6) Common Mistakes
- Forgetting to wrap results with
list()in Python 3 (map/filter return iterators). - Using
reduce()without importing it fromfunctools. - Writing overly complex lambdas — better to use
deffor clarity.
💡 Try It Yourself
- Use
map()to convert a list of strings to uppercase. - Use
filter()to extract numbers divisible by 3 from a list. - Use
reduce()to find the maximum number in a list.
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