Map, Filter and Reduce

Learn Python’s map, filter, and reduce functions with examples. Understand how to transform, filter, and aggregate data using functional programming.

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Map, Filter and Reduce

In Python, map, filter, and reduce are powerful higher-order functions that apply other functions to sequences or iterables. They allow concise and functional-style processing of data.

map()

The map() function applies a specified function to each item in an iterable (e.g., list, tuple) and returns a new iterable with the results.

Syntax:

map(function, iterable)
  • function: A function to apply to each element.
  • iterable: The sequence of elements to process.

Example:

# List of numbers
numbers = [1, 2, 3, 4, 5]
 
# Double each number using `map`
doubled = map(lambda x: x * 2, numbers)
 
print(list(doubled))  # Output: [2, 4, 6, 8, 10]

filter()

The filter() function filters elements from an iterable based on a predicate (a function that returns True or False). It returns an iterable containing only the elements that satisfy the condition.

Syntax:

filter(predicate, iterable)
  • predicate: A function that evaluates to True or False.
  • iterable: The sequence of elements to filter.

Example:

# List of numbers
numbers = [1, 2, 3, 4, 5]
 
# Get only even numbers
evens = filter(lambda x: x % 2 == 0, numbers)
 
print(list(evens))  # Output: [2, 4]

Key Notes:

  • If the predicate function returns False, the element is excluded.
  • Useful for filtering based on conditions or rules.

reduce()

The reduce() function reduces a sequence of elements into a single value by repeatedly applying a specified function. It is part of the functools module.

Syntax:

from functools import reduce
reduce(function, iterable[, initializer])
  • function: A function that takes two arguments and reduces them to one.
  • iterable: The sequence of elements to process.
  • initializer (optional): A starting value for the reduction.

Example:

from functools import reduce
 
# List of numbers
numbers = [1, 2, 3, 4, 5]
 
# Calculate the sum of the numbers
total = reduce(lambda x, y: x + y, numbers)
 
print(total)  # Output: 15

Key Notes:

  • The function is applied cumulatively:

    • First step: 1 + 2 = 3
    • Second step: 3 + 3 = 6
    • Third step: 6 + 4 = 10
    • Fourth step: 10 + 5 = 15

  • Adding an initializer can specify a starting point:

    result = reduce(lambda x, y: x + y, numbers, 10)
print(result)  # Output: 25 (10 + 15)

Example Combining All Three

from functools import reduce
 
# List of numbers
numbers = [1, 2, 3, 4, 5]
 
# Step 1: Double each number
doubled = map(lambda x: x * 2, numbers)
 
# Step 2: Filter out numbers greater than 5
filtered = filter(lambda x: x > 5, doubled)
 
# Step 3: Sum up the filtered numbers
result = reduce(lambda x, y: x + y, filtered)
 
print(result)  # Output: 18 (6 + 8 + 10)

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