5 Powerful Python Features You Might Not Be Using (and How to Apply Them)

Python is a beautiful language: simple yet powerful. This article highlights five advanced features that many developers overlook and demonstrates how to use them effectively.

Lambda Functions

A lambda is a small anonymous function without a name, useful for simple expressions.

x = lambda a, b: a * b
print(x(5, 6))  # prints '30'

x = lambda a: a*3 + 3
print(x(3))  # prints '12'

Lambda functions can accept any number of arguments, but their body must be a single expression.

Map Function

map()

applies a given function to each element of an iterable (list, dict, etc.), providing a clean and readable way to transform data.

def square_it_func(a):
    return a * a
x = map(square_it_func, [1, 4, 7])
print(x)  # prints '[1, 16, 49]'

def multiplier_func(a, b):
    return a * b
x = map(multiplier_func, [1, 4, 7], [2, 5, 8])
print(x)  # prints '[2, 20, 56]'

Filter Function

filter()

works like map() but returns only the elements for which the provided function returns True.

# Our numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

# Function that filters out all numbers which are odd
def filter_odd_numbers(num):
    if num % 2 == 0:
        return True
    else:
        return False

filtered_numbers = filter(filter_odd_numbers, numbers)
print(filtered_numbers)  # filtered_numbers = [2, 4, 6, 8, 10, 12, 14]

The filter() function evaluates each element and keeps only those that satisfy the condition.

Itertools Module

The itertools module provides a collection of tools for handling iterators, enabling complex operations with concise code.

from itertools import *
# Easy joining of two lists into a list of tuples
for i in izip([1, 2, 3], ['a', 'b', 'c']):
    print(i)  # ('a', 1) ('b', 2) ('c', 3)

# The count() function returns an iterator that produces consecutive integers forever.
for i in izip(count(1), ['Bob', 'Emily', 'Joe']):
    print(i)  # (1, 'Bob') (2, 'Emily') (3, 'Joe')

# dropwhile() returns an iterator that skips elements until the condition becomes false.
def check_for_drop(x):
    print('Checking: ', x)
    return x > 5
for i in dropwhile(check_for_drop, [2, 4, 6, 8, 10, 12]):
    print('Result: ', i)

# groupby() groups consecutive identical elements.
a = sorted([1, 2, 1, 3, 2, 1, 2, 3, 4, 5])
for key, value in groupby(a):
    print(key, list(value))

Generator Functions

Generator functions behave like iterators and can be used in for loops, reducing memory usage compared to building full lists.

# (1) Using a for loop
numbers = []
for i in range(1000):
    numbers.append(i + 1)
total = sum(numbers)

# (2) Using a generator
def generate_numbers(n):
    num, numbers = 1, []
    while num < n:
        numbers.append(num)
        num += 1
    return numbers
total = sum(generate_numbers(1000))

# (3) range() vs xrange()
total = sum(range(1000 + 1))
# In Python 2, xrange() would generate numbers lazily.

Generators are ideal when processing very large sequences or when memory is limited, such as on mobile devices or edge computing platforms.