itertools Advanced

Introduction

The itertools module provides advanced iterator functions. Functions like islice, takewhile, dropwhile, and groupby enable powerful data processing patterns.

islice - Slice Iterators

import itertools

numbers = range(20)

# Get first 5
first_five = list(itertools.islice(numbers, 5))
print(first_five)  # [0, 1, 2, 3, 4]

# Skip first 5, get next 5
next_five = list(itertools.islice(numbers, 5, 10))
print(next_five)  # [5, 6, 7, 8, 9]

# Get every other element
every_other = list(itertools.islice(numbers, None, None, 2))
print(every_other)  # [0, 2, 4, 6, 8, ...]

takewhile and dropwhile

import itertools

numbers = [1, 4, 6, 4, 1]

# Take while condition is true
taken = list(itertools.takewhile(lambda x: x < 5, numbers))
print(taken)  # [1, 4]

# Drop while condition is true, then yield rest
dropped = list(itertools.dropwhile(lambda x: x < 5, numbers))
print(dropped)  # [6, 4, 1]

groupby

import itertools

data = [
    {"name": "Alice", "dept": "A"},
    {"name": "Bob", "dept": "A"},
    {"name": "Charlie", "dept": "B"},
]

for dept, group in itertools.groupby(data, key=lambda x: x["dept"]):
    print(f"Department {dept}:")
    for item in group:
        print(f"  {item['name']}")

compress and filterfalse

import itertools

data = [1, 2, 3, 4, 5]
selectors = [True, False, True, False, True]

# Select items where selector is True
selected = list(itertools.compress(data, selectors))
print(selected)  # [1, 3, 5]

# Filter items where predicate is False
filtered = list(itertools.filterfalse(lambda x: x > 3, data))
print(filtered)  # [1, 2, 3]

pairwise and accumulate

import itertools

numbers = [1, 2, 3, 4, 5]

# Pairwise consecutive elements
pairs = list(itertools.pairwise(numbers))
print(pairs)  # [(1,2), (2,3), (3,4), (4,5)]

# Accumulate running totals
import operator
total = list(itertools.accumulate(numbers, operator.add))
print(total)  # [1, 3, 6, 10, 15]

Practice Problems

1. Use islice for pagination
2. Implement takewhile for filtering
3. Use groupby for grouping data
4. Create compress pattern
5. Use accumulate for running calculations