Comprehensions & generators
List/dict/set comprehensions, generator expressions, and lazy iteration.
Generate Data with Python Comprehensions and Generators
Shows list, dict compregensions and generator expressions plus a Fibonacci generator to produce data lazily.
# Data generation helpers using comprehensions and generators
from itertools import islice
def fibonacci(limit):
"""Generate Fibonacci numbers up to a limit."""
a, b = 0, 1
while a <= limit:
yield a
a, b = b, a + b
def main():
# List comprehension: squares of even numbers
square…How to Accumulate Values with a Generator in Python
This generator yields the running total of an iterable's elements, producing a cumulative sum with each step.
def accum(iterable):
total = 0
for item in iterable:
total += item
yield total
# Demo
if __name__ == "__main__":
data = [1, 2, 3, 4, 5]
print(list(accum(data))) # [1, 3, 6, 10, 15]
# Also works with any iterable, e.g., range
print(list(accum(range(1, 6)))) # [1, 3, 6, 10, 15]How to Build a Backpressure Generator Pause Producer Demo in Python
Demonstrates a producer–consumer pattern with a fixed-size buffer that pauses production when full, simulating backpressure.
import time
import collections
def producer(buffer, max_size, items):
"""Adds items to the buffer until full, then pauses."""
for item in items:
while len(buffer) >= max_size:
print(f"Buffer full ({len(buffer)}/{max_size}) — producer paused")
time.sleep(0.1)
buffer.appe…How to Build a Sliding Window Generator in Python
Create a generator that yields fixed-size overlapping slices of a sequence, useful for efficient windowed iteration.
def sliding_window(sequence, size):
for i in range(len(sequence) - size + 1):
yield sequence[i:i + size]
if __name__ == "__main__":
data = [1, 2, 3, 4, 5]
n = 3
for window in sliding_window(data, n):
print(window)How to Close a Generator and Handle GeneratorExit in Python
This Python code demonstrates how to explicitly close a generator using the close() method and handle the GeneratorExit exception through a finally block to run cleanup logic.
def countdown(n):
try:
while n > 0:
yield n
n -= 1
finally:
print(f"Generator closed after countdown completed")
if __name__ == "__main__":
gen = countdown(5)
print(next(gen))
print(next(gen))
gen.close()
print("Generator closed explicitly")How to Compress a Generator with a Boolean Mask in Python
Filters items from a generator based on a parallel boolean mask, yielding only the items where the mask is True.
def compress(generator, mask):
for item, keep in zip(generator, mask):
if keep:
yield item
if __name__ == "__main__":
data = [1, 2, 3, 4, 5]
mask = [True, False, True, False, True]
result = list(compress(iter(data), mask))
print(result)How to Create a Generator Context Manager in Python with contextlib
Create a custom context manager with the @contextlib.contextmanager decorator to manage resources using a generator function.
import contextlib
@contextlib.contextmanager
def temporary_directory():
"""Yield a string and clean up after the block exits."""
print("Creating temp directory...")
dir_name = "/tmp/example"
try:
yield dir_name
finally:
print(f"Removing {dir_name}...")
if __name__ == "__main__":
…How to Create a Line-Numbered Generator with enumerate start in Python
This Python code defines a generator that yields lines prefixed with their index, using enumerate's start parameter to offset numbering.
def line_numbered_lines(lines, start=1):
for idx, line in enumerate(lines, start):
yield f"{idx:3} {line}"
if __name__ == "__main__":
sample = ["first line", "second", "third"]
for numbered in line_numbered_lines(sample, start=10):
print(numbered)How to Create a Pairwise Generator with zip and tee in Python
Build a memory-efficient generator that yields successive overlapping pairs from any iterable using zip and tee.
from itertools import tee
def pairwise(iterable):
"""Yield successive overlapping pairs from iterable."""
a, b = tee(iterable)
next(b, None)
return zip(a, b)
if __name__ == "__main__":
values = [1, 2, 3, 4, 5]
print(list(pairwise(values)))
print(list(pairwise("hello")))How to Create an Infinite Arithmetic Sequence Generator in Python
Build a memory-efficient generator that yields an infinite arithmetic progression and extract the first N values with list comprehension.
"""Count generator infinite arithmetic progression"""
def arithmetic_counter(start=0, step=1):
"""Generate an infinite arithmetic sequence."""
current = start
while True:
yield current
current += step
if __name__ == "__main__":
counter = arithmetic_counter(1, 3)
result = [next(c…How to Delegate Iteration to a Subgenerator with yield from in Python
Use yield from to delegate iteration from one generator to a subgenerator, flattening nested generator output into a single sequence.
def subgenerator():
yield "first"
yield "second"
yield "third"
def delegate():
yield "before delegation"
yield from subgenerator()
yield "after delegation"
if __name__ == "__main__":
for item in delegate():
print(item)How to Filter Data with Predicates in Python
This helper filters a list with a predicate using a list comprehension, plus a lazy generator version that yields matches one by one.
def filter_data(data, predicate):
"""Return a list containing only items that pass the predicate."""
return [item for item in data if predicate(item)]
def filter_data_lazy(data, predicate):
"""Generator version: yields items that pass the predicate one by one."""
for item in data:
if predicat…How to Generate Cartesian Product Combinations in Python
Use itertools.product to generate every combination across multiple iterables, a pattern common for product variant generation.
from itertools import product
def generate_cartesian_combinations(*iterables):
"""Generate all Cartesian product combinations of given iterables."""
return list(product(*iterables))
if __name__ == "__main__":
colors = ["red", "green", "blue"]
sizes = ["S", "M", "L"]
styles = ["t-shirt", "hoodie"]…How to Generate Combinations with Replacement in Python
Generate all r-length combinations with repetition from a list using the standard library itertools.combinations_with_replacement function.
from itertools import combinations_with_replacement
items = ['A', 'B', 'C']
r = 2
combos = list(combinations_with_replacement(items, r))
for combo in combos:
print(combo)
if __name__ == "__main__":
print(f"Total combinations with replacement: {len(combos)}")How to Generate Fibonacci Numbers in Python Without Recursion
Build an efficient infinite Fibonacci sequence using a generator function with O(1) memory and no recursion overhead.
def fib(n):
a, b = 0, 1
for _ in range(n):
yield a
a, b = b, a + b
if __name__ == "__main__":
count = 10
result = list(fib(count))
print(result)How to Generate Permutations of Length r in Python
Generate all ordered arrangements of length r from a given list of elements using itertools.permutations.
from itertools import permutations
def generate_permutations(elements, r):
"""Generate all r-length permutations of the given elements."""
return list(permutations(elements, r))
if __name__ == "__main__":
elements = ['A', 'B', 'C']
r = 2
result = generate_permutations(elements, r)
print(f"Ele…How to Generate Primes with a Generator in Python
Generate prime numbers up to a limit using the Sieve of Eratosthenes wrapped in a generator expression for lazy evaluation.
def prime_generator(limit):
sieve = [True] * (limit + 1)
sieve[0] = sieve[1] = False
for i in range(2, int(limit ** 0.5) + 1):
if sieve[i]:
for j in range(i * i, limit + 1, i):
sieve[j] = False
return (num for num, is_prime in enumerate(sieve) if is_prime)
if __n…How to Generate a Collatz Sequence in Python
Generate the Collatz sequence for a given positive integer by repeatedly applying the 3n+1 rule until reaching 1.
def collatz_sequence(n):
if n <= 0:
raise ValueError("n must be a positive integer")
sequence = [n]
while n != 1:
if n % 2 == 0:
n = n // 2
else:
n = 3 * n + 1
sequence.append(n)
return sequence
if __name__ == "__main__":
start = 7
result…How to Group Data in Python with defaultdict and Comprehensions
Group a list of items by a computed key using a defaultdict-based generator helper and an alternative dictionary comprehension approach.
from collections import defaultdict
def group_by(data, key_func):
"""Group items in data by the value returned by key_func."""
result = defaultdict(list)
for item in data:
result[key_func(item)].append(item)
return dict(result)
def group_by_comprehension(data, key_func):
"""Same grouping …How to Implement takewhile Generator in Python
A generator that yields items from an iterable until a condition fails, like itertools.takewhile.
def takewhile(predicate, iterable):
for item in iterable:
if not predicate(item):
break
yield item
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5, 1, 2, 3]
result = list(takewhile(lambda x: x < 4, numbers))
print(result)How to Implement the Iterator Protocol in Python
A manual iterator class using __iter__ and __next__, compared with an equivalent generator using yield.
class ManualCounter:
def __init__(self, limit):
self.limit = limit
self.current = 0
def __iter__(self):
return self
def __next__(self):
if self.current >= self.limit:
raise StopIteration
value = self.current
self.current += 1
return valu…How to Lazily Transform Items in Python with a Generator
Map a transform function over an iterable lazily with a generator so items are processed on demand, not up front.
def lazy_map(items, transform):
for item in items:
yield transform(item)
def double(x):
return x * 2
def upper(s):
return s.upper()
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
doubled = lazy_map(numbers, double)
print("Doubled numbers:", end=" ")
for value in doubled:
…How to Merge Multiple Iterables with a Generator in Python
This code defines a generator function that 'chains' or merges multiple iterables into a single iterator, which is then converted to a list.
def chain(*iterables):
for iterable in iterables:
yield from iterable
def main():
list1 = [1, 2, 3]
tuple1 = (4, 5)
set1 = {6, 7}
string1 = "89"
result = list(chain(list1, tuple1, set1, string1))
print(result)
if __name__ == "__main__":
main()How to Parse CSV Rows as Generator Dicts in Python
Reads a CSV file and yields each row as a dictionary one at a time using a generator, so the file is processed lazily.
import csv
from pathlib import Path
def csv_to_dicts(filepath):
with open(filepath, mode="r", newline="", encoding="utf-8") as file:
reader = csv.DictReader(file)
for row in reader:
yield row
if __name__ == "__main__":
sample_csv = Path("sample_data.csv")
sample_csv.write_text…Browse by section
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Comprehensions & generators — Python code examples
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