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How to Cycle Through a List Infinitely with itertools
This code uses itertools.cycle to create an infinite iterator over a list and returns the first n items from that cycle.
from itertools import cycle
def demonstrate_cycle(items, cycles=3):
"""
Cycle through a list infinitely using itertools.cycle.
Returns the first n items from the infinite cycle.
"""
cycled = cycle(items)
result = [next(cycled) for _ in range(len(items) * cycles)]
return result
if __name__…How to Convert a List to an Iterator in Python with iter()
This code converts a list into an iterator using the built-in iter() function and retrieves items sequentially with next(), handling exhaustion with StopIteration.
def main():
# Original list
fruits = ["apple", "banana", "cherry"]
# Convert the list to an iterator using iter()
fruit_iterator = iter(fruits)
# Retrieve items one at a time with next()
print(next(fruit_iterator)) # apple
print(next(fruit_iterator)) # banana
print(next(fruit_iterat…How to Create an Iterator Class with Dunder Methods in Python
A minimal Counter class implementing __iter__ and __next__ to act as a self-iterating iterator, yielding numbers from start to end-1.
class Counter:
def __init__(self, start=0, end=5):
self.current = start
self.end = end
def __iter__(self):
return self
def __next__(self):
if self.current >= self.end:
raise StopIteration
value = self.current
self.current += 1
return val…How to Use Python's next() Builtin with a Default Sentinel Value
A wrapper function that returns the next item from an iterator, or a default sentinel value when the iterator is exhausted.
def get_next_or_default(iterator, default=None):
"""Return the next item from an iterator, or default if exhausted."""
return next(iterator, default)
if __name__ == "__main__":
fruits = iter(["apple", "banana", "cherry"])
print(get_next_or_default(fruits)) # apple
print(get_next_or…How to Create an Iterable Class with __iter__ and __next__ in Python
Build custom iterable classes in Python by implementing the __iter__ and __next__ dunder methods to yield items on demand.
class EvenNumbers:
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
result = self.current
self.current += 2
return resul…How to Implement Iterator Protocol on a Custom Class in Python
Create a custom iterable class by defining the __iter__ and __next__ methods, enabling use in for loops and list conversions.
class Countdown:
"""Iterator that counts down from start to 0."""
def __init__(self, start):
self.start = start
self.current = start
def __iter__(self):
return self
def __next__(self):
if self.current < 0:
raise StopIteration
value = self.current
…Chunk an Iterable into Batches with a Generator in Python
Yield fixed-size batches from any iterable lazily using itertools.islice inside a generator function.
from itertools import islice
def chunked(iterable, size):
iterator = iter(iterable)
while True:
batch = list(islice(iterator, size))
if not batch:
break
yield batch
if __name__ == "__main__":
data = range(10)
for batch in chunked(data, 3):
print(batch)Drop n items then yield rest generator
A generator that skips the first n items of an iterable and then yields the remaining items one by one.
def drop(n, items):
"""Yield every item except the first n from items."""
it = iter(items)
for _ in range(n):
next(it, None) # skip first n items
yield from it
if __name__ == "__main__":
numbers = [10, 20, 30, 40, 50]
result = list(drop(2, 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 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…How to Repeat a Generator Cycle Single Value in Python
Build a generator that repeats a single value across multiple cycles, each cycle adding an extra repetition to mark its completion.
def repeat_with_cycle(value, cycle_limit, repetitions):
"""
Repeats a single value until reaching a cycle limit,
then yields the value one more time to demonstrate a full cycle.
Args:
value: The single value to repeat.
cycle_limit: Number of repetitions per cycle.
repetitio…How to Use starmap() to Unpack Tuple Arguments in Python
Use itertools.starmap to apply a function to each tuple in an iterable, unpacking tuple elements as separate arguments and returning an iterator of results.
from itertools import starmap
def multiply(a, b):
return a * b
if __name__ == "__main__":
pairs = [(2, 3), (4, 5), (6, 7), (8, 9)]
results = list(starmap(multiply, pairs))
print(results)Merge Sorted Iterators with a Heap Generator in Python
Merge multiple sorted iterators into a single sorted stream using a heap and generator, yielding values lazily in order.
import heapq
def merge_sorted_iterators(*iterators):
heap = []
for idx, iterator in enumerate(iterators):
try:
value = next(iterator)
heapq.heappush(heap, (value, idx, iterator))
except StopIteration:
continue
while heap:
value, idx, iterator = …Browse by section
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