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How to Split a List into Chunks in Python
Split a list into fixed-size sublists using a simple list comprehension with slicing.
def chunk_list(lst, size):
"""Split a list into sublists of given size."""
return [lst[i:i + size] for i in range(0, len(lst), size)]
if __name__ == "__main__":
sample = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(chunk_list(sample, 3))Convert All Markdown Files in a Folder to HTML in Python
Batch convert every .md file in a folder to .html using the `markdown` library with the 'extra' extensions.
import os
import markdown
from pathlib import Path
def convert_md_folder_to_html(input_folder="markdown_files", output_folder="html_pages"):
input_path = Path(input_folder)
output_path = Path(output_folder)
output_path.mkdir(exist_ok=True)
for md_file in input_path.glob("*.md"):
with open…How to Stream Large CSV Files in Python
Process a large CSV file in memory-efficient chunks using Python's csv module, yielding batches of rows instead of loading everything at once.
import csv
from pathlib import Path
def process_csv_in_chunks(file_path, chunk_size=1000):
"""Yield rows from a large CSV file in chunks without loading all into memory."""
with open(file_path, 'r', newline='') as f:
reader = csv.DictReader(f)
chunk = []
for row in reader:
…Split CSV Files into Smaller Chunks in Python
Splits a large CSV file into multiple smaller chunk files, preserving the header row in each chunk.
import csv
import os
def split_csv(input_file, chunk_size=1000, output_prefix="chunk"):
"""Split a large CSV file into smaller chunks."""
with open(input_file, 'r', newline='') as infile:
reader = csv.reader(infile)
header = next(reader)
file_count = 1
row_count = 0
…Batch Rows in Chunks with a Generator in Python
Group a list of row dicts into fixed-size chunks using a generator that yields one slice per call.
from typing import Iterator, List
def batch_rows(rows: List[dict], batch_size: int) -> Iterator[List[dict]]:
for i in range(0, len(rows), batch_size):
yield rows[i:i + batch_size]
if __name__ == "__main__":
sample_rows = [
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"},
…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)How to Batch Embed a List of Strings in Python
Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.
class MockEncoder:
def __init__(self, dim=8, seed=42):
self.dim = dim
self.seed = seed
def embed(self, text):
# Deterministic pseudo-random embedding based on text content
hash_val = hash(text)
import random
rng = random.Random(hash_val + self.seed)
retu…Batch Rename Hundreds of Files in Python
Rename all files with a given extension inside a folder using a sequential counter and a custom prefix.
import os
from pathlib import Path
def batch_rename_files(directory: str, prefix: str, extension: str = ".txt") -> None:
"""Rename all files with given extension in directory to prefix_{counter}.ext."""
path = Path(directory)
if not path.is_dir():
print(f"Directory '{directory}' does not exist.")
…Convert Markdown to HTML in Python (Batch)
Convert every Markdown file in a directory to HTML with the Python markdown library, saving each result with an .html extension.
import markdown
from pathlib import Path
def convert_md_to_html(source_dir: str, dest_dir: str) -> list[str]:
src = Path(source_dir)
dst = Path(dest_dir)
dst.mkdir(parents=True, exist_ok=True)
converted_files = []
for md_file in src.glob("*.md"):
html_content = markdown.markdown(md_file.…Create ICS Calendar Invites in Python
This script generates a batch of calendar invites in the ICS format using the ics library.
import ics
from datetime import datetime, timedelta
def create_invites(batch):
calendar = ics.Calendar()
for event_data in batch:
event = ics.Event()
event.name = event_data["name"]
event.begin = event_data["start"]
event.end = event_data["end"]
event.description = even…How to Batch Resize Images in Python with pathlib and Pillow
Batch resize all JPG images from a source folder and save to a destination folder using pathlib and Pillow.
from pathlib import Path
from PIL import Image
def batch_resize_images(src_dir: str, dest_dir: str, size: tuple[int, int] = (800, 600)) -> None:
src_path = Path(src_dir)
dest_path = Path(dest_dir)
dest_path.mkdir(parents=True, exist_ok=True)
for img_path in src_path.glob("*.jpg"):
if not …How to Resize Hundreds of Images in Batch with Python
Resize every image in a folder to a target size using Pillow, creating a new subfolder for processed files.
import os
from PIL import Image
def resize_images_in_batch(directory, output_size=(800, 600)):
if not os.path.exists(directory):
print(f"Directory {directory} does not exist.")
return
output_dir = os.path.join(directory, "resized")
os.makedirs(output_dir, exist_ok=True)
for filename in…How to Track Checkpoint Offset After Batch Commit in Python
A batch processor that tracks the last successfully committed offset after processing records in batches, advancing the checkpoint only when each batch commits successfully.
import json
from typing import Any
class BatchProcessor:
"""Tracks checkpoint offset after committing batches."""
def __init__(self, batch_size: int = 3):
self.batch_size = batch_size
self.offset = 0 # last successfully committed offset (exclusive)
self.total_committed = 0
def …How to route late-arriving data to a side output in Python
Separate late-arriving events from a streaming data batch into a dead-letter side output list using a timestamp threshold.
from collections import defaultdict
def late_arriving_side_output(events, late_threshold_ts):
"""
Mock a streaming pipeline that separates late-arriving data events
into a side output list (e.g., for dead-letter analysis).
events: list of (timestamp, data) tuples, timestamps as ints.
late_thresho…How to Implement a Batch Requests Flush Interval in Python
A simple async batcher that accumulates items and flushes them either when a max batch size is reached or after a time-based flush interval.
import asyncio
from collections import deque
class Batcher:
def __init__(self, flush_interval=0.5, max_batch=5):
self.flush_interval = flush_interval
self.max_batch = max_batch
self.queue = deque()
self.lock = asyncio.Lock()
async def add(self, item):
async with self.l…Limit Concurrency with asyncio.Semaphore in Python
Use asyncio.Semaphore to cap how many async tasks run at once, throttling a batch of coroutines to a set concurrency limit.
import asyncio
import random
async def fetch_data(i: int, semaphore: asyncio.Semaphore) -> str:
async with semaphore:
print(f"Task {i} starts")
await asyncio.sleep(random.uniform(0.1, 0.5))
print(f"Task {i} finishes")
return f"Result {i}"
async def main() -> None:
semaphore …How to Build a Batch Operations Multi-Status 207 Mock Server in Python
Build a mock HTTP server that accepts a batch of operations and returns HTTP 207 Multi-Status with per-operation status codes in JSON.
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
class BatchHandler(BaseHTTPRequestHandler):
def do_POST(self):
if self.path != "/batch":
self.send_response(404)
self.end_headers()
return
content_length = int(self.headers.get("Content-Leng…Batch Consume Process Commit Pattern in Python
A mock batch processor that accumulates items in a queue, processes full batches, commits successful or failed results, and flushes remaining items.
import random
import threading
import time
from collections import deque
class MockBatchProcessor:
def __init__(self, process_func, commit_func, batch_size=5):
self.queue = deque()
self.batch_size = batch_size
self.process_func = process_func
self.commit_func = commit_func
de…How to Mock a Kafka Producer Batch Send in Python
Simulate a Kafka producer in Python that sends batched JSON events with mock partitions and latency for testing streaming pipelines without a real broker.
import json
import random
import time
from datetime import datetime
class MockKafkaProducer:
def __init__(self, topic):
self.topic = topic
self.sent_messages = []
def send(self, value, key=None):
message = {
"topic": self.topic,
"key": key,
"value"…How to Simulate a Micro-Batch Interval Trigger in Python
A dataclass-based mock that emits batch numbers at fixed intervals, mimicking a micro-batch streaming scheduler for testing and development.
import time
from dataclasses import dataclass, field
from typing import List, Callable
@dataclass
class MicroBatchTriggerMock:
batch_interval_seconds: float = 0.5
max_batches: int = 5
_batches_emitted: int = 0
_next_emit_time: float = field(init=False, default=0)
def start(self, on_batch: Callab…Kafka Consumer Poll Loop Mock in Python
Simulate a Kafka consumer poll loop with a mock class, process messages in batches, and commit offsets to understand streaming consumption patterns.
import time
class MockKafkaConsumer:
def __init__(self, topic, messages):
self.topic = topic
self.messages = list(messages)
self.position = 0
def poll(self, timeout_ms=100):
if self.position >= len(self.messages):
time.sleep(timeout_ms / 1000)
return []…How to Mock Redis Pipeline Batch Commands in Python
Create a lightweight MockRedis class that simulates Redis pipeline batching with SET, GET, and DELETE operations for testing without a live server.
import redis
import time
class MockRedis:
def __init__(self):
self.data = {}
def pipeline(self):
return MockPipeline(self)
def execute(self, commands):
results = []
for cmd in commands:
op, args = cmd[0], cmd[1:]
if op == "SET":
se…How to implement a write-behind cache with async queue in Python
Build an async write-behind cache that queues writes in memory and flushes them in batches to persistent storage.
import asyncio
from collections import deque
from dataclasses import dataclass
@dataclass
class CacheEntry:
key: str
value: str
class WriteBehindCache:
def __init__(self, flush_interval=1.0):
self.cache = {}
self.queue = deque()
self.flush_interval = flush_interval
self._f…How to use Redis MGET MSET pipeline in Python
Store multiple keys atomically and read them efficiently with Redis MSET/MGET, then batch commands with a pipeline to cut round trips.
import redis # v4.x+ required
r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)
# Sample data to store
r.flushdb()
data = {"name": "Alice", "age": "30", "city": "Berlin"}
# MSET: store multiple key-value pairs in one command
r.mset(data)
# MGET: fetch multiple keys in one round trip
keys =…Browse by section
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- Open a sample, read How it works, and copy the code block
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Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.