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How to Process Text in Python
This code processes multiline text by splitting lines, stripping whitespace, counting words and characters, and converting to lowercase.
def process_text(text):
lines = text.split("\n")
clean_lines = []
for line in lines:
stripped = line.strip()
if stripped:
tokens = stripped.split()
title_case = stripped.lower()
clean_lines.append({
"raw": stripped,
"word_c…How to Process Text Lines with Lists and Loops in Python
This code processes a list of text lines by stripping whitespace, converting to uppercase, and reporting character counts per line and totals.
def process_text(lines):
"""Convert a list of text lines to uppercase and report line statistics."""
processed = []
total_chars = 0
for index, line in enumerate(lines, start=1):
cleaned = line.strip().upper()
processed.append(cleaned)
total_chars += len(cleaned)
pri…How to Count Word Frequencies in Python with Counter and Sets
This code processes a text string by lowercasing, splitting into words, counting frequencies with Counter, and extracting unique and sorted word lists using sets.
from collections import Counter
def process_text(text):
words = text.lower().split()
word_counts = Counter(words)
unique_words = set(words)
sorted_words = sorted(unique_words)
return {
"total_words": len(words),
"unique_words": len(unique_words),
"word_frequencies": di…Memory efficient map over large file in Python
A generator-based streaming map that processes a large file line by line without loading the whole file into memory.
import sys
def process_lines(file_path):
"""Memory-efficient map over a large file: yields processed lines."""
with open(file_path, 'r') as f:
for line in f:
# Example mapping: strip whitespace and uppercase
yield line.strip().upper()
if __name__ == "__main__":
# Use a sma…Find Zombie Processes on Linux with Python
Parse the output of `ps -eo pid,stat,comm` to detect processes in zombie state (Z) on a Linux system and report their PIDs and commands.
#!/usr/bin/env python3
import os
import subprocess
def find_zombie_processes():
"""Find zombie processes (state 'Z') running on Linux."""
try:
result = subprocess.run(['ps', '-eo', 'pid,stat,comm'], capture_output=True, text=True, check=True)
zombies = []
for line in result.stdout.stri…How to Build a CLI with argparse in Python
Create a beginner-friendly command-line tool in Python that processes multiple filenames with optional flags for verbose output and uppercase conversion.
import argparse
def main():
parser = argparse.ArgumentParser(
description="A simple CLI to process files with optional verbose mode."
)
parser.add_argument("filenames", nargs="+", help="Files to process")
parser.add_argument("-v", "--verbose", action="store_true", help="Print extra details")
…How to Detect Applications Consuming Excessive Memory in Python
Use psutil to list the top memory-using processes by RSS and print their names, PIDs, and memory usage in MB.
import psutil
def find_top_memory_processes(limit=5):
"""Return top `limit` processes by memory usage (RSS)."""
processes = []
for proc in psutil.process_iter(['pid', 'name', 'memory_info']):
try:
info = proc.info
mem = info['memory_info'].rss if info['memory_info'] else 0…How to Kill Zombie Processes Matching a Name in Python
Scans running processes with ps, finds zombies whose command name matches a pattern, and attempts to kill them with SIGKILL.
import subprocess
import re
import signal
def find_zombies(name_pattern):
"""Find PIDs of zombie processes matching the given pattern."""
result = subprocess.run(["ps", "-eo", "pid,stat,comm"], capture_output=True, text=True)
zombies = []
for line in result.stdout.splitlines()[1:]: # Skip header
…How to Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…How to Send and Receive Messages Between Processes with multiprocessing.Pipe in Python
Use multiprocessing.Pipe to create a two-way connection between two processes, send a message from parent to child, and receive a reply back.
import multiprocessing
def child_process(conn):
"""Receive from parent and send back a response."""
message = conn.recv()
print(f"Child received: {message}")
conn.send("Hello from child!")
if __name__ == "__main__":
parent_conn, child_conn = multiprocessing.Pipe()
process = multiprocessing…How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…How to Share a Dict and List Between Processes with multiprocessing Manager in Python
This code demonstrates how to share a dictionary and a list between multiple processes using multiprocessing.Manager, enabling safe concurrent updates.
import multiprocessing as mp
def worker(shared_dict, shared_list, name):
shared_dict[name] = name.upper()
shared_list.append(name)
print(f"{name} added to shared structures")
def main():
with mp.Manager() as manager:
shared_dict = manager.dict()
shared_list = manager.list()
…How to Share a Queue Between Processes in Python
Use multiprocessing.Queue to pass work from a producer process to multiple consumer processes, coordinating with a sentinel stop message.
import multiprocessing
import time
def producer(queue, items):
for item in items:
queue.put(item)
time.sleep(0.1)
queue.put("STOP")
def consumer(queue, name):
while True:
item = queue.get()
if item == "STOP":
break
print(f"{name} processed: {item}")
…How to Use pool.map for CPU-Bound Tasks in Python
Distribute CPU-intensive functions across processes with multiprocessing.Pool.map and measure the performance gain.
from multiprocessing import Pool
import time
def cpu_bound_task(n):
"""Mock CPU-bound work: compute sum of squares."""
total = 0
for i in range(n):
total += i * i
return total
if __name__ == "__main__":
numbers = [10_000_000, 12_000_000, 8_000_000, 15_000_000]
start = time.perf_count…How to spawn multiple worker processes in Python with multiprocessing.Process
Spawns three separate worker processes using multiprocessing.Process, runs them concurrently, and waits for all to finish before printing a completion message.
import multiprocessing
import time
def worker(name):
print(f"Worker {name} started")
time.sleep(1)
print(f"Worker {name} finished")
return name
if __name__ == "__main__":
processes = []
for i in range(3):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p…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…At Least Once with Idempotent Consumer in Python
Implements a thread-safe idempotent consumer that processes each unique message exactly once, even when a producer sends duplicates under an at-least-once delivery model.
import threading
import time
import uuid
from collections import Counter
class IdempotentConsumer:
def __init__(self):
self.processed = set()
self._lock = threading.Lock()
def consume(self, message_id, payload):
with self._lock:
if message_id in self.processed:
…How to implement an idempotency key store in Python
Build an in-memory idempotency key store with TTL that processes a request once and reuses the cached result for duplicate calls.
import hashlib
import time
from typing import Dict, Optional
class IdempotencyStore:
"""Simple in-memory idempotency key store with mock processing."""
def __init__(self, ttl_seconds: int = 3600) -> None:
self.ttl = ttl_seconds
self._store: Dict[str, tuple[str, float]] = {}
def _is_expi…How to Mock Spark Streaming Micro-Batches in Python
Simulate Spark's micro-batch streaming with a simple deque-based class that collects events over time and processes them in timed batches.
import time
from collections import deque
from datetime import datetime
class MicroBatchStream:
def __init__(self, batch_interval_sec=2):
self.batch_interval = batch_interval_sec
self.source = deque()
self.processed = []
def add_events(self, events):
self.source.extend(events…Browse by section
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