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How to Implement a Recent Counter with a Deque in Python
Implements a RecentCounter class that uses a deque to count ping requests within the last 3000 milliseconds.
from collections import deque
import time
class RecentCounter:
def __init__(self):
self.hits = deque()
def ping(self, t: int) -> int:
self.hits.append(t)
while self.hits and self.hits[0] < t - 3000:
self.hits.popleft()
return len(self.hits)
if __name__ == "__mai…How to Map Strings to Uppercase in Python
Loops through a list of strings and builds a new list with each string converted to uppercase.
strings = ["hello", "world", "python", "skillset"]
uppercased = []
for s in strings:
uppercased.append(s.upper())
print(uppercased)How to Remove Duplicates in Python Preserving Order
Removes duplicate items from a list while keeping the first occurrence order intact using a set for fast membership checks.
def remove_duplicates_preserving_order(items):
seen = set()
result = []
for item in items:
if item not in seen:
seen.add(item)
result.append(item)
return result
if __name__ == "__main__":
sample = [3, 1, 2, 1, 3, 4, 2, 5]
unique_items = remove_duplicates_preserv…How to Replace Outliers Beyond Threshold with Cap in Python
Replace values that fall below a lower threshold or above an upper threshold by capping them to the threshold values using a simple Python function.
def replace_outliers_with_cap(data, lower_threshold=None, upper_threshold=None):
"""Replace values beyond given thresholds with the threshold values (capping)."""
if lower_threshold is None and upper_threshold is None:
raise ValueError("At least one threshold must be provided.")
capped_data = …Insert Multiple Values Into a Sorted List in Python
Insert multiple values into an already-sorted list while keeping it sorted using the bisect.insort function.
import bisect
def insert_sorted(sorted_list, values):
for value in values:
bisect.insort(sorted_list, value)
return sorted_list
if __name__ == "__main__":
original = [1, 3, 5, 7, 9]
new_values = [4, 6, 2, 8, 0]
result = insert_sorted(original, new_values)
print(f"Original: {original}"…Pair Elements with Next Cyclic Neighbor in Python
Create tuples pairing every element with its next element, wrapping around to the first element for the last one.
def cyclic_pairs(lst):
if not lst:
return []
return [(lst[i], lst[(i + 1) % len(lst)]) for i in range(len(lst))]
if __name__ == "__main__":
sample = [1, 2, 3, 4, 5]
result = cyclic_pairs(sample)
print(result)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 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 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 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:
…Normalize Data in Python with Comprehensions and Generators
Clean a list by dropping None values with a comprehension, then min-max normalize it using a lazy generator expression — a beginner-friendly data preparation pattern.
import statistics
# Sample raw data including missing and outlier-ish values
raw = [22, 18, None, 25, 30, 19, 22, 17, None, 28, 24]
# Clean the data: drop None values using a list comprehension
clean = [x for x in raw if x is not None]
# Normalize using min-max scaling with a generator expression
min_val = min(clea…How to Chunk a Long Document for RAG Retrieval in Python
Split text into overlapping chunks at sentence boundaries using a custom Python function suitable for RAG retrieval pipelines.
import re
from pathlib import Path
def chunk_document(text, chunk_size=500, overlap=100):
"""Split text into overlapping chunks suitable for RAG retrieval."""
# Normalize whitespace
text = re.sub(r'\s+', ' ', text).strip()
chunks = []
start = 0
while start < len(text):
end = min(s…How to Parse an LLM Response in Python
This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.
import json
from typing import Any, Dict, List
def parse_llm_response(response: str) -> Dict[str, Any]:
"""Parse a JSON string from an LLM response, handling common edge cases."""
# Remove code fences if present
cleaned = response.strip()
if cleaned.startswith("How to Stream Tokens from a Mock LLM in Python
Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.
import time
from typing import Generator
def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
"""Simulate an LLM streaming tokens word by word."""
for word in text.split():
yield word
time.sleep(delay)
if __name__ == "__main__":
sample = "Hello world! This is…How to Summarize Old Conversation Turns in Python
Compress old conversation turns into a brief summary while keeping recent turns intact for LLM context management.
from datetime import datetime, timedelta
def summarize_old_turns(conversation, max_turns=5):
"""Compress turns older than max_turns into a brief summary."""
if len(conversation) <= max_turns:
return conversation, ""
old_turns = conversation[:-max_turns]
recent_turns = conversation[-max_turns…Route Tool Call Name to Python Handler Dict
Routes a tool call name to the correct Python handler function using a dictionary lookup, returning an error for unknown tools.
def get_name():
return {"name": "Alice"}
def get_age():
return {"age": 30}
def get_email():
return {"email": "alice@example.com"}
handlers = {
"get_name": get_name,
"get_age": get_age,
"get_email": get_email,
}
def route(tool_call):
handler = handlers.get(tool_call["name"])
if handl…Check Service Ping Status and Exit Code in Python
Ping a list of hosts, print OK/FAIL per host, and exit with a non-zero code when any host is unreachable.
import subprocess
import sys
SERVICES = [
"8.8.8.8",
"1.1.1.1",
"invalid-host",
]
def main():
failed = []
for host in SERVICES:
result = subprocess.run(
["ping", "-c", "1", "-W", "2", host],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
…How to Automatically Download Every Favicon from a List of Websites in Python
Download each website's favicon.ico file by constructing its URL, making a GET request, and saving the binary content locally.
import requests
from urllib.parse import urlparse
import os
websites = [
"https://www.google.com",
"https://www.github.com",
"https://www.stackoverflow.com"
]
def download_favicon(url):
parsed = urlparse(url)
favicon_url = f"{parsed.scheme}://{parsed.netloc}/favicon.ico"
response = requests.g…How to Download a List of URLs to a Directory in Python
This script downloads a list of URLs into a specified directory, creating the folder if needed and keeping original filenames.
import urllib.request
from pathlib import Path
def download_urls(url_list, directory):
"""Download each URL in url_list into directory, keeping original filenames."""
save_dir = Path(directory)
save_dir.mkdir(parents=True, exist_ok=True)
for url in url_list:
filename = url.rstrip('/').spl…How to Hash Duplicate Photos and Delete Copies in Python
This script hashes image files in a directory using SHA-256 and deletes duplicate copies while keeping the first occurrence, ideal for cleaning up photo libraries.
from pathlib import Path
import hashlib
def file_hash(path, chunk_size=8192):
hasher = hashlib.sha256()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(chunk_size), b""):
hasher.update(chunk)
return hasher.hexdigest()
def delete_duplicate_photos(directory):
directory …How to Map Network Drive Paths to Local Paths in Python
Convert mock SMB network drive paths (like 'S:\reports\q1.xlsx') to local placeholder paths and back using a simple mapping dictionary in Python.
"""Map mock SMB network drive paths to local placeholder paths."""
from dataclasses import dataclass
@dataclass(frozen=True)
class NetworkDrive:
letter: str
remote_path: str
DRIVES = {
"S:": NetworkDrive("S", r"\\server01\shares\sales"),
"M:": NetworkDrive("M", r"\\server02\media\movies"),
"X:": …Monitor Website Uptime with Python
Periodically check if a website is reachable and its HTTP status is 200, logging the status with timestamps.
import requests
import time
def check_website(url):
try:
response = requests.get(url, timeout=5)
if response.status_code == 200:
return True
else:
return False
except requests.ConnectionError:
return False
except requests.Timeout:
return Fals…ETL in Python: Extract CSV, Transform Dict, Load JSON
Build a simple ETL pipeline in Python that reads a CSV file, transforms each row (stripping whitespace and converting numeric fields), and writes the result to JSON.
import csv
import json
from pathlib import Path
def extract_csv(file_path):
"""Read CSV file and return list of row dictionaries."""
with Path(file_path).open('r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
return list(reader)
def transform_dicts(rows):
"""Transform ro…Filter Records by Required Fields in Python
Filter a list of dictionaries, keeping only records where every required field is present and not None.
def filter_records(records, required_fields):
"""Return only records that have all required fields non-null."""
return [
record for record in records
if all(record.get(field) is not None for field in required_fields)
]
if __name__ == "__main__":
sample_records = [
{"name": "Al…Browse by section
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