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How to Implement an Exactly-Once Deduplication Store in Python
Implement a Python class that deduplicates keys exactly once, tracking first-seen timestamps and duplicate counts.
from datetime import datetime
from typing import Any, Hashable
class ExactlyOnceStore:
def __init__(self) -> None:
self._seen: set[Hashable] = set()
self._first_seen: dict[Hashable, datetime] = {}
self._counts: dict[Hashable, int] = {}
def add(self, key: Hashable, value: Any = None) …Idempotent Consumer Event Processing in Python
Track processed event IDs to skip duplicates and count event types for a reliable, idempotent consumer.
import json
from collections import defaultdict
class EventProcessor:
def __init__(self):
self.processed_ids = set()
self.counts = defaultdict(int)
def process_event(self, event):
event_id = event["id"]
if event_id in self.processed_ids:
return {"status": "skipped"…How to Mock a Hash Join on Large and Small Tables in Python
This code efficiently joins a large dataset (1000 rows) with a small lookup table (20 rows) by building a dictionary hash lookup, mimicking a hash join strategy used in big data systems.
import random
from pprint import pprint
# Large table: 1000 rows (id, group_id, value)
large = [{"id": i, "group_id": random.randint(1, 20), "value": random.random() * 100} for i in range(1000)]
# Small table: 20 rows (group_id, label)
small = [{"group_id": g, "label": f"Group-{g}"} for g in range(1, 21)]
# Mock a …Build a Data Helper Class in Python for ML Pipelines
A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.
from typing import List, Dict, Any
import json
class DataHelper:
"""Beginner-friendly helpers for ML data pipelines."""
def __init__(self, data: List[Dict[str, Any]]):
self.data = data
self.keys = list(data[0].keys()) if data else []
def summary(self) -> Dict[str, Any]:
"…How to Define Dagster ML Assets in Python
Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.
from dagster import asset
@asset
def raw_features():
return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}
@asset
def normalized_features(raw_features):
values = raw_features["sepal_length"]
mean = sum(values) / len(values)
std = (sum((x - mean) ** 2 for x in values) / len(values…How to Build a Simple Binary Protocol Parser Mock in Python
Defines a mock binary protocol with field definitions, encoding, and decoding to simulate network packet parsing for A/B testing and experiment setup.
class SimpleProtocol:
def __init__(self, name, version):
self.name = name
self.version = version
self.fields = []
def add_field(self, field_name, field_size):
self.fields.append((field_name, field_size))
def parse(self, data):
if len(data) != sum(size for _, size i…How to Calculate Secondary Metrics in Python
Computes distribution, variability, and spread of a numeric dataset using Python's statistics and collections modules.
import random
import statistics
from collections import Counter
def explore_secondary_metrics(data):
"""Calculate secondary metrics: distribution, variability, and spread."""
if not data:
return "No data provided"
total = sum(data)
mean = statistics.mean(data)
median = statistics.medi…How to Limit a Result Set to Top N Rows in Python
Sort a list of dictionaries by a numeric key and return only the top N results, formatted as a readable ranked list.
import random
def top_n_mock(limit: int = 5):
"""Return a formatted top-N result set as a mock example."""
# Simulated data source
scores = [
{"name": "Alice", "score": 87},
{"name": "Bob", "score": 92},
{"name": "Charlie", "score": 78},
{"name": "Diana", "score": 95},
…How to Optimize SQLite Database Performance in Python
A Python helper that creates an index, enables WAL mode, and tunes synchronous settings to optimize SQLite database performance.
import sqlite3
DATABASE_PATH = "beginners.db"
UNOPTIMIZED_TABLE_SCHEMA = """
CREATE TABLE IF NOT EXISTS users (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT NOT NULL
)
"""
def optimize_database(db_path: str = DATABASE_PATH) -> dict:
with sqlite3.connect(db_path) as connection:
curs…How to Speed Up Column Lookups with DataFrame Index in Python
Use pandas set_index to make repeated column value lookups O(1)-style fast instead of scanning the whole DataFrame each time.
import pandas as pd
# Mock dataset with duplicate customer IDs
data = {"customer_id": [101, 102, 103, 101, 104, 102],
"order_amount": [250.0, 85.5, 300.0, 175.25, 420.0, 95.75]}
df = pd.DataFrame(data)
df = df.set_index("customer_id")
# Simulated lookup request
search_id = 102
# Fast index-based lookup (no…How to enforce a unique index constraint in Python
Mock a database unique index in Python that rejects duplicate rows based on one or more columns.
class MockIndex:
def __init__(self, columns):
self.columns = columns
self._values = set()
def insert(self, row):
key = tuple(row[col] for col in self.columns)
if key in self._values:
raise ValueError(f"Duplicate key {key} for columns {self.columns}")
self._v…How to Create Secure Session Cookies in Python with Secure, HttpOnly, and SameSite Flags
This code demonstrates how to create a secure session cookie using Python's stdlib, setting Secure, HttpOnly, and SameSite attributes to protect against common web vulnerabilities.
import http.cookies
import secrets
class SessionManager:
def __init__(self):
self.cookie = http.cookies.SimpleCookie()
def create_session_cookie(self, session_id=None):
session_id = session_id or secrets.token_hex(16)
self.cookie["session"] = session_id
self.cookie["session"][…How to Mock a Permissions Policy in Python
A lightweight Python class that simulates a browser Permissions-Policy header by tracking allowed/ denied feature permissions with get, set, reset, and bulk operations.
class PermissionsPolicy:
def __init__(self):
self._features = {
"geolocation": "self",
"camera": "self",
"microphone": "self",
"payment": "self",
"usb": "self",
}
def get_feature_policy(self, feature):
return self._features.ge…How to Revoke Tokens with a Blacklist Set in Python
A minimal TokenBlacklist class using a Python set to revoke, batch-revoke, check, and remove expired tokens for simple token invalidation.
import time
class TokenBlacklist:
def __init__(self):
self.blacklisted_tokens = set()
def revoke(self, token):
self.blacklisted_tokens.add(token)
print(f"Token {token} revoked. Blacklist size: {len(self.blacklisted_tokens)}")
def revoke_batch(self, tokens):
before = len(s…How to Set X-Frame-Options DENY in Flask with a Mock Response
Set the X-Frame-Options header to DENY in a Flask response to prevent clickjacking, and verify it with Flask's test client.
from flask import Flask, Response
app = Flask(__name__)
@app.route("/")
def index():
response = Response("Hello, World!")
response.headers["X-Frame-Options"] = "DENY"
return response
if __name__ == "__main__":
with app.test_client() as client:
resp = client.get("/")
print(resp.get_da…How to Set a SameSite Cookie in Python
Set a SameSite cookie attribute in Python using the standard library's SimpleCookie class.
from http.cookies import SimpleCookie
def set_same_site_cookie(name, value, same_site="Lax"):
cookie = SimpleCookie()
cookie[name] = value
cookie[name]["path"] = "/"
cookie[name]["samesite"] = same_site
return cookie[name].OutputString()
if __name__ == "__main__":
print(set_same_site_cookie("…How to Mock a Feature Flag Rollout Percentage in Python
Simulate a percentage-based feature flag rollout by hashing a user ID to deterministically enable features for a subset of users.
import random
from dataclasses import dataclass
@dataclass
class FeatureFlag:
name: str
rollout_percentage: int
def is_feature_enabled(feature_flag: FeatureFlag, user_id: str) -> bool:
hashed_id = hash(user_id) % 100
return hashed_id < feature_flag.rollout_percentage
if __name__ == "__main__":
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