Python Code
Samples
Easy snippets you can copy, study, and run in the browser editor.
How to Broadcast a Small Lookup Table in Python
Simulates broadcasting a small lookup table by iterating key-value pairs and emitting packed rows to subscribers with deterministic output.
import random
# Generate a deterministic mock broadcast of a small lookup table
# with 5 keys and random integer values (seeded for reproducibility)
data = {
"sensor_a": 22,
"sensor_b": 87,
"sensor_c": 43,
"sensor_d": 65,
"sensor_e": 31,
}
# Simulate a broadcast to subscribers by iterating and p…Sliding Window Streaming Mock in Python
A simple Python class that maintains a sliding window of recent streaming values and computes the running average.
import time
import random
class StreamingMock:
"""Produces a stream of numbers using a sliding window."""
def __init__(self, window_size=5):
self.window = []
self.window_size = window_size
def push(self, value):
"""Add a value, sliding the window forward."""
s…How to Build a Simple ML Pipeline with ZenML in Python
Build a mock machine learning pipeline with ZenML steps for data loading, training, and evaluation, and run it to print the final accuracy.
from zenml import pipeline, step
@step
def load_data() -> dict:
"""Simulate loading data from a source."""
return {"accuracy": 0.0, "loss": 1.0}
@step
def train_model(data: dict) -> dict:
"""Simulate training a model."""
data["accuracy"] = 0.95
data["loss"] = 0.1
return data
@step
def eva…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 Load CSV Training Data in Python Without Pandas
Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.
import csv
from pathlib import Path
def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
"""Load CSV training data and return headers plus rows as dictionaries."""
with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
reader = csv.DictReader…How to Mock MLflow log_params and log_metrics in Python
Use unittest.mock to patch MLflow's log_param and log_metric, run the training function, and verify logging calls without touching a real tracking server.
from unittest.mock import Mock, patch
import mlflow
def train_model():
mlflow.log_param("learning_rate", 0.01)
mlflow.log_param("epochs", 10)
mlflow.log_metric("accuracy", 0.95)
mlflow.log_metric("loss", 0.05)
return "Training completed"
if __name__ == "__main__":
with patch("mlflow.log_par…How to Mock train_test_split in Python for Unit Testing
Build a lightweight mock of sklearn's train_test_split to unit test ML pipeline code without needing the full library or deterministic random state.
import numpy as np
from sklearn.model_selection import train_test_split
from unittest.mock import patch
def mock_train_test_split(X, y, test_size=0.25, random_state=None, **kwargs):
"""A simple mock implementation of train_test_split."""
n_samples = len(X)
n_test = int(n_samples * test_size)
n_train =…How to Run Batch Predictions with a Mock Model in Python
Build a lightweight mock model class and run predictions across a batch of samples, returning results as a plain Python list.
import numpy as np
class MockModel:
def __init__(self, weights):
self.weights = np.array(weights)
def predict(self, X):
return X @ self.weights
def predict_batch(model, batch):
"""Run predictions for a batch of samples and return results as a list."""
return model.predict(np.array(ba…How to Simulate an Airflow ML Pipeline in Python
Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.
from datetime import datetime, timedelta
import time
class MLPipeline:
def __init__(self, pipeline_name):
self.pipeline_name = pipeline_name
self.steps = []
def add_step(self, step_name, duration_seconds):
self.steps.append({"name": step_name, "duration": duration_seconds})
def …How to Trigger Model Retraining on Drift in Python
Automatically detects accuracy drift in a mock ML model and triggers retraining when performance falls below a threshold.
import random
import time
class MockModel:
def __init__(self, name):
self.name = name
self.accuracy = 0.85
self.version = 1
def train(self, data_size):
# Simulate training time and accuracy improvement
time.sleep(0.1)
drift = random.uniform(-0.02, 0.02)
…Load CSV Training Data Without Pandas in Python
This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.
import csv
from pathlib import Path
def load_csv(path):
"""Load CSV file into list of dicts without pandas."""
rows = []
with open(path, newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(dict(row))
return rows
if __name__ == "__m…How to Build a Guardrail Metrics Monitor in Python
This code implements a mock monitor that records metric values, checks them against thresholds, and summarizes pass/alert statistics.
import random
import time
from collections import defaultdict
class GuardrailMetricsMonitor:
def __init__(self):
self.metrics = defaultdict(list)
self.thresholds = {
"prompt_toxicity": 0.8,
"response_length": 500,
"latency_ms": 1000,
}
def record(s…Simulate a Ramp Rollout Percentage in Python
Simulates a percentage-based ramp rollout with deterministic seeding, returning success/failure/in-progress counts for a mock user population.
import random
from enum import Enum
class RolloutStatus(Enum):
SUCCESS = "success"
FAILED = "failed"
IN_PROGRESS = "in_progress"
def simulate_ramp_rollout(total_users: int, percentage: int, seed: int = 42) -> dict:
"""
Simulates a mock ramp rollout for a given percentage of users.
Returns sta…Broadcast a Small Reference Table in Python
Simulates SQL-style broadcasting of a small lookup table against a larger fact table in memory for mockups or load tests.
import random
def broadcast_mock(target, source, columns):
result = {}
for col in columns:
if col in target and col in source:
result[col] = target[col] + [source[col][i % len(source[col])] for i in range(len(target[col]))]
elif col in target:
result[col] = target[col]
…Hash index equality mock concept in Python
A simple hash index class in Python that stores key-value pairs in buckets and demonstrates basic equality-based lookup.
class HashIndex:
def __init__(self):
self._buckets = {}
def insert(self, key, value):
"""Insert a key-value pair into the hash index."""
index = hash(key) % 10
if index not in self._buckets:
self._buckets[index] = []
self._buckets[index].append((key, value))…How to Mock Date Sharding by Range in Python
Split a date interval into fixed-size contiguous shards, returning each window as an ISO date string pair.
from datetime import date, timedelta
def shard_ranges(start_date, end_date, shard_days=7):
if start_date > end_date:
raise ValueError("start_date cannot be after end_date")
shards = []
current = start_date
while current <= end_date:
shard_end = min(current + timedelta(days=shard_days …How to Replicate Data Across All Shards in Python
Mocks a global table that replicates a key-value pair to every shard, ensuring reads return the same value from any shard.
from dataclasses import dataclass
from typing import Dict, List
@dataclass
class Shard:
id: str
data: Dict[str, int]
class GlobalTable:
def __init__(self, shards: List[Shard]):
self._shards = {s.id: s for s in shards}
def set_value(self, key: str, value: int) -> None:
"""Replicate …How to Validate Data Before Scaling in Python
A reusable Python helper that validates required fields and constraint checks on data rows before entering a database pipeline, improving data quality and throughput.
def validate_data(data, required_fields, constraints=None):
"""
Basic validation helper demonstrating data-quality workflows
before scaling (catches bad rows early, improves throughput).
"""
constraints = constraints or {}
errors = []
for field in required_fields:
if field not in d…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…Simulate PostgreSQL Vacuum to Reclaim Space in Python
A Python class that safely rewrites a data file to remove deleted rows and reclaim physical space, mimicking PostgreSQL's VACUUM operation.
import shutil
import os
class VacuumCleaner:
"""Simulates PostgreSQL-style vacuum reclaiming dead space in a file."""
def __init__(self, filepath, fill_ratio=0.7, dead_marker="[DELETED]"):
self.filepath = filepath
self.fill_ratio = fill_ratio
self.dead_marker = dead_marker
…Fetch Secrets from a Mock Secrets Manager in Python
Build a minimal in-memory secrets manager that stores and retrieves secret values, raising a KeyError for missing names.
import json
class SecretsManager:
"""Mock secrets manager that returns secrets from a local store."""
def __init__(self, store=None):
self.store = store or {
"api_key": "mock-api-key-123",
"db_password": "s3cret-p@ss",
"jwt_secret": "dev-only-secret"
}
…How to Check Negotiated Cipher Suite in Python
Connect to a TLS server with Python's ssl module and print the negotiated protocol version and cipher suite details.
import ssl
import socket
def get_cipher_suites(hostname, port=443):
context = ssl.create_default_context()
context.set_ciphers("DEFAULT:@SECLEVEL=2")
with socket.create_connection((hostname, port), timeout=5) as sock:
with context.wrap_socket(sock, server_hostname=hostname) as ssock:
…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 Hash Passwords with bcrypt in Python
Hash a plaintext password with bcrypt using a randomly generated salt, then verify a plaintext attempt against the stored hash.
import bcrypt
def hash_password(password: str) -> str:
"""Hash a password using bcrypt with a generated salt."""
salt = bcrypt.gensalt()
return bcrypt.hashpw(password.encode("utf-8"), salt).decode("utf-8")
def check_password(password: str, hashed: str) -> bool:
"""Verify a plaintext password against …Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
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.