Python Code
Samples
Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Validate dict schema at pipeline boundary in Python
This code validates a dictionary against a TypedDict schema at a pipeline boundary, enforcing required fields and types with custom error messages.
from typing import Any, TypedDict
class Person(TypedDict):
name: str
age: int
email: str
def validate_person(data: dict[str, Any]) -> Person:
errors: list[str] = []
if not isinstance(data.get("name"), str) or not data["name"].strip():
errors.append("name must be a non-empty string")
…How to Mock CloudFront Invalidation Paths in Python
Build a sorted, deduplicated list of CloudFront invalidation paths from a set of file paths, adding implicit index.html entries.
import argparse
def build_invalidation_paths(files, include_index=True):
"""
Create CloudFront invalidation paths from a list of files.
Converts file names to root-relative paths and optionally adds /index.html.
"""
paths = []
for f in files:
f = f.strip()
if not f:
…How to Validate AWS Security Group Ingress Rules in Python
Validates AWS security group ingress rules (protocol, port ranges, CIDR, description) and returns a list of errors or OK.
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class SecurityGroupRule:
protocol: str
port_range: tuple
cidr: str
description: str = ""
def validate_ingress_rule(rule: SecurityGroupRule) -> List[str]:
"""Validate a security group ingress rule against common AWS pat…How to Validate Data Fields and Types in Python
Validate required fields and type correctness in a Python dictionary with small helper functions, returning a list of clear error messages.
import json
from typing import Any, Dict, List
def validate_data(data: Dict[str, Any], required_fields: List[str]) -> List[str]:
"""Check required fields exist and are non-empty. Return list of errors."""
errors = []
for field in required_fields:
value = data.get(field)
if value is None o…Mock CloudWatch put_metric_data in Python
Simulate AWS CloudWatch put_metric_data with validation and formatted output for local testing without AWS.
import json
from datetime import datetime, timezone
def put_metric_data(namespace, metric_data_list):
"""
Mock AWS CloudWatch put_metric_data.
Validates and prints the metrics that would be sent.
"""
timestamp = datetime.now(timezone.utc).isoformat()
print(f"[MockCloudWatch] Received request …How to Validate Data with a Simple Dict-Based Rules Helper in Python
Validates a dictionary against a set of callable rules, printing pass/fail per field and returning an overall boolean.
import json
from pathlib import Path
from typing import Any, Callable
def validate_data(
data: dict[str, Any],
rules: dict[str, Callable[[Any], bool]],
path: Path | None = None,
) -> bool:
"""Validate a dict against a set of simple rules."""
all_valid = True
for field, validator in rules.item…How to Validate Data with ThreadPoolExecutor in Python
This code shows how to validate a list of numbers concurrently using ThreadPoolExecutor, dramatically speeding up slow validation tasks by running them in parallel threads.
import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
@dataclass
class Result:
is_valid: bool
value: int
def validate(value: int) -> Result:
time.sleep(0.1) # simulate slow validation (API call, DB check)
return Result(is_valid=0 < value < 100, value=value…How to Use TypedDict for Data Validation in Python
Define a TypedDict schema and validate raw dictionary input with type hints for safer, more readable data handling.
from typing import Any, Dict, List, Optional, Union, TypedDict, Literal
class Product(TypedDict):
product_id: int
name: str
price: Union[int, float]
in_stock: bool
tags: Optional[List[str]]
def validate_product(data: Dict[str, Any]) -> Product:
product_id: int = int(data["product_id"])
na…How to Validate Data in Python with Typing Hints
Build a runtime validation helper that checks values against Python type hints like Optional, list, and basic types.
from typing import Any, Optional, Union, TypeVar, get_origin, get_args
T = TypeVar("T")
def validate(value: Any, expected_type: type) -> Optional[str]:
"""Returns an error message if value doesn't match expected_type, else None."""
# Handle Optional[...] types
origin = get_origin(expected_type)
if or…How to Validate Dataclass Fields with Python Type Hints
A beginner-friendly helper that checks if instance attributes match their declared type hints using dataclasses and get_type_hints.
from typing import Any, TypeVar, get_type_hints
from dataclasses import dataclass
T = TypeVar("T")
@dataclass
class User:
name: str
age: int
email: str
def validate_fields(obj: Any) -> dict[str, bool]:
"""Check if object attributes match declared type hints."""
hints = get_type_hints(obj.__class…How to Validate Data in Python for Beginners
A beginner-friendly Python class for validating required fields, types, ranges, and allowed choices in dict payloads.
import json
from typing import Any, Dict, List, Optional, Union
class Validator:
"""A simple validate data helper designed for beginners."""
def __init__(self, data: Union[Dict[str, Any], List[Any]]):
self.data = data
self.errors: Dict[str, str] = {}
def validate_required(self, field: s…How to Validate JWT Claims (exp, iss, aud) in Python
This code demonstrates how to decode and validate a JWT's essential claims—expiration (exp), issuer (iss), and audience (aud)—using the PyJWT library, returning clear error messages for common validation failures.
import jwt
from datetime import datetime, timezone, timedelta
SECRET = "mock-secret"
def validate_token(token, expected_iss, expected_aud):
try:
decoded = jwt.decode(
token,
SECRET,
algorithms=["HS256"],
options={"require": ["exp", "iss", "aud"]},
…How to Validate Request Body JSON Against a Schema in Python
Build a lightweight schema validator to check required fields, types, string lengths, allowed values, and nested objects in a JSON request body.
import json
def validate_against_schema(data, schema, path=""):
errors = []
if not isinstance(data, dict):
errors.append(f"{path}: expected object, got {type(data).__name__}")
return errors
for field, rules in schema.items():
field_path = f"{path}.{field}" if path else field
…Sort Python list by query param order_by
Sort a list of dataclass objects dynamically by a field name passed as a query param, with asc/desc direction support.
from dataclasses import dataclass
@dataclass
class Item:
name: str
price: int
def sort_items(items, order_by, direction="asc"):
if order_by not in ("name", "price"):
raise ValueError(f"Unsupported sort field: {order_by}")
reverse = direction.lower() == "desc"
return sorted(items, key=l…How to Invalidate a Cache in Python with lru_cache
This code demonstrates how to clear the cache of an @lru_cache decorated function in Python using cache_clear(), showing the effect on cached results.
from functools import lru_cache
import time
@lru_cache(maxsize=None)
def expensive_operation(key):
return f"Computed value for {key} at {time.time():.6f}"
def invalidate_cache():
expensive_operation.cache_clear()
if __name__ == "__main__":
print(expensive_operation("alpha"))
print(expensive_operatio…How to Mock Cache Tag Invalidation in Python
Use unittest.mock.patch with wraps to verify tagged cache entries are invalidated correctly.
import unittest
from unittest.mock import patch
def get_cached_data(cache, key):
"""Return data from cache if present and valid, else None."""
if cache.get(key, {}).get("valid", False):
return cache[key]["data"]
return None
def invalidate_tag_mock(cache, tag):
"""Invalidate all cache entries …How to Validate and Cache Data with Redis in Python
A beginner-friendly helper that validates email, phone, and age data and caches validated entries in Redis for 5 minutes.
import redis
import json
from functools import wraps
class DataValidator:
def __init__(self, host="localhost", port=6379, db=0):
self.cache = redis.Redis(host=host, port=port, db=db)
self.validators = {
"email": lambda v: "@" in v and "." in v.split("@")[-1],
"phone": lambd…Rate Limiting with a Simple Python RateLimiter Class
A beginner-friendly Python rate limiter that tracks call timestamps and enforces a maximum number of calls within a rolling time window, with a helper to validate positive integers.
import time
class RateLimiter:
def __init__(self, max_calls, period_seconds):
self.max_calls = max_calls
self.period_seconds = period_seconds
self.calls = []
def is_allowed(self):
now = time.time()
while self.calls and now - self.calls[0] >= self.period_seconds:
…How to Build a Microservice Helper in Python
A beginner-friendly Python helper that validates input, normalizes service responses, and simulates user management—showing clean patterns for microservice development.
import json
from typing import Any, Dict, List
class DataValidator:
"""Simple validator for common data patterns."""
@staticmethod
def is_valid_email(value: str) -> bool:
"""Check if value looks like an email."""
return "@" in value and "." in value.split("@")[-1]
@staticmethod
…Zero Trust Service Auth Mock in Python
A simple HMAC-based token issuance and validation mock that enforces zero trust between microservices.
import hmac
import hashlib
import json
import time
class ZeroTrustAuth:
def __init__(self, secret_key):
self.secret_key = secret_key
self.service_tokens = {}
def issue_token(self, service_name, ttl=300):
payload = {
"service": service_name,
"issued_at": int(tim…Create a Minimal Great Expectations Suite Mock in Python
Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.
import json
class GreatExpectationsSuite:
"""A minimal mock of a Great Expectations suite."""
def __init__(self, suite_name, expectations=None):
self.suite_name = suite_name
self.expectations = expectations or []
def add_expectation(self, expectation_type, column=None, kwargs=None):
…How to Build a Data Validation Schema in Python
Create a lightweight validation schema using dataclasses and lambda validators to check fields in a dictionary.
import re
from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class Field:
name: str
validator: Callable[[Any], bool]
required: bool = True
def validate(self, value: Any) -> bool:
if not self.required and value is None:
return True
return …K-Fold Cross Validation in Python: A Simple Implementation
Implements k-fold cross validation from scratch, splitting data into folds and computing MSE scores for a baseline mean-predictor model.
import random
from statistics import mean
def cross_validation_scores(data, labels, k=5, seed=42):
random.seed(seed)
indices = list(range(len(data)))
random.shuffle(indices)
fold_size = len(indices) // k
folds = []
for i in range(k):
if i == k - 1:
folds.append(indices[i *…How to create a global control holdout group in Python
This code implements a deterministic global control holdout group, randomly selecting a fraction of users to be excluded from feature rollouts for experiment validation.
import random
class GlobalControl:
def __init__(self, population_size, holdout_fraction=0.2, seed=42):
random.seed(seed)
self.population_size = population_size
self.holdout_fraction = holdout_fraction
self.holdout_size = int(population_size * holdout_fraction)
self.holdout_…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.