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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Cache Data in Redis with Python
A beginner-friendly Redis cache helper that stores JSON strings with a TTL and retrieves them with the redis-py client.
import redis
class DataCache:
def __init__(self, host="localhost", port=6379, db=0):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
def cache_data(self, key, value, ttl=60):
self.client.setex(key, ttl, value)
def get_cached_data(self, key):
return …How to Cache Data in Redis with Python
Build a simple Redis cache wrapper that stores and retrieves JSON data with automatic TTL and serialization.
import redis
import json
import time
class Cache:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db)
self.default_ttl = default_ttl
def get(self, key):
value = self.client.get(key)
if value is None:
…How to Cache Function Results with Redis in Python
A RedisCache helper class caches function results using a decorator, with JSON serialization and TTL-based expiry.
import redis
import json
from functools import wraps
class RedisCache:
def __init__(self, host='localhost', port=6379, db=0, ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.ttl = ttl
def cached(self, key_prefix):
def decorator(func):
…How to Serialize Cache Values with JSON and Pickle in Python
Serialize cache values using JSON for simple types or pickle for arbitrary objects, with robust error handling for unsupported types like mocks.
import json
import pickle
from unittest.mock import Mock
def serialize(value, method="json"):
"""Serialize a cache value using JSON or pickle with type checking."""
if method == "json":
try:
return json.dumps(value).encode("utf-8")
except TypeError as e:
raise ValueErro…How to Use Redis as a Cache in Python
A beginner-friendly RedisCache helper that stores, retrieves, and deletes JSON values with automatic TTL expiration using the redis-py client.
import json
import time
import redis
class RedisCache:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.default_ttl = default_ttl
def set(self, key, value, ttl=None):
"""Store a v…Simple Redis Cache Helper in Python
Build a minimal Redis-backed cache with TTL, JSON serialization, and automated fetching to speed up repeated expensive lookups.
import time
import redis
import json
class SimpleCache:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.default_ttl = default_ttl
def get(self, key):
value = self.client.get(key)…How to Deduplicate Messages in Python by ID
This code consumes a mock inbox of JSON messages and deduplicates them by message ID, keeping either the first or last occurrence.
import json
from collections import OrderedDict
mock_inbox = [
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 2, "message": "world", "timestamp": "2024-01-01T10:01:00Z"},
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 3, "message": "test", "times…Export Metrics with OTLP Mock in Python
Simulates system metric collection and exports them as an OTLP-like JSON payload using only Python's standard library.
from dataclasses import dataclass, asdict
import json
import random
import time
@dataclass
class Metric:
name: str
value: float
timestamp: int
unit: str = "1"
def collect_system_metrics() -> list[Metric]:
"""Mock metric collection for OTLP export simulation."""
now = int(time.time())
re…Generate Synthetic CPU Utilization Metrics in Python
Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.
from datetime import datetime, timedelta
import random
import json
def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
"""Generate realistic CPU utilization samples for a given time window."""
timestamps = []
values = []
now = datetime.utcnow()
start_time = now - timede…How to Do Structured JSON Line Logging in Python
Create a simple JSON-lines logger that writes one JSON object per line to stdout with timestamp, level, message, and custom context fields.
import json
import sys
from datetime import datetime
class JsonLineLogger:
def __init__(self, stream=sys.stdout):
self.stream = stream
def log(self, level, message, **context):
record = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"level": level,
"me…How to Do Structured JSON Logging in Python
Create a custom logging formatter that outputs each log entry as a single JSON line with timestamp, level, logger name, and message.
import json
import logging
from datetime import datetime
class JsonFormatter(logging.Formatter):
def format(self, record):
log_entry = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"level": record.levelname,
"logger": record.name,
"message": record.ge…How to Ship Logs to an Aggregator Endpoint in Python
Ship batched log entries to a mock HTTP aggregator endpoint with proper error handling and response status.
import json
import requests
from datetime import datetime, timezone
LOG_ENTRIES = [
{"timestamp": "2024-01-15T10:00:00Z", "level": "INFO", "message": "Server started"},
{"timestamp": "2024-01-15T10:00:05Z", "level": "WARN", "message": "High memory usage"},
{"timestamp": "2024-01-15T10:00:10Z", "level": "E…Python Observability Data Helper for Beginners
A beginner-friendly Python helper to log events, record metrics, summarize observability data, and export it as JSON.
import json
from datetime import datetime
from collections import defaultdict
class ObservabilityDataHelper:
"""Helper for exploring basic observability data patterns."""
def __init__(self):
self.events = []
self.metrics = defaultdict(list)
def log_event(self, service, level, message):
…Backward Compatible Schema Evolution in Python
A mock schema validator that evolves JSON schemas while preserving backward compatibility by keeping old fields and validating required ones.
import json
from copy import deepcopy
class SchemaValidator:
def __init__(self, schema):
self.schema = schema
def evolve(self, new_schema):
"""Evolve mock schema while keeping backward compatibility."""
for field in self.schema:
if field not in new_schema:
…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
…How to Implement a Data Helper for Microservices in Python
Create a reusable helper class to serialize, deserialize, and wrap data for microservice communication using dataclasses and JSON.
import json
from dataclasses import dataclass, asdict
from typing import Any, Dict, List
@dataclass
class ServiceResponse:
status: str
data: Any
message: str = ""
class DataHelper:
"""Simple helper for microservice data handling."""
@staticmethod
def serialize(data: Dict[str, Any]) -> str:…Delta Lake ACID Transaction Log Mock in Python
Simulates Delta Lake's transactional log with JSON files for atomic commits, versioned operations, and crash recovery
import json
import time
from pathlib import Path
class DeltaLog:
def __init__(self, path):
self.log_dir = Path(path)
self.log_dir.mkdir(parents=True, exist_ok=True)
self.version = 0
def _write_txn(self, action, payload):
txn = {
"version": self.version,
…How to Create a Mock Iceberg Snapshot Manifest in Python
Build a mock Iceberg snapshot manifest structure with metadata and data entries using Python dictionaries and JSON.
import json
from datetime import datetime, timezone
def create_mock_manifest(snapshot_id: int, file_paths: list[str]) -> dict:
"""Create a mock Iceberg snapshot manifest structure."""
manifest_file = {
"manifest_path": f"/warehouse/table/metadata/snap-{snapshot_id}-m0.avro",
"manifest_length"…How to Create a Mock Kafka Producer in Python
Build a Kafka producer that generates mock streaming records with JSON serialization and error handling for local testing.
import json
import time
from kafka import KafkaProducer
from kafka.errors import KafkaError
def create_mock_producer(bootstrap_servers="localhost:9092", topic="input-topic"):
"""Create a Kafka producer that generates mock streaming data."""
producer = KafkaProducer(
bootstrap_servers=bootstrap_servers…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]:
"…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 Load, Save, and Split JSON Data in Python
Provides helper functions to load, save, and split JSON dictionary data for simple ML pipeline preprocessing.
import json
from pathlib import Path
def load_json_data(file_path):
"""Load JSON data from a file, returning an empty dict if missing."""
path = Path(file_path)
if path.exists():
with path.open("r", encoding="utf-8") as f:
return json.load(f)
return {}
def save_json_data(data, f…Generate a Mock Multi-Armed Bandit Report in Python
Simulate a multi-armed bandit experiment with random pulls and rewards, then output a JSON report with per-arm statistics.
import random
import json
def generate_mock_bandit_report(num_arms=5, num_rounds=100, seed=42):
random.seed(seed)
arms = ["A", "B", "C", "D", "E"][:num_arms]
true_means = {arm: random.uniform(0.3, 0.7) for arm in arms}
pulls = {arm: 0 for arm in arms}
rewards = {arm: 0 for arm in arms}
for _ …How to Generate Multivariate JSON Mock Data in Python
This script generates mock multivariate JSON-compatible data with measurements and boolean flags for testing and experimentation pipelines.
import json
def multivariate_mock(row_count: int = 3) -> list:
"""Generate mock multivariate data as list of JSON-compatible dicts."""
records = []
for i in range(row_count):
record = {
"id": i + 1,
"measurements": {
"temperature": 20.5 + i * 1.5,
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