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Validate dictionary data with sets in Python
Validate a dictionary against required keys and allowed value sets, returning a list of validation errors.
def validate_data(data, required_keys, allowed_values=None):
"""
Validate a dictionary against required keys and optional allowed value sets.
Returns a list of validation errors (empty list if valid).
"""
errors = []
# Check for missing required keys
missing = set(required_keys) - set(…How to Make a Python Class Hashable with __eq__ and __hash__
Define __eq__ and __hash__ together on a Python class so equal instances share the same hash and work correctly in sets and dictionary keys.
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def __eq__(self, other):
if not isinstance(other, Point):
return NotImplemented
return self.x == other.x and self.y == other.y
def __hash__(self):
return hash((self.x, self.y))
def __repr…Sort list by multiple keys with tuple ordering in Python
Sort a list of dictionaries by multiple criteria — surname, age, then score descending — using a tuple key and negation.
def sort_multi_key(data):
# Sorts by surname, then age, then score descending
return sorted(
data,
key=lambda person: (
person['surname'].lower(),
person['age'],
-person['score'] # negative to reverse sort by score
)
)
if __name__ == "__main__"…Stable sort preserving equal order demo in Python
Demonstrates Python's stable sort, showing that elements with equal sort keys retain their original relative order.
from operator import itemgetter
def stable_sort_demo():
data = [(3, "first"), (1, "second"), (3, "third"), (1, "fourth"), (2, "fifth")]
print("Original:", data)
# Sort by first element (the tuple's first value), keeping relative order of equal items
sorted_data = sorted(data, key=itemgetter(0))
…Dict Comprehension to Map Keys to Lengths in Python
Build a dictionary that maps each word to its character count using a dictionary comprehension.
words = ["apple", "banana", "cherry", "date", "elderberry"]
word_lengths = {word: len(word) for word in words}
print(word_lengths)Group Consecutive Keys in Python with itertools.groupby
Group consecutive equal elements in a list using the itertools.groupby generator, printing each key and its values.
from itertools import groupby
data = [1, 1, 2, 2, 3, 1, 1, 4, 4, 4]
for key, group in groupby(data):
group_list = list(group)
print(f"Key: {key}, Values: {group_list}")JSON Mode Prompt Schema Output in Python
Extract a user object to JSON with explicit schema keys, ready for LLM JSON-mode prompts.
import json
from typing import Any, Dict
def extract_user_as_json(user: Dict[str, Any]) -> str:
"""Extract a user object and return it as JSON using explicit schema keys."""
schema_fields = ("id", "name", "email", "is_active")
user_subset = {key: user[key] for key in schema_fields if key in user}
ret…Rotate API keys in Python by updating an .env template
Replace an old API key with a new one inside an .env template file, with a guard for missing keys.
import json
from pathlib import Path
def rotate_api_keys(env_template_path: Path, old_key: str, new_key: str) -> None:
"""Replace an old API key with a new one in an .env template file."""
content = env_template_path.read_text()
if old_key not in content:
print(f"Error: '{old_key}' not found in {e…ETL in Python: Extract CSV, Transform Dicts, Load JSON
Build a simple ETL pipeline that reads a CSV, normalizes keys and converts price to float, then writes structured JSON.
import csv
import json
from pathlib import Path
def etl_csv_to_json(csv_path: str, json_path: str) -> None:
"""Extract CSV, transform rows to dicts, load to JSON."""
with open(csv_path, mode='r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
records = list(reader)
# Trans…How to Clean and Format Data in Python
This code loads JSON data, cleans records by removing empty fields and normalizing text, then summarizes the results with counts and unique keys.
import json
from pathlib import Path
def load_data(filepath: str) -> dict:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def clean_records(records: list[dict]) -> list[dict]:
"""Remove empty fields and normalize text to lowercase."""…Test a Python Pipeline with Fixture Sample Rows
Test pipeline functions with sample rows provided by a pytest fixture, verifying required keys and value constraints.
import pytest
def get_value(data: dict, key: str):
return data.get(key)
def sample_rows():
return [
{"name": "Alice", "age": 30, "city": "London"},
{"name": "Bob", "age": 25, "city": "Paris"},
{"name": "Charlie", "age": 35, "city": "Berlin"},
]
@pytest.fixture
def sample_data(…Build a Simple Log Graph in Python
Create a basic one-dimensional bar chart from log lines by counting occurrences of leading numeric keys.
import heapq
def log_graph(log_lines: list[str]) -> str:
"""Build a simple per-line, one-dimensional visual graph from log entries."""
counts: dict[int, int] = {}
for line in log_lines:
tokens = line.split()
if tokens:
try:
idx = int(tokens[0])
exce…Route Messages to Handlers with a Python Dict
This code demonstrates a simple message routing pattern using a dictionary to map topic keys to handler functions, with a default handler for unmatched topics.
def route_message(message, routing_table):
"""Route a message to the correct handler based on the topic key."""
topic = message.get("topic", "default")
handler = routing_table.get(topic, routing_table.get("default"))
return handler(message)
def handle_orders(message):
return f"Orders handler proc…Exactly Once Idempotent Consumer Store in Python
A mock key-value store that guarantees exactly-once processing by rejecting duplicate message keys in a message or event stream.
from collections import defaultdict
class ExactlyOnceStore:
def __init__(self):
self.processed = defaultdict(set)
self.data = {}
def consume(self, key, value):
if key in self.data:
return False
self.data[key] = value
return True
def get_processed_count…How to mock RabbitMQ queue binding with routing keys in Python
A mock demonstration of binding a queue to an exchange with multiple routing keys in RabbitMQ using Python and pika, without a real broker connection.
import pika
import sys
def bind_queue_with_routing(channel, queue_name, exchange_name, routing_keys):
"""
Mock RabbitMQ queue binding with routing keys.
Prints the binding configuration instead of connecting to a real broker.
"""
for routing_key in routing_keys:
binding = {
"q…Cache Asides in Python with a Read-Through Loader
Implements a cache-aside pattern with a read-through loader that fetches missing keys from a backing data store and caches them.
class DataStore:
"""Mock database with a few records."""
def __init__(self):
self.data = {1: "Alice", 2: "Bob", 3: "Charlie"}
def get(self, key):
print(f"Loading key {key} from database")
return self.data.get(key)
class CacheAsideLoader:
"""Cache-aside pattern with a read-thr…Cache Warming with Python: Preload Hot Keys
Demonstrates a simple LRU-like cache with a warm method that preloads hot keys with mock values using OrderedDict.
import time
from collections import OrderedDict
class CacheWarm:
def __init__(self, capacity=3):
self.capacity = capacity
self.cache = OrderedDict()
self.hot_keys = []
def warm(self, keys):
"""Preload hot keys into cache with mock values."""
for key in keys:
…How to Implement Namespaced Cache Keys for Tenant Isolation in Python
Build a tenant-aware cache wrapper that prefixes keys with tenant and namespace, and test it with mocks.
from keyvaluestore import SimpleCache
from unittest.mock import patch
class TenantCache(SimpleCache):
def __init__(self, tenant_id, namespace="default"):
super().__init__()
self.tenant_id = tenant_id
self.namespace = namespace
def _key(self, key):
return f"tenant:{self.tenant_…How to Iterate Redis Keys with SCAN in Python
Iterate all Redis keys matching a pattern using the SCAN command with a mock client to simulate pagination.
import redis
def scan_keys(client, pattern="*", count=10):
keys = []
cursor = 0
while True:
cursor, batch = client.scan(cursor=cursor, match=pattern, count=count)
keys.extend(batch)
if cursor == 0:
break
return keys
if __name__ == "__main__":
# Mock Redis clien…How to use Redis MGET MSET pipeline in Python
Store multiple keys atomically and read them efficiently with Redis MSET/MGET, then batch commands with a pipeline to cut round trips.
import redis # v4.x+ required
r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)
# Sample data to store
r.flushdb()
data = {"name": "Alice", "age": "30", "city": "Berlin"}
# MSET: store multiple key-value pairs in one command
r.mset(data)
# MGET: fetch multiple keys in one round trip
keys =…How to Mock a Baggage Context (Key-Value Store) in Python
This code implements an in-memory key-value mock of a baggage context, letting you set, get, check, and delete keys for tracing-style metadata.
class BaggageContext:
def __init__(self):
self._store = {}
def set(self, key, value):
self._store[key] = value
return value
def get(self, key, default=None):
return self._store.get(key, default)
def has(self, key):
return key in self._store
def delete(sel…How to Redact Secrets from Log Messages in Python
Build a lightweight RedactingFormatter class that replaces sensitive tokens like passwords and API keys with [REDACTED] before log messages are printed.
class RedactingFormatter:
def __init__(self, secrets):
self.secrets = secrets
def redact(self, message):
for secret in self.secrets:
message = message.replace(secret, "[REDACTED]")
return message
def format(self, record):
message = record["message"]
ret…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) …Modeling a Hive Metastore Table Schema in Python
A dataclass that mimics a Hive metastore table schema—columns, partition keys, storage format, and location—with helper methods for description and mutation.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class HiveTable:
"""Simple mock of a Hive metastore table schema."""
name: str
database: str = "default"
columns: List[Dict[str, str]] = field(default_factory=list)
partition_keys: List[Dict[str, str]] = f…Browse by section
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