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Return Proper HTTP Status Codes Table in Python
Mock HTTP status code table with proper numeric and textual representations, including formatted status lines and a filtered table view.
# Mock HTTP status code table with proper numeric and textual representations
codes = {
200: "OK",
201: "Created",
204: "No Content",
301: "Moved Permanently",
302: "Found",
304: "Not Modified",
400: "Bad Request",
401: "Unauthorized",
403: "Forbidden",
404: "Not Found",
50…
How to Propagate Context Variables with asyncio in Python
Use Python's ContextVar with asyncio to carry deadline information across concurrent tasks and propagate context automatically.
import asyncio
from contextvars import ContextVar
from datetime import datetime
deadline = ContextVar("deadline", default=None)
async def worker(name):
current = deadline.get()
if current:
print(f"{name} sees deadline: {current}")
else:
print(f"{name} sees no deadline")
await asyncio.…
How to implement rate limiting in Python
Build a simple sliding-window rate limiter in Python that enforces a max number of calls per time period and formats data with timestamps.
import time
class RateLimiter:
def __init__(self, max_calls, period):
self.max_calls = max_calls
self.period = period
self.calls = []
def allow(self):
now = time.time()
# Remove calls older than the period window
self.calls = [t for t in self.calls if now -…
Generate Prometheus Text Exposition Format in Python
Mock a Prometheus metrics endpoint by formatting metrics into the text exposition format with HELP, TYPE, and sample lines.
import time
from random import randint
# Mock a Prometheus metrics endpoint output
metrics = {
"http_requests_total": {
"help": "Total number of HTTP requests",
"type": "counter",
"samples": [
{"labels": {"method": "get", "code": "200"}, "value": randint(1000, 9999)},
…
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 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 Build an Anti-Corruption Layer in Python
Translate messy legacy system data into a clean domain model using an anti-corruption layer in Python.
class MockLegacySystem:
"""Simulates a legacy system with messy data formats."""
def get_user_data(self):
# Legacy format: fields are abbreviated and types are inconsistent
return {
"usr_id": "USR-123",
"usr_nm": "john_doe",
"email_addrs": "John.Doe@example.c…
How to Explode an Array Column in Python
This code demonstrates a mock explode operation that converts an array column into multiple rows, similar to Spark's explode function.
import json
def explode_array_column(data, column):
"""Mock explode: split array column into multiple rows."""
exploded = []
for row in data:
values = row.get(column, [])
for value in values:
new_row = dict(row)
new_row[column] = value
exploded.append(n…
Mock RDD in Python: Simulate Spark RDD Lazy Transformations
Simulate Apache Spark RDD behavior in Python with lazy maps, filters, partitions, and a collect action.
import random
def mock_rdd(data, num_slices=2):
"""
A simple simulation of Spark RDD behavior with lazy evaluation,
transformations, and an action.
"""
class SimpleRDD:
def __init__(self, data, num_slices=2):
self.data = data
self.num_slices = num_slices
…
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…
Compare Model A vs Model B Metrics in Python
A script that simulates and compares metrics between two ML models, showing a formatted diff table for quick insight.
import random
def compare_a_b(samples=5):
"""Mock comparison of model A vs model B predictions."""
metrics = ["accuracy", "precision", "recall", "f1"]
print(f"{'Metric':<12}{'Model A':>10}{'Model B':>10}{'Diff':>10}")
print("-" * 42)
random.seed(42)
for metric in metrics:
a = round(r…
How to Compute a Confusion Matrix in Python
Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.
from collections import defaultdict
def compute_confusion_matrix(y_true, y_pred, labels):
"""Compute confusion matrix using Python dicts and nested lists."""
label_index = {label: i for i, label in enumerate(labels)}
matrix = [[0] * len(labels) for _ in range(len(labels))]
for true, pred in zip(y…
Build a Full Text Search Index in Python
Create a simple inverted index for full-text search with the standard library, supporting multi-word AND queries across documents.
import re
from collections import defaultdict
class SimpleTextIndex:
def __init__(self):
self.index = defaultdict(list)
self.documents = {}
def add_document(self, doc_id, text):
self.documents[doc_id] = text
words = set(re.findall(r'\w+', text.lower()))
for word in wo…
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 Expand a Contract and Migrate Data in Python
Expand an old data contract by renaming fields and adding defaults, then migrate to a final version with deepcopy isolation.
import json
from copy import deepcopy
# Mock data representing a user record (old contract)
old_contract = {
"id": 1,
"name": "Alice",
"email": "alice@example.com",
"age": 30,
"status": "active"
}
# Expanded contract: adds fields with defaults and renames some fields
expand_rules = {
"id": "u…
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