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How to Export a Conda Environment YAML File in Python
Generate a mock conda environment YAML export with a reusable Python function and the PyYAML library.
import yaml
def conda_env_mock(name="demo_env", channels=None, packages=None):
channels = channels or ["defaults"]
packages = packages or [
"python=3.11",
"pip",
"numpy=1.24.3",
"pandas=2.0.3",
]
env_dict = {
"name": name,
"channels": channels,
…How to Generate a Mock Rollbar Error Report in Python
Create a realistic fake Rollbar error report with random timestamps, levels, messages, and counts for testing and demos.
import json
import random
import time
from datetime import datetime, timedelta
def mock_rollbar_report(n_errors=5):
messages = [
"TypeError: unsupported operand type(s) for +: 'int' and 'str'",
"KeyError: 'user_id'",
"ValueError: invalid literal for int() with base 10: 'abc'",
"At…How to Generate a Mock devcontainer.json Config in Python
Build a reproducible devcontainer.json file with Python, composing name, image, extensions, forwarded ports, and a post-create command as a dict.
import json
from pathlib import Path
def create_devcontainer_config(
image: str = "mcr.microsoft.com/devcontainers/python:3.11",
name: str = "python-dev-container",
ports: list[int] | None = None,
post_create: str | None = None,
) -> dict:
config = {
"name": name,
"image": image,
…How to Run Coverage Report and Generate HTML in Python
Use the coverage module to measure test coverage, save the report, and generate an HTML report in Python.
import coverage
import unittest
def add(a, b):
return a + b
class TestAdd(unittest.TestCase):
def test_add_positive(self):
self.assertEqual(add(2, 3), 5)
if __name__ == "__main__":
cov = coverage.Coverage(source=["__main__"])
cov.start()
suite = unittest.defaultTestLoader.loadTestsFro…How to configure ruff linter rules in pyproject.toml with Python
This Python script generates a pyproject.toml file with ruff linter rules, including selected and ignored rules, per-file ignores, and complexity limits.
from pathlib import Path
def configure_ruff_rules(project_dir: str = "my_project") -> None:
"""Create a pyproject.toml with ruff linter rules for mock usage."""
pyproject_path = Path(project_dir) / "pyproject.toml"
pyproject_path.parent.mkdir(parents=True, exist_ok=True)
config = """[tool.ruff]
line-…Build a Python Performance Profiler That Generates Readable Reports
Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.
import cProfile
import pstats
import io
from pathlib import Path
def slow_function():
total = 0
for i in range(500_000):
total += i ** 2
return total
def fast_function():
total = sum(i * i for i in range(500_000))
return total
def profile_functions():
profiler = cProfile.Profile()
…How to Build a Producer-Consumer Pattern with asyncio.Queue in Python
This code implements a classic producer-consumer pattern using asyncio.Queue to coordinate one producer task that generates items and two consumer tasks that process them concurrently, with a sentinel value to signal completion.
import asyncio
import random
async def producer(queue, item_count):
for i in range(item_count):
item = random.randint(1, 100)
await queue.put(item)
print(f"Produced: {item}")
await asyncio.sleep(0.1)
await queue.put(None) # Sentinel to signal end
async def consumer(queue, n…Fuzz Test Random Bytes Input Crash in Python
A simple fuzz test generates random byte inputs and runs a parser to find unexpected crashes.
import random
def parse_header(data: bytes) -> dict:
"""Parse a fake binary header format."""
if len(data) < 8:
raise ValueError("header too short")
magic = data[:4]
if magic != b'PARS':
raise ValueError("bad magic")
version = data[4]
if version != 1:
raise ValueErro…Generate Fake User Data with Faker in Python
Use the Faker library to generate realistic fake user profiles with names, emails, phone numbers, and addresses for tests or demos.
from faker import Faker
fake = Faker()
def generate_user():
return {
"name": fake.name(),
"email": fake.email(),
"phone": fake.phone_number(),
"address": fake.address().replace("\n", ", "),
}
if __name__ == "__main__":
user = generate_user()
for key, value in user.ite…How to Use Hypothesis Strategies for Lists of Text in Python
Generate random lists of non-empty strings with Hypothesis and verify that joining them with a comma-and-space separator meets expected length and containment invariants.
from hypothesis import given, strategies as st
from hypothesis import example
@given(st.lists(st.text(min_size=1, max_size=10), min_size=1, max_size=5))
def test_joined_string_length(items):
"""Each text is non-empty; a joined string should be at least as long
as the number of items (separator adds character…Generate an OpenAPI Spec from Mock Routes in Python
This Python script generates an OpenAPI 3.0 specification from a simple mock routes dictionary, mapping each HTTP method to response examples.
import json
from pathlib import Path
def generate_openapi_spec(routes: dict, title: str = "Mock API", version: str = "1.0.0") -> dict:
paths = {}
for route, methods in routes.items():
path_item = {}
for method, response_data in methods.items():
method = method.lower()
…How to Add a Correlation ID Tracing Header in Python
A mock middleware generates or preserves a correlation ID header and logs structured JSON messages with it for API request tracing.
import uuid
import json
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class Request:
headers: dict = field(default_factory=dict)
def get(self, key, default=None):
return self.headers.get(key, default)
class CorrelationIdMiddleware:
def __init__(self, header_name…How to Propagate X-Request-ID in Python
Generate a unique request ID when one is missing and pass it through API calls for distributed tracing.
import uuid
def generate_request_id() -> str:
"""Generate a unique request ID similar to X-Request-ID header."""
return str(uuid.uuid4())
def propagate_request_id(request_id: str | None) -> str:
"""Return the request ID for propagation, generating one if missing."""
if request_id:
return re…How to Aggregate Periodic Snapshot Data in Python
Generates mock snapshot data and groups values into periods to compute average aggregates with Python's standard library.
import random
from collections import defaultdict
def snapshot_aggregate(n=10, period=3):
data = defaultdict(list)
for i in range(n):
key = f"item_{i % period}"
data[key].append(random.randint(1, 100))
return dict(data)
def aggregate_periodic(snapshots, period=3):
result = {}
for …How to Build a Mock Change Data Capture Event Stream in Python
Generate a deterministic list of mock CDC events with event IDs, stream positions, payloads, and timestamps for testing streaming pipelines.
from itertools import count
from random import choice, randint, seed
from datetime import datetime, timedelta
seed(42) # Make output deterministic
event_types = ["INSERT", "UPDATE", "DELETE"]
table_names = ["users", "orders", "products", "payments"]
counter = count(1)
def mock_cdc_event(stream_index: int) -> dict:
…How to create a stable cache key from function arguments in Python
Generate a stable SHA-256 cache key from normalized function arguments, with keyword order normalized and tests using mocks.
import hashlib
import json
from unittest.mock import Mock
def make_cache_key(*args, **kwargs):
"""Normalize args/kwargs into a stable hash key for caching."""
normalized = {
"args": [repr(arg) for arg in args],
"kwargs": {key: repr(value) for key, value in sorted(kwargs.items())}
}
pa…Generate Mock CPU and Memory Metrics in Python
Build a mock_host_metrics() generator that outputs realistic CPU and memory usage percentages for monitoring demos and tests.
import time
import random
def mock_host_metrics():
"""Generate mock CPU and memory metrics for a host."""
cpu_percent = round(random.uniform(10.0, 95.0), 1)
memory_percent = round(random.uniform(20.0, 90.0), 1)
memory_used_mb = round(random.uniform(512, 8192), 1)
return {
"timestamp": in…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)},
…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…Generate Synthetic SRE Metrics and Calculate Availability in Python
Create realistic service metrics with random latency, error rate, and request counts, then compute availability and summarize the stream for SLO checks.
from datetime import datetime, timedelta
import random
def generate_service_metrics(service_name: str, minutes: int = 30) -> list[dict]:
"""Generate synthetic SRE metrics for a service across recent minutes."""
metrics = []
now = datetime.now()
for i in range(minutes):
timestamp = now - t…How to Calculate Apdex Score from Latency Data in Python
Generate simulated latency samples and compute the Apdex score to gauge user satisfaction with an application's performance.
import random
import statistics
def generate_latencies(count=100, base=100, stddev=30):
return [max(0, random.gauss(base, stddev)) for _ in range(count)]
def apdex(latencies, threshold=200):
satisfied = sum(1 for lat in latencies if lat < threshold)
tolerating = sum(1 for lat in latencies if lat >= thres…How to Calculate Percentile Latency in Python
Generate mock latency samples with occasional spikes and compute 50th, 90th, 95th, and 99th percentile values in milliseconds.
import random
import statistics
def generate_latency_samples(n=1000):
"""Generate realistic mock latency data (ms) with occasional spikes."""
samples = []
for _ in range(n):
# Normal case: ~50ms with jitter
base = random.gauss(50, 5)
# 2% spike chance: slow downstream or GC pause
…How to Create a Deep Health Check Database in Python
Setup a SQLite-backed health check database, insert mock data with response times and statuses, and generate a report ordered by most recent check.
import sqlite3
from datetime import datetime, timedelta
from pathlib import Path
DB_PATH = Path("deep_health_check.db")
def setup_database():
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS health_checks (
id INTEGER PRIMARY KEY AU…How to Create a Deployment Environment Tag in Python
Generate a standardized deployment tag string by combining service and environment names with an f-string.
def mock_env_tag(service, environment):
return f"{service}-{environment}"
if __name__ == "__main__":
service = "api-gateway"
environment = "production"
tag = mock_env_tag(service, environment)
print(f"Deployment tag: {tag}")Browse by section
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