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How to Mock Auto Scaling Policy Scale Out in Python
Define a mock auto-scaling function that scales out capacity by a factor up to a max, simulating AWS-like events.
def mock_scale_out(current_capacity: int, max_capacity: int, scale_factor: int = 1) -> tuple:
"""
Mock auto-scaling policy: scales out by the specified factor
if capacity allows, capped at max_capacity.
"""
if current_capacity >= max_capacity:
return current_capacity, False
new_cap…How to Mock GCP Cloud Functions HTTP Events in Python
Simulate a GCP Cloud Functions HTTP event with a Python mock handler that constructs a realistic event payload and returns a JSON response.
import json
from datetime import datetime, timezone
def mock_http_event(data):
"""Simulate a GCP Cloud Function HTTP event."""
event = {
"event_id": "mock-event-12345",
"timestamp": datetime.now(timezone.utc).isoformat(),
"event_type": "google.cloud.functions.http",
"resource"…How to Parse Cloud JSON Data in Python
A helper function that safely parses JSON payloads from cloud services into a clean dict with defaults and error handling.
import json
from typing import Dict, Any
def parse_cloud_data(payload: str) -> Dict[str, Any]:
"""Parse a JSON payload from a cloud service into a clean dict."""
try:
data = json.loads(payload)
return {
"status": data.get("status", "unknown"),
"region": data.get("region…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…Data Conversion Helper Functions in Python
A set of beginner-friendly helper functions to convert between JSON strings and Python data, parse dates, and read/write files using pathlib.
from datetime import datetime
from pathlib import Path
import json
def to_json(data, indent=2):
"""Convert Python data to pretty-printed JSON string."""
return json.dumps(data, indent=indent, default=str)
def from_json(json_string):
"""Parse JSON string back into Python data."""
return json.loads(jso…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 Format Data with Python's datetime and JSON Helpers
A beginner-friendly set of helper functions to format dates and safely read/write JSON files in Python.
from datetime import datetime
from pathlib import Path
import json
def format_today(pattern: str = "%Y-%m-%d") -> str:
"""Return today's date formatted with the given pattern."""
return datetime.now().strftime(pattern)
def load_json(file_path: str) -> dict:
"""Read and parse a JSON file safely."""
…How to Mock Fabric Connections in Python for Task Testing
Create a lightweight MockConnection class to replace fabric.Connection and test task functions without SSH.
from fabric import Connection
class MockConnection:
"""Minimal mock of fabric.Connection for task testing."""
def __init__(self):
self.commands = []
def run(self, command, **kwargs):
self.commands.append(command)
return f"OK: {command}"
def deploy(conn):
"""Deploy the app:…How to Mock Twine Upload to TestPyPI in Python
Simulate a twine upload to TestPyPI with a dry-run mock function that validates distribution files and prints the intended upload action without any network call.
import subprocess
import sys
# Mock twine upload to TestPyPI using subprocess dry-run
def mock_twine_upload(dist_file: str, repo_url: str = "https://test.pypi.org/legacy/") -> None:
"""Simulate twine upload by checking dist file and printing intended action."""
if not dist_file.endswith((".whl", ".tar.gz")):
…How to Parametrize Tests in Python with pytest
This code demonstrates how to use pytest's @pytest.mark.parametrize decorator to run a single test function against multiple input sets, ensuring comprehensive coverage with minimal code duplication.
import pytest
def multiply(a, b):
return a * b
@pytest.mark.parametrize("x, y, expected", [
(2, 3, 6),
(4, 5, 20),
(0, 10, 0),
(7, 1, 7),
])
def test_multiply(x, y, expected):
result = multiply(x, y)
assert result == expected, f"multiply({x}, {y}) = {result}, expected {expected}"
if _…How to Type Check a Mock with pyright in Python
Shows how pyright validates a mock function against a TypedDict and Callable signature before runtime.
from typing import TypedDict, Callable
class User(TypedDict):
id: int
name: str
def get_user_name(user_id: int, get_user: Callable[[int], User]) -> str:
user = get_user(user_id)
return user["name"]
def mock_get_user(user_id: int) -> User:
return {"id": user_id, "name": f"User {user_id}"}
if…Mock pip-compile to Resolve Requirements in Python
A mock function that mimics pip-compile by converting a requirements.in file into pinned, locked package versions.
import subprocess
import tempfile
from pathlib import Path
def compile_requirements_mock(requirements_in: str) -> str:
"""Mock pip-compile: resolve a simple requirements.in into a locked format."""
lines = [line.strip() for line in requirements_in.splitlines() if line.strip() and not line.startswith("#")]
…How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Use ThreadPoolExecutor in Python for Parallel Processing
Use ThreadPoolExecutor with executor.map to run a function over many inputs concurrently and collect ordered results.
def worker(item):
return item * item
if __name__ == "__main__":
from concurrent.futures import ThreadPoolExecutor
numbers = list(range(1, 11))
with ThreadPoolExecutor(max_workers=4) as executor:
results = list(executor.map(worker, numbers))
print("Input: ", numbers)
print("Results:", …How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…How to Use pool.map for CPU-Bound Tasks in Python
Distribute CPU-intensive functions across processes with multiprocessing.Pool.map and measure the performance gain.
from multiprocessing import Pool
import time
def cpu_bound_task(n):
"""Mock CPU-bound work: compute sum of squares."""
total = 0
for i in range(n):
total += i * i
return total
if __name__ == "__main__":
numbers = [10_000_000, 12_000_000, 8_000_000, 15_000_000]
start = time.perf_count…How to Vectorize a Function with a Pure Python Fallback
Create a decorator that calls a scalar function directly for a single value and routes list inputs to a pure-Python fallback for vectorized processing without NumPy.
import math
def fallback_vectorize(func, fallback=None):
"""Vectorize a scalar function with a pure-Python fallback for lists."""
if fallback is None:
fallback = lambda x: [func(i) for i in x]
def wrapped(*args):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
retur…How to set a timeout with asyncio.wait_for in Python
Use asyncio.wait_for to bound an async function with a timeout, catching TimeoutError when it exceeds the limit.
import asyncio
async def slow_task():
await asyncio.sleep(3)
return "finished"
async def main():
try:
result = await asyncio.wait_for(slow_task(), timeout=1)
print(result)
except asyncio.TimeoutError:
print("Task timed out")
if __name__ == "__main__":
asyncio.run(main())How to use ThreadPoolExecutor for concurrent tasks in Python
Run blocking functions in parallel with ThreadPoolExecutor and as_completed, cutting total runtime from 5 sequential sleeps to about 1 second.
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
def fetch_data(item):
"""Simulate a slow operation with a fixed delay."""
time.sleep(0.2)
return item * 2
def main():
items = [1, 2, 3, 4, 5]
start = time.perf_counter()
with ThreadPoolExecutor(max_workers=3) as ex…Fix and Test a Regression Bug in Python with Unit Tests
This code implements a circle area function that raises ValueError for negative radii, then runs basic tests and a regression check for that edge case.
import math
def calculate_area(radius):
"""Calculate the area of a circle given its radius."""
if radius < 0:
raise ValueError("Radius cannot be negative")
return math.pi * radius ** 2
def main():
test_cases = [0, 1, 2.5, 5, 10]
print("Circle Area Calculator")
print("-" * 30)
…How to Assert Exceptions in Python with pytest.raises
Use pytest.raises as a context manager to assert that a function raises an expected exception and inspect its message in pytest tests.
import pytest
def divide(a, b):
if b == 0:
raise ValueError("Cannot divide by zero")
return a / b
def test_divide_by_zero():
with pytest.raises(ValueError) as exc_info:
divide(10, 0)
assert str(exc_info.value) == "Cannot divide by zero"
assert "zero" in str(exc_info.value)
def te…How to Group Data by Key in Python with Type Hints
Group a list of dictionaries by a specified key using a typed helper function and print a summary of each group.
from typing import Any, Dict, List, TypeVar, Union
T = TypeVar("T")
def group_by(data: List[Dict[str, Any]], key: str) -> Dict[Any, List[Dict[str, Any]]]:
"""Group a list of dictionaries by a given key."""
grouped: Dict[Any, List[Dict[str, Any]]] = {}
for item in data:
value = item.get(key)
…How to Mock open() in Python for Reading File Data
This example shows how to mock Python's built-in open() function using unittest.mock to simulate file reading without touching the disk.
import builtins
from unittest.mock import patch
def read_file_data(filename):
with open(filename, 'r') as f:
return f.read()
def mock_read_data():
fake_data = "This is mocked file content"
class FakeFile:
def __enter__(self):
return self
def __exit__(self, *args):…Browse by section
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