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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 _…pytest mark slow skip integration
Uses pytest markers to select fast tests, skip unfinished ones, and run integration checks with verbose output.
import pytest
def test_fast():
assert 1 + 1 == 2
@pytest.mark.slow
def test_slow():
import time
time.sleep(1)
assert 5 * 5 == 25
@pytest.mark.skip(reason="Not ready for production")
def test_skipped():
assert 2 + 2 == 5
@pytest.mark.integration
def test_integration():
database = {"users": […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 ProcessPoolExecutor for CPU Parallel Map in Python
Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.
from concurrent.futures import ProcessPoolExecutor
import math
def compute_square(num):
return num * num
def is_prime(n):
if n < 2:
return False
for i in range(2, int(math.sqrt(n)) + 1):
if n % i == 0:
return False
return True
if __name__ == "__main__":
numbers = rang…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 a Bounded Buffer with threading.Condition in Python
Implement a thread-safe bounded buffer using threading.Condition and show a producer–consumer example with exact output.
import threading
import time
import random
class BoundedBuffer:
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.condition = threading.Condition()
def put(self, item):
with self.condition:
while len(self.buffer) >= self.capacity:
…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…Profile Memory Usage with tracemalloc Snapshot Diff in Python
Use tracemalloc to take two memory snapshots, compute a diff, and print the top changes (size and count) by line number.
import tracemalloc
def profile_memory():
tracemalloc.start()
# Allocate some objects to track
data = [i * 2 for i in range(10000)]
text = "x" * 5000
nested = {"key": [1, 2, 3], "value": (4, 5)}
# Take first snapshot
snapshot1 = tracemalloc.take_snapshot()
# Free some mem…Capture stdout and stderr with pytest capsys
Use pytest's capsys fixture to capture and assert on standard output and error streams in your tests.
import pytest
# Function under test
def greet(name):
print(f"Hello, {name}!")
print(f"Error: {name} not found", file=sys.stderr)
def test_captures_stdout_and_stderr(capsys):
greet("Alice")
captured = capsys.readouterr()
assert "Hello, Alice!" in captured.out
assert "Error: Alice not foun…Characterization Test for Legacy Python Code
Capture the exact output of a legacy Python function for known inputs, creating a characterization test that documents current behavior before refactoring.
def legacy_behavior(value):
"""Legacy function that returns a tuple with unconventional types."""
if value == "special":
return None, "legacy-special"
elif value > 100:
return value, "large"
elif value > 0:
return value * 2, "positive-doubled"
elif value == 0:
…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…How to Parametrize pytest Tests with Multiple Input Cases in Python
This code shows how to use pytest's @pytest.mark.parametrize decorator to run the same test function across multiple input-output combinations, checking that an add function behaves correctly for each case.
import pytest
def add(a, b):
return a + b
@pytest.mark.parametrize("a,b,expected", [
(1, 2, 3),
(5, 5, 10),
(-1, 1, 0),
(0, 0, 0),
(10, -3, 7),
])
def test_add(a, b, expected):
assert add(a, b) == expected
if __name__ == "__main__":
pytest.main([__file__, "-v"])How to Run Test Coverage with pytest-cov in Python
Run pytest with coverage reporting using pytest-cov on a temporary project and see line-by-line coverage output.
import os
import subprocess
import tempfile
from pathlib import Path
def sample_function(x: int) -> int:
"""A simple function to demonstrate coverage."""
if x > 0:
return x * 2
else:
return -x
def run_pytest_with_coverage() -> str:
"""Run pytest with coverage on a temp project and r…How to Snapshot Test JSON with Mock in Python
Use pytest-snapshot to capture the exact output of a JSON-loading function, with and without mocking json.loads, so future changes are automatically detected.
import json
from unittest.mock import Mock, patch
import pytest
def load_config(data):
config = json.loads(data)
return {"host": config["host"], "port": config["port"]}
def test_load_config_snapshot(snapshot):
mock_data = json.dumps({"host": "localhost", "port": 8080, "extra": "ignored"})
result = …How to Test Hypotheses with Property-Based Check in Python
A Python search that checks an integer property (palindrome divisible by digit sum) and returns the first counterexample within a range, with exactly reproduced output from the code.
def is_property_satisfied(n):
"""
Demonstrates a mathematically inspired property:
checks whether n is both a palindrome and divisible by its digit sum.
"""
s = str(n)
if s != s[::-1]:
return False
digit_sum = sum(int(d) for d in s)
return digit_sum != 0 and n % digit_sum == 0
…How to Test Properties with Random Inputs in Python
Write a simple property-based test in Python using random string generation to verify that string invariants like reverse-twice identity and uppercase idempotence always hold.
import random
import string
def generate_random_string(length: int) -> str:
"""Generate a random alphanumeric string of given length."""
chars = string.ascii_letters + string.digits
return "".join(random.choice(chars) for _ in range(length))
def reverse_twice_is_identity(s: str) -> bool:
"""Propert…How to Use TypedDict for Data Validation in Python
Define a TypedDict schema and validate raw dictionary input with type hints for safer, more readable data handling.
from typing import Any, Dict, List, Optional, Union, TypedDict, Literal
class Product(TypedDict):
product_id: int
name: str
price: Union[int, float]
in_stock: bool
tags: Optional[List[str]]
def validate_product(data: Dict[str, Any]) -> Product:
product_id: int = int(data["product_id"])
na…How to Verify Formatted Output with an Approval Test in Python
Write a small Python approval test that verifies a function's exact formatted output using unittest.
import sys
from io import StringIO
import unittest
def generate_output(name, score):
return f"Player: {name} | Score: {score:03d}"
class TestFormattedOutput(unittest.TestCase):
def test_output_format(self):
expected = "Player: Alice | Score: 042"
result = generate_output("Alice", 42)
…Table-Driven Tests in Python (unittest)
Run a single unittest test against many input cases using a list of tuples and subTest.
import unittest
def add(a, b):
return a + b
class TestAddFunction(unittest.TestCase):
def test_add_with_table(self):
cases = [
(1, 2, 3),
(-1, 1, 0),
(0, 0, 0),
(2, -3, -1),
]
for x, y, expected in cases:
with self.subTest(x…How to Structure a Three-Tier Layered Architecture in Python
A mock three-tier architecture with presentation, business, and data layers that process a user request from input to response.
class PresentationLayer:
def __init__(self, business_layer):
self.business = business_layer
def handle_request(self, user_id):
print(f"[Presentation] Received request for user {user_id}")
data = self.business.process_user(user_id)
print(f"[Presentation] Response: {data}")
…Build a Mock REST API with PUT and GET in Python
A minimal mock REST server implementing idempotent PUT for resource replacement and GET for retrieval, built with Python's http.server module.
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
from urllib.parse import urlparse
mock_db = {}
class MockAPIHandler(BaseHTTPRequestHandler):
def do_PUT(self):
parsed = urlparse(self.path)
resource_id = parsed.path.strip("/").split("/")[-1]
content_length = int(self.…How to Implement Pagination with Offset and Limit in Python
A mock API pagination pattern that parses page and per_page query parameters, computes offset and limit, and slices a list of items for a specific page.
def paginate(items, page, per_page):
offset = (page - 1) * per_page
return items[offset:offset + per_page]
def parse_query_params(query_string):
params = {}
if query_string:
for pair in query_string.split("&"):
key, value = pair.split("=")
params[key] = value
page …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 deduplicate messages by ID in Python
Track seen message IDs in a set to skip duplicate messages and store unique content in a dict, with exact output showing which messages were added or skipped.
import time
class MessageStore:
def __init__(self):
self.seen_ids = set()
self.messages = {}
def add(self, message_id, content, timestamp=None):
timestamp = timestamp or time.time()
if message_id in self.seen_ids:
return False
self.seen_ids.add(message_…Browse by section
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