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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": […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…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 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 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)
…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_…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 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…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 Use Log Levels DEBUG INFO WARNING ERROR in Python
Demonstrates Python's logging levels (DEBUG, INFO, WARNING, ERROR) with basicConfig and a logger, showing how severity filtering controls output.
import logging
# Configure a mock logger to demonstrate log levels
logging.basicConfig(level=logging.DEBUG, format="%(levelname)s: %(message)s")
logger = logging.getLogger("mock_logger")
# Simulate events at each severity level
logger.debug("Detailed diagnostic info")
logger.info("General system operation")
logger.w…Compaction Small Files Mock in Python
Simulates a small-files compaction job by creating small mock files and merging them into a single output file using Python's standard library.
from pathlib import Path
import tempfile
import os
def create_small_files(directory: Path, file_count: int = 5, lines_per_file: int = 3):
"""Create several small mock files with sample content."""
directory.mkdir(exist_ok=True)
for i in range(file_count):
file_path = directory / f"part-{i:04d}.tx…How to Broadcast a Small Lookup Table in Python
Simulates broadcasting a small lookup table by iterating key-value pairs and emitting packed rows to subscribers with deterministic output.
import random
# Generate a deterministic mock broadcast of a small lookup table
# with 5 keys and random integer values (seeded for reproducibility)
data = {
"sensor_a": 22,
"sensor_b": 87,
"sensor_c": 43,
"sensor_d": 65,
"sensor_e": 31,
}
# Simulate a broadcast to subscribers by iterating and p…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…How to Mock Shadow Mode Inference in Python
Simulates running multiple candidate models in shadow mode by adding randomized delays and returning their outputs alongside a primary model's output.
import random
import time
def shadow_mode_inference(candidates, mock_delay=0.1):
"""
Simulates running multiple candidate models in 'shadow mode'
by adding tiny randomized delays and returning their outputs
alongside the primary model's output.
"""
primary_output = "primary: answer"
shado…Generate a Mock Multi-Armed Bandit Report in Python
Simulate a multi-armed bandit experiment with random pulls and rewards, then output a JSON report with per-arm statistics.
import random
import json
def generate_mock_bandit_report(num_arms=5, num_rounds=100, seed=42):
random.seed(seed)
arms = ["A", "B", "C", "D", "E"][:num_arms]
true_means = {arm: random.uniform(0.3, 0.7) for arm in arms}
pulls = {arm: 0 for arm in arms}
rewards = {arm: 0 for arm in arms}
for _ …How to Mock a GitHub Actions Workflow in Python
Build a dataclass-based model of a GitHub Actions workflow and simulate its execution to validate steps and outputs before deployment.
import json
from dataclasses import dataclass, asdict
from typing import List, Dict, Any
@dataclass
class Step:
name: str
run: str
@dataclass
class Job:
name: str
steps: List[Step]
runs_on: str = "ubuntu-latest"
@dataclass
class Workflow:
name: str
jobs: List[Job]
def to_github_a…How to simulate GitLab CI stages in Python
Build a lightweight Python mock of GitLab CI pipeline stages to test job sequencing and output locally.
def mock_gitlab_ci_stages():
stages = ["build", "test", "deploy"]
stage_status = {}
for stage in stages:
jobs = []
if stage == "build":
jobs = ["compile", "package"]
elif stage == "test":
jobs = ["unit", "integration", "e2e"]
elif stage == "deploy":…Browse by section
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Guide: free Python code samples library
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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.