Python Code
Samples
Easy snippets you can copy, study, and run in the browser editor.
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):…
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 Parse Data with Type Hints in Python
A beginner-friendly helper that parses simple dictionary- or list-like strings into typed Python structures using modern typing annotations.
from typing import Any, Dict, List, Union
def parse_data(raw: str) -> Union[Dict[str, Any], List[Any], str]:
"""Parse a simple string into structured data using type hints."""
cleaned = raw.strip()
if not cleaned:
return {}
if cleaned.startswith("{") and cleaned.endswith("}"):
…
How to Share Fixtures Across Tests with pytest conftest
Learn how to define pytest fixtures in conftest.py and control their scope (function, module, session) so every test in a directory reuses the same setup and teardown.
import pytest
@pytest.fixture
def sample_data():
"""Simple fixture available to all tests in this directory."""
return {"name": "Alice", "age": 30}
@pytest.fixture(scope="session")
def session_data():
"""Fixture created once per test session."""
return {"session_id": 12345}
@pytest.fixture(scope="mo…
How to Use Basic Type Hints (int, str) for Return Values in Python
Declare a simple function with int and str type hints and a typed return value in Python.
def greet(name: str, age: int) -> str:
return f"{name} is {age} years old."
if __name__ == "__main__":
print(greet("Alice", 30))
How to Use Literal Type Hints in Python
Use typing.Literal to restrict a function parameter to specific allowed string values and get static type checking.
from typing import Literal
def get_status_message(status: Literal["active", "inactive", "pending"]) -> str:
"""Return a message based on the status value."""
if status == "active":
return "Account is active"
elif status == "inactive":
return "Account is inactive"
else:
return "…
How to Use Python Type Hints for Beginners
Build a data helper module with basic type hints — Union, Optional, List, Dict, Any, and TypeVar — to make your code clearer and safer.
from typing import Any, Union, Optional, List, Dict, Tuple, Callable, TypeVar
T = TypeVar("T")
def describe(value: Any) -> str:
"""Return a human-readable description of the value's type."""
if isinstance(value, list):
return f"list of {len(value)} items"
elif isinstance(value, dict):
ret…
How to Use TypedDict and Dataclasses in Python
Create typed data structures with TypedDict and dataclasses, then use them as helper functions for describing objects in a type-safe way.
from typing import TypedDict, NotRequired, Optional
from dataclasses import dataclass
class User(TypedDict):
name: str
age: NotRequired[int]
email: Optional[str]
@dataclass
class Product:
id: int
title: str
price: float = 0.0
def describe_user(user: User) -> str:
age = user.get("age",…
How to Use TypedDict for Structured Dict Typing in Python
Define and use TypedDict to add type hints to dictionaries, improving code clarity and enabling static type checking in your Python projects.
from typing import TypedDict
class User(TypedDict):
name: str
age: int
email: str
def greet(user: User) -> str:
return f"Hello {user['name']}, age {user['age']}, contact {user['email']}"
if __name__ == "__main__":
alice: User = {"name": "Alice", "age": 30, "email": "alice@example.com"}
pr…
How to Use Union Type Hints in Python
This code demonstrates how to use Union type hints to specify that a parameter can accept multiple types (int, float, str) and handle them accordingly.
from typing import Union
def process_value(value: Union[int, float, str]) -> str:
if isinstance(value, (int, float)):
return f"Number: {value * 2}"
return f"String: {value.upper()}"
if __name__ == "__main__":
print(process_value(10))
print(process_value(3.14))
print(process_value("hello"))
How to Use mock.assert_called_with in Python
Verify that a MagicMock received a call with specific positional and keyword arguments using assert_called_with in unittest.
import unittest
from unittest.mock import MagicMock
class TestMockAssertions(unittest.TestCase):
def test_assert_called_with(self):
# Create a mock object
mock = MagicMock()
# Call the mock with specific arguments
mock.send_email("alice@example.com", subject="Greetings", body="Hel…
How to Validate Dataclass Fields with Python Type Hints
A beginner-friendly helper that checks if instance attributes match their declared type hints using dataclasses and get_type_hints.
from typing import Any, TypeVar, get_type_hints
from dataclasses import dataclass
T = TypeVar("T")
@dataclass
class User:
name: str
age: int
email: str
def validate_fields(obj: Any) -> dict[str, bool]:
"""Check if object attributes match declared type hints."""
hints = get_type_hints(obj.__class…
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 Write a Contract Test with Mock in Python
Use unittest.mock to verify a consumer's expectations match the provider's response shape in a Python contract test.
from unittest.mock import Mock
# Contract test: verify consumer expects data shape that provider delivers.
# We mock the provider and assert the consumer's calls match the agreed contract.
def fetch_user(provider_client, user_id):
"""Consumer code: expects provider to return {'id', 'name', 'email'}."""
respo…
How to Write a pytest Test Function with assert Equal in Python
Define simple pytest test functions that use assert to verify result equality and run them with pytest.main.
import pytest
def add(a, b):
return a + b
def test_add_positive_numbers():
result = add(2, 3)
assert result == 5
def test_add_negative_numbers():
result = add(-2, -3)
assert result == -5
def test_add_mixed_numbers():
result = add(2, -3)
assert result == -1
if __name__ == "__main__":
…
How to Write pytest Test Function Assert Equal in Python
Write three pytest test functions that assert the result of an add() function equals an expected numeric value.
import pytest
def add(a, b):
return a + b
def test_add_positive_numbers():
assert add(2, 3) == 5
def test_add_negative_numbers():
assert add(-1, -2) == -3
def test_add_mixed_numbers():
assert add(5, -3) == 2
if __name__ == "__main__":
pytest.main([__file__, "-v"])
How to mark known bugs with pytest xfail in Python
Use @pytest.mark.xfail to mark tests that are expected to fail due to known bugs, with optional strict mode to control pass/fail behavior.
import pytest
def divide(a: int, b: int) -> float:
if b == 0:
raise ZeroDivisionError("Cannot divide by zero")
return a / b
@pytest.mark.xfail(reason="Known bug: division returns int instead of float", strict=False)
def test_divide_integer_division():
result = divide(10, 4)
assert isinstanc…
How to use Optional type hint in Python
Use the Optional type hint to indicate a parameter can be a string or None, with an example function that handles both cases.
from typing import Optional
def greet(name: Optional[str]) -> str:
if name is None:
return "Hello, anonymous!"
else:
return f"Hello, {name}!"
if __name__ == "__main__":
print(greet("Alice"))
print(greet(None))
How to use unittest mock side_effect with a sequence in Python
Demonstrates using Mock.side_effect with a list to return different values per call and raise an exception at a specific call in unittest.
import unittest
from unittest.mock import Mock
class TestMockSideEffectSequence(unittest.TestCase):
def test_side_effect_sequence(self):
mock = Mock()
mock.side_effect = [1, 2, 3, Exception("boom")]
self.assertEqual(mock(), 1)
self.assertEqual(mock(), 2)
self.asser…
Interface Segregation with Fake Test Implementations in Python
Defines segregated abstract interfaces (Printer, Scanner) and uses a FakePrinter to record calls for unit testing without real resources.
from abc import ABC, abstractmethod
class Printer(ABC):
@abstractmethod
def print_document(self, doc: str) -> str:
pass
class Scanner(ABC):
@abstractmethod
def scan_document(self) -> str:
pass
class MultiFunctionPrinter(Printer, Scanner):
def print_document(self, doc: str) -> …
Create a Data Helper Class in Python
A reusable DataHelper class that saves and loads JSON and CSV files from a configurable base directory, with automatic header detection for CSV.
import json
import csv
from pathlib import Path
class DataHelper:
def __init__(self, base_path="."):
self.base_path = Path(base_path)
self.base_path.mkdir(exist_ok=True)
def save_json(self, data, filename):
path = self.base_path / filename
with open(path, "w") as f:
…
How to Aggregate Mock API Routes by Method in Python
Groups mock API routes by path and method, collecting response bodies and counts into a nested dictionary structure.
from collections import defaultdict
def aggregate_mock_routes(routes):
"""Aggregate mock API routes by method and aggregate their response bodies."""
aggregated = defaultdict(lambda: defaultdict(list))
for route in routes:
method = route["method"]
path = route["path"]
response = …
How to Build a Simple Service Discovery Registry in Python
A lightweight in-memory service registry class using a dict — register, deregister, and discover services with host, port, and version.
class ServiceRegistry:
def __init__(self):
self._services = {}
def register(self, name, host, port, version="1.0"):
self._services[name] = {
"host": host,
"port": port,
"version": version
}
def deregister(self, name):
return self._servic…
How to Build a Weighted Random Load Balancer in Python
A Python load balancer mock that distributes requests across servers based on configurable weights using a cumulative weighted random selection algorithm.
import random
from collections import Counter
SERVERS = {
"server-a": 50,
"server-b": 30,
"server-c": 20,
}
def weighted_random_server(servers: dict[str, int]) -> str:
"""Select a server based on its weight (higher weight = more likely)."""
total_weight = sum(servers.values())
rand = random.…
Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
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.