Python Code
Samples
Easy snippets you can copy, study, and run in the browser editor.
How to Convert Data in Parallel with ThreadPoolExecutor in Python
This example demonstrates converting a list of items in parallel using ThreadPoolExecutor, showing performance gains over serial processing.
import time
from concurrent.futures import ThreadPoolExecutor
def convert_data(item):
"""Simulate a CPU/IO-bound conversion task."""
time.sleep(0.05) # simulate work
return item.upper()
if __name__ == "__main__":
items = [f"item_{i}" for i in range(20)]
start = time.perf_counter()
serial_…How to Validate Data with ThreadPoolExecutor in Python
This code shows how to validate a list of numbers concurrently using ThreadPoolExecutor, dramatically speeding up slow validation tasks by running them in parallel threads.
import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
@dataclass
class Result:
is_valid: bool
value: int
def validate(value: int) -> Result:
time.sleep(0.1) # simulate slow validation (API call, DB check)
return Result(is_valid=0 < value < 100, value=value…Dataclass with Type Hints Fields in Python
Create a data class with typed fields and default values, then instantiate and inspect it.
from dataclasses import dataclass
@dataclass
class Person:
name: str
age: int
email: str = "unknown@example.com"
is_active: bool = True
if __name__ == "__main__":
person = Person(name="Alice", age=30)
print(person)
print(f"Name: {person.name}, Age: {person.age}, Email: {person.email}, A…Design Data Helpers with Python TypedDict and Literal
Use TypedDict, Literal, and Union to define typed data shapes and parse values in Python.
from typing import TypedDict, Literal, Optional, Union, List
class User(TypedDict):
name: str
age: int
role: Literal["admin", "user", "guest"]
def describeUser(data: User) -> str:
return f"{data['name']} ({data['age']}) — {data['role']}"
def parse_value(item: Union[int, str, None]) -> str:
if it…Format Data with Type Hints in Python
Build a validated person dict with modern type hints and optional list handling.
from typing import Any, Dict, List, Optional, Union
JsonValue = Union[str, int, float, bool, None, List["JsonValue"], Dict[str, "JsonValue"]]
def format_person(name: str, age: int, hobbies: Optional[List[str]] = None) -> Dict[str, Any]:
"""Build a person dict with validated typing."""
if not name or age < 0:…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 Filter Data in Python with Type Hints
A reusable filter_data helper uses optional predicates and numeric bounds with modern Python type hints.
from typing import Iterable, TypeVar, Callable, Any
T = TypeVar("T")
def filter_data(
items: Iterable[T],
predicate: Callable[[T], bool] | None = None,
*,
min_value: float | None = None,
max_value: float | None = None,
) -> list[T]:
"""Filter items by predicate and/or numeric bounds."""
r…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):…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 Sort Data in Python
Sort sequences with type-safe helpers that handle mixed data with a string fallback.
from typing import Any, TypeVar, Protocol, Sequence, Iterable
T = TypeVar("T")
Comparable = TypeVar("Comparable", bound="Comparable")
class Sortable(Protocol):
def __lt__(self, other: Any) -> bool: ...
S = TypeVar("S", bound=Sortable)
def sort_data(data: Sequence[S], *, reverse: bool = False) -> list[S]:
"…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 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…Builder pattern for mocking complex objects in Python
Use a fluent Builder to construct realistic mock objects with defaults, enabling readable test data setup.
class User:
def __init__(self):
self.name = "default"
self.age = 0
self.email = "unknown@example.com"
self.address = "unknown"
def __repr__(self):
return f"User(name={self.name!r}, age={self.age}, email={self.email!r}, address={self.address!r})"
class UserBuilder:
…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 Build an MVP Presenter View Mock in Python
A minimal MVP (Model-View-Presenter) mock showing a Presenter controlling a SlideDeck model with slide navigation and typed state via dataclasses.
from dataclasses import dataclass, field
from typing import List
@dataclass
class SlideDeck:
title: str
slides: List[str] = field(default_factory=list)
current_index: int = 0
def next_slide(self) -> str:
if self.current_index < len(self.slides) - 1:
self.current_index += 1
…How to Implement a Data Helper Class in Python
Build a beginner-friendly DataHelper class using dataclasses and key system design patterns like Command, Strategy, and Map.
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
@dataclass
class DataHelper:
"""A beginner-friendly data utility with common system design patterns."""
data: List[Dict[str, Any]] = field(default_factory=list)
def add_record(self, r…How to Implement a Simple Event Bus in Python
Create a publish-subscribe event bus using dataclasses and defaultdict to decouple event producers from consumers.
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Callable, Dict, List, Set
@dataclass
class EventBus:
_subscribers: Dict[str, List[Callable]] = field(
default_factory=lambda: defaultdict(list)
)
def subscribe(self, event_type: str, handler: Callable…How to Implement the Prototype Pattern with Deep Copy in Python
Implements the Prototype design pattern using copy.deepcopy to clone complex objects without sharing mutable state.
import copy
from dataclasses import dataclass, field
from typing import List
@dataclass
class Engine:
horsepower: int
@dataclass
class Car:
brand: str
engine: Engine
accessories: List[str] = field(default_factory=list)
def clone_prototype(car: Car) -> Car:
return copy.deepcopy(car)
if __name__ …How to Implement the Repository Pattern in Python with an In-Memory Dict
Stores, retrieves, updates, and deletes user records in memory using a Repository abstraction over a plain dict, isolating data access from business logic.
class UserRepository:
def __init__(self):
self._storage = {}
self._next_id = 1
def create(self, name, email):
user_id = self._next_id
self._next_id += 1
self._storage[user_id] = {"id": user_id, "name": name, "email": email}
return self._storage[user_id]
def…Python MVC Pattern Example (Model-View-Controller)
A minimal, runnable Model-View-Controller (MVC) example in pure Python that separates data, presentation, and logic.
class Model:
def __init__(self):
self.data = {"title": "Initial Title", "content": "Initial Content"}
def get_data(self):
return self.data
def update_data(self, title=None, content=None):
if title:
self.data["title"] = title
if content:
self.data["c…Convert Protobuf to JSON and Dict in Python
Provides static helper methods to convert between protobuf messages, JSON strings, and Python dictionaries using the google.protobuf library.
from google.protobuf.json_format import MessageToJson, Parse
import json
class DataConverter:
"""Helper class to convert between protobuf messages and common formats."""
@staticmethod
def to_json(message, indent=2):
"""Convert a protobuf message to JSON string."""
return MessageToJson(me…Create a Data Helper in Python for gRPC-style APIs
This code builds a simple DataHelper class that mimics gRPC request/response handling with in-memory storage, JSON serialization, and basic CRUD operations for beginners.
import json
from dataclasses import dataclass, asdict
from typing import Dict, Any
@dataclass
class User:
user_id: int
name: str
email: str
class DataHelper:
"""Simple helper to demonstrate gRPC-like data handling for beginners."""
def __init__(self) -> None:
self._users: Dict[int, Use…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.