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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Extract Data by Type from a List in Python: Numbers and Strings
Loop through a mixed list to filter out numeric and string values into separate lists.
def extract_numbers(items):
"""Extract all numeric values from a mixed list."""
numbers = []
for item in items:
if isinstance(item, (int, float)) and not isinstance(item, bool):
numbers.append(item)
return numbers
def extract_strings(items):
"""Extract all string values from a…How to Validate List Data in Python
A beginner-friendly validation helper that checks if data is a list, enforces minimum length, and optionally verifies item types with clear error messages.
def validate_data(data, expected_types=None, min_length=1):
"""Validate that data is a non-empty list and optionally check item types."""
if not isinstance(data, list):
return False, f"Expected a list, got {type(data).__name__}"
if len(data) < min_length:
return False, f"List must have…How to check list items by type and emptiness in Python
Loop through a list with enumerate(), classify each item as empty, number, or text, and print a formatted status for each element.
def check_data(data):
"""Check each item in a list and print whether it's valid."""
for i, item in enumerate(data):
if item is None or item == "":
status = "empty"
elif isinstance(item, (int, float)):
status = "number"
else:
status = "text"
pr…Validate dataclass fields with __post_init__ in Python
Add custom validation to a Python dataclass inside __post_init__, raising ValueError or TypeError for invalid field values.
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class Product:
name: str
price: float
quantity: int = 1
category: Optional[str] = None
def __post_init__(self):
if not self.name or not isinstance(self.name, str):
raise ValueError("name must be a…How to Validate JSON Output Against a Dict Schema in Python
Validate JSON-like data against a simple dict schema with type checking and descriptive error messages using only the Python standard library.
from typing import Dict, Any, List, Union
def validate_json(data: Any, schema: Dict[str, str]) -> List[str]:
"""
Validate JSON-like data against a simple dict schema.
Schema format: {field_name: expected_type} where type is one of:
'str', 'int', 'float', 'bool', 'list', 'dict', 'any'
Returns list …How to Validate Data in a Python Pipeline
A helper module to validate common record types — email, positive integer, and non-empty string list — before processing data in a pipeline.
from typing import Any, Iterable
def is_valid_email(email: str) -> bool:
"""Basic email check: one '@', no spaces, dot after '@'."""
if "@" not in email or " " in email:
return False
local, _, domain = email.partition("@")
return bool(local) and "." in domain
def is_positive_int(value: Any)…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…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 Build a Data Validation Schema in Python
Create a lightweight validation schema using dataclasses and lambda validators to check fields in a dictionary.
import re
from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class Field:
name: str
validator: Callable[[Any], bool]
required: bool = True
def validate(self, value: Any) -> bool:
if not self.required and value is None:
return True
return …Browse by section
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