This article explores ten underrated Python libraries that can help automate tasks, debug faster, and improve coding efficiency.
- Rich: Terminal beautification
- PyWhatKit: Automation tasks
- Pydantic: Data validation
- Black: Code formatting
- HTTPie: API testing
- Typer: Building CLI applications
- IceCream: Debugging
- Poetry: Package management
- Faker: Generating fake data
- Pyppeteer: Browser automation
An analysis showing that structured outputs can sometimes perform worse than unstructured ones in certain tasks for different LLM models, emphasizing the importance of testing both approaches.
main.py
import json from pydantic import BaseModel, EmailStr, ValidationError, validator
class Employee(BaseModel): name: str age: int email: EmailStr department: str employee_id: str
@validator("employee_id")
def validate_employee_id(cls, v):
if not v.isalnum() or len(v) != 6:
raise ValueError("Employee ID must be exactly 6 alphanumeric characters")
return v
Load and parse the JSON data
with open("employees.json", "r") as f: data = json.load(f)
Validate each employee record
for record in data: try: employee = Employee(**record) print(f"Valid employee record: {employee.name}") except ValidationError as e: print(f"Invalid employee record: {record 'name' » }") print(f"Errors: {e.errors()}"
