Tags: python* + pydantic*

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  1. A list of 10 Python libraries that can improve your workflow and make coding easier.

    1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
    2. **tenacity:** Elegant retrying mechanism for functions that might fail.
    3. **diskcache:** Persistent caching system backed by a disk (SQLite).
    4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
    5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
    6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
    7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
    8. **pyrsistent:** Immutable data structures for functional programming.
    9. **structlog:** Library for creating structured, parseable logs.
    10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
    2025-07-18 Tags: , , , , , by klotz
  2. Model Context Protocol server to run Python code in a sandbox using Pyodide in Deno, isolated from the operating system.
    2025-04-06 Tags: , , , , , , , by klotz
  3. 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
  4. 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.
    2024-12-12 Tags: , , , by klotz
  5. # 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()}"
    2024-03-26 Tags: , , by klotz

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