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Declarations that automatically generate initialization, representation, equality, and hashing methods for Python classes.
Distinct from Kotlin Data Class Declarations: Distinct from Kotlin Data Class Declarations: focuses on Python's attrs library for data class generation, not Kotlin's language feature.
Explore 5 awesome GitHub repositories matching data & databases · Python Data Class Declarations. Refine with filters or upvote what's useful.
HTTParty este o bibliotecă client HTTP pentru Ruby concepută pentru construirea de clienți API standardizați. Abstractizează primitivele de rețea de nivel scăzut într-o interfață de nivel înalt, permițând dezvoltatorilor să încapsuleze URI-urile de bază și configurațiile cererilor în clase pentru a menține interacțiuni consistente cu serviciile web la distanță. Biblioteca automatizează transformarea datelor brute de răspuns de rețea în structuri native Ruby folosind un sistem bazat pe registru care parsează conținutul pe baza headerelor de răspuns. De asemenea, oferă mecanisme specializate pentru trimiterea datelor de formular multipart și streaming-ul payload-urilor mari pentru a menține eficiența memoriei în timpul transferurilor de fișiere. Proiectul acoperă o gamă largă de capabilități de rețea, inclusiv execuția operațiunilor HTTP standard, transmisia payload-urilor JSON și gestionarea conexiunilor TLS securizate. Include instrumente pentru autentificarea cererilor, decompresia răspunsurilor și logarea traficului de rețea în scopuri de depanare. Biblioteca oferă, de asemenea, o interfață de linie de comandă pentru interogarea serviciilor web și vizualizarea datelor de răspuns ca obiecte structurate.
Allows defining base URIs and request defaults within Ruby classes to encapsulate service-specific settings.
attrs is a Python library that automatically generates initialization, representation, equality, hashing, and ordering methods from declarative class attribute definitions. At its core, it provides a class decorator metaprogramming framework that intercepts class creation to rewrite the class body, producing dunder methods without manual boilerplate. The library includes a comprehensive attribute validation toolkit with built-in validators for type checks, range constraints, regex matching, length limits, and logical composition of validation rules. The library distinguishes itself through it
Lists attributes and automatically generates initialization, representation, equality, and hashing methods.
Tortoise ORM is an asynchronous object-relational mapper for Python that mirrors Django's model and queryset API while running on asyncio. It defines database tables as Python classes with typed fields and supports foreign key, many-to-many, and one-to-one relations, providing a chainable query API for filtering, annotating, grouping, and prefetching related objects without blocking the event loop. The ORM includes a built-in migration engine that detects model changes, generates migration files, and applies or reverts schema changes through a command-line tool. It connects to PostgreSQL, MyS
Defines database tables as Python classes with typed fields and relations, automatically generating schema.
Flask-SQLAlchemy is a toolkit that integrates the SQLAlchemy relational database toolkit with the Flask web framework. It enables relational data modeling by defining database table structures as Python classes and manages the persistence and retrieval of database records within a web application. The project binds database session lifecycles to the active application request context to ensure automatic connection cleanup. It provides specialized utilities for web data access, including query result pagination and a mechanism to automatically trigger 404 Not Found responses when a requested d
Creates table structures using declarative classes that map application objects to database rows.
Flask-SQLAlchemy is a relational database toolkit that integrates the SQLAlchemy object-relational mapper into web applications. It serves as a database session manager and schema toolkit, providing the necessary infrastructure to define data models and execute queries within a request lifecycle. The project is distinguished by its multi-database routing engine, which uses bind-keys to map different models to multiple distinct database engines. It also includes a SQL query auditing tool that captures and logs executed statements and timing data for a single request to identify performance bot
Uses a declarative class system to automatically manage database metadata and naming conventions.