semvec/pypi-packages huggingface dataset:
langchain 1.2.10
Building applications with LLMs through composability
License: MIT
Requires Python: <4.0.0,>=3.10.0

# 🦜️🔗 LangChain

[![PyPI - Version](https://img.shields.io/pypi/v/langchain?label=%20)](https://pypi.org/project/langchain/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)

Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.

## Quick Install

```bash
pip install langchain
```

## 🤔 What is this?

LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and [more](https://docs.langchain.com/oss/python/integrations/providers/overview). LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.

We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.

LangChain [agents](https://docs.langchain.com/oss/python/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)

## 📖 Documentation

For full documentation, see the [API reference](https://reference.langchain.com/python/langchain/langchain/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview). You can also chat with the docs using [Chat LangChain](https://chat.langchain.com).

## 📕 Releases & Versioning

See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.

## 💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
statslibx 0.2.2
StatsLibx - Librería de estadística descriptiva, inferencial y computacional
License: MIT
Requires Python: >=3.8

# 📦 StatsLibX

StatsLibX es un paquete de Python diseñado para proporcionar una solución sencilla, eficiente y flexible para manejar volumenes de datos.

Este proyecto surge con la idea de ofrecer una alternativa moderna, intuitiva y ligera que permita a desarrolladores y entusiastas integrar la **estadistica descriptiva, inferencial y computacional (En desarrollo)** sin complicaciones, con multiples funcionalidades y utilidades pensadas para el futuro.

GitHub del Proyecto: [https://github.com/GhostAnalyst30/StatsLibX](https://github.com/GhostAnalyst30/StatsLibX)

## ✨ Características principales

- ⚡ Rápido y eficiente: optimizado para ofrecer un rendimiento suave incluso en tareas exigentes.

- 🧩 Fácil de usar: una API limpia para que empieces en segundos.

- 🔧 Altamente extensible: personalízalo según tus necesidades.

- 📚 Documentación clara: ejemplos simples y prácticos.

- 🔮 Diseñado con visión a futuro: construido para escalar y adaptarse.

## 🚀 Ejemplo rápido
```python
from statslibx import DescriptiveStats, InferentialStats, UtilsStats
from statslibx.datasets import load_iris()

data = load_iris()

stats = DescriptiveStats(data) # InferentialStats(data), UtilsStats()

stats.summary()
```
Para ver mas funciones: [https://github.com/GhostAnalyst30/StatsLibX/blob/main/how_use_statslibx.ipynb](https://github.com/GhostAnalyst30/StatsLibX/blob/main/how_use_statslibx.ipynb)

##  📦 Instalación
```bash
pip install statslibx
```

## 👩‍💻 ¡Usalo en la terminal! (De forma preliminar)
```bash
statslibx describe .\archive.csv # Devuelve una descripcion de la data
statslibx quality .\archive.csv # Devuelve la calidad de los datos
statslibx preview .\archive.csv # Devuelve una visualizacion de los datos
```

🤝 Contribuciones

¡Todas las mejoras e ideas son bienvenidas! 

E-mail: ascendraemmanuel@gmail.com