pandasai 3.0.0

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications. It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more.

LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.

A pandas-based library to visualize and compare datasets.

Web Client for Visualizing Pandas Objects

PyOptimus is a Python library that brings together the power of various data processing engines like Pandas, Dask, cuDF, Dask-cuDF, Vaex, and PySpark under a single, easy-to-use API. It offers over 100 functions for data cleaning and processing, including handling strings, processing dates, URLs, and emails. PyOptimus also provides out-of-the-box functions for data exploration and quality fixing. One of the key features of PyOptimus is its ability to handle large datasets efficiently, allowing you to use the same code to process data on your laptop or on a remote cluster of GPUs.

In this blog post, we’ll utilize the powerful libraries Matplotlib, Numpy and Pandas to perform data generation and visualization. We’ll discuss the programming concepts, methods, and functionalities used in this script.

An extension to pandas describe function.

Mage is a tool for building and deploying data pipelines.

Visualizing large time series with plotly