> browse/registry

> pandas

Assists with loading, cleaning, transforming, and analyzing tabular data using pandas. Use when importing CSV/Excel/SQL data, handling missing values, performing groupby aggregations, merging datasets, working with time series, or building analysis-ready datasets. Trigger words: pandas, dataframe, csv, groupby, merge, time series, data cleaning.

> pandas-ai

PandasAI enables natural language queries on pandas DataFrames using LLMs. Learn to ask questions in plain English, generate charts, clean data, and integrate with OpenAI and local models for conversational data analysis.

> pandera

Expert guidance for Pandera, the Python library for validating pandas and Polars DataFrames with expressive schemas. Helps developers define data contracts, validate data pipelines, and catch data quality issues before they corrupt downstream systems.

> polars

Polars is a blazingly fast DataFrame library written in Rust with a Python API. Learn eager and lazy evaluation, expressions, groupby, joins, and how Polars outperforms pandas for large datasets through parallel execution.

> ibis

Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps developers write analytics code once and run it anywhere without rewriting SQL for each database.

Large-scale data processing with PySpark DataFrames, SQL, UDFs, and window functions.