Kirby (by Kadoa)
The autonomous AI web agent for reliable, structured data extraction at scale.

Scale your Pandas workflows from a single core to a cluster with a single line of code.
Modin is a high-performance, distributed dataframe library designed to accelerate Pandas by distributing computation across all available CPU cores or cluster nodes. Architecturally, Modin sits as a layer between the user's Python code and a distributed execution engine such as Ray, Dask, or Unidist. Unlike Pandas, which is single-threaded and limited by the Python Global Interpreter Lock (GIL), Modin utilizes a sophisticated partitioning logic that divides dataframes along both axes, enabling truly parallelized operations on operations like groupby, join, and map. In the 2026 market landscape, Modin remains a critical bridge for data scientists who need to scale their local exploratory data analysis (EDA) to enterprise-scale datasets without rewriting code in Spark or SQL. Following the acquisition of its primary commercial developer, Ponder, by Snowflake in 2023, Modin has been deeply integrated into the Snowflake ecosystem via Snowpark, though it maintains its robust, independent open-source presence for local and cloud-agnostic environments. It is the gold standard for 'transparent scaling,' ensuring that legacy Pandas codebases can handle memory-intensive workloads that would traditionally cause Out-Of-Memory (OOM) errors.
Implements ~90% of the Pandas API, allowing for drop-in replacement of the pandas module.
The autonomous AI web agent for reliable, structured data extraction at scale.
The open-source Python framework for reproducible, maintainable, and modular data science code.
The premier community-driven cloud environment for high-performance data science and machine learning.
The open-source gold standard for programmatic workflow orchestration and complex data pipelines.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Partitions data along both rows and columns to maximize parallelism across all operations.
Supports Ray, Dask, and Unidist as execution engines without changing user code.
Utilizes the Plasma object store (via Ray) to share data across processes without serialization overhead.
Includes an experimental OmniSci backend for GPU-accelerated operations and lazy execution paths.
Parallelizes file reading operations (CSV, Parquet) across all available cores.
Native connectors for AWS S3, GCP, and Snowflake for high-speed cloud data access.
Pandas fails with OOM errors when loading files larger than available RAM.
Registry Updated:2/7/2026
Applying complex functions to 100M+ rows takes hours in single-threaded Pandas.
Developers use Pandas locally but have to rewrite code in PySpark for production clusters.