pandas is widely regarded as the most popular tool in data science, used by millions of data scientists to prepare, transform, and analyze data in machine learning workflows. Despite its remarkable success and widespread adoption, it suffers from severe usability challenges at scale.
At Ponder, we build easy-to-use, enterprise-ready tools that support rapid experimentation with data at scale, centered around making pandas more scalable. Our work builds on Modin and Lux, two open-source projects that supercharge the capabilities of pandas, all without requiring users to change the existing ways they work with data. Modin is a scalable "drop-in replacement" for pandas, meaning that data scientists can seamlessly scale up to large datasets, without the users having to change a single line of code. Lux is a visualization tool for pandas that automatically identifies visual insights on large and complex datasets, again without changing a single line of code. Both project have been adopted by more than 30+ data teams across a variety of industries and sectors, with over 2.5M total downloads and more than 10k stars on GitHub. For more information, see: http://ponder.io.
