Modin aims to not only optimize pandas, but also provide a comprehensive, integrated toolkit for data scientists. We are actively developing data science tools such as DataFrame spreadsheet integration, DataFrame algebra, progress bars, SQL queries on DataFrames, and more. Join us on Slack and Discourse for the latest updates!
Modin also supports these experimental APIs on top of pandas that are under active development.
read_csv_glob()– read multiple files in a directory
read_sql()– add optional parameters for the database connection
read_custom_text()– read custom text data from file
read_pickle_distributed()– read multiple files in a directory
to_pickle_distributed()– write to multiple files in a directory
DataFrame partitioning API#
Modin DataFrame provides an API to directly access partitions: you can extract physical partitions from
DataFrame, modify their structure by reshuffling or applying some
functions, and create a DataFrame from those modified partitions. Visit
pandas partitioning API documentation to learn more.
Modin Spreadsheet API#
The Spreadsheet API for Modin allows you to render the dataframe as a spreadsheet to easily explore your data and perform operations on a graphical user interface. The API also includes features for recording the changes made to the dataframe and exporting them as reproducible code. Built on top of Modin and SlickGrid, the spreadsheet interface is able to provide interactive response times even at a scale of billions of rows. See our Modin Spreadsheet API documentation for more details.
Visual progress bar for Dataframe operations such as groupby and fillna, as well as for file reading operations such as read_csv. Built using the tqdm library and Ray execution engine. See Progress Bar documentation for more details.
A minimal set of operators that can be composed to express any dataframe query for use in query planning and optimization. See our paper for more information, and full documentation is coming soon!
SQL on Modin Dataframes#
Read about Modin Dataframe support for SQL queries in this recent blog post. Check out the Modin SQL documentation as well!
Distributed XGBoost on Modin#
Modin provides an implementation of distributed XGBoost machine learning algorithm on Modin DataFrames. See our Distributed XGBoost on Modin documentation for details about installation and usage, as well as Modin XGBoost architecture documentation for information about implementation and internal execution flow.
Logging with Modin#
Modin logging offers users greater insight into their queries by logging internal Modin API calls, partition metadata, and system memory. Logging is disabled by default, but when it is enabled, log files are written to a local .modin directory at the same directory level as the notebook/script used to run Modin. See our Logging with Modin documentation for usage information.
Batch Pipeline API#
Modin provides an experimental batched API that pipelines row parallel queries. See our Batch Pipline API Usage Guide for a walkthrough on how to use this feature, as well as Batch Pipeline API documentation for more information about the API.
An experimental GitHub Action on pull request has been added to Modin, which automatically runs the Modin codebase against fuzzydata, a random dataframe workflow generator. The resulting workflow that was used to test Modin codebase can be downloaded as an artifact from the GitHub Actions tab for further inspection. See fuzzydata for more details.