@@ -374,5 +374,33 @@ datasets.
374374
375375You see more dask examples at https://examples.dask.org.
376376
377+ Use Modin
378+ ---------
379+
380+ Modin _ is a scalable dataframe library, which aims to be a drop-in replacement API for pandas and
381+ provides the ability to scale pandas workflows across nodes and CPUs available. It is also able
382+ to work with larger than memory datasets. To start working with Modin you just need
383+ to replace a single line of code, namely, the import statement.
384+
385+ .. code-block :: ipython
386+
387+ # import pandas as pd
388+ import modin.pandas as pd
389+
390+ After you have changed the import statement, you can proceed using the well-known pandas API
391+ to scale computation. Modin distributes computation across nodes and CPUs available utilizing
392+ an execution engine it runs on. At the time of Modin 0.27.0 the following execution engines are supported
393+ in Modin: Ray _, Dask _, `MPI through unidist `_, HDK _. The partitioning schema of a Modin DataFrame partitions it
394+ along both columns and rows because it gives Modin flexibility and scalability in both the number of columns and
395+ the number of rows.
396+
397+ For more information refer to `Modin's documentation `_ or the `Modin's tutorials `_.
398+
399+ .. _Modin : https://github.com/modin-project/modin
400+ .. _`Modin's documentation` : https://modin.readthedocs.io/en/latest
401+ .. _`Modin's tutorials` : https://github.com/modin-project/modin/tree/master/examples/tutorial/jupyter/execution
402+ .. _Ray : https://github.com/ray-project/ray
377403.. _Dask : https://dask.org
404+ .. _`MPI through unidist` : https://github.com/modin-project/unidist
405+ .. _HDK : https://github.com/intel-ai/hdk
378406.. _dask.dataframe : https://docs.dask.org/en/latest/dataframe.html
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