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