Pandas on Ray

Pandas on Ray is an early stage DataFrame library that wraps Pandas and transparently distributes the data and computation. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous Pandas notebooks while experiencing a considerable speedup from Pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use Pandas on Ray just like you would Pandas.

# import pandas as pd
import ray.dataframe as pd

Currently, we have part of the Pandas API implemented and are working toward full functional parity with Pandas.

Using Pandas on Ray on a Single Node

In order to use the most up-to-date version of Pandas on Ray, please follow the instructions on the installation page

Once you import the library, you should see something similar to the following output:

>>> import ray.dataframe as pd

Waiting for redis server at to respond...
Waiting for redis server at to respond...
Starting local scheduler with the following resources: {'CPU': 4, 'GPU': 0}.

View the web UI at http://localhost:8889/notebooks/ray_ui36796.ipynb?token=ac25867d62c4ae87941bc5a0ecd5f517dbf80bd8e9b04218

If you do not see output similar to the above, please make sure that you have built Ray using the instructions on the installation page

One you have executed import ray.dataframe as pd, you’re ready to begin running your Pandas pipeline as you were before. Please note, the API is not yet complete. For some methods, you may see the following:

NotImplementedError: To contribute to Pandas on Ray, please visit

If you would like to request a particular method be implemented, feel free to open an issue. Before you open an issue please make sure that someone else has not already requested that functionality.

Using Pandas on Ray on a Cluster

Currently, we do not yet support running Pandas on Ray on a cluster. Coming Soon!


You can find an example on our recent blog post or on the Jupyter Notebook that we used to create the blog post.