Using Modin

Modin is an early stage DataFrame library that wraps pandas and transparently distributes the data and computation, accelerating your pandas workflows with one line of code change. 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 Modin, 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 Modin just like you would pandas, since the API is identical to pandas.


# import pandas as pd
import modin.pandas as pd

That’s it. You’re ready to use Modin on your previous pandas notebooks.

We currently have most of the pandas API implemented and are working toward full functional parity with pandas (as well as even more tools and features ).

Using Modin on a Single Node

In local (without a cluster) modin will create and manage a local (dask or ray) cluster for the execution

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

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

>>> import modin.pandas 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

Once you have executed import modin.pandas as pd, you’re ready to begin running your pandas pipeline as you were before.

APIs Supported

Please note, the API is not yet complete. For some methods, you may see the following:

NotImplementedError: To contribute to Modin, please visit

We have compiled a list of currently supported methods.

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 Modin on a Cluster (experimental)

Modin is able to utilize Ray’s built-in autoscaled cluster. However, this usage is still under heavy development. To launch a Ray autoscaled cluster using Amazon Web Service (AWS), you can use the file examples/cluster/aws_example.yaml as the config file when launching an autoscaled Ray cluster. For the commands, refer to the autoscaler documentation.

We will provide a sample config file for private servers and other cloud service providers as we continue to develop and improve Modin’s cluster support.

Advanced usage (experimental)

In some cases, it may be useful to customize your Ray environment. Below, we have listed a few ways you can solve common problems in data management with Modin by customizing your Ray environment. It is possible to use any of Ray’s initialization parameters, which are all found in Ray’s documentation.

import ray
import modin.pandas as pd

Modin will automatically connect to the Ray instance that is already running. This way, you can customize your Ray environment for use in Modin!

Exceeding memory (Out of core pandas)

Modin experimentally supports out of core operations. See more on the Out of Core documentation page.

Reducing or limiting the resources Modin can use

By default, Modin will use all of the resources available on your machine. It is possible, however, to limit the amount of resources Modin uses to free resources for another task or user. Here is how you would limit the number of CPUs Modin used in your bash environment variables:

export MODIN_CPUS=4

You can also specify this in your python script with os.environ. Make sure you update the CPUS before you import Modin!:

import os
os.environ["MODIN_CPUS"] = "4"
import modin.pandas as pd

If you’re using a specific engine and want more control over the environment Modin uses, you can start Ray or Dask in your environment and Modin will connect to it. Make sure you start the environment before you import Modin!

import ray
import modin.pandas as pd

Specifying num_cpus limits the number of processors that Modin uses. You may also specify more processors than you have available on your machine, however this will not improve the performance (and might end up hurting the performance of the system).


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