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Scale your pandas workflows by changing one line of code

To use Modin, replace the pandas import:

# import pandas as pd
import modin.pandas as pd

Installation

Modin can be installed from PyPI:

pip install modin

If you don't have Ray or Dask installed, you will need to install Modin with one of the targets:

pip install modin[ray] # Install Modin dependencies and Ray to run on Ray
pip install modin[dask] # Install Modin dependencies and Dask to run on Dask
pip install modin[all] # Install all of the above

Modin will automatically detect which engine you have installed and use that for scheduling computation!

Pandas API Coverage

pandas Object	Ray Engine Coverage	Dask Engine Coverage
pd.DataFrame
pd.Series
pd.read_*

Choosing a Compute Engine

If you want to choose a specific compute engine to run on, you can set the environment variable MODIN_ENGINE and Modin will do computation with that engine:

export MODIN_ENGINE=ray  # Modin will use Ray
export MODIN_ENGINE=dask  # Modin will use Dask

This can also be done within a notebook/interpreter before you import Modin:

import os

os.environ["MODIN_ENGINE"] = "ray"  # Modin will use Ray
os.environ["MODIN_ENGINE"] = "dask"  # Modin will use Dask

import modin.pandas as pd

Note: You should not change the engine after you have imported Modin as it will result in undefined behavior

Which engine should I use?

If you are on Windows, you must use Dask. Ray does not support Windows. If you are on Linux or Mac OS, you can install and use either engine. There is no knowledge required to use either of these engines as Modin abstracts away all of the complexity, so feel free to pick either!

Advanced usage

In Modin, you can start a custom environment in Dask or Ray and Modin will connect to that environment automatically. For example, if you'd like to limit the amount of resources that Modin uses, you can start a Dask Client or Initialize Ray and Modin will use those instances. Make sure you've set the correct environment variable so Modin knows which engine to connect to!

For Ray:

import ray
ray.init(plasma_directory="/path/to/custom/dir", object_store_memory=10**10)
# Modin will connect to the existing Ray environment
import modin.pandas as pd

For Dask:

from distributed import Client
client = Client(n_workers=6)
# Modin will connect to the Dask Client
import modin.pandas as pd

This gives you the flexibility to start with custom resource constraints and limit the amount of resources Modin uses.

Full Documentation

Visit the complete documentation on readthedocs: https://modin.readthedocs.io

Scale your pandas workflow by changing a single line of code.

import modin.pandas as pd
import numpy as np

frame_data = np.random.randint(0, 100, size=(2**10, 2**8))
df = pd.DataFrame(frame_data)

To use Modin, you do not need to know how many cores your system has and you do not need to specify how to distribute the data. In fact, you can continue using your previous pandas notebooks while experiencing a considerable speedup from Modin, even on a single machine. Once you’ve changed your import statement, you’re ready to use Modin just like you would pandas.

Faster pandas, even on your laptop

The modin.pandas DataFrame is an extremely light-weight parallel DataFrame. Modin transparently distributes the data and computation so that all you need to do is continue using the pandas API as you were before installing Modin. Unlike other parallel DataFrame systems, Modin is an extremely light-weight, robust DataFrame. Because it is so light-weight, Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.

In pandas, you are only able to use one core at a time when you are doing computation of any kind. With Modin, you are able to use all of the CPU cores on your machine. Even in read_csv, we see large gains by efficiently distributing the work across your entire machine.

import modin.pandas as pd

df = pd.read_csv("my_dataset.csv")

Modin is a DataFrame designed for datasets from 1MB to 1TB+

We have focused heavily on bridging the solutions between DataFrames for small data (e.g. pandas) and large data. Often data scientists require different tools for doing the same thing on different sizes of data. The DataFrame solutions that exist for 1KB do not scale to 1TB+, and the overheads of the solutions for 1TB+ are too costly for datasets in the 1KB range. With Modin, because of its light-weight, robust, and scalable nature, you get a fast DataFrame at small and large data. With preliminary cluster and out of core support, Modin is a DataFrame library with great single-node performance and high scalability in a cluster.

Modin Architecture

We designed Modin to be modular so we can plug in different components as they develop and improve:

Visit the Documentation for more information!

modin.pandas is currently under active development. Requests and contributions are welcome!

More information and Getting Involved

• Documentation
• Ask questions or participate in discussions on our Discourse
• Join our mailing list [email protected]
• Submit bug reports to our GitHub Issues Page
• Contributions are welcome! Open a pull request