Welcome to the Modin Discourse!


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


Modin can be installed from PyPI:

pip install modin

Full Documentation

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

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

Modin uses Ray to provide an effortless way
to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed
DataFrame libraries, Modin provides seamless integration and compatibility with existing
pandas code. Even using the DataFrame constructor is identical.

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

import modin.pandas as pd

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

Modin is a DataFrame designed for datasets from 1KB 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.pandas is currently under active development. Requests and contributions are welcome!

Using the Discourse

If you would like to ask a question or are thinking of contributing, feel free to create a post!

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