To use Modin, replace the pandas import:

# import pandas as pd
import modin.pandas as pd

Modin uses Ray or Dask 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.

Installation and choosing your compute engine

Modin can be installed from PyPI:

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!

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

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")