To use Modin, replace the pandas import:
Scale your pandas workflow by changing a single line of code¶
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:
pip install modin
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
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¶
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 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 1MB 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 1MB and 1TB+.
Modin is currently under active development. Requests and contributions are welcome!
If you are interested in contributions please refer to ‘developer documentation’ section, where you can find ‘Getting started’ guide, system architecture and internal implementation details docs and lots of other useful information.
- Using Modin
- Out of Core in Modin
- Distributed XGBoost on Modin (experimental)
- SQL on Modin Dataframes
- Modin Spreadsheets API
- Progress Bar
- System Architecture
- High-Level Architectural View
- System View
- Subsystem/Container View
- Component View
- DataFrame Partitioning
- Query Compiler
- Modin DataFrame
- Execution Engine/Framework
- Internal abstractions
- Supported Execution Frameworks and Memory Formats
- Module/Class View
- Partition API in Modin