Integrates with existing projects

Built with the broader community

Dask is open source and freely available. It is developed in coordination with other community projects like Numpy, Pandas, and Scikit-Learn.

Numpy

Dask arrays scale Numpy workflows, enabling multi-dimensional data analysis in earth science, satellite imagery, genomics, biomedical applications, and machine learning algorithms.

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Pandas

Dask dataframes scale Pandas workflows, enabling applications in time series, business intelligence, and general data munging on big data.

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Scikit-Learn

Dask-ML scales machine learning APIs like Scikit-Learn and XGBoost to enable scalable training and prediction on large models and large datasets.

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Familiar for Python users

and easy to get started

Dask uses existing Python APIs and data structures to make it easy to switch between Numpy, Pandas, Scikit-learn to their Dask-powered equivalents.

You don't have to completely rewrite your code or retrain to scale up.

Learn About Dask APIs »
# Arrays implement the Numpy API
import dask.array as da
x = da.random.random(size=(10000, 10000),
                     chunks=(1000, 1000))
x + x.T - x.mean(axis=0)
# Dataframes implement the Pandas API
import dask.dataframe as dd
df = dd.read_csv('s3://.../2018-*-*.csv')
df.groupby(df.account_id).balance.sum()
# Dask-ML implements the Scikit-Learn API
from dask_ml.linear_model \
  import LogisticRegression
lr = LogisticRegression()
lr.fit(train, test)

Scale up to clusters

or just use it on your laptop

Dask's schedulers scale to thousand-node clusters and its algorithms have been tested on some of the largest supercomputers in the world.

But you don't need a massive cluster to get started. Dask ships with schedulers designed for use on personal machines. Many people use Dask today to scale computations on their laptop, using multiple cores for computation and their disk for excess storage.

Learn About Dask Schedulers »

Customizable

Enabling you to parallelize internal systems

Not all computations fit into a big dataframe.

Dask exposes lower-level APIs letting you build custom systems for in-house applications. This helps open source leaders parallelize their own packages and helps business leaders scale custom business logic.


Powered by Dask

These software projects are built on Dask, or use Dask to power components of their infrastructure.

pangeo logo

Pangeo

A community effort for big data geoscience in the cloud

scikit-learn logo

Scikit-learn

Machine learning in Python

xarray logo

XArray

Brings the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures

datashader logo

Datashader

Visualization packages for large data

scikit-image logo

Scikit-image

A collection of algorithms for image processing in Python

intake logo

Intake

A lightweight package for finding, investigating, loading and disseminating data

prefect logo

Prefect

A workflow management system, designed for modern infrastructure

mdanalysis logo

MDAnalysis

A Python toolkit to analyze molecular dynamics trajectories generated by a wide range of popular simulation packages

stumpy logo

Stumpy

A Python library that can be used for a variety of time series data mining tasks

featuretools logo

Featuretools

A Python framework for automated feature engineering

tpot logo

TPOT

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

iris logo

Iris

A Python library for analysing and visualising Earth science data

SatPy

Library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats

Streamz

A package to help build pipelines to manage continuous streams of data

Scikit-allel

Provides utilities for exploratory analysis of large scale genetic variation data

tsfresh

Automatic extraction of relevant features from time series


Supported By

We thank these institutions for generously supporting the project

New-BSD Licensed

© 2019 Dask core developers