Dask

Dask is an open-source Python library designed for parallel computing, enabling users to scale their data analysis and machine learning tasks efficiently. It integrates seamlessly with existing Python libraries, such as NumPy and Pandas, allowing for the handling of large datasets that exceed memory limits. Dask provides dynamic task scheduling and can operate on single machines or distributed clusters, making it versatile for various computational environments. With features like real-time data streaming and a user-friendly dashboard for monitoring performance, Dask empowers data scientists to optimize their workflows and accelerate their computations significantly.

Dask — Python Library for Large Datasets

Python in Plain English

Dask is a flexible parallel computing library in Python that allows users to harness the power of their CPU cores and perform distributed computing on larger-than-memory datasets.

Dask — Parallelism for Analytics at Scale

Analytics Vidhya

Dask is one of the wonderful tools that exist in the Python ecosystem which allows the scaling of data workloads for datasets that typically do not fit in memory in a ‘typical’ workstation. I will be…...

Dask for Python and Machine Learning

Analytics Vidhya

Recently I encountered a very interesting Python library called DASK. It is an open-source python library with an exclusive feature of parallelism and scalability. It can either be scaled on a local…

You are using Dask wrong!

Level Up Coding

Unleash the Full Power of Parallel Computing in your Data Projects Photo by Jason Yuen on Unsplash Are you tired of waiting forever for your data analysis to be complete? Do you wish there was a way ...

What is Dask and How Does it Work?

Towards Data Science

This article will first address what makes Dask special and then explain in more detail how Dask works. So: what makes Dask special? Python has a rich ecosystem of data science libraries including…

Why every Data Scientist should use Dask?

Towards Data Science

Dask is simply the most revolutionary tool for data processing that I have encountered. If you love Pandas and Numpy but were sometimes struggling with data that would not fit into RAM then Dask is…

Scalable Machine Learning with Dask on Google Cloud

Towards Data Science

Dask has been reviewed by many and compared to various other tools, including Spark, Ray and Vaex. Developed in coordination with other community projects like Numpy, Pandas, and Scikit-Learn, it is…

DASK HACK: Efficiently Distributing Large Auxiliary Data Across Your Workers

Towards Data Science

once_per_worker is a utility to create dask.delayed objects around functions that you only want to ever run once per distributed worker. This is useful when you have some large data baked into your…

Parallelizing Feature Engineering with Dask

Towards Data Science

In this article, we'll use Dask to run an automated feature engineering calculation in parallel, reducing run time by using all our resources and building a framework for scaling to large datasets.

Dask DataFrame is not Pandas

Towards Data Science

This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next…

Supercharging Hyperparameter Tuning with Dask

Towards Data Science

Dask improves scikit-learn parameter search speed by over 100x, and Spark by over 40x. Hyperparameter tuning is a crucial, and often painful, part of building machine learning models.

Distributed Machine Learning with Python and Dask — Introduction

Towards Data Science

Dask will help you scale your Data Science skills using Python. You will be able to work with BIG DATA and scale your code, boosting your productivity. Welcome Data Science lover! You are interested…