What do you think?
Rate this book
Data Science with Python and Dask
Summary
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.
About the Book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.
What's inside
About the Reader
For data scientists and developers with experience using Python and the PyData stack.
About the Author
Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.
Table of Contents
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.
About the Book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.
What's inside
About the Reader
For data scientists and developers with experience using Python and the PyData stack.
About the Author
Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.
Table of Contents
- GenresProgramming
296 pages, Paperback
Published July 30, 2019
7 people are currently reading
40 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 2 of 2 reviews
September 23, 2020
This book is a solid introduction to the dask API. If you're fairly new to the data science world and are already dealing with larger-than-memory datasets in Python, this is the book for you. In particular, I enjoyed the explanations of DAG scheduling through kitchen analogies.
If, on the other hand, you're already solid in the Python DS ecosystem and are able to read the dask API documentation, then you will likely find yourself skipping large parts of this book, only to find the rest often lacking in detail.
In particular, I would've liked to know a bit more about dask internals, schedulers, performance optimizations, diagnosing performance problems, explanations of the more low-level API such as blockwise and map_overlap, deployment, best practices for writing code with dask, and many many more.
If you have any recommendations, then do please comment here. In the meantime, I will continue exploring dask by reading the dask github repo, documentation and issues, and the wonderful Designing Data-Intensive Applications
If, on the other hand, you're already solid in the Python DS ecosystem and are able to read the dask API documentation, then you will likely find yourself skipping large parts of this book, only to find the rest often lacking in detail.
In particular, I would've liked to know a bit more about dask internals, schedulers, performance optimizations, diagnosing performance problems, explanations of the more low-level API such as blockwise and map_overlap, deployment, best practices for writing code with dask, and many many more.
If you have any recommendations, then do please comment here. In the meantime, I will continue exploring dask by reading the dask github repo, documentation and issues, and the wonderful Designing Data-Intensive Applications
May 21, 2023
It had a few very helpful points on Pandas, but the rest of the book did not convince me.
Displaying 1 - 2 of 2 reviews