Data Science at Scale with Python and Dask

Jesse Daniel

ISBN 10: 1617295604 / ISBN 13: 9781617295607

Published by Manning Publications, US, 2019

About this Item

Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark.   Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book introduces Dask Data Frames and teaches helpful code patterns to streamline the reader's analysis.   Key Features Working with large structured datasets Writing DataFrames Cleaningand visualizing DataFrames Machine learning with Dask-ML Working with Bags and Arrays   Written for data engineers and scientists with experience using Python. Knowledge of the PyData stack (Pandas, NumPy, and Scikit-learn) will be helpful. No experience with low-level parallelism is required.   About the technology Dask is a self-contained, easily extendible library designed to query, stream, filter, and consolidate huge datasets.   Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.

Seller Inventory # LU-9781617295607

Bibliographic Details

• Title: Data Science at Scale with Python and Dask
• Publisher: Manning Publications, US
• Publication Date: 2019
• Language: English
• ISBN 10: 1617295604
• ISBN 13: 9781617295607
• Binding: Paperback
• Condition: New

About this title

Synopsis

Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark.

 

Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book introduces Dask Data Frames and teaches helpful code patterns to streamline the reader’s analysis.

 

Key Features

• Working with large structured datasets
• Writing DataFrames
• Cleaningand visualizing DataFrames
• Machine learning with Dask-ML
• Working with Bags and Arrays

 

Written for data engineers and scientists with experience using Python. Knowledge of the PyData stack (Pandas, NumPy, and Scikit-learn) will be helpful. No experience with low-level parallelism is required.

 

About the technology

Dask is a self-contained, easily extendible library designed to query, stream, filter, and consolidate huge datasets.

 

Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.

About the Author

Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.