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Data Science at Scale with Python and Dask

Af: Jesse Daniel Engelsk Paperback

Data Science at Scale with Python and Dask

Af: Jesse Daniel Engelsk Paperback
Tjek vores konkurrenters priser

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.

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Tjek vores konkurrenters priser
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Fragt: 39 kr
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Tjek vores konkurrenters priser

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.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 296
ISBN-13: 9781617295607
Indbinding: Paperback
Udgave:
ISBN-10: 1617295604
Kategori: Programmering / softwareudvikling
Udg. Dato: 11 okt 2019
Længde: 20mm
Bredde: 190mm
Højde: 235mm
Forlag: Manning Publications
Oplagsdato: 11 okt 2019
Forfatter(e): Jesse Daniel
Forfatter(e) Jesse Daniel

Kategori Programmering / softwareudvikling

ISBN-13 9781617295607

Sprog Engelsk

Indbinding Paperback

Sider 296

Udgave

Længde 20mm

Bredde 190mm

Højde 235mm

Udg. Dato 11 okt 2019

Oplagsdato 11 okt 2019

Forlag Manning Publications