Langue: anglais
Edité par Manning (edition First Edition), 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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Langue: anglais
Edité par Manning Publications, US, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
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Ajouter au panierPaperback. Etat : New. 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.
Langue: anglais
Edité par Manning Publications, New York, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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Ajouter au panierPaperback. Etat : new. Paperback. 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 readers 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. Large datasets tend to be distributed, non-uniform, and prone to change. Teaching readers how to build distributed data projects that can handle huge amounts of data, this edition introduces Dask DataFrames and teaches helpful code patterns to streamline the reader's analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Langue: anglais
Edité par Manning Publications 2019-10-04, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. pap/psc edition. 276 pages. 9.00x7.25x0.50 inches. In Stock.
Langue: anglais
Edité par Manning Publications, US, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
EUR 50,98
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Ajouter au panierPaperback. Etat : New. 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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Langue: anglais
Edité par Manning Publications|Manning, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierKartoniert / Broschiert. Etat : New. SummaryDask 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 alr.