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Ajouter au panierPaperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
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Ajouter au panierPaperback. Etat : Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Ajouter au panierPaperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Ajouter au panierpaperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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Ajouter au panierEtat : New. Brand New.
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 Company, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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Ajouter au panierEtat : New. pp. 400.
Langue: anglais
Edité par Manning Publications Company, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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Ajouter au panierEtat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Langue: anglais
Edité par Manning Publications Company, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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Ajouter au panierEtat : New. pp. 400.
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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Ajouter au panierEtat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Ajouter au panierEtat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Langue: anglais
Edité par Pearson,, 2019
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Ajouter au panierSoft cover. Etat : New. ISBN:9781617295607.
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Ajouter au panierEtat : New. 2019. Paperback. . . . . .
Langue: anglais
Edité par Manning Publications 2019-10-11, 2019
ISBN 10 : 1617295604 ISBN 13 : 9781617295607
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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
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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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Ajouter au panierPaperback. Etat : New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Ajouter au panierEtat : NEW.
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.