Data science scale python dask de jesse daniel (17 resultados)

Paperback. Condición: 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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Paperback. Condición: 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.…

Condición: 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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Paperback. Condición: 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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Condición: 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.

Paperback. Condición: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…