Data Science at Scale with Python and Dask

Jesse Daniel

ISBN 10: 1617295604 / ISBN 13: 9781617295607

Verlag: Manning Publications, US, 2019

Beschreibung

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.

Bestandsnummer des Verkäufers LU-9781617295607

Bibliografische Details

• Titel: Data Science at Scale with Python and Dask
• Verlag: Manning Publications, US
• Veröffentlichungsdatum: 2019
• Sprache: Englisch
• ISBN-10: 1617295604
• ISBN-13: 9781617295607
• Bindung: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

Summary

Dask 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 already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.

About the Technology

An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.

About the Book

Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.

What's inside

• Working with large, structured and unstructured datasets
• Visualization with Seaborn and Datashader
• Implementing your own algorithms
• Building distributed apps with Dask Distributed
• Packaging and deploying Dask apps

About the Reader

For data scientists and developers with experience using Python and the PyData stack.

About the Author

Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.

Table of Contents

PART 1 - The Building Blocks of scalable computing

1. Why scalable computing matters
2. Introducing Dask

PART 2 - Working with Structured Data using Dask DataFrames

3. Introducing Dask DataFrames
4. Loading data into DataFrames
5. Cleaning and transforming DataFrames
6. Summarizing and analyzing DataFrames
7. Visualizing DataFrames with Seaborn
8. Visualizing location data with Datashader

PART 3 - Extending and deploying Dask

9. Working with Bags and Arrays
10. Machine learning with Dask-ML
11. Scaling and deploying Dask

Über die Autorin bzw. den Autor

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.