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 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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Author: Jesse Daniel has five years of experience writing applications in Python, including three years working with in the Py Data stack (Pandas, Num Py, Sci Py, 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.
| Author | Jesse |
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Manning Publications Data science with python and dask, paperback/jesse c. daniel
