July 2019
Intermediate to advanced
296 pages
9h 1m
English
Content preview from Data Science with Python and Dask
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Part 2 Working with structured data using Dask DataFrames
Now that you have a basic understanding of how Dask makes it possible to both work with large datasets and take advantage of parallelism, you’re ready to get some hands-on experience working with a real dataset to learn how to solve common data science challenges with Dask. Part 2 focuses on Dask DataFrames—a parallelized implementation of the ever-popular Pandas DataFrame—and how to use them to clean, analyze, and visualize large structured datasets.
Chapter 3 opens the part by explaining how Dask parallelizes Pandas DataFrames and describing why some parts of the Dask DataFrame API are different from its Pandas counterpart.
Chapter 4 jumps into the first part of the data science ...
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