Data Science with Python and Dask
- Downloads：2202
- Type：Epub+TxT+PDF+Mobi
- Create Date：2021-08-26 09:54:02
- Update Date：2025-09-07
- Status：finish
- Author：Jesse C. Daniel
- ISBN：1617295604
- Environment：PC/Android/iPhone/iPad/Kindle
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Summary
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
Why scalable computing matters
Introducing Dask
PART 2 - Working with Structured Data using Dask DataFrames
Introducing Dask DataFrames
Loading data into DataFrames
Cleaning and transforming DataFrames
Summarizing and analyzing DataFrames
Visualizing DataFrames with Seaborn
Visualizing location data with Datashader
PART 3 - Extending and deploying Dask
Working with Bags and Arrays
Machine learning with Dask-ML
Scaling and deploying Dask
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
Why scalable computing matters
Introducing Dask
PART 2 - Working with Structured Data using Dask DataFrames
Introducing Dask DataFrames
Loading data into DataFrames
Cleaning and transforming DataFrames
Summarizing and analyzing DataFrames
Visualizing DataFrames with Seaborn
Visualizing location data with Datashader
PART 3 - Extending and deploying Dask
Working with Bags and Arrays
Machine learning with Dask-ML
Scaling and deploying Dask
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