Data Science with Python and Dask

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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! 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.

Author(s): Jesse C. Daniel
Edition: 1
Publisher: Manning Publications
Year: 2019

Language: English
Pages: 296
City: Shelter Island, NY
Tags: Amazon Web Services; Machine Learning; Python; Classification; Concurrency; Parallel Programming; Data Visualization; Pipelines; Scalability; Clusters; Docker; Naive Bayes; Logistic Regression; scikit-learn; NLTK; NumPy; pandas; Seaborn; Dask; Scheduling

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