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Dr. Argenis Leon - Data Processing with Optimus: Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

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Dr. Argenis Leon Data Processing with Optimus: Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark
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Data Processing with Optimus: Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark: summary, description and annotation

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Written by the core Optimus team, this comprehensive guide will help you to understand how Optimus improves the whole data processing landscape

Key Features
  • Load, merge, and save small and big data efficiently with Optimus
  • Learn Optimus functions for data analytics, feature engineering, machine learning, cross-validation, and NLP
  • Discover how Optimus improves other data frame technologies and helps you speed up your data processing tasks
Book Description

Optimus is a Python library that works as a unified API for data cleaning, processing, and merging data. It can be used for handling small and big data on your local laptop or on remote clusters using CPUs or GPUs.

The book begins by covering the internals of Optimus and how it works in tandem with the existing technologies to serve your data processing needs. Youll then learn how to use Optimus for loading and saving data from text data formats such as CSV and JSON files, exploring binary files such as Excel, and for columnar data processing with Parquet, Avro, and OCR. Next, youll get to grips with the profiler and its data types - a unique feature of Optimus Dataframe that assists with data quality. Youll see how to use the plots available in Optimus such as histogram, frequency charts, and scatter and box plots, and understand how Optimus lets you connect to libraries such as Plotly and Altair. Youll also delve into advanced applications such as feature engineering, machine learning, cross-validation, and natural language processing functions and explore the advancements in Optimus. Finally, youll learn how to create data cleaning and transformation functions and add a hypothetical new data processing engine with Optimus.

By the end of this book, youll be able to improve your data science workflow with Optimus easily.

What you will learn
  • Use over 100 data processing functions over columns and other string-like values
  • Reshape and pivot data to get the output in the required format
  • Find out how to plot histograms, frequency charts, scatter plots, box plots, and more
  • Connect Optimus with popular Python visualization libraries such as Plotly and Altair
  • Apply string clustering techniques to normalize strings
  • Discover functions to explore, fix, and remove poor quality data
  • Use advanced techniques to remove outliers from your data
  • Add engines and custom functions to clean, process, and merge data
Who this book is for

This book is for Python developers who want to explore, transform, and prepare big data for machine learning, analytics, and reporting using Optimus, a unified API to work with Pandas, Dask, cuDF, Dask-cuDF, Vaex, and Spark. Although not necessary, beginner-level knowledge of Python will be helpful. Basic knowledge of the CLI is required to install Optimus and its requirements. For using GPU technologies, youll need an NVIDIA graphics card compatible with NVIDIAs RAPIDS library, which is compatible with Windows 10 and Linux.

Table of Contents
  1. Hi Optimus!
  2. Data Loading, Saving, and File Formats
  3. Data Wrangling
  4. Combining, Reshaping, and Aggregating Data
  5. Data Visualization and Profiling
  6. String Clustering
  7. Feature Engineering
  8. Machine Learning
  9. Natural Language Processing
  10. Hacking Optimus
  11. Optimus as a Web Service

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Data Processing with Optimus

Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

Dr. Argenis Leon

Luis Aguirre

BIRMINGHAMMUMBAI

Data Processing with Optimus

Copyright 2021 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Publishing Product Manager: Devika Battike

Senior Editor: David Sugarman

Content Development Editor: Joseph Sunil

Technical Editor: Manikandan Kurup

Copy Editor: Safis Editing

Project Coordinator: Aparna Ravikumar Nair

Proofreader: Safis Editing

Indexer: Pratik Shirodkar

Production Designer: Sinhayna Bais

First published: September 2021

Production reference: 1290721

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80107-956-3

www.packt.com

Contributors
About the authors

Dr. Argenis Leon created Optimus, an open-source python library built over PySpark aimed to provide an easy-to-use API to clean, process, and merge data at scale. Since 2012, Argenis has been working on big data-related projects using Postgres, MongoDB, Elasticsearch for social media data collection and analytics. In 2015, he started working on Machine learning projects in Retail, AdTech, and Real Estate in Venezuela and Mexico. In 2019 he created Bumblebee, a low-code open-source web platform to clean and wrangle big data using CPU and GPUs using NVIDIA RAPIDS. Nowadays Argenis is Co-founder and CTO of boitas.com (backed by YCombinator) a wholesale marketplace for SMB in Latin America.

Luis Aguirre began working with web development projects for Mood Agency in 2018, creating sites for brands from all across Latin America. One year later he started working on Bumblebee, a low-code web platform to transform data that uses Optimus. In mid-2020 he started participating in the Optimus project as a core developer; focusing on creating the easiest-to-use experience for both projects. In 2021 he started working on the Optimus REST API, a tool to allow requests from the web focused on data wrangling.

About the reviewer

Sergio Snchez Zavala, originally from Tijuana, Baja California, Mexico, is a self-described hip-hop head, public policy wonk, and data nerd. He is dedicated to making research, open source tools, and resources transparent, reproducible, and accessible. He is also the creator of @tacosdedatos, an online community for learning data analysis, engineering, and visualization best practices and techniques in Spanish. He is currently a data engineer at Alluma, a social enterprise in the social tech space providing people-centered, policy-driven technology solutions and consulting services to state, county, and local government agencies, nonprofits, and other partners.

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