Written by the core Optimus team, this comprehensive guide will help you to understand how Optimus 2.0 improves the whole data processing landscape Key Features * Load, merge, and save small and big data efficiently with Optimus * Discover how to output data for analytics, reporting, and machine learning with the help of real-world examples * Understand how Optimus improves other data frame technologies and helps you to speed up your data processing Book Description Optimus is a Python library aimed to be the unified API for data cleaning, processing, and merging that can be used for small and big data on your local and big clusters using CPUs or GPUs. The book begins by covering the internals of Optimus and showing you how it works in tandem with the existing technologies to serve data processing user needs. You'll 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. As you advance, you'll learn about the profiler and profiler data types, a unique feature of Optimus Dataframe that helps you get an overview of the data quality in every column. You'll also discover how to create data cleaning and transformation functions and add a hypothetical new data processing engine. Next, you'll explore plots available in Optimus such as histogram, frequency charts, scatter, and box plots, and learn how Optimus lets you connect to any other library like Plotly and Altair. Finally, you'll delve into advanced applications such as feature engineering, machine learning, and natural language processing functions available in Optimus and explore the road ahead with the Optimus project. By the end of this book, you'll be able to improve your Data Science workflow with Optimus with ease. What you will learn * Reshape and pivot data to output the needed format * Discover how to draw histograms, frequency charts, scatter plot, and box plots * Connect Optimus with popular python visualization libraries such as Plotly and Altair * Familiarize yourself with Optimus functions for machine learning, NLP, and feature engineering * Add custom functions to clean, process, and merge data * Find out how to add a new engine to the Optimus backend 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 Spark, Dask on CPUs, or GPUs.

Book Information
ISBN 9781801079563
Author Argenis Leon
Format Paperback
Page Count 303
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited