Actions for Data Processing with Optimus [electronic resource]

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Data Processing with Optimus [electronic resource] / Leon, Argenis

Author
Leon, Argenis
Published
Packt Publishing, 2021.
Edition
1st edition.
Physical Description
1 online resource (300 pages)
Additional Creators
Aguirre, Luis and Safari, an O'Reilly Media Company
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ISBN
9781801079563
9781801077750
Digital File Characteristics
text file
Note
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. 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. Next, you'll get to grips with the profiler and its data types - a unique feature of Optimus Dataframe that assists with data quality. You'll 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. You'll 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, you'll 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, you'll 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 pre...
Copyright Note
Copyright © 2021 Packt Publishing 2021
Issuing Body
Made available through: Safari, an O'Reilly Media Company.
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