Book Image

Data Processing with Optimus

By : Dr. Argenis Leon, Luis Aguirre
Book Image

Data Processing with Optimus

By: Dr. Argenis Leon, Luis Aguirre

Overview of this book

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.
Table of Contents (16 chapters)
1
Section 1: Getting Started with Optimus
4
Section 2: Optimus – Transform and Rollout
10
Section 3: Advanced Features of Optimus

Chapter 1: Hi Optimus!

Optimus is a Python library that loads, transforms, and saves data, and also focuses on wrangling tabular data. It provides functions that were designed specially to make this job easier for you; it can use multiple engines as backends, such as pandas, cuDF, Spark, and Dask, so that you can process both small and big data efficiently.

Optimus is not a DataFrame technology: it is not a new way to organize data in memory, such as arrow, or a way to handle data in GPUs, such as cuDF. Instead, Optimus relies on these technologies to load, process, explore, and save data.

Having said that, this book is for everyone, mostly data and machine learning engineers, who want to simplify writing code for data processing tasks. It doesn't matter if you want to process small or big data, on your laptop or in a remote cluster, if you want to load data from a database or from remote storage – Optimus provides all the tools you will need to make your data processing task easier.

In this chapter, we will learn about how Optimus was born and all the DataFrame technologies you can use as backends to process data. Then, we will learn about what features separate Optimus from all the various kinds of DataFrame technologies. After that, we will install Optimus and Jupyter Lab so that we will be prepared to code in Chapter 2, Data Loading, Saving, and File Formats.

Finally, we will analyze some of Optimus's internal functions to understand how it works and how you can take advantage of some of the more advanced features.

A key point: this book will not try to explain how every DataFrame technology works. There are plenty of resources on the internet that explain the internals and the day-to-day use of these technologies. Optimus is the result of an attempt to create an expressive and easy to use data API and give the user most of the tools they need to complete the data preparation process in the easiest way possible.

The topics we will be covering in this chapter are as follows:

  • Introducing Optimus
  • Installing everything you need to run Optimus
  • Using Optimus
  • Discovering Optimus internals