Data Processing with Optimus

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 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, you'll need an NVIDIA graphics card compatible with NVIDIA's 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

In 2015, 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 with pyspark pre-version 1.0 for retail projects doing market basket analysis for National brands in Venezuela. In 2017 he co-founded Iron AI- a digital marketing agency focused on using AI to get customer insight and create unique working in AdTech and Real Estate projects in Mexico. In 2019 he created Bumblebee, a low-code open-source web platform to clean and wrangle big data using CPU/GPUs.