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Data Processing with Optimus
Data Processing with Optimus

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

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Profile Icon Dr. Argenis Leon Profile Icon Luis Aguirre Contreras
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₹3276.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4 Ratings)
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Arrow left icon
Profile Icon Dr. Argenis Leon Profile Icon Luis Aguirre Contreras
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₹3276.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (4 Ratings)
Paperback Sep 2021 300 pages 1st Edition
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Data Processing with 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

Technical requirements

To take full advantage of this chapter, please ensure you implement everything specified in this section.

Optimus can work with multiple backend technologies to process data, including GPUs. For GPUs, Optimus uses RAPIDS, which needs an NVIDIA card. For more information about the requirements, please go to the GPU configuration section.

To use RAPIDS on Windows 10, you will need the following:

  • Windows 10 version 2004 (OS build 202001.1000 or later)
  • CUDA version 455.41 in CUDA SDK v11.1

You can find all the code for this chapter in https://github.com/PacktPublishing/Data-Processing-with-Optimus.

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • 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

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.

Who is this book 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.

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
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Length: 300 pages
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Language : English
ISBN-13 : 9781801079563
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Table of Contents

15 Chapters
Section 1: Getting Started with Optimus Chevron down icon Chevron up icon
Chapter 1: Hi Optimus! Chevron down icon Chevron up icon
Chapter 2: Data Loading, Saving, and File Formats Chevron down icon Chevron up icon
Section 2: Optimus – Transform and Rollout Chevron down icon Chevron up icon
Chapter 3: Data Wrangling Chevron down icon Chevron up icon
Chapter 4: Combining, Reshaping, and Aggregating Data Chevron down icon Chevron up icon
Chapter 5: Data Visualization and Profiling Chevron down icon Chevron up icon
Chapter 6: String Clustering Chevron down icon Chevron up icon
Chapter 7: Feature Engineering Chevron down icon Chevron up icon
Section 3: Advanced Features of Optimus Chevron down icon Chevron up icon
Chapter 8: Machine Learning Chevron down icon Chevron up icon
Chapter 9: Natural Language Processing Chevron down icon Chevron up icon
Chapter 10: Hacking Optimus Chevron down icon Chevron up icon
Chapter 11: Optimus as a Web Service Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(4 Ratings)
5 star 75%
4 star 25%
3 star 0%
2 star 0%
1 star 0%
Manuel Perche Sep 04, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As an enthusiast of Python and big data I found this book very useful. It explains everything in detail and provides the necessary context for developers just starting to learn, Optimus provides all the tools and simplifies what you need to handle data processing.
Amazon Verified review Amazon
Javier Sep 08, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Currently I work on a major tech company in Latin America and I can say that this book is a very valuable asset since it greatly speeds up the process of working with all kinds of datasets as well as processing units like CPU, GPU and so fort.The data wrangling section teaches you everything you need to know regarding the possible data transformation using different functions all trough the advanced features of optimus, it is highly accessible singe it lets you hack with it in order to implement your own engines if necessary.Definitely a great choice in the Data Science field.
Amazon Verified review Amazon
Maria Rubio Sep 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is incredibly valuable for those learning Python and Pandas and looking to improve their workflow. Optimus makes data processing better, and this detailed guide helped me achieve just that.
Amazon Verified review Amazon
srija Oct 13, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is excellent to expertise in pandas and their applications. Also, it is a detailed guide for learning Optimus, a great tool that simplifies the tedious data processing tasks.
Amazon Verified review Amazon
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