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Data Processing with Optimus

You're reading from   Data Processing with Optimus Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801079563
Length 300 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Dr. Argenis Leon Dr. Argenis Leon
Author Profile Icon Dr. Argenis Leon
Dr. Argenis Leon
Luis Aguirre Contreras Luis Aguirre Contreras
Author Profile Icon Luis Aguirre Contreras
Luis Aguirre Contreras
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Optimus
2. Chapter 1: Hi Optimus! FREE CHAPTER 3. Chapter 2: Data Loading, Saving, and File Formats 4. Section 2: Optimus – Transform and Rollout
5. Chapter 3: Data Wrangling 6. Chapter 4: Combining, Reshaping, and Aggregating Data 7. Chapter 5: Data Visualization and Profiling 8. Chapter 6: String Clustering 9. Chapter 7: Feature Engineering 10. Section 3: Advanced Features of Optimus
11. Chapter 8: Machine Learning 12. Chapter 9: Natural Language Processing 13. Chapter 10: Hacking Optimus 14. Chapter 11: Optimus as a Web Service 15. Other Books You May Enjoy

Experimenting with user-defined functions

Optimus tries to provide the most commonly used functions out of the box so that you can focus on your work instead of writing code. Of course, there are times when you will need to write custom functions to accomplish a task.

Before we deep dive into user-defined functions (UDF), let's explore a couple of scenarios regarding how data can be processed. Two such scenarios are known as vectorized and non-vectorized execution. This is important to understand because it can have a very big impact on performance.

Vectorized execution refers to operations that are performed on multiple components of a vector at the same time, in one statement. A vector is just a list of elements like the following:

[0, 1, 2, 3, 4, 5]

In the case of non-vectorized operations, the functions are executed in every element, one at a time. In the previous list, we need to pass every element to execute an operation. That's why using vectorized functions...

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