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 7: Feature Engineering

Now that we have covered some considerable ground on how to shape our data as needed, let's talk about feature engineering.

If you want to create a machine learning model, you input data. This input data includes the features that an algorithm needs to create a model. These features need to have specific characteristics; for example, it cannot have null values or the data needs to comply and have specific probability distributions.

With featuring engineering, you can prepare the input dataset so that it complies with the algorithm's requirements, and also improve the performance of the machine learning model, thereby creating new features with data we already have.

So, in this chapter, we will be covering the following topics:

  • Handling missing values
  • Handling outliers
  • Binning
  • Variable transformation
  • One-hot encoding
  • Feature splitting
  • Scaling