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

Data quality

In Optimus, we call the process of counting the number of values in a column that match a specific profiler data type data quality. For example, if the profiler data type in a column is URL, Optimus will count the number of values in a column that do the following:

  • Match the URL format, such as "google.com".
  • Do NOT match the URL format, such as "google".
  • It will also count the null values.

Optimus has many data types in the profiler, which are inferred with a combination of regular expressions and number type detection. For reference, in the following table, we list the profiler data types and the Python data types:

Figure 5.1 – Optimus profiler datatypes

These data types are inferred when you run the profiler. Also, you can change the profiler if you are sure that a profiler datatype should have a specific data type:

from optimus import Optimus 
op = Optimus("pandas")
df = op.load...