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

Loading a file

As explained in the previous chapter, to load a dataframe, we have the .load accessor in the Optimus instance, which lets us to load from different sources, such as the local filesystem, databases, and alternative filesystems (S3, HDFS, and more).

The most useful and readable function from this accessor is df.load.file, which allows us to infer encoding data on the loading process so that we can forget about extra configuration when loading a new dataset. This following is an example:

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

In the preceding code, we are simply loading a JSON file. Internally, Optimus detects its format and creates the dataframe. It can also load formats such as CSV, JSON, XML, Excel, Parquet, Avro, ORC, and HDF5. If required, you can also call any specific method for its type, as shown here:

Figure 2.5 – Specific file format loading methods available...