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

    Posted By: yoyoloit
    Data Processing with Optimus: Supercharge big data preparation tasks for analytics and machine learning with Optimus

    Data Processing with Optimus
    by Argenis Leon, Luis Aguirre

    English | 2021 | ISBN: 1801079560 | 301 pages | True (PDF EPUB) | 8.34 MB

    Written by the core Optimus team, this comprehensive guide will help you to understand how Optimus improves the whole data processing landscape
    Key Features

    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

    Book 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.
    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

    Who this book is 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.
    Table of Contents

    Hi Optimus!
    Data Loading, Saving, and File Formats
    Data Wrangling
    Combining, Reshaping, and Aggregating Data
    Data Visualization and Profiling
    String Clustering
    Feature Engineering
    Machine Learning
    Natural Language Processing
    Hacking Optimus
    Optimus as a Web Service