September 2024
Beginner to intermediate
456 pages
11h 53m
English
Overview
Python Data Cleaning and Preparation Best Practices delivers a hands-on approach to tackling data quality issues using Python. By focusing on effective methods for cleaning structured and unstructured data, this guide equips you with essential tools for maximizing the value of your data assets. It also covers ingestion, validation, and transformation processes to ensure your data is well-prepared for analysis.
What this Book will help me do
- Master the ingestion of data from various formats and sources into standardized pipelines.
- Efficiently analyze and profile data to identify and address quality gaps and inconsistencies.
- Apply advanced techniques to clean, transform, and encode structured datasets with Python.
- Develop skills to handle missing values, outliers, and categorical variables effectively.
- Learn workflows for processing unstructured data like text, images, and audio using Python.
Author(s)
Maria Zervou is an experienced data scientist and Python expert with a passion for teaching data cleaning and preparation techniques. She has worked across various industries, solving real-world data challenges and refining data pipelines for better results. Her engaging teaching style makes complex topics accessible to everyone.
Who is it for?
This book is designed for data professionals, including data scientists, data engineers, and analysts, who seek to improve their data preparation and cleaning abilities. If you have a basic understanding of Python and aim to work more efficiently with data, this book is an excellent resource to expand your skills and ensure data quality in your projects.
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