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Python Data Cleaning and Preparation Best Practices
By :

Python Data Cleaning and Preparation Best Practices
By:
Overview of this book
Professionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone.
To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio.
By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.
Table of Contents (19 chapters)
Preface
Part 1: Upstream Data Ingestion and Cleaning
Chapter 1: Data Ingestion Techniques
Chapter 2: Importance of Data Quality
Chapter 3: Data Profiling – Understanding Data Structure, Quality, and Distribution
Chapter 4: Cleaning Messy Data and Data Manipulation
Chapter 5: Data Transformation – Merging and Concatenating
Chapter 6: Data Grouping, Aggregation, Filtering, and Applying Functions
Chapter 7: Data Sinks
Part 2: Downstream Data Cleaning – Consuming Structured Data
Chapter 8: Detecting and Handling Missing Values and Outliers
Chapter 9: Normalization and Standardization
Chapter 10: Handling Categorical Features
Chapter 11: Consuming Time Series Data
Part 3: Downstream Data Cleaning – Consuming Unstructured Data
Chapter 12: Text Preprocessing in the Era of LLMs
Chapter 13: Image and Audio Preprocessing with LLMs
Index
Customer Reviews