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Python Data Cleaning and Preparation Best Practices: A practical guide to organizing and handling data from various sources and formats using Python
| dc.contributor.author | Zervou, Maria | |
| dc.date.accessioned | 2024-12-11T02:32:34Z | |
| dc.date.available | 2024-12-11T02:32:34Z | |
| dc.date.issued | 2024 | |
| dc.identifier.isbn | 978-1837634743 | |
| dc.identifier.isbn | 1837634742 | |
| dc.identifier.uri | https://thuvienso.hoasen.edu.vn/handle/123456789/15967 | |
| dc.description | 456 pages | vi |
| dc.description.abstract | 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. | vi |
| dc.description.tableofcontents | Table of Contents Data Ingestion Techniques Importance of Data Quality Data Profiling – Understanding Data Structure, Quality, and Distribution Cleaning Messy Data and Data Manipulation Data Transformation – Merging and Concatenating Data Grouping, Aggregation, Filtering, and Applying Functions Data Sinks Detecting and Handling Missing Values and Outliers Normalization and Standardization Handling Categorical Features Consuming Time Series Data Text Preprocessing in the Era of LLMs Image and Audio Preprocessing with LLMs | vi |
| dc.language.iso | en | vi |
| dc.publisher | Packt Publishing | vi |
| dc.subject | Python | vi |
| dc.subject | Data | vi |
| dc.title | Python Data Cleaning and Preparation Best Practices: A practical guide to organizing and handling data from various sources and formats using Python | vi |
| dc.type | Book | vi |