MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03107nam a22001937a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250915092031.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250903b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781837634743 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
DC 005.133 Z55 2024 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Zervou, Maria. |
| 9 (RLIN) |
67213 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Python data cleaning and preparation best practices : |
| Remainder of title |
a practical guide to organizing and handling data from various sources and formats using Python / |
| Statement of responsibility, etc. |
Maria Zervou. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
UK : |
| Name of publisher, distributor, etc. |
Packt Publishing Ltd, |
| Date of publication, distribution, etc. |
©2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi, 431 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
23 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset Key Features Maximize the value of your data through effective data cleaning methods Enhance your data skills using strategies for handling structured and unstructured data Elevate the quality of your data products by testing and validating your data pipelines Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Ingest data from different sources and write it to the required sinks Profile and validate data pipelines for better quality control Get up to speed with grouping, merging, and joining structured data Handle missing values and outliers in structured datasets Implement techniques to manipulate and transform time series data Apply structure to text, image, voice, and other unstructured data Who this book is for Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language). |
| 9 (RLIN) |
67402 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Circulation |
