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Key benefits

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

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Python Data Cleaning and Preparation Best Practices

$44.99

$44.99

$39.99

Total $ 129.97

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Shipping Details

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Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

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Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Rest of the World:

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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Russian Federation
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Amazon Customer Oct 25, 2024

The book is great in guiding readers from basic data cleaning methods on structured datasets, such as normalization, standardization, and encoding of categorical features, to more sophisticated techniques like text preprocessing and image/audio handling.The sections on natural language processing (NLP) and handling multimedia data are particularly valuable. The use of Python throughout the book ensures that concepts are not just theoretical but also applicable in real-world scenarios. Code examples help readers immediately apply methods discussed in the text, enhancing the hands-on experience and the book is full of different use cases that you can find in real scenarios.The introduction of unstructured data processing with focus on large language models (LLMs) and AI is great and the examples really applicable

Amazon Verified review

J Gil Oct 08, 2024

This booked has been key in strengthening my understanding of data preparation. It has built my confidence in tackling the issues faced in real applications. My favourite aspect of it is how every section starts with a clear outline of how the content covered ties back to issues teams face in building their products in the real world. It progressively goes into the details and explains clearly why they matter.The code for each section is provided in github repos and I found the step-by-step walkthroughs to be clear and actionable as a beginner/intermediate user of python. The setup steps are also detailed without any assumptions being made so you can start from scratch. The practical aspect of this book is very well executed and clearly helps with the understanding and confidence on the topic.Real world tools across streaming, SQL warehouses, noSQL databases, and how/when to use them is also covered (eg. BigQuery, Kafka, Databricks SQL). This has been super useful in connecting the dots between different parts of architectures.

Amazon Customer Oct 20, 2024

As a practitioner in the data field, I found this book to be incredibly practical and comprehensive for tackling data cleaning and preparation across various data types and sources, from structured to unstructured. The coverage of the latest techniques for processing text, audio, and image data with LLMs really stood out, offering practical insights I can apply directly in my projects.

Spiros Zervos Oct 09, 2024

The book excels in demonstrating both structured and unstructured data handling, offering end-to-end code examples for practical implementation. Its sections on optimizing and tuning operations like joining and merging are especially strong, showing how these techniques can significantly impact code performance. The detailed testing methods included help users understand the performance trade-offs of their operations. Additionally, the chapter on large language models (LLMs) is a highlight, showing how to combine modern techniques with traditional problem-solving approaches, bridging older and newer technologies.

Python Data Cleaning and Preparation Best Practices: A practical guide to organizing and handling data from various sources and formats using Python

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