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Python Data Cleaning and Preparation Best Practices
Python Data Cleaning and Preparation Best Practices

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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Python Data Cleaning and Preparation Best Practices

Data Ingestion Techniques

Data ingestion is a critical component of the data life cycle and sets the foundation for subsequent data transformation and cleaning. It involves the process of collecting and importing data from various sources into a storage system where it can be accessed and analyzed. Effective data ingestion is crucial for ensuring data quality, integrity, and availability, which directly impacts the efficiency and accuracy of data transformation and cleaning processes. In this chapter, we will dive deep into the different types of data sources, explore various data ingestion methods, and discuss their respective advantages, disadvantages, and real-world applications.

In this chapter, we’ll cover the following topics:

  • Ingesting data in batch mode
  • Ingesting data in streaming mode
  • Real-time versus semi-real-time ingestion
  • Data sources technologies
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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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 27, 2024
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781837632909
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Product Details

Publication date : Sep 27, 2024
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781837632909
Category :
Languages :
Concepts :

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Table of Contents

18 Chapters
Part 1: Upstream Data Ingestion and Cleaning Chevron down icon Chevron up icon
Chapter 1: Data Ingestion Techniques Chevron down icon Chevron up icon
Chapter 2: Importance of Data Quality Chevron down icon Chevron up icon
Chapter 3: Data Profiling – Understanding Data Structure, Quality, and Distribution Chevron down icon Chevron up icon
Chapter 4: Cleaning Messy Data and Data Manipulation Chevron down icon Chevron up icon
Chapter 5: Data Transformation – Merging and Concatenating Chevron down icon Chevron up icon
Chapter 6: Data Grouping, Aggregation, Filtering, and Applying Functions Chevron down icon Chevron up icon
Chapter 7: Data Sinks Chevron down icon Chevron up icon
Part 2: Downstream Data Cleaning – Consuming Structured Data Chevron down icon Chevron up icon
Chapter 8: Detecting and Handling Missing Values and Outliers Chevron down icon Chevron up icon
Chapter 9: Normalization and Standardization Chevron down icon Chevron up icon
Chapter 10: Handling Categorical Features Chevron down icon Chevron up icon
Chapter 11: Consuming Time Series Data Chevron down icon Chevron up icon
Part 3: Downstream Data Cleaning – Consuming Unstructured Data Chevron down icon Chevron up icon
Chapter 12: Text Preprocessing in the Era of LLMs Chevron down icon Chevron up icon
Chapter 13: Image and Audio Preprocessing with LLMs Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(4 Ratings)
5 star 100%
4 star 0%
3 star 0%
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1 star 0%
Amazon Customer Oct 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
J Gil Oct 08, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Verified review Amazon
Amazon Customer Oct 20, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Spiros Zervos Oct 09, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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