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Python Data Cleaning and Preparation Best Practices

Python Data Cleaning and Preparation Best Practices

By : Maria Zervou
4.8 (6)
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Python Data Cleaning and Preparation Best Practices

Python Data Cleaning and Preparation Best Practices

4.8 (6)
By: Maria Zervou

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)
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1
Part 1: Upstream Data Ingestion and Cleaning
9
Part 2: Downstream Data Cleaning – Consuming Structured Data
14
Part 3: Downstream Data Cleaning – Consuming Unstructured Data

Data Profiling – Understanding Data Structure, Quality, and Distribution

Data profiling refers to scrutinizing, understanding, and validating datasets to learn more about their underlying structure, patterns, and quality. It is a critical step in data management and ingestion as it can enhance data quality and accuracy and ensure compliance with regulatory standards. In this chapter, you will learn how to perform profiling with different tools and how to change your tactics as the data volume increases.

In this chapter, we will deep dive into the following topics:

  • Understanding data profiling
  • Data profiling with the pandas profiler
  • Data validation with Great Expectations
  • Comparing Great Expectations and the pandas profiler – when to use what
  • How to profile big data volumes

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