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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

Binary encoding

Binary encoding is a technique used for encoding categorical features by converting each category into binary code. Each unique category is represented by a unique binary pattern, where each digit (0 or 1) in the pattern corresponds to the presence or absence of that category. Binary encoding is particularly useful for handling high-cardinality categorical features while reducing dimensionality.

When to use binary encoding

Binary encoding can be considered in the following scenarios:

  • Dimensionality reduction: You want to reduce the dimensionality of the dataset while still capturing information contained within the categorical feature. Binary encoding is particularly useful in this scenario.
  • Efficiency: You need an efficient encoding method that results in a compact representation of categorical data and can be easily processed by ML algorithms.

Let’s look at a use case.

Use case – customer subscription prediction

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