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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

Understanding data profiling

If you have never heard of data profiling before starting this chapter, it is a comprehensive process that involves analyzing and examining data from various sources to gain insights into the structure, quality, and overall characteristics of a dataset. Let’s start by describing the main goals of data profiling.

Identifying goals of data profiling

Data profiling helps us understand the structure and quality of the data. As a result, we can get a better idea of the best way to organize the different datasets, identify potential data integration challenges, assess data quality, and identify and address issues that may affect the reliability and trustworthiness of the data.

Let’s deep dive into the three main goals of data profiling.

Data structure

One of the main goals of data profiling is to understand the data’s structure. This entails examining the data types, formats, and relationships between different data fields...

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Python Data Cleaning and Preparation Best Practices
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