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

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

Python Data Cleaning and Preparation Best Practices: A practical guide in Python for organizing and handling data from various sources and formats Paperback / softback - 2024

by Maria Zervou

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Paperback / softback. New. Take your data preparation skills to the next level by converting any type of data asset into a structured, properly formatted, and readily usable dataset Key Features Maximize the value of your data with effective data-cleaning methods Transform your data skills with strategies for handling structured and unstructured data Learn to elevate the quality of your data products by testing and validating your data pipelines Book DescriptionData 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, caused by data that is inaccurate, incomplete, or inconsistent. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, missing valuable insights that are difficult or impossible to obtain from structured data alone. To tackle these challenges, you will go on a journey to the upstream data pipeline, which includes the ingestion of data from various sources, validation and profiling of the data for high-quality end tables, and writing the data to different sinks. Subsequently, you will acquire knowledge on handling structured data by performing essential tasks like cleaning and encoding datasets and handling missing values and outliers. The journey concludes by demystifying the manipulation of unstructured data with simple techniques that unlock their potential. You will be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques for structuring images, videos, and audio. By the end of the book, you will have achieved mastery of the techniques of data cleaning and preparation for both structured and unstructured data.What you will learn Ingest data from different sources and write them to required sinks Profile and validate data pipelines for better quality control Master 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 Who this book is forWhether you're a Data Analyst, Data Engineer, Data Scientist, or any data professional who relishes the task of data preparation and cleaning, this book is for you. It’s an ideal resource for upskilling in data cleaning concepts and expanding your knowledge across all types of data, from tabular to audio and video. Working knowledge of Python programming is needed to get the most out of the book
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Details

  • Title Python Data Cleaning and Preparation Best Practices: A practical guide in Python for organizing and handling data from various sources and formats
  • Author Maria Zervou
  • Binding Paperback
  • Condition New
  • Pages 456
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2024-09-27
  • Bookseller's Inventory # B9781837634743
  • ISBN 9781837634743 / 1837634742
  • Weight 1.71 lbs (0.78 kg)
  • Dimensions 9.25 x 7.5 x 0.92 in (23.50 x 19.05 x 2.34 cm)
  • Category Computers - Data Base Management
  • Quantity available 10

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From the publisher

Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset

Key Features:

- 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

Book 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

Who this book is 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.

Table of Contents

- Data Ingestion Techniques

- Importance of Data Quality

- Data Profiling - Understanding Data Structure, Quality, and Distribution

- Cleaning Messy Data and Data Manipulation

- Data Transformation - Merging and Concatenating

- Data Grouping, Aggregation, Filtering, and Applying Functions

- Data Sinks

- Detecting and Handling Missing Values and Outliers

- Normalization and Standardization

- Handling Categorical Features

- Consuming Time Series Data

- Text Preprocessing in the Era of LLMs

- Image and Audio Preprocessing with LLMs

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