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Practical Python Data Wrangling and Data Quality: Getting Started with Reading, Cleaning, and Analyzing Data,Used
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The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargonfree way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Collect data from structured data files, web pages, and APIs Perform basic statistical analyses to make meaning from datasets Visualize and present data in clear and compelling ways
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has four hundred thirteen pages. It provides in-depth insights into data wrangling and quality.
- Q: What are the dimensions of this book? A: The book measures seven point zero one inches in length, zero point eight seven inches in width, and nine point one nine inches in height.
- Q: What type of binding does this book have? A: This book is available in paperback binding. This makes it lightweight and easy to handle for reading.
- Q: How do I start learning from this book? A: You can start by reading the introductory chapters that cover Python basics. Follow along with practical examples to clean and analyze data.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It covers foundational concepts in an accessible and jargon-free manner.
- Q: Can I use this book for advanced data analysis? A: Yes, seasoned professionals can also benefit from this book. It includes expert advice and advanced techniques for effective data evaluation.
- Q: How should I store this book? A: Store this book in a dry, cool place to maintain its quality. Avoid direct sunlight to prevent fading of the cover and pages.
- Q: Is this book safe for all ages? A: Yes, this book is safe for all ages. It focuses on practical data skills without any inappropriate content.
- Q: What if my book arrives damaged? A: If your book arrives damaged, you should contact the seller for a replacement. Most sellers have a return policy for damaged items.
- Q: How can I apply the techniques in this book? A: You can apply the techniques by working on real datasets provided in the book. Practice using Python to enhance your data skills.
- Q: Is there a glossary or index in this book? A: Yes, the book includes a glossary and index for easy reference. This helps you quickly find key terms and concepts.
- Q: Does this book cover data visualization? A: Yes, the book covers data visualization techniques. It teaches how to present data clearly and compellingly using Python.
- Q: What programming language does this book focus on? A: This book focuses on Python, specifically version three point eight and above. It provides practical examples using this programming language.
- Q: Will I learn about APIs in this book? A: Yes, this book includes content on collecting data from APIs. It’s an essential skill for modern data analysis.
- Q: Can I find examples for cleaning data in this book? A: Yes, the book provides numerous examples for cleaning data. These practical techniques will help you improve your data quality.