Practical Python Data Wrangling and Data Quality by Susan E McGregor (Author)

Practical Python Data Wrangling and Data Quality: Getting Started with Reading, Cleaning, and Analyzing Data (1st Edition) by Susan E. McGregor is a hands-on guide designed to teach data wrangling and data cleaning techniques using Python. Aimed at beginners and intermediate learners, this book provides a clear, step-by-step approach to handling raw data, transforming it into a clean and usable format for analysis. It covers essential Python libraries, such as Pandas and NumPy, for reading, cleaning, and transforming data efficiently. Through practical examples and case studies, readers learn to manage real-world datasets, handle missing values, remove duplicates, deal with inconsistent formatting, and prepare data for machine learning and analysis. McGregor emphasizes the importance of data quality throughout the book, showing readers how to identify and fix common issues that can hinder data analysis and decision-making.

Key Points:

1. Step-by-Step Introduction to Data Wrangling: The book provides a beginner-friendly approach to data wrangling, guiding readers through the process of preparing data for analysis using Python.

2. Focus on Real-World Datasets: Practical examples and case studies are used throughout the book, helping readers work with real-world data and solve common data-related problems in industry.

3. Hands-On Python Examples: The book teaches key Python libraries like Pandas and NumPy, providing readers with the skills to read, clean, and transform data into a usable format for analysis.

4. Handling Missing and Inconsistent Data: McGregor covers important techniques for addressing missing values, inconsistencies, and duplicates, ensuring that data quality is maintained throughout the wrangling process.

5. Data Cleaning for Analysis and Machine Learning: The book highlights techniques to clean data specifically for analysis and machine learning, ensuring datasets are ready for deeper insights and model-building.

6. Practical Solutions for Data Quality Issues: The author provides practical solutions for dealing with common data quality problems, such as inconsistent formatting, incorrect data types, and errors during data collection.

7. Efficient Data Transformation Techniques: Readers learn how to use Python to reshape, filter, and merge data, transforming raw datasets into clean, structured formats.

8. Exploratory Data Analysis (EDA): The book includes an introduction to exploratory data analysis, where readers learn to inspect and visualize data to understand its underlying structure before further analysis.

9. Tips for Automation and Scaling: The book includes tips on automating the data wrangling process and scaling data operations, preparing readers to work with large datasets in real-world scenarios.

10. Data Wrangling Best Practices: The book emphasizes best practices for data wrangling, focusing on maintaining data integrity, consistency, and quality throughout the entire cleaning process.

Conclusion:

Practical Python Data Wrangling and Data Quality is an invaluable resource for anyone looking to enhance their data manipulation and cleaning skills with Python. Through practical examples and a clear, step-by-step approach, Susan E. McGregor equips readers with the knowledge necessary to work with messy, real-world datasets, preparing them for further data analysis or machine learning tasks. Whether you're a beginner or someone looking to strengthen your data preparation techniques, this book provides the tools needed to handle and clean data effectively, ensuring high-quality results.

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Writer                 ✤              Susan E. McGregor (Author)