Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook will show you tools and techniques for cleaning and handling data with Python for better outcomes. You will begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources.

Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate data to get it into a useful form. The current edition emphasizes advanced techniques like machine learning and AI-specific approaches to data cleaning along with the conventional ones. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.

By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.

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Spis treści

Python Data Cleaning Cookbook. Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI - Second Edition eBook -- spis treści

• 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
• 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
• 3. Taking the Measure of Your Data
• 4. Identifying Outliers in Subsets of Data
• 5. Using Visualizations for the Identification of Unexpected Values
• 6. Cleaning and Exploring Data with Series Operations
• 7. Identifying and Fixing Missing Values
• 8. Encoding, Transforming, and Scaling Features
• 9. Fixing Messy Data When Aggregating
• 10. Addressing Data Issues When Combining DataFrames
• 11. Tidying and Reshaping Data
• 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines