Python Data Cleaning Cookbook
Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Created by Michael Walker
Learn how to prepare your data for analysis by tackling real-world data cleaning challenges using Python. Explore practical techniques for handling messy, inconsistent, or incomplete datasets so you can make better decisions and get reliable results from your data projects.
Packt | May 2024 | 486 min
What You Will Learn
You will work through practical, hands-on examples using Python libraries such as pandas, NumPy, Matplotlib, scikit-learn, and OpenAI. Each step focuses on real data problems, showing you how to clean, validate, and prepare datasets for analysis or machine learning. By building reusable functions and workflows, you will gain confidence in managing new data challenges.
Key Features
- Master advanced data cleaning for machine learning and NLP applications
- Apply modern Python tools like pandas, NumPy, and OpenAI for efficient data wrangling
- Diagnose and resolve common data issues to ensure accurate analysis and insights
Target Audience
This content is ideal for data analysts, scientists, and developers who already have a working knowledge of Python and want to improve their data cleaning skills. If you often deal with messy or complex datasets and want to prepare your data for machine learning, AI, or deeper analysis, you will find actionable solutions and techniques here.
