Data Wrangling with Python: Tips and Tools to Make Your Life Easier

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don’t need to know a thing about the Python programming language to get started.

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.

• Quickly learn basic Python syntax, data types, and language concepts
• Work with both machine-readable and human-consumable data
• Scrape websites and APIs to find a bounty of useful information
• Clean and format data to eliminate duplicates and errors in your datasets
• Learn when to standardize data and when to test and script data cleanup
• Explore and analyze your datasets with new Python libraries and techniques
• Use Python solutions to automate your entire data-wrangling process

Table of Contents

Chapter 1. Introduction to Python
Chapter 2. Python Basics
Chapter 3. Data Meant to Be Read by Machines
Chapter 4. Working with Excel Files
Chapter 5. PDFs and Problem Solving in Python
Chapter 6. Acquiring and Storing Data
Chapter 7. Data Cleanup: Investigation, Matching, and Formatting
Chapter 8. Data Cleanup: Standardizing and Scripting
Chapter 9. Data Exploration and Analysis
Chapter 10. Presenting Your Data
Chapter 11. Web Scraping: Acquiring and Storing Data from the Web
Chapter 12. Advanced Web Scraping: Screen Scrapers and Spiders
Chapter 13. APIs
Chapter 14. Automation and Scaling
Chapter 15. Conclusion
Appendix A. Comparison of Languages Mentioned
Appendix B. Python Resources for Beginners
Appendix C. Learning the Command Line
Appendix D. Advanced Python Setup
Appendix E. Python Gotchas
Appendix F. IPython Hints
Appendix G. Using Amazon Web Services