A comprehensive, self-contained guide to pandas, the library at the heart of data work in Python — the tool that loads, cleans, reshapes, combines,…

Welcome to Pandas: Data Wrangling in Python.

A comprehensive, self-contained guide to pandas, the library at the heart of data work in Python — the tool that loads, cleans, reshapes, combines, and analyzes the messy tabular data of the real world before any model or chart ever sees it. If a Python data-science or machine-learning project starts with a CSV, a database table, or a spreadsheet, pandas is almost certainly the first thing it touches. This book teaches it from first principles: the Series and DataFrame, reading and writing data, selecting and cleaning, the split-apply-combine of groupby, merging and reshaping, time series, performance, and the end-to-end data-analysis workflow. It blends intuition, the concepts behind the API, and runnable code.

This title is part of the ShriIra library and is free to read in full, right here — our small contribution to making world-class knowledge easy to reach.

A note on reading it: open the Contents menu at the top of the reader to jump between chapters, use the Aa menu to set a comfortable text size, theme (light, sepia, or night), and single- or two-page layout. Your place is saved automatically, so you can always pick up where you left off.

We hope it serves you well.

— Shriira Press

Contents

1. Chapter 1 — What Is Pandas?
2. Chapter 2 — Series and DataFrame: The Core Data Structures
3. Chapter 3 — Reading and Writing Data: I/O
4. Chapter 4 — Indexing and Selection: loc, iloc, and Boolean Filtering
5. Chapter 5 — Cleaning Data: Missing Values, Duplicates, and Types
6. Chapter 6 — Transforming Columns: apply, map, and Vectorized Operations
7. Chapter 7 — GroupBy: Split-Apply-Combine
8. Chapter 8 — Combining Data: Merge, Join, and Concat
9. Chapter 9 — Reshaping Data: Pivot, Melt, and Tidy Data
10. Chapter 10 — Time Series
11. Chapter 11 — Categorical Data, Text, and Advanced Types
12. Chapter 12 — Performance, Scaling, and the Ecosystem
13. Chapter 13 — The Data Analysis Workflow and the Profession
14. Appendix A — Glossary and API Quick Reference
15. Appendix B — Further Reading and Resources