- 10 minutes to pandas
- Intro to data structures
- Essential basic functionality
- IO tools (text, CSV, HDF5, …)
- PyArrow Functionality
- Indexing and selecting data
- MultiIndex / advanced indexing
- Copy-on-Write (CoW)
- Merge, join, concatenate and compare
- Reshaping and pivot tables
- Working with text data
- Working with missing data
- Duplicate Labels
- Categorical data
- Nullable integer data type
- Nullable Boolean data type
- Chart visualization
- Table Visualization
- Group by: split-apply-combine
- Windowing operations
- Time series / date functionality
- Time deltas
- Options and settings
- Enhancing performance
- Scaling to large datasets
- Sparse data structures
- Migration guide for the new string data type (pandas 3.0)
- Frequently Asked Questions (FAQ)
- Cookbook
User Guide
The User Guide covers all of pandas by topic area. Each of the subsections introduces a topic (such as “working with missing data”), and discusses how pandas approaches the problem, with many examples throughout.
How to read these guides
In these guides you will see input code inside code blocks such as:
import pandas as pd
pd.DataFrame({'A': [1, 2, 3]})