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, …

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

pandas pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Install pandas now!

For a quick overview of pandas functionality, see 10 Minutes to pandas. You can also reference the pandas cheat sheet for a succinct guide for manipulating data with pandas.

These classes are not to be confused with classes from the pandas-stubs package which has classes in addition to those that occur in pandas for type-hinting. In addition, public functions in …

While standard Python / NumPy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data …

How do I read and write tabular data? How do I select a subset of a DataFrame? How do I create plots in pandas? How to create new columns derived from existing columns How to calculate …

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

Try pandas in your browser (experimental) You can try pandas in your browser with the following interactive shell without needing to install anything on your system.

Flags refer to attributes of the pandas object. Properties of the dataset (like the date is was recorded, the URL it was accessed from, etc.) should be stored in DataFrame.attrs.