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!

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 access methods, …

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 …

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.

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.

For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the PyData stack (SciPy, NumPy, Matplotlib, and more) is with Anaconda, a cross-platform …

Getting started New to pandas? Check out the getting started guides. They contain an introduction to pandas’ main concepts and links to additional tutorials.

pandas aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open …

If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them.

An introductory workshop by Stefanie Molin designed to quickly get you up to speed with pandas using real-world datasets. It covers getting started with pandas, data wrangling, and data visualization (with …