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!

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.

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, …

pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these data sources is provided by function with the …

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.

The primary pandas data structure. Parameters: datandarray (structured or homogeneous), Iterable, dict, or DataFrame Dict can contain Series, arrays, constants, dataclass or list-like objects. If data is …

Essential basic functionality # Here we discuss a lot of the essential functionality common to the pandas data structures. To begin, let’s create some example objects like we did in the 10 minutes to pandas …

Package overview # 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. It aims to be the …

Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more. Mission pandas aims to …

10 minutes to pandas # This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook. Customarily, we import as follows: