About pandas

History of development

In 2008, pandas development began at AQR Capital Management. By the end of 2009 it had been open sourced, and is actively supported today by a community of like-minded individuals around the world who contribute their valuable time and energy to help make open source pandas possible. Thank you to all of our contributors.

Since 2015, pandas is a NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world-class open-source project.

Timeline

• 2008: Development of pandas started
• 2009: pandas becomes open source
• 2012: First edition of Python for Data Analysis is published
• 2015: pandas becomes a NumFOCUS sponsored project
• 2018: First in-person core developer sprint

Library Highlights

• A fast and efficient DataFrame object for data manipulation with integrated indexing;

• Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;

• Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;

• Flexible reshaping and pivoting of data sets;

• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;

• Columns can be inserted and deleted from data structures for size mutability;

• Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;

• High performance merging and joining of data sets;

• Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;

• Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;

• Highly optimized for performance, with critical code paths written in Cython or C.

• 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 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 source data analysis / manipulation tool available in any language.

Vision

A world where data analytics and manipulation software is:

• Accessible to everyone
• Free for users to use and modify
• Flexible
• Powerful
• Easy to use
• Fast

Values

Is in the core of pandas to be respectful and welcoming with everybody, users, contributors and the broader community. Regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion, or nationality.