pandas Ecosystem

Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. This is encouraging because it means pandas is not only helping users to handle their data tasks but also that it provides a better starting point for developers to build powerful and more focused data tools. The creation of libraries that complement pandas’ functionality also allows pandas development to remain focused around it’s original requirements.

This is an in-exhaustive list of projects that build on pandas in order to provide tools in the PyData space.

We’d like to make it easier for users to find these project, if you know of other substantial projects that you feel should be on this list, please let us know.

Statistics and Machine Learning


Statsmodels is the prominent python “statistics and econometrics library” and it has a long-standing special relationship with pandas. Statsmodels provides powerful statistics, econometrics, analysis and modeling functionality that is out of pandas’ scope. Statsmodels leverages pandas objects as the underlying data container for computation.


Use pandas DataFrames in your scikit-learn ML pipeline.



The Vincent project leverages Vega (that in turn, leverages d3) to create plots . It has great support for pandas data objects.


Hadley Wickham’s ggplot2 is a foundational exploratory visualization package for the R language. Based on “The Grammer of Graphics” it provides a powerful, declarative and extremely general way to generate bespoke plots of any kind of data. It’s really quite incredible. Various implementations to other languages are available, but a faithful implementation for python users has long been missing. Although still young (as of Jan-2014), the yhat/ggplot project has been progressing quickly in that direction.


Although pandas has quite a bit of “just plot it” functionality built-in, visualization and in particular statistical graphics is a vast field with a long tradition and lots of ground to cover. The Seaborn project builds on top of pandas and matplotlib to provide easy plotting of data which extends to more advanced types of plots then those offered by pandas.


Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.

Domain Specific


Geopandas extends pandas data objects to include geographic information which support geometric operations. If your work entails maps and geographical coordinates, and you love pandas, you should take a close look at Geopandas.