Useful Pandas Features

A tutorial on 10 useful Pandas features:

http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/

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.

http://pandas.pydata.org/pandas-docs/version/0.15.0/ecosystem.html

Seaborn

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

https://github.com/mwaskom/seaborn

Examples/tutorial:

http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb

Vincent: A Python to Vega translator

The folks at Trifacta are making it easy to build visualizations on top of D3 with Vega. Vincent makes it easy to build Vega with Python.


https://github.com/wrobstory/vincent

Bokeh

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.

http://bokeh.pydata.org/en/latest/tutorial/index.html

ggplot

ggplot is an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.

https://github.com/yhat/ggplot

Qgrid

Qgrid is an IPython extension which uses SlickGrid to render pandas DataFrames within an IPython notebook. It's being developed for use in Quantopian's hosted research environment, and this repository holds the latest source code:

https://github.com/quantopian/qgrid


Demo:

http://nbviewer.ipython.org/github/quantopian/qgrid/blob/master/qgrid_demo.ipynb