dtale

I played around with many of these before: - https://github.com/quantopian/qgrid - https://github.com/man-group/dtale I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it. If I really want to show everything, I just use: ``` with pd.option_context('display.max_rows', None): print(df) ```

Some other interesting projects from financial services firms: https://perspective.finos.org/ (JP Morgan) https://github.com/jpmorganchase/regular-table (JP Morgan) https://github.com/man-group/dtale (Man Numeric) https://github.com/deshaw/pyflyby (DE Shaw)

My company, Man Numeric, has really been pushing for open-source projects and we were able to open source this client for visualizing pandas data structures. The motivation for building such a tool was born out of a conversion from SAS to Python and we needed a replacement for SAS's Insight function. It integrates really nicely into a jupyter notebook or python terminal. Under the hood its a small Flask service with a React front-end (particularly react-virtualized & chart.js) which can be instantiated by passing it a Pandas data structure. It can handle datasets of any size (as long as the data fits in your RAM) by only loading & drawing what data is viewable in the dimensions of your browser. I have a demo running on PythonAnywhere: http://andrewschonfeld.pythonanywhere.com/dtale/iframe/2 Let me know what you think? I also have anothe