Debugging large, stateful Python applications can be a hassle. In Django, projects, for example, your logic can be spread across multiple files containing models, views, managers, etc.
This tool will allow you to control debugging code in one module from within another module. This can be very helpful in helping disentangle complex, stateful code.
This is a tool that allows you to build processing pipelines in Python. You factor your code into small, easily testable components that are then wired together into a task graph.
A native-ish backend like TkAgg is valuable because it instantly pops up an interactive plot, which I find really useful for the quick-n-dirty data exploration tasks I usually use Pandashells for. You don't need the interactive backend and can use the --savefig option on plots to save images or html, but that takes time -- and I usually want my results now!! :)
I would love help making a conda package. I've never done that before. I didn't spend a lot of time looking, but I didn't find conda packages for some of the dependencies (e.g. gatspy). If you now how to work around that, I'd welcome the help.
Thanks for the feedback. As you may know, getting matplotlib to work properly with backends like TkAgg can be quite a chore when installing using pip (non-python libraries are needed). The Pandashells install process could be improved. I intend on creating a conda package for Pandashells that should handle the dependency issue you raise, but I just haven't had time to do so yet. I am hoping the detailed examples in the docs will suffice until I do.
Pandashells is a set of cli tools that opens up the power of the python data stack to the bash prompt. Brings dataframe manipulation, visualization and statistical modeling to the unix pipeline workflow.