As a grad student I was definitely a polygot programmer, but I've since really settled into an almost 100% python workflow, except when I used highly optimized

As a grad student I was definitely a polygot programmer, but I've since really settled into an almost 100% python workflow, except when I used highly optimized simulation packages that run on massively parallel machines. The reason why python stuck for me was a combination of the simplicity of the language, its ability to just let me get work done and the robust scientific library support, not to mention that I rarely need to step out python to do things. Need to set up a quick and dirty batch processing queue to churn through a series of simulations on my workstation: `import multiprocessing` and set up a worker pool. Need to automate the creation of several hundred simulation config scripts, simple with standard library string templating. Need to work with hdf5 files: `import h5py`, etc, etc. That and its been dead simple to wrap existing c code with cython and write small compiled modules for cpu intensive tasks when needed. And IPython for interactive data exploration and development. So from that perspective, I'd be much more interested in calling Julia from within Python, or writing some small function that needs optimization in Julia and then calling it from Python, than the other way around.