It's because to a reasonably approximation of "None" - none of the actual data science runs in Python, it's all hyper customized libraries which do run (close to) metal fast once the data has been loaded into the appropriate data structures. Pandas is a shim on top of Numpy, which heavily leverages the Fortran77 BLAS/LAPACK libraries.
Python is used at the top of the stack because it's an easy language to learn, you can get started fast, and, for places where performance is important - nothing is running in Python anyways.
> Python is used at the top of the stack because it's an easy language to learn, you can get started fast, and, for places where performance is important - nothing is running in Python anyways.
Also interactivity and quick feedback cycle, stuff like Jupyter Notebooks (né IPython Notebooks, a spinoff from the IPython project), matplotlib, ...
Python is used at the top of the stack because it's an easy language to learn, you can get started fast, and, for places where performance is important - nothing is running in Python anyways.