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, ...

> why has python become to standard for data science?

Because it's glue, so its speed doesn't matter overly much.

> Is it Library support or purely community based?

That's a dichotomy which doesn't really make sense. Python has cultivated and attracted attention from scientific communities from the start: the matrix-sig (a special interest group focusing on array computing packages) was created back in '95 and a number of their suggestions were added as language-level conveniences (that continues to this day, `@` was recently added as the "matrix multiplication" operator).

Most Python data science offloads the data crunching to Numpy, an optimized array based processing library.

This is feasible to reduce interpreter overhead considerably by using array programming:

c = a + b #This is valid Python code.

Where a and b are two same sized numpy arrays. Numpy typically handles the add in an optimized SIMD function.

For me, I have known Python since the 90s but only started using it as a daily driver a few years ago because there were big Microsoft and C/Java groups within my org and choosing one would alienate the other. So I used Python because it was good enough and non-threatening (and free and tons of libraries and healthy community, etc). I would have chosen javascript if there were as many packages. Also considered R, but hadn’t used it at the time.

I think Python is a good example of how being good enough is better than being awesome. And then it builds inertia through use and packages and friend of a friend recommendations.