Python is becoming one of the most dominant programming languages in the world and is designed to appeal to a wide range of users, from hardcore data scientists to university students.<\/p>\n\n\n\n
Python’s strength in data science can be traced back to the efforts of its scientific computing pioneers.<\/p>\n\n\n\n
Guido van Rossum, the founder of Python, remained friendly to his competitors and the scientific community and encouraged these groups to improve Python for their needs.<\/p>\n\n\n\n
This minimized the need for the project to fork and enabled Python to do so many great things.<\/p>\n\n\n\n
For improvement opportunities, Peter Wang, co-founder, and CEO of Anaconda noted that Python has packaging issues.<\/p>\n\n\n\n
While it is laudable that one can connect existing libraries with the Python glue, one still has to figure out how to put all these libraries together.<\/p>\n\n\n\n
Anaconda’s distribution makes it easier to take hard-to-compile things like Fortran and make them work efficiently with Python.<\/p>\n\n\n\n
Within data science, the focus has been strengthened to further improve Python’s performance.<\/p>\n\n\n\n
Finally, it is to be expected that Python’s popularity will translate into numerous contributors eager to confront any obstacles to growth.<\/p>\n\n\n