I had extensive experience with Python for my doctoral in computation mathematics. In my work, I use SAS. They are different tools for different purposes. SAS is more a dedicated software for doing statistical analysis while Python is a generic programming language which would let you do a lot more. This is what it comes down to - SAS is more dedicated to data analysis and Python is more general. It is important to keep this distinction in mind. Python can probably do everything SAS does, but for more data related tasks, SAS already developed really comprehensive tools to deal with them. If your work only has a limited number of things you need to do - for example, data reporting on large gigabytes amount of data, then go with SAS. If your work needs a lot of complex computation and they are always changing - for example, you are researching the most innovative way to simulate the stock market, possibly with advance parallelisation, then a generic programming language would be better. I could never have written my phd thesis in SAS because it would be a nightmare to write the advance algorithms in SAS. In work, I wouldn't really use Python because SAS has dedicated tools to deal with large data (though Python is catching up too).
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