I use of both Python (pandas) and base SAS at work for UK government. I have lots of experience in SAS, and enjoy using it. The macro language allows for very

I use of both Python (pandas) and base SAS at work for UK government. I have lots of experience in SAS, and enjoy using it. The macro language allows for very succinct solutions to difficult data manipulation problems. However, given SAS's huge expense it's difficult for me to identify any 'killer' areas where it's significantly better than open source tools. Indeed, I find pandas faster and easier to use for many problems. I find it hugely frustrating that the government pays so much money for SAS licences and training when most people use it for simple use cases, where they would be better picking up transferable skills (e.g. Python, SQL, R). My understanding is that that SAS supposed to be good at processing very large datasets because it uses RAM efficiently (only the PDV is stored in RAM). But in reality, a small minority of users are processing datasets that are too big for RAM (e.g. 16gb+) and there are probably better tools for the job in this use case. One user here comments that SAS is like an 'improved Excel'. In fact, I find pandas much closer to Excel than SAS because (in ipython notebook at least), you get nice visual representations of your tables, and it usually isn't difficult to translate an Excel operation into a pandas one. I especially like the multi-index and pivot table based capabilities. With a background in VBA for Excel, it's also relatively easy to pick up Python. None of this is quite so obvious in SAS, which has quite an unusual data step and macro programming language. It's very powerful, but is quite unintuitive to begin with due to a complete reliance on the program data vector.