Data processing the "SAS way" in Python

[GenDemo]

Hi All

I know this is not a Python forum, but I am appealing to my fellow SAS users for advise, as I think you will understand what I mean and what I am after.
I am very new to Python, but a veteran SAS & SQL user.
My company doesn't want to dish out the money to get me a SAS licence, hence I am tying to learn Python. However, I am finding the data processing part of it immensely frustrating. This process is just highlighting to me (ONCE AGAIN) how awesome SAS (and SQL) is. 
Is there a way for me to do data processing in Python in a way which is similar to either SAS's data steps; or a SQL type query?
For example, I have CSV files that I generate from out DB or BI Tool, and export to use in Python for regression or time series analysis. I need to import this CSV, format columns and dates, do date calculations, lag functions etc. Then I might need to join data sets together. 
From here I need to do plots and then the statistical modelling itself - the plotting and modelling I can do in Python (though its not at the level I would like as I am used to in SAS).Is R perhaps a better tool to do this in? can I do my data processing in R, in Python and still use Python for the modelling?

i know this is a very open-ended and subjective question... but I'd greatly appreciate some advice.

[SASKiwi]

Another question to ask your company is whether they are happy for you to use unsupported software and indeed what is their policy regarding unsupported software? By that I mean having access to professional tech support, not getting questions answered on stackoverflow....

IMHO Python, R etc are fine for work that is not directly business-critical, but I wouldn't want to be doing actual business-critical work on unsupported software.

[GenDemo]

Yeah I totally agree with you - but its a moot point.

Python/R is free, so they said lets use that rather than having to pay money. So I suppose, to your question, they are happy with open-source software.

[SASKiwi]

@GenDemo  - I have no problem if it is official company policy. However often the implications of these decisions are not thought through. How do you avoid being blamed when you can't find the answer to an unsupported software problem?

Just trying to point out that both commercial and open source software have their place.

[LinusH]

Not being knwledgable in Python, but there are claims it can be perfectially used for data mangament tasks.

And a few ETL tools uses Python behind the scenes.

So I think it's partly matter of habit/knowledge.

There are lots of resources online, so you perhaps just have to invest time in this.

Good luck!