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I have been using R and RStudio for prototyping and model building and due to some persisting problems (which would only be applicable to the environment that I am using in) we have decided to use python. I am looking to know:

  1. is there a python development environment like "RStudio Server"?
  2. how easy/hard to enable multi-threading/multi-core processing in Python for decision trees/gradient boosting?

  3. can data persist in python dev environment server? Meaning: Can I save the dev environment with code and data frames and come back the next day to access/pick up where I left?

  4. Can you connect to Oracle database from Python?

  5. Is there a way I can import .RData to python development environment?

Why ask these questions here?

While looking up on search engines, I am mostly getting unreliable results, training insitutes promotions, outdated blogs and whitepapers from industry giants. I need a reliable answer.

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  • $\begingroup$ 2): Algos in sklearn and also the interfaces to XGBoost and lightGBM are as parallelized as their analogons in R. $\endgroup$ – Michael M Apr 4 '18 at 16:00
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1) Anaconda Spyder, maybe

2) sklearn, random forest has an option to select the number of jobs, and it will take care of parallelizing

3) i don't think so, but you can pickle objects and load them up. you can probably do something like create a variable_name - value dictionary and just pickle that

4) never tried

5) according to https://stackoverflow.com/questions/21288133/loading-rdata-files-into-python, I don't think so

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    $\begingroup$ Why pickling when you have feather format? $\endgroup$ – Aditya Apr 5 '18 at 4:43
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I can try to answer the 3rd question. You can use Jupyter + Python Kernel (install Anaconda to use conda commands with python Kernel). Jupyter allows keep your code and notes and graphs in one notebook and save it.

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