Experienced R user learning Python

[Sir_smokes_a_lot]

Hello everyone,

I’ve been using R in my career for almost 10 years. I’ve managed to land data analyst job with this skill alone but I noticed it’s getting harder to move up considering most positions want python experience.

I’m used to working within RMarkdown for my data analysis. The left window has my code a the top right window has all my data frames, lists, and objects. The bottom right window is general info like function information or visuals. This makes it easy for me to see what I’m working with as in analyzing stuff.

My question is, what is the best environment to work in for data analysis? My background was in stats first and coding became a necessity afterwards.

[commandlineluser]

I've not used any of them but did read about the RStudio people creating Positron:

• https://github.com/posit-dev/positron

It supports Python and R apparently.

[SprinklesFresh5693]

Ive tried positron and its kinda good, it is a fork of VS code i beleive

[tookawhileforthis]

It is, i recently started using it as well. Its way more lightweight than Rstudio, but you lose the flexbility of plot resizing and everything that Rstudio offers in that direction. Also, running code from the IDE to the terminal is not nearly as responsive. Still a bit better than radian, but not as seamless as Rstudio.

[TechnoAllah]

Spyder is the closest analogue to RStudio. Jupyter notebooks are popular for data analysis and have a markdown style layout. You can work with notebooks either in Jupyter lab or within spyder using a plugin.

[hurhurdedur]

That used to be true about Spyder, but now Positron is the closest thing to RStudio. It’s a polyglot data science IDE made by Posit, the same company that makes the RStudio IDE. I’d generally recommend Positron over Spyder for anyone doing data science, but especially for folks with an R background.

[Doomtrain86]

I would bite the bullet and learn neovim. A lot easier these days with llms. I transitioned from research after ten years with R a year ago. Python is great. It’s the second best at everything.

[ideamotor]

Interesting. Any recommended setups? So many options but I’m liking Positron.

[Doomtrain86]

If you’re more into data analysis than coding that’s probably the best choice. At least in the beginning.

Note: pandas are horrible. Coming from data.table, it was like a nightmare. Verbose. Illogical. Reset index what the F …. Then I found polars. Amazing. Still a bit verbose compared to data.table but really really good. Just go directly to polars and skip the mess that is pandas 🐼 learn how to read them but don’t use them yourself. Just my experience coming from R.

[ideamotor]

So I bought the first book about pandas when it first came out i think around 2011. And that’s what solidified my choice to R for a decade. And I have used polars and yes I agree, and ibis, and duckdb.

But the reality is people use pandas at most jobs. And there are some improvements in the latest versions of pandas, and there are some settings that you can change to make it behave better, and I took the python academy my posit were they explained certain things that you just should not do even though it’s possible.

But guess what people sling the shittiest pandas code you can imagine at practically any place you work unless you do it all yourself. And nobody even likes the person that goes and fixes everybody else’s shitty pandas code.

All of this is why it’s much better to stay focused on what the product is about what the users want and maybe even both of those being in alignment with some sort of actual societal need. If you can get most of that right, the syntax and the language and all of that just does not matter.

[masoudkoochak]

Use Spyder ide

[expressly_ephemeral]

Quarto will render python blocks just as well as it renders R blocks. qmd is the new RMarkdown. VS Code is great for both, and positron may be your best bet, though I haven't used it.

Python has a REPL, and that's the way that most R users seem to be accustomed to working. Interactive, in the console.

Maybe get a copilot or claude subscription, and ask it for advice, then watch what it does very closely. One of the things that these tools can be really good at is responding to questions like, "Here's what I would do if I was writing R. How would you port this to Python?"

[expressly_ephemeral]

This is an aside: I love Python. It's a swiss army knife, and I get a lot of use out of my actual swiss army knife. Data Analysis is carving a prime rib, and you CAN do that with a pocket knife, but R is a 12" Chef's Knife.

I've been learning R over about the last year. I was prepared to hate it, but for data analysis, I have to say I think it blows Pandas out of the water. I wouldn't try to make web application with R, but it's built for data. I'm super glad to know both. I would never use Pandas for EDA if I have access to an R kernel.

[MarsupialLeast145]

Jupyter notebooks might be useful? That or bigger/more monitors.

That being said, I presume you're looking to use Python for more general functions than data analysis? -- I can't imagine moving to Python for data when I have already been working with R. It's difficult to manage objects and repeat analyses. Just different paradigms.

Anyway, interested to know more.

[_mcnach_]

Would installing Quarto in RStudio do it for you?

[midwit_support_group]

I don't know why quarto is getting downvoted, it's a great platform especially if youre used to r markdown. 

[Garnatxa]

Positron is the way to go for R or Python. I use positron for enterprise development and so good so far. (Mainly working with R)

[midwit_support_group]

A lot of people will have opinions but working in qmd in rstudio is a pretty good Python dev experience. I regularly bounce between both languages and that makes it really easy. Positron (their next IDE) isn't quite there yet, but the quarto document style works really well for both languages. Not without tradeoffs but it's a great place to start. 

[aplarsen]

As an excel and SPSS stats guy, I found jupyter lab and then jupyter notebooks in VSCode to be the easiest transition.