Arguably Spark solves a problem that does not exist anymore: single node performance with tools like DuckDB and Polars is so good that there’s no need for more complex orchestration anymore, and these tools are sufficiently user-friendly that there is little point to switching to Pandas for smaller datasets.

> Pandas is widely adopted and deeply integrated into the Python ecosystem.

This is pretty laughable. Yes there are very DS specific tools that make good use of Pandas, but `to_pandas` in Polars trivially solves this. The fact that Pandas always feels like injecting some weird DSL into existing Python code bases is one of the major reasons why I really don't like it.

> If you are dealing with huge data sets, you are probably using Spark or something like Dask already where jobs can run in the cloud. If you need speed and efficiency on your local machine, you use NumPy outright. And if you really, really need speed, you rewrite it in C/C++.

Have you used Polars at all? Or for that matter written significant Pandas outside of a notebook? The number one benefit of Polars, imho, is that Polars works using Expressions that allow you to trivially compose and reuse fundamental logic when working with data in a way the works well with other Python code. This solves the biggest problem with Pandas is that it does not abstract well.

Not to mention that Pandas is really poor dataframe experience outside of it's original use case which was financial time series. The entire multi-index experience is awful and I know that either you are calling 'reset_index' multiple times in your Pandas logic or you have bugs.

> once most of its users realise that it offers them no actual practical advantages over Pandas

What? Speed and better nested data support (arrays/JSON) alone are extremely useful to every data scientist.

My produtivity skyrocketed after switching from pandas to polars.

"Data Science" has never been related to academic research, it has always emerged in a business context. I wouldn't say that researchers at Deep Mind are "data scientists", they are academic researchers who focus on shipping papers. If you're in a pure research environment, nobody cares if you write everything in Matlab.

But the last startup I was at tried to take a similar approach to research was unable to ship a functioning product and will likely disappear in a year from now. FAIR has been largely disbanded in favor of the way more shipping-centric MSL, and the people I know at Deep Mind are increasingly finding themselves under pressure to actually produce things.

Since you've been hanging out in an ivory tower then you might be unaware that during the peek DS frenzy (2016-2019) there were companies where data scientists were allowed to live entirely in notebooks and it was someone else's problem to ship their notebooks. Today if you have that expectation you won't last long at most companies, if you can even find a job in the first place.

On top of that, I know quite a few people at the major LLM teams and, based on my conversations, all of them are doing pretty serious data engineering work to get things shipped even if they were hired for there modeling expertise. It's honestly hard to even run serious experiments at the scale of modern day LLMs without being pretty proficient at data engineering related tasks.

This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets, and to_pandas() gives you backwards compatibility with other libraries. However, plotnine is completely compatible with Polars dataframe objects.

You can always convert from Polars to Pandas. Plotnine will do it automatically for you, even.

I love R, but how can you make that claim when R uses three distinct object-oriented systems all at the same time? R might seem stable only because it carries along with it 50 years of history of programming languages (part of it's charm, where else can you see the generic function approach to OOP in a language that's still evolving?)

Finally, as someone who wrote a lot of R pre-tidyverse, I've seen the entire ecosystem radically change over my career.

The R ecosystem has had a similar evolution with the tidyverse, it was just a little further ago. As for Matlab, I initially learned statistical programming with it a long time ago, but I’m not sure I’ve ever seen it in the wild. I don’t know what’s going on there.

I’m actually quite partial to R myself, and I used to use it extensively back when quick analysis was more valuable to my career. Things have probably progressed, but I dropped it in favor of python because python can integrate into production systems whereas R was (and maybe still is) geared towards writing reports. One of the best things to happen recently in data science is the plotnine library, bringing the grammar of graphics to python imho.

The fact is that today, if you want career opportunities as a data scientist, you need to be fluent in python.

Outside bioconductor or the tidyverse in R can be just as unstable due to CRAN's package requirements.