In short I think it's hard to strike an appropriate balance between these but this seems to be a good intro level book.
> Copyright 2016
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.
Yes, there is Octave but often the toolboxes aren't available or compatible so you're rewriting everything anyway. And when you start rewriting things for Octave you learn/remember what trash Matlab actually is as a language or how big a pain doing anything that isn't what Mathworks expects actually is.
To be fair: Octave has extended Matlab's syntax with amazing improvements (many inspired by numpy and R). It really makes me angry that Mathworks hasn't stolen Octave's innovations and I hate every minute of not being able to broadcast and having to manually create temp variables because you can't chain indexing whenever I have to touch actual Matlab. So to be clear Octave is somewhat pleasant and for pure numerical syntax superior to numpy.
But the siren call of Python is significant. Python is not the perfect language (for anything really) but it is a better-than-good language for almost everything and it's old enough and used by so many people that someone has usually scratched what's itching already. Matlab's toolboxes can't compete with that.
Finally, as someone who wrote a lot of R pre-tidyverse, I've seen the entire ecosystem radically change over my career.
Since Pandas lacks Polars' concept of an Expression, it's actually quite challenging to programmatically interact with non-trivial Pandas queries. In Polars the query logic can be entirely independent of the data frame while still referencing specific columns of the data frame. This makes Polars data frames work much more naturally with typical programming abstractions.
Pandas multi-index is a bad idea in nearly all contexts other than it's original use case: financial time series (and I'll admit, if you're working with purely financial time series, then Pandas feels much better). Sufficiently large Pandas code bases are littered with seemingly arbitrary uses of 'reset_index', there are many times where multi-index will create bugs, and, most important, I've never seen any non-financial scenario where anyone has ever used Multi-index to their advantage.
Finally Pandas is slow, which is honestly the least priority for me personally, but using Polars is so refreshing.
What other data frames have you used? Having used R's native dataframes extensively (the way they make use of indexing is so much nicer) in addition to Polars both are drastically preferable to Pandas. My experience is that most people use Pandas because it has been the only data frame implementation in Python. But personally I'd rather just not use data frames if I'm forced to used Pandas. Could you expand on what you like about Pandas over other data frames models you've worked with?
I like how in Pandas (and in R), I can quickly load data sets up in a manner that lets me do relational queries using familiar syntax. For my Elite: Dangerous project, because I couldn't get Pandas to work for me (which the reader should chalk up to my ignorance and not any deficiency of Pandas itself), I ended up using the SQLAlchemy ORM with Marshmallow to load the data into SQLite or PostgreSQL. Looking back at the work, I probably ought to have thrown it into a JSON-aware data warehouse somehow, which I think is how the guy behind Spansh does it, but I'm not a big data guy (yet) and have a lot to learn about what's possible.
What other framework would you replace it with?
No, polars or spark is not a good answer, those are optimized for data engineering performance, not a holistic approach to data science.
Can you expand on why Polars isn't optimised for a holistic approach to data science?
It is a curse I know. I would also choose a better interface. Performance is meh to me, I use SQL if i want to do something at scale that involves row/column data.
Today all serious DS work will ultimately become data engineering work anyway. The time when DS can just fiddle around in notebooks all day has passed.
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++.
Polars is trying to solve an issue that just doesn't exist for the vast majority of users.
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.
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.
Oh yeah? Well in my ivory tower the work stops being serious once it becomes engineering, how do you like that elitism?!
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.
Reminds me of how thinking using frequencies rather than computing probabilities is easier and can avoid errors (e.g. a 99% accurate test being positive does not mean 99% likelihood of having disease for a disease with a 1/10,000 prevalence in population).
Everything it does can be done reasonable well with list comprehensions and objects that support type annotations and runtime type checking (if needed).
Pandas code is untestable, unreadable, hard to refactor and impossible to reuse.
Trillions of dollars are wasted every year by people having to rewrite pandas code.
The thousand-plus data integrity tests I've written in pandas tell a different story...
I see this take somewhat often, and usually with similar lack of nuance. How do you come to this? In other cases where I've seen this it's from people who haven't worked in any context where performance or scientific computing ecosystem interoperability matters - missing a massive part of the picture. I've struggled to get through to them before. Genuine question.
Pandas insist you never use a for loop. So, I feel guilty if I ever need a throwaway variable on the way to creating a new column. Sometimes methods are attached to objects, other times they aren't. And if you need to use a function that isn't vectorised, you've got to do df.apply anyway. You have to remember to change the 'axis' too. Plotting is another thing that I can't get my head around. Am I supposed to use Pandas' helpers like df.plot() all the time? Or ditch it and use the low level matplotlib directly? What is idiomatic? I cannot find answers to much of it, even with ChatGPT. Worse, I can't seem to create a mental model of what Pandas expects me to do in a given situation.
Pandas has disabused me of the notion that Python syntax is self-explanatory and executable-pseudocode. I find it terrible to look at. Matlab was infinitely more enjoyable.
Regarding your plotting question: use seaborn when you can, but you’ll still need to know matplotlib.
I pretty much consider anyone who likes it to have Stockholm syndrome.
A for loop is a lot about the "how" but apply, join etc are much closer to the "what".
That said, the polars/narwals style API is better than pandas API for sure. More readable and composable, simpler (no index) and a bit less weird overall.
But the code base I work on has thousands and THOUSANDS of lines of Pandas churning through big data, and I can't remember the last time it lead to a bug or error in production.
We use pandas + static schema wrapper + type checker, so you'll have to get exotic to break things.
* Mypy dependency and really bad PEP compliance * Sub-optimal runtime check decorators * Subclasses pd.DataFrame, so using e.g. .assign(...) makes the type checker think it's still the same type, but now you just violated your own schema
So I wrote my own library that solves all these issues, but it's currently company-internal. I've been meaning to push for open-sourcing it, but just haven't had the time.
BenGosub•2mo ago
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