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Python is not a great language for data science

• lenerdenator 1y ago
  > I think people way over-index Python as the language for data science. It has limitations that I think are quite noteworthy. There are many data-science tasks I’d much rather do in R than in Python.1 I believe the reason Python is so widely used in data science is a historical accident, plus it being sort-of Ok at most things, rather than an expression of its inherent suitability for data-science work. Python doesn't need to be the best at any one thing; it just has to be serviceable for a lot of things. You can take someone who has expertise in a completely different domain in software (web dev, devops, sysadmin, etc.) and introduce them to the data science domain without making them learn an entirely new language and toolchain.
  • dmurray 1y ago
    That's not why it's used in data science though. Lots of data scientists use Python all day and have no concept of ever working in a different field. It's used in data science because it's used in data science.
    • mohaine 1y ago
      But data science usually isn't an island. Use whatever you want on your one off personal projects but use something more non-data science friendly if you ever want your model to run directly in a production workflow. Productionizing R models is quite painful. The normal way is to just rewrite it not in R.
      • dmurray 1y ago
        I've soured a lot on directly productionizing data science code. It's normally an unmaintainable mess. If you write it in R and then rewrite it in C (better: rewrite it in English with the R as helpful annotations, then have someone else rewrite it in C), at least there is some chance you've thought about the abstractions and operations that are actually necessary for your problem.
    • lenerdenator 1y ago
      That's probably true now, but at one point, they were looking for people to start doing data science, and were pulling people from other domains.
    • vkazanov 1y ago
      It's used in data science because no other language has this level of library support. And it got this unprecedented level of support because right from the start it made its focus clear syntax and (perceived) simplicity. There is also a sort of cumulative effect from being nice for algorithmic work. Guido's long-term strategy won over numerous other strong candidates for this role.
      • passivegains 1y ago
        I think the key thing not obvious to most data scientists is they're not using python because it meets their needs, it's because we've failed them. twice. 1. data scientists aren't programmers, so why do they need a programming language? the tools they should be using don't exist. they'd need programmers to make them, and all we have to offer is... more programming languages. 2. the giant problem at the heart of modern software: the most important feature of a modern programming language is being easy to read and write. this feature is conspicuously absent from most important languages. they're trapped. they can't do what they need without a programming language but there are only a handful they can possibly use. the real reason python ended up with such good library support is they never really had a choice.
        • vkazanov 1y ago
          When the first scientific libraries were written for python, most alternatives didn't even consider being readable, or convenient. The choice was more like C/Cpp/Fortran vs Python. And then Python went into a self-reinforcing loop, with scientific community coming up with more and more ways to improve Python support for the kind of interactive work that was required for data analysis. Think ipython -> jupyter -> jupyter forks and other python-centric notebook systems. So when data analysis evolved into data science and machine learning, gpu-first library vendors already faced a crowd of people knowing python. It is crazy how right now one can utilize 100s of gpus through these bits of dirty python wrapped in json.
          • aragilar 1y ago
            I think you're forgetting perl (plus other unix utils) and matlab. PDL (perl data language) was a thing, as was IDL (and other similar tools).
    • bsder 1y ago
      Partially, but it's also because 90% of your work in "data science" isn't direct analysis. You need to get the data from somewhere. Do you need to scrape that because Python is okay at scraping? Oh, after its scraped, we looked at it and it's in ObtuseBinaryFormat0.0.LOL.Beta and, what do you know, somebody wrote a converter for that for Python. And we need to clean all the broken entries out of that and Python is decent at that. etc. The trick is that while Python may or may not be anybody's first choice for a particular task, Python is an okay second or third choice for most tasks. So, you can learn Python. Or you learn <best language> and <something else>. And if <something else> is Python, was <best language> sufficiently better than Python to be worth spending the time learning?
• forgotpwd16 1y ago
  Article is well written but fails to address its own thesis by postponing it to a sequel article. At its current state only alludes that Python is not great because requires specialized packages. (And counterexample is R for which also used a package.)
  • stevenpetryk 1y ago
    Totally agree. The author's most significant example is two code snippets that are quite similar and both pretty nice.
  • puzzlingcaptcha 1y ago
    The 'sequel' is also online: https://blog.genesmindsmachines.com/p/python-is-not-a-great-language-for-2e0 https://blog.genesmindsmachines.com/p/python-is-not-a-great-...
    • forgotpwd16 1y ago
      Thanks! In such serial articles usually there's link to the end pointing to the next one so, since there wasn't any, thought next one hadn't been written. This one indeed addresses the thesis. The TL;DR, taken directly from the article, >The core problems I see with Python as a language for data science are call-by-reference semantics, lack of built-in concepts of missing values, lack of built-in vectorization, and lack of non-standard evaluation.
• yeahwhatever10 1y ago
  A little late for this
  • ASalazarMX 1y ago
    "Not great" doesn't necessarily mean "bad", it can be interpreted as "good", or even "very good". An honest title would have explicitly qualified how suitable the author found it was. That the author avoided saying Python was a bad language outright speaks a great deal of its suitability. Well, that, and the majority data science in practice.
• iLemming 1y ago
  From many practical points, Clojure is great for data. And you can even leverage python libs via clj-python.
  • phforms 1y ago
    In the past few years I have seen some serious efforts from the Clojure community to make Clojure more attractive for data science. Check out the Scicloj[1] group and their data science stack/toolkit Noj[2] (still in beta) as well as the high-performance tabular data processing library tech.ml.dataset (TMD)[3]. - [1] https://scicloj.github.io https://scicloj.github.io - [2] https://scicloj.github.io/noj https://scicloj.github.io/noj - [3] https://github.com/techascent/tech.ml.dataset https://github.com/techascent/tech.ml.dataset
    • geokon 1y ago
      What's worth emphasizing is that you're not marrying in to an ecosystem of libs. There are a lot of separate pieces that you can typically use separately. I do climate data work without most of Scicloj's tools, but I do use tech.ml.dataset extensively
• deleted 1y ago
  [deleted]
• paulfharrison 1y ago
  R is so good in part because of the efforts of people like Di Cook, Hadley Wickham, and Yihui Xie to create an software environment that they like working in. It also helps that in R any function can completely change how its arguments are evaluated, allowing the tidyverse packages to do things like evaluate arguments in the context of a data frame or add a pipe operator as a new language feature. This is a very dangerous feature to put in the hands of statisticians, but it allows more syntactic innovation than is possible in Python.
  • cb321 1y ago
    Like Python, R is a 2 (+...) language system. C/Fortran backends are needed for performance as problems scale up. Julia and Nim [1] are dynamic and static approaches (respectively) to 1 language systems. They both have both user-defined operators and macros. Personally, I find the surface syntax of Julia rather distasteful and I also don't live in PLang REPLs / emacs all day long. Of course, neither Julia nor Nim are impractical enough to make calling C/Fortran all that hard, but the communities do tend to implement in the new language without much prompting. [1] https://nim-lang.org/ https://nim-lang.org/
• Lyngbakr 1y ago
  I was a bit disappointed to discover that this was essentially an R vs. Python article, which is a data science trope. I've been in the field for 20+ years now and while I used to be firmly on team R, I now think that we don't really have a good language for data science. I had high hopes for Julia and even Clojure's data landscape looks interesting, but given the momentum of Python I don't see how it could be usurped at this point.
  • vkazanov 1y ago
    It is EVERYWHERE. I recently had to interview a bunch of data scientists, and only one of them knew SQL. Surely, all of then worked with python. I bet none of them even heard of R.
    • Lyngbakr 1y ago
      Yikes. Were they experienced data scientists or straight out of school? I find it very odd (and a bit scary) that they didn't know SQL.
      • garciasn 1y ago
        Experienced Data Scientists and/or those straight out of school are EXTREMELY lacking in valuable SQL experience and always have been. Take a DS with 25 years experience in SAS, many of them are great with DATAstep, but have far less experience using PROC SQL for querying the data in the most effective way--even if they were pulling the data down with pass-through via SAS/ACCESS. Often they'd be doing very simplistic querying and then manipulating via DATAstep prior to running whatever modeling and/or reporting PROCs later, rather than pushing it upstream into a far faster native database SQL pull via pass-through. Back in 2008/2009, I saved 30h+ runtime on a regular report by refactoring everything in SQL via pass-through as opposed to the data scientists' original code that simply pulled the data down from the external source and manipulated it in DATAstep. Moving from 30h to 3m (Oracle backend) freed up an entire FTE to do more than babysit a long-running job 3x a week to multiple times per day.
    • garciasn 1y ago
• RobinL 1y ago
  I think a lot of this comes down to the question: Why aren't tables first class citizens in programming languages? If you step back, it's kind of weird that there's no mainstream programming language that has tables as first class citizens. Instead, we're stuck learning multiple APIs (polars, pandas) which are effectively programming languages for tables. R is perhaps the closest, because it has data.frame as a 'first class citizen', but most people don't seem to use it, and use e.g. tibbles from dplyr instead. The root cause seems to be that we still haven't figured out the best language to use to manipulate tabular data yet (i.e. the way of expressing this). It feels like there's been some convergence on some common ideas. Polars is kindof similar to dplyr. But no standard, except perhaps SQL. FWIW, I agree that Python is not great, but I think it's also true R is not great. I don't agree with the specific comparisons in the piece.
  • jna_sh 1y ago
    I know the primary data structure in Lua is called a table, but I’m not very familiar with them and if they map to what’s expected from tables in data science.
    • TheSoftwareGuy 1y ago
      IIRC those are basically hash tables, which are first-class citizens in many languages already
    • deleted 1y ago
      [deleted]
    • Jtsummers 1y ago
      Lua's tables are associative arrays, at least fundamentally. There's more to it than that, but it's not the same as the tables/data frames people are using with pandas and similar systems. You could build that kind of framework on top of Lua's tables, though. https://www.lua.org/pil/2.5.html https://www.lua.org/pil/2.5.html
  • kelipso 1y ago
    People use data.table in R too (my favorite among those but it’s been a few years). data.table compared to dplyr is quite a contrast in terms of language to manipulate tabular data.
• serjester 1y ago
  Seems like their critique boils down to two areas - pandas limitations and fewer built ins to lean on. Personally I've found polars has solved most of the "ugly" problems that I had with pandas. It's way faster, has an ergonomic API, seamless pandas interop and amazing support for custom extensions. We have to keep in mind Pandas is almost 20 years old now. I will agree that Shiny is an amazing package, but I would argue it's less important now that LLMs will write most of your code.
• kasperset 1y ago
  R data science people generally come to data science field from life science or stats field. Python data science people generally originate from other fields that are mostly engineering focused. Again this may not apply to all the cases but that is my general observation. Recently I am seeing that Python is heavily pushed for all data science related things. Sometimes objectively Python may not be the best option especially for stats. It is hard to change something after it becomes the "norm" regardless of its usability.
• exabrial 1y ago
  The problem is there's so much momentum behind it that's hard to course correct. PyTorch is now a goliath.
• jakobnissen 1y ago
  Excellent article - except that the author probably should have gated their substantiation of the claim behind a cliffhanger, as other commenters have mentioned. The author's priorities are sensible, and indeed with that set of priorities, it makes sense to end up near R. However, they're not universal among data scientists. I've been a data scientist for eight years, and have found that this kind of plotting and dataframe wrangling is only part of the work. I find there is usually also some file juggling, parsing, and what the author calls "logistics". And R is terrible at logistics. It's also bad at writing maintainable software. If you care more about logistics and maintenance, your conclusion is pushed towards Python - which still does okay in the dataframes department. If you're ALSO frequently concerned about speed, you're pushed towards Julia. None of these are wrong priorities. I wish Julia was better at being R, but it isn't, and it's very hard to be both R and useful for general programming. Edit: Oh, and I should mention: I also teach and supervise students, and I KEEP seeing students use pandas to solve non-table problems, like trying to represent a graph as a dataframe. Apparently some people are heavily drawn to use dataframes for everything - if you're one of those people, reevaluate your tools, but also, R is probably for you.
  • ActorNightly 1y ago
    >Excellent article Except its not. Data science in python pretty much requires you to use numpy. So his example of mean/variance code is a dumb comparison. Numpy has mean and variance functions built in for arrays. Even when using raw python in his example, some syntax can be condesed quite a bit: groups = defaultdict(list) [groups[(row['species'], row['island'])].append(row['body_mass_g']) for row in filtered] It takes the same amount of mental effort to learn python/numpy as it does with R. The difference is, the former allows you to integrate your code into any other applicaiton.
    • ModernMech 1y ago
      I dunno. Numpy has its own data types, its own collections, its own semantics which are all different enough from Python, I think it's fair to consider it a DSL on its own. It'd be one thing if it was just, operator overloading to provide broadcasting for python, but Numpy's whole existence is to patch the various shortcomings Python has in DS.
• whyenot 1y ago
  What makes Python a great language for data science, is that so many people are familiar with it, and that it is an easy language to read. If you use a more obscure language like Clojure, Common Lisp, Julia, etc., many people will not be familiar with the language and unable to read or review your code. Peer review is fundamental to the scientific endeavor. If you only optimize on what is the best language for the task, there are clearly better languages than Python. If you optimize on what is best for science then I think it is hard not to argue that Python (and R) are the best choices. In science, just getting things done is not enough. Other people need to be able to read and understand what you are doing. BTW AI is not helping and in fact is leading to a generation of scientists who know how to write prompts, but do not understand the code those prompts generate or have the ability to peer review it.
  • iLemming 1y ago
    I can't speak for Julia - never used it; never used Common Lisp for analyzing data (I don't think it's very "data-oriented" for the modern age and the shape of data), but Clojure is really not "obscure" - it only looks weird for the first fifteen minutes or so; once you start using it - it is one of the most straightforward and reasonable languages out there - it is in fact simpler than Python and Javascript. Immutable-by-default makes it far much easier to reason about the code. And OMG, it is so much more data-oriented - it's crazy that more people don't use it. Most never even heard about it.
    • MarsIronPI 1y ago
      Common Lisp fan here, but not a data scientist. Why do you say to avoid CL for data analysis? Not trying to flame or anything, just curious about your experience with it.
      • iLemming 1y ago
        I don't have great experience of using CL for analyzing data, because of "why?", if I already have another Lisp that is simply amazing for data. Clojure, unlike lists in traditional Lisps, based on composable, unified abstraction for its collections, they are lazy by default and literal readable data structures, they are far easier to introspect and not so "opaque" compared to anything - not just CL (even Python), they are superb for dealing with heterogeneous data. Clojure's cohesive data manipulation story is where Common Lisp's lists-and-symbols just can't match.
• huherto 1y ago
  For what is worth. The Kotlin folks have been adding some cool features and tools for data analysis. https://kotlinlang.org/docs/data-analysis-overview.html https://kotlinlang.org/docs/data-analysis-overview.html
• niemandhier 1y ago
  Python is just a language that: 1. Is easy to read 2. Was easy to extend in languages that people who work with scientific data happen to like. When I did my masters we hacked around in the numpy source and contributed here and there while doing astrophysics. Stuff existed in Java and R, but we had learned C in the first semester and python was easier to read and contrary to MATLAB numpy did not need a license. When data science came into the picture, the field was full of physicists that had done similar things. They brought their tools as did others.
  • jillesvangurp 1y ago
    The main feature of Python is that it is approachable by people who have never programmed before. They might have a vague notion of wanting to instruct a computer to first do this and then do that. Imperative programming is their starting point. And Python delivers that. It was designed as a scripting language whose primary use indeed was to script together other things. It always was good at that and that was the main thing it was used for in the nineties. It got popular once Linux distributions started relying on a lot of python scripts (e.g. Red Hat and Debian). As a side effect it was present on a lot of Linux and Unix systems early on. Scientists in the early 2000s and late nineties had access to workstations running Linux and Unix. So, Python was simply the approachable thing that was just there already. And because it's so easy, there are lots of people getting into Python. So it got its own dynamic of generations of researchers in all sorts of fields knowing about Python being the goto thing to reach for. It never really was the best at anything it does. That wasn't even a goal. It's a bit slow. A bit verbose/clumsy compared to some of the alternatives that some data scientists prefer. It lacks a lot of features other languages have. Etc. This doesn't matter because it is simple and easy. The type of users that are new to programming are looking for something simple that they can understand. Not the platonic ideal of a language that mathematicians or computer scientists might prefer. Python is the modern equivalent of BASIC which had this role before python was created. It wasn't that amazing. But early home computers had it as part of their OS. E.g. the Commodore 64 that was my first computer had an interactive Basic shell with the ability to load games from a tape as the main OS experience.
• jswelker 1y ago
  Inherited Python code is a mixed bag. Inherited R code is a nightmare.
• mushufasa 1y ago
  Languages inherently have network effects; most people around the world learn English so they can talk with other professionals who also know English, not because they are passionate about Charles Dickens. My take (and my own experience) is that python won because the rest of the team knows it. I prefer R but our web developers don't know it, and it's way better for me to write code that the rest of our team can review, extend, and maintain.
• pacbard 1y ago
  When you think about a data science pipeline, you really have three separate steps: [Data Preparation] --> [Data Analysis] --> [Result Preparation] Neither Python or R does a good job at all of these. The original article seems to focus on challenges in using Python for data preparation/processing, mostly pointing out challenges with Pandas and "raw" Python code for data processing. This could be solved by switching to something like duckdb and SQL to process data. As far as data analysis, both Python and R have their own niches, depending on field. Similarly, there are other specialized languages (e.g., SAS, Matlab) that are still used for domain-specific applications. I personally find result preparation somewhat difficult in both Python and R. Stargazer is ok for exporting regression tables but it's not really that great. Graphing is probably better in R within the ggplot universe (I'm aware of the python port).
• huherto 1y ago
  Isn't the author saying that Python + Pandas is almost as good as R, but Python without Pandas is less powerful than R. I can't help to conclude that Python is as good as R because I still have the choice of using Pandas when I need it. What did I get wrong?
  • paddleon 1y ago
    you missed the "almost as" in your first sentence. also, we didn't define "good".
• programmertote 1y ago
  Disclaimer: I have nothing against R or Python and I'm not partial to either. Python, the language itself, might not be a great language for data science. BUT the author can use Pandas or Polars or another data-science-related library/framework in Python to get the job done that s/he was trying to write in R. I could read both her R and Pandas code snippets and understand them equally. This article reads just like, "Hey, I'm cooking everything by making all ingredients from scratch and see how difficult it is!".
• NuSkooler 1y ago
  You could end it with "Python is not a great language". Now, is Python a SUCCESSFUL language? Very.
• rdtsc 1y ago
  They basically advocate using R. I think it depends what they mean by "data science" and if the person will be doing just data science. If that's the case then R may be better. As in their whole career is going to built on that domain. But let's say they are on a general computer science track, now they'll probably benefit from learning Python more than R, simply because they can use it for other purposes. > Either way, I’ll not discuss it further here. I’ll also not consider proprietary languages such as Matlab or Mathematica, or fairly obscure languages lacking a wide ecosystem of useful packages, such as Octave. I feel, to most programming folks R is in the same category. R is to them what Octave is to the author. R is nice nice, but do they really want to learn a "niche" language, even if it has better some features than Python? Is holding a whole new paradigm, syntax, library ecosystem in your head worth it?
• solatic 1y ago
  Shell is the best language for data science. Pick the best tools for each of getting data, cleaning data, transforming data, and visualizing data, then stitch them together by sheer virtue of the fact that text is the universal interoperable protocol and files are the universal way of saving intermediate stages of data. Best part is, write a --help, and you can load them into LLMs as tools to help the LLMs figure it out for you. Fight me.
  • xn 1y ago
    redo[1] with shell scripts has become my goto method of dealing with multi-step data problems. It makes it easy to review each step of data retrieval, clean-up, transformation, etc. I use mlr, sqlite, rye, souffle, and goawk in the shell scripts, and visidata to interactively review the intermediate files. 1. https://redo.readthedocs.io/en/latest/ https://redo.readthedocs.io/en/latest/
• spicybbq 1y ago
  Part 2 is here: https://blog.genesmindsmachines.com/p/python-is-not-a-great-language-for-2e0 https://blog.genesmindsmachines.com/p/python-is-not-a-great-...
• drchaim 1y ago
  Python was a great language for data science, when data science become a mainstream thing. it was easy to think about the structures (iterators) it was easy to extend. it had a good community. And for that, people start extending it via libraries. There are plenty more alternatives now.
• thom 1y ago
  I think this expectation that data science code is a thing you write basically top to bottom to get some answers out, put them in a graph and move on with your life is not a useful lens through which to evaluate two programming languages. R definitely is an efficient DSL for doing stats this way, but it’s a painful way to build a durable piece of software. Python is nowhere near perfect but I’ve seen fewer codebases that made my eyes bleed, however pretty the graphs might look.
• amai 1y ago
  The example would better be written in SQL. So according to the author that would make SQL a great language for data science. SQL also supports tables natively. This conclusion is of course ridiculous and shows the shallow reasoning in this article.
• constantcrying 1y ago
  Python is also an embarrassingly bad language for numerics. It comes without support for different floating point types does not have an n-D Array data type and is extremely slow. At the same time it is an absolute necessity to know if you are doing numerics. What this shows, at least to me, is that it is "good enough" and that the million integrations, examples and pieces of documentation matter more than whether the peculiarities of the language work in favor of its given use case, as long as the shortcomings can be mostly addressed.
  • slashdave 1y ago
    Native python is hopeless for numerics, which is why just about everyone just uses numpy, which solves all of these issues. Of course, a separate package. But the strength of python is that it can fairly seamlessly incorporate these kinds of packages that add core capabilities. Another important example: pytorch.
    • kbr2000 1y ago
      https://en.wikipedia.org/wiki/Ousterhout's_dichotomy https://en.wikipedia.org/wiki/Ousterhout's_dichotomy
• coolThingsFirst 1y ago
  Python just has poor aesthetics. __init__(self) is unacceptable in a language in 2025. Ruby would've been a much better choice. Sloppiness in language design is just a bad idea.
  • stOneskull 1y ago
    there's @dataclass in 2025
• semiinfinitely 1y ago
  correct, its only the best on that we have
• keeeba 1y ago
  As a fairly extensive user of both Python and R, I net out similarly. If I want to wrangle, explore, or visualise data I’ll always reach for R. If I want to build ML/DL models or work with LLM’s I will usually reach for Python. Often in the same document - nowadays this is very easy with Quarto.
  • Joel_Mckay 1y ago
    Python has a list of issues fundamentally broken in the language, and relies heavily on integrated library bindings to operate at reasonable speeds/accuracy. Julia allows embedding both R and Python code, and has some very nice tools for drilling down into datasets: https://www.queryverse.org/ https://www.queryverse.org/ It is the first language I've seen in decades that reduces entire paradigms into single character syntax, often outperforming both C and Numpy in many cases. =3
    • pphysch 1y ago
      Deeply ironic for a Julia proponent to smear a popular language as "fundamentally broken" without evidence. https://yuri.is/not-julia/ https://yuri.is/not-julia/
      • Joel_Mckay 1y ago
        Python threading and computational errata issues go back a long time. It is a popular integration "glue" language, but is built on SWiG wrappers to work around its many unresolved/unsolvable problems. Not a "smear", but rather a well known limitation of the language. Perhaps your environment context works differently than mine. It is bizarre people get emotionally invested in something so trivial and mundane. Julia is at v1.12.2 so YMMV, but Queryverse is a lot of fun =3
      • kelipso 1y ago
        This is like one of those people posting Dijkstra’s letter advocating for 0-based indexing without ever having read or understood what they posted.
• IshKebab 1y ago
  Python's not a great language for anything. Maybe for teaching programming I guess (except then you end up with people that only know Python).
• aorist 1y ago
  > Examples include converting boxplots into violins or vice versa, turning a line plot into a heatmap, plotting a density estimate instead of a histogram, performing a computation on ranked data values instead of raw data values, and so on. Most of this is not about Python, it’s about matplotlib. If you want the admittedly very thoughtful design of ggplot in Python, use plotnine > I would consider the R code to be slightly easier to read (notice how many quotes and brackets the Python code needs) This isn’t about Python, it’s about the tidyverse. The reason you can use this simpler syntax in R is because it’s non-standard-evaluation allows packages to extend the syntax in a way Python does not expose: http://adv-r.had.co.nz/Computing-on-the-language.html http://adv-r.had.co.nz/Computing-on-the-language.html
  • npalli 1y ago
    Python is nothing without it’s batteries.
    • jskherman 1y ago
      Python is its batteries.
      • iLemming 1y ago
        But why whenever I try to use it, it tries to hurt me like it's kicking me right in my batteries?
    • pphysch 1y ago
      The design and success of e.g. Golang is pretty strong support for the idea that you can't and shouldn't separate a language from its broader ecosystem of tooling and packages.
      • LtWorf 1y ago
        The success of python is due to not needing a broader ecosystem for A LOT of things. They are of course now abandoning this idea.
• drnick1 1y ago
  I suppose it depends on what exactly is meant by "data science." If find that for stochastic simulations, C++ and the Eigen library are unbeatable. You get the readability of high-level code with the performance of low-level code thanks to the "zero-cost abstractions" of Eigen. If by data science you mean loading data to memory and running canned routines for regression, classification and other problems, then Python is great and mostly calls C/FORTRAN binaries under the hood, so Python itself has relatively little overhead.
• KaiserPro 1y ago
  The observation I make here is in that first python example with the penguins, what the fuck is that? It makes it look like perl, on a bad day, or worse autogenerated javascript. Why on earth is it so many levels deep in objects?
• johnea 1y ago
  They could have just left the last three words off of that title 8-/ Python is not a great language First, the white space requirements are a bad flashback to 1970s fortran. Second, it is the language that is least compatible with itself.
• progval 1y ago
  The pure Python code in the last example is more verbose than it needs to be. groups = {} for row in filtered: key = (row['species'], row['island']) if key not in groups: groups[key] = [] groups[key].append(row['body_mass_g']) can be rewritten as: groups = collections.defaultdict(list) for row in filtered: groups[(row['species'], row['island'])].append(row['body_mass_g']) and variance = sum((x - mean) ** 2 for x in values) / (n - 1) std_dev = math.sqrt(variance) as: std_dev = statistics.stddev(values)
  • ashdev 1y ago
    Disagree. In the first instance, the original code is readable and tells me exactly what's what. In your example, you're sacrificing readability for being clever. Clear code(even if verbose) is better than being clever.
    • billyoyo 1y ago
      Using a very common utility in the standard library is to avoid reinventing the wheel is not "clean code"? defaultdict is ubiquitous in modern python, and is far from a complicated concept to grasp.
      • ux266478 1y ago
        I don't think that's the right metaphor to use here, it exists at a different level than what I would consider "reinventing the wheel". That to me is more some attempt to make a novel outward-facing facet of the program when there's not much reason to do so. For example, reimplementing shared memory using a custom kernel driver as your IPC mechanism, despite it not doing anything that shared memory doesn't already do. The difference between the examples is so trivial I'm not really sure why the parent comment felt compelled to complain.
    • MarsIronPI 1y ago
      I think code clarity is subjective. I find the second easier to read because I have to look at less code. When I read code, I instinctively take it apart and see how it fits together, so I have no problem with the second approach. Whereas the first approach is twice as long so it takes me roughly twice as long to read.
• taeric 1y ago
  I'm heavily inclined to agree with the general thought, but I balk at the low level code showing why a language is bad at something. In this specific case, without the tidyverse, R isn't exactly peaches and cream. As annoying as it is to admit it, python is a great language for data science almost strictly because it has so many people doing data science with it. The popularity is, itself, a benefit.
• shevy-java 1y ago
  > I think people way over-index Python as the language for data science. It has limitations that I think are quite noteworthy. There are many data-science tasks I’d much rather do in R than in Python. R is kind of a super-specialized language. Python is much more general purpose. R failed to evolve, let's be honest. Python won via jupyter - I see this used ALL the time in universities. R is used too, but mostly for statistics related courses only, give or take. Perhaps R is better for its niche, but Python has more momentum and in thus, dominates over R. That's simply the reality of the situation. It is like the bulldozer moving forward, at a fast speed. > I say “This is great, but could you quickly plot the data in this other way?” Ok so ... he would have to adjust R code too, right? And finding good info on that is simply harder. He says he has experience with universities. Well, I do too, and my experience is that people are WAY better with python than with R. You simply see that more students will drop out from R than from python. That's also simply the reality of the situation. > They appear to be sufficiently cumbersome or confusing that requests that I think should be trivial frequently are not. I am sure the reverse also applies. Pick some python library, do something awesome, then tell the R students to do the same. I bet he will have the same problems. > So many times, I felt that things that would be just a few lines of simple R code turned out to be quite a bit longer and fairly convoluted. Ok, so here he is trolling. Flat out - I said it. I wrote a LOT of python and quite a bit of R. There is no way in life that the R code is more succinct than the python code for about 90% of the use cases out there. Sorry, that's simply not the case. R is more verbose. > Here is the relevant code in R, using the tidyverse approach: penguins |> filter(!is.na(body_mass_g)) |> group_by(species, island) |> summarize( This is like perl. They also don't adapt. R is going to lose grounds. This professor just hasn't realised that he is slowly becoming a fossil himself, by being unable to see that x is better than y.
  • oivey 1y ago
    > R failed to evolve, let's be honest. Python won via jupyter Ju = Julia Pyt = Python Er = R R is not only supported in Jupyter, it was there from the start. I’ve never written a single line of R. It is bizarre how little people know about their tools.
    • 1y ago
• rob_c 1y ago
  Refuses to learn tool so tool is broken... There is no problem with python for this. If you hate boiler plate job the club, get llms to generate it for you and move on to doing real work (or get involved in improving the language or libraries directly)
• nicechianti 1y ago
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• hekkle 1y ago
  For those who thought the article was TL;DR, the author argues. - A General programming language like Python is good enough for data science but isn't specifically designed for it. - A language that is specifically designed for Data Science like R is better at Data Science. Who would have thought?
• fnord77 1y ago
  Python is not a great language
  • hekkle 1y ago
    Not great at what? I agree that Python is not great at anything specifically, but it is good at almost everything, and that's what makes it great.
    • deleted 1y ago
      [deleted]
    • fnord77 1y ago
      which is nicer? float(len(str)) or str.len().float() Also, there's numerous ways of doing the same thing, despite that being contrary to python's philosophy
      • hekkle 1y ago
        Actchooally: Python's philosophy (the zen of python) states: There should be one-- and preferably only one --obvious way to do it. So, the answer is to pick the obvious choice, which in this case will depend on the style guide of the project you are working on, and whether they prefer the lisp style syntax or the OO property of style.
• psunavy03 1y ago
  I'm not sure what that last example is meant to be other than an anti-Python caricature. If you're implementing calculating things like standard deviations by hand, that's not real-world coding, that's the undergraduate harassment package which should end with a STEM bachelor's. Of course there's a bunch of loops and things; you're exposing what has to happen in both R and Python under the hood of all those packages.
  • roadside_picnic 1y ago
    > that's not real-world coding It's pretty clear the post is focused on the context of work being done in an academic research lab. In that context I think most of the points are pretty valid, but most of the real world benefit I've experience from using Python is being able to work more closely with engineering (even on non-Python teams). I shipped R code to a production environment once over my career and it felt incredibly fragile. R is great for EDA, but really doesn't work well for iteratively building larger software projects. R is has a great package system, but it's not so great when you need abstraction in between.
    • SubiculumCode 1y ago
      Yeah, to me, R has never really been a.language I'd choose to program with...it's a statistical powerhouse to analyze datasets with great packages / SOTA statistical methods, etc, not a roduction tool.
• aussieguy1234 1y ago
  I felt forced to use python when I gave langgraph agents a go. Worked quite well, but the TS/JS langgraph version is way behind. React agents are just a few lines of code, compared to 50 odd lines for the same thing in JS/TS. Better to use a different language, even one i'm not familiar with, to be able to maintain a few lines of code vs 50 lines.
• yodsanklai 1y ago
  Maybe R is fine for people who use it all the time? but as SWE that occasionally needs to do some data analysis, I find it much easier to rely on tools I know rather than R. R is pretty convoluted as a language.
• dragonwriter 1y ago
  The bare python/stdlib example used (as well as bare python and avoiding add-on data science oriented libraries not being the way most people would use python for data science) is just...bad? (And, by bad here I mean showing signs of deliberately avoiding stdlib features in order to increase the appearance of the things the author then complains about.) A better stdlib-only version would be: from palmerpenguins import load_penguins import math from itertools import groupby from statistics import fmean, stdev penguins = load_penguins() # Convert DataFrame to list of dictionaries penguins_list = penguins.to_dict('records') # create key function for grouping/sorting by species/island def key_func(x): return x['species'], x['island'] # Filter out rows where body_mass_g is missing and sort by species and island filtered = sorted((row for row in penguins_list if not math.isnan(row['body_mass_g'])), key=key_func) # Group by species and island groups = groupby(filtered, key=key_func) # Calculate mean and standard deviation for each group results = [] for (species, island), group in groups: values = [row['body_mass_g'] for row in group] mean_value = fmean(values) sd_value = stdev(values, xbar=mean_value) results.append({ 'species': species, 'island': island, 'body_weight_mean': mean_value, 'body_weight_sd': sd_value })
• rossdavidh 1y ago
  Speaking as a python programmer who has occasionally done work in R: yes, of course. Python is not a great language for anything; it's a pretty good language for just about anything. That is, and always has been, its strength. If you're doing data science all day, you should learn R, even if it's so weird at first (for somebody coming from a C-style language) that it seems way harder; R is made for the way statisticians work and think, not the way computer programmers work and think. If you're doing data science all day, you should start thinking and working like a statistician and working in R, and the fact that it seems to bend your mind is probably at least in part good, because a statistician needs to think differently than a programmer. I work in python, though, almost all of the time.
  • ZhiqiangWang 1y ago
    Agree, the argument is well made in sklearn API design paper https://arxiv.org/abs/1309.0238 https://arxiv.org/abs/1309.0238
• deleted 1y ago
  [deleted]
• actuallyalys 1y ago
  As much as I like Python and personally prefer it to R, I don’t really disagree. But I’m not sure R is a great language for data science either—it has its own weaknesses, e.g., writing custom loops (or functional equivalents with map or reduce) was pretty clunky last I tried it. The other thing is that a lot of R’s strengths are really the tidyverse’s. Some of that is to R’s credit as an extensible language that enables a skilled API designer to really shine of course, but I think there’s no reason Python the language couldn’t have similar libraries. In fact it has, in plotnine. (I haven’t tried Polars yet but it does at least seem to have a more consistent API.)
• dcreater 1y ago
  Fixed title: Python is not a great language for data science if pandas/polars/ibis did not exist
  • mike_ivanov 1y ago
    Please read the article. It literally shows pandas code as an example.
• roadside_picnic 1y ago
  So I've been writing Python for around 20 years now, and doing data science/ML work for around 15. Despite being a Python programmer first I spent a good 5 years using R exclusively. There's a lot of things I genuinely love about R and I strongly believe that R is unfairly maligned by devs... but there's a good reason I have written exclusively Python for DS work for the last 5 years. > Python is pretty good for deep learning. There’s a reason PyTorch is the industry standard. When I’m talking about data science here, I’m specifically excluding deep learning. I've written very little deep learning code over my career, but made very frequent use of the GPU and differentiable programming for non-deep learning specific tasks. In general Python is much easier to write quantitative programs that make use of the hardware, and you have a lot more options when your problem doesn't fit into RAM. > I have been running a research lab in computational biology for over two decades. I've been working nearly exclusively in industry for these two decades and a major reason I find Python just better is it's much, much easier to interface with other parts of engineering when you're a using truly general purpose PL. I've actually never worked for a pure Python shop, but it's generally much easier to get production ML/DS solutions into prod when working with Python. > Data science as I define it here involves a lot of interactive exploration of data and quick one-off analyses or experiments This re-iterates the previous difference. In my experience I would call this "step one" in all my DS related work. The first step is to understand the problem and de-risk. But the vast majority of code and work is related to delivering a scalable product. You can say that's not part of "data science", but if you did you'd have a hard time finding a job on most of the teams I've worked on. All that said, my R vs Python experience has boiled down to: If your end result is a PDF report, R is superior. If your end result is shipping a product, then Python is superior. And my experience has been that, outside of university labs, there aren't a lot of jobs out there for DS folks who only want to deliver PDFs.
• _yb2s 1y ago
  Doing computational biology for several decades in about a dozen languages, I do think R is a much better language for data science, but in practice I end up using Python almost every time because it has more libraries, and it’s easier to find software engineers and collaborators to work on Python. However, R makes for much simpler cleaner code, less silent errors, and the 1 indexing makes dealing with biological sequences much less hassle.
  • 3eb7988a1663 1y ago
    Pardon? Less silent errors? R has quite a few foot guns around permissively parsing user intention. Which does make it handy for exploratory analysis, but a lot more fragile when you want production code. Just a simple one that can get you, R is 1-indexed. Yet if you have a vector, accessing myvec[0] is not an error. Alternatively, if you had say, a vector length of 3 and do myvec[10] that gets NA (an otherwise legal value). Or you could make an assignment past the end of the vector myvec[15] <- 3.14 , which will silently extend the array, inserting NAs
    • _yb2s 1y ago
      All languages have things like that, but from my experience developing a number of widely used scientific software packages for both Python and R, those sort of bugs happen a lot more in Python. Rs copy on write, for example makes it a much more functional language without unintended side effects- Python is very inconsistent in that regard, with different variable types and data structures behaving entirely different in identical situations.
  • _Wintermute 1y ago
    In my experience R is king of happily chugging along spitting out nonsense results when it should have errored 100 lines ago.
• drtournier 1y ago
  JavaScript is not a great language for web development either, yet…
• janalsncm 1y ago
  Python is versatile which is what makes it popular. You can load back and forth from a GPU using well-tested libraries. You can memmap things if you need to. If your loops are too slow you can rewrite the hot loops in rust or C. You can read and write from most file formats in a couple of lines.
• plaidfuji 1y ago
  Python is a pretty bad language for tabular data analysis and plotting, which seems to be the actual topic of this post. R is certainly better, hell Tableau, Matlab, JMP, Prism and even Excel are all better in many cases. Pandas+seaborn has done a lot, but seaborn still has frustrating limits. And pandas is essentially a separate programming language. If your data is already in a table, and you’re using Python, you’re doing it because you want to learn Python for your next job. Not because it’s the best tool for your current job. The one thing Python has on all those other options is $$$. You will be far more employable than if you stick to R. And the reason for that is because Python is one of the best languages for data and ML engineering, which is about 80% of what a data science job actually entails.
  • getnormality 1y ago
    ...unless your data engineering job happens on a database, in which case R's dbplyr is far better than anything Python has to offer.
  • jampekka 1y ago
    > And pandas is essentially a separate programming language. I'd say dplyr/tidyverse is a lot more a separate programming language to R than pandas is to Python.
  • fastasucan 1y ago
    > And pandas is essentially a separate programming language. No it isn't.
• _ZeD_ 1y ago
  Sooo... Is this a post about python envy?
• morshu9001 1y ago
  Data science is the one thing I consider Python especially good at
• culebron21 1y ago
  This was underwhelming. I work with Python and Pandas, and I can show examples of much clumsier workflows I run into. The most often, you get dataframe[(dataframe.column1 == something) & ~dataframe.column2.isna()] constucts, which show that python syntax falls short here, and isn't suitable for such manipulations. Unfortunately, there's no alternative, and I don't see R as much easier, there are plenty of ugly things as well there. There's Julia -- it has serious drawbacks, like slow cold start if you launch a Julia script from the shell, which makes it unsuitable for CLI workflows. Otherwise you have to switch to compiled languages, with their tradeoffs.
  • markkitti 1y ago
    > Unfortunately, there's no alternative, and I don't see R as much easier, there are plenty of ugly things as well there. Have you tried Polars? It really discourages the inefficient creation of intermediate boolean arrays such as in the code that you are showing. > There's Julia -- it has serious drawbacks, like slow cold start if you launch a Julia script from the shell, which makes it unsuitable for CLI workflows. Julia has gotten significantly better over time with regard to startup, especially with regard to plotting. There is definitely a preference for REPL or notebook based development to spread the costs of compilation over many executions. Compilation is increasingly modular with package based precompilation as well as ahead-of-time compilation modes. I do appreciate that typical compilation is an implicit step making the workflow much more similar to a scripting language than a traditionally compiled language. I also do appreciate that traditional ahead-of-time static compilation to binary executable is also available now for deployment. After a day of development in R or Python, I usually start regretting that I am not using Julia because I know yesterday's code could be executing much faster if I did. The question really becomes do I want to pay with time today or over the lifetime of the project.
    • jampekka 1y ago
      > Have you tried Polars? It really discourages the inefficient creation of intermediate boolean arrays such as in the code that you are showing. The problem is not usually inefficiency, but syntactic noise. Polars does remove that in some cases, but in general gets even more verbose (apparently by design), which gets annoying fast when doing explorative data analysis.
• slowhadoken 1y ago
  Sounds like a skill issue
• gyulai 1y ago
  I think, the lesson learned from › Python v. R ‹ is that people prefer doing data science in a general purpose language that is also okay-ish for data science over a language that's purpose-built for data science but suffers from diseconomies. Specifically: Imagine a new database or something like that has just come out. Now, the audience that wants to wire it into applications and the audience that wants to tap it to extract data for analytics put their weight together to create the demand for the Python library. The economies for that work out better than if you had to create two different libraries in two different languages to satisfy those two groups of demand.
  • LanceH 1y ago
    You mention a good point of using Python to put out the results. I think munging the input into a clean enough data set that you can work on is another place Python excels compared to analysis specific tools like R.
• Surac 1y ago
  I at the moment try to learn python as a hobby language. I use c c++ and c# to earn my money. MY biggest problem is finding good examples that are up to date. I spent a whole day learning that there a four (I think) ways to do formatting strings. This „bloat“ in syntax makes even a simple print very heavy to digest. I don’t even bother using v2 python only v3. Also using whitespaces to block things together sounds appealing but in reality you need to use editors that can indent and unindent whole blocks or I never get it right
  • lenkite 1y ago
    15 years ago, Python programmers used to mock Perl by quoting the Zen of Python: "There should be one - and preferably only one - obvious way to do it.". This was in stark contrast to Perl's TIMTOWTDI motto: "There Is More Than One Way To Do It." The Zen of Python is sadly now an absolute lie.
    • gbacon 1y ago
      Rather than an absolute lie, I’m more inclined to characterize it as naïve or black-and-white thinking, outside of CRUD apps and undergraduate intro projects.
  • Stratoscope 1y ago
    You seem to be making things more difficult for yourself than they need to be. For the strings, just use f-strings and forget all the others. You can even do things like this for debugging: >>> class User: ... pass ... user = User() ... user.name = "Surac" ... >>> print(f"{user.name=}") user.name='Surac' >>> For the block indenting, what editor are you using? Pretty much every modern editor lets you select a block and indent/unindent with Tab/Shift+Tab. VS Code and PyCharm are both free and are great for Python coding. They each have a full debugger, which is invaluable when you are learning a language.
    • louistsi 1y ago
      I think their point is that it's not clear to someone with 0% Python experience which of the /many/ different ways of doing things (like string interpolation) is the "correct" / idiomatic way.
• deleted 1y ago
  [deleted]
• Havoc 1y ago
  Realistically it’s winning because it’s accessible rather than perfectly suited
• willvarfar 1y ago
  My experience was that data science was doable but clunky and ugly with pandas. It got slightly better with polars. Only really slightly better. Then, for me at least, it jumped lightyears ahead with duckdb. These days I run some big query on an OLAP database and download the results to parquet stored on the local disk of a cloud notebook VM and then mine it to bits with duckdb reading straight from these parquet files. The notebooks end up with very clear SQL queries and results (most notebook servers support SQL cells with highlighting and completion etc), and small pockets of python cells for doing those corner case things that an imperative language makes easier. So when I get to the bottom of the article where it shows the difference between Python and R, I'm screaming "wouldn't that look better in SQL?!" :)
  • mettamage 1y ago
    Huh, as a frequent polars user, I'll try duckdb.
    • knorke 1y ago
      well, duckdb works very well with pandas, too
    • willvarfar 1y ago
      I think you can query and join polars dataframes if you call to_arrow() on them too.
      • fastasucan 1y ago
        You dont need to do that afaik. You just query them.
  • goatlover 1y ago
    So you're saying you prefer SQL to dataframes. I prefer dataframes and staying in the native language.
    • willvarfar 1y ago
• sheepscreek 1y ago
  I really didn’t understand the author’s grievances. The only concrete example they illustrated was one where they concluded that Python without Pandas is verbose and ugly to achieve the same outcome, hence Python is not great for Data Science. That’s a bad argument or a naive and obvious one; depending on how you look at it. Python wasn’t designed for Data Science. It is not a DSL for it. MATLAB was arguably designed for scientific computing, and yet it’s the most disliked language in the StackOverflow liked/disliked index. Here’s a different way to look at it. A good programming language is like the weather in a city. I would love to live somewhere where it’s 72F/23C all year round. But if it’s in the middle of nowhere and I’ve got no friends to hang out with, would I? I don’t think so. FWIW, Python is like Sweden or Finland, with shitty weather for 6 months of the year yet thriving against all odds. PS: I think the article’s topic is a bit click-batey (not a particularly useful discussion) because it’s polarizing and no one will be 100% right about it. It’s perhaps best thought of as an opinion piece.
• moi2388 1y ago
  You had me at “Python is not a great language”
• neuropacabra 1y ago
  I expected the author will complain rightfully about the tooling, including linters, formatters and package managers. Things improved drastically over the years with Astral’s ruff, uv and alpha stage ty. But the article says that very exotic syntax is more readable. I think this is mostly about the libraries, where honestly I equally don’t like matplotlib and R’s ggplot. But I would not think it’s language problem. I was hoping to find some performance benchmarks or something more than feelings about certain block of code. Don’t get me wrong I am also not a die hard fan of Python although I have written a lot or production code in it. Mentioning bloated, boilerplate code…I am afraid author should look on Java or any modern JavaScript project.
• BiteCode_dev 1y ago
  Notice how the article load_penguins() example starts neatly after all the messy parts of data science are done and stops right before the next pain starts. It lives in a sterile, idealized world. Python is a great language for data science in practice because it turns out data science is also: - gluing a lot of data sources - cleaning up a ton of terribly shaped data - validation and error handling - I/O, networking, and format conversion - emboarding non-programmers into programming - wrapping a lot of compiled languages' libs or plugging system - prototyping stuff and exposing that prototype to some people - turning prototypes into more permanent projects And it turns out Python and its ecosystem are good at those while remaining decent at the other things. There are other languages excellent at some of those, or some of the other things, but rarely good at most. And because humanity is vast, diverse, and constantly renewing, being the second best at those is eventually always winning. Because whoever you are, you will be annoyed at not having the best experience at task X. But you would be mortified if you had the worst experience at doing task Y and Z. And task X, Y, and Z change depending on who you ask. And you want to get things done, while days have 24 hours. As usual, to understand the Python phenomenon, you have to see the whole picture. Not your little corner of the bubble. Not the ideal world in your head either. Life is not a maths problem with a clearly laid out premise and an elegant answer. That's the same debate about why PHP won the web in 2000 no matter the size of the spaghetti plate, why Windows stayed used for so long despite it being terrible, why people keep using iphones after all the abuses, etc. There is more to it than the use case you have every day. People have needs you don't haven't thought about. So it's not "let the language war begin". It's, "dude, get more experience, go work with accountants, ngos, govs and logistic chains, go work in china, africa and south america, go from a startup to schools to corporate, satisfy the geeks, the artists and the business people, than we'll talk".
• codeptualize 1y ago
  Wait, so there is one example, which shows the R and Python equivalents are pretty much the same.. I was all hyped up, ready to see the amazing examples and arguments that would convince me to pick up R, and it gave me absolutely nothing (except quotes and brackets..). Disappointing.
• jakubmazanec 1y ago
  I wish people used Julia more. Few years ago I reimplemented some MATLAB code for a novel algorithm [1] I wanted to use in my dissertation about psychometrics and Julia was great language to work with - and also the code ran for 20 minutes instead of 60. [1] https://link.springer.com/article/10.1007/s11336-017-9581-x https://link.springer.com/article/10.1007/s11336-017-9581-x
  • maratc 1y ago
    How important was this saving of 40 minutes for the whole timeline of the project of writing your dissertation about psychometrics?
    • jakubmazanec 1y ago
      Very important. This was only a simulated dataset, the final analysis would be done on a much larger one (sadly, in the end didn't finish it, because of unrelated reasons). Also, the rewrite didn't take long; the final Julia code was small, few hundreds, or maybe a thousand lines.
• mfld 1y ago
  This really calls for an A/B speed programming test of Python vs. R practitioners.
• HelloNurse 1y ago
  Guess what, doing a relatively complex but standard task (filtering and aggregating example penguins) with a specialized and ossified library (Pandas) is better than doing it "bare.handed" with basic lists and dicts. More terse, more efficient, less error prone, hopefully more numerically accurate, as if Python had an ecosystem of well designed libraries on par with R.
• sarusso 1y ago
  The main flaw of this article is comparing a general-purpose language built with production systems in mind (Python) with a domain-specific language designed for interactive analysis (R)... Beware of comparing apples and oranges, because productizing R code typically requires rewriting it in another language.
• poulpy123 1y ago
  But python is a great language for data science. As the anglos say: the proof is in the pudding, and the fact it is massively used for data science prove it is great at data science. You will say that not everything that is successful is great, and you will be right, but the success of python came organically, and not because of advertisement, de facto monopoly, politics, money, or first-arrived-advantage. Although there is one cause that isn't intrinsic to python but from the people who built numpy. The fact there is a single numerical library, extremely easy to use, fast and extensive in the whole ecosystem was very very huge
• Pinegulf 1y ago
  Once the data is clean and neatly in standard format this becomes a matter of preference. Work experience says that 90% of work is gathering, cleaning and transforming data from different sources. In this capacity Python has more options available.
• markkitti 1y ago
  I tried this in Julia with TidierData.jl, and it looks quite similar to the R version. using TidierData, DataFrames using PalmerPenguins: load penguins = load() @chain penguins begin DataFrame @drop_missing(body_mass_g) @group_by(species, island) @summarize( body_weight_mean = mean(body_mass_g), body_weight_std = std(body_mass_g) ) show(_, allrows=true) end
• ebonnafoux 1y ago
  In the article > Contrast this with equivalent code that is full of logistics, where I’m using only basic Python language features and no special data wrangling package: n = len(values) # Calculate mean mean = sum(values) / n # Calculate standard deviation variance = sum((x - mean) \* 2 for x in values) / (n - 1) std_dev = math.sqrt(variance) He doesn' t know about the statistics package in the standart library of Python (https://docs.python.org/3/library/statistics.html https://docs.python.org/3/library/statistics.html). Of course, if you do not know to use Python, you will have a lot of boilerplate.
• analog31 1y ago
  >>> Without fail, from the students that use Python, the response is: “This will take me a bit. Let me sit down at my desk and figure it out and then I’ll be back.” This is completely aside, but I wouldn't hold this against the students or Python. The students may be following an age-old rule of office politics: "Never troubleshoot in front of an audience." And why this is more prevalent among the students who use Python, well... sample size of 30.
• Vaslo 1y ago
  My team has all moved slowly from R to Python. There was no pressure to do so. R has a clunky feel with a bunch of modules that can be a challenge to automate. Python’s general purpose use beats whatever superior modules R has all day. If someone wants the same package on Python from R it’s probably out there. While plotting may be clunky, I just don’t see r as much better. Plus in 2025 I can just provide a sample of data and what plot I want in an LLM and I get zero shot code of the plot I want. Author sounds very academic to me.
• orochimaaru 1y ago
  It’s not. Julia is better, much better. But Julia came too late. A lot of data science code is already in Python. That’s where it’s going to stay because rewriting code is time consuming. My guess is we will continue to improve Python gradually and keep refactoring the code.
  • 1gn15 1y ago
    > It’s not. Julia is better, much better. But Julia came too late. Sounds a lot like "worse is better". Python is the worse option, incomplete and inelegant, but is much more practical due to being there first and receiving the bulk of the attention.
  • fithisux 1y ago
    Julia macros are a game changers. You do not need a DSL.
• hmokiguess 1y ago
  My issue with Python is that it makes it too easy to do things wrong, it accepts all and anyone. It’s too inclusive and permissive, which is great for expression and creativity but bad for exact sciences and rigid disciplines. In certain matters opinions and cargo cult programming are often a detriment for science. Unfortunately for high level abstractions it’s not that simple to do it right without sacrificing speed, so the industry forces the hand of the community in a lot of ways.
• skeeter2020 1y ago
  This is not about "Python is not a great language for data science" but the author's expertise and affection for R. I guess that title wouldn't get as many clicks.
• fithisux 1y ago
  Personally I use R for the occasional script or some tidyverse quick processing. But the language has many rough edges 1. non standard eval is very weird, rlang fixes these shortcomings 2. unintuitive names or functions not belonging to packages, base has a mix of functions 3. S3 mixes with naming, no problem personally with S3 and S7 is even better, but mixing S3 names with ordinary names is unintuitive, keep snake case 4. data.frames are unintuitive, tidyverse fixes this 5. f(a=) seriously? or working with unintuitive functions in body for discrete ranges of function arguments? 6. no imports per file in packages, I can live with this .. still ... 7. AST functions are unintuitive R has some excellent parts: non-standard evaluation, AST in the base language, lazy evaluation but it is being killed by the bad parts I think all the external fixes and sanity in names should go into base but it will take a lot of time if it ever happens due to legacy. Julia fixes many of these not as elegantly as R but it's pragmatic approach is too attractive.
• nyrikki 1y ago
  > Contrast this with equivalent code that is full of logistics, where I’m using only basic Python language features and no special data wrangling package While I am not a python cheerleader, but a user because the reality is that it is a pretty good glue language, the above is a bit of a problem. Duckdb, pandas, numpy etc.. is what makes python nice. About a decade ago I worked at a major BI software company and ran into another silly problem when trying to evangelize R, wikis kbs and search engines don’t like single letter search terms. So it didn’t matter how much better R was at the time, people found learning it more difficult than it should have been.
• another_twist 1y ago
  I think TypeScript will shine here. Especially for data output pipelines so we can emit strongly typed datasets. Also add to the fact that TS based exploratory code can potentially plot SVG via d3 and maybe even exported to a webpage.
• Decabytes 1y ago
  Python pays the bills. If it was up to me I'd use a different language, but there is no denying that its got a strong story in just about every field now. As I've gotten older, I've come to realize that programming languages are vehicles for solving computer based problems, and I've learned to find joy in solving those problems in whatever language my company/project is using. But in my personal projects, my favorite language to use it Dart.
• prepend 1y ago
  Doesn’t need to be great, just needs to be good enough.
• northlondoner 1y ago
  There is a similar tread, regarding life-time of projects, such as which ecosystem is better for long-term maintainability: https://news.ycombinator.com/item?id=46055463 https://news.ycombinator.com/item?id=46055463
• northlondoner 1y ago
  Has anybody else noticed how much Python took from Scala for type hints? I was using Scala around 2015 and when I see type hints, immediately recognise its similarity to Scala's approach.
• CephalopodMD 1y ago
  Python is the 2nd best language for almost everything
  • iLemming 1y ago
    Yup, paradoxically, it's also the 2nd worst language for almost everything.
• knorke 1y ago
  okay, click bait worked on me. but the claims are weak. basically "Python is not a great language... because it's not that of a domain language than R" mediocre!
• LelouBil 1y ago
  Kotlin is trying to be one with notebooks[0], I even heard they have fancy code generation so that your dynamic data can still have typed properties (after the first evaluations, members corresponding to your field names are generated, or something to that extent I never used it) [0] https://kotlinlang.org/docs/kotlin-notebook-overview.html https://kotlinlang.org/docs/kotlin-notebook-overview.html
• drtc 1y ago
  I work as a data scientist where I do lots of exploratory work using Python+Pandas/Polars+Jupyter notebooks, I have to say that I agree with the title of the article. I feel like I'm using Python more and more in a way that is just not native to it. Strict typing is simply necessary at some point in time, but I don't get any of the performance benefits or compile-time warnings that other languages provide. But more than Pandas/Polars/DuckDB, I think it is the plotting ecosystem that keeps me in the Python universe. Seaborn, altair, plotnine all take ggplot's GoG and bring it to Python and I'm really grateful for that. I don't see an alternative to Python for someone who needs to: 1) Work with data that fits in memory and is mostly tabular (thus Pandas/Python), 2) needs to visualize this data often, 3) does exploratory work (Jupyter notebooks).
• egecant 1y ago
  This article reminds me of another great article comparing R to Python: "Why pandas feels clunky when coming from R" (https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-from-r/ https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-...)
• ineedasername 1y ago
  TLDR: thinks R is better for DS Of course, if your DS is mixed with ML & modern AI you just: pip install rpy2 But then, why choose? No need to be dogmatic, if R is nice for you: install.packages("reticulate")