Python is not a great language
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.
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Python is not a great language for data science
121–130 of 339 posts
Re: Python is not a great language for data science
#122What 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 ta…
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. Immutab…
I tried to get into Clojure, but a lot of the JVM hosted languages require some Java experience. Same thing with Scala and Kotlin or F# on .NET.
The early tooling was also pretty dependent on Vim or Emacs. Maybe it's all easier now with VSCode or something like that.
Re: Python is not a great language for data science
#123I 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 'firs…
What is a table other than an array of structs?
Well it could be a struct of arrays.
Nitpicking aside, a nice library for doing “table stuff” without “the whole ass big table framework” would be nice.
It’s not hard to roll this stuff by hand, but again, a nicer way wouldn’t be bad.
Re: Python is not a great language for data science
#124> 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 sl…
> This isn’t about Python, it’s about the tidyverse.
> it’s non-standard-evaluation allows packages to extend the syntax in a way Python does not expose
Well this is a fundamental difference between Python and R.
Re: Python is not a great language for data science
#125> 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 sl…
Python is nothing without it’s batteries.
I hear this so much from Python people -- almost like they are paid by the word to say it. Is it different from Perl, Ruby, Java, or C# (DotNet)? Not in my experience, except people from those communities don't repeat that phrase so much.
The irony here: We are talking about data science. 98% of "data science" Python projects start by creating a virtual env and adding Pandas and NumPy which have numerous (really: squillions of) dependencies outside the foundation library.
Re: Python is not a great language for data science
#126I 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 'firs…
Dplyr is quite happy with data.frame. R is built around tabular data. Other statistical languages are too, such as Stata.
Re: Python is not a great language for data science
#127I 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 'firs…
There's a number of structures that I think are missing in our major programming languages. Tables are one. Matrices are another. Graphs, and relatedly, state machines are tools that are grossly underused because of bad language-level support. Finally, not a structure per se, but I think most languages that are batteries-included enough to included a regex engine should have a a full-fledged PEG parsing engines. Most…
> There's a number of structures that I think are missing in our major programming languages. Tables are one. Matrices are another.
Re: Python is not a great language for data science
#128As 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.
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/ It is the first language I've seen in decades that reduces entire paradigms into single character syntax, often outperforming…
Deeply ironic for a Julia proponent to smear a popular language as "fundamentally broken" without evidence.
Re: Python is not a great language for data science
#129> 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 sl…
>> I would consider the R code to be slightly easier to read (notice how many quotes and brackets the Python code needs)
Oh god no, do people write R like that, pipes at the end? Elixir style pipe-operators at the beginning is the way.
And if you really wanted to "improve" readability by confusing arguments/functions/vars just to omit quotes, python can do that, you'll just need a wrapper object and getattr hacks to get from `my_magic_strings.foo` -> `'foo'`. As for the brackets.. ok that's a legitimate improvement, but again not language related, it's library API design for function sigs.
Re: Python is not a great language for data science
#130Earlier quoted context omitted.
There's a number of structures that I think are missing in our major programming languages. Tables are one. Matrices are another. Graphs, and relatedly, state machines are tools that are grossly underused because of bad language-level support. Finally, not a structure per se, but I think most languages that are batteries-included enough to included a regex engine should have a a full-fledged PEG parsing engines. Most…
> There's a number of structures that I think are missing in our major programming languages. Tables are one. Matrices are another. I disagree. Most programmers will go their entire career and never need a matrix data structure. Sure, they will use libraries that use matrices, but never use them directly themselves. It seems fine that matrices are not a separate data type in most modern programming languages.
Unless you think "most programmers" === "shitty webapp developers", I strongly disagree. Matrices are first class, important components in statistics, data analysis, graphics, video games, scientific computing, simulation, artificial intelligence and so, so much more.
And all of those programmers are either using specialized languages, (suffering problems when they want to turn their program into a shitty web app, for example), or committing crimes against syntax like
rotation_matrix.matmul(vectorized_cat)