Earlier quoted context omitted.
Python is nothing without it’s batteries.
What language is used to write the batteries
C/C++, in large part
Python is not a great language for data science
101–110 of 339 posts
Re: Python is not a great language for data science
#102Python 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.
Re: Python is not a great language for data science
#103I 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?
The difference is semantics.
What is a paragraph but an array of sentences? What is a sentence but an array of words? What's a word but an array of letters? You can do this all the way down. Eventually you need to assign meaning to things, and when you do, it helps to know what the thing actually is, specifically, because an array of structs can be many things that aren't a table.
Re: Python is not a great language for data science
#104Re: Python is not a great language for data science
#105I 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.
Re: Python is not a great language for data science
#106The 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 v…
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.
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.
Re: Python is not a great language for data science
#107What 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…
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.
Re: Python is not a great language for data science
#108Maybe 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.
Re: Python is not a great language for data science
#109The 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 v…
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.
The 2nd version is the most idiomatic.
Re: Python is not a great language for data science
#110I 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…
APL Is great
Perfect solution for doing analysis on tables. Wes McKinney (inventor of pandas is rumored to have been inspired by it too).
My problem with APL is 1.) the syntax is less amazing at other more mundane stuff, and 2.) the only production worthy versions are all commercial. I'm not creating something that requires me to pay for a development license as well as distribution royalties.