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.
there's @dataclass in 2025
Python is not a great language for data science
141–150 of 339 posts
Re: Python is not a great language for data science
#142The 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…
> (n - 1)
It's also funny that one would write their own standard deviation function and include Bessel's correction. Usually if I'm manually re-implementing a standard deviation function it's because I'm afraid the implementors blindly applied the correction without considering whether or not it's actually meaningful for the given analysis. At the very least, the correct name for what's implemented there should really be `sample_std_dev`.
Re: Python is not a great language for data science
#143Python 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 th…
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.
Re: Python is not a great language for data science
#144I'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.
> 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 i…
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.
Re: Python is not a great language for data science
#145Doing 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.
Re: Python is not a great language for data science
#146> 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…
R is more of a statistical software than a programming language. So, if you are a so-called "statistician," then R will feel familiar to you
No, R is a serious general purpose programming language that is great for building almost any type of complex scientific software with. Projects like Bioconductor are a good example.
Re: Python is not a great language for data science
#147> 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…
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.
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 and . And if is Python, was sufficiently better than Python to be worth spending the time learning?
Re: Python is not a great language for data science
#148JavaScript is not a great language for web development either, yet…
Re: Python is not a great language for data science
#149The 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…
> (n - 1) It's also funny that one would write their own standard deviation function and include Bessel's correction. Usually if I'm manually re-implementing a standard deviation function it's because I'm afraid the implementors blindly applied the correction without considering whether or not it's actually meaningful for the given analysis. At the very least, the correct name for what's implemented there should real…
It is sadly really inconsistent. The stdlib statistics has two separate functions, stdev for sample and pstdev for population. Numpy and pandas both have .std() with ddof (delta degrees of freedom) as a parameter, but numpy defaults to 0 (population) and pandas to 1 (sample).
Re: Python is not a great language for data science
#150Python 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.