16 ms·

Why Is Python Growing So Quickly?

254 points by lainon 9y ago

monkmartinez 9y ago
> Why Is Python Growing So Quickly? Really easy to answer: It is stupid fun to program with Python. There are libraries for everything you can imagine and they are generally very easy to use. Once you grok the virtual environment thing, writing apps/programs/scripts is just a matter of creating a new env and installing the libs you need. Testing ideas with Jupyter notebook is fun, fast and rewarding. Pycharm is awesome. VSCode with Python just works. You can automate tons of boring stuff (Thanks Al!)... and the list goes on... and on...
- pageandrew 9y ago
  for whatever reason, the word "grok" annoys me to no end
  - castis 9y ago
    In other words, you don't grok "grok"?
  - mark-r 9y ago
    The origin of the term is fascinating: https://en.wikipedia.org/wiki/Grok https://en.wikipedia.org/wiki/Grok
  - deleted 9y ago
    [deleted]
  - antod 9y ago
    Heh. There was a python web framework called grok.
  - Zarath 9y ago
    For me it's the fact that no English words end in "ok".
  - drcongo 9y ago
    I thought it was just me.
- pvg 9y ago
  I think a big part of the answer for SO is the popularity and inscrutable nature of pandas. 'Popular, useful and incomprehensible' make for excellent Stack Overflow growth.
  - newen 9y ago
    Got to agree. I used to google pandas problems for remotely complex stuff because it wasn't really intuitive how to do it in pandas. Now I have a better context for how simple such things can be because of data.table in R.
- rpazyaquian 9y ago
  You're listing off tooling, libraries, and frameworks written with or targeting Python, which isn't Python itself. As soon as you want to go off the rails in any of those things, Python gets as complicated as any other language. My take on the matter is that Python is gaining traction due to its market share of the Data Science/Machine Learning field, which is what's really gaining traction and growing quickly. Had Perl or Ruby been the premier language to use for DS/ML, they would be growing instead of Python.
  - schneems 9y ago
    This. Most schools are moving away from C or Java being their primary language. Most comp sci professors acknowledge the usefulness of a scripting language and so when they need to pick one, they pick one they have the most familiarity with. Due to numpy and scipy this tends to be Python over Ruby. Also many things about python that I don't like such as `len` being a function rather than a method on a list are very trivial when coming from a language like C which has hordes of tiny inconsistencies. The whitespace thing in python is pretty gimmicky but does also hold a certain amount of pedantic appeal.
    - xapata 9y ago
      The Python design FAQ explains why Len is a built-in function instead of a method. Guido wanted a guarantee that whenever he called it, it'd return a whole number. As just another method, you might find a new type that defines a length in fractional terms, which would break any code wanting to use that as a list index, etc.
      - goatlover 9y ago
        But that would be on your for misusing the length method. All the built-in and popular libraries would still define length properly.
        xapata 9y ago
        Perhaps, but "your" mistake might impede the popularity of the language. A wide range of well-designed community libraries are a major reason to choose Python. Take a look at the design rationale for the new __matmul__ magic method. It's not for the standard library, it's for the community.
- guelo 9y ago
  Are you suggesting that we ignore the article's data-driven conclusion that it is because of pandas and the rise of data science and machine learning?
  - rpazyaquian 9y ago
    That is exactly what I am thinking myself.
  - fish_fan 9y ago
    I think you're reading the article wrong—pandas is a symptom, not a cause. That said, it seems foolish to ignore the clear data science use case. But that's also been a theme for at least five years now.
  - njharman 9y ago
    No they're explaining why Pandas was written in Python and why data science and machine learning projects choose Python over R or Java or Haskell, etc.
- CJefferson 9y ago
  I have come to the conclusion that part of the reason python is so fun is because it is slow. There are so many lovely and easy to use libraries in Python. I took a couple and tried to be inspired by them to write a C++ library, maybe for addition to boost. However, if you are writing C++, people expect your library to be fast and memory efficient -- you can't go around sticking the entirety of a parsed file in a std::string. Also, people then start saying your code better interoperate with their templated classes well, and they want to replace the memory management entirely, and it goes on... Of course, the resulting libraries are very fast and very clever, but they have lost the joy. They also take much longer to write, maintain and use.
  - paulddraper 9y ago
    It's a funny way of phrasing it, but you're not wrong. C++ libraries should allow people to squeeze every bit of performance juice out of them (e.g. custom allocator). Python libraries should be intuitive to use. --- Naturally, there are exceptions, like numpy.
    - fencepost 9y ago
      C++ may let you "squeeze every bit of performance," but in a lot of cases it does so by handing you a lot of rope. You might use it to make a rope bridge across that performance gap, but how good your bridge is depends a lot on you - and if you just manage to hang yourself with it, well, that's on you as well. With Python it may take longer to get across because you detour a mile over to a more solidly constructed bridge, but you still get there and for most cases you get there fast enough.
  - harshgupta 9y ago
    Part of the reason is same side network effect. It makes sense to create new libraries in languages having great existing libraries.
  - barrkel 9y ago
    Watch out for conflating abstraction level with speed. Coding at a low level of abstraction means sweating the small stuff, but it's easy to fool yourself into thinking you're making things fast - when actually, you're micro-managing. Painting with broader brushstrokes with less concern for micro-efficiencies lets you block out a solution faster. Another part of the problem with C++ is that the components come with so many rough edges that need to be filed down that you can't help getting swamped in a certain level of detail, unless you start with a large toolbox or framework that lets you work back at a higher level again. You ought to have the equivalent of Java's file stream, buffer and text decoder as three separate bits that you can compose, so that putting a whole file in a string is never even a temptation. But if you start out with a sparse toolbox, it's a slog.
- tw1010 9y ago
  That description doesn't explain why there has been such a sudden growth in recent years. Python is an old language. The real reason it is growing so much just recently is of course due to ML.
  - dwaltrip 9y ago
    > The real reason it is growing so much just recently is of course due to ML. This is certainly a potential explanation. But I don't think it is self-evident that the trend of recent growth is entirely due to this. Edit: The article supports the claim - I should have read it before commenting.
    - euyyn 9y ago
      It doesn't need to be self-evident, as it's what the article is showing with data.
      - dwaltrip 9y ago
        My bad, this is what I get for not reading the article. I added a note to my comment, thanks.
- jdc0589 9y ago
  > Once you grok the virtual environment thing it's incredible how hard it is to get some people on board with this. I've gotten used to good project-local package management in other langauges, I can't imaging working with anything that depended on stuff happening to be available in a global namespace ever again.
  - dom0 9y ago
    There are still widely used packages that don't work with pip, like PyQt.
  - whipoodle 9y ago
    It is a bit more of a pain to work with than similar features in other languages. Like as far as I know you can't just move a venv, you have to rebuild it in the new place. Whereas `node_modules` is just a dir. Also, there is a weird layering where venv abstracts away installed eggs/wheels/whatever they're calling them these days, but then since that's still painful to work with, you need another layer on top like virtualenvtools or burrito or whatever it is. (Sorry, it's been a couple years so my memory is fuzzy)
    - Zaak 9y ago
      Yeah, Python's environment/import logic is really convoluted. It evolved organically with little forethought, and only got a minimal pruning with Python 3.
      - chrshawkes 9y ago
        It was essentially created by one dude no?
    - qaq 9y ago
      "a bit"? honestly I would say it's much closer to a lot more pain
      - whipoodle 9y ago
- gdulli 9y ago
  > It is stupid fun to program with Python Around 2009 I started questioning if I even enjoyed coding anymore. I was primarily using PHP and Java. Rather than quit the profession I tried Python and suddenly it was fun again. 8 years later, it still is. Python just spoke my language. My career is based around using Python because it's the way I enjoy the work. Everything else, like the actual properties of Python relative to other languages, is irrelevant to me if using another language would make the work unenjoyable again. (Though if Python didn't measure up very well compared to other languages it wouldn't have become my go-to in the first place.)
  - cutler 9y ago
    Try Clojure and I promise you will have even more fun.
  - alvivar 9y ago
    Maybe you should try F#, to me both are kind of fun compatible (plus the functional focus that is kind of fun by itself).
  - Daishiman 9y ago
    Same thing here. I worked with PHP and Java apps and it was just pain having to deal with the awkwardness of PHP's designs and Java's boilerplate. I actually went into sysadmin roles because of that. Python gave me hope that there's fun development roles to be had. Nowadays the panorama for good languages, thankfully, is much better. PHP is no longer that much of a dumpster fire with modern frameworks, and Java has decided to adopt better defaults and drop the XML madness. Not to mention all the good languages that have come up (Elixir, Clojure, Rust, etc).
- blauditore 9y ago
  I recently worked with Python for a couple of months, for the first time. Even though reading code and writing simple parts is fairly easy, I found several details to be quite confusing, like variable scope, or the various underscore conventions/variables. Also, I struggled to find a good free IDE (there was no money for PyCharm), which made debugging a hassle. Also, dependency management with pip feels a bit quirky compared to e.g. Maven or npm. All in all, I found it neither much more or less fun to program with than e.g. Java, C# or TS. It might be a better fit for small projects than others, though.
  - wslh 9y ago
    IDE? Visual Studio Code. Python is not perfect but the final code is simple to write and understand years later.
    - bedros 9y ago
      I use sublime for python development, don't use fancy debug features, just print statements
      - cableshaft 9y ago
        I generally use Notepad++. Python lets you get away with it, for the most part. Other languages would be a pain in the butt to write whole programs in it.
  - pletnes 9y ago
    Pycharm and visual studio have free editions. Vim, emacs, Atom, VS Code, sublime - there are many options.
    - blauditore 9y ago
      I think it was the remote debugger I was missing, and as far as I could tell there was no such feature in any free editor/IDE, probably because it's not a language-native feature afaik.
  - 9y ago
- walterstucco 9y ago
  The real answer is: data scientist everywhere They usually don't know better
  - wwarner 9y ago
    zing!
  - copperx 9y ago
    What would they be using if they knew better? R?
    - walterstucco 9y ago
      It's ok They'd make a mess with every language... A beautiful mess, don't get me wrong There are better languages but there isn't enough "science cargo cult" around them And honestly there aren't as many code snippets to copy from My hopes are in luna[1] p.s. distributing load or using multi cores is still shitty in python, the more dataset will grow in size, the more python will struggle [1] http://www.luna-lang.org/ http://www.luna-lang.org/
- vturner 9y ago
  This is an honest question, what's the size of projects you have written in Python? For me, once a project gets beyond some size, the aggravation only grows of debugging old code and adding new. Mostly, I see this is as a lack of compilation and types in Python. How do others overcome this and enjoy large Python projects?
  - andybak 9y ago
    OK. Bear with me here. This might be overstating my case... In some sense - there are no large projects. Only projects that have failed at being modular. Maintainability involves keeping clear boundaries between functional units so that you can keep enough code in your head at any one time to reason about it. If we assume that Python is capable of handling more code than you can keep in your head at any given time, then surely the problem is simply one of making sure your modules are nicely self-contained from each other.
    - vturner 9y ago
      Agreed, modularity definitely helps us minimize the mental load of understanding all the objects at once. But how does it handle this situation: Write function in module A that expects some complex data structure that comes in. In a static typed language, you put a type on the argument and thus guarantee, to some degree, the data in the object can be safely manipulated. Over in module B we call that function. Because Python doesn't enforce types, I can pass in some type of data structure that might behave well in many situations, but in some that function is going to fail because I didn't properly construct the data structure. How do other Python projects avoid this? Do they avoid large data structures of multiple fields?
      - outworlder 9y ago
        > Write function in module A that expects some complex data structure that comes in. In a static typed language, you put a type on the argument and thus guarantee, to some degree, the data in the object can be safely manipulated. Let's question some of the assumptions. Do you NEED the entire thing to be passed around? If your functions are small and only do one thing, can't they be fed the portion of the data they need and only manipulate that? If they are getting unnecessary data, soon someone will like the convenience and that piece of data will now become necessary. I have seen Java projects with humongous "DAO" objects being passed across all apps. Even if Java had Haskell's type system, at some point it breaks down. So, you do not enforce that on Python itself. Rather, you have to apply due diligence and ensure your data structures are clean, and functions do what they are supposed to do and no more. > I can pass in some type of data structure that might behave well in many situations, but in some that function is going to fail because I didn't properly construct the data structure. Then you construct the structure in a single place, and that should have some sanity checks and sane defaults. Even if you are using a static typed language, there's no guarantee that what's inside the data structure even makes sense, only that the right type of structure is being passed on. That's helpful, but not as much as people would think. And you write tests. Lots of tests. It is amazing the amount of Python code that gets written without proper testing. And Java, for that matter. Only Python programmers have less excuses, it's easier to replace what you need for testing.
- mmagin 9y ago
  I actually love forcing people into a fairly consistent coding style, but everything else about the language irks me compared to my previous extensive use of Perl (on a large enterprise software product). One particularly big thing, that it doesn't have much of a distinct compilation phase. You'll write some script you want to run to do some one-off task that will take the next hour, only to find it crashed after 20 minutes when it hits some boneheaded syntax error you made.
  - makecheck 9y ago
    For that though you can use linting tools such as "pylint"; catches a lot of errors that might otherwise be runtime exceptions.
  - dwaltrip 9y ago
    For this reason, I generally test scripts like that with a small slice of the data, or small set of dummy data. Then one can also verify that the output is reasonable -- which is good to do even with a compiled language that would catch all syntax errors ahead of time.
  - shalabhc 9y ago
    Take a look at mypy and pytype - there is a drive in the Python community to add a verification system to the language.
  - cbr 9y ago
    only to find it crashed after 20 minutes when it hits some boneheaded syntax error you made I find mypy to be pretty useful for this.
- IamNotAtWork 9y ago
  I haven't overcome the virtual env thing. There are a few virtual environment libraries, not sure which one to use. Then it is awkward to have to change your entire environment over by activating it and for it to be global. What if I want to work on two different projects in two different terminals? Why can't I activate an environment by just CDing into a directory with my project? Then there is the GIL. I still don't know if multithreaded python apps are really MT or just faking it. And yes, I know you can do event driven stuff and that does look very interesting.
  - orf 9y ago
    Use "python3 -m venv" You can activate two different environments in two different terminals. Why wouldn't you be able to? You can automatically activate venvs when you cd into a directory with virtualenvwrapper. Rule of thumb: if a thread goes into C it's releasing the GIL. Not always, but often. Like reading a file, or a library like like numpy or Pillow.
- hyperpallium 9y ago
  Java could have libraries almost as easy to use as python, but doesn't. However, some things can't be as easy as in Python, such as sympy, which does symbolic processing of in-python expressions (differentiation, integration etc). I actually don't find python easy to use - every time I use it, I have to look up how to do for loops (what? range? zero-based? does it include the last one?), and the docs don't have links for arguments (even though they require specific types), because no static types. But the libraries absolutely are easy to use. And because many are written in C (unlike most of Java's librries), you're arguably running C.
- submeta 9y ago
  +1 for PyCharm. With it I realized how much I was missing with Vim/Emacs/Sublime (even though I configured a lot in those editors).
- mixmastamyk 9y ago
  > Once you grok the virtual environment thing I wish this would die, it just scares off newbies. In my twenty years of Python I encountered conflicting reqs once or twice. It's easy to direct folks to the documentation in those circumstances rather than cramming it down every newbie's throat---YAGNI. Now that pip can install with --user and containers are popular, the problem isn't even possible in production in many circumstances.
  - donarb 9y ago
    Virtual environments are not just for conflicting requirements. I write code for clients on my laptop and don't want to clog up my packages directory with project specific libraries that I'll never use myself. Once the project is done and I turn code over to a client, I can delete the virtual env and all the installed libraries. Virtual envs also allow me to have multiple versions of Python installed on my machine. This discussion is a lot like version control. How many people made daily (hourly) duplicates of project folders before learning the benefits of version control?
    - mixmastamyk 9y ago
      A use case that newbs need not concern themselves with. You normally won't get a version control lecture during every programming language tutorial either.
- deleted 9y ago
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alienreborn 9y ago
because readability is one of the most, if not the most, important quality for mainstream programming language adoption.
- cremp 9y ago
  Golang forces readability; might want to give it a shot.
  - lilbobbytables 9y ago
    Just the other day I started looking back into Golang. Syntactically, and with Gofmt, it is quite readable. But then they have to go and ruin it by making single letter variables a common thing.
- pyre 9y ago
  But significant whitespace means that I can't write code like this: sub (@) { return map { "@{[ $_[0] . $_ ]}" } @$_ }; Disclaimer: The majority of the programmers that I have known in real life that have a significant (and very vocal) dislike of Python's whitespace requirements have all written code like that above (this is my from-memory reproduction of actual code that one of them wrote). Python sucks because it's "restricting" them from being "expressive" with their code.
  - minimaxir 9y ago
    For statistical programming in particular, readability of your code by others is the most important part of the coding style.
    - BurningFrog 9y ago
      That's absolutely true of any programming.
      - neuronexmachina 9y ago
binthere 9y ago
I've started to use python a lot for 3d scripting on Blender and Maya. On Blender this is the only option.
deleted 9y ago
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gallerdude 9y ago
As someone introduced to Python and C two semesters ago, I'm not surprised at all. If I want to be really nerdy and crank some performance, I write C. If I want to test ideas, write to a text file, or basically anything else, I use Python. Python just makes it all super easy and nice. I respect the performance gains of C, but ease of use will always win.
acomjean 9y ago
It's the libraries I think. that's why I started using it. I don't think it's particularly different syntactically from most other languages. The built in data types are nice, though why they insist on using different names from other languages (lists, dictionaries ...) Better editors help too.
- redblacktree 9y ago
  I may be biased, but I much prefer "dictionary" to "hash table" and "list" to "array." They provide much better metaphors to real-life things, instead of requiring people to learn and use additional jargon.
baldfat 9y ago
I have mostly left Python and moved to R, Racket and Lua for my needs. It is hard to see how Python won't stay the predominant first language for decades to come just due to momentum and the good reputation. My first language was Basic and my second language was C64 Assembly and Pascal. I would have killed for Python as a kid.
tw1010 9y ago
The really interesting question though is how big of an influence meta-posts like this one has on the growth of one programming language over another. How would you model that?
- mark-r 9y ago
  Have you ever known someone to pick a language based on a blog post?
  - tw1010 9y ago
    Javascript programmers base a lot of their choice of framework on where the hypetrain is heading, which blog posts can have a lot of influence over.
deepnotderp 9y ago
Because programming in Python is great for productivity, has lots of useful libraries, is rather bearable/enjoyable depending on your view point and for 90% of people/companies you don't need the speed of anything more than PyPy or Cython because, believe me, you're not at Facebook or Google scale, and even those companies often use Python (Google most famously). Btw, for those of you who dislike the dynamic typing (e.g. myself) of Python, check out Cython, it's really heaven.
thisisit 9y ago
Mostly because the whole Data Science and ML stuff is catching on. Everyone and their grandma now wants to do something related to AI, ML etc. The easiest language for these is? Python. Hence the growth. Once upon a time it was all about cloud, SaaS and building a webapp. Hence the growth of JS.
- choxi 9y ago
  The chart makes a pretty compelling case that this is the right answer. The top trending libraries are pandas, numpy, and matplotlib (both flask and django are probably trending at the same baseline rate of any top 10 popular language). These are all libraries heavily used in data science. We also know that ML and AI are new macro trends in the tech industry. Python's elegant syntax isn't really evidence for why Python is trending now as opposed to any other time in the last decade.
  - bsilvereagle 9y ago
    > These are all libraries heavily used in data science. These are all libraries heavily used in any technical computing field. The cost of MATLAB, the simplicity of Jupyter, and the community around Python have caused Python to slowly be a language used for general technical computing, not just data science.
h4l0 9y ago
Python has been continuously becoming the entry point of computer programming. I think the language reached that goal and now people who started with python are contributing in different fields. Personally I met with python thanks to course assignments. It helped me to finish my projects way faster than any other language. I loved its simplicity and now even in my complex projects, I tend to solve my problems in "python way". Also modularity, almost non-existent boiler-plate code, etc kind of things attract data scientists without computer science background.
alunchbox 9y ago
the colleges and programming intro courses for my university are starting off with python now. When I went to uni it was a language called processing which is a simple version of Java. College started out with JavaScript moving into .Net. Some my teacher I kept in contact with moved over the courses to python which might also be a factor in python popularity. Not to mention High schools programming classes also starting with python or js instead of QBasic.
maxerickson 9y ago
Related discussion of the previous post: https://news.ycombinator.com/item?id=15186025 https://news.ycombinator.com/item?id=15186025
AlexCoventry 9y ago
TL;DR, machine learning.
- goatlover 9y ago
  ML plus Pandas, Matplotlib, SciPy, Jupyter Notebooks, etc.
beamatronic 9y ago
Easy package management. If Java made it just as easy to add packages, and run in an interpreted mode, it might have had similar success. Actually years ago there was something called "jython"
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outworlder 9y ago
It's not only because of data science of machine learning. Big, high profile projects are being done in Python (eg. Openstack). I am at a big company, and it seems most new projects are being done in Python. No one asks why, it is just accepted. Now the question is why. I have no explanation, other than maybe that's because universities have been teaching python more often, and it happens to be practical. I just wish everyone were using Python 3.
deckar01 9y ago
Is stack overflow traffic a good indicator of library growth? It seems like libraries with really good documentation would require fewer stack overflow questions regardless of their popularity.
csense 9y ago
Now that powerful machines are commonplace, the biggest limiting factor of software creation is developer productivity. Python's so popular because it maximizes developer productivity.
igravious 9y ago
I say all the following as an avowed Rubyist 1) Computational Linguistics libraries 2) Machine Learning libraries 3) Still one of the blessed languages internal to Google? and BFDL was employed for a while there 4) Jupyter Notebooks 5) Coder Dojo approved language I used to think that all dynamic languages (Ruby/Python/Javascript/Perl/…) were of a par but I have to admit that Python has an awful lot of mindshare and it's beginning to exhibit strong network effects.
- aphextron 9y ago
  >I used to think that all dynamic languages (Ruby/Python/Javascript/Perl/…) were of a par but I have to admit that Python has an awful lot of mindshare and it's beginning to exhibit strong network effects. Ruby is a wonderful language with a really unfortunate standard implementation. It's easily 5 times slower than Node at practically anything, which is sad because Ruby is infinitely better than Javascript. I really think this is what it comes down to and why Rails has been dying off.
  - goatlover 9y ago
    Agreed on Ruby as a language, but Node also has the advantage of being isomorphic. It's hard to beat JS when it's the browser language, and then you provide a really nice server side platform for it. Plus there has been a reaction to Rails size and complexity. I kind of hate that everyone thinks of Rails when Ruby is mentioned. But I thought Sinatra was a really nice microframework.
    - flavio81 9y ago
      > but Node also has the advantage of being isomorphic. It's hard to beat JS when it's the browser language This has repeated often but so far I haven't found a really convincing argument. All my friends, who know C, C++, C#, Java (and other languages) had no problem in picking up Javascript/ES7 for programming the browser using whatever you need. I think this argument was exciting for the front-end developers who wanted to try doing things on the server but couldn't be bothered into learning a new language. So now with Node.js they are happy and satisfied, until they realize that the problem is not the language; the problem is that server programming is a different matter altogether, no matter what the language is.
ben_jones 9y ago
Because it's easy for people to learn with no prior knowledge in programming. It's taught in many intro classes now and as time goes on the simple fact will cause the language to be used more in production environments as well.
ralmidani 9y ago
Python has a reputation for being a good "beginner" or "scripting" language. Describing it as such is really a disservice. One of things that makes Python a killer language (apart from its readable syntax and straightforward programming model) is how it enables you to jump in at any skill level and be immediately productive, without hampering your ability to get things done quickly and elegantly as your skills grow. There are faster, trendier languages out there, but I suspect Python will still be thriving when many of them have become footnotes.
- runevault 9y ago
  The interesting thing, to me, with Python is it can fall out of favor but it seems very good at coming back. Look when Ruby got big and became the most popular scripting language. yet Python kept trucking on, until the data science explosion that made it having things like Numpy and Scipy matter so much (and then from there libs like Tensorflow).
- blhack 9y ago
  This has been such a frustration of mine when mentoring people through learning to code. Python is a great beginner language, but it isn't a language that is only for beginners.
ihaveajob 9y ago
This just about sums it up for me: https://xkcd.com/353/ https://xkcd.com/353/
Animats 9y ago
based on Stack Overflow question visits Is Python use really growing? Or is this a Stack Overflow thing? I thought it was being displaced by Javascript Everywhere. Python had a 5-year setback from the Python 3 transition, but we're now mostly past that.
- ZanyProgrammer 9y ago
  I think if anything the SO stats underestimate the pervasiveness and popularity of Python, even outside the data science world.
- cutler 9y ago
  It's certainly growing here in the UK. From being level with Ruby 5 or 6 years ago the Python:Ruby jobs ratio is now 2.9. I've also watched Django recently edge ahead of Rails for the first time.
- deleted 9y ago
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- fish_fan 9y ago
  Well, most devs I know would rather use python for server-side and scripting, but you're not wrong. Python never found a way to do cross platform UI as well as react native; it's on every browser; you don't have to unlearn brace syntax. That said; the idea of something like numpy in javascript is years off and laughable.
- goatlover 9y ago
  Javascript isn't displacing Python or any other language in data science. The web doesn't encompass all of computing.
jackschultz 9y ago
The most interesting part of the post is the conclusion where the author says that he's a data scientist who uses R and doesn't feel the need to switch to Python! He also has very good thoughts, and very important for beginners to know, about how there isn't a single right way to learn programming. > In any case, data science is an exciting and growing field, and there’s plenty of room for multiple languages to thrive. My main conclusion is to encourage developers early in their career to consider building skills in data science. We’ve seen here that it’s among the fastest-growing components of the software development ecosystem, and one that’s become relevant across many industries.
- make3 9y ago
  How it that interesting? Statisticians and economists everywhere use R instead of Python
  - jackschultz 9y ago
    The fact that the author wrote a sizable post about how big Python is and is growing, but then says he's more of an R type of person instead of Python. Obviously that's interesting to see when authors aren't giant fans of what they're trying to promote.
  - CuriouslyC 9y ago
    What's particularly interesting is that you can tell someone's academic pedigree from whether they use R or Python. As you mentioned, R is very popular in stats, while Python is very popular in computer science. In fact, you can tell whether other academic fields are more influenced by stats or comp. sci by this measure as well. R in econ is a great example of this, while fields that build simulations to do research tend to skew Python.
pinnbert 9y ago
My python grows quickly.
alephnil 9y ago
Python is what BASIC was in the 1970s and 80s, in a good way. It enables people that normally would not program to do things that they otherwise could not be done with traditional software with a GUI, that being web based, apps or desktop applications. It is a stepping stone into computing that otherwise would have been inaccessible for many. Unlike BASIC, the design of the language is good enough that it can be used in professional applications, and not only being a stepping stone.
darkmagnus 9y ago
The community is very welcoming, in my experience.
INTPenis 9y ago
It's dead simple and people complain about speed but I use it for massive distributed backends handling hundreds of messages each second and scaling without problem. And this was way back when twisted was the only asynchronous game in town. So I'm not surprised it's growing.
aaronchall 9y ago
I'm a Python answerer on Stack Overflow, I teach Python at NYU, and whaddaya know, I write Python for my day job. Why is it growing so quickly? I speculate: Lots of people want to learn to program - and Python is easy to learn and teach. I think it blows other languages out of the water. I'm going to compare it to its closest competitor, Ruby. Ruby is also open-source, high level, popular relative to other languages (which means you can probably get help if you need it), it has lots of libraries (meaning you can do a lot with it), ok tutorials, and a decent package manager. On the downside, Ruby is also a very large language, which makes it much harder to learn to mastery. I think Python blows Ruby out of the water in terms of how easy Python is to learn relative to Ruby. I don't encourage people to learn Ruby. I spent a long time (months) trying to learn it. I still don't tell people I know it. I used to think that Ruby's syntax is twice as much to learn - now I think it might be even more than that. Ruby's YACC file is ~918 lines long[0], while Python's grammar file is 149 lines long. ~$ wc cpython/Grammar/Grammar 149 879 6472 cpython/Grammar/Grammar Even if 1/2 of Ruby's grammar output is whitespace, that's more than twice as much grammar to learn as Python's grammar[1]. I could compare Python to other languages, but I don't think they come close to matching the upsides of Python. I still want to learn other languages, like Haskell, lisps, and C, to a level of mastery - but since I can be so productive so fast with Python, the other goals are something I work on in my spare time without a bunch of urgency. [0] https://stackoverflow.com/a/16629318/541136 https://stackoverflow.com/a/16629318/541136 [1] https://docs.python.org/3/reference/grammar.html https://docs.python.org/3/reference/grammar.html PS I can see that this comparison to Ruby isn't popular due to the downvoting - but nobody is contradicting my point.
- cbanks 9y ago
  Cough. Python's cpython/Grammar/Grammar uses a compact EBNF notation. Converted to yacc it is about 850 lines long.
sinemetu11 9y ago
How do people create and manage internal python packages that work together as an ensemble? I've found the packaging system with setup.py dependency_links to be pretty cumbersome and not well supported. How do people version these internal packages and manage them? Running your own pypi seems nontrivial and using github eggs/releases isn't very functional either. My experience with python has been positive but I don't understand how people who want to create separate libraries for specific functionality tie these codebases together without using deprecated features or containerizing everything.
- IanCal 9y ago
  Gemfury provide a service which might be useful. If you need to keep things on premise, is running your own pypi particularly hard? I've never tried so maybe there's a bunch of hidden complexity but there are some packages to run one and even a docker image: https://hub.docker.com/r/codekoala/pypi/builds/bwe6cdn4swgyiruoolkpqp/ https://hub.docker.com/r/codekoala/pypi/builds/bwe6cdn4swgyi...
- ipmb 9y ago
  pip2pi makes it really easy to run your own PyPI. All you need is something that can serve static files (Nginx, S3, etc.) https://github.com/wolever/pip2pi https://github.com/wolever/pip2pi
deleted 9y ago
[deleted]
microDude 9y ago
Really simple answer... University undergraduate classes in a variety of courses use it as their default language, so it is a bunch of students trying to complete their homework assignments.
Derbasti 9y ago
I don't like how Numpy, Matplotlib and Pandas are portrayed as "Data Science". There is nothing particular to "Data" in these three packages. All I see is "Science". All scientists need numerical simulations or evaluations, plotting, and statistics. That's what these libraries do. Not "Data Science", but "Science". The correlation with TensorFlow/Keras suggests Data Science, maybe. But I bet there is a similar correlation with scikit-learn, Scipy, scikit-image, basemap, and heck, Matlab, Julia and R--and those each suggest different scientific endevours. TL;DR: In my opinion, Python is becoming the language of Science, which includes, but is not limited to, Data Science.
- cjbillington 9y ago
  I'm still at a loss as to what type of science there is that doesn't have data. "Data science" to me seems like a buzzword that has connotations of a field or industry starting to use science where they previously didn't. Over here in academia it's just "science", or possibly "data analysis" if you want to emphasise that that's the part of your project you're working on right now as opposed to data collection.
  - xapata 9y ago
    Theoretical physicists don't always use data. Many scientists sometimes use case studies instead of systematic data collection.
    - cjbillington 9y ago
      Eh. Case studies is still data. And the theoretical physicists that don't use data are criticised as not really doing science. Most theorists I know (I am in physics) are heavily data-driven.
- PeterisP 9y ago
  You seem to treat "data science" as a subset of science, but it's really not - it generally is used to refer to the application of scientific methods to data analysis outside of "the field of science" in various business environments, and that is a much wider field with more people than those actually working in science.
dasil003 9y ago
The obvious answer to me is the slow trickle down of people from universities as CS programs have migrated from Java/Scheme to Python. This also overlaps with Stack Overflow usage, so I think that explains a lot.
blunte 9y ago
#1 - it comes installed on most Linux and on Mac (even though different versions can be a bit of a pain sometimes) #2 - it's dead simple to do things; the syntax is, to me at least, about as direct and uncomplicated as you can get (and without being overly wordy!) #3 - so many handy libraries... it's good for full projects, and it's great for integrating and gluing things together I wouldn't call programming in Python fun, per se, but I do call "getting things done quickly and easily" fun.
russellbeattie 9y ago
Python is a great language, despite its quirks. Imagine how popular Python would be if it had decent basic documentation and a single active version? The confusion caused by Python 3 caused at least a decade of slow adoption and the docs, well, that's pretty much a lost cause. (Compare https://docs.python.org/3/library/os.path.html https://docs.python.org/3/library/os.path.html to https://nodejs.org/api/path.html https://nodejs.org/api/path.html - the latter has a nice summary at the beginning, an index of methods at the top of the page listing everything below, method inputs and return values are noted for each method clearly, concise non-interactive terminal examples that you can copy/paste for each method is given, the exact version the method was added, a collapsed history of changes and any error types thrown. My only assumption is that the Python maintainers must have a side deal with O'Reilly to keep the docs in the worst format ever as a source of continued revenue for both.)
- mjw1007 9y ago
  I'd say that, over the last twenty years, Python's docs have gone from excellent to good to middling to excellent to good to middling (dropping as the language expanded and the generally expected standard went up, and improving when they did one major revamp). So I agree that another revamp would be good now, but I historically the docs have been a plus as much as they've been a minus, relative to Python's alternatives.
  - dom0 9y ago
    Much plain reference data is missing from the docs (like return types). Its neat to describe a function, but if you don't hand out the API, then the docs are not all that useful.
- antod 9y ago
  I'm doing mostly Ruby now and I miss the Python docs.
- apple4ever 9y ago
  Python's documentation is terrible! I do like Python (only been using it for about a year), but the docs make it so hard to figure out how to do something.
Will_Do 9y ago
I don't think SO questions is the best way to gauge the popularity of a language and especially not a package. I big reason why Pandas is so popular is because its API is so complicated. I probably have 10x more experience using Pandas than Django. And yet, when I'm doing something in Pandas I'm visiting the documentation far more often than Django. Let's compare the top level APIs: >>> import pandas as pd >>> len(dir(pd)) 177 >>> import django >>> len(dir(django)) 11 Also, I find the data manipulation Pandas provides is far inferior to the R Hadleyverse and is one of the few things that R does better than Python.
- dom0 9y ago
  Django has a very deep package tree, there are tons of stuff in there — a lot of things you wouldn't expect.
  - yen223 9y ago
    Django admin could fill a section by itself.
davesque 9y ago
My simple answer to this, apart from the arguments laid down in the article, is that the Python core devs (and community) have found a unique balance between sobriety and adventurousness. I think this explains why the language was adopted by data scientists and ML researchers. My complaints with other languages usually center around them being too preoccupied with theoretical or academic concerns (e.g. Haskell) or being too loose in adopting new features or opaque syntax (e.g. Javascript). Also, Python is probably the most concise and easy-to-read imperative language that has gained wide acceptance.
rdiddly 9y ago
OK Stackoverflow, that's enough charticles about this language or that, and which one's growing and which one gets used by whom at what time of day and in relation to which other query etc. It's like they didn't expect the attention they got for these, and now they're addicted to it. Nothing against Python... because if anything my underlying point might be that language doesn't ultimately matter that much. But besides, there still remains the same question as always: Doesn't the posing of Stackoverflow questions tend to reflect more who's starting to use a language, than the sum total of who uses it per se? (Since presumably questions taper off with time.) If you're measuring growth or adoption, as in this case, sure, it might seem like a reasonable approximation, but it doesn't tell you anything about long-term use (by people who become experts and stop visiting Stackoverflow) or about attrition (people who quit using it and move on to something else). Thus any trend Stackoverflow can see, probably overemphasizes short-term phenomena. Which is great if you're doing algorithmic trading on the stock market and make your money by responding to the market's every short-term boom & bust (and other investors' overconfidence & diaper-crappings respectively), but not so great if you're choosing a language to which you'll devote months of study.
mountaineer 9y ago
Python has been in the top 3 of hiring posts within "Who is hiring" threads for at least 4 years now[1] so interesting to see other datasets noting Python's popularity as well. 1. https://hntrends.com https://hntrends.com
rurban 9y ago
Besides the obvious answer: pseudocode alike, also don't forget: worse is better. Of all major languages is by far the slowest, hardest to extend and by now easily surpassed PHP and JavaScript with its number of design quirks.
njharman 9y ago
It sucks less than the alternatives.
cleandreams 9y ago
Why is Python So Aggravating? That's a question that bugs me. I can't believe the level of obviousness of the things that are wrong. That is because we write complex code. Things like direct access to data members (which is 'pythonic'!) are cruel blows to software productivity for complex code. Ugh, Ugh, let me count the ways: No compiler to check the code. The other day I dumped 800 extra lines into a file as a typo. Many, many duplicate functions. Of our whole test suite, how many tests failed? One!!! Ugh. This is not even to mention the topics of Black.Holes.In.Your.Code because you can't tell what types the functions receive. I really think python is driven by non-programmers who consume rather than produce complex code and the teachers who teach these people. I believe the problems will increase over time. Does anyone know the story of Hack at Facebook? I heard the lead developer give a talk once. Be afraid.
- flavio81 9y ago
  > This is not even to mention the topics of Black.Holes.In.Your.Code because you can't tell what types the functions receive. Use aptly named parameters, always, add assert() statements for type checking whenever you feel mistakes could happen. I can bet, based on my experience, that it's much "safer" (less error-prone) to use a language that allows calling functions with named parameters (where the order of parameters doesn't matter and you know exactly which parameter are you sending a value into), than a language like C++ or Java which has a static typing system but has no named parameteres, plus depends on argument order to call a function. You should try using Python in this way.
  - cleandreams 9y ago
    We do that. Try developing a complex code base with a medium sized team. It becomes very hard to make changes!!! You just need compiler support, IMHO. Python offers ease and fun, and that has led to an explosion of functionality. But there is a tradeoff. And at some point we will hit that wall, as an industry.
    - mahyarm 9y ago
      Just use kotlin or golang. You get a modern language and you get types. And you still have java libraries with kotlin. I totally understand the lack of typing & static compilation making things painful once you reach a scale where you can't put the codebase in your head. Asserts, unit tests and variable names are poor substitutes. As an interm solution have you tried converting your internal python code to be fully typed with 'optional types'?
bdcravens 9y ago
Many have listed out the technical reasons, but I think there's a cultural aspect: I think many people are turned off by the community of languages like Ruby. Sometimes you just want a great tool, not unicorns and kittens.
- discreteevent 9y ago
  Your comment is a little bit snarky. But I have to admit that stuff does put me off
- swat535 9y ago
  What exactly are you talking about here? Are you referring to Github, Shopify, Basecamp and many other functional businesses scaled and built on the shoulders of ruby "unicorns" and "kittens" ? Having worked with both languages, I can confirm that they simply have different view points with tradeoffs, and that in itself is a matter of personal opinion. But claiming one community does not build great tools because you view them as "kittens" is ridiculous. Also please do not generalize things like "most people" prefer X. [EDIT] I sincerely hope this comment does not come off as rude. I am just trying to understand OP's point of view
  - newen 9y ago
    I think he means those intro to ruby tutorials where they make ruby (and programming in general) sound all magical, where it's so easy to program and programming is just unicorns and kittens or whatever. It becomes tiring and dumb after a while.
    - newen 9y ago
      Add in ducks and geese and a whole slew of other animals and I end up wondering if I'm programming in a zoo with ruby lol.
  - Daishiman 9y ago
    From my outsider perspective, equivalent Ruby tools advertise themselves a the Most Awesome Thing Ever(TM), with well-designed pages, funny characters and exceedingly strong developer personalities. In comparison, big Python projects have a lower profile, a more serious, adult tone, and generally don't brag too much about their capabilities. In practice I've seen that the Python community's greater skepticism towards new packages means that libraries have evolved more slowly but with more certainty, whereas Ruby libs have been swapped out as fashions come an go. Witness the fact that Rails decided to package Coffeescript at its core, whereas Django has no strong opinions on frontend matters. Also look at Django's mature deprecation policy compared to pretty much everything else.
vgy7ujm 9y ago
It's beyond my comprehension... it's so .. ugly .. and dead .. and unexciting ... zzzzzzz
luord 9y ago
Heh, most of the questions I've answered are in the Flask and/or Vue tags; not surprised to see them related and I'm happy that python's popularity keeps on growing.

254 points by lainon 9y ago

> Why Is Python Growing So Quickly? Really easy to answer: It is stupid fun to program with Python. There are libraries for everything you can imagine and they are generally very easy to use. Once you grok the virtual environment thing, writing apps/programs/scripts is just a matter of creating a new env and installing the libs you need. Testing ideas with Jupyter notebook is fun, fast and rewarding. Pycharm is awesome. VSCode with Python just works. You can automate tons of boring stuff (Thanks Al!)... and the list goes on... and on...

pageandrew 9y ago
for whatever reason, the word "grok" annoys me to no end
- castis 9y ago
  In other words, you don't grok "grok"?
- mark-r 9y ago
  The origin of the term is fascinating: https://en.wikipedia.org/wiki/Grok https://en.wikipedia.org/wiki/Grok
- deleted 9y ago
  [deleted]
- antod 9y ago
  Heh. There was a python web framework called grok.
- Zarath 9y ago
  For me it's the fact that no English words end in "ok".
- drcongo 9y ago
  I thought it was just me.
pvg 9y ago
I think a big part of the answer for SO is the popularity and inscrutable nature of pandas. 'Popular, useful and incomprehensible' make for excellent Stack Overflow growth.
- newen 9y ago
  Got to agree. I used to google pandas problems for remotely complex stuff because it wasn't really intuitive how to do it in pandas. Now I have a better context for how simple such things can be because of data.table in R.
rpazyaquian 9y ago
You're listing off tooling, libraries, and frameworks written with or targeting Python, which isn't Python itself. As soon as you want to go off the rails in any of those things, Python gets as complicated as any other language. My take on the matter is that Python is gaining traction due to its market share of the Data Science/Machine Learning field, which is what's really gaining traction and growing quickly. Had Perl or Ruby been the premier language to use for DS/ML, they would be growing instead of Python.
- schneems 9y ago
  This. Most schools are moving away from C or Java being their primary language. Most comp sci professors acknowledge the usefulness of a scripting language and so when they need to pick one, they pick one they have the most familiarity with. Due to numpy and scipy this tends to be Python over Ruby. Also many things about python that I don't like such as `len` being a function rather than a method on a list are very trivial when coming from a language like C which has hordes of tiny inconsistencies. The whitespace thing in python is pretty gimmicky but does also hold a certain amount of pedantic appeal.
  - xapata 9y ago
    The Python design FAQ explains why Len is a built-in function instead of a method. Guido wanted a guarantee that whenever he called it, it'd return a whole number. As just another method, you might find a new type that defines a length in fractional terms, which would break any code wanting to use that as a list index, etc.
    - goatlover 9y ago
      But that would be on your for misusing the length method. All the built-in and popular libraries would still define length properly.
      - xapata 9y ago
        Perhaps, but "your" mistake might impede the popularity of the language. A wide range of well-designed community libraries are a major reason to choose Python. Take a look at the design rationale for the new __matmul__ magic method. It's not for the standard library, it's for the community.
guelo 9y ago
Are you suggesting that we ignore the article's data-driven conclusion that it is because of pandas and the rise of data science and machine learning?
- rpazyaquian 9y ago
  That is exactly what I am thinking myself.
- fish_fan 9y ago
  I think you're reading the article wrong—pandas is a symptom, not a cause. That said, it seems foolish to ignore the clear data science use case. But that's also been a theme for at least five years now.
- njharman 9y ago
  No they're explaining why Pandas was written in Python and why data science and machine learning projects choose Python over R or Java or Haskell, etc.
CJefferson 9y ago
I have come to the conclusion that part of the reason python is so fun is because it is slow. There are so many lovely and easy to use libraries in Python. I took a couple and tried to be inspired by them to write a C++ library, maybe for addition to boost. However, if you are writing C++, people expect your library to be fast and memory efficient -- you can't go around sticking the entirety of a parsed file in a std::string. Also, people then start saying your code better interoperate with their templated classes well, and they want to replace the memory management entirely, and it goes on... Of course, the resulting libraries are very fast and very clever, but they have lost the joy. They also take much longer to write, maintain and use.
- paulddraper 9y ago
  It's a funny way of phrasing it, but you're not wrong. C++ libraries should allow people to squeeze every bit of performance juice out of them (e.g. custom allocator). Python libraries should be intuitive to use. --- Naturally, there are exceptions, like numpy.
  - fencepost 9y ago
    C++ may let you "squeeze every bit of performance," but in a lot of cases it does so by handing you a lot of rope. You might use it to make a rope bridge across that performance gap, but how good your bridge is depends a lot on you - and if you just manage to hang yourself with it, well, that's on you as well. With Python it may take longer to get across because you detour a mile over to a more solidly constructed bridge, but you still get there and for most cases you get there fast enough.
- harshgupta 9y ago
  Part of the reason is same side network effect. It makes sense to create new libraries in languages having great existing libraries.
- barrkel 9y ago
  Watch out for conflating abstraction level with speed. Coding at a low level of abstraction means sweating the small stuff, but it's easy to fool yourself into thinking you're making things fast - when actually, you're micro-managing. Painting with broader brushstrokes with less concern for micro-efficiencies lets you block out a solution faster. Another part of the problem with C++ is that the components come with so many rough edges that need to be filed down that you can't help getting swamped in a certain level of detail, unless you start with a large toolbox or framework that lets you work back at a higher level again. You ought to have the equivalent of Java's file stream, buffer and text decoder as three separate bits that you can compose, so that putting a whole file in a string is never even a temptation. But if you start out with a sparse toolbox, it's a slog.
tw1010 9y ago
That description doesn't explain why there has been such a sudden growth in recent years. Python is an old language. The real reason it is growing so much just recently is of course due to ML.
- dwaltrip 9y ago
  > The real reason it is growing so much just recently is of course due to ML. This is certainly a potential explanation. But I don't think it is self-evident that the trend of recent growth is entirely due to this. Edit: The article supports the claim - I should have read it before commenting.
  - euyyn 9y ago
    It doesn't need to be self-evident, as it's what the article is showing with data.
    - dwaltrip 9y ago
      My bad, this is what I get for not reading the article. I added a note to my comment, thanks.
jdc0589 9y ago
> Once you grok the virtual environment thing it's incredible how hard it is to get some people on board with this. I've gotten used to good project-local package management in other langauges, I can't imaging working with anything that depended on stuff happening to be available in a global namespace ever again.
- dom0 9y ago
  There are still widely used packages that don't work with pip, like PyQt.
- whipoodle 9y ago
  It is a bit more of a pain to work with than similar features in other languages. Like as far as I know you can't just move a venv, you have to rebuild it in the new place. Whereas `node_modules` is just a dir. Also, there is a weird layering where venv abstracts away installed eggs/wheels/whatever they're calling them these days, but then since that's still painful to work with, you need another layer on top like virtualenvtools or burrito or whatever it is. (Sorry, it's been a couple years so my memory is fuzzy)
  - Zaak 9y ago
    Yeah, Python's environment/import logic is really convoluted. It evolved organically with little forethought, and only got a minimal pruning with Python 3.
    - chrshawkes 9y ago
      It was essentially created by one dude no?
  - qaq 9y ago
    "a bit"? honestly I would say it's much closer to a lot more pain
    - whipoodle 9y ago
gdulli 9y ago
> It is stupid fun to program with Python Around 2009 I started questioning if I even enjoyed coding anymore. I was primarily using PHP and Java. Rather than quit the profession I tried Python and suddenly it was fun again. 8 years later, it still is. Python just spoke my language. My career is based around using Python because it's the way I enjoy the work. Everything else, like the actual properties of Python relative to other languages, is irrelevant to me if using another language would make the work unenjoyable again. (Though if Python didn't measure up very well compared to other languages it wouldn't have become my go-to in the first place.)
- cutler 9y ago
  Try Clojure and I promise you will have even more fun.
- alvivar 9y ago
  Maybe you should try F#, to me both are kind of fun compatible (plus the functional focus that is kind of fun by itself).
- Daishiman 9y ago
  Same thing here. I worked with PHP and Java apps and it was just pain having to deal with the awkwardness of PHP's designs and Java's boilerplate. I actually went into sysadmin roles because of that. Python gave me hope that there's fun development roles to be had. Nowadays the panorama for good languages, thankfully, is much better. PHP is no longer that much of a dumpster fire with modern frameworks, and Java has decided to adopt better defaults and drop the XML madness. Not to mention all the good languages that have come up (Elixir, Clojure, Rust, etc).
blauditore 9y ago
I recently worked with Python for a couple of months, for the first time. Even though reading code and writing simple parts is fairly easy, I found several details to be quite confusing, like variable scope, or the various underscore conventions/variables. Also, I struggled to find a good free IDE (there was no money for PyCharm), which made debugging a hassle. Also, dependency management with pip feels a bit quirky compared to e.g. Maven or npm. All in all, I found it neither much more or less fun to program with than e.g. Java, C# or TS. It might be a better fit for small projects than others, though.
- wslh 9y ago
  IDE? Visual Studio Code. Python is not perfect but the final code is simple to write and understand years later.
  - bedros 9y ago
    I use sublime for python development, don't use fancy debug features, just print statements
    - cableshaft 9y ago
      I generally use Notepad++. Python lets you get away with it, for the most part. Other languages would be a pain in the butt to write whole programs in it.
- pletnes 9y ago
  Pycharm and visual studio have free editions. Vim, emacs, Atom, VS Code, sublime - there are many options.
  - blauditore 9y ago
    I think it was the remote debugger I was missing, and as far as I could tell there was no such feature in any free editor/IDE, probably because it's not a language-native feature afaik.
- 9y ago
walterstucco 9y ago
The real answer is: data scientist everywhere They usually don't know better
- wwarner 9y ago
  zing!
- copperx 9y ago
  What would they be using if they knew better? R?
  - walterstucco 9y ago
    It's ok They'd make a mess with every language... A beautiful mess, don't get me wrong There are better languages but there isn't enough "science cargo cult" around them And honestly there aren't as many code snippets to copy from My hopes are in luna[1] p.s. distributing load or using multi cores is still shitty in python, the more dataset will grow in size, the more python will struggle [1] http://www.luna-lang.org/ http://www.luna-lang.org/
vturner 9y ago
This is an honest question, what's the size of projects you have written in Python? For me, once a project gets beyond some size, the aggravation only grows of debugging old code and adding new. Mostly, I see this is as a lack of compilation and types in Python. How do others overcome this and enjoy large Python projects?
- andybak 9y ago
  OK. Bear with me here. This might be overstating my case... In some sense - there are no large projects. Only projects that have failed at being modular. Maintainability involves keeping clear boundaries between functional units so that you can keep enough code in your head at any one time to reason about it. If we assume that Python is capable of handling more code than you can keep in your head at any given time, then surely the problem is simply one of making sure your modules are nicely self-contained from each other.
  - vturner 9y ago
    Agreed, modularity definitely helps us minimize the mental load of understanding all the objects at once. But how does it handle this situation: Write function in module A that expects some complex data structure that comes in. In a static typed language, you put a type on the argument and thus guarantee, to some degree, the data in the object can be safely manipulated. Over in module B we call that function. Because Python doesn't enforce types, I can pass in some type of data structure that might behave well in many situations, but in some that function is going to fail because I didn't properly construct the data structure. How do other Python projects avoid this? Do they avoid large data structures of multiple fields?
    - outworlder 9y ago
      > Write function in module A that expects some complex data structure that comes in. In a static typed language, you put a type on the argument and thus guarantee, to some degree, the data in the object can be safely manipulated. Let's question some of the assumptions. Do you NEED the entire thing to be passed around? If your functions are small and only do one thing, can't they be fed the portion of the data they need and only manipulate that? If they are getting unnecessary data, soon someone will like the convenience and that piece of data will now become necessary. I have seen Java projects with humongous "DAO" objects being passed across all apps. Even if Java had Haskell's type system, at some point it breaks down. So, you do not enforce that on Python itself. Rather, you have to apply due diligence and ensure your data structures are clean, and functions do what they are supposed to do and no more. > I can pass in some type of data structure that might behave well in many situations, but in some that function is going to fail because I didn't properly construct the data structure. Then you construct the structure in a single place, and that should have some sanity checks and sane defaults. Even if you are using a static typed language, there's no guarantee that what's inside the data structure even makes sense, only that the right type of structure is being passed on. That's helpful, but not as much as people would think. And you write tests. Lots of tests. It is amazing the amount of Python code that gets written without proper testing. And Java, for that matter. Only Python programmers have less excuses, it's easier to replace what you need for testing.
mmagin 9y ago
I actually love forcing people into a fairly consistent coding style, but everything else about the language irks me compared to my previous extensive use of Perl (on a large enterprise software product). One particularly big thing, that it doesn't have much of a distinct compilation phase. You'll write some script you want to run to do some one-off task that will take the next hour, only to find it crashed after 20 minutes when it hits some boneheaded syntax error you made.
- makecheck 9y ago
  For that though you can use linting tools such as "pylint"; catches a lot of errors that might otherwise be runtime exceptions.
- dwaltrip 9y ago
  For this reason, I generally test scripts like that with a small slice of the data, or small set of dummy data. Then one can also verify that the output is reasonable -- which is good to do even with a compiled language that would catch all syntax errors ahead of time.
- shalabhc 9y ago
  Take a look at mypy and pytype - there is a drive in the Python community to add a verification system to the language.
- cbr 9y ago
  only to find it crashed after 20 minutes when it hits some boneheaded syntax error you made I find mypy to be pretty useful for this.
IamNotAtWork 9y ago
I haven't overcome the virtual env thing. There are a few virtual environment libraries, not sure which one to use. Then it is awkward to have to change your entire environment over by activating it and for it to be global. What if I want to work on two different projects in two different terminals? Why can't I activate an environment by just CDing into a directory with my project? Then there is the GIL. I still don't know if multithreaded python apps are really MT or just faking it. And yes, I know you can do event driven stuff and that does look very interesting.
- orf 9y ago
  Use "python3 -m venv" You can activate two different environments in two different terminals. Why wouldn't you be able to? You can automatically activate venvs when you cd into a directory with virtualenvwrapper. Rule of thumb: if a thread goes into C it's releasing the GIL. Not always, but often. Like reading a file, or a library like like numpy or Pillow.
hyperpallium 9y ago
Java could have libraries almost as easy to use as python, but doesn't. However, some things can't be as easy as in Python, such as sympy, which does symbolic processing of in-python expressions (differentiation, integration etc). I actually don't find python easy to use - every time I use it, I have to look up how to do for loops (what? range? zero-based? does it include the last one?), and the docs don't have links for arguments (even though they require specific types), because no static types. But the libraries absolutely are easy to use. And because many are written in C (unlike most of Java's librries), you're arguably running C.
submeta 9y ago
+1 for PyCharm. With it I realized how much I was missing with Vim/Emacs/Sublime (even though I configured a lot in those editors).
mixmastamyk 9y ago
> Once you grok the virtual environment thing I wish this would die, it just scares off newbies. In my twenty years of Python I encountered conflicting reqs once or twice. It's easy to direct folks to the documentation in those circumstances rather than cramming it down every newbie's throat---YAGNI. Now that pip can install with --user and containers are popular, the problem isn't even possible in production in many circumstances.
- donarb 9y ago
  Virtual environments are not just for conflicting requirements. I write code for clients on my laptop and don't want to clog up my packages directory with project specific libraries that I'll never use myself. Once the project is done and I turn code over to a client, I can delete the virtual env and all the installed libraries. Virtual envs also allow me to have multiple versions of Python installed on my machine. This discussion is a lot like version control. How many people made daily (hourly) duplicates of project folders before learning the benefits of version control?
  - mixmastamyk 9y ago
    A use case that newbs need not concern themselves with. You normally won't get a version control lecture during every programming language tutorial either.
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alienreborn 9y ago

because readability is one of the most, if not the most, important quality for mainstream programming language adoption.

cremp 9y ago
Golang forces readability; might want to give it a shot.
- lilbobbytables 9y ago
  Just the other day I started looking back into Golang. Syntactically, and with Gofmt, it is quite readable. But then they have to go and ruin it by making single letter variables a common thing.
pyre 9y ago
But significant whitespace means that I can't write code like this: sub (@) { return map { "@{[ $_[0] . $_ ]}" } @$_ }; Disclaimer: The majority of the programmers that I have known in real life that have a significant (and very vocal) dislike of Python's whitespace requirements have all written code like that above (this is my from-memory reproduction of actual code that one of them wrote). Python sucks because it's "restricting" them from being "expressive" with their code.
- minimaxir 9y ago
  For statistical programming in particular, readability of your code by others is the most important part of the coding style.
  - BurningFrog 9y ago
    That's absolutely true of any programming.
    - neuronexmachina 9y ago

binthere 9y ago

I've started to use python a lot for 3d scripting on Blender and Maya. On Blender this is the only option.

deleted 9y ago

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gallerdude 9y ago

As someone introduced to Python and C two semesters ago, I'm not surprised at all. If I want to be really nerdy and crank some performance, I write C. If I want to test ideas, write to a text file, or basically anything else, I use Python. Python just makes it all super easy and nice. I respect the performance gains of C, but ease of use will always win.

acomjean 9y ago

It's the libraries I think. that's why I started using it. I don't think it's particularly different syntactically from most other languages. The built in data types are nice, though why they insist on using different names from other languages (lists, dictionaries ...) Better editors help too.

redblacktree 9y ago
I may be biased, but I much prefer "dictionary" to "hash table" and "list" to "array." They provide much better metaphors to real-life things, instead of requiring people to learn and use additional jargon.

baldfat 9y ago

I have mostly left Python and moved to R, Racket and Lua for my needs. It is hard to see how Python won't stay the predominant first language for decades to come just due to momentum and the good reputation. My first language was Basic and my second language was C64 Assembly and Pascal. I would have killed for Python as a kid.

tw1010 9y ago

The really interesting question though is how big of an influence meta-posts like this one has on the growth of one programming language over another. How would you model that?

mark-r 9y ago
Have you ever known someone to pick a language based on a blog post?
- tw1010 9y ago
  Javascript programmers base a lot of their choice of framework on where the hypetrain is heading, which blog posts can have a lot of influence over.

deepnotderp 9y ago

Because programming in Python is great for productivity, has lots of useful libraries, is rather bearable/enjoyable depending on your view point and for 90% of people/companies you don't need the speed of anything more than PyPy or Cython because, believe me, you're not at Facebook or Google scale, and even those companies often use Python (Google most famously). Btw, for those of you who dislike the dynamic typing (e.g. myself) of Python, check out Cython, it's really heaven.

thisisit 9y ago

Mostly because the whole Data Science and ML stuff is catching on. Everyone and their grandma now wants to do something related to AI, ML etc. The easiest language for these is? Python. Hence the growth. Once upon a time it was all about cloud, SaaS and building a webapp. Hence the growth of JS.

choxi 9y ago
The chart makes a pretty compelling case that this is the right answer. The top trending libraries are pandas, numpy, and matplotlib (both flask and django are probably trending at the same baseline rate of any top 10 popular language). These are all libraries heavily used in data science. We also know that ML and AI are new macro trends in the tech industry. Python's elegant syntax isn't really evidence for why Python is trending now as opposed to any other time in the last decade.
- bsilvereagle 9y ago
  > These are all libraries heavily used in data science. These are all libraries heavily used in any technical computing field. The cost of MATLAB, the simplicity of Jupyter, and the community around Python have caused Python to slowly be a language used for general technical computing, not just data science.

h4l0 9y ago

Python has been continuously becoming the entry point of computer programming. I think the language reached that goal and now people who started with python are contributing in different fields. Personally I met with python thanks to course assignments. It helped me to finish my projects way faster than any other language. I loved its simplicity and now even in my complex projects, I tend to solve my problems in "python way". Also modularity, almost non-existent boiler-plate code, etc kind of things attract data scientists without computer science background.

alunchbox 9y ago

the colleges and programming intro courses for my university are starting off with python now. When I went to uni it was a language called processing which is a simple version of Java. College started out with JavaScript moving into .Net. Some my teacher I kept in contact with moved over the courses to python which might also be a factor in python popularity. Not to mention High schools programming classes also starting with python or js instead of QBasic.

maxerickson 9y ago

Related discussion of the previous post: https://news.ycombinator.com/item?id=15186025 https://news.ycombinator.com/item?id=15186025

AlexCoventry 9y ago

TL;DR, machine learning.

goatlover 9y ago
ML plus Pandas, Matplotlib, SciPy, Jupyter Notebooks, etc.

beamatronic 9y ago

Easy package management. If Java made it just as easy to add packages, and run in an interpreted mode, it might have had similar success. Actually years ago there was something called "jython"

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outworlder 9y ago

It's not only because of data science of machine learning. Big, high profile projects are being done in Python (eg. Openstack). I am at a big company, and it seems most new projects are being done in Python. No one asks why, it is just accepted. Now the question is why. I have no explanation, other than maybe that's because universities have been teaching python more often, and it happens to be practical. I just wish everyone were using Python 3.

deckar01 9y ago

Is stack overflow traffic a good indicator of library growth? It seems like libraries with really good documentation would require fewer stack overflow questions regardless of their popularity.

csense 9y ago

Now that powerful machines are commonplace, the biggest limiting factor of software creation is developer productivity. Python's so popular because it maximizes developer productivity.

igravious 9y ago

I say all the following as an avowed Rubyist 1) Computational Linguistics libraries 2) Machine Learning libraries 3) Still one of the blessed languages internal to Google? and BFDL was employed for a while there 4) Jupyter Notebooks 5) Coder Dojo approved language I used to think that all dynamic languages (Ruby/Python/Javascript/Perl/…) were of a par but I have to admit that Python has an awful lot of mindshare and it's beginning to exhibit strong network effects.

aphextron 9y ago
>I used to think that all dynamic languages (Ruby/Python/Javascript/Perl/…) were of a par but I have to admit that Python has an awful lot of mindshare and it's beginning to exhibit strong network effects. Ruby is a wonderful language with a really unfortunate standard implementation. It's easily 5 times slower than Node at practically anything, which is sad because Ruby is infinitely better than Javascript. I really think this is what it comes down to and why Rails has been dying off.
- goatlover 9y ago
  Agreed on Ruby as a language, but Node also has the advantage of being isomorphic. It's hard to beat JS when it's the browser language, and then you provide a really nice server side platform for it. Plus there has been a reaction to Rails size and complexity. I kind of hate that everyone thinks of Rails when Ruby is mentioned. But I thought Sinatra was a really nice microframework.
  - flavio81 9y ago
    > but Node also has the advantage of being isomorphic. It's hard to beat JS when it's the browser language This has repeated often but so far I haven't found a really convincing argument. All my friends, who know C, C++, C#, Java (and other languages) had no problem in picking up Javascript/ES7 for programming the browser using whatever you need. I think this argument was exciting for the front-end developers who wanted to try doing things on the server but couldn't be bothered into learning a new language. So now with Node.js they are happy and satisfied, until they realize that the problem is not the language; the problem is that server programming is a different matter altogether, no matter what the language is.

ben_jones 9y ago

Because it's easy for people to learn with no prior knowledge in programming. It's taught in many intro classes now and as time goes on the simple fact will cause the language to be used more in production environments as well.

ralmidani 9y ago

Python has a reputation for being a good "beginner" or "scripting" language. Describing it as such is really a disservice. One of things that makes Python a killer language (apart from its readable syntax and straightforward programming model) is how it enables you to jump in at any skill level and be immediately productive, without hampering your ability to get things done quickly and elegantly as your skills grow. There are faster, trendier languages out there, but I suspect Python will still be thriving when many of them have become footnotes.

runevault 9y ago
The interesting thing, to me, with Python is it can fall out of favor but it seems very good at coming back. Look when Ruby got big and became the most popular scripting language. yet Python kept trucking on, until the data science explosion that made it having things like Numpy and Scipy matter so much (and then from there libs like Tensorflow).
blhack 9y ago
This has been such a frustration of mine when mentoring people through learning to code. Python is a great beginner language, but it isn't a language that is only for beginners.

ihaveajob 9y ago

This just about sums it up for me: https://xkcd.com/353/ https://xkcd.com/353/

Animats 9y ago

based on Stack Overflow question visits Is Python use really growing? Or is this a Stack Overflow thing? I thought it was being displaced by Javascript Everywhere. Python had a 5-year setback from the Python 3 transition, but we're now mostly past that.

ZanyProgrammer 9y ago
I think if anything the SO stats underestimate the pervasiveness and popularity of Python, even outside the data science world.
cutler 9y ago
It's certainly growing here in the UK. From being level with Ruby 5 or 6 years ago the Python:Ruby jobs ratio is now 2.9. I've also watched Django recently edge ahead of Rails for the first time.
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fish_fan 9y ago
Well, most devs I know would rather use python for server-side and scripting, but you're not wrong. Python never found a way to do cross platform UI as well as react native; it's on every browser; you don't have to unlearn brace syntax. That said; the idea of something like numpy in javascript is years off and laughable.
goatlover 9y ago
Javascript isn't displacing Python or any other language in data science. The web doesn't encompass all of computing.

jackschultz 9y ago

The most interesting part of the post is the conclusion where the author says that he's a data scientist who uses R and doesn't feel the need to switch to Python! He also has very good thoughts, and very important for beginners to know, about how there isn't a single right way to learn programming. > In any case, data science is an exciting and growing field, and there’s plenty of room for multiple languages to thrive. My main conclusion is to encourage developers early in their career to consider building skills in data science. We’ve seen here that it’s among the fastest-growing components of the software development ecosystem, and one that’s become relevant across many industries.

make3 9y ago
How it that interesting? Statisticians and economists everywhere use R instead of Python
- jackschultz 9y ago
  The fact that the author wrote a sizable post about how big Python is and is growing, but then says he's more of an R type of person instead of Python. Obviously that's interesting to see when authors aren't giant fans of what they're trying to promote.
- CuriouslyC 9y ago
  What's particularly interesting is that you can tell someone's academic pedigree from whether they use R or Python. As you mentioned, R is very popular in stats, while Python is very popular in computer science. In fact, you can tell whether other academic fields are more influenced by stats or comp. sci by this measure as well. R in econ is a great example of this, while fields that build simulations to do research tend to skew Python.

pinnbert 9y ago

My python grows quickly.

alephnil 9y ago

Python is what BASIC was in the 1970s and 80s, in a good way. It enables people that normally would not program to do things that they otherwise could not be done with traditional software with a GUI, that being web based, apps or desktop applications. It is a stepping stone into computing that otherwise would have been inaccessible for many. Unlike BASIC, the design of the language is good enough that it can be used in professional applications, and not only being a stepping stone.

darkmagnus 9y ago

The community is very welcoming, in my experience.

INTPenis 9y ago

It's dead simple and people complain about speed but I use it for massive distributed backends handling hundreds of messages each second and scaling without problem. And this was way back when twisted was the only asynchronous game in town. So I'm not surprised it's growing.

aaronchall 9y ago

I'm a Python answerer on Stack Overflow, I teach Python at NYU, and whaddaya know, I write Python for my day job. Why is it growing so quickly? I speculate: Lots of people want to learn to program - and Python is easy to learn and teach. I think it blows other languages out of the water. I'm going to compare it to its closest competitor, Ruby. Ruby is also open-source, high level, popular relative to other languages (which means you can probably get help if you need it), it has lots of libraries (meaning you can do a lot with it), ok tutorials, and a decent package manager. On the downside, Ruby is also a very large language, which makes it much harder to learn to mastery. I think Python blows Ruby out of the water in terms of how easy Python is to learn relative to Ruby. I don't encourage people to learn Ruby. I spent a long time (months) trying to learn it. I still don't tell people I know it. I used to think that Ruby's syntax is twice as much to learn - now I think it might be even more than that. Ruby's YACC file is ~918 lines long[0], while Python's grammar file is 149 lines long. ~$ wc cpython/Grammar/Grammar 149 879 6472 cpython/Grammar/Grammar Even if 1/2 of Ruby's grammar output is whitespace, that's more than twice as much grammar to learn as Python's grammar[1]. I could compare Python to other languages, but I don't think they come close to matching the upsides of Python. I still want to learn other languages, like Haskell, lisps, and C, to a level of mastery - but since I can be so productive so fast with Python, the other goals are something I work on in my spare time without a bunch of urgency. [0] https://stackoverflow.com/a/16629318/541136 https://stackoverflow.com/a/16629318/541136 [1] https://docs.python.org/3/reference/grammar.html https://docs.python.org/3/reference/grammar.html PS I can see that this comparison to Ruby isn't popular due to the downvoting - but nobody is contradicting my point.

cbanks 9y ago
Cough. Python's cpython/Grammar/Grammar uses a compact EBNF notation. Converted to yacc it is about 850 lines long.

sinemetu11 9y ago

How do people create and manage internal python packages that work together as an ensemble? I've found the packaging system with setup.py dependency_links to be pretty cumbersome and not well supported. How do people version these internal packages and manage them? Running your own pypi seems nontrivial and using github eggs/releases isn't very functional either. My experience with python has been positive but I don't understand how people who want to create separate libraries for specific functionality tie these codebases together without using deprecated features or containerizing everything.

IanCal 9y ago
Gemfury provide a service which might be useful. If you need to keep things on premise, is running your own pypi particularly hard? I've never tried so maybe there's a bunch of hidden complexity but there are some packages to run one and even a docker image: https://hub.docker.com/r/codekoala/pypi/builds/bwe6cdn4swgyiruoolkpqp/ https://hub.docker.com/r/codekoala/pypi/builds/bwe6cdn4swgyi...
ipmb 9y ago
pip2pi makes it really easy to run your own PyPI. All you need is something that can serve static files (Nginx, S3, etc.) https://github.com/wolever/pip2pi https://github.com/wolever/pip2pi

deleted 9y ago

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microDude 9y ago

Really simple answer... University undergraduate classes in a variety of courses use it as their default language, so it is a bunch of students trying to complete their homework assignments.

Derbasti 9y ago

I don't like how Numpy, Matplotlib and Pandas are portrayed as "Data Science". There is nothing particular to "Data" in these three packages. All I see is "Science". All scientists need numerical simulations or evaluations, plotting, and statistics. That's what these libraries do. Not "Data Science", but "Science". The correlation with TensorFlow/Keras suggests Data Science, maybe. But I bet there is a similar correlation with scikit-learn, Scipy, scikit-image, basemap, and heck, Matlab, Julia and R--and those each suggest different scientific endevours. TL;DR: In my opinion, Python is becoming the language of Science, which includes, but is not limited to, Data Science.

cjbillington 9y ago
I'm still at a loss as to what type of science there is that doesn't have data. "Data science" to me seems like a buzzword that has connotations of a field or industry starting to use science where they previously didn't. Over here in academia it's just "science", or possibly "data analysis" if you want to emphasise that that's the part of your project you're working on right now as opposed to data collection.
- xapata 9y ago
  Theoretical physicists don't always use data. Many scientists sometimes use case studies instead of systematic data collection.
  - cjbillington 9y ago
    Eh. Case studies is still data. And the theoretical physicists that don't use data are criticised as not really doing science. Most theorists I know (I am in physics) are heavily data-driven.
PeterisP 9y ago
You seem to treat "data science" as a subset of science, but it's really not - it generally is used to refer to the application of scientific methods to data analysis outside of "the field of science" in various business environments, and that is a much wider field with more people than those actually working in science.

dasil003 9y ago

The obvious answer to me is the slow trickle down of people from universities as CS programs have migrated from Java/Scheme to Python. This also overlaps with Stack Overflow usage, so I think that explains a lot.

blunte 9y ago

#1 - it comes installed on most Linux and on Mac (even though different versions can be a bit of a pain sometimes) #2 - it's dead simple to do things; the syntax is, to me at least, about as direct and uncomplicated as you can get (and without being overly wordy!) #3 - so many handy libraries... it's good for full projects, and it's great for integrating and gluing things together I wouldn't call programming in Python fun, per se, but I do call "getting things done quickly and easily" fun.

russellbeattie 9y ago

Python is a great language, despite its quirks. Imagine how popular Python would be if it had decent basic documentation and a single active version? The confusion caused by Python 3 caused at least a decade of slow adoption and the docs, well, that's pretty much a lost cause. (Compare https://docs.python.org/3/library/os.path.html https://docs.python.org/3/library/os.path.html to https://nodejs.org/api/path.html https://nodejs.org/api/path.html - the latter has a nice summary at the beginning, an index of methods at the top of the page listing everything below, method inputs and return values are noted for each method clearly, concise non-interactive terminal examples that you can copy/paste for each method is given, the exact version the method was added, a collapsed history of changes and any error types thrown. My only assumption is that the Python maintainers must have a side deal with O'Reilly to keep the docs in the worst format ever as a source of continued revenue for both.)

mjw1007 9y ago
I'd say that, over the last twenty years, Python's docs have gone from excellent to good to middling to excellent to good to middling (dropping as the language expanded and the generally expected standard went up, and improving when they did one major revamp). So I agree that another revamp would be good now, but I historically the docs have been a plus as much as they've been a minus, relative to Python's alternatives.
- dom0 9y ago
  Much plain reference data is missing from the docs (like return types). Its neat to describe a function, but if you don't hand out the API, then the docs are not all that useful.
antod 9y ago
I'm doing mostly Ruby now and I miss the Python docs.
apple4ever 9y ago
Python's documentation is terrible! I do like Python (only been using it for about a year), but the docs make it so hard to figure out how to do something.

Will_Do 9y ago

I don't think SO questions is the best way to gauge the popularity of a language and especially not a package. I big reason why Pandas is so popular is because its API is so complicated. I probably have 10x more experience using Pandas than Django. And yet, when I'm doing something in Pandas I'm visiting the documentation far more often than Django. Let's compare the top level APIs: >>> import pandas as pd >>> len(dir(pd)) 177 >>> import django >>> len(dir(django)) 11 Also, I find the data manipulation Pandas provides is far inferior to the R Hadleyverse and is one of the few things that R does better than Python.

dom0 9y ago
Django has a very deep package tree, there are tons of stuff in there — a lot of things you wouldn't expect.
- yen223 9y ago
  Django admin could fill a section by itself.

davesque 9y ago

My simple answer to this, apart from the arguments laid down in the article, is that the Python core devs (and community) have found a unique balance between sobriety and adventurousness. I think this explains why the language was adopted by data scientists and ML researchers. My complaints with other languages usually center around them being too preoccupied with theoretical or academic concerns (e.g. Haskell) or being too loose in adopting new features or opaque syntax (e.g. Javascript). Also, Python is probably the most concise and easy-to-read imperative language that has gained wide acceptance.

rdiddly 9y ago

OK Stackoverflow, that's enough charticles about this language or that, and which one's growing and which one gets used by whom at what time of day and in relation to which other query etc. It's like they didn't expect the attention they got for these, and now they're addicted to it. Nothing against Python... because if anything my underlying point might be that language doesn't ultimately matter that much. But besides, there still remains the same question as always: Doesn't the posing of Stackoverflow questions tend to reflect more who's starting to use a language, than the sum total of who uses it per se? (Since presumably questions taper off with time.) If you're measuring growth or adoption, as in this case, sure, it might seem like a reasonable approximation, but it doesn't tell you anything about long-term use (by people who become experts and stop visiting Stackoverflow) or about attrition (people who quit using it and move on to something else). Thus any trend Stackoverflow can see, probably overemphasizes short-term phenomena. Which is great if you're doing algorithmic trading on the stock market and make your money by responding to the market's every short-term boom & bust (and other investors' overconfidence & diaper-crappings respectively), but not so great if you're choosing a language to which you'll devote months of study.

mountaineer 9y ago

Python has been in the top 3 of hiring posts within "Who is hiring" threads for at least 4 years now[1] so interesting to see other datasets noting Python's popularity as well. 1. https://hntrends.com https://hntrends.com

rurban 9y ago

Besides the obvious answer: pseudocode alike, also don't forget: worse is better. Of all major languages is by far the slowest, hardest to extend and by now easily surpassed PHP and JavaScript with its number of design quirks.

njharman 9y ago

It sucks less than the alternatives.

cleandreams 9y ago

Why is Python So Aggravating? That's a question that bugs me. I can't believe the level of obviousness of the things that are wrong. That is because we write complex code. Things like direct access to data members (which is 'pythonic'!) are cruel blows to software productivity for complex code. Ugh, Ugh, let me count the ways: No compiler to check the code. The other day I dumped 800 extra lines into a file as a typo. Many, many duplicate functions. Of our whole test suite, how many tests failed? One!!! Ugh. This is not even to mention the topics of Black.Holes.In.Your.Code because you can't tell what types the functions receive. I really think python is driven by non-programmers who consume rather than produce complex code and the teachers who teach these people. I believe the problems will increase over time. Does anyone know the story of Hack at Facebook? I heard the lead developer give a talk once. Be afraid.

flavio81 9y ago
> This is not even to mention the topics of Black.Holes.In.Your.Code because you can't tell what types the functions receive. Use aptly named parameters, always, add assert() statements for type checking whenever you feel mistakes could happen. I can bet, based on my experience, that it's much "safer" (less error-prone) to use a language that allows calling functions with named parameters (where the order of parameters doesn't matter and you know exactly which parameter are you sending a value into), than a language like C++ or Java which has a static typing system but has no named parameteres, plus depends on argument order to call a function. You should try using Python in this way.
- cleandreams 9y ago
  We do that. Try developing a complex code base with a medium sized team. It becomes very hard to make changes!!! You just need compiler support, IMHO. Python offers ease and fun, and that has led to an explosion of functionality. But there is a tradeoff. And at some point we will hit that wall, as an industry.
  - mahyarm 9y ago
    Just use kotlin or golang. You get a modern language and you get types. And you still have java libraries with kotlin. I totally understand the lack of typing & static compilation making things painful once you reach a scale where you can't put the codebase in your head. Asserts, unit tests and variable names are poor substitutes. As an interm solution have you tried converting your internal python code to be fully typed with 'optional types'?

bdcravens 9y ago

Many have listed out the technical reasons, but I think there's a cultural aspect: I think many people are turned off by the community of languages like Ruby. Sometimes you just want a great tool, not unicorns and kittens.

discreteevent 9y ago
Your comment is a little bit snarky. But I have to admit that stuff does put me off
swat535 9y ago
What exactly are you talking about here? Are you referring to Github, Shopify, Basecamp and many other functional businesses scaled and built on the shoulders of ruby "unicorns" and "kittens" ? Having worked with both languages, I can confirm that they simply have different view points with tradeoffs, and that in itself is a matter of personal opinion. But claiming one community does not build great tools because you view them as "kittens" is ridiculous. Also please do not generalize things like "most people" prefer X. [EDIT] I sincerely hope this comment does not come off as rude. I am just trying to understand OP's point of view
- newen 9y ago
  I think he means those intro to ruby tutorials where they make ruby (and programming in general) sound all magical, where it's so easy to program and programming is just unicorns and kittens or whatever. It becomes tiring and dumb after a while.
  - newen 9y ago
    Add in ducks and geese and a whole slew of other animals and I end up wondering if I'm programming in a zoo with ruby lol.
- Daishiman 9y ago
  From my outsider perspective, equivalent Ruby tools advertise themselves a the Most Awesome Thing Ever(TM), with well-designed pages, funny characters and exceedingly strong developer personalities. In comparison, big Python projects have a lower profile, a more serious, adult tone, and generally don't brag too much about their capabilities. In practice I've seen that the Python community's greater skepticism towards new packages means that libraries have evolved more slowly but with more certainty, whereas Ruby libs have been swapped out as fashions come an go. Witness the fact that Rails decided to package Coffeescript at its core, whereas Django has no strong opinions on frontend matters. Also look at Django's mature deprecation policy compared to pretty much everything else.

vgy7ujm 9y ago

It's beyond my comprehension... it's so .. ugly .. and dead .. and unexciting ... zzzzzzz

luord 9y ago

Heh, most of the questions I've answered are in the Flask and/or Vue tags; not surprised to see them related and I'm happy that python's popularity keeps on growing.