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This week our Director ordered a total rewrite of two years of work in Python. His rationale: it's what everyone else uses in this space. No reason specific to our use case, just simply to follow the herd. I realise that a large community translates into easy hiring and rich ecosystems, but I despise the mentality as it promotes a monoculture.
Python keeps growing in number of users because it’s easy to get started, has libraries to load basically any data, and to perform any task. It’s frequently the second best language but it’s the second best language for anything. By the time a python programmer has «graduated» to learning a second language, exponential growth has created a bunch of new python programmers, most of which don’t consider themselves progr…
Also if I (a programmer) want to write really really fast code I'm probably reaching for tools like tensorflow, numpy, or jax. So there's not much incentive for me to switch to a more efficient language when as near as I can tell the best tooling for dealing with SIMD or weird gpu bullshit seems to be being created for python developers. If you want to write fast code do it in c/rust/whatever, if you want to write really fast code do it in python with .
For a very specific definition of the word "fast" at least.
Python keeps growing in number of users because it’s easy to get started, has libraries to load basically any data, and to perform any task. It’s frequently the second best language but it’s the second best language for anything. By the time a python programmer has «graduated» to learning a second language, exponential growth has created a bunch of new python programmers, most of which don’t consider themselves progr…
There are also programmers who are tired of chasing pointers and simply want to get stuff done. E.g. people who once wrote "robust" code in Rust but were "outcompeted" left and right by coworkers who churn out shiny new things at 10x the speed.
With Python, testing, good hygiene and a bit of luck you can write core that is maybe 99% reliable. It is very, very hard to get to (100-eps)% for eps Anything else, especially if there isn't a huge premium on speed, meh - Python is almost always sufficient, and not in the way.
I think one reason for Python growing popularity is because it's become the default tool in some domains whether it is the best tool or not. This week our Director ordered a total rewrite of two years of work in Python. His rationale: it's what everyone else uses in this space. No reason specific to our use case, just simply to follow the herd. I realise that a large community translates into easy hiring and rich eco…
Comparing Python to Java 8 and saying it’s more readable isn’t showing much. And it’s not very portable the second dependencies with native code (which is common since pure Python is too inefficient for many tasks) are used. I think Python is popular for two reasons: - it’s believed to be beginner friendly compared to other languages. I’m not really sure why - maybe the whitespace? - it has an enormous set of librari…
1. Near zero boilerplate. Python's boilerplate is usually no more than setting up a class and then calling a method. Often you can skip the class and just call it directly. This is probably the biggest strength; to give you an example, my standard test for a languages approachability is "how much work do I need to put in to get a very simple JSON file from a web URL" (nothing fancy like POST, just an HTTP GET). With python, a call to urllib.request.urlretrieve and then a call json.loads are all you need. In Java, you need to manually implement a bunch of boilerplate code that looks horrible, need to think about the size of your response in memory and often need to pass in configuration options that should be a case of "works by default" but aren't because the standard is ages old so it needs to be manually toggled on. Part of that is that the Java stdlib consists largely of reference implementations rather than actual implementations, which means that anyone who wants to implement something in Java will usually end up falling back to the stdlib interfaces, which in turn suffer from being stdlib interfaces, so a lot of "should really be on by default" expectations aren't on by default since the stdlib is where code goes to die, so you instead start piling on features.
2. Interop with C. I hold the opinion that C is a great language that doesn't work very well once you get to any form of scaling. Python allowing developers to take the slowest parts of their code and writing it in C to speed it up is one of the easiest speed gains to make and it avoids the biggest bumps that come with using C as your primary language in terms of project structuring.
3. Library support is as you say, good. If you need it, there's probably a package for it. Pypi is dependency-wise kind of a disaster but if you know how to set up requirements.txt, it works really well. Most libraries ship with "sane" defaults too.
All of these combine to a language that's easy to prototype, easy to expand
It's important to keep in mind that pythons biggest successes aren't in the speed-focused, low-memory environments where every speedgain is necessary. Its success lies in conventional desktops and servers, which have much more processing power and often have more leniency in being a bit slower.
With python you can write something in 3 hours what would take a day in another language, at the cost that instead of being lightning fast and done in 10 seconds, you need to wait 30 seconds. That's an issue for some environments but in 99% of the cases that's not a problem.
That isn't to say the language is perfect (no language is), but speed of development at the cost of slightly slower execution time is the main reason why python got popular.
Earlier quoted context omitted.
There are also programmers who are tired of chasing pointers and simply want to get stuff done. E.g. people who once wrote "robust" code in Rust but were "outcompeted" left and right by coworkers who churn out shiny new things at 10x the speed.
I use mostly Python, and a bit of Rust. With Python, testing, good hygiene and a bit of luck you can write core that is maybe 99% reliable. It is very, very hard to get to (100-eps)% for eps Anything else, especially if there isn't a huge premium on speed, meh - Python is almost always sufficient, and not in the way.
Earlier quoted context omitted.
I admit I have a blind spot for Python, because I use PHP in my day job, so when I need to do some scripting, I mostly use PHP. But admittedly Python is a lot friendlier than some alternatives (Perl, Shell scripts etc.), and more universal than others (PHP being mostly used for web dev), so that's why people choosing a scripting language for their tool/library tend to choose Python.
I use Python all the time both for my personal stuff and for some side-projects at work, so this isn't a dunk on Python, but honestly it feels like a circular thing: it's popular because it's popular. I wouldn't say it's friendlier than the alternatives, Perl and Shell scripts sure, but not when compared to Javascript, Ruby or Lua. Now, if you're talking about libraries, support, etc. then sure, Python wins hands dow…
The story of Ruby is altogether different: they made the fatal mistake of not defining a C foreign function interface in the standard, otherwise I imagine we'd be seeing numerical computation and ML libraries with a Ruby interface today. Still, Ruby lives on in Metasploit, and in Sorbet and Crystal.
I have been a heavy Python user now about 15 years, but for me now I'm increasingly reaching for modern JavaScript and particularly TypeScript to do the things I would have traditionally done with Python. ES modules, fat arrow expressions, and all the other nice new syntax and library features have made the language so more pleasant to use. In many ways the ergonomics of TypeScript in particular are far superior to P…
I am going this way too. I recently tried to onboard a few contractors with limited python experience. It took several deep sessions to work out why they couldn't get an environment set up. After 10+ years I've never come across the install certificates command. There's still no way to get a dev environment with a specific version of Python working with one command that works reliably everywhere. pyenv isn't even pac…
https://github.com/n-riesco/ijavascript
I used this for interactive debugging AWS API calls using the AWS Javascript SDK. It worked great!
Python keeps growing in number of users because it’s easy to get started, has libraries to load basically any data, and to perform any task. It’s frequently the second best language but it’s the second best language for anything. By the time a python programmer has «graduated» to learning a second language, exponential growth has created a bunch of new python programmers, most of which don’t consider themselves progr…
>and they don’t care - or know about - concurrency, memory efficiency, L2 cache misses due to pointer chasing. Also if I (a programmer) want to write really really fast code I'm probably reaching for tools like tensorflow, numpy, or jax. So there's not much incentive for me to switch to a more efficient language when as near as I can tell the best tooling for dealing with SIMD or weird gpu bullshit seems to be being…
Very limited view which ignores way too many areas.
Earlier quoted context omitted.
Realistically something that takes 1 second in C++ will take 10 seconds (if you write efficient python and lean heavily on fast libraries) to 10 minutes in python. But the rest of your point stands
Damn! Is the rule of thumb really a 10x performance hit between Python/C++? I don’t doubt you’re correct, I’m just thinking of all the unnecessary cycles I put my poor CPU through.