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I teach javascript to students coming from Java and Python and, while I do agree npm could be better, students find it to be so much more intuitive rather than

I teach javascript to students coming from Java and Python and, while I do agree npm could be better, students find it to be so much more intuitive rather than have some IDE handle packages (in the case of java) or be stored somewhere in your system path with python. Being able to see the dependency within node_modules and make that connection between you saying "i want to install this" and it appears in your project file system makes a bunch of sense for beginners.

paulie_a 8y ago
I honestly don't get why I was down voted but mkvirtualenv, workon xyz, pip install -r requirements.txt isn't exactly a huge barrier to entry with a python project. Personally I always saw the node_modules directory as something to be ignored. It's a crowded bunch of junk.
- mythrwy 8y ago
  Fully agree. NPM is a complete three ring circus compared to pip and a requirements.txt file.
- deleted 8y ago
  [deleted]
- lostcolony 8y ago
  I admit I haven't used Python that much, but I'd say about 90% of the libraries and projects I had interest in didn't have a requirements.txt. I've used Node a LOT, and I have yet to have a library or project -not- have a package.json. I'll readily admit that Python has the -ability- to be a well maintained environment. THe fact it doesn't default and practically enforce it has led to a culture where it's not often used. And half the mindshare uses conda environments instead.
- dagenix 8y ago
  I didn't downvote you. However, I do disagree with what you are saying. In Python, the absolute most trivial case, is already a big pain. In order to get a project started, you need to: 1) create a virtual environment (which you can do with virtualenv, python -m venv, virtualenvwrapper, pyenv-virtualenv, Pipenv, poetry, or, Conda - but lets ignore Conda from here on out). 2) next, you may need to activate the virtual environment - but, its easy to forget to do this or to activate the wrong one. And, depending on how you created the virtual environment, you have to do it differently. 3) once activated, you then need to install your dependencies - possibly using pip, or, maybe using Pipenv or poetry. 4) Depending on the type of project you are setting up, you may also need to create a setup.py file, otherwise you won't be able to install the new project you are working on into a virtual environment. 5) Then, you probably need to configure your IDE to use your virtual environment - depending on how you created it your IDE may pick it up automatically, but, it probably didn't. Then, you can get down to work. But, thats the easy case - the more painful case is when you want to either deploy your project or you want to update it. If you didn't use Pipenv or poetry, you're going to need to create a requirements file - probably with pip freeze. You can then go to a different virtual environment and do a pip install -r to install the requirements from that file. Of course, when developing your code, you may have installed modules like py.test that you don't want to install on your production system - but pip doesn't know the difference between a development and a runtime dependency, so, you either need to edit the requirements file generated by pip freeze by hand, or, just live with deploying code you don't want to to production. If you used Pipenv or poetry, at least then you can keep development and runtime dependencies separate. However, both of these tools are less available than pip, so, this generally means you have to install them on your production system - which, given that they are newer, tends to be awkward to do since it may involve pulling down code from github directly. Alternatively, you can do a pip freeze to create a requirements file, but, then you are back to pull in dependencies you may not want. The next thing you're going to want to do is to update some dependencies. If all you have is a requirements.txt file, well, you are pretty much out of luck. If it was created by pip freeze, its going to include all of your transitive dependencies - good luck remembering which ones you use directly and which ones you don't. Maybe you didn't use pip freeze to create it, however, and you created it by hand. Well, now you'll know which dependencies you actually are using, since, you only put those in the file - however, the problem then becomes that since you didn't list your transitive dependencies, whenever you install the requirements, you could get a different set of transitive dependencies - and if you accidentally started using one of them without realizing it, this could break your production system. So, maybe you listed all of your dependencies in your setup.py file - if so, you can always delete your virtual environment, reinstall everything from your setup.py file, and then re-generate your requirements file. However, doing that is a massive, massive pain since it involves a number of commands. If you try to do this, odds are that your setup.py and your requirements files start to fall out of sync and you give up on one or the other of them. Pipenv helps - a bit. Its more of a replacement for the requirements file than for the setup.py file - which leads to the odd problem of not knowing if you should list your requirements in both places or try to have one include the other. Whats made more fun, is that Pipenv's interface includes a bunch of options that don't make much sense (pipenv install includes the options "--selective-upgrade", "--keep-outdated", "--skip-lock", and "--ignore-pipfile" and its not really all that clear what they are supposed to do). What I'd like to be able to do is to either update either a single dependency OR update them all, at my discretion. I assume that some combination or its arcane options are supposed to allow you to update a single dependency without updating all of them - however, if so, its not clear which one is supposed to do that as it seems like both "--selective-upgrade" and "--keep-outdated" might do that. However, worse than not knowing what option you should use, it seems like neither of them actually does work: https://github.com/pypa/pipenv/issues/966 https://github.com/pypa/pipenv/issues/966 has been open for a while and has been dismissed by the maintainers as not a problem, then "fixed", then acknowledged that it didn't actually work, and then they went dark. So, as it stands, if you try to update any dependency, Pipenv is probably going to insist on updating everything - so, have fun testing that. Poetry is probably the strongest contender for making this whole mess sane. But, for reasons that seem to completely defy logic, Pipenv is getting most of the attention in this space. It appears to be mostly a one person project - and so tying a project to it feels risky. Despite all that, it does work pretty well, but, there are still a lot of features that would be great to see and it would be really great to see it get some more attention and manpower.
  - paulie_a 8y ago
    While I can understand your frustrations in some of those cases, but it almost seems like the issues are being overcomplicated. I have worked with complex code bases and three commands got me running. Occasionally requirements.txt might fail me, so yes I might have to install a package manually. But for most projects I am up and running in 2-3 minutes. I don't bother with pipenv, or poetry. I use the kiss method.
    - dagenix 8y ago
      How do you create the requirements.txt file - by hand, or by pip freeze? If by hand, how do you make sure to lock the versions of your transitive dependencies? If by pip freeze, how do you keep track of what you actually depend on as opposed to what your dependencies depend on? How do you update your dependencies? Do you modify requirements.txt directly? If so, how do you keep it in sync with setup.py? How do you find updated versions of your dependencies - do you search pypi by hand and then update the file? Or, do you leave the versions of your dependencies unlocked in requirements.txt and ask pip to re-install everything to pull in the updated ones? And if its the latter case, how do you then lock them down again so that if you do multiple deployments you'll always get the same set of dependencies installed? I'm really not trying to say that there is anything wrong with the way you work - for some types of work, these aren't big issues. And, if these issues don't matter for your use case, well, that's awesome and keep on rocking it. However, for use cases where these issue do matter, the current Python dependency landscape is a bit of a dumpster fire - there are 50 ways to do everything and none of them work well. And worse, none of them really seem designed to solve the problem. And its not like its the UNIX philosophy at play, where tools are designed to solve one problem at a time and you can solve complex problems by composing them. I've spent a ton of time trying to make a reasonable workflow that handles the update and deploy lifecycle well, and no combination of tools seems to do it. (Except poetry - that tool, while not perfect, seems to actually be trying to fix this).
      - paulie_a 8y ago
        Pip freeze is dead simple and I upgrade packages as needed. I've rarely manually updated requirements.txt. I literally make a venv, pip install what I need and I am off and running. My editor/ide automatically recognizes the environment. I can't imagine how much simpler it could be. I've been doing it for years without any problems. Dependcies, versioning etc are incredibly simple. A couple commands and I am up and running, boom, done. I probably don't even have time to get a cup of coffee in that workflow. I also can deploy to QA and production when necessary fairly fast, although that might take a few more minutes. Having a proper requirements set of files for production is a 30 second trivial task and can be automated easily.
jjjensen90 8y ago
Just so you know, 95% of the few hundred Java devs I've worked with in the last few years used maven or gradle on the command line to handle packages, including junior developers. I think the Java = IDE concept is outdated (although IntelliJ IDEA is an amazingly well-integrated IDE with Java).

paulie_a 8y ago

I honestly don't get why I was down voted but mkvirtualenv, workon xyz, pip install -r requirements.txt isn't exactly a huge barrier to entry with a python project. Personally I always saw the node_modules directory as something to be ignored. It's a crowded bunch of junk.

mythrwy 8y ago
Fully agree. NPM is a complete three ring circus compared to pip and a requirements.txt file.
deleted 8y ago
[deleted]
lostcolony 8y ago
I admit I haven't used Python that much, but I'd say about 90% of the libraries and projects I had interest in didn't have a requirements.txt. I've used Node a LOT, and I have yet to have a library or project -not- have a package.json. I'll readily admit that Python has the -ability- to be a well maintained environment. THe fact it doesn't default and practically enforce it has led to a culture where it's not often used. And half the mindshare uses conda environments instead.
dagenix 8y ago
I didn't downvote you. However, I do disagree with what you are saying. In Python, the absolute most trivial case, is already a big pain. In order to get a project started, you need to: 1) create a virtual environment (which you can do with virtualenv, python -m venv, virtualenvwrapper, pyenv-virtualenv, Pipenv, poetry, or, Conda - but lets ignore Conda from here on out). 2) next, you may need to activate the virtual environment - but, its easy to forget to do this or to activate the wrong one. And, depending on how you created the virtual environment, you have to do it differently. 3) once activated, you then need to install your dependencies - possibly using pip, or, maybe using Pipenv or poetry. 4) Depending on the type of project you are setting up, you may also need to create a setup.py file, otherwise you won't be able to install the new project you are working on into a virtual environment. 5) Then, you probably need to configure your IDE to use your virtual environment - depending on how you created it your IDE may pick it up automatically, but, it probably didn't. Then, you can get down to work. But, thats the easy case - the more painful case is when you want to either deploy your project or you want to update it. If you didn't use Pipenv or poetry, you're going to need to create a requirements file - probably with pip freeze. You can then go to a different virtual environment and do a pip install -r to install the requirements from that file. Of course, when developing your code, you may have installed modules like py.test that you don't want to install on your production system - but pip doesn't know the difference between a development and a runtime dependency, so, you either need to edit the requirements file generated by pip freeze by hand, or, just live with deploying code you don't want to to production. If you used Pipenv or poetry, at least then you can keep development and runtime dependencies separate. However, both of these tools are less available than pip, so, this generally means you have to install them on your production system - which, given that they are newer, tends to be awkward to do since it may involve pulling down code from github directly. Alternatively, you can do a pip freeze to create a requirements file, but, then you are back to pull in dependencies you may not want. The next thing you're going to want to do is to update some dependencies. If all you have is a requirements.txt file, well, you are pretty much out of luck. If it was created by pip freeze, its going to include all of your transitive dependencies - good luck remembering which ones you use directly and which ones you don't. Maybe you didn't use pip freeze to create it, however, and you created it by hand. Well, now you'll know which dependencies you actually are using, since, you only put those in the file - however, the problem then becomes that since you didn't list your transitive dependencies, whenever you install the requirements, you could get a different set of transitive dependencies - and if you accidentally started using one of them without realizing it, this could break your production system. So, maybe you listed all of your dependencies in your setup.py file - if so, you can always delete your virtual environment, reinstall everything from your setup.py file, and then re-generate your requirements file. However, doing that is a massive, massive pain since it involves a number of commands. If you try to do this, odds are that your setup.py and your requirements files start to fall out of sync and you give up on one or the other of them. Pipenv helps - a bit. Its more of a replacement for the requirements file than for the setup.py file - which leads to the odd problem of not knowing if you should list your requirements in both places or try to have one include the other. Whats made more fun, is that Pipenv's interface includes a bunch of options that don't make much sense (pipenv install includes the options "--selective-upgrade", "--keep-outdated", "--skip-lock", and "--ignore-pipfile" and its not really all that clear what they are supposed to do). What I'd like to be able to do is to either update either a single dependency OR update them all, at my discretion. I assume that some combination or its arcane options are supposed to allow you to update a single dependency without updating all of them - however, if so, its not clear which one is supposed to do that as it seems like both "--selective-upgrade" and "--keep-outdated" might do that. However, worse than not knowing what option you should use, it seems like neither of them actually does work: https://github.com/pypa/pipenv/issues/966 https://github.com/pypa/pipenv/issues/966 has been open for a while and has been dismissed by the maintainers as not a problem, then "fixed", then acknowledged that it didn't actually work, and then they went dark. So, as it stands, if you try to update any dependency, Pipenv is probably going to insist on updating everything - so, have fun testing that. Poetry is probably the strongest contender for making this whole mess sane. But, for reasons that seem to completely defy logic, Pipenv is getting most of the attention in this space. It appears to be mostly a one person project - and so tying a project to it feels risky. Despite all that, it does work pretty well, but, there are still a lot of features that would be great to see and it would be really great to see it get some more attention and manpower.
- paulie_a 8y ago
  While I can understand your frustrations in some of those cases, but it almost seems like the issues are being overcomplicated. I have worked with complex code bases and three commands got me running. Occasionally requirements.txt might fail me, so yes I might have to install a package manually. But for most projects I am up and running in 2-3 minutes. I don't bother with pipenv, or poetry. I use the kiss method.
  - dagenix 8y ago
    How do you create the requirements.txt file - by hand, or by pip freeze? If by hand, how do you make sure to lock the versions of your transitive dependencies? If by pip freeze, how do you keep track of what you actually depend on as opposed to what your dependencies depend on? How do you update your dependencies? Do you modify requirements.txt directly? If so, how do you keep it in sync with setup.py? How do you find updated versions of your dependencies - do you search pypi by hand and then update the file? Or, do you leave the versions of your dependencies unlocked in requirements.txt and ask pip to re-install everything to pull in the updated ones? And if its the latter case, how do you then lock them down again so that if you do multiple deployments you'll always get the same set of dependencies installed? I'm really not trying to say that there is anything wrong with the way you work - for some types of work, these aren't big issues. And, if these issues don't matter for your use case, well, that's awesome and keep on rocking it. However, for use cases where these issue do matter, the current Python dependency landscape is a bit of a dumpster fire - there are 50 ways to do everything and none of them work well. And worse, none of them really seem designed to solve the problem. And its not like its the UNIX philosophy at play, where tools are designed to solve one problem at a time and you can solve complex problems by composing them. I've spent a ton of time trying to make a reasonable workflow that handles the update and deploy lifecycle well, and no combination of tools seems to do it. (Except poetry - that tool, while not perfect, seems to actually be trying to fix this).
    - paulie_a 8y ago
      Pip freeze is dead simple and I upgrade packages as needed. I've rarely manually updated requirements.txt. I literally make a venv, pip install what I need and I am off and running. My editor/ide automatically recognizes the environment. I can't imagine how much simpler it could be. I've been doing it for years without any problems. Dependcies, versioning etc are incredibly simple. A couple commands and I am up and running, boom, done. I probably don't even have time to get a cup of coffee in that workflow. I also can deploy to QA and production when necessary fairly fast, although that might take a few more minutes. Having a proper requirements set of files for production is a 30 second trivial task and can be automated easily.

jjjensen90 8y ago

Just so you know, 95% of the few hundred Java devs I've worked with in the last few years used maven or gradle on the command line to handle packages, including junior developers. I think the Java = IDE concept is outdated (although IntelliJ IDEA is an amazingly well-integrated IDE with Java).