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python import yaml - but which one? [duplicate]

This question already has answers here:
Python: How do I find which pip package a library belongs to?
(2 answers)
Closed 24 days ago.
Ok, so you clone a repo, there's an import
import yaml
ok, so you do pip install yaml and you get:
ERROR: No matching distribution found for yaml
Ok, so you look for a package with yaml in it, and there's like a gazillion of them... usually adding py in front does the job, but...
How on earth should I know which one was used?!
And it's not just yaml, oh no... there's:
import cv2 # python-opencv
import PIL # Pillow
and the list goes on and on...
How can I know which import uses which package? Shouldn't there be a PEP for this? Or a naming convention, e.g. import is always the same as the package name?
There's a similar topic here, if you're not frustrated enough :)

[When I clone a repo,] How can I know which import uses which package?
In short: it is the cloned code's responsibility to explain this, and it is an expected courtesy that the cloned code includes an installer that will take care of it.
If this is just some random person's bundle of .py files on GitHub with no installation instructions, look for notes in the associated documentation; failing that, make an issue on the tracker. (Or just give up. Maybe look for a better-engineered project that does the same thing.)
However, most "serious", contemporary Python projects are meant to be installed by using some form of packaging system. These have evolved over the years, and best practices have changed many times; but generally speaking, a properly "packaged" and "distributed" project will have either a setup.py or (newer; better in many ways, but not universally adopted yet) pyproject.toml file at the top level.
A pyproject.toml file is a config file in TOML format that simply describes a bunch of project metadata. This requires a build backend conforming to PEP 517. For a while, this required third-party tools, such as Poetry; but the standard setuptools can handle this since version 40.8.0. (As of this writing, the current release is 65.7.0.)
A setup.py script is executable code that pip will invoke after downloading a package from PyPI (or another package index). Generally, this script will use either setuptools or distutils (the predecessor to setuptools; it has finally been officially deprecated in 3.10, and will be removed in 3.12) to install the project, by calling a function named setup and passing it a big dict with some project metadata.
Security warning: this file is still executable code. It is arbitrary code, and it doesn't have to be following the standard conventions. Also, the package that is actually downloaded from PyPI doesn't necessarily match the project's source shown on GitHub (or another Git provisioning website), if such is even available. (This problem also affects package managers in other languages and ecosystems, notably npm for Javascript.)
With the setup.py based approach, package dependencies are specified using a keyword argument to the setup function. The specification has changed many times; currently, projects still using a setup.py should use the install_requires keyword argument.
With the pyproject.toml based approach, using setuptools' backend, dependencies will be an array (using JSON terminology, as TOML is a superset) stored under project.dependencies. This will vary for other backends; for example, Poetry expects this information under tool.poetry.dependencies.
In any event, pip freeze will output a list of what's installed in the current environment. It's a somewhat common practice for developers to test the code in a virtual environment where the dependencies are installed, dump this output to a requirements.txt file, and include that as documentation.
[When I want to use a third-party library in my own code,] How can I know which import uses which package?
It's worth considering the question the other way around, too: given that we have installed OpenCV for Python using pip install opencv-python, and want to use it in our own code, how do we know to import cv2 specifically?
The answer: there is no convention, and certainly no requirement for the installed package name to match the PyPI name, nor the GitHub etc. repository name. Read the documentation. Everyone who intends for their code to be used as a library, will be more than willing to show how, on at least a basic level.

Watch for requirements.txt . Big projects usually have it. You can import packages from this file. Else just google.

Keep in mind that it might not be a pip package.
Probably what is happening is that the main script is trying to import a secondary script (yaml.py, in this case) with functions or utils for the main script to use.
Check if the repo contains a file named yaml.py. If it's the case make sure to run the main script while the yaml.py is in the same directory.
Also, check for a requirements.txt file.
You can install all the requirements inside the file running in shell this line:
pip install -r *path to your requirements.txt*
Hope that this helps.

Any package on PyPI or cloned from some online repository is free to set itself up with a base directory name it chooses. That base directory xyz determines the import xyz line. Additionally a package name on PyPI doesn't have to match the repository name where its source code revisions are kept (assuming there is any).
This has the disadvantage that there is no one-to-one relation between package name, repo and/or import-line. But the advantage is that you e.g. can install Pillow, which is backwards compatible with PIL and still use import PIL instead of changing all your sources to use import Pillow as PIL.
If the repo you clone has a requirements.txt look there, you can also look in the setup.py for extra_require. But there is no guarantee that these are available, or contain the names of the packages to install (e.g. I use a generic setup.py that reads its info from a datastructure in the __init__.py file when creating/installing a package).
yaml seems to be a reserved name on PyPI (at least when I tried to upload a package with that name a few years ago). So that might be the reason the package is named PyYAML, although the Py is not very informative as the python code will not function in another programming language. PyPI' search is not very helpful as it relevance ordering is not relevant (at least not for yaml).
PyPI has no entry in the metadata for the import line, but you could extract that from .whl package file as the import line is the top level directory that doesn't match .dist-info. This is normally not possible from a .tar.gz` package file. I don't know of any site that does this kind of automatic scraping.
You can click through the packages on PyPI, after searching the import term, and hope you find something that matches the import in the documentation, but that is no guarantee you get the right one.
You might be best of searching for import yaml here on stackoverflow, and hope that the question or the answer mentions the package name.

thank you very much for your help and ideas. Big thanks to Karl Knechter for his exhaustive answer.
tl;dr: I think using some sort of "package" / "distribution" as a standard, would make everyone's lives easier.
However, my question was half-theoretical, to point out something I'd call, an incoherence in Python. You are of course right, there should be setuptools or requirements.txt or at least some documentation. But, if there isn't any, we're prone to error or additional browsing.
GospelBG pointed out something important. There could be a script yaml.py in the main folder and we need to check and/or guess.
Most importantly, naming imports differently than packages is just plainly misleading. There should be a naming convention or a PEP for this. Again, you can of course eventually get the proper package etc., but it's not explicit and obvious, and it should be! Because in programming, we like it that way, don't we?
I'm no seasoned dev in Python and I'm learning C++, but e.g. in C++, you import a header file with a particular name and static or dynamic libraries by their filename. Now I know this is very "step-by-step, on foot method", but at least you use the exact filenames.
On the upper level you have CMake, which would be an equivalent of setuptools where using find_package or find_library you can import package / library. To be honest, I'm not sure if all packages have the exact equivalent name, but at least the ones I used, did match.
Thanks again for your help and answers! I'm open for discussion and comments :)

How to add a folder to Python path?

Basically, I can only reference my other files as modules when they are in a very specific location:
C:\Users\Dave\Desktop\Programming\Python.
If I want to create a new folder for a large project with multiple modules, say
C:\Users\Dave\Desktop\Programming\Python\Project1,
I can no longer import any modules and keep getting a ModuleNotFoundError. I've looked into it and it seems I need to add that folder to the Python Path somehow, but I couldn't find any answers on how to do it. My computer runs on Windows 10 if that matters.

I think the immediate solution to your problem/the answer to your question would be to use sys.path.append() at the top of your script.
import sys
sys.path.append("<ABSOLUTE/PATH/TO/YOUR/CUSTOM/MODULES/FOLDER>")
import custom_module
This isn't an ideal solution for any kind of prod use, however. Based on what you wrote in your question, this may not be what you're looking for. More info might help to craft a more stable solution:
Are you using virtual environments when running python? Do you just run python script.py or is there a specific version of python you're using that's installed elsewhere than the common C:\Program Files\Python?
When you say that you work on a project with multiple modules, does that mean there are custom modules that you/someone wrote or is it just that that project uses non-standard library modules, i.e. you had to pip install them?
Can we get an example of the code you're running and the folder structure of your project/where the modules are that you need?

Python: Is it possible to create a package out of multiple external libraries?

This issue has been driving me insane for the past few days.
So basically, I'm trying to port over a Pure Python project to a proper PyCharm project. This is to basically improve code quality and project structure.
I wish it was as simple as basically creating a virtualenv to house everything, but it isn't. This project will eventually be developed simultaneously by multiple developers with Git as source control, and the default libraries will be modified. I presume this means that the libraries should ideally be tracked by Git in the end. Virtualenv shouldn't help here as far as I know because it's not portable between systems (or at least that's still being tested).
This project will also be, in the future, deployed to a Centos server.
So the only plan I can think of to successfully pull off this would be to simply bring in all of the libraries (which was done using pip install -t Libraries <ExampleLibrary>) into a single folder, with a __init__.py inside, and use them from other python files as a package within the Pycharm project.
Is this possible / recommended? I tried various methods to reference these libraries, but they all don't work during runtime. Somehow when the files in the library import something else from their own package, an ImportError is raised saying that there's no such module.
Will accept any other suggestions too.
Using Pycharm Community Edition.
EDIT: After having a good night's rest I think the crux of the issue is really just project organization. Before I ported it over to Pycharm the project worked as expected, but this had all of the python files in the root directory, and the libraries in a subfolder of the root, with every project file having the same boilerplate code:
import os, sys
absFilePath = os.path.dirname(os.path.abspath(__file__));
sys.path.insert(1, absFilePath + "/lib")
I was hoping that by using Pycharm to help me flesh out the packages, I could avoid having repeated boilerplate code.

Note: Not full solution.
The addition of the template code below forces the file containing the code to be in the same directory as the libs folder.
For Pycharm, all I had to do was mark the libs folder as a source folder. Even with the addition of the template code to the file, the modified libraries still work as expected.
For the Python Shell, this template code is still needed:
import os
import sys
absFilePath = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(1, absFilePath + "/lib")

What is the proper way to work with shared modules in Python development?

I'm working toward adopting Python as part of my team's development tool suite. With the other languages/tools we use, we develop many reusable functions and classes that are specific to the work we do. This standardizes the way we do things and saves a lot of wheel re-inventing.
I can't seem to find any examples of how this is usually handled with Python. Right now I have a development folder on a local drive, with multiple project folders below that, and an additional "common" folder containing packages and modules with re-usable classes and functions. These "common" modules are imported by modules within multiple projects.
Development/
Common/
Package_a/
Package_b/
Project1/
Package1_1/
Package1_2/
Project2/
Package2_1/
Package2_2/
In trying to learn how to distribute a Python application, it seems that there is an assumption that all referenced packages are below the top-level project folder, not collateral to it. The thought also occurred to me that perhaps the correct approach is to develop common/framework modules in a separate project, and once tested, deploy those to each developer's environment by installing to the site-packages folder. However, that also raises questions re distribution.
Can anyone shed light on this, or point me to a resource that discusses this issue?

If you have common code that you want to share across multiple projects, it may be worth thinking about storing this code in a physically separate project, which is then imported as a dependency into your other projects. This is easily achieved if you host your common code project in github or bitbucket, where you can use pip to install it in any other project. This approach not only helps you to easily share common code across multiple projects, but it also helps protect you from inadvertently creating bad dependencies (i.e. those directed from your common code to your non common code).
The link below provides a good introduction to using pip and virtualenv to manage dependencies, definitely worth a read if you and your team are fairly new to working with python as this is a very common toolchain used for just this kind of problem:
http://dabapps.com/blog/introduction-to-pip-and-virtualenv-python/
And the link below shows you how to pull in dependencies from github using pip:
How to use Python Pip install software, to pull packages from Github?

The must-read-first on this kind of stuff is here:
What is the best project structure for a Python application?
in case you haven't seen it (and follow the link in the second answer).
The key is that each major package be importable as if "." was the top level directory, which means that it will also work correctly when installed in a site-packages. What this implies is that major packages should all be flat within the top directory, as in:
myproject-0.1/
myproject/
framework/
packageA/
sub_package_in_A/
module.py
packageB/
...
Then both you (within your other packages) and your users can import as:
import myproject
import packageA.sub_package_in_A.module
etc
Which means you should think hard about #MattAnderson's comment, but if you want it to appear as a separately-distributable package, it needs to be in the top directory.
Note this doesn't stop you (or your users) from doing an:
import packageA.sub_package_in_A as sub_package_in_A
but it does stop you from allowing:
import sub_package_in_A
directly.

...it seems that there is an assumption that all referenced packages
are below the top-level project folder, not collateral to it.
That's mainly because the current working directory is the first entry in sys.path by default, which makes it very convenient to import modules and packages below that directory.
If you remove it, you can't even import stuff from the current working directory...
$ touch foo.py
$ python
>>> import sys
>>> del sys.path[0]
>>> import foo
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: No module named foo
The thought also occurred to me that perhaps the correct approach is
to develop common/framework modules in a separate project, and once
tested, deploy those to each developer's environment by installing to
the site-packages folder.
It's not really a major issue for development. If you're using version control, and all developers check out the source tree in the same structure, you can easily employ relative path hacks to ensure the code works correctly without having to mess around with environment variables or symbolic links.
However, that also raises questions re distribution.
This is where things can get a bit more complicated, but only if you're planning to release libraries independently of the projects which use them, and/or having multiple project installers share the same libraries. It that's the case, take a look at distutils.
If not, you can simply employ the same relative path hacks used in development to ensure you project works "out of the box".

I think that this is the best reference for creating a distributable python package:
link removed as it leads to a hacked site.
also, don't feel that you need to nest everything under a single directory. You can do things like
platform/
core/
coremodule
api/
apimodule
and then do things like from platform.core import coremodule, etc.

Automatically fetching latest version of a file on import

I have a module that I want to keep up to date, and I'm wondering if this is a bad idea:
Have a module (mod1.py) in the
site-packages directory that copies a
different module from some other
location into the site-packages
directory, and then imports * from
that module.
import shutil
from distutils.sysconfig import get_python_lib
p_source = r'\\SourceSafeServer\mod1_current.py'
p_local = get_python_lib() + r'\mod1_current.py'
shutil.copyfile(p_source, p_local)
from mod1_current import *
Now I can do this in any module, and it will always be the latest version:
from mod1 import function1
This works.... but is there a better way of doing this?
Update
Here is the current process... there is a project under source-control that has a single module: mod1.py There is also a setup.py Running setup.py copies mod1.py to the site-packages directory.
Developers that use the module must run setup.py to update the module. Sometimes, they don't and not having the latest version causes problems.
I want to be able to just check-in the a new version, and any code that imports that module will automatically grab the latest version every time, without anyone having to run setup.py

Do you really want to do this? This means you could very easily roll code to a production app simply by committing to source control. I would consider this a nasty side-effect for someone who isn't aware of your setup.
That being said this seems like a pretty good solution - you may want to add some exception-handling around the network file calls as those are prone to failure.

In some cases, we put .pth files in the Python site-packages directory. The .pth files name our various SVN checkout directories.
No install. No copy.
.pth files are described here.

The original strategy of having other developers copy mod1.py into their site-packages in order to use the module sounds like it's the real problem. Why aren't they just using the same source control are you are?
This auto-copying will make it hard to do rollbacks, especially if other developers copy your strategy. Imagine this same system used for dozens and dozens of files. And then imagine you actually do want to use a version of mod1.py that is not the latest for something.