Traps for the Unwary in Python's Import System
Python's import system is powerful, but also quite complicated. Until the release of Python 3.3, there was no comprehensive explanation of the expected import semantics, and even following the release of 3.3, the details of how sys.path is initialised are still somewhat challenging to figure out.
Even though 3.3 cleaned up a lot of things, it still has to deal with various backwards compatibility issues that can cause strange behaviour, and may need to be understood in order to figure out how some third party frameworks operate.
Furthermore, even without invoking any of the more exotic features of the import system, there are quite a few common misteps that come up regularly on mailing lists and Q&A sites like Stack Overflow.
This essay only officially covers Python versions back to Python 2.6. Much of it applies to earlier versions as well, but I won't be qualifying any of the explanations with version details before 2.6.
As will all my essays on this site, suggestions for improvement or requests for clarification can be posted on BitBucket.
The missing __init__.py trap
This particular trap applies to 2.x releases, as well as 3.x releases up to and including 3.2.
Prior to Python 3.3, filesystem directories, and directories within zipfiles, had to contain an __init__.py in order to be recognised as Python package directories. Even if there is no initialisation code to run when the package is imported, an empty __init__.py file is still needed for the interpreter to find any modules or subpackages in that directory.
This has changed in Python 3.3: now any directory on sys.path with a name that matches the package name being looked for will be recognised as contributing modules and subpackages to that package.
The __init__.py trap
This particular trap is an all new trap added in Python 3.3: if a subdirectory encountered on sys.path as part of a package import contains an __init__.py file, then the Python interpreter will create a single directory package containing only modules from that directory, rather than finding all appropriately named subdirectories as described in the previous section.
This happens even if there are other preceding subdirectories on sys.path that match the desired package name, but do not include an __init__.py file.
This complexity is primarily forced on us by backwards compatibility constraints - without it, some existing code would have broken when Python 3.3 made the presence of __init__.py files in packages optional.
However, it is also useful in that it makes it possible to explicitly declare that a package is closed to additional contributions. All of the standard library currently works that way, although some packages may open up their namespaces to third party contributions in future releases (specifically, it is almost certain the encodings package will be open to additions in Python 3.4).
The double import trap
This next trap exists in all current versions of Python, including 3.3, and can be summed up in the following general guideline: "Never add a package directory, or any directory inside a package, directly to the Python path".
The reason this is problematic is that every module in that directory is now potentially accessible under two different names: as a top level module (since the directory is on sys.path) and as a submodule of the package (if the higher level directory containing the package itself is also on sys.path).
As an example, Django (up to and including version 1.3) is guilty of setting up exactly this situation for site-specific applications - the application ends up being accessible as both app and site.app in the module namespace, and these are actually two different copies of the module. This is a recipe for confusion if there is any meaningful mutable module level state, so this behaviour has been eliminated from the default project layout in version 1.4 (site-specific apps will always need to be fully qualified with the site name, as described in the release notes).
Unfortunately, this is still a really easy guideline to violate, as it happens automatically if you attempt to run a module inside a package from the command line by filename rather than using the -m switch.
Consider a simple package layout like the following (I typically use package layouts along these lines in my own projects - a lot of people hate nesting tests inside package directories like this, and prefer a parallel hierarchy, but I prefer the ability to use explicit relative imports to keep module tests independent of the package name):
project/ setup.py example/ __init__.py foo.py tests/ __init__.py test_foo.py
What's surprising about this layout is that all of the following ways to invoke test_foo.py probably won't work due to broken imports (either failing to find example for absolute imports like import example.foo or from example import foo, complaining about relative imports in a non-package or beyond the toplevel package for explicit relative imports like from .. import foo, or issuing even more obscure errors if some other submodule happens to shadow the name of a top-level module used by the test, such as an example.json module that handled serialisation or an example.tests.unittest test runner):
# These commands will most likely *FAIL* due to problems with the way # the import state gets initialised, even if the test code is correct # working directory: project/example/tests ./test_foo.py python test_foo.py python -m package.tests.test_foo python -c "from package.tests.test_foo import main; main()" # working directory: project/package tests/test_foo.py python tests/test_foo.py python -m package.tests.test_foo python -c "from package.tests.test_foo import main; main()" # working directory: project example/tests/test_foo.py python example/tests/test_foo.py # working directory: project/.. project/example/tests/test_foo.py python project/example/tests/test_foo.py # The -m and -c approaches don't work from here either, but the failure # to find 'package' correctly is easier to explain in this case
That's right, that long list is of all the methods of invocation that are quite likely to break if you try them, and the error messages won't make any sense if you're not already intimately familiar not only with the way Python's import system works, but also with how it gets initialised.
For a long time, the only way to get sys.path right with this kind of setup was to either set it manually in test_foo.py itself (hardly something novice, or even many veteran, Python programmers are going to know how to do) or else to make sure to import the module instead of executing it directly:
# working directory: project python -c "from package.tests.test_foo import main; main()"
Since Python 2.6, however, the following also works properly:
# working directory: project python -m package.tests.test_foo
This last approach is actually how I prefer to use my shell when programming in Python - leave my working directory set to the project directory, and then use the -m switch to execute relevant submodules like test or command line tools. If I need to work in a different directory for some reason, well, that's why I also like to have multiple shell session open.
While I'm using an embedded test case as an example here, similar issues arise any time you execute a script directly from inside a package without using the -m switch from the parent directory in order to ensure that sys.path is initialised correctly (e.g. the pre-1.4 Django project layout gets into trouble by running manage.py from inside a package - the 1.4+ layout solves that by moving manage.py outside the package directory).
The fact that most methods of invoking Python code from the command line break when that code is inside a package, and the two that do work are highly sensitive to the current working directory is all thoroughly confusing for a beginner. I personally believe it is one of the key factors leading to the perception that Python packages are complicated and hard to get right.
This problem isn't even limited to the command line - if test_foo.py is open in Idle and you attempt to run it by pressing F5, or if you try to run it by clicking on it in a graphical filebrowser, then it will fail in just the same way it would if run directly from the command line.
There's a reason the general "no package directories on sys.path" guideline exists, and the fact that the interpreter itself doesn't follow it when determining sys.path is the root cause of all sorts of grief.
However, even if there are improvements in this area in future versions of Python (see PEP 395), this trap will still exist in all current versions.
Importing the main module twice
This is a variant of the above double import problem that doesn't require any erroneous sys.path entries.
It's specific to the situation where the main module is also imported as an ordinary module, effectively creating two instances of the same module under different names.
As with any double-import problem, if the state stored in __main__ is significant to the correct operation of the program, or if there is top-level code in the main module that has non-idempotent side effects, then this duplication can cause obscure and surprising errors.
This is just one more reason why main modules in more complex applications should be kept fairly minimal - it's generally far more robust to move most of the functionality to a function or object in a separate module, and just import and load that from the main module. That way, importing the main module twice becomes harmless. Keeping main modules small and simple also helps to avoid a few potential problems with object serialisation as well as with the multiprocessing package.
More exotic traps
The above are the common traps, but there are others, especially if you start getting into the business of overriding the default import system.
I may add more details on each of these over time:
- the weird signature of __import__
- the influence of the module globals (__import__, __path__, __package__)
- issues with threads prior to 3.3
- the lack of PEP 302 support in the default machinery prior to 3.3
- non-cooperative package portions in pre-3.3 namespace packages
- sys.path initialisation variations
- more on the issues with pickle, multiprocessing and the main module (see PEP 395)
- __main__ is not always a top level module (thanks to -m)
- the fact modules are allowed to replace themselves in sys.modules during import