In Python how can one tell if a module comes from a C extension?

Question

What is the correct or most robust way to tell from Python if an imported module comes from a C extension as opposed to a pure Python module? This is useful, for example, i

Answer 1

@Cecil Curry's function is excellent. Two minor comments: firsly, the _elementtree example raises a TypeError with my copy of Python 3.5.6. Secondly, as @crld points out, it's also helpful to know if a module contains C extensions, but a more portable version might help. More generic versions (with Python 3.6+ f-string syntax) may therefore be:

from importlib.machinery import ExtensionFileLoader, EXTENSION_SUFFIXES
import inspect
import logging
import os
import os.path
import pkgutil
from types import ModuleType
from typing import List

log = logging.getLogger(__name__)


def is_builtin_module(module: ModuleType) -> bool:
    """
    Is this module a built-in module, like ``os``?
    Method is as per :func:`inspect.getfile`.
    """
    return not hasattr(module, "__file__")


def is_module_a_package(module: ModuleType) -> bool:
    assert inspect.ismodule(module)
    return os.path.basename(inspect.getfile(module)) == "__init__.py"


def is_c_extension(module: ModuleType) -> bool:
    """
    Modified from
    https://stackoverflow.com/questions/20339053/in-python-how-can-one-tell-if-a-module-comes-from-a-c-extension.

    ``True`` only if the passed module is a C extension implemented as a
    dynamically linked shared library specific to the current platform.

    Args:
        module: Previously imported module object to be tested.

    Returns:
        bool: ``True`` only if this module is a C extension.

    Examples:

    .. code-block:: python

        from cardinal_pythonlib.modules import is_c_extension

        import os
        import _elementtree as et
        import numpy
        import numpy.core.multiarray as numpy_multiarray

        is_c_extension(os)  # False
        is_c_extension(numpy)  # False
        is_c_extension(et)  # False on my system (Python 3.5.6). True in the original example.
        is_c_extension(numpy_multiarray)  # True

    """  # noqa
    assert inspect.ismodule(module), f'"{module}" not a module.'

    # If this module was loaded by a PEP 302-compliant CPython-specific loader
    # loading only C extensions, this module is a C extension.
    if isinstance(getattr(module, '__loader__', None), ExtensionFileLoader):
        return True

    # If it's built-in, it's not a C extension.
    if is_builtin_module(module):
        return False

    # Else, fallback to filetype matching heuristics.
    #
    # Absolute path of the file defining this module.
    module_filename = inspect.getfile(module)

    # "."-prefixed filetype of this path if any or the empty string otherwise.
    module_filetype = os.path.splitext(module_filename)[1]

    # This module is only a C extension if this path's filetype is that of a
    # C extension specific to the current platform.
    return module_filetype in EXTENSION_SUFFIXES


def contains_c_extension(module: ModuleType,
                         import_all_submodules: bool = True,
                         include_external_imports: bool = False,
                         seen: List[ModuleType] = None,
                         verbose: bool = False) -> bool:
    """
    Extends :func:`is_c_extension` by asking: is this module, or any of its
    submodules, a C extension?

    Args:
        module: Previously imported module object to be tested.
        import_all_submodules: explicitly import all submodules of this module?
        include_external_imports: check modules in other packages that this
            module imports?
        seen: used internally for recursion (to deal with recursive modules);
            should be ``None`` when called by users
        verbose: show working via log?

    Returns:
        bool: ``True`` only if this module or one of its submodules is a C
        extension.

    Examples:

    .. code-block:: python

        import logging

        import _elementtree as et
        import os

        import arrow
        import alembic
        import django
        import numpy
        import numpy.core.multiarray as numpy_multiarray

        log = logging.getLogger(__name__)
        logging.basicConfig(level=logging.DEBUG)  # be verbose

        contains_c_extension(os)  # False
        contains_c_extension(et)  # False

        contains_c_extension(numpy)  # True -- different from is_c_extension()
        contains_c_extension(numpy_multiarray)  # True

        contains_c_extension(arrow)  # False

        contains_c_extension(alembic)  # False
        contains_c_extension(alembic, include_external_imports=True)  # True
        # ... this example shows that Alembic imports hashlib, which can import
        #     _hashlib, which is a C extension; however, that doesn't stop us (for
        #     example) installing Alembic on a machine with no C compiler

        contains_c_extension(django)

    """  # noqa
    assert inspect.ismodule(module), f'"{module}" not a module.'

    if seen is None:  # only true for the top-level call
        seen = []  # type: List[ModuleType]
    if module in seen:  # modules can "contain" themselves
        # already inspected; avoid infinite loops
        return False
    seen.append(module)

    # Check the thing we were asked about
    is_c_ext = is_c_extension(module)
    if verbose:
        log.info(f"Is module {module!r} a C extension? {is_c_ext}")
    if is_c_ext:
        return True
    if is_builtin_module(module):
        # built-in, therefore we stop searching it
        return False

    # Now check any children, in a couple of ways

    top_level_module = seen[0]
    top_path = os.path.dirname(top_level_module.__file__)

    # Recurse using dir(). This picks up modules that are automatically
    # imported by our top-level model. But it won't pick up all submodules;
    # try e.g. for django.
    for candidate_name in dir(module):
        candidate = getattr(module, candidate_name)
        # noinspection PyBroadException
        try:
            if not inspect.ismodule(candidate):
                # not a module
                continue
        except Exception:
            # e.g. a Django module that won't import until we configure its
            # settings
            log.error(f"Failed to test ismodule() status of {candidate!r}")
            continue
        if is_builtin_module(candidate):
            # built-in, therefore we stop searching it
            continue

        candidate_fname = getattr(candidate, "__file__")
        if not include_external_imports:
            if os.path.commonpath([top_path, candidate_fname]) != top_path:
                if verbose:
                    log.debug(f"Skipping, not within the top-level module's "
                              f"directory: {candidate!r}")
                continue
        # Recurse:
        if contains_c_extension(
                module=candidate,
                import_all_submodules=False,  # only done at the top level, below  # noqa
                include_external_imports=include_external_imports,
                seen=seen):
            return True

    if import_all_submodules:
        if not is_module_a_package(module):
            if verbose:
                log.debug(f"Top-level module is not a package: {module!r}")
            return False

        # Otherwise, for things like Django, we need to recurse in a different
        # way to scan everything.
        # See https://stackoverflow.com/questions/3365740/how-to-import-all-submodules.  # noqa
        log.debug(f"Walking path: {top_path!r}")
        try:
            for loader, module_name, is_pkg in pkgutil.walk_packages([top_path]):  # noqa
                if not is_pkg:
                    log.debug(f"Skipping, not a package: {module_name!r}")
                    continue
                log.debug(f"Manually importing: {module_name!r}")
                # noinspection PyBroadException
                try:
                    candidate = loader.find_module(module_name)\
                        .load_module(module_name)  # noqa
                except Exception:
                    # e.g. Alembic "autogenerate" gives: "ValueError: attempted
                    # relative import beyond top-level package"; or Django
                    # "django.core.exceptions.ImproperlyConfigured"
                    log.error(f"Package failed to import: {module_name!r}")
                    continue
                if contains_c_extension(
                        module=candidate,
                        import_all_submodules=False,  # only done at the top level  # noqa
                        include_external_imports=include_external_imports,
                        seen=seen):
                    return True
        except Exception:
            log.error("Unable to walk packages further; no C extensions "
                      "detected so far!")
            raise

    return False


# noinspection PyUnresolvedReferences,PyTypeChecker
def test() -> None:
    import _elementtree as et

    import arrow
    import alembic
    import django
    import django.conf
    import numpy
    import numpy.core.multiarray as numpy_multiarray

    log.info(f"contains_c_extension(os): "
             f"{contains_c_extension(os)}")  # False
    log.info(f"contains_c_extension(et): "
             f"{contains_c_extension(et)}")  # False

    log.info(f"is_c_extension(numpy): "
             f"{is_c_extension(numpy)}")  # False
    log.info(f"contains_c_extension(numpy): "
             f"{contains_c_extension(numpy)}")  # True
    log.info(f"contains_c_extension(numpy_multiarray): "
             f"{contains_c_extension(numpy_multiarray)}")  # True  # noqa

    log.info(f"contains_c_extension(arrow): "
             f"{contains_c_extension(arrow)}")  # False

    log.info(f"contains_c_extension(alembic): "
             f"{contains_c_extension(alembic)}")  # False
    log.info(f"contains_c_extension(alembic, include_external_imports=True): "
             f"{contains_c_extension(alembic, include_external_imports=True)}")  # True  # noqa
    # ... this example shows that Alembic imports hashlib, which can import
    #     _hashlib, which is a C extension; however, that doesn't stop us (for
    #     example) installing Alembic on a machine with no C compiler

    django.conf.settings.configure()
    log.info(f"contains_c_extension(django): "
             f"{contains_c_extension(django)}")  # False


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)  # be verbose
    test()

Answer 2

First, I don't think this is at all useful. It's very common for modules to be pure-Python wrappers around a C extension module—or, in some cases, pure-Python wrappers around a C extension module if it's available, or a pure Python implementation if not.

For some popular third-party examples: numpy is pure Python, even though everything important is implemented in C; bintrees is pure Python, even though its classes may all be implemented either in C or in Python depending on how you build it; etc.

And this is true in most of the stdlib from 3.2 on. For example, if you just import pickle, the implementation classes will be built in C (what you used to get from cpickle in 2.7) in CPython, while they'll be pure-Python versions in PyPy, but either way pickle itself is pure Python.

---

But if you do want to do this, you actually need to distinguish three things:

• Built-in modules, like sys.
• C extension modules, like 2.x's cpickle.
• Pure Python modules, like 2.x's pickle.

And that's assuming you only care about CPython; if your code runs in, say, Jython, or IronPython, the implementation could be JVM or .NET rather than native code.

You can't distinguish perfectly based on __file__, for a number of reasons:

• Built-in modules have no __file__ at all. (This is documented in a few places—e.g., the Types and members table in the inspect docs.) Note that if you're using something like py2app or cx_freeze, what counts as "built-in" may be different from a standalone installation.
• A pure-Python module may have a .pyc/.pyo file without having a .py file in a distributed app.
• A module in a a package installed as a single-file egg (which is common with easy_install, less so with pip) will have either a blank or useless __file__.
• If you build a binary distribution, there's a good chance your whole library will be packed in a zip file, causing the same problem as single-file eggs.

---

In 3.1+, the import process has been massively cleaned up, mostly rewritten in Python, and mostly exposed to the Python layer.

So, you can use the importlib module to see the chain of loaders used to load a module, and ultimately you'll get to BuiltinImporter (builtins), ExtensionFileLoader (.so/.pyd/etc.), SourceFileLoader (.py), or SourcelessFileLoader (.pyc/.pyo).

You can also see the suffixes assigned to each of the four, on the current target platform, as constants in importlib.machinery. So, you could check that the any(pathname.endswith(suffix) for suffix in importlib.machinery.EXTENSION_SUFFIXES)), but that won't actually help in, e.g., the egg/zip case unless you've already traveled up the chain anyway.

---

The best heuristics anyone has come up with for this are the ones implemented in the inspect module, so the best thing to do is to use that.

The best choice will be one or more of getsource, getsourcefile, and getfile; which is best depends on which heuristics you want.

A built-in module will raise a TypeError for any of them.

An extension module ought to return an empty string for getsourcefile. This seems to work in all the 2.5-3.4 versions I have, but I don't have 2.4 around. For getsource, at least in some versions, it returns the actual bytes of the .so file, even though it should be returning an empty string or raising an IOError. (In 3.x, you will almost certainly get a UnicodeError or SyntaxError, but you probably don't want to rely on that…)

Pure Python modules may return an empty string for getsourcefile if in an egg/zip/etc. They should always return a non-empty string for getsource if source is available, even inside an egg/zip/etc., but if they're sourceless bytecode (.pyc/etc.) they will return an empty string or raise an IOError.

The best bet is to experiment with the version you care about on the platform(s) you care about in the distribution/setup(s) you care about.

Answer 3

While Cecil Curry's answer works (and was very informative, as was abarnert's, I might add) it will return False for the "top level" of a module even if it includes sub-modules that use the C extension (e.g. numpy vs. numpy.core.multiarray).

While probably not as robust as it could be, the following is working for my use current use cases:

def is_c(module):

    # if module is part of the main python library (e.g. os), it won't have a path
    try:
        for path, subdirs, files in os.walk(module.__path__[0]):

            for f in files:
                ftype = f.split('.')[-1]
                if ftype == 'so':
                    is_c = True
                    break
        return is_c

    except AttributeError:

        path = inspect.getfile(module)
        suffix = path.split('.')[-1]

        if suffix != 'so':

            return False

        elif suffix == 'so':

            return True

is_c(os), is_c(im), is_c(et), is_c_extension(ma), is_c(numpy)
# (False, False, True, True, True)

Answer 4

tl;dr

See the "In Search of Perfection" subsection below for the well-tested answer.

As a pragmatic counterpoint to abarnert's helpful analysis of the subtlety involved in portably identifying C extensions, Stack Overflow Productions™ presents... an actual answer.

The capacity to reliably differentiate C extensions from non-C extensions is incredibly useful, without which the Python community would be impoverished. Real-world use cases include:

• Application freezing, converting one cross-platform Python codebase into multiple platform-specific executables. PyInstaller is the standard example here. Identifying C extensions is critical to robust freezing. If a module imported by the codebase being frozen is a C extension, all external shared libraries transitively linked to by that C extension must be frozen with that codebase as well. ^{Shameful confession: I contribute to PyInstaller.}
• Application optimization, either statically to native machine code (e.g., Cython) or dynamically in a just-in-time manner (e.g., Numba). For self-evident reasons, Python optimizers necessarily differentiate already compiled C extensions from uncompiled pure-Python modules.
• Dependency analysis, inspecting external shared libraries on behalf of end users. In our case, we analyze a mandatory dependency (Numpy) to detect local installations of this dependency linking against non-parallelized shared libraries (e.g., the reference BLAS implementation) and inform end users when this is the case. Why? Because we don't want the blame when our application underperforms due to improper installation of dependencies over which we have no control. ^{Bad performance is your fault, hapless user!}
• Probably other essential low-level stuff. Profiling, maybe?

We can all agree that freezing, optimization, and minimizing end user complaints are useful. Ergo, identifying C extensions is useful.

The Disagreement Deepens

I also disagree with abarnert's penultimate conclusion that:

The best heuristics anyone has come up with for this are the ones implemented in the inspect module, so the best thing to do is to use that.

No. The best heuristics anyone has come up with for this are those given below. All stdlib modules (including but not limited to inspect) are useless for this purpose. Specifically:

• The inspect.getsource() and inspect.getsourcefile() functions ambiguously return None for both C extensions (which understandably have no pure-Python source) and other types of modules that also have no pure-Python source (e.g., bytecode-only modules). Useless.
• importlib machinery only applies to modules loadable by PEP 302-compliant loaders and hence visible to the default importlib import algorithm. Useful, but hardly generally applicable. The assumption of PEP 302 compliance breaks down when the real world hits your package in the face repeatedly. For example, did you know that the __import__() built-in is actually overriddable? ^{This is how we used to customize Python's import mechanism – back when the Earth was still flat.}

abarnert's ultimate conclusion is also contentious:

…there is no perfect answer.

There is a perfect answer. Much like the oft-doubted Triforce of Hyrulean legend, a perfect answer exists for every imperfect question.

Let's find it.

In Search of Perfection

The pure-Python function that follows returns True only if the passed previously imported module object is a C extension: ^{For simplicity, Python 3.x is assumed.}

import inspect, os
from importlib.machinery import ExtensionFileLoader, EXTENSION_SUFFIXES
from types import ModuleType

def is_c_extension(module: ModuleType) -> bool:
    '''
    `True` only if the passed module is a C extension implemented as a
    dynamically linked shared library specific to the current platform.

    Parameters
    ----------
    module : ModuleType
        Previously imported module object to be tested.

    Returns
    ----------
    bool
        `True` only if this module is a C extension.
    '''
    assert isinstance(module, ModuleType), '"{}" not a module.'.format(module)

    # If this module was loaded by a PEP 302-compliant CPython-specific loader
    # loading only C extensions, this module is a C extension.
    if isinstance(getattr(module, '__loader__', None), ExtensionFileLoader):
        return True

    # Else, fallback to filetype matching heuristics.
    #
    # Absolute path of the file defining this module.
    module_filename = inspect.getfile(module)

    # "."-prefixed filetype of this path if any or the empty string otherwise.
    module_filetype = os.path.splitext(module_filename)[1]

    # This module is only a C extension if this path's filetype is that of a
    # C extension specific to the current platform.
    return module_filetype in EXTENSION_SUFFIXES

If it looks long, that's because docstrings, comments, and assertions are good. It's actually only six lines. Eat your elderly heart out, Guido.

Proof in the Pudding

Let's unit test this function with four portably importable modules:

• The stdlib pure-Python os.__init__ module. ^{Hopefully not a C extension.}
• The stdlib pure-Python importlib.machinery submodule. ^{Hopefully not a C extension.}
• The stdlib _elementtree C extension.
• The third-party numpy.core.multiarray C extension.

To wit:

>>> import os
>>> import importlib.machinery as im
>>> import _elementtree as et
>>> import numpy.core.multiarray as ma
>>> for module in (os, im, et, ma):
...     print('Is "{}" a C extension? {}'.format(
...         module.__name__, is_c_extension(module)))
Is "os" a C extension? False
Is "importlib.machinery" a C extension? False
Is "_elementtree" a C extension? True
Is "numpy.core.multiarray" a C extension? True

All's well that ends.

How to do this?

The details of our code are quite inconsequential. Very well, where do we begin?

1. If the passed module was loaded by a PEP 302-compliant loader (the common case), the PEP 302 specification requires the attribute assigned on importation to this module to define a special __loader__ attribute whose value is the loader object loading this module. Hence:
   1. If this value for this module is an instance of the CPython-specific importlib.machinery.ExtensionFileLoader class, this module is a C extension.
2. Else, either (A) the active Python interpreter is not the official CPython implementation (e.g., PyPy) or (B) the active Python interpreter is CPython but this module was not loaded by a PEP 302-compliant loader, typically due to the default __import__() machinery being overridden (e.g., by a low-level bootloader running this Python application as a platform-specific frozen binary). In either case, fallback to testing whether this module's filetype is that of a C extension specific to the current platform.

Eight line functions with twenty page explanations. Thas just how we rolls.