Published: April 2, 2017
by Tobias Pleyer
Tags: python, import

A closer look at Python’s import mechanism

This is the first in a series of articles about Python’s import mechanism. I am planning to give a detailed look on the entire call stack, from the very low level interface to the very high level interface and its manipulation facilities.

Outline of the Series

  1. What is a module?
  2. The low level import interface
  3. Bootstrapping and the importlib module
  4. How to customize Python’s import behaviour

What is a module to Python?

Before we can speak of the import process itself, we first have to know what the end result is: a Python module. The module will be the accessible interface on the Python level to what has been imported.

Remark: All my findings, references and source code depictions are based on the CPython GitHub repository checked out at tag v3.6.1rc1.

The direct answer to this question is: Like everything in Python, a module is an object. An instance of PyModuleObject (defined in Objects/moduleobject.c) to be more precise. Said a little bit over simplified, a module is nothing more than a little bit book keeping around a dictionary (also known as hash map).

One of the nice things about Python is that it is not trying to hide its internals. Surely some of the low level C interface gets lost on the way, but where possible things are made available almost unchanged. The internal md_dict member of the PyModuleObject object, which is the dictionary underneath a module holding its content, is exposed as the __dict__ attribute of the module:

And can be accessed as soon as the module has been imported:

>>> import itertools
>>> itertools.__dict__
{'__loader__': <class '_frozen_importlib.BuiltinImporter'>, 'combinations_with_replacement': <class 'itertools.combinations_with_replacement'>, '__spec__': ModuleSpec(name='itertools', loader=<class '_frozen_importlib.BuiltinImporter'>, origin='built-in'), 'groupby': <class 'itertools.groupby'>, 'islice': <class 'itertools.islice'>, 'combinations': <class 'itertools.combinations'>, '__name__': 'itertools', 'cycle': <class 'itertools.cycle'>, 'filterfalse': <class 'itertools.filterfalse'>, '_grouper': <class 'itertools._grouper'>, '__package__': '', 'tee': <built-in function tee>, '__doc__': 'Functional tools for creating and using iterators.\n\nInfinite iterators:\ncount(start=0, step=1) --> start, start+step, start+2*step, ...\ncycle(p) --> p0, p1, ... plast, p0, p1, ...\nrepeat(elem [,n]) --> elem, elem, elem, ... endlessly or up to n times\n\nIterators terminating on the shortest input sequence:\naccumulate(p[, func]) --> p0, p0+p1, p0+p1+p2\nchain(p, q, ...) --> p0, p1, ... plast, q0, q1, ... \nchain.from_iterable([p, q, ...]) --> p0, p1, ... plast, q0, q1, ... \ncompress(data, selectors) --> (d[0] if s[0]), (d[1] if s[1]), ...\ndropwhile(pred, seq) --> seq[n], seq[n+1], starting when pred fails\ngroupby(iterable[, keyfunc]) --> sub-iterators grouped by value of keyfunc(v)\nfilterfalse(pred, seq) --> elements of seq where pred(elem) is False\nislice(seq, [start,] stop [, step]) --> elements from\n       seq[start:stop:step]\nstarmap(fun, seq) --> fun(*seq[0]), fun(*seq[1]), ...\ntee(it, n=2) --> (it1, it2 , ... itn) splits one iterator into n\ntakewhile(pred, seq) --> seq[0], seq[1], until pred fails\nzip_longest(p, q, ...) --> (p[0], q[0]), (p[1], q[1]), ... \n\nCombinatoric generators:\nproduct(p, q, ... [repeat=1]) --> cartesian product\npermutations(p[, r])\ncombinations(p, r)\ncombinations_with_replacement(p, r)\n', 'takewhile': <class 'itertools.takewhile'>, 'permutations': <class 'itertools.permutations'>, 'product': <class 'itertools.product'>, 'zip_longest': <class 'itertools.zip_longest'>, 'chain': <class 'itertools.chain'>, 'count': <class 'itertools.count'>, 'compress': <class 'itertools.compress'>, 'starmap': <class 'itertools.starmap'>, '_tee_dataobject': <class 'itertools._tee_dataobject'>, 'accumulate': <class 'itertools.accumulate'>, 'repeat': <class 'itertools.repeat'>, 'dropwhile': <class 'itertools.dropwhile'>, '_tee': <class 'itertools._tee'>}

That’s it about modules. As far as Python is concerned they are just objects of a certain type. When dealing with modules on the Python level, i.e. in .py files, they are nothing but objects holding attributes and one builtin function, __dir__. The contents of the module - functions, variables and sub-modules, are stored in __dict__ in the process of importing the module. The details of that will be the focus of the next part of the series.

How to write a module?

Basically a module can have one of two forms:

  1. Any pure Python file (.py extension)
  2. C/C++ extension code (.so extension)

In the first case Python will open the file, parse its content and thereby “learn” what the module is about and what its content is. Important to know is that Python will actually execute the code contained in the module! That is the reason why files that are meant to be used as scripts and as modules try to split these two functionalities. The common idiom used for that is this:

The construct __name__ == ‘__main__’ is a guard. When Python executes a script, this script is more or less the main driving script ( __main__ ). Python will set the __name__ variable accordingly. If, on the other hand, the file is just imported and the portion of the code below the if construct will never be reached, thus never be executed.

The second case is different. The content of the module will be C/C++ code, not native Python syntax. This means Python cannot tell how to use that code as is. What Python needs is a “recipe” that help it integrate the code in the Python eco system. Most importantly it needs to know how to initialize it. This is done via an import hook. Every module written in C/C++ needs to export one function, PyInit_modulename, where modulename is the name used in the import:

This function is the only means of communication between the import mechanism and the module to be imported. Python expects this function to return a pointer to a PyObject, which will be casted to the module in the process. In order for this function to return a valid module it needs to make use of a few helping constructs. The best is to have a look at an example. Directly taken from the collections module (Modules/_collectionsmodule.c):

/* module level code ********************************************************/

"High performance data structures.\n\
- deque:        ordered collection accessible from endpoints only\n\
- defaultdict:  dict subclass with a default value factory\n\

static struct PyMethodDef module_functions[] = {
    {"_count_elements", _count_elements,    METH_VARARGS,   _count_elements_doc},
    {NULL,       NULL}          /* sentinel */

static struct PyModuleDef _collectionsmodule = {
    PyModuleDef_HEAD_INIT,  /* m_base */
    "_collections",         /* m_name */
    module_doc,             /* m_doc */
    -1,                     /* m_size */
    module_functions,       /* m_methods */
    NULL,                   /* m_slots */
    NULL,                   /* m_traverse */
    NULL,                   /* m_clear */
    NULL                    /* m_free */

    PyObject *m;

    m = PyModule_Create(&_collectionsmodule);
    if (m == NULL)
        return NULL;

    if (PyType_Ready(&deque_type) < 0)
        return NULL;
    PyModule_AddObject(m, "deque", (PyObject *)&deque_type);

    defdict_type.tp_base = &PyDict_Type;
    if (PyType_Ready(&defdict_type) < 0)
        return NULL;
    PyModule_AddObject(m, "defaultdict", (PyObject *)&defdict_type);

    PyModule_AddObject(m, "OrderedDict", (PyObject *)&PyODict_Type);

    if (PyType_Ready(&dequeiter_type) < 0)
        return NULL;
    PyModule_AddObject(m, "_deque_iterator", (PyObject *)&dequeiter_type);

    if (PyType_Ready(&dequereviter_type) < 0)
        return NULL;
    PyModule_AddObject(m, "_deque_reverse_iterator", (PyObject *)&dequereviter_type);

    return m;

As the comment string in the code example already says, this is the module level code. As can be seen above, every module needs to have a definition of its methods (PyMethodDef) and itself (PyModuleDef). The PyMethodDef structure instructs which functions should be available in the module. The PyModuleDef structure holds important information about the module, like its name, the documentation string its functions and so on. I added comments to the PyModuleDef structure above to show the names of the structure fields. A module only needs to define those that are needed. The attributes of the module, e.g. the class OrderedDict, are added in the PyInit_ function via the helper function PyModule_AddObject, which results in a new entry in the internal hash table (dictionary) of the module (see above).

We now can conclude this first part of the series and concentrate on the import procedure itself in the following parts.