cpython

located nonempty set of objects with a particular member object type, called the element type."

s to stack indices, and the variables are accessed through those. Just like C can access arguments passed on the stack using constant offsets from the stack pointer. Here is the code for the normal call fast path: https://github.com/python/cpython/blob/62be74290aca26d16f3f5... f = _PyFrame_New_NoTrack(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = f->f_localsplus; for (i = 0; i < nargs; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); This sets up a stack frame for the callee (on the heap, yes), then copies the arguments (which are pointers to PyObject) into slots in that stack frame numbered consecutively from 0. Access to these arguments inside the callee is via the LOAD_FAST bytecode instruction: https://github.com/python/cpython/blob/master/Python/ceval.c... case TARGET(LOAD_FAST): { PyObject *value = GETLOCAL(oparg); ... which uses the GETLOCAL macro: https://github.com/python/cpython/blob/master/Python/ceval.c... #define GETLOCAL(i) (fastlocals[i]) Nowhere are name-value bindings allocated dynamically in this normal case. Python does perform dictionary manipulation for keyword args, but not for positional ones. For normal positional arguments, the mechanism is exactly equivalent to a C (or whatever) compiler pushing arguments onto the stack. Local variables are exactly what you claimed that they were not, namely names for storage locations (stack slots). I'm done with this thread now.

ere's not much to do about it now. However - creating, sharing and installing packages in python is THE problem. And I'm not saying it's an unsolvable problem - that's what makes it so frustrating! You have a thriving, healthy community of package maintainers creating amazing libraries that allow regular engineers like me to get work done out of the box. And in many ways, as other posters have pointed out, python paved the way for making this experience better than the hell that is c++ package management. Sure. But the current user experience of trying to give somebody an application that happens to be written in python? The story of how you take some python code you wrote, bottle it up, and make it work on somebody else's system? And the story for how they take that code and make it available to themselves on their own system? It. is. atrocious. Which one of these tools, concepts, commands and filetypes do I need to care about: package, module, egg, wheel, bdist, sdist, distutils, setuptools, easy_install, pip, pipenv, poetry, PEX, PyInstaller, py2exe, PyPI, conda, miniconda, anaconda, virtualenv, venv, requirements.txt, setup.py, pyproject.toml, pipfile, site-packages, dist-packages, $PYTHONPATH or motherfucking .pth files??!?! Ever been curious how sys.path gets populated at startup? Gaze into the darkness: https://github.com/python/cpython/blob/master/PC/getpathp.c And to anyone saying "Just use a virtualenv": that is so, so not an answer to this eldritch horror. Let me ask you this - when you installed chrome, did you have to create a new virtualenv? No. If you want to effectively distribute code to lots of people, you need more than a god damn virtualenv. The whole world isn't just scientists tooling around in sandboxes.

f specialized data classes which store Python bytecode and provide the framework for its execution, like PyCodeObject, PyFrameObject and PyFunctionObject. Note that these don't know anything about how the bytecode is produced, nor about how to actually execute it. But they do know about things like local variables (so PyCodeObject will store the list of locals, and PyFrameObject will allocate space for them), so Python-the-language starts creeping in here. - The parser which produces AST (which is itself a bunch of Python objects), and the bytecode compiler that produces PyCodeObjects out of that AST. - And finally, the actual interpreter, that ties it all together by providing semantics for the bytecode contained in PyCodeObjects. This layering is even visible in the Python source itself (https://github.com/python/cpython/tree/v3.7.0) - the first three things live under ./Objects in the source tree, and the parser and the interpreter is under ./Python (with some bits under ./Grammar and ./Parser). So, roughly speaking, ./Objects is the object model, and ./Python is the language proper. The headers are interdependent, unfortunately, but it's not that hard to break them apart if anyone cared to.

python/blob/86b89916d1b0a26c1e77f... multiprocessing Queue serializing data to send it using that Pipe https://github.com/python/cpython/blob/86b89916d1b0a26c1e77f...