python-to-binary

py2bin turns Python into machine code using nothing but the Python standard library. No Cython, Nuitka, mypyc, Rust, C, C++, PyInstaller, PPCI, bootloader, assembler, linker or SDK - and no gcc or clang at any point. The only thing a build needs is an interpreter.

pip install python-to-binary
py2bin compile-capi app.py --target darwin-arm64 -o app

Source, issues and the full documentation: https://github.com/yu314-coder/python_to_binary

0.8.9 is a large correctness and speed release. Eleven measured operations got faster - in on a list and a try that does not raise now beat the interpreter - and several wrong answers were found and fixed on the way, including a __bool__ that raised being read as true and a memory leak in every try. See Release notes below.

Platforms

What compile-capi - the tier that turns your program into machine code that drives CPython - can target today.

📦 marks a target a complete third-party GUI application has been built for and run on real hardware rather than only a corpus: ManimStudio - 10,100 lines, pywebview, Pillow, manim - as a Windows x86-64 .exe, a macOS arm64 app and a Linux arm64 executable, all three working. iOS is not a py2bin target; that app's iPad/iPhone build is a separate native Swift port embedding CPython for arm64-iphoneos, and is not this compiler's work.

An iPad can build all three of them, though. py2bin has no compiler, assembler or linker behind it - it writes the machine code and the Mach-O, PE and ELF itself - so a cross-build is arithmetic and file writing, which a sandbox allows. Run inside the embedded Python of the iPad app:

Nothing on that path needs subprocess, ctypes, fork or exec - every target still compiles with all of them removed from the interpreter, which is a test. The iPad is a build machine and nothing else: an App Store app cannot exec an arbitrary binary, so there is no such thing as a py2bin artifact that runs on the tablet that made it.

Each working target is held to the same standard: an 889-program corpus is compiled for it and every program's output and exit code compared against CPython's. macOS agrees on 878 and differs on 7; a 100-program slice run through Wine agrees on 93 and differs on 5. The differences are the same on every platform and are inherent rather than open - CPython's "Did you mean" needs a Python frame to suggest from, and the repr of a compiled function really is a builtin function's.

The native tier (py2bin compile, no CPython at all) targets all six.

What it guarantees

Names the program binds are the program's. Integers do not stop at 64 bits. Floats stay floats and -0.0 stays distinct. Evaluation order is Python's, and a __len__ or __getitem__ runs exactly once. Exceptions - class, message, __cause__, traceback, what except matches - are the interpreter's.

Where it knowingly differs: a generator expression is built eagerly; builtins.len and builtins.str replaced at run time are not observed (print is); attribute access is slower than the interpreter's, because matching it means reading ob_type out of an object this treats as opaque - which is what lets one binary run against a CPython it was not built against.

Inside an except block, the exception being handled is not on record. sys.exc_info() answers None there, and an exception raised from a handler gets no __context__ - so a traceback loses its "during handling of the above exception" chain. Both come from one thing: the handler takes the exception rather than also setting it as the thread's handled exception, and CPython's PyErr_SetHandledException is not in the vetted table. The exception itself, its type, message, __cause__ from an explicit raise ... from, and what except matches are all correct; it is the implicit chaining and the introspection that are missing.

A call that names an argument is still slow. helper(t, step=1) measured 0.13x the interpreter and is now 0.18x. The caller no longer allocates a tuple and a dict and rebuild the keyword's name for every call - vectorcall carries the values in one array with a tuple of names built at start-up beside it - but the callee still turns kwnames back into a dict and looks each parameter up in it. Matching the names directly against the tuple, which is what CPython does, is what would close the rest, and it is not done yet.

The paths through it

Three ways to turn a program into an artifact. They trade the same three things against each other, and which one you want depends on which you care about.

compile is the fastest and the smallest. Python AST → py2bin IR → optimizer → handwritten x86-64/ARM64 → ELF, PE or Mach-O. There is no interpreter in the artifact and none on the machine: 14× faster than CPython on that loop, in 32 KB that runs on a bare system. You pay for it in what it will accept - integers, floats, strings, control flow, your own functions - and it will not import a package at all.

freeze is the most complete. It ships your program beside an interpreter that runs it, so NumPy, Torch and a GUI toolkit all work exactly as they do now. Nothing is translated, so nothing is faster; the artifact is the largest of the three because an interpreter and every dependency are inside it.

compile-capi is the middle, and the one under active work. It translates ordinary Python into C that drives the CPython C API, then compiles that C with py2bin's own C compiler - the tier Nuitka occupies, with Nuitka's dependency on clang removed. Almost the whole language goes through, and anything the linked interpreter can import still works, so a real application with pywebview and Pillow compiles. Integer loops beat CPython because their locals are held in registers, and so is float arithmetic; attribute and method access are slower, because each is a real C-API call where the interpreter has a per-site cache that reads the object's internals directly. The per-feature table is below.

The loop above is deliberately unkind to compile-capi: its accumulator is compared against a parameter, which the register analysis cannot claim, so the fast path is off. On a loop it can claim, the same tier is 1.17× faster than CPython; a float loop, once the worst row at 0.32×, is 1.06×; and a call to a small helper is 2.10×, because the call stops existing and the loop around it becomes machine arithmetic.

Using it

pip install python-to-binary

Bundling a real application into a macOS .app that carries its own interpreter and packages:

py2bin compile-capi app.py --target darwin-arm64 \
  --app --name "My App" --icon icon.icns \
  --embed-python --site ../Resources/site-packages \
  --bundle-site /path/to/venv/lib/python3.14/site-packages \
  --prune-unused --zip-stdlib --include web \
  -o dist/MyApp.app --clean

--include PATH carries a file or directory beside the program - a web/ folder, templates, anything it opens at runtime rather than imports. make finds those on its own; this is how to name one it did not.

How it works

Nothing wraps a toolchain; each stage is a module you can read.

capi_emit.py        Python AST  ->  C that calls the CPython C API
capi_ints.py          which locals may live in a machine register
c_preprocessor.py   #include, macros, conditionals
c_frontend.py       C  ->  py2bin IR
native/ir.py        the IR itself
native/optimizer.py constant folding, dead code, write merging
native/arm64.py     IR  ->  ARM64 instructions
native/x86_64.py    IR  ->  x86-64, System V and Microsoft x64
native/formats/     Mach-O, PE32+, ELF
freezer.py          bundling: interpreter, packages, pruning, archives
cabi.py             the vetted CPython entry points

So compile-capi is five stages, all of them in this package: capi_emit → c_preprocessor → c_frontend → native.x86_64/native.arm64 → native.formats.macho/pe.

There is no import ctypes anywhere on that path, which a test asserts by compiling in a fresh interpreter and listing what got loaded. ctypes is standard library and would pass an imports-only-stdlib check, but it pulls in subprocess - and there are Pythons where a subprocess is not something a program may have.

Three questions, and nothing to type

py2bin make

It asks which file is the program, which machine it is for, and what shape it should take. Everything else is found or downloaded rather than typed - the other .py files beside it, the libraries it imports, an interpreter for the target, web/ and assets/ if they are there, and an icon if one is.

Two other ways in ship in the source distribution: build.py runs a clone with nothing installed, and get-py2bin.py fetches py2bin for a machine with neither - falling back to curl or wget where Python's own networking is kept away from the interpreter.

Bundling for Windows

The executable, the interpreter and the packages share one directory, and one command assembles it:

py2bin compile-capi app.py --target windows-x86_64 --crash-log \
  --runtime /path/to/embeddable-cpython \
  --bundle-site /path/to/site-packages \
  -o dist/win/MyApp.exe

Neither needs a path on this machine: --auto-fetch downloads the interpreter for the target, and --fetch-package NAME downloads and unpacks a project's wheel. Both are checked against a published hash and cached.

--bundle-site copies packages into Lib\site-packages and names it on the interpreter's path, which has to happen together: the embeddable CPython ships a pythonXY._pth naming exactly two places, and once it exists sys.path is those two and nothing else. Packages are invisible until the path file names them, and the program reports ModuleNotFoundError for a directory plainly on disk - silently, if it is windowed.

A wheel must also match the interpreter's ABI, not only its version: cp314 and cp314t differ by a character, the second is for the free-threaded build, and only one loads.

macOS bundles, signing and disk images

--app writes a .app; --embed-python makes it carry its own interpreter, so it runs on a Mac with no Python installed. The bundle is signed and sealed as the last step of the build, once everything is in place, and codesign --verify --deep --strict exits 0 on the result.

The signature is ad-hoc - no Apple Developer ID, no notarisation, since either needs a paid account and Apple's own tooling. That only matters to Gatekeeper, which inspects apps carrying a quarantine flag: copied from a USB stick there is none, downloaded through a browser there is, and then the app needs one trip through System Settings → Privacy & Security → Open Anyway.

--dmg writes a mountable disk image beside the bundle. No hdiutil is involved, because nothing in this library may reach for a subprocess; the filesystem is written byte by byte as ISO 9660 with Joliet, which macOS mounts with files executable - what an .app needs in order to launch.

py2bin compile-capi app.py --app --dmg -o dist/MyApp.app

What compile-capi supports

Every row is checked by compiling it, running it, running the same source under CPython, and requiring identical stdout and exit status.

A generator cannot be compiled the way the rest is - a C function has one entry and its locals die with its frame, so it cannot stop in the middle of itself. It is turned inside out instead: the body is cut into blocks at each yield, the blocks are numbered, and the function becomes a class whose __next__ dispatches on which block to run next, with the locals as attributes because they have to outlive a return. The class is then compiled by the machinery that already compiles classes, so there is no new C and nothing interpreted at run time.

That covers yield as a statement or as a value, send, yield from, straight-line code, if/else, while, for, break, continue and a bare return. next(g) is g.send(None) here as it is in the protocol, and yield from is written as the loop it is before the body is cut - which forwards iteration but not a send into the sub-generator, so a yield from whose value is used is refused rather than quietly answering None.

A try/except around a yield works, and the way it works is worth saying, because "the handler has to survive the suspension" sounds like it needs something the cut cannot give. It does not: an exception can only be raised while a block is running, so each block of the guarded region carries the handler and it is re-established on every entry rather than having to persist across one.

await is the same machine with a second name on it. Awaiting an object with __await__ means delegating to the iterator it answers with, and a state machine is one - so an async def compiles to the same class, plus __await__ returning itself, and await x is PEP 380's expansion of yield from x.__await__(). A real event loop then drives it through send exactly as it drives a coroutine: asyncio.run, asyncio.sleep and asyncio.gather all work on compiled coroutines.

A finally around a yield works, and so do with, async for and async with - see How finally and with are handled above for how, and for the two shapes that are still refused.

A refusal is a file:line:col error, never a silent approximation. On an 889-program corpus, 878 programs produce byte-identical output to CPython; the 7 that differ do so inherently (CPython's "Did you mean" needs a Python frame, "v" is "v" depends on interning) and 4 are refused outright.

How fast each one is

Measured on an Apple M4 (10 cores - 4 performance, 6 efficiency - 24 GB, macOS 27.0, arm64) against CPython 3.14.3, python.org framework build - the interpreter these binaries actually bind, which is not the same as whichever python3 is first on PATH and does not perform alike.

300,000 iterations per row, nine fresh processes each, median taken, timing only the hot loop so neither column pays for start-up. Higher is better. The harness and cases are in benchmarks/ in the repository.

Ratios are computed from the unrounded timings, so dividing the millisecond figures as shown gives a slightly different number in the last decimal.

One recorded run, the one in benchmarks/last-run.json in the repository. Repeat it and the figures move by a few per cent either way - which rows beat the interpreter, and by roughly how much, does not.

Where those numbers came from

Fourteen of the twenty-seven rows sit at 0.80× or better and six beat the interpreter outright. Most did not a short while ago.

The largest wins were not optimisations but mistakes being removed - a wrapper written in Python on the method path, a float analysis that did not exist, a string rebuilt on every iteration of a loop. And they all have one shape: something stops happening. Adding a cheap test in order to skip expensive work inside the interpreter was tried five times and measured flat or slower every time, because the extra call through the import table cost more than it saved.

Arithmetic loops win because a local the analysis picks out is held in a machine register - a long long for an integer, a double for a float - with an overflow check that falls back to unbounded arithmetic when an integer leaves the word. That is what CPython's specialising interpreter does, and doing anything less was what made this tier slower than not compiling at all.

Everything else loses, by a factor that tracks how many C-API calls the operation costs. Each one is a real call with the reference-count discipline around it, where the interpreter's specialised bytecode does the same work inline.

Method calls used to be far worse than that pattern predicted - 21×, where everything else paid 2-4×. Every compiled method was wrapped in functools.partialmethod to make it bind, and that wrapper's __get__ is written in Python, so each obj.method ran interpreted code before the call could start. CPython's own instancemethod does the same binding in C, and both are now within the general pattern.

Raising a class

raise ValueError names a class and raise ValueError("x") an instance, and the two want different things from the C API - asking type() for the class of a class answers type, the metaclass. The plainest raise a program can write therefore ended in SystemError: exception <class 'type'> is not a BaseException subclass, in compiled code of every kind. Fixed.

How finally and with are handled

A generator becomes a class with __next__, not a generator: never closed, never finalised by the collector, so the only ways out of a protected region are the ones the rewriter can see. The cleanup is not a real finally: - a yield returns from __next__, so one would fire on every suspension. It is attached to the raising path as a handler that runs it and re-raises, while the ordinary path jumps to a block holding the same cleanup. with expands into the try it stands for and takes that path, with __exit__ looked up once on the type and suppression honoured.

async for and async with take the same route, each written out as what it stands for. A return here is signalled by raising StopIteration, so the cleanup's handler had to learn to tell the frame leaving from a real failure - otherwise __aexit__ is handed a StopIteration where CPython passes None.

A finally that itself yields works too: the cleanup is a block and a block may suspend, reached the same way from both paths, with whatever was raised waiting in a name until it is done. A break or continue leaving the region runs a copy of the cleanup first, which is what it would have reached had it left the ordinary way.

One file, both ways

On macOS an application is a directory - Finder runs Contents/MacOS/<name> and Gatekeeper reads Contents/Info.plist beside it. Nuitka says so in its own help: --mode=app is "onefile except on macOS where it creates an app bundle". What stays open is how much is inside the bundle, and --onefile folds the payload into the bundle's own executable.

The packed bundle unpacks once into a content-addressed cache and runs from there. A self-extracting single executable is what py2bin builds on Windows and Linux, and Nuitka where it can - on macOS it declines that shape once pyobjc is in the graph, which is any pywebview program.

Measured against Nuitka

manim_app: 10,100 lines, pywebview + Pillow + pyobjc, built both ways on the same machine.

The two bundle tables were taken at 0.8.5 and have not been re-taken. They need the application's own virtualenv staged into wheels, which is not a build this repository can run on its own. Every other figure here was re-measured for 0.8.7.

Whole-process time - start-up included - median of 5, seconds, on the same Apple M4 against Nuitka 4.1.3 --standalone:

Two of those rows finish in under thirty milliseconds, so start-up is a large share of them - a real difference between the three rather than a distortion. The margins that are about generated code are the loops and the calls. This compiler's weaknesses do not show up in a five-loop benchmark at all; for those read the grid above, where method call and instantiation sit at 0.41×. Put a hot loop at module level instead of in a function and the loop advantage goes away, because module-scope names are not narrowed into registers.

What a build costs

Run time is what a user waits for; build memory is what decides whether the build runs at all. py2bin never starts a C toolchain. Peak resident set of the whole build process tree, sampled every 25 ms. Nuitka keeps a ccache and a module cache and py2bin keeps none, so both answers are given - py2bin's column is the same in each.

Cold - a first build, or CI without a warm cache:

Warm - Nuitka's cache in place, which is what a second build gets:

A small build costs a seventh of a warm Nuitka's and a fifteenth of a cold one, which is the whole reason an iPad can run one. The advantage narrows with program size - nothing here streams, so the curve is steeper - but on a cold build it survives to the largest case measured. Only against a warm Nuitka at 3,000 functions does it turn over.

Yes, the C toolchain is counted - it is most of Nuitka's column. The sampler walks the whole process tree and now reports what held the memory, so the total can name its parts rather than be asserted:

At scale it is the linker rather than the compiler that dominates. py2bin starts neither: it writes the object code and the container itself, so its tree is one Python process at every size.

Sampling finer matters here, and the interval was tightened from 25 ms to 10 ms after checking: clang processes are short-lived, and at 25 ms the same build measured 582 MB where 5 ms saw 643 MB. The published figures were understating Nuitka - which flattered it rather than py2bin, but was wrong either way.

Startup, print("x"), median of 13 runs, same M4:

Loops beat both because a local the analysis picks out is held in a register rather than on the heap, with the overflow check that falls back to unbounded arithmetic when it leaves the word. Calls still lose: an argument is boxed at the call and unboxed inside, where the interpreter's specialised call pays neither.

Release notes

Newest first. The full history is in the repository.

0.8.9 - verdicts, borrowed references, and a leak in every try

The largest release so far, and most of it is correctness that turned up while chasing speed. Fourteen of the twenty-seven measured rows sit at 0.80x or better and six beat the interpreter.

What moved. Each of these is the same case before and after; the grid above is the current measurement.

Two rows went the other way on purpose. exception raise/catch gave up 1.06x for 1.00x because the raising path now lifts the exception out so that the non-raising path builds nothing at all, and try spent a while at 0.65x while the leak below was fixed by releasing what it built, before it stopped building it.

A condition wants a verdict, not a value

Four places computed an object and then asked what it meant. if a and b evaluated the whole chain into a Python boolean; if x in xs looked True up by name on the builtins module and handed it to PyObject_IsTrue, when PySequence_Contains had already answered; isinstance(x, C) found the callable and dispatched through it, where PyObject_IsInstance is what the builtin does; 0 < i < n did it once per link.

Each goes straight through now. The short circuit in and/or is the C if guarding the next side, so a side that must not run has no code reached rather than a value discarded. A chained comparison whose operands cost nothing to read twice is rewritten into the and Python says it is, so each link picks up the machine comparison a two-sided one would have had - swapping the call alone measured nothing, because routing the chain through its own emitter had bypassed that path.

A __bool__ that raised was read as true. PyObject_IsTrue answers -1 with an exception set, and -1 is true in C, so a class whose __bool__ raised ran the body of the if and the program exited 0 where CPython stops. The same for a comparison that raised. Every verdict is checked where it is produced now. This was there before any of the work above and is the more important half of it.

Nothing pays for a reference it already holds

PyObject_CallOneArg, PyObject_SetAttr, PyObject_GetItem and PyObject_Vectorcall all borrow what they are given. The single-argument call

• the commonest call shape there is - took a reference and dropped it again, and so did the callable, the object in obj.field = v, the key in d[k] and d[k] = v, and every piece of an f-string. A pooled literal is the safest borrow there is: it lives in a static written once at start-up, so unlike a local it holds at module level too. All borrowed now, on rules that still refuse to borrow a global, because anything a call runs could rebind one.

A call that names an argument

f(a, key=b) built a tuple for the positional part and a dict for the rest, and made the keyword's name from a C string on every call - two allocations and a string build to pass one argument by name. Vectorcall carries the values in one array with their names in a tuple beside it, which is what CPython does and what every compiled function here already accepts.

The callee stopped rebuilding a dict too: without a ** parameter there is nothing to hand leftovers to, so each parameter looks through the names tuple instead, which for the one or two of each a real call has beats an allocation, a hash per entry and a probe per parameter. Functions with **kwargs keep the dict, which is what a dict is for. It is still the worst row measured.

def f(a) called as f(1, b=2) used to run and answer where CPython raises, because a key was only removed from the keywords when there was a ** to hand the rest to - so nothing could tell a keyword that had been taken from one that matched no parameter. The complaints are worded as CPython words them now, including which parameter got two values.

Everything a try was doing that it did not need to

The classes each clause catches were built before the body ran, so every try paid for them whether it needed them or not - and nothing released them where the body raised nothing. except (ValueError, TypeError) builds a fresh tuple each evaluation, so a try in a loop leaked one per turn: 400,000 turns held 40 MB against the interpreter's 15, and 800,000 held 65.

They are built in the handler now, where the body has already raised. Doing that with an exception set is what CPython refuses, so the exception is lifted with PyErr_GetRaisedException and put back once they are built - which is what the match needs anyway. A try that does not raise builds nothing at all, and what is never built cannot be leaked.

Names that were not the program's

A decorator written without the @ - greet = trace(greet) - kept calling the undecorated body, because a module-level def earned a direct C call keyed on the spelling alone. Reading a global above its assignment handed the program a raw NULL instead of raising NameError, and so did a class used above its class. A function that rebound itself through global kept calling its old body. The rule is positional now: a def earns the direct call only when it is the one thing binding that name at module scope, and only where it is already bound - so a function may still call one written below it, and recursion keeps the direct call.

A nested function could not call itself. Its own name is not bound when the capture is taken - the def being compiled is what binds it - so an ordinary nested fact raised NameError on its first recursive call. Mutual recursion between nested functions is refused at build time with an explanation, because capture-by-value cannot express it and failing at run time naming a function written plainly above is the worst way to say so.

Structure

A long function no longer needs a bigger stack frame than a short one. Every intermediate the C lowering needed took a stack slot and never gave it back, so a frame grew with a function's length: about 1,800 statements in one def reached the 512 KB budget and the build was refused, which generated code walks into without doing anything unusual. Forty thousand statements compile now.

Tuple unpacking boxed the length, boxed the expected count twice, ran two PyObject_RichCompare calls and asked PyObject_IsTrue of each - eleven C-API calls and five allocations for one machine comparison - and called tuple() first, allocating a copy per unpack. The length is a machine comparison now and a value that can answer for itself is taken apart where it stands, with tuple() kept for what needs it: a generator has neither length nor index.

Smaller. + on strings known to be exact skips the __add__ dispatch, and exactness composes so a + b + c converts throughout. Branches whose condition is already a constant are removed, in function bodies as well as the entry point - 24 operations across the benchmark suite, which is a small number and is stated rather than implied. The launcher scripts and the bootstrapper's PowerShell fallback quote or pass by environment what they were given, so nothing py2bin writes into a shell is read as shell.

The grid grew, and its headline got worse

Ten shapes were added, taking it from seventeen rows to twenty-seven. They were found by measuring what the suite did not cover, and most were worse than anything already in it - which is why the fraction beating the interpreter fell from seven of seventeen to six of twenty-seven while the number at 0.80x or better rose from ten to fourteen. A grid showing only the shapes a compiler is good at is a grid measuring itself. One old row was also measuring the wrong thing: string concatenation concatenated only literals, which fold at compile time, so the generated C held no concatenation at all.

0.8.8 - archives, and what they may not do

py2bin's safety story is that it never runs what it downloads - no setup.py, no pip, no install hooks. Unpacking is therefore the only moment a hostile wheel or runtime pack acts at all, and it was not being guarded well enough. Reported by a static scan; each was reproduced before it was fixed.

A symbolic link in an archive could write outside the build. Three callers each checked containment by resolving where a member would land and comparing strings. Both halves fail: resolve() runs before extraction, so a link the archive is about to create is not there to be resolved through - esc -> .. followed by esc/passwd passed both members and extractall then followed the link it had just made - and /tmp/outsider starts with /tmp/out. Reproduced on Python 3.11, writing a file outside the destination. Python 3.14 happens to stop it because extractall defaults to the data filter there; this project supports 3.10 upwards, so on most of that range nothing did.

Extraction now happens member by member through one shared implementation (py2bin.archives), never extractall, with every member judged before anything is written. Links are validated, not banned: a CPython framework is a structure of symbolic links, and a runtime pack that lost them would not run.

A wheel could escape on Windows through a backslash. ..\..\x is a single atom to a POSIX path - no .. part, not absolute, so it passed - and then becomes a traversal when Windows splits it again. One of the two copies of this rule refused the character and the other did not.

An archive could expand without limit. Member count was capped, which says nothing about size.

A fetched runtime is no longer trusted silently. --fetch-lock now exists on compile-capi as well as freeze, so the tier most people use can pin what it downloads. python.org publishes a GPG signature for the Windows embeddable CPython and no SHA-256, so there is nothing to verify against on a first fetch: that is now said out loud, with the digest and how to pin it, instead of being recorded quietly and compared against on later builds.

A refused download now reports as one line rather than a traceback, and a test asserts no fetching path can go back to unpacking its own way.

0.8.7 - long functions, and names that are not bound yet

A long function no longer needs a bigger stack frame than a short one. Every intermediate the C lowering needed took a stack slot and never gave it back, so a function's frame grew with its length rather than with how much of it was live at once. About eighteen hundred statements in a single def reached the 512 KB budget and the build was refused outright - which generated code walks into without doing anything unusual. Slots taken for a statement's temporaries are handed back when it finishes, and the frame is now built from the high-water mark rather than from what happens to be outstanding at the end. Forty thousand statements in one function compile and answer correctly, where 1,900 was refused before.

Two things had to be got right and both have tests. A local, or the scratch area the float formatter allocates the first time a program prints a double, outlives the statement that created it - reclaiming those handed a later statement slots something still held, which did not print wrong numbers but exhausted the heap. And reclaiming is per statement rather than per expression, which is what keeps a loop's condition alive across its own body.

A decorator written without the @ was skipped. greet = trace(greet) kept calling the undecorated body: a module-level def became a direct C call keyed on the name alone, so a later rebinding of that name was never consulted. A def now earns the direct call only when it is the one thing binding the name at module scope.

Three more of the same shape, all asking does the module bind this name where the question is is it bound yet: print(y) above y = 5 handed the program a raw NULL instead of raising NameError, the same for a class used above its class, and a call above its own def answered rather than raising. The replacement rule is positional, so a function may still call one written below it and recursion keeps the direct call. Nine tests pin it, five of which fail against 0.8.6; no measurable speed cost.

0.8.6 - repairing 0.8.5, and six machines

0.8.5 could not build anything through py2bin make or build.py. An append option nobody passed is None rather than an empty list, and the change that stopped --exclude fetching what it excluded read it without a default - so every --auto-fetch build without --exclude stopped with a TypeError. Fixed, and every append option is now checked for a default by a test.

The three questions offer six machines rather than five (linux-x86_64 was missing), Linux gains a one-file shape, and all sixteen target-and-shape combinations are driven through build.py end to end in testing.

A helper naming a module constant, or calling another helper, is now written out at its call site: bump(v) over weigh(v) collapses to v * SCALE + 1, 0.67x to 1.26x against the interpreter.

0.8.5 - a correctness sweep

A correctness sweep. Each of these produced a wrong result rather than an error, which is the worst way for a compiler to be wrong; all are now pinned by tests.

• A name the program bound was ignored - its own len, str, print or super lost to the builtin, and a module-level function called through a rebound name calling the wrong one.
• A bundle could not find the packages it carried, on Linux: the program asked CPython where it was, and CPython - handed no argument vector - answered with its own installation.
• sys.argv held one entry this compiler had put there, so a command-line tool could not read what it was asked to do. It is taken from the operating system now.
• len(5) answered -1 instead of raising, leaving the TypeError set.
• A two-piece f-string ran __add__; an f-string joins.
• A wheel's executable bit was dropped, so a package shipping a helper program could not start it.

Two that destroyed things: --clean removed whatever was at the output path, directory and contents; --include removed its own source when the output was in the same directory. Both are refused now.

New: --onefile for a macOS .app and for targets with no bundle; --exclude reaching the fetch rather than downloading what it excluded; a syntax error reported with file, line and column instead of a traceback.

Faster too - seven of sixteen measured operations now beat CPython, where two did. See the table below.

Licence

MIT. Full documentation, source and issues: https://github.com/yu314-coder/python_to_binary