# Differences between PyPy and CPython

This page documents the few differences and incompatibilities between the PyPy Python interpreter and CPython. Some of these differences are "by design", since we think that there are cases in which the behaviour of CPython is buggy, and we do not want to copy bugs.

Differences that are not listed here should be considered bugs of PyPy.

## Differences related to garbage collection strategies

The garbage collectors used or implemented by PyPy are not based on reference counting, so the objects are not freed instantly when they are no longer reachable. The most obvious effect of this is that files (and sockets, etc) are not promptly closed when they go out of scope. For files that are opened for writing, data can be left sitting in their output buffers for a while, making the on-disk file appear empty or truncated. Moreover, you might reach your OS's limit on the number of concurrently opened files.

If you are debugging a case where a file in your program is not closed properly, you can use the `-X track-resources` command line option. If it is given, a `ResourceWarning` is produced for every file and socket that the garbage collector closes. The warning will contain the stack trace of the position where the file or socket was created, to make it easier to see which parts of the program don't close files explicitly.

Fixing this difference to CPython is essentially impossible without forcing a reference-counting approach to garbage collection. The effect that you get in CPython has clearly been described as a side-effect of the implementation and not a language design decision: programs relying on this are basically bogus. It would be a too strong restriction to try to enforce CPython's behavior in a language spec, given that it has no chance to be adopted by Jython or IronPython (or any other port of Python to Java or .NET).

Even the naive idea of forcing a full GC when we're getting dangerously close to the OS's limit can be very bad in some cases. If your program leaks open files heavily, then it would work, but force a complete GC cycle every n'th leaked file. The value of n is a constant, but the program can take an arbitrary amount of memory, which makes a complete GC cycle arbitrarily long. The end result is that PyPy would spend an arbitrarily large fraction of its run time in the GC --- slowing down the actual execution, not by 10% nor 100% nor 1000% but by essentially any factor.

To the best of our knowledge this problem has no better solution than fixing the programs. If it occurs in 3rd-party code, this means going to the authors and explaining the problem to them: they need to close their open files in order to run on any non-CPython-based implementation of Python.

Here are some more technical details. This issue affects the precise time at which `__del__` methods are called, which is not reliable or timely in PyPy (nor Jython nor IronPython). It also means that weak references may stay alive for a bit longer than expected. This makes "weak proxies" (as returned by `weakref.proxy()`) somewhat less useful: they will appear to stay alive for a bit longer in PyPy, and suddenly they will really be dead, raising a `ReferenceError` on the next access. Any code that uses weak proxies must carefully catch such `ReferenceError` at any place that uses them. (Or, better yet, don't use `weakref.proxy()` at all; use `weakref.ref()`.)

Note a detail in the documentation for weakref callbacks:

> If callback is provided and not None, and the returned weakref object is still alive, the callback will be called when the object is about to be finalized.

There are cases where, due to CPython's refcount semantics, a weakref dies immediately before or after the objects it points to (typically with some circular reference). If it happens to die just after, then the callback will be invoked. In a similar case in PyPy, both the object and the weakref will be considered as dead at the same time, and the callback will not be invoked. (Issue #2030)

There are a few extra implications from the difference in the GC. Most notably, if an object has a `__del__`, the `__del__` is never called more than once in PyPy; but CPython will call the same `__del__` several times if the object is resurrected and dies again (at least it is reliably so in older CPythons; newer CPythons try to call destructors not more than once, but there are counter-examples). The `__del__` methods are called in "the right" order if they are on objects pointing to each other, as in CPython, but unlike CPython, if there is a dead cycle of objects referencing each other, their `__del__` methods are called anyway; CPython would instead put them into the list `garbage` of the `gc` module. More information is available on the blog [1] [2].

Note that this difference might show up indirectly in some cases. For example, a generator left pending in the middle is --- again --- garbage-collected later in PyPy than in CPython. You can see the difference if the `yield` keyword it is suspended at is itself enclosed in a `try:` or a `with:` block. This shows up for example as issue 736.

Using the default GC (called `minimark`), the built-in function `id()` works like it does in CPython. With other GCs it returns numbers that are not real addresses (because an object can move around several times) and calling it a lot can lead to performance problem.

Note that if you have a long chain of objects, each with a reference to the next one, and each with a `__del__`, PyPy's GC will perform badly. On the bright side, in most other cases, benchmarks have shown that PyPy's GCs perform much better than CPython's.

Another difference is that if you add a `__del__` to an existing class it will not be called:

    >>>> class A(object):
    ....     pass
    ....
    >>>> A.__del__ = lambda self: None
    __main__:1: RuntimeWarning: a __del__ method added to an existing type will not be called

Even more obscure: the same is true, for old-style classes, if you attach the `__del__` to an instance (even in CPython this does not work with new-style classes). You get a RuntimeWarning in PyPy. To fix these cases just make sure there is a `__del__` method in the class to start with (even containing only `pass`; replacing or overriding it later works fine).

Last note: CPython tries to do a `gc.collect()` automatically when the program finishes; not PyPy. (It is possible in both CPython and PyPy to design a case where several `gc.collect()` are needed before all objects die. This makes CPython's approach only work "most of the time" anyway.)

## Subclasses of built-in types

Officially, CPython has no rule at all for when exactly overridden method of subclasses of built-in types get implicitly called or not. As an approximation, these methods are never called by other built-in methods of the same object. For example, an overridden `__getitem__()` in a subclass of `dict` will not be called by e.g. the built-in `get()` method.

The above is true both in CPython and in PyPy. Differences can occur about whether a built-in function or method will call an overridden method of another object than `self`. In PyPy, they are often called in cases where CPython would not. Two examples:

    class D(dict):
        def __getitem__(self, key):
            return "%r from D" % (key,)

    class A(object):
        pass

    a = A()
    a.__dict__ = D()
    a.foo = "a's own foo"
    print a.foo
    # CPython => a's own foo
    # PyPy => 'foo' from D

    glob = D(foo="base item")
    loc = {}
    exec "print foo" in glob, loc
    # CPython => base item
    # PyPy => 'foo' from D

## Mutating classes of objects which are already used as dictionary keys

Consider the following snippet of code:

    class X(object):
        pass

    def __evil_eq__(self, other):
        print 'hello world'
        return False

    def evil(y):
        d = {X(): 1}
        X.__eq__ = __evil_eq__
        d[y] # might trigger a call to __eq__?

In CPython, __evil_eq__ might be called, although there is no way to write a test which reliably calls it. It happens if `y is not x` and `hash(y) == hash(x)`, where `hash(x)` is computed when `x` is inserted into the dictionary. If by chance the condition is satisfied, then `__evil_eq__` is called.

PyPy uses a special strategy to optimize dictionaries whose keys are instances of user-defined classes which do not override the default `__hash__`, `__eq__` and `__cmp__`: when using this strategy, `__eq__` and `__cmp__` are never called, but instead the lookup is done by identity, so in the case above it is guaranteed that `__eq__` won't be called.

Note that in all other cases (e.g., if you have a custom `__hash__` and `__eq__` in `y`) the behavior is exactly the same as CPython.

## Ignored exceptions

In many corner cases, CPython can silently swallow exceptions. The precise list of when this occurs is rather long, even though most cases are very uncommon. The most well-known places are custom rich comparison methods (like __eq__); dictionary lookup; calls to some built-in functions like isinstance().

Unless this behavior is clearly present by design and documented as such (as e.g. for hasattr()), in most cases PyPy lets the exception propagate instead.

## Object Identity of Primitive Values, `is` and `id`

Object identity of primitive values works by value equality, not by identity of the wrapper. This means that `x + 1 is x + 1` is always true, for arbitrary integers `x`. The rule applies for the following types:

> - `int`
> - `float`
> - `long`
> - `complex`
> - `str` (empty or single-character strings only)
> - `unicode` (empty or single-character strings only)
> - `tuple` (empty tuples only)
> - `frozenset` (empty frozenset only)
> - unbound method objects (for Python 2 only)

This change requires some changes to `id` as well. `id` fulfills the following condition: `x is y <=> id(x) == id(y)`. Therefore `id` of the above types will return a value that is computed from the argument, and can thus be larger than `sys.maxint` (i.e. it can be an arbitrary long).

Note that strings of length 2 or greater can be equal without being identical. Similarly, `x is (2,)` is not necessarily true even if `x` contains a tuple and `x == (2,)`. The uniqueness rules apply only to the particular cases described above. The `str`, `unicode`, `tuple` and `frozenset` rules were added in PyPy 5.4; before that, a test like `if x is "?"` or `if x is ()` could fail even if `x` was equal to `"?"` or `()`. The new behavior added in PyPy 5.4 is closer to CPython's, which caches precisely the empty tuple/frozenset, and (generally but not always) the strings and unicodes of length <= 1.

Note that for floats there "`is`" only one object per "bit pattern" of the float. So `float('nan') is float('nan')` is true on PyPy, but not on CPython because they are two objects; but `0.0 is -0.0` is always False, as the bit patterns are different. As usual, `float('nan') == float('nan')` is always False. When used in containers (as list items or in sets for example), the exact rule of equality used is "`if x is y or x == y`" (on both CPython and PyPy); as a consequence, because all `nans` are identical in PyPy, you cannot have several of them in a set, unlike in CPython. (Issue #1974). Another consequence is that `cmp(float('nan'), float('nan')) == 0`, because `cmp` checks with `is` first whether the arguments are identical (there is no good value to return from this call to `cmp`, because `cmp` pretends that there is a total order on floats, but that is wrong for NaNs).

## C-API Differences

The external C-API has been reimplemented in PyPy as an internal cpyext module. We support most of the documented C-API, but sometimes internal C-abstractions leak out on CPython and are abused, perhaps even unknowingly. For instance, assignment to a `PyTupleObject` is not supported after the tuple is used internally, even by another C-API function call. On CPython this will succeed as long as the refcount is 1. On PyPy this will always raise a `SystemError('PyTuple_SetItem called on tuple after use of tuple")` exception (explicitly listed here for search engines).

Another similar problem is assignment of a new function pointer to any of the `tp_as_*` structures after calling `PyType_Ready`. For instance, overriding `tp_as_number.nb_int` with a different function after calling `PyType_Ready` on CPython will result in the old function being called for `x.__int__()` (via class `__dict__` lookup) and the new function being called for `int(x)` (via slot lookup). On PyPy we will always call the __new__ function, not the old, this quirky behaviour is unfortunately necessary to fully support NumPy.

## Performance Differences

CPython has an optimization that can make repeated string concatenation not quadratic. For example, this kind of code runs in O(n) time:

    s = ''
    for string in mylist:
        s += string

In PyPy, this code will always have quadratic complexity. Note also, that the CPython optimization is brittle and can break by having slight variations in your code anyway. So you should anyway replace the code with:

    parts = []
    for string in mylist:
        parts.append(string)
    s = "".join(parts)

## Miscellaneous

- Hash randomization (`-R`) is ignored in PyPy. In CPython before 3.4 it has little point. Both CPython >= 3.4 and PyPy3 implement the randomized SipHash algorithm and ignore `-R`.

- You can't store non-string keys in type objects. For example:

      class A(object):
          locals()[42] = 3

  won't work.

- `sys.setrecursionlimit(n)` sets the limit only approximately, by setting the usable stack space to `n * 768` bytes. On Linux, depending on the compiler settings, the default of 768KB is enough for about 1400 calls.

- since the implementation of dictionary is different, the exact number of times that `__hash__` and `__eq__` are called is different. Since CPython does not give any specific guarantees either, don't rely on it.

- assignment to `__class__` is limited to the cases where it works on CPython 2.5. On CPython 2.6 and 2.7 it works in a bit more cases, which are not supported by PyPy so far. (If needed, it could be supported, but then it will likely work in many more case on PyPy than on CPython 2.6/2.7.)

- the `__builtins__` name is always referencing the `__builtin__` module, never a dictionary as it sometimes is in CPython. Assigning to `__builtins__` has no effect. (For usages of tools like RestrictedPython, see issue #2653.)

- directly calling the internal magic methods of a few built-in types with invalid arguments may have a slightly different result. For example, `[].__add__(None)` and `(2).__add__(None)` both return `NotImplemented` on PyPy; on CPython, only the latter does, and the former raises `TypeError`. (Of course, `[]+None` and `2+None` both raise `TypeError` everywhere.) This difference is an implementation detail that shows up because of internal C-level slots that PyPy does not have.

- on CPython, `[].__add__` is a `method-wrapper`, and `list.__add__` is a `slot wrapper`. On PyPy these are normal bound or unbound method objects. This can occasionally confuse some tools that inspect built-in types. For example, the standard library `inspect` module has a function `ismethod()` that returns True on unbound method objects but False on method-wrappers or slot wrappers. On PyPy we can't tell the difference, so `ismethod([].__add__) == ismethod(list.__add__) == True`.

- in CPython, the built-in types have attributes that can be implemented in various ways. Depending on the way, if you try to write to (or delete) a read-only (or undeletable) attribute, you get either a `TypeError` or an `AttributeError`. PyPy tries to strike some middle ground between full consistency and full compatibility here. This means that a few corner cases don't raise the same exception, like `del (lambda:None).__closure__`.

- in pure Python, if you write `class A(object): def f(self): pass` and have a subclass `B` which doesn't override `f()`, then `B.f(x)` still checks that `x` is an instance of `B`. In CPython, types written in C use a different rule. If `A` is written in C, any instance of `A` will be accepted by `B.f(x)` (and actually, `B.f is A.f` in this case). Some code that could work on CPython but not on PyPy includes: `datetime.datetime.strftime(datetime.date.today(), ...)` (here, `datetime.date` is the superclass of `datetime.datetime`). Anyway, the proper fix is arguably to use a regular method call in the first place: `datetime.date.today().strftime(...)`

- some functions and attributes of the `gc` module behave in a slightly different way: for example, `gc.enable` and `gc.disable` are supported, but "enabling and disabling the GC" has a different meaning in PyPy than in CPython. These functions actually enable and disable the major collections and the execution of finalizers.

- PyPy prints a random line from past #pypy IRC topics at startup in interactive mode. In a released version, this behaviour is suppressed, but setting the environment variable PYPY_IRC_TOPIC will bring it back. Note that downstream package providers have been known to totally disable this feature.

- PyPy's readline module was rewritten from scratch: it is not GNU's readline. It should be mostly compatible, and it adds multiline support (see `multiline_input()`). On the other hand, `parse_and_bind()` calls are ignored (issue #2072).

- `sys.getsizeof()` always raises `TypeError`. This is because a memory profiler using this function is most likely to give results inconsistent with reality on PyPy. It would be possible to have `sys.getsizeof()` return a number (with enough work), but that may or may not represent how much memory the object uses. It doesn't even make really sense to ask how much one object uses, in isolation with the rest of the system. For example, instances have maps, which are often shared across many instances; in this case the maps would probably be ignored by an implementation of `sys.getsizeof()`, but their overhead is important in some cases if they are many instances with unique maps. Conversely, equal strings may share their internal string data even if they are different objects---or empty containers may share parts of their internals as long as they are empty. Even stranger, some lists create objects as you read them; if you try to estimate the size in memory of `range(10**6)` as the sum of all items' size, that operation will by itself create one million integer objects that never existed in the first place. Note that some of these concerns also exist on CPython, just less so. For this reason we explicitly don't implement `sys.getsizeof()`.

- The `timeit` module behaves differently under PyPy: it prints the average time and the standard deviation, instead of the minimum, since the minimum is often misleading.

- The `get_config_vars` method of `sysconfig` and `distutils.sysconfig` are not complete. On POSIX platforms, CPython fishes configuration variables from the Makefile used to build the interpreter. PyPy should bake the values in during compilation, but does not do that yet.

- `"%d" % x` and `"%x" % x` and similar constructs, where `x` is an instance of a subclass of `long` that overrides the special methods `__str__` or `__hex__` or `__oct__`: PyPy doesn't call the special methods; CPython does---but only if it is a subclass of `long`, not `int`. CPython's behavior is really messy: e.g. for `%x` it calls `__hex__()`, which is supposed to return a string like `-0x123L`; then the `0x` and the final `L` are removed, and the rest is kept. If you return an unexpected string from `__hex__()` you get an exception (or a crash before CPython 2.7.13).

- In PyPy, dictionaries passed as `**kwargs` can contain only string keys, even for `dict()` and `dict.update()`. CPython 2.7 allows non-string keys in these two cases (and only there, as far as we know). E.g. this code produces a `TypeError`, on CPython 3.x as well as on any PyPy: `dict(**{1: 2})`. (Note that `dict(**d1)` is equivalent to `dict(d1)`.)

- PyPy3: `__class__` attribute assignment between heaptypes and non heaptypes. CPython allows that for module subtypes, but not for e.g. `int` or `float` subtypes. Currently PyPy does not support the `__class__` attribute assignment for any non heaptype subtype.

- In PyPy, module and class dictionaries are optimized under the assumption that deleting attributes from them are rare. Because of this, e.g. `del foo.bar` where `foo` is a module (or class) that contains the function `bar`, is significantly slower than CPython.

- Various built-in functions in CPython accept only positional arguments and not keyword arguments. That can be considered a long-running historical detail: newer functions tend to accept keyword arguments and older function are occasionally fixed to do so as well. In PyPy, most built-in functions accept keyword arguments (`help()` shows the argument names). But don't rely on it too much because future versions of PyPy may have to rename the arguments if CPython starts accepting them too.

- PyPy3: `distutils` has been enhanced to allow finding `VsDevCmd.bat` in the directory pointed to by the `VS%0.f0COMNTOOLS` (typically `VS140COMNTOOLS`) environment variable. CPython searches for `vcvarsall.bat` somewhere above that value.

- SyntaxError s try harder to give details about the cause of the failure, so the error messages are not the same as in CPython

- Dictionaries and sets are ordered on PyPy. On CPython < 3.6 they are not; on CPython >= 3.6 dictionaries (but not sets) are ordered.

- PyPy2 refuses to load lone `.pyc` files, i.e. `.pyc` files that are still there after you deleted the `.py` file. PyPy3 instead behaves like CPython. We could be amenable to fix this difference in PyPy2: the current version reflects our annoyance with this detail of CPython, which bit us too often while developing PyPy. (It is as easy as passing the `--lonepycfile` flag when translating PyPy, if you really need it.)

## Extension modules

List of extension modules that we support:

- Supported as built-in modules (in pypy/module/):

  > __builtin__ __pypy__ _ast _codecs _collections _continuation _ffi _hashlib _io _locale _lsprof _md5 _minimal_curses <https://doc.pypy.org/en/latest/config/objspace.usemodules._minimal_curses> _multiprocessing _random _rawffi _sha _socket _sre _ssl _warnings _weakref _winreg array binascii bz2 cStringIO cmath cpyext crypt errno exceptions fcntl gc imp itertools marshal math mmap operator parser posix pyexpat select signal struct symbol sys termios thread time token unicodedata zipimport zlib

  When translated on Windows, a few Unix-only modules are skipped, and the following module is built instead:

  > _winreg

- Supported by being rewritten in pure Python (possibly using `cffi`): see the lib_pypy/ directory. Examples of modules that we support this way: `ctypes`, `cPickle`, `cmath`, `dbm`, `datetime`... Note that some modules are both in there and in the list above; by default, the built-in module is used (but can be disabled at translation time).

The extension modules (i.e. modules written in C, in the standard CPython) that are neither mentioned above nor in lib_pypy/ are not available in PyPy. (You may have a chance to use them anyway with cpyext.)