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Python Library Reference |  |
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Python Library Reference | |
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The following types can be pickled:
None, True, and False
Attempts to pickle unpicklable objects will raise the PicklingError exception; when this happens, an unspecified number of bytes may have already been written to the underlying file. Trying to pickle a highly recursive data structure may exceed the maximum recursion depth, a RuntimeError will be raised in this case. You can carefully raise this limit with sys.setrecursionlimit().
Note that functions (built-in and user-defined) are pickled by ``fully qualified'' name reference, not by value. This means that only the function name is pickled, along with the name of module the function is defined in. Neither the function's code, nor any of its function attributes are pickled. Thus the defining module must be importable in the unpickling environment, and the module must contain the named object, otherwise an exception will be raised.3.5
Similarly, classes are pickled by named reference, so the same
restrictions in the unpickling environment apply. Note that none of
the class's code or data is pickled, so in the following example the
class attribute attr is not restored in the unpickling
environment:
class Foo:
attr = 'a class attr'
picklestring = pickle.dumps(Foo)
These restrictions are why picklable functions and classes must be defined in the top level of a module.
Similarly, when class instances are pickled, their class's code and data are not pickled along with them. Only the instance data are pickled. This is done on purpose, so you can fix bugs in a class or add methods to the class and still load objects that were created with an earlier version of the class. If you plan to have long-lived objects that will see many versions of a class, it may be worthwhile to put a version number in the objects so that suitable conversions can be made by the class's __setstate__() method.
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Python Library Reference | |
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