
``pickle`` --- Python object serialization
******************************************

The ``pickle`` module implements a fundamental, but powerful algorithm
for serializing and de-serializing a Python object structure.
"Pickling" is the process whereby a Python object hierarchy is
converted into a byte stream, and "unpickling" is the inverse
operation, whereby a byte stream is converted back into an object
hierarchy.  Pickling (and unpickling) is alternatively known as
"serialization", "marshalling," [1] or "flattening", however, to avoid
confusion, the terms used here are "pickling" and "unpickling".

This documentation describes both the ``pickle`` module and the
``cPickle`` module.

Warning: The ``pickle`` module is not intended to be secure against erroneous
  or maliciously constructed data.  Never unpickle data received from
  an untrusted or unauthenticated source.


Relationship to other Python modules
====================================

The ``pickle`` module has an optimized cousin called the ``cPickle``
module.  As its name implies, ``cPickle`` is written in C, so it can
be up to 1000 times faster than ``pickle``.  However it does not
support subclassing of the ``Pickler()`` and ``Unpickler()`` classes,
because in ``cPickle`` these are functions, not classes.  Most
applications have no need for this functionality, and can benefit from
the improved performance of ``cPickle``. Other than that, the
interfaces of the two modules are nearly identical; the common
interface is described in this manual and differences are pointed out
where necessary.  In the following discussions, we use the term
"pickle" to collectively describe the ``pickle`` and ``cPickle``
modules.

The data streams the two modules produce are guaranteed to be
interchangeable.

Python has a more primitive serialization module called ``marshal``,
but in general ``pickle`` should always be the preferred way to
serialize Python objects.  ``marshal`` exists primarily to support
Python's ``.pyc`` files.

The ``pickle`` module differs from ``marshal`` in several significant
ways:

* The ``pickle`` module keeps track of the objects it has already
  serialized, so that later references to the same object won't be
  serialized again. ``marshal`` doesn't do this.

  This has implications both for recursive objects and object sharing.
  Recursive objects are objects that contain references to themselves.
  These are not handled by marshal, and in fact, attempting to marshal
  recursive objects will crash your Python interpreter.  Object
  sharing happens when there are multiple references to the same
  object in different places in the object hierarchy being serialized.
  ``pickle`` stores such objects only once, and ensures that all other
  references point to the master copy.  Shared objects remain shared,
  which can be very important for mutable objects.

* ``marshal`` cannot be used to serialize user-defined classes and
  their instances.  ``pickle`` can save and restore class instances
  transparently, however the class definition must be importable and
  live in the same module as when the object was stored.

* The ``marshal`` serialization format is not guaranteed to be
  portable across Python versions.  Because its primary job in life is
  to support ``.pyc`` files, the Python implementers reserve the right
  to change the serialization format in non-backwards compatible ways
  should the need arise. The ``pickle`` serialization format is
  guaranteed to be backwards compatible across Python releases.

Note that serialization is a more primitive notion than persistence;
although ``pickle`` reads and writes file objects, it does not handle
the issue of naming persistent objects, nor the (even more
complicated) issue of concurrent access to persistent objects.  The
``pickle`` module can transform a complex object into a byte stream
and it can transform the byte stream into an object with the same
internal structure.  Perhaps the most obvious thing to do with these
byte streams is to write them onto a file, but it is also conceivable
to send them across a network or store them in a database.  The module
``shelve`` provides a simple interface to pickle and unpickle objects
on DBM-style database files.


Data stream format
==================

The data format used by ``pickle`` is Python-specific.  This has the
advantage that there are no restrictions imposed by external standards
such as XDR (which can't represent pointer sharing); however it means
that non-Python programs may not be able to reconstruct pickled Python
objects.

By default, the ``pickle`` data format uses a printable ASCII
representation. This is slightly more voluminous than a binary
representation.  The big advantage of using printable ASCII (and of
some other characteristics of ``pickle``'s representation) is that for
debugging or recovery purposes it is possible for a human to read the
pickled file with a standard text editor.

There are currently 3 different protocols which can be used for
pickling.

* Protocol version 0 is the original ASCII protocol and is backwards
  compatible with earlier versions of Python.

* Protocol version 1 is the old binary format which is also compatible
  with earlier versions of Python.

* Protocol version 2 was introduced in Python 2.3.  It provides much
  more efficient pickling of *new-style class*es.

Refer to **PEP 307** for more information.

If a *protocol* is not specified, protocol 0 is used. If *protocol* is
specified as a negative value or ``HIGHEST_PROTOCOL``, the highest
protocol version available will be used.

Changed in version 2.3: Introduced the *protocol* parameter.

A binary format, which is slightly more efficient, can be chosen by
specifying a *protocol* version >= 1.


Usage
=====

To serialize an object hierarchy, you first create a pickler, then you
call the pickler's ``dump()`` method.  To de-serialize a data stream,
you first create an unpickler, then you call the unpickler's
``load()`` method.  The ``pickle`` module provides the following
constant:

pickle.HIGHEST_PROTOCOL

   The highest protocol version available.  This value can be passed
   as a *protocol* value.

   New in version 2.3.

Note: Be sure to always open pickle files created with protocols >= 1 in
  binary mode. For the old ASCII-based pickle protocol 0 you can use
  either text mode or binary mode as long as you stay consistent.A
  pickle file written with protocol 0 in binary mode will contain lone
  linefeeds as line terminators and therefore will look "funny" when
  viewed in Notepad or other editors which do not support this format.

The ``pickle`` module provides the following functions to make the
pickling process more convenient:

pickle.dump(obj, file[, protocol])

   Write a pickled representation of *obj* to the open file object
   *file*.  This is equivalent to ``Pickler(file,
   protocol).dump(obj)``.

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or
   ``HIGHEST_PROTOCOL``, the highest protocol version will be used.

   Changed in version 2.3: Introduced the *protocol* parameter.

   *file* must have a ``write()`` method that accepts a single string
   argument. It can thus be a file object opened for writing, a
   ``StringIO`` object, or any other custom object that meets this
   interface.

pickle.load(file)

   Read a string from the open file object *file* and interpret it as
   a pickle data stream, reconstructing and returning the original
   object hierarchy.  This is equivalent to
   ``Unpickler(file).load()``.

   *file* must have two methods, a ``read()`` method that takes an
   integer argument, and a ``readline()`` method that requires no
   arguments.  Both methods should return a string.  Thus *file* can
   be a file object opened for reading, a ``StringIO`` object, or any
   other custom object that meets this interface.

   This function automatically determines whether the data stream was
   written in binary mode or not.

pickle.dumps(obj[, protocol])

   Return the pickled representation of the object as a string,
   instead of writing it to a file.

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or
   ``HIGHEST_PROTOCOL``, the highest protocol version will be used.

   Changed in version 2.3: The *protocol* parameter was added.

pickle.loads(string)

   Read a pickled object hierarchy from a string.  Characters in the
   string past the pickled object's representation are ignored.

The ``pickle`` module also defines three exceptions:

exception exception pickle.PickleError

   A common base class for the other exceptions defined below.  This
   inherits from ``Exception``.

exception exception pickle.PicklingError

   This exception is raised when an unpicklable object is passed to
   the ``dump()`` method.

exception exception pickle.UnpicklingError

   This exception is raised when there is a problem unpickling an
   object. Note that other exceptions may also be raised during
   unpickling, including (but not necessarily limited to)
   ``AttributeError``, ``EOFError``, ``ImportError``, and
   ``IndexError``.

The ``pickle`` module also exports two callables [2], ``Pickler`` and
``Unpickler``:

class class pickle.Pickler(file[, protocol])

   This takes a file-like object to which it will write a pickle data
   stream.

   If the *protocol* parameter is omitted, protocol 0 is used. If
   *protocol* is specified as a negative value or
   ``HIGHEST_PROTOCOL``, the highest protocol version will be used.

   Changed in version 2.3: Introduced the *protocol* parameter.

   *file* must have a ``write()`` method that accepts a single string
   argument. It can thus be an open file object, a ``StringIO``
   object, or any other custom object that meets this interface.

   ``Pickler`` objects define one (or two) public methods:

   dump(obj)

      Write a pickled representation of *obj* to the open file object
      given in the constructor.  Either the binary or ASCII format
      will be used, depending on the value of the *protocol* argument
      passed to the constructor.

   clear_memo()

      Clears the pickler's "memo".  The memo is the data structure
      that remembers which objects the pickler has already seen, so
      that shared or recursive objects pickled by reference and not by
      value.  This method is useful when re-using picklers.

      Note: Prior to Python 2.3, ``clear_memo()`` was only available on
        the picklers created by ``cPickle``.  In the ``pickle``
        module, picklers have an instance variable called ``memo``
        which is a Python dictionary.  So to clear the memo for a
        ``pickle`` module pickler, you could do the following:

           mypickler.memo.clear()

        Code that does not need to support older versions of Python
        should simply use ``clear_memo()``.

It is possible to make multiple calls to the ``dump()`` method of the
same ``Pickler`` instance.  These must then be matched to the same
number of calls to the ``load()`` method of the corresponding
``Unpickler`` instance.  If the same object is pickled by multiple
``dump()`` calls, the ``load()`` will all yield references to the same
object. [3]

``Unpickler`` objects are defined as:

class class pickle.Unpickler(file)

   This takes a file-like object from which it will read a pickle data
   stream. This class automatically determines whether the data stream
   was written in binary mode or not, so it does not need a flag as in
   the ``Pickler`` factory.

   *file* must have two methods, a ``read()`` method that takes an
   integer argument, and a ``readline()`` method that requires no
   arguments.  Both methods should return a string.  Thus *file* can
   be a file object opened for reading, a ``StringIO`` object, or any
   other custom object that meets this interface.

   ``Unpickler`` objects have one (or two) public methods:

   load()

      Read a pickled object representation from the open file object
      given in the constructor, and return the reconstituted object
      hierarchy specified therein.

      This method automatically determines whether the data stream was
      written in binary mode or not.

   noload()

      This is just like ``load()`` except that it doesn't actually
      create any objects.  This is useful primarily for finding what's
      called "persistent ids" that may be referenced in a pickle data
      stream.  See section *The pickle protocol* below for more
      details.

      **Note:** the ``noload()`` method is currently only available on
      ``Unpickler`` objects created with the ``cPickle`` module.
      ``pickle`` module ``Unpickler``s do not have the ``noload()``
      method.


What can be pickled and unpickled?
==================================

The following types can be pickled:

* ``None``, ``True``, and ``False``

* integers, long integers, floating point numbers, complex numbers

* normal and Unicode strings

* tuples, lists, sets, and dictionaries containing only picklable
  objects

* functions defined at the top level of a module

* built-in functions defined at the top level of a module

* classes that are defined at the top level of a module

* instances of such classes whose ``__dict__`` or ``__setstate__()``
  is picklable  (see section *The pickle protocol* for details)

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 the 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. [4]

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.


The pickle protocol
===================

This section describes the "pickling protocol" that defines the
interface between the pickler/unpickler and the objects that are being
serialized.  This protocol provides a standard way for you to define,
customize, and control how your objects are serialized and de-
serialized.  The description in this section doesn't cover specific
customizations that you can employ to make the unpickling environment
slightly safer from untrusted pickle data streams; see section
*Subclassing Unpicklers* for more details.


Pickling and unpickling normal class instances
----------------------------------------------

object.__getinitargs__()

   When a pickled class instance is unpickled, its ``__init__()``
   method is normally *not* invoked.  If it is desirable that the
   ``__init__()`` method be called on unpickling, an old-style class
   can define a method ``__getinitargs__()``, which should return a
   *tuple* containing the arguments to be passed to the class
   constructor (``__init__()`` for example).  The
   ``__getinitargs__()`` method is called at pickle time; the tuple it
   returns is incorporated in the pickle for the instance.

object.__getnewargs__()

   New-style types can provide a ``__getnewargs__()`` method that is
   used for protocol 2.  Implementing this method is needed if the
   type establishes some internal invariants when the instance is
   created, or if the memory allocation is affected by the values
   passed to the ``__new__()`` method for the type (as it is for
   tuples and strings).  Instances of a *new-style class* ``C`` are
   created using

      obj = C.__new__(C, *args)

   where *args* is the result of calling ``__getnewargs__()`` on the
   original object; if there is no ``__getnewargs__()``, an empty
   tuple is assumed.

object.__getstate__()

   Classes can further influence how their instances are pickled; if
   the class defines the method ``__getstate__()``, it is called and
   the return state is pickled as the contents for the instance,
   instead of the contents of the instance's dictionary.  If there is
   no ``__getstate__()`` method, the instance's ``__dict__`` is
   pickled.

object.__setstate__(state)

   Upon unpickling, if the class also defines the method
   ``__setstate__()``, it is called with the unpickled state. [5] If
   there is no ``__setstate__()`` method, the pickled state must be a
   dictionary and its items are assigned to the new instance's
   dictionary.  If a class defines both ``__getstate__()`` and
   ``__setstate__()``, the state object needn't be a dictionary and
   these methods can do what they want. [6]

   Note: For *new-style class*es, if ``__getstate__()`` returns a false
     value, the ``__setstate__()`` method will not be called.

Note: At unpickling time, some methods like ``__getattr__()``,
  ``__getattribute__()``, or ``__setattr__()`` may be called upon the
  instance.  In case those methods rely on some internal invariant
  being true, the type should implement either ``__getinitargs__()``
  or ``__getnewargs__()`` to establish such an invariant; otherwise,
  neither ``__new__()`` nor ``__init__()`` will be called.


Pickling and unpickling extension types
---------------------------------------

object.__reduce__()

   When the ``Pickler`` encounters an object of a type it knows
   nothing about --- such as an extension type --- it looks in two
   places for a hint of how to pickle it.  One alternative is for the
   object to implement a ``__reduce__()`` method.  If provided, at
   pickling time ``__reduce__()`` will be called with no arguments,
   and it must return either a string or a tuple.

   If a string is returned, it names a global variable whose contents
   are pickled as normal.  The string returned by ``__reduce__()``
   should be the object's local name relative to its module; the
   pickle module searches the module namespace to determine the
   object's module.

   When a tuple is returned, it must be between two and five elements
   long. Optional elements can either be omitted, or ``None`` can be
   provided as their value.  The contents of this tuple are pickled as
   normal and used to reconstruct the object at unpickling time.  The
   semantics of each element are:

   * A callable object that will be called to create the initial
     version of the object.  The next element of the tuple will
     provide arguments for this callable, and later elements provide
     additional state information that will subsequently be used to
     fully reconstruct the pickled data.

     In the unpickling environment this object must be either a class,
     a callable registered as a "safe constructor" (see below), or it
     must have an attribute ``__safe_for_unpickling__`` with a true
     value. Otherwise, an ``UnpicklingError`` will be raised in the
     unpickling environment.  Note that as usual, the callable itself
     is pickled by name.

   * A tuple of arguments for the callable object.

     Changed in version 2.5: Formerly, this argument could also be
     ``None``.

   * Optionally, the object's state, which will be passed to the
     object's ``__setstate__()`` method as described in section
     *Pickling and unpickling normal class instances*.  If the object
     has no ``__setstate__()`` method, then, as above, the value must
     be a dictionary and it will be added to the object's
     ``__dict__``.

   * Optionally, an iterator (and not a sequence) yielding successive
     list items.  These list items will be pickled, and appended to
     the object using either ``obj.append(item)`` or
     ``obj.extend(list_of_items)``.  This is primarily used for list
     subclasses, but may be used by other classes as long as they have
     ``append()`` and ``extend()`` methods with the appropriate
     signature.  (Whether ``append()`` or ``extend()`` is used depends
     on which pickle protocol version is used as well as the number of
     items to append, so both must be supported.)

   * Optionally, an iterator (not a sequence) yielding successive
     dictionary items, which should be tuples of the form ``(key,
     value)``.  These items will be pickled and stored to the object
     using ``obj[key] = value``. This is primarily used for dictionary
     subclasses, but may be used by other classes as long as they
     implement ``__setitem__()``.

object.__reduce_ex__(protocol)

   It is sometimes useful to know the protocol version when
   implementing ``__reduce__()``.  This can be done by implementing a
   method named ``__reduce_ex__()`` instead of ``__reduce__()``.
   ``__reduce_ex__()``, when it exists, is called in preference over
   ``__reduce__()`` (you may still provide ``__reduce__()`` for
   backwards compatibility).  The ``__reduce_ex__()`` method will be
   called with a single integer argument, the protocol version.

   The ``object`` class implements both ``__reduce__()`` and
   ``__reduce_ex__()``; however, if a subclass overrides
   ``__reduce__()`` but not ``__reduce_ex__()``, the
   ``__reduce_ex__()`` implementation detects this and calls
   ``__reduce__()``.

An alternative to implementing a ``__reduce__()`` method on the object
to be pickled, is to register the callable with the ``copy_reg``
module.  This module provides a way for programs to register
"reduction functions" and constructors for user-defined types.
Reduction functions have the same semantics and interface as the
``__reduce__()`` method described above, except that they are called
with a single argument, the object to be pickled.

The registered constructor is deemed a "safe constructor" for purposes
of unpickling as described above.


Pickling and unpickling external objects
----------------------------------------

For the benefit of object persistence, the ``pickle`` module supports
the notion of a reference to an object outside the pickled data
stream.  Such objects are referenced by a "persistent id", which is
just an arbitrary string of printable ASCII characters. The resolution
of such names is not defined by the ``pickle`` module; it will
delegate this resolution to user defined functions on the pickler and
unpickler. [7]

To define external persistent id resolution, you need to set the
``persistent_id`` attribute of the pickler object and the
``persistent_load`` attribute of the unpickler object.

To pickle objects that have an external persistent id, the pickler
must have a custom ``persistent_id()`` method that takes an object as
an argument and returns either ``None`` or the persistent id for that
object.  When ``None`` is returned, the pickler simply pickles the
object as normal.  When a persistent id string is returned, the
pickler will pickle that string, along with a marker so that the
unpickler will recognize the string as a persistent id.

To unpickle external objects, the unpickler must have a custom
``persistent_load()`` function that takes a persistent id string and
returns the referenced object.

Here's a silly example that *might* shed more light:

   import pickle
   from cStringIO import StringIO

   src = StringIO()
   p = pickle.Pickler(src)

   def persistent_id(obj):
       if hasattr(obj, 'x'):
           return 'the value %d' % obj.x
       else:
           return None

   p.persistent_id = persistent_id

   class Integer:
       def __init__(self, x):
           self.x = x
       def __str__(self):
           return 'My name is integer %d' % self.x

   i = Integer(7)
   print i
   p.dump(i)

   datastream = src.getvalue()
   print repr(datastream)
   dst = StringIO(datastream)

   up = pickle.Unpickler(dst)

   class FancyInteger(Integer):
       def __str__(self):
           return 'I am the integer %d' % self.x

   def persistent_load(persid):
       if persid.startswith('the value '):
           value = int(persid.split()[2])
           return FancyInteger(value)
       else:
           raise pickle.UnpicklingError, 'Invalid persistent id'

   up.persistent_load = persistent_load

   j = up.load()
   print j

In the ``cPickle`` module, the unpickler's ``persistent_load``
attribute can also be set to a Python list, in which case, when the
unpickler reaches a persistent id, the persistent id string will
simply be appended to this list. This functionality exists so that a
pickle data stream can be "sniffed" for object references without
actually instantiating all the objects in a pickle. [8]  Setting
``persistent_load`` to a list is usually used in conjunction with the
``noload()`` method on the Unpickler.


Subclassing Unpicklers
======================

By default, unpickling will import any class that it finds in the
pickle data. You can control exactly what gets unpickled and what gets
called by customizing your unpickler.  Unfortunately, exactly how you
do this is different depending on whether you're using ``pickle`` or
``cPickle``. [9]

In the ``pickle`` module, you need to derive a subclass from
``Unpickler``, overriding the ``load_global()`` method.
``load_global()`` should read two lines from the pickle data stream
where the first line will the name of the module containing the class
and the second line will be the name of the instance's class.  It then
looks up the class, possibly importing the module and digging out the
attribute, then it appends what it finds to the unpickler's stack.
Later on, this class will be assigned to the ``__class__`` attribute
of an empty class, as a way of magically creating an instance without
calling its class's ``__init__()``. Your job (should you choose to
accept it), would be to have ``load_global()`` push onto the
unpickler's stack, a known safe version of any class you deem safe to
unpickle. It is up to you to produce such a class.  Or you could raise
an error if you want to disallow all unpickling of instances.  If this
sounds like a hack, you're right.  Refer to the source code to make
this work.

Things are a little cleaner with ``cPickle``, but not by much. To
control what gets unpickled, you can set the unpickler's
``find_global`` attribute to a function or ``None``.  If it is
``None`` then any attempts to unpickle instances will raise an
``UnpicklingError``.  If it is a function, then it should accept a
module name and a class name, and return the corresponding class
object.  It is responsible for looking up the class and performing any
necessary imports, and it may raise an error to prevent instances of
the class from being unpickled.

The moral of the story is that you should be really careful about the
source of the strings your application unpickles.


Example
=======

For the simplest code, use the ``dump()`` and ``load()`` functions.
Note that a self-referencing list is pickled and restored correctly.

   import pickle

   data1 = {'a': [1, 2.0, 3, 4+6j],
            'b': ('string', u'Unicode string'),
            'c': None}

   selfref_list = [1, 2, 3]
   selfref_list.append(selfref_list)

   output = open('data.pkl', 'wb')

   # Pickle dictionary using protocol 0.
   pickle.dump(data1, output)

   # Pickle the list using the highest protocol available.
   pickle.dump(selfref_list, output, -1)

   output.close()

The following example reads the resulting pickled data.  When reading
a pickle-containing file, you should open the file in binary mode
because you can't be sure if the ASCII or binary format was used.

   import pprint, pickle

   pkl_file = open('data.pkl', 'rb')

   data1 = pickle.load(pkl_file)
   pprint.pprint(data1)

   data2 = pickle.load(pkl_file)
   pprint.pprint(data2)

   pkl_file.close()

Here's a larger example that shows how to modify pickling behavior for
a class. The ``TextReader`` class opens a text file, and returns the
line number and line contents each time its ``readline()`` method is
called. If a ``TextReader`` instance is pickled, all attributes
*except* the file object member are saved. When the instance is
unpickled, the file is reopened, and reading resumes from the last
location. The ``__setstate__()`` and ``__getstate__()`` methods are
used to implement this behavior.

   #!/usr/local/bin/python

   class TextReader:
       """Print and number lines in a text file."""
       def __init__(self, file):
           self.file = file
           self.fh = open(file)
           self.lineno = 0

       def readline(self):
           self.lineno = self.lineno + 1
           line = self.fh.readline()
           if not line:
               return None
           if line.endswith("\n"):
               line = line[:-1]
           return "%d: %s" % (self.lineno, line)

       def __getstate__(self):
           odict = self.__dict__.copy() # copy the dict since we change it
           del odict['fh']              # remove filehandle entry
           return odict

       def __setstate__(self, dict):
           fh = open(dict['file'])      # reopen file
           count = dict['lineno']       # read from file...
           while count:                 # until line count is restored
               fh.readline()
               count = count - 1
           self.__dict__.update(dict)   # update attributes
           self.fh = fh                 # save the file object

A sample usage might be something like this:

   >>> import TextReader
   >>> obj = TextReader.TextReader("TextReader.py")
   >>> obj.readline()
   '1: #!/usr/local/bin/python'
   >>> obj.readline()
   '2: '
   >>> obj.readline()
   '3: class TextReader:'
   >>> import pickle
   >>> pickle.dump(obj, open('save.p', 'wb'))

If you want to see that ``pickle`` works across Python processes,
start another Python session, before continuing.  What follows can
happen from either the same process or a new process.

   >>> import pickle
   >>> reader = pickle.load(open('save.p', 'rb'))
   >>> reader.readline()
   '4:     """Print and number lines in a text file."""'

See also:

   Module ``copy_reg``
      Pickle interface constructor registration for extension types.

   Module ``shelve``
      Indexed databases of objects; uses ``pickle``.

   Module ``copy``
      Shallow and deep object copying.

   Module ``marshal``
      High-performance serialization of built-in types.


``cPickle`` --- A faster ``pickle``
***********************************

The ``cPickle`` module supports serialization and de-serialization of
Python objects, providing an interface and functionality nearly
identical to the ``pickle`` module.  There are several differences,
the most important being performance and subclassability.

First, ``cPickle`` can be up to 1000 times faster than ``pickle``
because the former is implemented in C.  Second, in the ``cPickle``
module the callables ``Pickler()`` and ``Unpickler()`` are functions,
not classes. This means that you cannot use them to derive custom
pickling and unpickling subclasses.  Most applications have no need
for this functionality and should benefit from the greatly improved
performance of the ``cPickle`` module.

The pickle data stream produced by ``pickle`` and ``cPickle`` are
identical, so it is possible to use ``pickle`` and ``cPickle``
interchangeably with existing pickles. [10]

There are additional minor differences in API between ``cPickle`` and
``pickle``, however for most applications, they are interchangeable.
More documentation is provided in the ``pickle`` module documentation,
which includes a list of the documented differences.

-[ Footnotes ]-

[1] Don't confuse this with the ``marshal`` module

[2] In the ``pickle`` module these callables are classes, which you
    could subclass to customize the behavior.  However, in the
    ``cPickle`` module these callables are factory functions and so
    cannot be subclassed.  One common reason to subclass is to control
    what objects can actually be unpickled.  See section *Subclassing
    Unpicklers* for more details.

[3] *Warning*: this is intended for pickling multiple objects without
    intervening modifications to the objects or their parts.  If you
    modify an object and then pickle it again using the same
    ``Pickler`` instance, the object is not pickled again --- a
    reference to it is pickled and the ``Unpickler`` will return the
    old value, not the modified one. There are two problems here: (1)
    detecting changes, and (2) marshalling a minimal set of changes.
    Garbage Collection may also become a problem here.

[4] The exception raised will likely be an ``ImportError`` or an
    ``AttributeError`` but it could be something else.

[5] These methods can also be used to implement copying class
    instances.

[6] This protocol is also used by the shallow and deep copying
    operations defined in the ``copy`` module.

[7] The actual mechanism for associating these user defined functions
    is slightly different for ``pickle`` and ``cPickle``.  The
    description given here works the same for both implementations.
    Users of the ``pickle`` module could also use subclassing to
    effect the same results, overriding the ``persistent_id()`` and
    ``persistent_load()`` methods in the derived classes.

[8] We'll leave you with the image of Guido and Jim sitting around
    sniffing pickles in their living rooms.

[9] A word of caution: the mechanisms described here use internal
    attributes and methods, which are subject to change in future
    versions of Python.  We intend to someday provide a common
    interface for controlling this behavior, which will work in either
    ``pickle`` or ``cPickle``.

[10] Since the pickle data format is actually a tiny stack-oriented
     programming language, and some freedom is taken in the encodings
     of certain objects, it is possible that the two modules produce
     different data streams for the same input objects.  However it is
     guaranteed that they will always be able to read each other's
     data streams.
