## 3.14.3 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 functions to make this process more convenient:

`dump`(object, file[, bin])
  Write a pickled representation of object to the open file object file. This is equivalent to `Pickler(file, bin).dump(object)`. If the optional bin argument is true, the binary pickle format is used; otherwise the (less efficient) text pickle format is used (for backwards compatibility, this is the default).

  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.

`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.

`dumps`(object[, bin])
  Return the pickled representation of the object as a string, instead of writing it to a file. If the optional bin argument is true, the binary pickle format is used; otherwise the (less efficient) text pickle format is used (this is the default).

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

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

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

The `pickle` module also exports two callables3.4, `Pickler` and `Unpickler`:

class `Pickler`(file[, bin])
  This takes a file-like object to which it will write a pickle data stream. Optional bin if true, tells the pickler to use the more efficient binary pickle format, otherwise the ASCII format is used (this is the default).

  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`(object)
  Write a pickled representation of object to the open file object given in the constructor. Either the binary or ASCII format will be used, depending on the value of the bin flag 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: `clear_memo()` is 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()

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 object3.5.

`Unpickler` objects are defined as:

class `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.

`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 3.14.5 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.

#### Footnotes

... callables 3.4
  In the `pickle` module these callables are classes, which you could subclass to customize the behavior. However, in the `cPickle` modules these callables are factory functions and so cannot be subclassed. One of the common reasons to subclass is to control what objects can actually be unpickled. See section 3.14.6 for more details on security concerns.
... object 3.5
  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.