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pickle — Python object serialization

Python is a widely used general-purpose, high-level programming language. In this article, we will learn about pickling and unpickling in Python using the pickle module.

The pickle module is used for implementing binary protocols for serializing and de-serializing a Python object structure. 

• Pickling: It is a process where a Python object hierarchy is converted into a byte stream. 
• Unpickling: It is the inverse of the Pickling process where a byte stream is converted into an object hierarchy. 

• dumps() - This function is called to serialize an object hierarchy.
• loads() - This function is called to de-serialize a data stream.

1. pickle.HIGHEST_PROTOCOL 
   This is an integer value representing the highest protocol version available. This is considered as the protocol value which is passed to the functions dump(), dumps(). 
2. pickle.DEFAULT_PROTOCOL 
   This is an integer value representing the default protocol used for pickling whose value may be less than the value of the highest protocol. 

pickle.dump(obj, file, protocol = None, *, fix_imports = True) 
This function is equivalent to Pickler(file, protocol).dump(obj). This is used to write a pickled representation of obj to the open file object file.
The optional protocol argument is an integer that tells the pickler to use the given protocol. The supported protocols are 0 to HIGHEST_PROTOCOL. If not specified, the default is DEFAULT_PROTOCOL. If a negative number is specified, HIGHEST_PROTOCOL is selected.
If fix_imports is true and protocol is less than 3, the pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python

Example :The code defines a SimpleObject class and creates instances stored in a list. It uses the pickle module to serialize these instances and writes them to an io.StringIO object.

import pickle 
import io
class SimpleObject(object): 

    def __init__(self, name): 
        self.name = name 
        l = list(name) 
        l.reverse() 
        self.name_backwards = ''.join(l) 
        return

data = [] 
data.append(SimpleObject('pickle')) 
data.append(SimpleObject('cPickle')) 
data.append(SimpleObject('last')) 
out_s = io.StringIO() 
for o in data: 
    print ('WRITING: %s (%s)' % (o.name, o.name_backwards)) 
    pickle.dump(o, out_s) 
    out_s.flush() 

Output : 

WRITING: pickle (elkcip)
WRITING: cPickle (elkciPc)
WRITING: last (tsal)

pickle.dumps(obj, protocol = None, *, fix_imports = True) 

This function returns the pickled representation of the object as a bytes object.

Example : This code uses the 'pickle' module to serialize a list containing a dictionary with various data types (string, integer, and float). It converts the data into a binary data string and prints it. This binary string represents the serialized data and can be deserialized to reconstruct the original data structure using pickle.loads().

 import pickle 

data = [ { 'a':'A', 'b':2, 'c':3.0 } ] 
data_string = pickle.dumps(data) 
print ('PICKLE:', data_string )

Output :  

PICKLE: (lp0
(dp1
S'a'
p2
S'A'
p3
sS'c'
p4
F3.0
sS'b'
p5
I2
sa.

pickle.load(file, *, fix_imports = True, encoding = "ASCII", errors = "strict") 
This function is equivalent to Unpickler(file).load(). This function is used to read a pickled object representation from the open file object file and return the reconstituted object hierarchy specified.

Example : This code defines a 'SimpleObject' class and creates instances stored in a list. It uses the pickle module to serialize these instances and writes them to an io.StringIO object. Then, it reads and deserializes the pickled data from in_s using pickle.load(). The code demonstrates the complete cycle of pickling and unpickling, successfully printing the information about the unpickled objects.

import pickle 
import io
class SimpleObject(object): 

    def __init__(self, name): 
        self.name = name 
        l = list(name) 
        l.reverse() 
        self.name_backwards = ''.join(l) 
        return
data = [] 
data.append(SimpleObject('pickle')) 
data.append(SimpleObject('cPickle')) 
data.append(SimpleObject('last')) 
out_s = io.StringIO() 
for o in data: 
    print ('WRITING: %s (%s)' % (o.name, o.name_backwards)) 
    pickle.dump(o, out_s) 
    out_s.flush() 
in_s = io.StringIO(out_s.getvalue()) 
while True: 
    try: 
        o = pickle.load(in_s) 
    except EOFError: 
        break
    else: 
        print ('READ: %s (%s)' % (o.name, o.name_backwards)) 

Output

WRITING: pickle (elkcip)
WRITING: cPickle (elkciPc)
WRITING: last (tsal)
READ: pickle (elkcip)
READ: cPickle (elkciPc)
READ: last (tsal)

pickle.loads(bytes_object, *, fix_imports = True, encoding = "ASCII", errors = "strict") 
This function is used to read a pickled object representation from a bytes object and return the reconstituted object hierarchy specified.

Example : This code demonstrates pickling and unpickling data using the pickle module. It first serializes a list of dictionaries and then loads the pickled data back into another variable. It compares the original and unpickled data, showing that they are equal in content (data1 == data2) but not the same object in memory (data1 is not data2). This highlights the process of serialization and deserialization while preserving the data's integrity.

import pickle 
import pprint 
data1 = [ { 'a':'A', 'b':2, 'c':3.0 } ] 
print ('BEFORE:',) 
pprint.pprint(data1) 

data1_string = pickle.dumps(data1) 

data2 = pickle.loads(data1_string) 
print ('AFTER:',) 
pprint.pprint(data2) 

print ('SAME?:', (data1 is data2)) 
print ('EQUAL?:', (data1 == data2)) 

Output : 

BEFORE:[{'a': 'A', 'b': 2, 'c': 3.0}]
AFTER:[{'a': 'A', 'b': 2, 'c': 3.0}]
SAME?: False
EQUAL?: True
 

exception pickle.PickleError 
This exception inherits Exception. It is the base class for all other exceptions raised in pickling.

 exception pickle.PicklingError 
This exception inherits PickleError. This exception is raised when an unpicklable object is encountered by Pickler. 

 exception pickle.UnpicklingError 
This exception inherits PickleError. This exception is raised when there is a problem like data corruption or a security violation while unpickling an object. 

This class takes a binary file for writing a pickle data stream. 
dump(obj) - This function is used to write a pickled representation of obj to the open file object given in the constructor.

persistent_id(obj) - If persistent_id() returns None, obj is pickled as usual. This does nothing by default and exists so that any subclass can override it.

Dispatch_table - A pickler object’s dispatch table is a mapping whose keys are classes and whose values are reduction functions. 
By default, a pickler object will not have a dispatch_table attribute, and it will instead use the global dispatch table managed by the copyreg module.
Example : The below code creates an instance of pickle.Pickler with a private dispatch table that handles the SomeClass class especially.

f = io.BytesIO()
p = pickle.Pickler(f)
p.dispatch_table = copyreg.dispatch_table.copy()
p.dispatch_table[SomeClass] = reduce_SomeClass

This class takes a binary file for reading a pickle data stream. 
load() - This function is used to read a pickled object representation from the open file object file and return the reconstituted object hierarchy specified.

persistent_load(pid) - This raises an UnpicklingError by default.

find_class(module, name) - This function imports module if required and returns the object called name from it, where the module and name arguments are str objects.

The following types can be pickled : 

• None, True, and False
• integers, floating point numbers, complex numbers
• strings, bytes, bytearrays
• tuples, lists, sets, and dictionaries containing only picklable objects
• functions defined at the top level of a module (using def, not lambda)
• 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 the result of calling __getstate__() is picklable

This section explains the general mechanisms available to define, customize, and control how class instances are pickled and unpickled. 
No additional code is needed to make instances pickable. By default, pickle will retrieve the class and the attributes of an instance via introspection.
Classes can alter the default behaviour by providing one or several special methods :

• object.__getnewargs_ex__() 
  This method dictates the values passed to the __new__() method upon unpickling. The method must return a pair (args, kwargs) where args is a tuple of positional arguments and kwargs a dictionary of named arguments for constructing the object. 
• object.__getnewargs__() 
  This method supports only positive arguments. It must return a tuple of arguments args which will be passed to the __new__() method upon unpickling. 
• object.__getstate__() 
  If this method is defined by classes, it is called and the returned object is pickled as the contents for the instance, instead of the contents of the instance’s dictionary. 
• object.__setstate__(state) 
  If this method is defined by classes, it is called with the unpickled state. The pickled state must be a dictionary and its items are assigned to the new instance’s dictionary. 
• object.__reduce__() 
  The __reduce__() method takes no argument and shall return either a string or preferably a tuple. 
• object.__reduce_ex__(protocol) 
  This method is similar to __reduce__ method. It takes a single integer argument. The main use for this method is to provide backwards-compatible reduce values for older Python releases. 

Example : Handling Stateful Objects 

This example 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

This code defines a TextReader class for reading and numbering lines in a text file, with custom serialization and deserialization using the pickle module. It removes the file object during pickling and restores it during unpickling to maintain line numbering. The code demonstrates creating a TextReader, reading lines, and successfully preserving its state through pickling and unpickling, allowing for continued reading from the last line.

import pickle
class TextReader:
    """Print and number lines in a text file."""
 
    def __init__(self, filename):
        self.filename = filename
        self.file = open(filename)
        self.lineno = 0
 
    def readline(self):
        self.lineno + 1
        line = self.file.readline()
        if not line:
            return None
        if line.endswith('\n'):
            line = line[:-1]
        return "%i: %s" % (self.lineno, line)
 
    def __getstate__(self):
        state = self.__dict__.copy()
        del state['file']
        return state
 
    def __setstate__(self, state):
        self.__dict__.update(state)
        file = open(self.filename)
        for _ in range(self.lineno):
            file.readline()
        self.file = file
  
reader = TextReader("Geeksforgeeks.txt")
print(reader.readline())
print(reader.readline())
new_reader = pickle.loads(pickle.dumps(reader))
print(new_reader.readline())

Output  

0: hi geeks!, this is line 1.
0: This is line 2.
0: hi geeks!, this is line 1.

And here is a editor for PickleViewer: 
Click here for the Github Repo 
Click here for the download of version 0.7.5 
Or Click here for the latest release

 

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