Python Data Persistence - Pickle Module

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Pythons terminology for serialization and deserialization is pickling and unpickling respectively. The pickle module in Python library, uses very Python specific data format. Hence, non-Python applications may not be able to deserialize pickled data properly. It is also advised not to unpickle data from un-authenticated source.

The serialized (pickled) data can be stored in a byte string or a binary file. This module defines dumps() and loads() functions to pickle and unpickle data using byte string. For file based process, the module has dump() and load() function.

Pythons pickle protocols are the conventions used in constructing and deconstructing Python objects to/from binary data. Currently, pickle module defines 5 different protocols as listed below −

Example - Object representation

The pickle module consists of dumps() function that returns a string representation of pickled data.

main.py

from pickle import dumps
dct={"name":"Ravi", "age":23, "Gender":"M","marks":75}
dctstring=dumps(dct)
print (dctstring)

Output

b'\x80\x03}q\x00(X\x04\x00\x00\x00nameq\x01X\x04\x00\x00\x00Raviq\x02X\x03\x00\x00\x00ageq\x03K\x17X\x06\x00\x00\x00Genderq\x04X\x01\x00\x00\x00Mq\x05X\x05\x00\x00\x00marksq\x06KKu

Example - Loading String in dictionary

Use loads() function, to unpickle the string and obtain original dictionary object.

main.py

from pickle import dumps, loads

dct={"name":"Ravi", "age":23, "Gender":"M","marks":75}
dctstring=dumps(dct)

dct=loads(dctstring)
print (dct)

Output

{'name': 'Ravi', 'age': 23, 'Gender': 'M', 'marks': 75}

Example - Persisting Objects on Disk

Pickled objects can also be persistently stored in a disk file, using dump() function and retrieved using load() function.

main.py

import pickle
f=open("data.txt","wb")
dct={"name":"Ravi", "age":23, "Gender":"M","marks":75}
pickle.dump(dct,f)
f.close()

#to read
import pickle
f=open("data.txt","rb")
d=pickle.load(f)
print (d)
f.close()

Output

{'name': 'Ravi', 'age': 23, 'Gender': 'M', 'marks': 75}

The pickle module also provides, object oriented API for serialization mechanism in the form of Pickler and Unpickler classes.

As mentioned above, just as built-in objects in Python, objects of user defined classes can also be persistently serialized in disk file. In following program, we define a User class with name and mobile number as its instance attributes. In addition to the __init__() constructor, the class overrides __str__() method that returns a string representation of its object.

class User:
   def __init__(self,name, mob):
      self.name=name
      self.mobile=mob
   def __str__(self):
return ('Name: {} mobile: {} '. format(self.name, self.mobile))

Example - Serializing Object

To pickle object of above class in a file we use pickler class and its dump() method.

main.py

from pickle import Pickler

class User:
   def __init__(self, name, email, mob):
      self.name=name
      self.email=email
      self.mobile=mob
   def __str__(self):
      return ('Name: {} Email: {} mobile: {} '. format(self.name, self.email, self.mobile))

user1=User('Rajani', 'raj@gmail.com', '1234567890')
file=open('userdata','wb')
Pickler(file).dump(user1)
file.close()

Example - Deserializing Object

Conversely, Unpickler class has load() method to retrieve serialized object as follows −

main.py

from pickle import Unpickler

class User:
   def __init__(self, name, email, mob):
      self.name=name
      self.email=email
      self.mobile=mob
   def __str__(self):
      return ('Name: {} Email: {} mobile: {} '. format(self.name, self.email, self.mobile))
      
file=open('userdata','rb')
user1=Unpickler(file).load()
print (user1)

Output

Name: Rajani Email: raj@gmail.com mobile: 1234567890