In this Article we're going to see :-
serializing object *(a process called serialization)
de-serializing object *(a process called de-serialization)
let's start with the definations :-
Serialization :-
Serialization is the process of converting data structures or object state into a format that can be stored or saved in memory for latter use, that format in which the object state saved will follow some rules which will help in de-serializing the object later.
De-serialization :-
De-serialization is just opposite of serialization in De-serialization ,we get the object state back to recreate the original object from the serialized format.
Now that we have understand defination of serialization and de-serialization.
let's see some place where they are very helpful :-
While sending data over the internet the data is transfered mostly in Json form (JSON is a format that encodes objects in a string and deserialization it (convert string -> object))
Hugh Machine Learning Model that are trained on hugh amount of data ,need to stored in some form for later use ,we cannot re-train them again and again , that is where serialization help
to store ML model state.Big Data system uses serialization and deseralization to store large amount of data.
we have seen only few applications, serialization and deserialization are pretty much applied whererever there is need to store data in some form to use it for later use.
Now we going to look how to do Serialzation and Deserializatiom in Python
Pickle :-
Python pickle module is used for serializing and de-serializing a Python object structure. Any object in Python can be pickled so that it can be saved on disk.
Pickling is a way to convert a python object (list, dict, etc.) into a character stream
It saves the find the .pkl format
methods available :-
dump() − The dump() method serializes to an open file (file-like object).
dumps() − Serializes to a string
load() − Deserializes from an open-like object.
loads() − Deserializes from a string.
Serializing the object
import pickle class Student: def __init__(self,firstname,lastname,age,standard): self.firstname = firstname self.lastname = lastname self.age = age self.standard = standard def showinfo(self): print(f"Firstname :- {self.firstname}") print(f"Lastname :- {self.lastname}") print(f"Age :- {self.age}") print(f"Standarad :- {self.standard}") student1 = Student("Adarsh","Raven",21,"12th") student2 = Student("Ankit",'Raven',24,'11th') # Student Info print("Student1 :- ") student1.showinfo() print("\nStudent2 :- ") student2.showinfo() # Serializing object picked_student1 = pickle.dumps(student1) picked_student2 = pickle.dumps(student2) # Object stored in Byte steam print("serialized student",picked_student1) Output :- Student1 :- Firstname :- Adarsh Lastname :- Raven Age :- 21 Standarad :- 12th Student2 :- Firstname :- Ankit Lastname :- Raven Age :- 24 Standarad :- 11th serialized student b'\x80\x03c__main__\nStudent\nq\x00)\x81q\x01}q\x02(X\t\x00\x00\x00firstnameq\x03X\x06\x00\x00\x00Adarshq\x04X\x08\x00\x00\x00lastnameq\x05X\x05\x00\x00\x00Ravenq\x06X\x03\x00\x00\x00ageq\x07K\x15X\x08\x00\x00\x00standardq\x08X\x04\x00\x00\x0012thq\tub.' Deserializing the same object
orignal_student1 = pickle.loads(picked_student1) orignal_student2 = pickle.loads(picked_student2) print("\n\nAfter Getting object back from the saved state :- \n") print("Student1 :- ") orignal_student1.showinfo() print("\nStudent2 :- ") orignal_student2.showinfo() Output :- After Getting object back from the saved state :- Student1 :- Firstname :- Adarsh Lastname :- Raven Age :- 21 Standarad :- 12th Student2 :- Firstname :- Ankit Lastname :- Raven Age :- 24 Standarad :- 11th there are also other ways available to do the same task
we have covered in the above example, it will mention resources to those if you're intrested :-
:-)
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