python serialization | OnlineITGuru
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Python is the top programming language to develop several kinds of applications. This language is suggested by many developers to develop all kinds of applications including large and small. Today many people opt for these programming languages due to its great extensive features. Moreover, this programming language has a rich library set. And this is one of the reasons for many developers to opt for this programming language. As mentioned in the previous points, besides the rich library set offers several important features. One of these important features is Serialization. Today, in this article of the blog, I’m going to explain the concept of Serialization in python.

There is no end for some phrases to be written. One such phrase is NECESSITY IS THE MOTHER OF INVENTION. Today scientists do not invent anything without the need?  Hence prior to knowing about the serialization in python, let us initially know

Why do we need serialization?

Data is the biggest gift that the internet has given to the people in these days. This data plays a major role in the analysis of the business.  Some companies get data from their previous traffic. While the other approach some other third parties for the data.  Moreover, in these modern days, it is not possible for all the people to work at the same place. Hence these people make use of the internet to transfer the data from one place to another. But it takes a lot for time on the internet to transfer the actual data from place to the other.

So in order to send the same amount of data, people use some techniques to transfer from place to the other in a short span of time. Moreover, with this technique, people can send data through the internet at low weight. As mentioned above, there are many ways to send data over the internet. One such way is Serialization.

So  I hope you people have got enough idea of the need for serialization, now lets us move forward to

What is Serialization?

Serialization is the process of converting the object (or) data into some other format where they can be stored and retrieved later. Since the data is transformed and stored in another format,  it allows the features of restoring and deserializing the data from the serialized form. Besides the data conversion, this serialization also allows us to reduce the data size. Hence this data gets fit in the given data storage (or) the bandwidth.

python serialization | OnlineITGuru

The reverse process of this serialization is known as Deserialization. The terms serialization and deserialization were also known as Pickling and unpicking in python.

So I hope you people have got enough idea regarding the serialization, let us move forward with

Module interface for Picking and Unpicking:

In the pickle module, data is python specific. Hence, it is important to write the essential code, while performing the serializing and deserializing.  For the purpose of serialization, these programming languages use dumps () and for deserialization, it uses loads(). Through picking, we can convert the python object hierarchy to the binary format that can be stored.  And in order to pickle the object, we need to import the pickle module and call the dumps function. And this utilizes the object to pickled as the parameter.

import pickle
​
class Animal:
    def __init__(self, number_of_paws, color):
        self.number_of_paws = number_of_paws
        self.color = color
​
class Sheep(Animal):
    def __init__(self, color):
        Animal.__init__(self, 4, color)
​
mary = Sheep("white")
​
print (str.format("My sheep mary is {0} and has {1} paws", mary.color, mary.number_of_paws))
my_pickled_mary = pickle.dumps(mary)
​
print ("Would you like to see her pickled? Here she is!")
print (my_pickled_mary)

If you observe the above code,  we have created an instance for the sheep class and then transforming this in the array of bytes. Moreover, we people can easily store this bytes array on the binary file (or) in the database field. And we can restore this file any time, as per the requirement

As mentioned above, since the python data is python specific, it does not impose any restriction on the external data like JSON (or) XDR. But this makes the inability for the non -python programs to reconstruct the picked python objects. 

Get more knowledge on Serialization  from live experts at python online Course 

And for the purpose of pickling, it uses several different protocols, let us discuss, one by one in details

Python Picking Protocols:

python has five different versions for the purpose of pickling. They are:

a) Protocol Version ‘0’:  It the original as well as the human-readable protocol. It is backward compactable and supports the earlier version of python.

b)Protocol Version 1: This is an old binary format. This format is compactable with the earlier version of python

c)Protocol Version 2: This version is added in python 2.3. And it provides more efficient pickling of new-style-classes.

d)Protocol Version 3: This version is introduced in Python 3.0.  This version supports byte objects and cannot be unpickled by Python 2.x. This protocol version suits best at the time of the compatibility of other python versions.

e)Protocol Version 4:  This version came into existence from python 3.4.  This supports large objects with different kinds and certain data format optimizations.

But in order to serialize the data using the fundamental objects of data in python, I personally suggest you utilize the marshal module. This module supports the function to read and write the python value into the binary format. So let us discuss briefly Marshal

What is Marshal?

This Marshal module provides the feature of object serialization that is similar to that of the pickle module. Even though this module does not provide support for the data maintenance and the transmission of the objects, it supports the interpreter to perform read and write operations to the compiled version of the python modules. Some people call this marshal module as internal object serialization. And this is so-called due to the varying data format usage.   This module defines the load() and dump() functions to read and write the marshaled objects

dump(): It supports the objects with standard data types. While marshaling these python objects, it returns a similar byte object

loads(): By using this function, one can convert a byte object to the python object.  And if the conversion fails to provide the required python object, it returns the  TypeError (or) the ValueError.

How Pickling is beneficial?

If an application requires a minimal amount of data persistency, then pickling suits best. This picking helps in saving the data on the disk.  Moreover, this picking is the best option while working with objects related to machine learning. Since this model does not require any rewrites, this model suits best w.r.t time management.

Where pickling can be applied?

This picking can be applied to different data types. These data types include boolean, integers, floats, complex numbers and so on.  But anyway in order to pickle any datatypes, it is essential to pickle the functions as well as the classes.  Moreover, this pickling is python specific, we cannot use this across the different programming languages. Besides the file executed in the python version is not compactable to execute with the other python versions

Each concept has some pros and cons. Besides the cons, this serialization is the best practice that simplifies the storage of the data scientist.  And many developers today inform that it is one of the best features to implement the data conversation. In simple words, these picking and unpickling are the best ways to transform the data. And you people can the practical knowledge of this serialization from live industry experts at python online training

 
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