kapil-malviya/serialization-deserialization

Serialization refers to the process of converting a data object (e.g., Python objects ) into a format (e.g., JSON ) that allows us to store or transmit the data and then recreate the object when needed using the reverse process of deserialization

Serialization and Deserialization in Python

Welcome to the serialization-deserialization in Python repository! This repository contains a collection of codes and explanations related to the concepts of serialization and deserialization in Python. We explore the usage of the json module for JSON serialization and deserialization, as well as pickling and unpickling objects using Python's pickle module.

Table of Contents

• Introduction
• Usage
• Contributing

Introduction

Serialization is the process of converting complex data structures, such as objects, dictionaries, or lists, into a format that can be easily stored or transmitted. Deserialization, on the other hand, involves reconstructing the original data structure from the serialized format.

In this repository, we explore two common methods of serialization and deserialization:

1. JSON Serialization and Deserialization: We demonstrate how to use Python's built-in json module to serialize Python objects into JSON format and deserialize JSON data back into Python objects.

2. Pickling and Unpickling: We delve into pickling, a Python-specific serialization process, using the pickle module. Pickling allows you to serialize and deserialize Python objects, maintaining their state.

Usage

Each subdirectory within this repository contains Python scripts that demonstrate specific serialization and deserialization concepts. To run these examples, follow these steps :

1. Clone the repository:

   git clone https://github.com/kapil-malviya/serialization-deserialization.git

2. Navigate to the desired subdirectory:

   cd serialization-deserialization/json-module-for-serialization

3. Run the Python script:

   python3 serialize.py

Contributing

Contributions to this repository are welcome! If you'd like to add more examples, improve existing ones, or fix issues, please open a pull request.

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