- Python Introduction
- Python - Overview
- Python - History and Evolution
- Python - Significance in Modern Development
- Python - Understanding Python's design philosophy and the Zen of Python
- Python - Installing Python and Setting up the Development Environment
- Python - Setting Up the Development Environment
- Python - Basic Syntax and Execution of Python Scripts
- Python - Getting Started to Python
- Python Basics
- Python - Control flow (If statements, Loops - For and While)
- Python - Operators (Arithmetic, Comparison, Logical, Assignment)
- Python - Arithmetic Operators
- Python - Comparison Operators
- Python - Logical Operators
- Python - Assignment Operators
- Python - Integers (int)
- Python - Floats (float)
- Python - Strings (str)
- Python - Loop Control Statements
- Python - Loops
- Python - If Statements
- Python - Python's built-in functions
- Python - Using Built-in Functions
- Python - Examples of Common Built-in Functions
- Python Data Structures
- Python - Understanding Lists, Tuples, Sets, and Dictionaries
- Python - Lists
- Python - Tuples
- Python - Sets
- Python - Dictionaries
- Python - Operations on Data Structures (Indexing, Slicing, Appending, Removing)
- Python - Advanced Usage
- Python - Advanced Data Manipulation Techniques (List Comprehension, Dictionary comprehension)
- Python - List Comprehensions
- Python - Dictionary Comprehensions
- Python Functions and Modules
- Python - Defining a Function
- Python - Function arguments and return values
- Python - Function Arguments
- Python - Return Values
- Python - Defining and calling functions
- Python - Using the Return Value
- Python - Calling the Function with Arguments
- Python - Creating a Module
- Python - Importing a Module
- Python - Creating a Package
- Python - Importing from a Package
- Python - Creating and importing modules and packages
- Python - Introduction to Python standard libraries.
- Python - File and Directory Access
- Python - Internet Data Handling
- Python - Networking
- Python - Data Persistence and Serialization
- Python - Data Compression and Archiving
- Python - Concurrent Execution
- Python - Mathematics
- Python - Date and Time
- Python - Development Tools
- Python Object-Oriented Programming
- Python - Understanding classes and objects
- Python - Understanding Classes
- Python - Understanding Objects
- Python - Inheritance, encapsulation, and polymorphism
- Python - The constructor, class variables, and instance variables
- Python - Magic methods and their importance
- Python Error Handling and Debugging
- Python - Exceptions
- Python - Working Code Sample Demonstrating Both
- Python - Handling exceptions with try-except blocks
- Python - Syntax Errors versus Exceptions
- Python - Using logging for Debugging and Error tracking
- Python Working with Files
- Python - Reading from and writing to files
- Python - Writing to a File
- Python - Reading from a File
- Python - Combined Example of Reading and Writing
- Python - Understanding file handling modes
- Python - Common File Handling Modes
- Python - Working with different file formats (text, CSV, JSON)
- Python - Working with Text Files
- Python - Working with JSON Files
- Python - Working with CSV Files
- Python Web Scraping
- Python - Introduction to Beautiful Soup and requests library
- Python - Beautiful Soup
- Python - Requests Library
- Python - Legality of Web Scraping
- Python - Overview of web scraping and its legality
- Python - Combined Example: Using Both Requests and Beautiful Soup
- Python - Conclusion
Python - Data Persistence and Serialization
Data Persistence and Serialization in Python
Persistence and seriality are closely related. Serialization means taking a potentially complex data structure and converting it into a string of bytes. Persistence is storing data in a way that will persist beyond the run-time of your program.
Benefits of Serialization
- Persistence: Save the state of your program's objects so they can be loaded later.
- Data Transfer: Transmit data between different systems or processes.
- Caching: Store complex objects for faster access later.
There are some common serializations in Python:
json:
Data interchange between servers and online apps frequently uses the JSON format, which can be used to encode and decode data.
pickle:
Allows Python objects to be converted to byte streams and back again, as well as to be serialized and deserialized as object structures.
CSV:
It is a simple format for storing data in tabular format. It is easy to read and write and is ideal for datasets.
Example using Pickle:
import pickle
# Create a dictionary to serialize
data = {"name": "John", "age": 30, "city": "New York"}
# Serialize the dictionary
with open("data.pkl", "wb") as file:
pickle.dump(data, file)
# Deserialize the dictionary
with open("data.pkl", "rb") as file:
loaded_data = pickle.load(file)
print(loaded_data) # output: {'name': 'John', 'age': 30, 'city': 'New York'}Example using JSON:
import json
# Create a list of dictionaries to serialize
data = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 35}]
# Serialize the list of dictionaries
with open("data.json", "w") as file:
json.dump(data, file)
# Deserialize the JSON data
with open("data.json", "r") as file:
loaded_data = json.load(file)
print(loaded_data) # output: [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 35}]Choose the Right Method
- Pickle is used in complex Python objects within your application.
- JSON is used when we exchange data with other systems or languages.
- CSV is suitable for tabular data that can be easily represented in a spreadsheet-like format.
