Python in Practice: Mastering Database Management with ORM Techniques

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Python’s versatility extends to its ability to interact with databases efficiently. In this article, we’ll dive into Object-Relational Mapping (ORM) techniques in Python for database management. ORM allows you to work with databases using Python objects, simplifying data manipulation and reducing the need for complex SQL queries. Whether you’re a beginner or an experienced developer, understanding ORM techniques is essential for building scalable and maintainable database-driven applications. We’ll provide detailed examples, practical use cases, and expected outputs to help you master ORM techniques in Python.


Before we delve into ORM techniques in Python, make sure you have the following prerequisites:

  1. A basic understanding of Python programming.
  2. Python installed on your system.
  3. Familiarity with database concepts and SQL (Structured Query Language) is helpful but not mandatory.

Understanding ORM:

ORM is a programming technique that allows you to interact with a relational database using objects, classes, and methods instead of writing raw SQL queries. Python offers various ORM libraries, with SQLAlchemy and Django ORM being popular choices.


Let’s explore SQLAlchemy, a versatile and widely-used ORM library in Python.


You can install SQLAlchemy using pip:

pip install sqlalchemy

Defining a Model:

In SQLAlchemy, you define a model (table) using a Python class. Each attribute of the class corresponds to a column in the table.

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class Employee(Base):
    __tablename__ = 'employees'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    age = Column(Integer)
    position = Column(String)

Creating a Database Session:

To interact with the database, you need to create a session.

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine('sqlite:///mydatabase.db')  # Connect to an SQLite database
Session = sessionmaker(bind=engine)
session = Session()

Inserting Data:

You can insert data into the database using the model class.

# Create new records
employee1 = Employee(name="Alice", age=25, position="Manager")
employee2 = Employee(name="Bob", age=22, position="Developer")
# Add records to the session
# Commit the changes

Querying Data:

Querying data is straightforward with SQLAlchemy.

# Query data
employees = session.query(Employee).all()
# Print the results
for employee in employees:
    print(f"ID: {}, Name: {}, Age: {employee.age}, Position: {employee.position}")


ID: 1, Name: Alice, Age: 25, Position: Manager
ID: 2, Name: Bob, Age: 22, Position: Developer

Updating and Deleting Data:

Updating and deleting data is also simplified.

# Update data
employee1.age = 26
employee1.position = "Senior Developer"
# Delete data
# Commit the changes
Author: user