Data Archive

SQLAlchemy

What is SQLAlchemy?

SQLAlchemy is a Python SQL toolkit and Object Relational Mapper (ORM) that provides tools to interact with databases in a more Pythonic way. It allows you to work with relational databases such as MySQL, PostgreSQL, SQLite, and others without writing raw SQL queries manually.

SQLAlchemy vs. sqlite3

Key Features of SQLAlchemy

  1. Database Connectivity

    • Provides a unified interface to connect to different databases.

    • Uses connection strings to establish a database connection.

    • Example:

      from sqlalchemy import create_engine
 
engine = create_engine('mysql+pymysql://user:password@localhost:3306/database_name')

  2. SQL Query Execution

    • Allows execution of raw SQL queries using Pandas:

      import pandas as pd
df = pd.read_sql("SELECT  FROM customers", engine)

  3. ORM (Object Relational Mapping)

    • Converts database tables into Python objects (classes).

    • Eliminates the need to write SQL queries manually.

    • Example:

      from sqlalchemy.orm import declarative_base
from sqlalchemy import Column, Integer, String
 
Base = declarative_base()
 
class Customer(Base):
    __tablename__ = 'customers'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    phone_number = Column(String)

  4. Transaction Management

    • Provides robust control over commit and rollback operations.
    • Ensures data integrity by handling failures safely.

  5. Efficient Query Building

    • Allows writing Pythonic queries instead of raw SQL.

    • Example:

      from sqlalchemy.orm import sessionmaker
 
Session = sessionmaker(bind=engine)
session = Session()
 
customers = session.query(Customer).filter_by(name="John Doe").all()

  6. Supports Multiple Databases

    • Works with MySQL, PostgreSQL, SQLite, SQL Server, etc.
    • Easily switch databases without changing the core logic.

Why Use SQLAlchemy?

  • Reduces SQL complexity: Write Python code instead of SQL queries.
  • Prevents SQL injection: ORM prevents unsafe queries.
  • Improves maintainability: Easier to refactor code.
  • Handles connection pooling: Manages database connections efficiently.
  • Works with Pandas: Can load and save data directly to databases.

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