Table of Content

How to Use PostgreSQL with Python: A Beginner's Guide

Prerequisites for Connecting Python to MySQL

Essential Python Libraries to Use with PostgreSQL

Step-by-Step Guide to Connect Python with PostgreSQL

Real-World Use Cases of Python with PostgreSQL

Best Practices for Efficient Use of PostgreSQL in Python

Conclusion

Utilizing data

How to Use PostgreSQL with Python: A Beginner's Guide

October 24, 2025

When using databases in Python, PostgreSQL is a solid and effective choice. Pairing PostgreSQL with Python allows developers to create efficient data-driven applications, streamline workflows, and handle complex data tasks. At Zignuts Technolab, our development experts often leverage this combination to build powerful, scalable backend systems for clients. This guide covers essential tools and offers step-by-step instructions for using PostgreSQL with Python, including direct SQL queries, Object Relational Mapping (ORM), and asynchronous interaction.

Prerequisites for Connecting Python to MySQL

Before getting started, ensure you have the following tools installed:

Python

Make sure Python is on your system. You can check by running:

If it's not installed, download the latest version from Download Python

Postgresql

Confirm that PostgreSQL is installed on your system. You can check by running:

If it's not installed, download the latest version from Download PostgreSQL

Essential Python Libraries to Use with PostgreSQL

psycopg2: A PostgreSQL adapter for Python. Ideal for direct, low-level interaction.
SQLAlchemy: A Python SQL toolkit and ORM. Simplifies database access and modelling.
asyncpg: A fast PostgreSQL client library designed for asyncio.
SQLAlchemy asyncio support: Enables the use of asyncpg with SQLAlchemy ORM for modern async applications.

Step-by-Step Guide to Connect Python with PostgreSQL

Step 1: Install Required Libraries

Step 2: Setup PostgreSQL

Start a PostgreSQL shell session:

Add PostgreSQL to the path, then run:

Create a database and a user using the PostgreSQL shell:

Step 3: Connect to PostgreSQL

Option 1: Connect Using psycopg2

Option 2: Use SQLAlchemy (Sync) with psycopg2

Code

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import declarative_base, sessionmaker

Base = declarative_base()

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String)

engine = create_engine("postgresql+psycopg2://your_username:your_password@localhost/your_db_name")
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()

# CREATE
session.add(User(name="Bob"))
session.commit()

# READ
users = session.query(User).all()
for user in users:
    print(user.id, user.name)

# UPDATE
user = session.query(User).filter_by(id=1).first()
if user:
    user.name = "Bobby"
    session.commit()

# DELETE
user = session.query(User).filter_by(id=1).first()
if user:
    session.delete(user)
    session.commit()

session.close()

Option 3: Use SQLAlchemy (Async) with asyncpg

Code

import asyncio
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import declarative_base, sessionmaker
from sqlalchemy import Column, Integer, String, select

Base = declarative_base()

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String)

async def main():
    engine = create_async_engine("postgresql+asyncpg://your_username:your_password@localhost/your_db_name")
    async with engine.begin() as conn:
        await conn.run_sync(Base.metadata.create_all)

    async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

    async with async_session() as session:
        # CREATE
        session.add(User(name="Charlie"))
        await session.commit()

        # READ
        result = await session.execute(select(User))
        users = result.scalars().all()
        for user in users:
            print(user.id, user.name)

        # UPDATE
        user = await session.get(User, 1)
        if user:
            user.name = "Charles"
            await session.commit()

        # DELETE
        user = await session.get(User, 1)
        if user:
            await session.delete(user)
            await session.commit()

asyncio.run(main())

Option 4: Use asyncpg (Raw Async PostgreSQL Access)

Code

import asyncio
import asyncpg

async def main():
    conn = await asyncpg.connect(
        user="your_username",
        password="your_password",
        database="your_db_name",
        host="localhost"
    )

    # CREATE table
    await conn.execute("""
        CREATE TABLE IF NOT EXISTS users (
            id SERIAL PRIMARY KEY,
            name TEXT
        )
    """)

    # INSERT
    await conn.execute("INSERT INTO users(name) VALUES($1)", "David")

    # SELECT
    rows = await conn.fetch("SELECT * FROM users")
    for row in rows:
        print(dict(row))

    # UPDATE
    await conn.execute("UPDATE users SET name = $1 WHERE id = $2", "Dave", 1)

    # DELETE
    await conn.execute("DELETE FROM users WHERE id = $1", 1)

    await conn.close()

asyncio.run(main())

Real-World Use Cases of Python with PostgreSQL

Web applications using frameworks like Django, Flask, or FastAPI
Data engineering pipelines with PostgreSQL as the main store
Automation systems and schedulers for business tasks
AI and ML applications that store embeddings or training metadata
Reporting dashboards that source data from PostgreSQL

Best Practices for Efficient Use of PostgreSQL in Python

Use connection pooling in production (SQLAlchemy handles this internally)
Close connections explicitly
Avoid hardcoding credentials; use environment variables

.env file example:

connector.py:

Add .env to .gitignore
Consider using ORMs like SQLAlchemy for complex applications to simplify data modelling and queries.
Use prepared statements or parameterised queries to avoid SQL injection.
Monitor and log long-running queries.
Index frequently queried columns.
Use Alembic for versioned schema migrations.

Conclusion

Using PostgreSQL with Python is a popular and effective choice for backend development, data processing, and scalable services. By selecting the right library and following best practices, you can create reliable, efficient, and secure applications. Whether you are starting with basic scripts or deploying asynchronous services, PostgreSQL and Python offer the flexibility and performance necessary for modern development.

Darshan Thakkar

Passionate developer with expertise in building scalable web applications and solving complex problems. Loves exploring new technologies and sharing coding insights.