Connect a Python application to Lakebase Postgres

Summary: Connecting Python to Lakebase Postgres requires choosing between three drivers: psycopg (v3, synchronous), psycopg2 (legacy synchronous), and asyncpg (asyncio-based async). The guide covers project setup, .env connection string configuration, and full CRUD examples for all three drivers. Pick this over the SQLAlchemy or Django guides when you need raw SQL driver code rather than an ORM.

Connect a Python application to Lakebase Postgres

Learn how to run SQL queries from Python using psycopg, psycopg2, or asyncpg

Pre-built prompt for connecting Python applications to Neon View prompt

This guide describes how to create a Neon project and connect to it from a Python application using popular Postgres drivers. We'll cover Psycopg 3, the latest generation of the popular synchronous adapter, its predecessor Psycopg 2 (psycopg2), and asyncpg, an asynchronous adapter for use with asyncio.

You'll learn how to connect to the database from a Python application and perform basic Create, Read, Update, and Delete (CRUD) operations.

Prerequisites

A Neon account. If you do not have one, see Sign up.
Python 3.8 or later. If you do not have Python installed, install it from the Python website.

Create a Neon project

If you do not have one already, create a Neon project.

Navigate to the Projects page in the Neon Console.
Click New Project.
Specify your project settings and click Create Project.

Your project is created with a ready-to-use database named neondb. In the following steps, you will connect to this database from your Python application.

Create a Python project

For your Python project, create a project directory, set up a virtual environment, and install the required libraries.

Create a project directory and change into it.
Bash
```
mkdir neon-python-quickstart
cd neon-python-quickstart
```
Open the directory in your preferred code editor (for example, VS Code, PyCharm).

Create and activate a Python virtual environment. This isolates your project's dependencies from your system's Python environment.

MacOS / Linux / Windows Subsystem for Linux (WSL)

Bash

# Create a virtual environment
python3 -m venv venv

# Activate the virtual environment
source venv/bin/activate

Windows

Bash

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
.\venv\Scripts\activate

Install the required libraries using pip.
- psycopg: The modern, synchronous database adapter for connecting to Postgres (Psycopg 3). Recommended for new projects.
- psycopg2-binary: An older, widely-used synchronous database adapter.
- asyncpg: The asynchronous database adapter for connecting to Postgres.
- python-dotenv: A helper library to manage environment variables.
Bash
```
pip install "psycopg[binary]" psycopg2-binary asyncpg python-dotenv
```
If you're not sure which driver to use, use psycopg (v3). It's the actively maintained successor to psycopg2. This guide provides examples for all three in case your project already depends on one of the others.

Store your Neon connection string

Create a file named .env in your project's root directory. This file will securely store your database connection string.

In the Neon Console, select your project on the Dashboard.
Click Connect in the Console nav to open the Connect to your branch modal.
Copy the connection string, which includes your password.
Add the connection string to your .env file as shown below.
text
```
DATABASE_URL="postgresql://[user]:[password]@[neon_hostname]/[dbname]?sslmode=require&channel_binding=require"
```
Replace [user], [password], [neon_hostname], and [dbname] with your actual database credentials.

Examples

This section provides example Python scripts that demonstrate how to connect to your Neon database and perform basic operations such as creating a table, reading data, updating data, and deleting data.

Create a table and insert data

In your project directory, create a file named create_table.py and add the code for your preferred library. This script connects to your Neon database, creates a table named books, and inserts some sample data into it.

psycopg (v3)

create_table.py

import os

import psycopg
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Get the connection string from the environment variable
conn_string = os.getenv("DATABASE_URL")

try:
    with psycopg.connect(conn_string) as conn:
        print("Connection established")

        # Open a cursor to perform database operations
        with conn.cursor() as cur:
            # Drop the table if it already exists
            cur.execute("DROP TABLE IF EXISTS books;")
            print("Finished dropping table (if it existed).")

            # Create a new table
            cur.execute("""
                CREATE TABLE books (
                    id SERIAL PRIMARY KEY,
                    title VARCHAR(255) NOT NULL,
                    author VARCHAR(255),
                    publication_year INT,
                    in_stock BOOLEAN DEFAULT TRUE
                );
            """)
            print("Finished creating table.")

            # Insert a single book record
            cur.execute(
                "INSERT INTO books (title, author, publication_year, in_stock) VALUES (%s, %s, %s, %s);",
                ("The Catcher in the Rye", "J.D. Salinger", 1951, True),
            )
            print("Inserted a single book.")

            # Data to be inserted
            books_to_insert = [
                ("The Hobbit", "J.R.R. Tolkien", 1937, True),
                ("1984", "George Orwell", 1949, True),
                ("Dune", "Frank Herbert", 1965, False),
            ]

            # Insert multiple books at once
            cur.executemany(
                "INSERT INTO books (title, author, publication_year, in_stock) VALUES (%s, %s, %s, %s);",
                books_to_insert,
            )

            print("Inserted 3 rows of data.")
            # The transaction is committed automatically when the 'with' block exits in psycopg (v3)

except Exception as e:
    print("Connection failed.")
    print(e)

psycopg2

create_table.py

import os

import psycopg2
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Get the connection string from the environment variable
conn_string = os.getenv("DATABASE_URL")
conn = None

try:
    with psycopg2.connect(conn_string) as conn:
        print("Connection established")

        # Open a cursor to perform database operations
        with conn.cursor() as cur:
            # Drop the table if it already exists
            cur.execute("DROP TABLE IF EXISTS books;")
            print("Finished dropping table (if it existed).")

            # Create a new table
            cur.execute("""
                CREATE TABLE books (
                    id SERIAL PRIMARY KEY,
                    title VARCHAR(255) NOT NULL,
                    author VARCHAR(255),
                    publication_year INT,
                    in_stock BOOLEAN DEFAULT TRUE
                );
            """)
            print("Finished creating table.")

            # Insert a single book record
            cur.execute(
                "INSERT INTO books (title, author, publication_year, in_stock) VALUES (%s, %s, %s, %s);",
                ("The Catcher in the Rye", "J.D. Salinger", 1951, True),
            )
            print("Inserted a single book.")

            # Data to be inserted
            books_to_insert = [
                ("The Hobbit", "J.R.R. Tolkien", 1937, True),
                ("1984", "George Orwell", 1949, True),
                ("Dune", "Frank Herbert", 1965, False),
            ]

            # Insert multiple books at once
            cur.executemany(
                "INSERT INTO books (title, author, publication_year, in_stock) VALUES (%s, %s, %s, %s);",
                books_to_insert,
            )

            print("Inserted 3 rows of data.")

            # Commit the changes to the database
            conn.commit()

except Exception as e:
    print("Connection failed.")
    print(e)

asyncpg

create_table.py

import asyncio
import os

import asyncpg
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

async def run():
    # Get the connection string from the environment variable
    conn_string = os.getenv("DATABASE_URL")
    conn = None

    try:
        conn = await asyncpg.connect(conn_string)
        print("Connection established")

        # Drop the table if it already exists
        await conn.execute("DROP TABLE IF EXISTS books;")
        print("Finished dropping table (if it existed).")

        # Create a new table
        await conn.execute("""
            CREATE TABLE books (
                id SERIAL PRIMARY KEY,
                title VARCHAR(255) NOT NULL,
                author VARCHAR(255),
                publication_year INT,
                in_stock BOOLEAN DEFAULT TRUE
            );
        """)
        print("Finished creating table.")

        # Insert a single book record (using $1, $2 for placeholders)
        await conn.execute(
            "INSERT INTO books (title, author, publication_year, in_stock) VALUES ($1, $2, $3, $4);",
            "The Catcher in the Rye",
            "J.D. Salinger",
            1951,
            True,
        )
        print("Inserted a single book.")

        # Data to be inserted
        books_to_insert = [
            ("The Hobbit", "J.R.R. Tolkien", 1937, True),
            ("1984", "George Orwell", 1949, True),
            ("Dune", "Frank Herbert", 1965, False),
        ]

        # Insert multiple books at once
        await conn.executemany(
            "INSERT INTO books (title, author, publication_year, in_stock) VALUES ($1, $2, $3, $4);",
            books_to_insert,
        )
        print("Inserted 3 rows of data.")

    except Exception as e:
        print("Connection failed.")
        print(e)
    finally:
        if conn:
            await conn.close()

# Run the asynchronous function
asyncio.run(run())

The above code does the following:

Load the connection string from the .env file.
Connect to the Neon database.
Drop the books table if it already exists to ensure a clean slate.
Create a table named books with columns for id, title, author, publication_year, and in_stock.
Insert a single book record.
Insert multiple book records.
Commit the changes to the database (in psycopg, this happens automatically on exiting the with block).

Run the script using the following command:

Bash

python create_table.py

When the code runs successfully, it produces the following output:

Output

Connection established
Finished dropping table (if it existed).
Finished creating table.
Inserted a single book.
Inserted 3 rows of data.

Read data

In your project directory, create a file named read_data.py. This script connects to your Neon database and retrieves all rows from the books table.

psycopg (v3)

read_data.py

import os

import psycopg
from dotenv import load_dotenv

load_dotenv()

conn_string = os.getenv("DATABASE_URL")

try:
    with psycopg.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Fetch all rows from the books table
            cur.execute("SELECT * FROM books ORDER BY publication_year;")
            rows = cur.fetchall()

            print("\n--- Book Library ---")
            for row in rows:
                print(
                    f"ID: {row[0]}, Title: {row[1]}, Author: {row[2]}, Year: {row[3]}, In Stock: {row[4]}"
                )
            print("--------------------\n")

except Exception as e:
    print("Connection failed.")
    print(e)

psycopg2

read_data.py

import os

import psycopg2
from dotenv import load_dotenv

load_dotenv()

conn_string = os.getenv("DATABASE_URL")
conn = None

try:
    with psycopg2.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Fetch all rows from the books table
            cur.execute("SELECT * FROM books ORDER BY publication_year;")
            rows = cur.fetchall()

            print("\n--- Book Library ---")
            for row in rows:
                print(
                    f"ID: {row[0]}, Title: {row[1]}, Author: {row[2]}, Year: {row[3]}, In Stock: {row[4]}"
                )
            print("--------------------\n")

except Exception as e:
    print("Connection failed.")
    print(e)

asyncpg

read_data.py

import asyncio
import os

import asyncpg
from dotenv import load_dotenv

load_dotenv()

async def run():
    conn_string = os.getenv("DATABASE_URL")
    conn = None

    try:
        conn = await asyncpg.connect(conn_string)
        print("Connection established")

        # Fetch all rows from the books table
        rows = await conn.fetch("SELECT * FROM books ORDER BY publication_year;")

        print("\n--- Book Library ---")
        for row in rows:
            # asyncpg rows can be accessed by index or column name
            print(
                f"ID: {row['id']}, Title: {row['title']}, Author: {row['author']}, Year: {row['publication_year']}, In Stock: {row['in_stock']}"
            )
        print("--------------------\n")

    except Exception as e:
        print("Connection failed.")
        print(e)
    finally:
        if conn:
            await conn.close()

asyncio.run(run())

The above code does the following:

Load the connection string from the .env file.
Connect to the Neon database.
Use a SQL SELECT statement to fetch all rows from the books table, ordered by publication_year.
Print each book's details in a formatted output.

Run the script using the following command:

Bash

python read_data.py

When the code runs successfully, it produces the following output:

Output

Connection established

--- Book Library ---
ID: 2, Title: The Hobbit, Author: J.R.R. Tolkien, Year: 1937, In Stock: True
ID: 3, Title: 1984, Author: George Orwell, Year: 1949, In Stock: True
ID: 1, Title: The Catcher in the Rye, Author: J.D. Salinger, Year: 1951, In Stock: True
ID: 4, Title: Dune, Author: Frank Herbert, Year: 1965, In Stock: False
--------------------

Update data

In your project directory, create a file named update_data.py. This script connects to your Neon database and updates the stock status of the book 'Dune' to True.

psycopg (v3)

update_data.py

import os

import psycopg
from dotenv import load_dotenv

load_dotenv()
conn_string = os.getenv("DATABASE_URL")

try:
    with psycopg.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Update a data row in the table
            cur.execute(
                "UPDATE books SET in_stock = %s WHERE title = %s;", (True, "Dune")
            )
            print("Updated stock status for 'Dune'.")

except Exception as e:
    print("Connection failed.")
    print(e)

psycopg2

update_data.py

import os

import psycopg2
from dotenv import load_dotenv

load_dotenv()
conn_string = os.getenv("DATABASE_URL")
conn = None

try:
    with psycopg2.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Update a data row in the table
            cur.execute(
                "UPDATE books SET in_stock = %s WHERE title = %s;", (True, "Dune")
            )
            print("Updated stock status for 'Dune'.")

            # Commit the changes
            conn.commit()

except Exception as e:
    print("Connection failed.")
    print(e)

asyncpg

update_data.py

import asyncio
import os

import asyncpg
from dotenv import load_dotenv

load_dotenv()

async def run():
    conn_string = os.getenv("DATABASE_URL")
    conn = None
    try:
        conn = await asyncpg.connect(conn_string)
        print("Connection established")

        # Update a data row in the table
        await conn.execute(
            "UPDATE books SET in_stock = $1 WHERE title = $2;", True, "Dune"
        )
        print("Updated stock status for 'Dune'.")

    except Exception as e:
        print("Connection failed.")
        print(e)
    finally:
        if conn:
            await conn.close()

asyncio.run(run())

The above code does the following:

Load the connection string from the .env file.
Connect to the Neon database.
Use a SQL UPDATE statement to change the in_stock status of the book 'Dune' to True.
Commit the changes to the database.

Run the script using the following command:

Bash

python update_data.py

After running this script, you can run read_data.py again to verify that the row was updated.

Bash

python read_data.py

When the code runs successfully, it produces the following output:

Output

Connection established

--- Book Library ---
ID: 2, Title: The Hobbit, Author: J.R.R. Tolkien, Year: 1937, In Stock: True
ID: 3, Title: 1984, Author: George Orwell, Year: 1949, In Stock: True
ID: 1, Title: The Catcher in the Rye, Author: J.D. Salinger, Year: 1951, In Stock: True
ID: 4, Title: Dune, Author: Frank Herbert, Year: 1965, In Stock: True
--------------------

You can see that the stock status for 'Dune' has been updated to True.

Delete data

In your project directory, create a file named delete_data.py. This script connects to your Neon database and deletes the book '1984' from the books table.

psycopg (v3)

delete_data.py

import os

import psycopg
from dotenv import load_dotenv

load_dotenv()
conn_string = os.getenv("DATABASE_URL")

try:
    with psycopg.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Delete a data row from the table
            cur.execute("DELETE FROM books WHERE title = %s;", ("1984",))
            print("Deleted the book '1984' from the table.")

except Exception as e:
    print("Connection failed.")
    print(e)

psycopg2

delete_data.py

import os

import psycopg2
from dotenv import load_dotenv

load_dotenv()
conn_string = os.getenv("DATABASE_URL")
conn = None

try:
    with psycopg2.connect(conn_string) as conn:
        print("Connection established")
        with conn.cursor() as cur:
            # Delete a data row from the table
            cur.execute("DELETE FROM books WHERE title = %s;", ("1984",))
            print("Deleted the book '1984' from the table.")

            # Commit the changes
            conn.commit()

except Exception as e:
    print("Connection failed.")
    print(e)

asyncpg

delete_data.py

import asyncio
import os

import asyncpg
from dotenv import load_dotenv

load_dotenv()

async def run():
    conn_string = os.getenv("DATABASE_URL")
    conn = None
    try:
        conn = await asyncpg.connect(conn_string)
        print("Connection established")

        # Delete a data row from the table
        await conn.execute("DELETE FROM books WHERE title = $1;", "1984")
        print("Deleted the book '1984' from the table.")

    except Exception as e:
        print("Connection failed.")
        print(e)
    finally:
        if conn:
            await conn.close()

asyncio.run(run())

The above code does the following:

Load the connection string from the .env file.
Connect to the Neon database.
Use a SQL DELETE statement to remove the book '1984' from the books table.
Commit the changes to the database.

Run the script using the following command:

Bash

python delete_data.py

After running this script, you can run read_data.py again to verify that the row was deleted.

Bash

python read_data.py

When the code runs successfully, it produces the following output:

Output

Connection established

--- Book Library ---
ID: 2, Title: The Hobbit, Author: J.R.R. Tolkien, Year: 1937, In Stock: True
ID: 1, Title: The Catcher in the Rye, Author: J.D. Salinger, Year: 1951, In Stock: True
ID: 4, Title: Dune, Author: Frank Herbert, Year: 1965, In Stock: True
--------------------

You can see that the book '1984' has been successfully deleted from the books table.

Next steps: Using an ORM or framework

While this guide demonstrates how to connect to Neon using raw SQL queries, for more advanced and maintainable data interactions in your Python applications, consider using an Object-Relational Mapping (ORM) framework. ORMs not only let you work with data as objects but also help manage schema changes through automated migrations keeping your database structure in sync with your application models.

Explore the following resources to learn how to integrate ORMs with Neon:

Next steps: Neon backend services

Add Object Storage: S3-compatible file storage that branches with your database
Call an LLM with AI Gateway: Access foundation models from Anthropic, OpenAI, Google, and more with one credential

Resources

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Neon Docs

Connect a Python application to Lakebase Postgres

Connect a Python application to Lakebase Postgres

Prerequisites

Create a Neon project

Create a Python project

Store your Neon connection string

Examples

Create a table and insert data

Read data

Update data

Delete data

Next steps: Using an ORM or framework

Next steps: Neon backend services

Resources

Neon Docs

Connect a Python application to Lakebase Postgres

Prerequisites

Create a Neon project

Create a Python project

Store your Neon connection string

Examples

Create a table and insert data

Read data

Update data

Delete data

Next steps: Using an ORM or framework

Next steps: Neon backend services

Resources

Related docs (Languages)