2253 SQL 917d8914

SQL API Reference

Query databases and DataFrames using SQL with reactive integration.

Overview

marimo enables mixing Python and SQL through mo.sql(). Query Python DataFrames, databases, files, and APIs, with results returned as DataFrames.

Installation

pip install "marimo[sql]"

Basic Usage

mo.sql

Execute SQL queries.

import marimo as mo

# Query a Python DataFrame
result = mo.sql(f"SELECT * FROM {df} WHERE value > 100")

# With f-string interpolation for reactivity
threshold = mo.ui.slider(0, 100, value=50)
result = mo.sql(f"SELECT * FROM {df} WHERE value > {threshold.value}")

Creating SQL Cells

Three methods in the editor:

1. Right-click "add cell" → "SQL cell"
2. Convert empty cell via context menu
3. Click SQL button at notebook bottom

Querying DataFrames

Reference Python DataFrames directly by variable name:

# Python cell
import pandas as pd
sales_df = pd.DataFrame({"product": ["A", "B"], "revenue": [100, 200]})

# SQL cell
result = mo.sql(f"""
    SELECT product, revenue
    FROM {sales_df}
    WHERE revenue > 150
""")

Database Connections

UI Method

Click "Add Database Connection" in the SQL cell to configure:

• PostgreSQL
• MySQL
• SQLite
• DuckDB
• Snowflake
• BigQuery

Code Method

# SQLAlchemy
from sqlalchemy import create_engine
engine = create_engine("postgresql://user:pass@host/db")

# Use in SQL
result = mo.sql(f"SELECT * FROM users LIMIT 10", engine=engine)

Supported: SQLAlchemy, SQLModel, Ibis, DuckDB, ClickHouse Connect.

Output Types

Configure output format:

# Native DuckDB (recommended for large data)
result = mo.sql(f"SELECT * FROM {df}", output="native")

# Options: "native", "lazy-polars", "pandas", "polars", "auto"

Querying Files

# CSV
result = mo.sql(f"SELECT * FROM 'data.csv'")

# Parquet
result = mo.sql(f"SELECT * FROM 'data.parquet'")

# Remote files
result = mo.sql(f"SELECT * FROM 'https://example.com/data.csv'")

# S3
result = mo.sql(f"SELECT * FROM 's3://bucket/path/data.parquet'")

Reactive Queries

SQL integrates with marimo's reactivity:

# Cell 1
category = mo.ui.dropdown(["Electronics", "Books", "Clothing"])

# Cell 2 - re-runs when dropdown changes
filtered = mo.sql(f"""
    SELECT * FROM products
    WHERE category = '{category.value}'
""")

Best Practices

• Use output="native" for large datasets
• Reference DataFrames with {df} syntax
• Use f-strings for reactive parameter binding
• Database tables take precedence over local DataFrames with same name