---
title: "Database: SQLite"
original_url: "https://tds.s-anand.net/#/sqlite?id=database-sqlite"
downloaded_at: "2025-06-08T23:26:00.500923"
---

[Database: SQLite](#/sqlite?id=database-sqlite)
-----------------------------------------------

Relational databases are used to store data in a structured way. You’ll often access databases created by others for analysis.

PostgreSQL, MySQL, MS SQL, Oracle, etc. are popular databases. But the most installed database is [SQLite](https://www.sqlite.org/index.html). It’s embedded into many devices and apps (e.g. your phone, browser, etc.). It’s lightweight but very scalable and powerful.

Watch these introductory videos to understand SQLite and how it’s used in Python (34 min):

[![SQLite Introduction - Beginners Guide to SQL and Databases (22 min)](https://i.ytimg.com/vi_webp/8Xyn8R9eKB8/sddefault.webp)](https://youtu.be/8Xyn8R9eKB8)

[![SQLite Backend for Beginners - Create Quick Databases with Python and SQL (13 min)](https://i.ytimg.com/vi_webp/Ohj-CqALrwk/sddefault.webp)](https://youtu.be/Ohj-CqALrwk)

There are many non-relational databases (NoSQL) like [ElasticSearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html), [MongoDB](https://www.mongodb.com/docs/manual/), [Redis](https://redis.io/docs/latest/), etc. that you should know about and we may cover later.

Core Concepts:

```
-- Create a table
CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT UNIQUE,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

-- Insert data
INSERT INTO users (name, email) VALUES
    ('Alice', 'alice@example.com'),
    ('Bob', 'bob@example.com');

-- Query data
SELECT name, COUNT(*) as count
FROM users
GROUP BY name
HAVING count > 1;

-- Join tables
SELECT u.name, o.product
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE o.status = 'pending';Copy to clipboardErrorCopied
```

Python Integration:

```
import sqlite3
from pathlib import Path
import pandas as pd

async def query_database(db_path: Path, query: str) -> pd.DataFrame:
    """Execute SQL query and return results as DataFrame.

    Args:
        db_path: Path to SQLite database
        query: SQL query to execute

    Returns:
        DataFrame with query results
    """
    try:
        conn = sqlite3.connect(db_path)
        return pd.read_sql_query(query, conn)
    finally:
        conn.close()

# Example usage
db = Path('data.db')
df = await query_database(db, '''
    SELECT date, COUNT(*) as count
    FROM events
    GROUP BY date
''')Copy to clipboardErrorCopied
```

Common Operations:

1. **Database Management**

   ```
   -- Backup database
   .backup 'backup.db'

   -- Import CSV
   .mode csv
   .import data.csv table_name

   -- Export results
   .headers on
   .mode csv
   .output results.csv
   SELECT * FROM table;Copy to clipboardErrorCopied
   ```
2. **Performance Optimization**

   ```
   -- Create index
   CREATE INDEX idx_user_email ON users(email);

   -- Analyze query
   EXPLAIN QUERY PLAN
   SELECT * FROM users WHERE email LIKE '%@example.com';

   -- Show indexes
   SELECT * FROM sqlite_master WHERE type='index';Copy to clipboardErrorCopied
   ```
3. **Data Analysis**

   ```
   -- Time series aggregation
   SELECT
       date(timestamp),
       COUNT(*) as events,
       AVG(duration) as avg_duration
   FROM events
   GROUP BY date(timestamp);

   -- Window functions
   SELECT *,
       AVG(amount) OVER (
           PARTITION BY user_id
           ORDER BY date
           ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
       ) as moving_avg
   FROM transactions;Copy to clipboardErrorCopied
   ```

Tools to work with SQLite:

* [SQLiteStudio](https://sqlitestudio.pl/): Lightweight GUI
* [DBeaver](https://dbeaver.io/): Full-featured GUI
* [sqlite-utils](https://sqlite-utils.datasette.io/): CLI tool
* [Datasette](https://datasette.io/): Web interface

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Spreadsheet: Excel, Google Sheets](#/spreadsheets)

[Next

Version Control: Git, GitHub](#/git)