pandas.read_sql dtype consistency across SQLAlchemy backends

Integration Testing with In-Memory Engines

To avoid using production credentials during integration testing, pandas.read_sql can be paired with a SQLAlchemy in-memory SQLite engine (sqlite:///:memory:). This allows seeding fixture data via to_sql to verify query logic and data processing pipelines in a disposable environment.

Backend-Dependent Type Inference

While this approach validates row counts and value accuracy, pandas performs dtype inference based on the database backend. Because SQLite uses dynamic typing, the resulting DataFrame dtypes may differ from those produced by production backends like PostgreSQL or MySQL, particularly when handling NULL values or specific numeric precisions.

This creates uncertainty when writing test assertions that rely on df.dtypes, as a test passing on SQLite may not reflect the exact type coercion encountered in production.

• Should test suites assert exact pandas dtypes when using an in-memory SQLite proxy for other SQL dialects?
• Is there a recommended configuration to force consistent dtype coercion across different SQLAlchemy engines during read_sql execution?