Which is faster: read_sql in Pandas or direct database connection?

pandas.read_sql is a high-level function that internally uses drivers to connect to a database and execute an SQL query, then converts the result into a DataFrame. Direct connection to the database (for example, via psycopg2 for PostgreSQL or pyodbc for MS SQL) and manual data processing can be faster if you optimize queries and processing, as you control each step.

However, in most cases, the performance difference between read_sql and direct connection is negligible, and read_sql wins in terms of convenience and integration with Pandas.

If maximum speed is crucial, you can:

• Use direct connection and fetchmany for batch data loading.
• Optimize SQL queries.
• Use specialized libraries for fast loading (e.g., pyarrow for parsing).

Ultimately, read_sql is good for quick and convenient analysis, while direct connection can be faster with fine-tuning and large volumes of data.