Following on from a story I wrote comparing the speed of Pandas and Polars libraries in terms of reading and writing data — from and to — a Postgres database I thought it might be interesting to do a similar comparison between Pandas and Psycopg2.
If you need to get data from or to a Postgres database table from or to a local file, read on for the winner.
You can find the Pandas v Polars article at the link below:
I don’t think I need to explain much about what Pandas is. Its use in Python code is ubiquitous and is one of the main tools that people use to load, explore, visualise and process large amounts of data in Python.
Psycopg is one of the most popular PostgreSQL database libraries for the Python programming language. It implements the Python Database API Specification v2.0, allowing Python applications to communicate with PostgreSQL databases.
Psycopg is designed for efficiency and thread safety. It provides a high-level, Pythonic interface for connecting to a PostgreSQL database, executing SQL statements, managing transactions, and fetching results, while also offering low-level access to PostgreSQL-specific features for advanced use cases.
Using Psycopg, Python applications can perform a variety of database operations. These include executing SQL queries and commands, manipulating large object storage in PostgreSQL, managing transactions, and handling notifications from the PostgreSQL database.
The library also supports a variety of PostgreSQL features, such as prepared statements, multiple cursors, asynchronous notifications, and COPY commands for bulk data transfers. Additionally, it supports advanced data types and methods provided by PostgreSQL, including geometric types, arrays, hstore, JSON, and others.