Python’s rich ecosystem of data science tools is a big draw for users. The only downside of such a broad and deep collection is that sometimes the best tools can get overlooked.
Here’s a rundown of some of the best newer or less-known data science projects available for Python. Some, like Polars, are getting more attention but still deserve wider notice. Others, like ConnectorX, are hidden gems.
ConnectorX
Most data sits in a database somewhere, but computation typically happens outside of it. Getting data to and from the database for actual work can be a slowdown. ConnectorX loads data from databases into many common data-wrangling tools in Python, and it keeps things fast by minimizing the work required. Most of the data loading can be done in just a couple of lines of Python code and an SQL query.
Like Polars (which I’ll discuss shortly), ConnectorX uses a Rust library at its core. This allows for optimizations like being able to load from a data source in parallel with partitioning. Data in PostgreSQL, for instance, can be loaded this way by specifying a partition column.
