SQLite3 interface

SQLite3 1 is a popular in-memory database. It is faster than classical relational databases like PostgreSQL, but less stable under massive parallel access, and requires all contained data sets to be loaded into memory, which might fill up too much of your RAM, especially for large data sets.

As with all other databases in the unified import interface of the getML Python API, you have to first connect to it - using connect_sqlite3().

Import from SQLite3

By selecting an existing table from your database in the from_db() class method, you can create a new DataFrame containing all its data. Alternatively you can use the read_db() and read_query() methods to replace the content of the current DataFrame instance or append further rows based on either a table or a specific query.

Export to SQLite3

You can also write your results back into the SQLite3 database. By providing a name for the destination table in getml.models.MultirelModel.transform(), the features generated from your raw data will be written back. Passing it into getml.models.MultirelModel.predict() generates predictions of the target variables to new, unseen data and stores the result into the corresponding table.