> I honestly can't think of a single reason why anyone would choose to use one.
In the case of SQLAlchemy (referenced in the article), many people use it as a database connection abstraction rather than an ORM. It's kinda the equivalent of "ODBC" for certain Python libraries like Pandas.
For instance, in Pandas you can write your dataframe to the database by going `df.to_sql(tblname, sqlachemycon)` where sqlalchemycon is the connection instance to any database that SQLAlchemy supports.
In the case of SQLAlchemy (referenced in the article), many people use it as a database connection abstraction rather than an ORM. It's kinda the equivalent of "ODBC" for certain Python libraries like Pandas.
For instance, in Pandas you can write your dataframe to the database by going `df.to_sql(tblname, sqlachemycon)` where sqlalchemycon is the connection instance to any database that SQLAlchemy supports.
https://pandas.pydata.org/pandas-docs/version/0.23.4/generat...
I use SQLAlchemy for this purpose alone and write straight up SQL. I've never used the ORM parts of SQLAlchemy.