I am trying to connect and write a Pandas Dataframe to a Postgres custom schema on Supabase. Below is the code. from sqlalchemy import create_engine def server_access(): # Create SQLAlchemy connection string conn_str = ( f"postgresql+psycopg2://{"[USER]"}:{"[PASSWORD]"}" f"@{"[HOST]"}:{[PORT]}/{"[SCHEMA]"}?client_encoding=utf8" ) engine = create_engine(url = conn_str) return engine engine = server_access() df.to_sql('tbl', engine, if_exists='append', index=False) As per the documentation I've followed the steps to expose custom schema but it didn't worked. However, if I try using public schema the same code works fine without defining client_encoding in connection string. How to connect and write a Pandas Dataframe using SQLAlchemy on a custom schema on Supabase? Edit: Sample connection string: postgresql+psycopg2://userassword@host:6543/custom_schema?client_encoding=utf8 After replacing custom_schema with postgres it works fine. Error: conn = _connect(dsn, connection_factory=connection_factory, **kwasync) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sqlalchemy.exc.OperationalError: (psycopg2.OperationalError) server didn't return client encoding (Background on this error at: https://sqlalche.me/e/20/e3q8) Continue reading...