def pd_to_sqlDB(input_df: pd.DataFrame,
                table_name: str,
                db_name: str = 'default.db') -> None:

    '''Take a Pandas dataframe `input_df` and upload it to `table_name` SQLITE table

    Args:
        input_df (pd.DataFrame): Dataframe containing data to upload to SQLITE
        table_name (str): Name of the SQLITE table to upload to
        db_name (str, optional): Name of the SQLITE Database in which the table is created. 
                                 Defaults to 'default.db'.
    '''

    # Step 1: Setup local logging
    import logging
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s %(levelname)s: %(message)s',
                        datefmt='%Y-%m-%d %H:%M:%S')

    # Step 2: Find columns in the dataframe
    cols = input_df.columns
    cols_string = ','.join(cols)
    val_wildcard_string = ','.join(['?'] * len(cols))

    # Step 3: Connect to a DB file if it exists, else crete a new file
    con = sqlite3.connect(db_name)
    cur = con.cursor()
    logging.info(f'SQL DB {db_name} created')

    # Step 4: Create Table
    sql_string = f"""CREATE TABLE {table_name} ({cols_string});"""
    cur.execute(sql_string)
    logging.info(f'SQL Table {table_name} created with {len(cols)} columns')

    # Step 5: Upload the dataframe
    rows_to_upload = input_df.to_dict(orient='split')['data']
    sql_string = f"""INSERT INTO {table_name} ({cols_string}) VALUES ({val_wildcard_string});"""    
    cur.executemany(sql_string, rows_to_upload)
    logging.info(f'{len(rows_to_upload)} rows uploaded to {table_name}')
  
    # Step 6: Commit the changes and close the connection
    con.commit()
    con.close()