pyodbc

I would like to send a large pandas.DataFrame to a remote server running MS SQL. The way I do it now is by converting a data_frame object to a list of tuples and then send it away with pyODBC's executemany() function. It goes something like this: import pyodbc as pdb list_of_tuples = convert_df(data_frame) connection = pdb.connect(cnxn_str) cursor = connection.cursor() cursor.fast_executemany = True cursor.executemany(sql_statement, list_of_tuples) connection.commit() cursor.close() connection.close() I then started to wonder if things can be sped up (or at least more readable) by using data

Below is my code that I'd like some help with. I am having to run it over 1,300,000 rows meaning it takes up to 40 minutes to insert ~300,000 rows. I figure bulk insert is the route to go to speed it up? Or is it because I'm iterating over the rows via for data in reader: portion? #Opens the prepped csv file with open (os.path.join(newpath,outfile), 'r') as f: #hooks csv reader to file reader = csv.reader(f) #pulls out the columns (which match the SQL table) columns = next(reader) #trims any extra spaces columns = [x.strip(' ') for x in columns] #starts SQL statement query = 'bulk insert into