title: Pandas dataframe insert into SQL Server taking too long with execute and executemany
body: <p>I have a pandas dataframe with 27 columns and ~45k rows that I need to insert into a SQL Server table.</p>
  <p>I am currently using with the below code and it takes 90 mins to insert:</p>
  <pre><code>conn = pyodbc.connect('Driver={ODBC Driver 17 for SQL Server};\
                     Server=@servername;\
                     Database=dbtest;\
                     Trusted_Connection=yes;')
  cursor = conn.cursor()  #Create cursor
  
  
  
   for index, row in t6.iterrows():
  
      cursor.execute(&quot;insert into dbtest.dbo.test( col1, col2, col3, col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,,col27)\
                                                          values (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)&quot;,
                                                          row['col1'],row['col2'], row['col3'],,row['col27'])
  </code></pre>
  <p>I have also tried to load using executemany and that takes even longer to complete, at nearly 120mins.</p>
  <p>I am really looking for a faster load time since I need to run this daily.</p>