title: python pandas to_sql with sqlalchemy : how to speed up exporting to MS SQL?
body: <p>I have a dataframe with ca 155,000 rows and 12 columns.
  If I export it to csv with dataframe.to_csv , the output is an 11MB file (which is produced instantly).</p>
  
  <p>If, however, I export to a Microsoft SQL Server with the to_sql method, it takes between 5 and 6 minutes!
  No columns are text: only int, float, bool and dates. I have seen cases where ODBC drivers set nvarchar(max) and this slows down the data transfer, but it cannot be the case here.</p>
  
  <p>Any suggestions on how to speed up the export process? Taking 6 minutes to export 11 MBs of data makes the ODBC connection practically unusable.</p>
  
  <p>Thanks!</p>
  
  <p>My code is:</p>
  
  <pre><code>import pandas as pd
  from sqlalchemy import create_engine, MetaData, Table, select
  ServerName = "myserver"
  Database = "mydatabase"
  TableName = "mytable"
  
  engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database)
  conn = engine.connect()
  
  metadata = MetaData(conn)
  
  my_data_frame.to_sql(TableName,engine)
  </code></pre>