content: import pandas as pd
  from sqlalchemy import create_engine, MetaData, Table, select
  
  # Parameters
  TableName = "data"
  
  DB = {
      'drivername': 'mssql+pyodbc',
      'servername': 'DAVID-THINK',
      #'port': '5432',
      #'username': 'lynn',
      #'password': '',
      'database': 'BizIntel',
      'driver': 'SQL Server Native Client 11.0',
      'trusted_connection': 'yes',
      'legacy_schema_aliasing': False
  }
  
  # Create the connection
  engine = create_engine(DB['drivername'] + '://' + DB['servername'] + '/' + DB['database'] + '?' + 'driver=' + DB['driver'] + ';' + 'trusted_connection=' + DB['trusted_connection'], legacy_schema_aliasing=DB['legacy_schema_aliasing'])
  conn = engine.connect()
  
  # Required for querying tables
  metadata = MetaData(conn)
  
  # Table to query
  tbl = Table(TableName, metadata, autoload=True, schema="dbo")
  #tbl.create(checkfirst=True)
  
  # Select all
  sql = tbl.select()
  
  # run sql code
  result = conn.execute(sql)
  
  # Insert to a dataframe
  df = pd.DataFrame(data=list(result), columns=result.keys())
  
  # Close connection
  conn.close()
  
  print('Done')
  
  df.to_csv('DimDate.csv', index=False)
  df.to_excel('DimDate.xls', index=False)
  df.to_csv('DimDate.txt', index=False)
  