title: [Python to MS SQL]: Alternative to DataFrame.to_sql without using sqlalchemy.create_engine() and using pypyodbc
body: <p>Scenario:</p>
  <p>1.I am trying to insert the dataframe directly into SQL Table.</p>
  <pre><code>engine_azure = sqlalchemy.create_engine(sqlchemy_conn_str,echo=True,fast_executemany = True, poolclass=NullPool)
  conn = engine_azure.connect()
  df_final_result.to_sql('Employee', engine_azure,schema='dbo', index = False, if_exists = 'replace')
  </code></pre>
  <p>2.So is there any alternative to the above .to_sql using pypyodbc connection?</p>
  <p>3.Below code we can use but i have 90 columns, so i want to avoid code with below iteration.</p>
  <pre><code>import pyodbc
  cnxn = pyodbc.connect(driver='{ODBC Driver 17 for SQL Server}', server='xyz', database='xyz',               
                 trusted_connection='yes'
  cursor = cnxn.cursor()
  for index, row in df2.iterrows():
       cursor.execute(&quot;INSERT INTO Employee(ContactNumber,Name,Salary,Address) values(?,?,?,?,?,?)&quot;, row.ContactNumber, row.Name, row.Salary,row['Address'])
  
  cnxn.commit()
  cnxn.close()
  </code></pre>