to_sql creates duplicate redundant constraints when exporting boolean columns
with pandas 0.16 and sqlalchemy, the to_sql method of a dataframe creates duplicate redundant constraints on each boolean column, at least when exporting to Microsoft SQL Server, because for each column it creates 2 constraints checking the field must be either 0 or 1. This is redundant and slows down the database. A short example is below. Of course you must check the output in the sql database. Also, the fact that there is no control and no documentation over the constraints created by to_sql is extremely frustrating

import pandas as pd
import numpy as np
from sqlalchemy import create_engine, MetaData, Table, select
import sqlalchemy as sqlalchemy

from sqlalchemy import create_engine, MetaData, Table, select
import sqlalchemy as sqlalchemy

ServerName = "cdodb-wb"
Database = "DealAnalysis"

engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database, encoding='latin1')
conn = engine.connect()

metadata = MetaData(conn)

df=pd.DataFrame(np.random.rand(20)>0.5, columns=['random_boolean'])

df.to_sql('python_test_table',engine,if_exists='replace')
