pandas read_sql con use sqlalchemy engine slow much than pymysql connection
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import pymysql
import time
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

connect = pymysql.Connect(
    host=host,
    port=port,
    user=user,
    passwd=passwd,
    db=db,
    charset='utf8'
)
cursor = connect.cursor()
sql = """SELECT * FROM table WHERE id = 1;"""

start_time = time.time()
cursor.execute(sql)
for row in cursor.fetchall():
    print(row)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))

start_time = time.time()
df_mysql = pd.read_sql(sql, con=connect)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))

con_url = "mysql+pymysql://{0}:{1}@{2}:{3}/{4}?charset={5}"
engine = create_engine(con_url.format(user, passwd, host, port, db, charset))
DBSession = sessionmaker(bind=engine)
session = DBSession()
start_time = time.time()
raw_data_df = pd.read_sql(sql, session.bind)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))

```
#### Problem description
pandas read_sql con use sqlalchemy engine slow much than pymysql connection


#### Expected Output

sql execute time:0.02592611312866211
sql execute time:0.028751611709594727
sql execute time:0.5061156749725342

#### Output of ``pd.show_versions()``
pandas ==0.23.0
