pd can't import cx_Oracle string datetype to pd.DataFrame correctly. Can't to cast data to correct type(str)
#### Problem description
I have some oracle table with next field datatype:

**

- ID|              NOT NULL NUMBER              
- VERSION|         NOT NULL NUMBER              
- STAT_ACTUAL|     NOT NULL NUMBER              
- REGION|          NOT NULL VARCHAR2(5 CHAR)    
- PARENTID|                 NUMBER              
- CITY|                 VARCHAR2(5 CHAR)

**    
...
and if I had trying to add them from cx_Oracle to pandas.DataFrame like that:


#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from sqlalchemy import *
#import ...

conn = cx_Oracle.connect('datbs/datbs@host/serv')
cursorr = conn.cursor()
SQL  = '''select 
    ID, VERSION, STAT_ACTUAL, REGION, PARENTID, CITY 
    from table where region = 'Euroupe' '''
df= pd.read_sql_query(SQL, con=conn)
print(df.dtypes)
#datatypes looks like:
```

**- ID                          int64
- VERSION                     int64
- STAT_ACTUAL                 int64
- REGION                     object
- PARENTID                    int64
- CITY                       object**

So, all my oracle varchar2 (wich looks in cx_Oracle as ('REGION', <class 'cx_Oracle.STRING'>)) are object in pandas.df!

I tried to cast datatypes using `pd.df.astype({"REGION": str})` - result havn't been taken. Objects
are objects.

Using advice from one python chat I tried  to use Oracle's TO_CHAR(REGION) in 
SQL variable definition. no result object - are objects


#### Expected Output
Correct import of cx_Oracle dtypes. If varchar2 -->String  then str, etc.
or way for correct cast types ( if `df['REGION'].dtypes` returns `object`, then `df['REGION'].astype({'REGION': str}).dtypes` returns `str`)


<details>

python 3.7.4

linux manjaro

cx_Oracle ==7.3.0

pandas== 0.25.3

sqlalchemy == 1.2.12

</details>
