Update web_handlers.py (#181)

* Update web_handlers.py

Hi, was trying to find how to handle LIKE statements in the SQL query via the REST API and came across your great library. I notice that you don't current have some code to parse SQL queries into a dataframe so have suggested some code...

* Update web_handlers.py

Added processing of SQL query to repalce tabs and new lines with spaces that are typical of formatted SQL queries.  Note that LIKE statments do not seem to be compatible with the requests. I suspect this is to do with the % symbol not being passed. See my Stack Overflow question https://stackoverflow.com/questions/76063389/percent-symbol-in-python-requests-causing-query-to-not-work?noredirect=1#comment134147387_76063389

---------

Co-authored-by: Åsmund Våge Fannemel <34712686+asmfstatoil@users.noreply.github.com>

tagreader/web_handlers.py

@@ -664,7 +664,10 @@ def generate_sql_query(
                 'dso="CHARINT=N;CHARFLOAT=N;CHARTIME=N;CONVERTERRORS=N" '
                 f'm="{max_rows}" to="30" s="1">'
             )
-
+        query = query.replace("\t"," ").replace('\n'," ") # Replace new lines and tabs that are typical in formatted SQL queries with spaces. 
+
+        # Need a solution to LIKE comments. These have a % symbol in the query that does not seem to pass through the request
+
         connection_string += f"<![CDATA[{query}]]></SQL>"
         return connection_string
 
@@ -706,10 +709,21 @@ def query_sql(self, query: str, parse: bool = True) -> Union[str, pd.DataFrame]:
         res_text = self.fetch_text(url, params=params)
         # For now just return result as text regardless of value of parse
         if parse:
-            raise NotImplementedError(
-                "Use parse=False to receive and handle text result instead"
-            )
-        return res_text
+            dict = res.json()['data'][0]
+
+            cols = []
+            for i in dict['cols']:
+                cols.append(i['n'])
+
+            rows = []
+
+            for i in dict['rows']:
+                element = []
+                for j in i['fld']:
+                    element.append(j['v'])
+                rows.append(element)
+            return pd.DataFrame(data=rows, columns=cols)
+        return res.text
 
 
 class PIHandlerWeb(BaseHandlerWeb):