Feature/removing pandas warning (#14)

* removing the Pandas 'sqlalchemy' warning by refactoring

* removing the Pandas 'sqlalchemy' warning by refactoring.

* reformatting

src/server/BreCal/database/sql_handler.py

@@ -26,7 +26,7 @@ class SQLHandler():
         with self.sql_connection.cursor(buffered=True) as cursor:
             cursor.execute("SHOW TABLES")
             schema = cursor.fetchall()
-        all_schemas = [schem[0] for schem in schema]
+            all_schemas = [schem[0] for schem in schema]
         return all_schemas
     
     def build_str_to_model_dict(self):
@@ -42,13 +42,51 @@ class SQLHandler():
 
     def read_mysql_table_to_df(self, table_name:str):
         """determine a {table_name}, which will be read from a mysql server. returns a pandas DataFrame with the respective data"""
-        df = pd.read_sql(sql=f"SELECT * FROM {table_name}", con=self.sql_connection)
+        with self.sql_connection.cursor(buffered=True) as cursor: #df = pd.read_sql(sql=f"SELECT * FROM {table_name}", con=self.sql_connection)
+            # 1.) get the column names
+            cursor.execute(f"DESCRIBE {table_name}")
+            cols = cursor.fetchall()
+            column_names = [col_name[0] for col_name in cols]
+            
+            # 2.) get the data tuples
+            cursor.execute(f"SELECT * FROM {table_name}")
+            data = cursor.fetchall()
+
+        # 3.) map the data tuples to the correct column names
+        data = [{k:v for k,v in zip(column_names, dat)} for dat in data]
+
+        # 4.) build a dataframe from the respective data models (which ensures the correct data type)
+        data_model = self.str_to_model_dict.get(table_name)
+        if data_model is not None:
+            df = pd.DataFrame([data_model(**dat) for dat in data])
+        else:
+            df = pd.DataFrame([dat for dat in data])
         return df
 
-    def mysql_to_df(self, query):
+    def mysql_to_df(self, query, table_name):
         """provide an arbitrary sql query that should be read from a mysql server {sql_connection}. returns a pandas DataFrame with the obtained data"""
-        df = pd.read_sql(query, self.sql_connection).convert_dtypes()
+        with self.sql_connection.cursor(buffered=True) as cursor: # df = pd.read_sql(query, self.sql_connection).convert_dtypes()
-        df = df.set_index('id', inplace=False) # avoid inplace updates, so the raw sql remains unchanged
+            # 1.) get the column names
+            cursor.execute(f"DESCRIBE {table_name}")
+            cols = cursor.fetchall()
+            column_names = [col_name[0] for col_name in cols]
+
+            # 2.) get the data tuples
+            cursor.execute(query)
+            data = cursor.fetchall()
+
+        # 3.) map the data tuples to the correct column names
+        data = [{k:v for k,v in zip(column_names, dat)} for dat in data]
+
+        # 4.) build a dataframe from the respective data models (which ensures the correct data type)
+        data_model = self.str_to_model_dict.get(table_name)
+        if data_model is not None:
+            df = pd.DataFrame([data_model(**dat) for dat in data])
+        else:
+            df = pd.DataFrame([dat for dat in data])
+        
+        if 'id' in df.columns:
+            df = df.set_index('id', inplace=False) # avoid inplace updates, so the raw sql remains unchanged
         return df
     
     def read_all(self, all_schemas):
@@ -64,7 +102,7 @@ class SQLHandler():
         mysql_df_dict = {}
         for schem in all_schemas:
             query = f"SELECT * FROM {schem}"
-            mysql_df_dict[schem] = self.mysql_to_df(query)
+            mysql_df_dict[schem] = self.mysql_to_df(query, table_name=schem)
         return mysql_df_dict
     
     def initialize_shipcall_participant_list(self):