diff --git a/influxdb.py b/influxdb.py
index 4fb047a..8ae5d51 100644
--- a/influxdb.py
+++ b/influxdb.py
@@ -166,8 +166,6 @@ class InfluxDataGetter:
             variable_name_for_query = var_param[0]
             parameter = "value" if len(var_param) == 1 else var_param[1]
 
-            variables_info[variable] = {}
-            variables_info[variable]["expired_ranges"] = []
             query = f"""
                 from(bucket: "{self._bucket}")
                     |> range(start: {times[0]}, stop: {times[1] + 1})
@@ -202,16 +200,26 @@ class InfluxDataGetter:
                         row_to_insert = row
                 except:
                     row_to_insert = row
-            try:
+            try: #row_to_insert might be None
                 if not row_to_insert.empty :
                     row_to_insert["timestamp"] = float(times[0])
-                    data_frame.loc[-1] = row_to_insert
+                    if data_frame.empty:
+                        data_frame = row_to_insert.to_frame().T
+                    else:
+                        data_frame.loc[-1] = row_to_insert
             except:
                 pass
+                
+            if data_frame.empty:
+                continue
+
             data_frame.drop(["result", "table"], axis=1, inplace=True)
             data_frame.sort_values(by=["timestamp"], inplace=True)
             data_frame.reset_index()
 
+            variables_info[variable] = {}
+            variables_info[variable]["expired_ranges"] = []
+            
             # Identify time windows for which the curve is expired
 
             for index, row in data_frame.iterrows():
@@ -225,25 +233,27 @@ class InfluxDataGetter:
             data_frame.drop(["expired"], axis=1, inplace=True)
             variables_info[variable]["df"] = data_frame
         res = None
-        
+        non_empty_variables = list(variables_info.keys())
+
         # Merge single curve dataframes to a global one
-        if len(variables) == 1:
-            res = variables_info[variables[0]]["df"]
-        elif len(variables) == 2:
-            res = merge_ordered(variables_info[variables[0]]["df"], variables_info[variables[1]]["df"], on=["timestamp", "relative"], suffixes=(None, None))
+
+        if len(non_empty_variables) == 0:
+            return df()
+        elif len(non_empty_variables) == 1:
+            res = variables_info[non_empty_variables[0]]["df"]
         else :
-            for i,variable in enumerate(variables):
+            for i in range(0, len(non_empty_variables)):
                 if i == 1:
-                    res = merge_ordered(variables_info[variables[0]]["df"], variables_info[variables[1]]["df"], on=["timestamp", "relative"], suffixes=(None, None))
+                    res = merge_ordered(variables_info[non_empty_variables[0]]["df"], variables_info[non_empty_variables[1]]["df"], on=["timestamp", "relative"], suffixes=(None, None))
                 elif i > 1:
-                    res = merge_ordered(res, variables_info[variables[i]]["df"], on=["timestamp", "relative"], suffixes=(None, None))
+                    res = merge_ordered(res, variables_info[non_empty_variables[i]]["df"], on=["timestamp", "relative"], suffixes=(None, None))
         
         # Forward fill missing values, then set data points to NaN for those which are expired
-        if len(variables) > 1:
+        if len(non_empty_variables) > 1:
             res.ffill(inplace=True)
             for variable, info in variables_info.items():
-                for range in info["expired_ranges"]:
-                    res.loc[(res["timestamp"] >= range[0]) & ((res["timestamp"] < range[1]) if len(range) == 2 else True), variable] = NaN
+                for expired_range in info["expired_ranges"]:
+                    res.loc[(res["timestamp"] >= expired_range[0]) & ((res["timestamp"] < expired_range[1]) if len(expired_range) == 2 else True), variable] = NaN
         
         # Change order of columns
         cols = res.columns.tolist()