replace pd.np to np

apps/app_results.py

@@ -499,11 +499,11 @@ def update_ts_graph(stations, species, sources):
     if len(dfdt.columns) == 2:
         print("TS: Only date and station")
         return returnError
-    dfdt["labels"] = pd.np.nan
+    dfdt["labels"] = np.nan
     stations = dfdt["Station"].unique()
     # species = dfdt.columns
 
-    dfdt["labels"].fillna(value=pd.np.nan, inplace=True)
+    dfdt["labels"].fillna(value=np.nan, inplace=True)
 
     # should never happen... but just in case
     if "Date" not in dfdt.columns:
@@ -821,7 +821,7 @@ def update_concentration_graph(sources, stations):
     conn.close()
     d = d / d.xs("PM10", level="Specie")
     d = d.reset_index()
-    # d.replace({0: pd.np.nan}, inplace=True)
+    # d.replace({0: np.nan}, inplace=True)
 
     for s in sources:
         data.append(go.Box(
@@ -1061,7 +1061,7 @@ def update_uncertainty_graph_norm(sources, species, stations):
             traces.append(
                 go.Box(
                     x=dftmpBS.loc[:, "Station"],
-                    y=[pd.np.nan],
+                    y=[np.nan],
                     name="No normalized DISP"
                 )
             )
@@ -1628,8 +1628,8 @@ def update_op_contribution_ts_graph(stations, species, plot_type, stationts, est
 
     else:
         estimators = {
-            "mean": pd.np.nanmean,
-            "median": pd.np.nanmedian
+            "mean": np.nanmean,
+            "median": np.nanmedian
         }
         freqs = {
             "monthly": "M",