Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: abkfenris

pipeline/s3_timeseries/pipeline.py

@@ -200,7 +200,16 @@ def monthly_ds(
                 )
                 df = df.groupby(df.columns, axis=1).first()
 
-            df = clean_up_dtypes_and_nas(df, na_values="NAN")
+            # Avoid attempting to convert the time column to numeric inside
+            # clean_up_dtypes_and_nas, which causes unnecessary exceptions.
+            if "time" in df.columns:
+                time_col = df["time"]
+                df_wo_time = df.drop(columns=["time"])
+                df_wo_time = clean_up_dtypes_and_nas(df_wo_time, na_values="NAN")
+                df_wo_time["time"] = time_col
+                df = df_wo_time
+            else:
+                df = clean_up_dtypes_and_nas(df, na_values="NAN")
             daily_dfs.append(df)
 
         df = pd.concat(daily_dfs, ignore_index=True)
@@ -382,7 +391,7 @@ def clean_up_dtypes_and_nas(
     for c in df.columns:
         try:
             df[c] = pd.to_numeric(df[c])
-        except Exception as e:
+        except (ValueError, TypeError) as e:
             print(f"Unable to convert column {c}: {e}")
 
     return df