diff --git a/PBS_Utils/scripts/plquery.py b/PBS_Utils/scripts/plquery.py
index 9638ab14e3488a6f90a87ce83fa40561894975af..845ee2c3ea587fbb5d9cad41b5ed9f703fca86f2 100755
--- a/PBS_Utils/scripts/plquery.py
+++ b/PBS_Utils/scripts/plquery.py
@@ -1014,10 +1014,12 @@ def job(settings: Settings, extend):
     if not settings.header:
         settings.header = [
             "jobid",
+            "Job_Name",
             "queue",
             "job_state",
             "substate",
             "stime",
+            "obittime",
             # 'Resource_List.nodect',
             "nodect",
             # 'Resource_List.walltime', 'resources_used.walltime',
@@ -1027,6 +1029,7 @@ def job(settings: Settings, extend):
             "real_runtime",
             "job_stuck",
             "vnode",
+            "Exit_status",
         ]
     df_jobs["substate"] = df_jobs.get("substate", default=pd.Series(dtype=object)).apply(
         lambda ser: "".join(state_to_strings(data.enum_lookup, "LONG__REVERSE_JOB_SUBSTATE", ser)).replace("JOB_SUBSTATE_", "")
@@ -1039,7 +1042,17 @@ def job(settings: Settings, extend):
         # this makes lots of rows.  Maybe have a split option for this data.
         # df_jobs['exec_vnode'] = df_jobs['exec_vnode'].apply(wrap_cast_to_vnode_list)
         df_jobs["exec_vnode"] = df_jobs.get("exec_vnode", default=pd.Series(dtype=object)).apply(wrap_cast_to_vnode_list)
-        df_jobs = split_column_lst_into_series(df_jobs, "exec_vnode", "vnode")
+        df_jobs['vnode'] = df_jobs["exec_vnode"].apply(lambda l: len(l))
+        # df_jobs = split_column_lst_into_series(df_jobs, "exec_vnode", "vnode")
+        int_cols = ['Exit_status', 'run_count']
+        for int_col in int_cols:
+            if int_col in settings.header:
+                df_jobs[int_col] = df_jobs[int_col].apply(lambda e: int(e) if e and e > 0 else '-')
+        time_cols = ['stime', 'etime', 'obittime', 'eligible_time', 'ctime']
+        for time_col in time_cols:
+            if time_col in settings.header:
+                df_jobs[time_col] = df_jobs[time_col].apply(lambda t: epoch_to_datetime(t) if t and t > 0 else '-')
+
     df = df_jobs
     df = df_apply_options(df, settings)
     df_display(df, settings)