diff --git a/rowers/dataroutines.py b/rowers/dataroutines.py
index 40ea6451..3e782ffc 100644
--- a/rowers/dataroutines.py
+++ b/rowers/dataroutines.py
@@ -32,7 +32,10 @@ import zipfile
 import os
 from rowers.models import strokedatafields
 import polars as pl
-from polars.exceptions import ColumnNotFoundError, SchemaError
+from polars.exceptions import (
+    ColumnNotFoundError, SchemaError, ComputeError,
+    InvalidOperationError
+)
 
 from rowingdata import (
     KinoMapParser,
@@ -689,29 +692,29 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
         return datadf
     try:
         datadf = datadf.with_columns((-pl.col('catch')).alias('catch'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError, InvalidOperationError):
         pass
 
     try:
         datadf = datadf.with_columns((pl.col('peakforceangle')+1000).alias('peakforceangle'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError, InvalidOperationError):
         pass
 
     try:
         datadf = datadf.with_columns((pl.col('hr')+10).alias('hr'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError, InvalidOperationError):
         pass
 
     # protect 0 spm values from being nulled
     try:
         datadf = datadf.with_columns((pl.col('spm')+1.0).alias('spm'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError, InvalidOperationError):
         pass
 
     # protect 0 workoutstate values from being nulled
     try:
         datadf = datadf.with_columns((pl.col('workoutstate')+1).alias('workoutstate'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError, InvalidOperationError):
         pass
 
     try:
@@ -2028,16 +2031,28 @@ def dataplep(rowdatadf, id=0, inboard=0.88, forceunit='lbs', bands=True, barchar
     if windowsize <= 3:
         windowsize = 5
 
-    df.with_columns(
-        (pl.col(" Cadence (stokes/min)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
-         ).alias(" Cadence (stokes/min)"))
-    df.with_columns(
-        (pl.col(" DriveLength (meters)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
-         ).alias(" DriveLength (meters)"))
-    df.with_columns(
-        (pl.col(" HRCur (bpm)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
-         ).alias(" HRCur (bpm)"))
-    df.with_columns((pl.col("forceratio").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()).alias("forceratio"))
+    try:
+        df.with_columns(
+            (pl.col(" Cadence (stokes/min)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
+             ).alias(" Cadence (stokes/min)"))
+    except ComputeError:
+        pass
+    try:
+        df.with_columns(
+            (pl.col(" DriveLength (meters)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
+             ).alias(" DriveLength (meters)"))
+    except ComputeError:
+        pass
+    try:
+        df.with_columns(
+            (pl.col(" HRCur (bpm)").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()
+             ).alias(" HRCur (bpm)"))
+    except ComputeError:
+        pass
+    try:
+        df.with_columns((pl.col("forceratio").map_batches(lambda x: savgol_filter(x.to_numpy(), windowsize, 3)).explode()).alias("forceratio"))
+    except ComputeError:
+        pass
 
     df = df.with_columns((pl.col(" DriveLength (meters)") / pl.col(" DriveTime (ms)") * 1.0e3).alias("drivespeed"))
 
@@ -2621,7 +2636,6 @@ def add_c2_stroke_data_db(strokedata, workoutid, starttimeunix, csvfilename,
                        ' WorkoutState': 4,
                        ' ElapsedTime (sec)': seconds,
                        'cum_dist': dist2,
-                       ' WorkoutState': 0,
                        })
 
     df.sort_values(by='TimeStamp (sec)', ascending=True)