fix nan averages

app.py

@@ -476,15 +476,19 @@ def update_leaderboard(dataframe: pd.DataFrame, selected_task_substrings: List[s
         return df_transformed
 
     if show_average:
-        numeric_df = filtered_dataframe.select_dtypes(include=[np.number])
-        means = numeric_df.mean(axis=1, skipna=False)
-        # means = filtered_dataframe.replace("-", float("nan")).mean(axis=1, skipna=False)
+        numeric_data = filtered_dataframe.replace("-", np.nan)
+        means = numeric_data.mean(axis=1, skipna=False)
+
         s_filtered_dataframe = _transform_floats(filtered_dataframe)
-        numeric_s_df = s_filtered_dataframe.select_dtypes(include=[np.number])
-        # s_means = s_filtered_dataframe.replace("-", float("nan")).mean(axis=1, skipna=False)
-        s_means = numeric_s_df.mean(axis=1, skipna=False)
-        filtered_dataframe.loc[:, "Average"] = np.where(filtered_dataframe.eq("-").any(axis=1), "-", means.round(2))
-        filtered_dataframe.loc[:, "Score"] = np.where(filtered_dataframe.eq("-").any(axis=1), "-", s_means.round(2))
+        s_numeric_data = s_filtered_dataframe.replace("-", np.nan)
+        s_means = s_numeric_data.mean(axis=1, skipna=False)
+
+        # Set Average and Score columns
+        # Use isna() to check for NaN values (which occur when any cell in the row was "-")
+        filtered_dataframe.loc[:, "Average"] = np.where(means.isna(), "-", means.round(2))
+        filtered_dataframe.loc[:, "Score"] = np.where(s_means.isna(), "-", s_means.round(2))
+
+        # Sort by Average
         filtered_dataframe = filtered_dataframe.sort_values(by=["Average"], ascending=False, na_position='last')
 
     return filtered_dataframe