Refactor salary processing in app.py: update salary conversion to drop NaN values before converting to integers, ensuring cleaner data handling and enhancing the robustness of projections.

app.py

@@ -145,7 +145,7 @@ with tab1:
                     st.write('replaced salary symbols')
                 except:
                     pass
-                projections['salary'] = projections['salary'].astype(int)
+                projections['salary'] = projections['salary'].dropna().astype(int)
                 projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
                 st.dataframe(projections.head(10))