Enhance player position count displays in Streamlit app by adding percentage calculations for QB, RB, WR, and TE rankings, improving insights into player performance across different tiers.

src/streamlit_app.py

@@ -463,19 +463,31 @@ def main():
     # Display results
     st.header("Player Rankings")
     st.dataframe(final_df, use_container_width=True)
-    qb_count_col, rb_count_col, wr_count_col, te_count_col = st.columns(4)
+    blank_left, qb_count_col, rb_count_col, wr_count_col, te_count_col, blank_right = st.columns(6)
     with qb_count_col:
         st.subheader("QB Counts")
-        st.write(f"Total QB top 12: {(final_df.loc[0:11, 'Pos'] == 'QB').sum()}")
+        st.write(f"Total QB top 12: {(final_df.loc[0:11, 'Pos'] == 'QB').sum()}, {((final_df.loc[0:11, 'Pos'] == 'QB').sum() / 12) * 100}%")
+        st.write(f"Total QB 13 - 24: {(final_df.loc[12:23, 'Pos'] == 'QB').sum()}, {((final_df.loc[12:23, 'Pos'] == 'QB').sum() / 12) * 100}%")
+        st.write(f"Total QB 25 - 36: {(final_df.loc[24:35, 'Pos'] == 'QB').sum()}, {((final_df.loc[24:35, 'Pos'] == 'QB').sum() / 12) * 100}%")
+        st.write(f"Total QB 37 - 48: {(final_df.loc[36:47, 'Pos'] == 'QB').sum()}, {((final_df.loc[36:47, 'Pos'] == 'QB').sum() / 12) * 100}%")
     with rb_count_col:
         st.subheader("RB Counts")
-        st.write(f"Total RB top 12: {(final_df.loc[0:11, 'Pos'] == 'RB').sum()}")
+        st.write(f"Total RB top 12: {(final_df.loc[0:11, 'Pos'] == 'RB').sum()}, {((final_df.loc[0:11, 'Pos'] == 'RB').sum() / 12) * 100}%")
+        st.write(f"Total RB 13 - 24: {(final_df.loc[12:23, 'Pos'] == 'RB').sum()}, {((final_df.loc[12:23, 'Pos'] == 'RB').sum() / 12) * 100}%")
+        st.write(f"Total RB 25 - 36: {(final_df.loc[24:35, 'Pos'] == 'RB').sum()}, {((final_df.loc[24:35, 'Pos'] == 'RB').sum() / 12) * 100}%")
+        st.write(f"Total RB 37 - 48: {(final_df.loc[36:47, 'Pos'] == 'RB').sum()}, {((final_df.loc[36:47, 'Pos'] == 'RB').sum() / 12) * 100}%")
     with wr_count_col:
         st.subheader("WR Counts")
-        st.write(f"Total WR top 12: {(final_df.loc[0:11, 'Pos'] == 'WR').sum()}")
+        st.write(f"Total WR top 12: {(final_df.loc[0:11, 'Pos'] == 'WR').sum()}, {((final_df.loc[0:11, 'Pos'] == 'WR').sum() / 12) * 100}%")
+        st.write(f"Total WR 13 - 24: {(final_df.loc[12:23, 'Pos'] == 'WR').sum()}, {((final_df.loc[12:23, 'Pos'] == 'WR').sum() / 12) * 100}%")
+        st.write(f"Total WR 25 - 36: {(final_df.loc[24:35, 'Pos'] == 'WR').sum()}, {((final_df.loc[24:35, 'Pos'] == 'WR').sum() / 12) * 100}%")
+        st.write(f"Total WR 37 - 48: {(final_df.loc[36:47, 'Pos'] == 'WR').sum()}, {((final_df.loc[36:47, 'Pos'] == 'WR').sum() / 12) * 100}%")
     with te_count_col:
         st.subheader("TE Counts")
-        st.write(f"Total TE top 12: {(final_df.loc[0:11, 'Pos'] == 'TE').sum()}")
+        st.write(f"Total TE top 12: {(final_df.loc[0:11, 'Pos'] == 'TE').sum()}, {((final_df.loc[0:11, 'Pos'] == 'TE').sum() / 12) * 100}%")
+        st.write(f"Total TE 13 - 24: {(final_df.loc[12:23, 'Pos'] == 'TE').sum()}, {((final_df.loc[12:23, 'Pos'] == 'TE').sum() / 12) * 100}%")
+        st.write(f"Total TE 25 - 36: {(final_df.loc[24:35, 'Pos'] == 'TE').sum()}, {((final_df.loc[24:35, 'Pos'] == 'TE').sum() / 12) * 100}%")
+        st.write(f"Total TE 37 - 48: {(final_df.loc[36:47, 'Pos'] == 'TE').sum()}, {((final_df.loc[36:47, 'Pos'] == 'TE').sum() / 12) * 100}%")
 
     
     # Position breakdown