Update app.py

app.py

@@ -8,6 +8,18 @@ import streamlit as st
 import pandas as pd
 from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
 
+
+pos_dict = {1 :'P',
+            2 :'C',
+            3 :'1B',
+            4 :'2B',
+            5 :'3B',
+            6 :'SS',
+            7 :'LF',
+            8 :'CF',
+            9 :'RF',
+            10 :'DH'}
+
 # Set Streamlit page configuration
 st.set_page_config(layout="wide")
 
@@ -39,12 +51,24 @@ Catch probability data is only available for outfielders.
 
 #### What is Catch Probability?
 *From MLB:*
-
 **Catch Probability** expresses the likelihood for a ball to be caught by an outfielder based on opportunity time, 
 distance needed, and direction. “Opportunity time” starts when the ball is released by the pitcher, 
 and “distance needed” is the shortest distance needed to make the catch.
 Learn more about how direction is accounted for here. [Read more about the details of how Catch Probability works here](https://www.mlb.com/news/statcast-introduces-catch-probability-for-2017-c217802340).
 
+*Columns:*
+- **Batter Name**: Name of the batter
+- **Pitcher Name**: Name of the pitcher
+- **Fielder Name**: Name of the fielder
+- **Position**: Position of the fielder
+- **Event**: Type of play
+- **Out**: Was the ball caught?
+- **Wall**: Did the fielder catch the ball at the wall?
+- **Back**: Did the fielder catch the ball while moving back?
+- **Stars**: Number of stars assigned to the play (1-5)
+- **Distance**: Distance required to make the catch in feet
+- **Hang Time**: Hang time of the ball in seconds
+- **Catch Rate**: Probability of the catch being made
 """
 
 # Display the markdown text in Streamlit
@@ -92,15 +116,29 @@ df_catch['distance'] = df_catch['distance'].astype(float).round(1)
 df_merge = df.merge(df_catch, on='play_id', how='right', suffixes=('', '_fielder')).reset_index(drop=True)
 
 # Format the 'catch_rate' column as a percentage
-df_merge['catch_rate'] = df_merge['catch_rate'].astype(float).apply(lambda x: f"{x:.2%}")
+df_merge['catch_rate'] = df_merge['catch_rate'].astype(float).apply(lambda x: f"{x:.0%}")
+
+
+df_merge['Position'] = df_merge['pos'].map(pos_dict)
+
+column_names = ['game_date','batter_name', 'pitcher_name', 'name_display_first_last', 'Position','event', 'out', 'wall', 'back', 'stars', 'distance', 'hang_time', 'catch_rate']
+
+column_names_display = ['Game Date','Batter Name', 'Pitcher Name', 'Fielder Name', 'Position','Event', 'Out', 'Wall', 'Back', 'Stars', 'Distance', 'Hang Time', 'Catch Rate']
+
+
+
 
-column_names = ['batter_name', 'pitcher_name', 'name_display_first_last', 'event', 'out', 'wall', 'back', 'stars', 'distance', 'hang_time', 'catch_rate']
 
 # Use a container to control the width of the AgGrid display
 with st.container():
     st.write("### Fielder Data")
     # Configure the AgGrid options
     gb = GridOptionsBuilder.from_dataframe(df_merge[column_names])
+    # Set display names for columns
+    for col, display_name in zip(column_names, column_names_display):
+        gb.configure_column(col, headerName=display_name)
+
+
     gb.configure_selection('single', use_checkbox=True)
     grid_options = gb.build()