Update pages/Player Comparision.py

pages/Player Comparision.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 import plotly.graph_objects as go
 
-# Title
 st.title("🤝 Cricket Player Comparison")
 
 # Load data with caching
@@ -21,26 +20,39 @@ except FileNotFoundError as e:
     st.error(f"File not found: {e}")
     st.stop()
 
-# Player selectors
+# Show bowling columns to debug (optional)
+# st.write("Bowling columns:", bowling_df.columns.tolist())
+
+# Select two players for comparison
 player1 = st.selectbox("Select First Player", sorted(batting_df["player_name"].unique()), key="p1")
 player2 = st.selectbox("Select Second Player", sorted(batting_df["player_name"].unique()), key="p2")
 
-# Filter player data and fill NaNs
+# Filter batting data and fill NaNs
 bat1 = batting_df[batting_df["player_name"] == player1].fillna(0)
 bat2 = batting_df[batting_df["player_name"] == player2].fillna(0)
 
+# Filter bowling data and fill NaNs
 bowl1 = bowling_df[bowling_df["player_name"] == player1].fillna(0)
 bowl2 = bowling_df[bowling_df["player_name"] == player2].fillna(0)
 
-# Define stats to compare
+# Batting metrics based on your CSV columns
 batting_metrics = ["Runs", "Average", "SR", "Fours", "Sixes"]
-bowling_metrics = ["Wickets", "Eco", "Average", "SR"]
 
-# Calculate averages or zeros if no data
+# Bowling metrics - check which columns exist in bowling_df before using
+possible_bowling_metrics = ["Wickets", "Eco", "Average", "SR"]
+bowling_metrics = [col for col in possible_bowling_metrics if col in bowling_df.columns]
+
+# Function to safely get average values
 def get_avg(df, cols):
-    if df.empty:
+    existing_cols = [c for c in cols if c in df.columns]
+    if df.empty or not existing_cols:
         return pd.Series({col: 0 for col in cols})
-    return df[cols].mean()
+    avg_series = df[existing_cols].mean()
+    # Fill missing cols with zero
+    for c in cols:
+        if c not in avg_series:
+            avg_series[c] = 0
+    return avg_series[cols]
 
 bat1_avg = get_avg(bat1, batting_metrics)
 bat2_avg = get_avg(bat2, batting_metrics)
@@ -48,7 +60,7 @@ bat2_avg = get_avg(bat2, batting_metrics)
 bowl1_avg = get_avg(bowl1, bowling_metrics)
 bowl2_avg = get_avg(bowl2, bowling_metrics)
 
-# Batting comparison plot
+# Batting Comparison Plot
 st.subheader("🏏 Batting Stats Comparison")
 fig_bat = go.Figure()
 fig_bat.add_trace(go.Bar(x=batting_metrics, y=bat1_avg, name=player1))
@@ -56,10 +68,13 @@ fig_bat.add_trace(go.Bar(x=batting_metrics, y=bat2_avg, name=player2))
 fig_bat.update_layout(barmode='group', yaxis_title='Value', title='Batting Metrics Comparison')
 st.plotly_chart(fig_bat)
 
-# Bowling comparison plot
-st.subheader("🎯 Bowling Stats Comparison")
-fig_bowl = go.Figure()
-fig_bowl.add_trace(go.Bar(x=bowling_metrics, y=bowl1_avg, name=player1))
-fig_bowl.add_trace(go.Bar(x=bowling_metrics, y=bowl2_avg, name=player2))
-fig_bowl.update_layout(barmode='group', yaxis_title='Value', title='Bowling Metrics Comparison')
-st.plotly_chart(fig_bowl)
+# Bowling Comparison Plot (only if bowling_metrics exist)
+if bowling_metrics:
+    st.subheader("🎯 Bowling Stats Comparison")
+    fig_bowl = go.Figure()
+    fig_bowl.add_trace(go.Bar(x=bowling_metrics, y=bowl1_avg, name=player1))
+    fig_bowl.add_trace(go.Bar(x=bowling_metrics, y=bowl2_avg, name=player2))
+    fig_bowl.update_layout(barmode='group', yaxis_title='Value', title='Bowling Metrics Comparison')
+    st.plotly_chart(fig_bowl)
+else:
+    st.info("Bowling stats columns missing or not found in the dataset.")