Update pages/1player_information.py

pages/1player_information.py

@@ -6,8 +6,7 @@ import matplotlib.pyplot as plt
 st.set_page_config(page_title="Career Insights", layout="wide")
 
 # Load data
-file_path = "Final_Team.csv"
-df = pd.read_csv(file_path)
+df = pd.read_csv("Team_Info.csv")
 
 # Get unique player names
 player_names = df["Player"].unique()
@@ -31,61 +30,84 @@ if selected_player:
         col1, col2 = st.columns(2)
         
         with col1:
-            # Pie Chart - Matches Played Across Formats
-            matches = [
-                player_data.get("Matches_Test", 0),
-                player_data.get("Matches_ODI", 0),
-                player_data.get("Matches_T20", 0),
-                player_data.get("Matches_IPL", 0)
+            # Bar Chart - 100s and 50s
+            hundreds = [
+                player_data.get("100s_Test", 0),
+                player_data.get("100s_ODI", 0),
+                player_data.get("100s_T20", 0),
+                player_data.get("100s_IPL", 0)
+            ]
+            fifties = [
+                player_data.get("50s_Test", 0),
+                player_data.get("50s_ODI", 0),
+                player_data.get("50s_T20", 0),
+                player_data.get("50s_IPL", 0)
             ]
             fig, ax = plt.subplots()
-            ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90)
-            ax.set_title(f"Matches Played by {selected_player}")
+            ax.bar(labels, hundreds, label="100s", color="gold")
+            ax.bar(labels, fifties, label="50s", color="blue", bottom=hundreds)
+            ax.set_ylabel("Count")
+            ax.set_title(f"Centuries & Fifties by {selected_player}")
+            ax.legend()
             st.pyplot(fig)
         
         with col2:
-            # Bar Chart - Runs Scored
-            batting_runs = [
-                player_data.get("batting_Runs_Test", 0),
-                player_data.get("batting_Runs_ODI", 0),
-                player_data.get("batting_Runs_T20", 0),
-                player_data.get("batting_Runs_IPL", 0)
+            # Line Chart - Strike Rate & Average
+            strike_rate = [
+                player_data.get("SR_Test", 0),
+                player_data.get("SR_ODI", 0),
+                player_data.get("SR_T20", 0),
+                player_data.get("SR_IPL", 0)
+            ]
+            batting_avg = [
+                player_data.get("Avg_Test", 0),
+                player_data.get("Avg_ODI", 0),
+                player_data.get("Avg_T20", 0),
+                player_data.get("Avg_IPL", 0)
             ]
             fig, ax = plt.subplots()
-            ax.bar(labels, batting_runs, color=["gold", "green", "blue", "red"])
-            ax.set_ylabel("Runs Scored")
-            ax.set_title(f"Runs Scored by {selected_player}")
+            ax.plot(labels, strike_rate, marker='o', linestyle='-', color='red', label="Strike Rate")
+            ax.plot(labels, batting_avg, marker='s', linestyle='--', color='green', label="Batting Average")
+            ax.set_ylabel("Value")
+            ax.set_title(f"Strike Rate & Batting Average of {selected_player}")
+            ax.legend()
             st.pyplot(fig)
     
     if show_bowling:
         col1, col2 = st.columns(2)
         
         with col1:
-            # Calculate Overs Bowled
-            overs_bowled = [
-                player_data.get("bowling_Test_Balls", 0) // 6,
-                player_data.get("bowling_ODI_Balls", 0) // 6,
-                player_data.get("bowling_T20_Balls", 0) // 6,
-                player_data.get("bowling_IPL_Balls", 0) // 6
+            # Bar Chart - Economy Rate
+            economy_rate = [
+                player_data.get("Econ_Test", 0),
+                player_data.get("Econ_ODI", 0),
+                player_data.get("Econ_T20", 0),
+                player_data.get("Econ_IPL", 0)
             ]
-            
-            # Bar Chart - Overs Bowled
             fig, ax = plt.subplots()
-            ax.bar(labels, overs_bowled, color=["purple", "orange", "cyan", "brown"])
-            ax.set_ylabel("Overs Bowled")
-            ax.set_title(f"Overs Bowled by {selected_player}")
+            ax.bar(labels, economy_rate, color=["purple", "orange", "cyan", "brown"])
+            ax.set_ylabel("Economy Rate")
+            ax.set_title(f"Economy Rate of {selected_player}")
             st.pyplot(fig)
         
         with col2:
-            # Line Chart - Wickets Taken
-            wickets_taken = [
-                player_data.get("bowling_Wickets_Test", 0),
-                player_data.get("bowling_Wickets_ODI", 0),
-                player_data.get("bowling_Wickets_T20", 0),
-                player_data.get("bowling_Wickets_IPL", 0)
+            # Scatter Plot - Bowling Average & Strike Rate
+            bowling_avg = [
+                player_data.get("Bowl_Avg_Test", 0),
+                player_data.get("Bowl_Avg_ODI", 0),
+                player_data.get("Bowl_Avg_T20", 0),
+                player_data.get("Bowl_Avg_IPL", 0)
+            ]
+            bowling_sr = [
+                player_data.get("Bowl_SR_Test", 0),
+                player_data.get("Bowl_SR_ODI", 0),
+                player_data.get("Bowl_SR_T20", 0),
+                player_data.get("Bowl_SR_IPL", 0)
             ]
             fig, ax = plt.subplots()
-            ax.plot(labels, wickets_taken, marker='o', linestyle='-', color='red')
-            ax.set_ylabel("Wickets Taken")
-            ax.set_title(f"Wickets Taken by {selected_player}")
-            st.pyplot(fig)
+            ax.scatter(labels, bowling_avg, color='blue', label="Bowling Average")
+            ax.scatter(labels, bowling_sr, color='red', label="Bowling Strike Rate")
+            ax.set_ylabel("Value")
+            ax.set_title(f"Bowling Average & Strike Rate of {selected_player}")
+            ax.legend()
+            st.pyplot(fig)