Update pages/player_stats.py

pages/player_stats.py

@@ -1,54 +1,118 @@
 import streamlit as st
 import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
 
 # Load the dataset
 df = pd.read_csv("cric_final.csv")
 
 st.title("🏏 Player Performance Dashboard")
 
-# ✅ Dropdown to Select Player
+# Dropdown to Select Player
 selected_player = st.selectbox("Select a Player", df["Player"].unique())
 
 # Get Player Data
 player_data = df[df["Player"] == selected_player]
 
 if not player_data.empty:
-    # ✅ Only 2 Tabs now: Batting and Bowling
-    tab1, tab2 = st.tabs(["📋 Batting Performance", "📋 Bowling Performance"])
+    # Replace None/NaN with "-" only in batting and bowling columns
+    batting_cols = [col for col in df.columns if "batting" in col.lower()]
+    bowling_cols = [col for col in df.columns if "bowling" in col.lower()]
+
+    player_data[batting_cols] = player_data[batting_cols].fillna("-")
+    player_data[bowling_cols] = player_data[bowling_cols].fillna("-")
+
+    # Only 2 tabs now: Batting Performance and Bowling Performance
+    tab1, tab2 = st.tabs(["📈 Batting Performance", "🎯 Bowling Performance"])
 
     with tab1:
-        st.subheader("Batting Performance")
-        st.write("**Matches Played**")
-        st.write(f"Test: {player_data.iloc[0]['batting_Matches_Test']}")
-        st.write(f"ODI: {player_data.iloc[0]['batting_Matches_ODI']}")
-        st.write(f"T20: {player_data.iloc[0]['batting_Matches_T20']}")
-        st.write(f"IPL: {player_data.iloc[0]['batting_Matches_IPL']}")
-
-        st.write("**Runs Scored**")
-        st.write(f"Test: {player_data.iloc[0]['batting_Runs_Test']}")
-        st.write(f"ODI: {player_data.iloc[0]['batting_Runs_ODI']}")
-        st.write(f"T20: {player_data.iloc[0]['batting_Runs_T20']}")
-        st.write(f"IPL: {player_data.iloc[0]['batting_Runs_IPL']}")
-
-        st.write("**50s and 100s**")
-        st.write(f"50s in Test: {player_data.iloc[0]['batting_50s_Test']}, 100s in Test: {player_data.iloc[0]['batting_100s_Test']}")
-        st.write(f"50s in ODI: {player_data.iloc[0]['batting_50s_ODI']}, 100s in ODI: {player_data.iloc[0]['batting_100s_ODI']}")
-        st.write(f"50s in T20: {player_data.iloc[0]['batting_50s_T20']}, 100s in T20: {player_data.iloc[0]['batting_100s_T20']}")
-        st.write(f"50s in IPL: {player_data.iloc[0]['batting_50s_IPL']}, 100s in IPL: {player_data.iloc[0]['batting_100s_IPL']}")
+        st.write("### 📊 Batting Performance")
+
+        # Bar Chart for Batting Runs
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.barplot(
+            x=["Test", "ODI", "T20", "IPL"],
+            y=player_data.iloc[0][["batting_Runs_Test", "batting_Runs_ODI", "batting_Runs_T20", "batting_Runs_IPL"]],
+            palette="magma",
+            ax=ax
+        )
+        ax.set_ylabel("Total Runs", fontsize=14)
+        ax.set_title(f"Batting Performance of {selected_player}", fontsize=16)
+        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
+        st.pyplot(fig)
+
+        # Side-by-Side Bar Chart for 50s & 100s
+        st.write("### Half-Centuries (50s) vs Centuries (100s)")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        x_labels = ["Test", "ODI", "T20", "IPL"]
+        x = range(len(x_labels))
+        
+        fifties = player_data.iloc[0][["batting_50s_Test", "batting_50s_ODI", "batting_50s_T20", "batting_50s_IPL"]]
+        hundreds = player_data.iloc[0][["batting_100s_Test", "batting_100s_ODI", "batting_100s_T20", "batting_100s_IPL"]]
+        
+        width = 0.4
+        
+        ax.bar([i - width/2 for i in x], fifties, width=width, label="50s", color="skyblue")
+        ax.bar([i + width/2 for i in x], hundreds, width=width, label="100s", color="orange")
+        
+        ax.set_xticks(x)
+        ax.set_xticklabels(x_labels)
+        ax.set_ylabel("Count", fontsize=14)
+        ax.set_title(f"50s vs 100s of {selected_player}", fontsize=16)
+        ax.legend()
+        ax.grid(axis="y", linestyle="--", alpha=0.7)
+        st.pyplot(fig)
+
+        # Pie Chart for Batting Runs Distribution
+        st.write("### Batting Runs Distribution")
+        runs = player_data.iloc[0][["batting_Runs_Test", "batting_Runs_ODI", "batting_Runs_T20", "batting_Runs_IPL"]]
+        labels = ["Test", "ODI", "T20", "IPL"]
+        fig_pie = px.pie(values=runs, names=labels, title="Batting Runs Distribution", color=labels)
+        st.plotly_chart(fig_pie)
+
+        # Line Chart for Batting Averages
+        st.write("### Batting Averages Over Different Formats")
+        batting_averages = player_data.iloc[0][["batting_Average_Test", "batting_Average_ODI", "batting_Average_T20", "batting_Average_IPL"]]
+        fig_line, ax = plt.subplots(figsize=(7, 5))
+        ax.plot(["Test", "ODI", "T20", "IPL"], batting_averages, marker='o', color='tab:blue')
+        ax.set_ylabel("Batting Average", fontsize=14)
+        ax.set_title(f"Batting Averages of {selected_player}", fontsize=16)
+        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
+        st.pyplot(fig_line)
 
     with tab2:
-        st.subheader("Bowling Performance")
-        st.write("**Matches Played**")
-        st.write(f"Test: {player_data.iloc[0]['bowling_Matches_Test']}")
-        st.write(f"ODI: {player_data.iloc[0]['bowling_Matches_ODI']}")
-        st.write(f"T20: {player_data.iloc[0]['bowling_Matches_T20']}")
-        st.write(f"IPL: {player_data.iloc[0]['bowling_Matches_IPL']}")
-
-        st.write("**Wickets Taken**")
-        st.write(f"Test: {player_data.iloc[0]['bowling_Test_Wickets']}")
-        st.write(f"ODI: {player_data.iloc[0]['bowling_ODI_Wickets']}")
-        st.write(f"T20: {player_data.iloc[0]['bowling_T20_Wickets']}")
-        st.write(f"IPL: {player_data.iloc[0]['bowling_IPL_Wickets']}")
+        st.write("### 🎯 Bowling Performance")
+
+        # Bar Chart for Bowling Wickets
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.barplot(
+            x=["Test", "ODI", "T20", "IPL"],
+            y=player_data.iloc[0][["bowling_Test_Wickets", "bowling_ODI_Wickets", "bowling_T20_Wickets", "bowling_IPL_Wickets"]],
+            palette="coolwarm",
+            ax=ax
+        )
+        ax.set_ylabel("Total Wickets", fontsize=14)
+        ax.set_title(f"Bowling Performance of {selected_player}", fontsize=16)
+        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
+        st.pyplot(fig)
+
+        # Pie Chart for Bowling Wickets Distribution
+        st.write("### Bowling Wickets Distribution")
+        wickets = player_data.iloc[0][["bowling_Test_Wickets", "bowling_ODI_Wickets", "bowling_T20_Wickets", "bowling_IPL_Wickets"]]
+        labels = ["Test", "ODI", "T20", "IPL"]
+        fig_pie_bowl = px.pie(values=wickets, names=labels, title="Bowling Wickets Distribution", color=labels)
+        st.plotly_chart(fig_pie_bowl)
+
+        # Line Chart for Bowling Average
+        st.write("### Bowling Averages Over Different Formats")
+        bowling_averages = player_data.iloc[0][["bowling_Test_Avg", "bowling_ODI_Avg", "bowling_T20_Avg", "bowling_IPL_Avg"]]
+        fig_line_bowl, ax = plt.subplots(figsize=(7, 5))
+        ax.plot(["Test", "ODI", "T20", "IPL"], bowling_averages, marker='o', color='tab:green')
+        ax.set_ylabel("Bowling Average", fontsize=14)
+        ax.set_title(f"Bowling Averages of {selected_player}", fontsize=16)
+        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
+        st.pyplot(fig_line_bowl)
 
 else:
     st.warning("⚠️ Player not found in the dataset.")