Update pages/1player_information.py

pages/1player_information.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
-import seaborn as sns
 
 # Set page configuration
 st.set_page_config(page_title="Career Insights", layout="wide")
@@ -34,69 +33,121 @@ page_bg = """
 st.markdown(page_bg, unsafe_allow_html=True)
 
 # App Title
-st.title("🏏Career Insights")
+st.title("🏏 Career Insights")
 
 # Load data
 file_path = "Final.csv"  # Ensure this file exists in your working directory
 df = pd.read_csv(file_path)
 
 # Get unique player names
-player_names = df["Player"].unique()
+player_names = sorted(df["Player"].unique())
 
-# Autocomplete player selection
-player_prefix = st.text_input("Enter at least first three letters of Player Name:")
+# Dropdown for Player Selection
+selected_player = st.selectbox("Select a Player:", player_names)
 
-if player_prefix and len(player_prefix) >= 3:
-    filtered_players = [name for name in player_names if name.lower().startswith(player_prefix.lower())]
-
-    if filtered_players:
-        selected_player = st.selectbox("Select Player", filtered_players)
-    else:
-        st.warning("No matching player found.")
-        selected_player = None
-else:
-    selected_player = None
+# Buttons for Batting & Bowling
+col1, col2 = st.columns(2)
+show_batting = col1.button("🏏 Show Batting Stats")
+show_bowling = col2.button("🎯 Show Bowling Stats")
 
 if selected_player:
     player_data = df[df["Player"] == selected_player].iloc[0]
 
-    # Pie Chart - Matches Played Across Formats
-    matches = [
-        player_data["Matches_Test"],
-        player_data["Matches_ODI"],
-        player_data["Matches_T20"],
-        player_data["Matches_IPL"]
-    ]
-    labels = ["Test", "ODI", "T20", "IPL"]
-
-    fig, ax = plt.subplots()
-    ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90, 
-           colors=["#87CEEB", "#90EE90", "#FFA07A", "#9370DB"])
-    ax.set_title(f"Matches Played by {selected_player}", fontsize=14)
-    st.pyplot(fig)
-
-    # Bar Chart - Runs Scored in Different Formats
-    batting_runs = [
-        player_data["batting_Runs_Test"],
-        player_data["batting_Runs_ODI"],
-        player_data["batting_Runs_T20"],
-        player_data["batting_Runs_IPL"]
-    ]
-    fig, ax = plt.subplots()
-    ax.bar(labels, batting_runs, color=["#FFD700", "#008000", "#1E90FF", "#FF4500"])
-    ax.set_ylabel("Runs Scored", fontsize=12)
-    ax.set_title(f"Runs Scored by {selected_player}", fontsize=14)
-    st.pyplot(fig)
-
-    # Line Chart - Batting Average Over Formats
-    batting_average = [
-        player_data["batting_Runs_Test"] / max(1, player_data["batting_Innings_Test"]),
-        player_data["batting_Runs_ODI"] / max(1, player_data["batting_Innings_ODI"]),
-        player_data["batting_Runs_T20"] / max(1, player_data["batting_Innings_T20"]),
-        player_data["batting_Runs_IPL"] / max(1, player_data["batting_Innings_IPL"])
-    ]
-    fig, ax = plt.subplots()
-    ax.plot(labels, batting_average, marker='o', linestyle='-', color='#FFA500', linewidth=2)
-    ax.set_ylabel("Batting Average", fontsize=12)
-    ax.set_title(f"Batting Average of {selected_player}", fontsize=14)
-    st.pyplot(fig)
+    if show_batting:
+        st.subheader(f"🏏 Batting Stats of {selected_player}")
+
+        # Pie Chart - Matches Played Across Formats
+        matches = [
+            player_data["Matches_Test"],
+            player_data["Matches_ODI"],
+            player_data["Matches_T20"],
+            player_data["Matches_IPL"]
+        ]
+        labels = ["Test", "ODI", "T20", "IPL"]
+
+        fig, ax = plt.subplots()
+        ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90, 
+               colors=["#87CEEB", "#90EE90", "#FFA07A", "#9370DB"])
+        ax.set_title(f"Matches Played by {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+        # Bar Chart - Runs Scored in Different Formats
+        batting_runs = [
+            player_data["batting_Runs_Test"],
+            player_data["batting_Runs_ODI"],
+            player_data["batting_Runs_T20"],
+            player_data["batting_Runs_IPL"]
+        ]
+        fig, ax = plt.subplots()
+        ax.bar(labels, batting_runs, color=["#FFD700", "#008000", "#1E90FF", "#FF4500"])
+        ax.set_ylabel("Runs Scored", fontsize=12)
+        ax.set_title(f"Runs Scored by {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+        # Line Chart - Batting Average Over Formats
+        batting_average = [
+            player_data["batting_Runs_Test"] / max(1, player_data["batting_Innings_Test"]),
+            player_data["batting_Runs_ODI"] / max(1, player_data["batting_Innings_ODI"]),
+            player_data["batting_Runs_T20"] / max(1, player_data["batting_Innings_T20"]),
+            player_data["batting_Runs_IPL"] / max(1, player_data["batting_Innings_IPL"])
+        ]
+        fig, ax = plt.subplots()
+        ax.plot(labels, batting_average, marker='o', linestyle='-', color='#FFA500', linewidth=2)
+        ax.set_ylabel("Batting Average", fontsize=12)
+        ax.set_title(f"Batting Average of {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+    if show_bowling:
+        st.subheader(f"🎯 Bowling Stats of {selected_player}")
+
+        # Pie Chart - Overs Bowled in Different Formats
+        overs_bowled = [
+            player_data["Overs_Bowled_Test"],
+            player_data["Overs_Bowled_ODI"],
+            player_data["Overs_Bowled_T20"],
+            player_data["Overs_Bowled_IPL"]
+        ]
+        fig, ax = plt.subplots()
+        ax.pie(overs_bowled, labels=labels, autopct="%1.1f%%", startangle=90,
+               colors=["#FF5733", "#C70039", "#900C3F", "#581845"])
+        ax.set_title(f"Overs Bowled by {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+        # Bar Chart - Wickets Taken Across Formats
+        wickets = [
+            player_data["Wickets_Test"],
+            player_data["Wickets_ODI"],
+            player_data["Wickets_T20"],
+            player_data["Wickets_IPL"]
+        ]
+        fig, ax = plt.subplots()
+        ax.bar(labels, wickets, color=["#FF5733", "#C70039", "#900C3F", "#581845"])
+        ax.set_ylabel("Wickets Taken", fontsize=12)
+        ax.set_title(f"Wickets Taken by {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+        # Line Chart - Bowling Economy Over Formats
+        bowling_economy = [
+            player_data["Economy_Test"],
+            player_data["Economy_ODI"],
+            player_data["Economy_T20"],
+            player_data["Economy_IPL"]
+        ]
+        fig, ax = plt.subplots()
+        ax.plot(labels, bowling_economy, marker='o', linestyle='-', color='#2E86C1', linewidth=2)
+        ax.set_ylabel("Bowling Economy", fontsize=12)
+        ax.set_title(f"Bowling Economy of {selected_player}", fontsize=14)
+        st.pyplot(fig)
+
+        # Line Chart - Bowling Strike Rate Over Formats
+        bowling_strike_rate = [
+            player_data["Strike_Rate_Test"],
+            player_data["Strike_Rate_ODI"],
+            player_data["Strike_Rate_T20"],
+            player_data["Strike_Rate_IPL"]
+        ]
+        fig, ax = plt.subplots()
+        ax.plot(labels, bowling_strike_rate, marker='o', linestyle='-', color='#1ABC9C', linewidth=2)
+        ax.set_ylabel("Bowling Strike Rate", fontsize=12)
+        ax.set_title(f"Bowling Strike Rate of {selected_player}", fontsize=14)
+        st.pyplot(fig)