Rename pages/Player Stats to pages/Player Stats.py

pages/Player Stats.py

@@ -0,0 +1,77 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+# Streamlit App Title
+st.title("🏏 Cricket Data Analysis 📊")
+
+# Load CSV Files using Streamlit Cache
+@st.cache_data
+def load_batting_data():
+    return pd.read_csv("Batting_10_Teams_Final.csv")
+
+@st.cache_data
+def load_bowling_data():
+    return pd.read_csv("Bowling_10_Teams_Final.csv")
+
+# Load Data
+try:
+    batting_df = load_batting_data()
+    bowling_df = load_bowling_data()
+except FileNotFoundError as e:
+    st.error(f"❌ File not found: {e}")
+    st.stop()
+
+# Main Page Filters
+selected_country = st.selectbox("Select Country", sorted(batting_df["Country"].unique()))
+option = st.selectbox("Choose the Player", sorted(batting_df[batting_df["Country"] == selected_country]["player_name"].unique()))
+selected_format = st.selectbox("Select Format", ["All"] + list(batting_df["Format"].unique()) + ["IPL"])
+
+st.write("You selected:", option)
+
+# Filter Data
+filtered_batting_df = batting_df[(batting_df["player_name"] == option) & (batting_df["Country"] == selected_country)]
+filtered_bowling_df = bowling_df[(bowling_df["player_name"] == option) & (bowling_df["Country"] == selected_country)]
+
+if selected_format != "All":
+    filtered_batting_df = filtered_batting_df[filtered_batting_df["Format"] == selected_format]
+    filtered_bowling_df = filtered_bowling_df[filtered_bowling_df["Format"] == selected_format]
+
+# Convert NaN values to zero
+filtered_batting_df = filtered_batting_df.fillna(0)
+filtered_bowling_df = filtered_bowling_df.fillna(0)
+
+# Drop Unnecessary Columns
+display_batting_df = filtered_batting_df.drop(columns=["player_name", "Country"]).reset_index(drop=True)
+display_bowling_df = filtered_bowling_df.drop(columns=["player_name", "Country"]).reset_index(drop=True)
+
+# Display Raw Data
+st.subheader(f"🏏 Batting Stats - {option}")
+st.dataframe(display_batting_df)
+
+st.subheader(f"🎯 Bowling Stats - {option}")
+st.dataframe(display_bowling_df)
+
+# Batting Efficiency
+filtered_batting_df["Balls per Four"] = filtered_batting_df["Balls"] / filtered_batting_df["Fours"]
+filtered_batting_df["Balls per Six"] = filtered_batting_df["Balls"] / filtered_batting_df["Sixes"]
+
+# Bowling Efficiency
+filtered_bowling_df["Balls per Wicket"] = filtered_bowling_df["Balls"] / filtered_bowling_df["Wickets"]
+
+# Batting Visualizations
+st.subheader(f"🏏 Boundary Efficiency - {option}")
+fig = px.bar(filtered_batting_df, x="Format", y="Balls per Four", title="Balls Taken per Four Across Formats", color="Format")
+st.plotly_chart(fig)
+
+fig = px.bar(filtered_batting_df, x="Format", y="Balls per Six", title="Balls Taken per Six Across Formats", color="Format")
+st.plotly_chart(fig)
+
+# Bowling Visualizations
+st.subheader(f"🎯 Bowling Efficiency - {option}")
+fig = px.bar(filtered_bowling_df, x="Format", y="Eco", title="Bowling Economy Across Formats", color="Format")
+st.plotly_chart(fig)
+
+fig = px.bar(filtered_bowling_df, x="Format", y="Balls per Wicket", title="Balls Taken per Wicket Across Formats", color="Format")
+st.plotly_chart(fig)