Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from st_aggrid import AgGrid, GridOptionsBuilder
from datetime import datetime, timedelta

# Set page config
st.set_page_config(
    page_title="Basketball Analytics Dashboard",
    page_icon="🏀",
    layout="wide"
)

# Generate sample basketball data
@st.cache_data
def generate_basketball_data():
    np.random.seed(42)
    
    # Teams
    teams = ["Lakers", "Warriors", "Celtics", "Bulls", "Heat", "Nets", "Nuggets", "Clippers"]
    
    # Generate player data
    players_data = []
    for i in range(150):
        team = np.random.choice(teams)
        position = np.random.choice(["PG", "SG", "SF", "PF", "C"])
        
        # Generate realistic stats based on position
        if position in ["PG", "SG"]:
            points = np.random.normal(15, 8)
            assists = np.random.normal(6, 3)
            rebounds = np.random.normal(4, 2)
            steals = np.random.normal(1.2, 0.8)
        elif position == "SF":
            points = np.random.normal(18, 6)
            assists = np.random.normal(4, 2)
            rebounds = np.random.normal(6, 2)
            steals = np.random.normal(1.0, 0.6)
        else:  # PF, C
            points = np.random.normal(12, 5)
            assists = np.random.normal(2, 1)
            rebounds = np.random.normal(9, 3)
            steals = np.random.normal(0.7, 0.4)
        
        # Ensure positive values
        points = max(0, points)
        assists = max(0, assists)
        rebounds = max(0, rebounds)
        steals = max(0, steals)
        
        players_data.append({
            "Player": f"Player_{i+1}",
            "Team": team,
            "Position": position,
            "Points": round(points, 1),
            "Assists": round(assists, 1),
            "Rebounds": round(rebounds, 1),
            "Steals": round(steals, 1),
            "Minutes": round(np.random.normal(28, 8), 1),
            "Field_Goal_Pct": round(np.random.normal(0.45, 0.08), 3),
            "Age": np.random.randint(19, 38),
            "Games_Played": np.random.randint(60, 82)
        })
    
    # Generate team stats
    team_stats = []
    for team in teams:
        team_stats.append({
            "Team": team,
            "Wins": np.random.randint(25, 60),
            "Losses": np.random.randint(22, 57),
            "Win_Percentage": 0,
            "Points_Per_Game": round(np.random.normal(110, 10), 1),
            "Points_Allowed": round(np.random.normal(108, 8), 1),
            "Rebounds_Per_Game": round(np.random.normal(45, 5), 1),
            "Assists_Per_Game": round(np.random.normal(25, 4), 1)
        })
    
    # Calculate win percentage
    for team in team_stats:
        total_games = team["Wins"] + team["Losses"]
        team["Win_Percentage"] = round(team["Wins"] / total_games, 3)
    
    return pd.DataFrame(players_data), pd.DataFrame(team_stats)

# Load data
players_df, teams_df = generate_basketball_data()

# Dashboard title
st.title("🏀 Basketball Analytics Dashboard")
st.markdown("---")

# Sidebar for filters
st.sidebar.header("Filters")
selected_teams = st.sidebar.multiselect("Select Teams", teams_df["Team"].unique(), default=teams_df["Team"].unique())
selected_positions = st.sidebar.multiselect("Select Positions", players_df["Position"].unique(), default=players_df["Position"].unique())

# Filter data
filtered_players = players_df[
    (players_df["Team"].isin(selected_teams)) & 
    (players_df["Position"].isin(selected_positions))
]
filtered_teams = teams_df[teams_df["Team"].isin(selected_teams)]

# Key Metrics Row
col1, col2, col3, col4 = st.columns(4)

with col1:
    st.metric("Total Players", len(filtered_players))

with col2:
    avg_points = filtered_players["Points"].mean()
    st.metric("Avg Points/Game", f"{avg_points:.1f}")

with col3:
    avg_assists = filtered_players["Assists"].mean()
    st.metric("Avg Assists/Game", f"{avg_assists:.1f}")

with col4:
    avg_rebounds = filtered_players["Rebounds"].mean()
    st.metric("Avg Rebounds/Game", f"{avg_rebounds:.1f}")

st.markdown("---")

# Charts Row
col1, col2 = st.columns(2)

with col1:
    st.subheader("Team Performance")
    fig_teams = px.bar(
        filtered_teams.sort_values("Win_Percentage", ascending=False),
        x="Team",
        y="Win_Percentage",
        title="Team Win Percentage",
        color="Win_Percentage",
        color_continuous_scale="RdYlGn"
    )
    fig_teams.update_layout(height=400)
    st.plotly_chart(fig_teams, use_container_width=True)

with col2:
    st.subheader("Points vs Assists")
    fig_scatter = px.scatter(
        filtered_players,
        x="Points",
        y="Assists",
        color="Position",
        size="Minutes",
        hover_data=["Player", "Team"],
        title="Player Performance: Points vs Assists"
    )
    fig_scatter.update_layout(height=400)
    st.plotly_chart(fig_scatter, use_container_width=True)

# Second row of charts
col1, col2 = st.columns(2)

with col1:
    st.subheader("Position Distribution")
    position_counts = filtered_players["Position"].value_counts()
    fig_pie = px.pie(
        values=position_counts.values,
        names=position_counts.index,
        title="Players by Position"
    )
    fig_pie.update_layout(height=400)
    st.plotly_chart(fig_pie, use_container_width=True)

with col2:
    st.subheader("Team Scoring")
    fig_scoring = px.bar(
        filtered_teams.sort_values("Points_Per_Game", ascending=False),
        x="Team",
        y=["Points_Per_Game", "Points_Allowed"],
        title="Team Offensive vs Defensive Performance",
        barmode="group"
    )
    fig_scoring.update_layout(height=400)
    st.plotly_chart(fig_scoring, use_container_width=True)

st.markdown("---")

# Data Tables
tab1, tab2 = st.tabs(["Player Stats", "Team Stats"])

with tab1:
    st.subheader("Player Statistics")
    
    # Configure grid options for players
    gb = GridOptionsBuilder.from_dataframe(filtered_players)
    gb.configure_pagination(paginationAutoPageSize=True)
    gb.configure_side_bar()
    gb.configure_selection('multiple', use_checkbox=True)
    gb.configure_default_column(groupable=True, value=True, enableRowGroup=True, editable=False)
    
    # Configure specific columns
    gb.configure_column("Points", type=["numericColumn", "numberColumnFilter"], precision=1)
    gb.configure_column("Assists", type=["numericColumn", "numberColumnFilter"], precision=1)
    gb.configure_column("Rebounds", type=["numericColumn", "numberColumnFilter"], precision=1)
    gb.configure_column("Field_Goal_Pct", type=["numericColumn", "numberColumnFilter"], precision=3)
    
    gridOptions = gb.build()
    
    grid_response = AgGrid(
        filtered_players,
        gridOptions=gridOptions,
        data_return_mode='FILTERED_AND_SORTED',
        update_mode='MODEL_CHANGED',
        fit_columns_on_grid_load=True,
        enable_enterprise_modules=True,
        height=400,
        width='100%'
    )

with tab2:
    st.subheader("Team Statistics")
    
    # Configure grid options for teams
    gb_teams = GridOptionsBuilder.from_dataframe(filtered_teams)
    gb_teams.configure_pagination(paginationAutoPageSize=True)
    gb_teams.configure_selection('single', use_checkbox=True)
    gb_teams.configure_default_column(groupable=True, value=True, enableRowGroup=True, editable=False)
    
    # Configure specific columns
    gb_teams.configure_column("Win_Percentage", type=["numericColumn", "numberColumnFilter"], precision=3)
    gb_teams.configure_column("Points_Per_Game", type=["numericColumn", "numberColumnFilter"], precision=1)
    gb_teams.configure_column("Points_Allowed", type=["numericColumn", "numberColumnFilter"], precision=1)
    
    gridOptions_teams = gb_teams.build()
    
    grid_response_teams = AgGrid(
        filtered_teams,
        gridOptions=gridOptions_teams,
        data_return_mode='FILTERED_AND_SORTED',
        update_mode='MODEL_CHANGED',
        fit_columns_on_grid_load=True,
        enable_enterprise_modules=True,
        height=300,
        width='100%'
    )

# Footer
st.markdown("---")
st.markdown("### Dashboard Features")
st.markdown("""
- **Interactive Filtering**: Use the sidebar to filter by teams and positions
- **Dynamic Visualizations**: Charts update based on your filters
- **Sortable Data Tables**: Click column headers to sort, use filters for detailed analysis
- **Multi-select Capability**: Select multiple rows in the player stats table for comparison
- **Responsive Design**: Dashboard adapts to different screen sizes
""")

st.markdown("*Data is simulated for demonstration purposes*")