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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| # Load Data (Replace with your CSV path) | |
| #@st.cache_data | |
| # def load_data(): | |
| # return pd.read_csv('All_Teams_updated.csv') | |
| df = pd.read_csv('All_Teams_updated.csv') | |
| # Create a Single Tab | |
| tab1, = st.tabs(["📊 Player Information"]) | |
| # Helper function to safely get values or return 0 if column doesn't exist | |
| def get_value(filtered_data, column_name): | |
| return filtered_data[column_name].values[0] if column_name in filtered_data.columns else 0 | |
| # Tab for Player Stats | |
| with tab1: | |
| # Streamlit App Title | |
| st.title("🏏 Player Dashboard") | |
| st.header("View Player Stats") | |
| # Creating columns for side-by-side layout | |
| col1, col2, col3 = st.columns([1.5, 1.5, 1]) | |
| with col1: | |
| # Select Team | |
| team_name = st.selectbox("🏏 Select Team:", df['Player_Team'].unique()) | |
| with col2: | |
| # Filter Players by Team | |
| team_players = df[df['Player_Team'] == team_name]['Player'].unique() | |
| player_name = st.selectbox("👤 Select Player:", team_players) | |
| with col3: | |
| # Select Player Type (Batsman/Bowler) | |
| player_type = st.radio("🔎 Select Type:", ["Batsman", "Bowler"]) | |
| # Filter Data for Selected Player | |
| filtered_data = df[(df['Player'] == player_name) & (df['Player_Team'] == team_name)] | |
| if filtered_data.empty: | |
| st.warning(f"No data found for {player_name} in {team_name}.") | |
| else: | |
| if player_type == "Batsman": | |
| # Extract Batting Data | |
| batsman_data = pd.DataFrame({ | |
| 'Format': ['IPL', 'Test', 'ODI', 'T20'], | |
| 'Matches': [get_value(filtered_data, f'Matches_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Innings': [get_value(filtered_data, f'batting_Innings_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Balls': [get_value(filtered_data, f'batting_Balls_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Runs': [get_value(filtered_data, f'batting_Runs_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Average': [get_value(filtered_data, f'batting_Average_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Strike Rate': [get_value(filtered_data, f'batting_SR_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| "Duck's" : [get_value(filtered_data, f'batting_Ducks_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| "NOT OUT" : [get_value(filtered_data, f'batting_Not Out_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| "4's" : [get_value(filtered_data, f'batting_Fours_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| "6's" : [get_value(filtered_data, f'batting_Sixes_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '50s': [get_value(filtered_data, f'batting_50s_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '100s': [get_value(filtered_data, f'batting_100s_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '200s': [get_value(filtered_data, f'batting_200s_{fmt}') if fmt != 'T20' else 0 for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '300s': [get_value(filtered_data, f'batting_300s_Test') if fmt == 'Test' else 0 for fmt in ['IPL', 'Test', 'ODI', 'T20']] | |
| }) | |
| st.subheader(f"Batting Stats for {player_name} ({team_name})") | |
| st.dataframe(batsman_data) | |
| # Extract Data Matches vs innings | |
| formats = ['Test', 'ODI', 'T20', 'IPL'] | |
| matches = [get_value(filtered_data, f'Matches_{fmt}') for fmt in formats] | |
| innings = [get_value(filtered_data, f'batting_Innings_{fmt}') for fmt in formats] | |
| # Create DataFrame | |
| comparison_data = pd.DataFrame({ | |
| 'Format': formats, | |
| 'Matches': matches, | |
| 'Innings': innings | |
| }) | |
| # Melt Data | |
| comparison_data_melted = comparison_data.melt(id_vars='Format', var_name='Category', value_name='Count') | |
| # Plot Grouped Bar Chart | |
| fig_comparison = px.bar( | |
| comparison_data_melted, | |
| x='Format', | |
| y='Count', | |
| color='Category', | |
| barmode='group', | |
| title=f'{player_name} - Matches vs Innings Across Formats', | |
| labels={'Count': 'Number of Matches/Innings', 'Format': 'Match Format'} | |
| ) | |
| st.plotly_chart(fig_comparison) | |
| ## Balls Vs Runs | |
| formats = ['IPL', 'Test', 'ODI', 'T20'] | |
| runs = [get_value(filtered_data, f'batting_Runs_{fmt}') for fmt in formats] | |
| balls = [get_value(filtered_data, f'batting_Balls_{fmt}') for fmt in formats] | |
| # Create DataFrame | |
| line_data = pd.DataFrame({ | |
| 'Format': formats, | |
| 'Runs': runs, | |
| 'Balls Faced': balls | |
| }) | |
| # Melt Data for Plotly | |
| line_data_melted = line_data.melt(id_vars='Format', var_name='Metric', value_name='Count') | |
| # Plot Line Chart | |
| fig_line = px.line( | |
| line_data_melted, | |
| x='Format', | |
| y='Count', | |
| color='Metric', | |
| markers=True, | |
| title=f'{player_name} - Runs vs Balls Faced Across Formats' | |
| ) | |
| # Display Chart in Streamlit | |
| st.plotly_chart(fig_line) | |
| # Extract Player Data for Pie Chart runs across all formats | |
| player_data = df[df['Player'] == player_name].iloc[0] | |
| run_columns = ['batting_Runs_Test', 'batting_Runs_ODI', 'batting_Runs_T20', 'batting_Runs_IPL'] | |
| runs_data = player_data[run_columns] | |
| plot_data = pd.DataFrame({'Format': ['Test', 'ODI', 'T20', 'IPL'], 'Runs': runs_data.values}) | |
| # Plot Pie Chart | |
| fig = px.pie(plot_data, names='Format', values='Runs', title=f'{player_name} - Distribution of Batting Runs Across all Formats') | |
| st.plotly_chart(fig) | |
| # Bar Chart for 4s and 6s | |
| fours_sixes_data = pd.DataFrame({ | |
| 'Format': ['IPL', 'Test', 'ODI', 'T20'], | |
| "4's": [get_value(filtered_data, f'batting_Fours_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| "6's": [get_value(filtered_data, f'batting_Sixes_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']] | |
| }) | |
| # Data Extraction 50,100,200,300,400 | |
| formats = ['Test', 'ODI', 'T20', 'IPL'] | |
| fifties = [get_value(filtered_data, f'batting_50s_{fmt}') for fmt in formats] | |
| hundreds = [get_value(filtered_data, f'batting_100s_{fmt}') for fmt in formats] | |
| double_hundreds = [get_value(filtered_data, f'batting_200s_{fmt}') for fmt in formats] | |
| triple_hundreds = [get_value(filtered_data, f'batting_300s_{fmt}') for fmt in formats] | |
| four_hundreds = [get_value(filtered_data, f'batting_400s_{fmt}') for fmt in formats] | |
| # Create DataFrame | |
| comparison_data = pd.DataFrame({ | |
| 'Format': formats, | |
| '50s': fifties, | |
| '100s': hundreds, | |
| '200s': double_hundreds, | |
| '300s': triple_hundreds, | |
| '400s' : four_hundreds | |
| }) | |
| # Melt Data for Plotly | |
| comparison_data_melted = comparison_data.melt(id_vars='Format', var_name='Category', value_name='Count') | |
| # Plot Grouped Bar Chart | |
| fig_comparison = px.bar( | |
| comparison_data_melted, | |
| x='Format', | |
| y='Count', | |
| color='Category', | |
| barmode='group', | |
| title=f'{player_name} - Milestones Across Formats (Grouped Bar Chart)', | |
| labels={'Count': 'Number of Milestones', 'Format': 'Match Format'} | |
| ) | |
| # Display Chart in Streamlit | |
| st.plotly_chart(fig_comparison) | |
| # Data Extraction ducks vs notouts | |
| formats = ['IPL', 'Test', 'ODI', 'T20'] | |
| not_outs = [get_value(filtered_data, f'batting_Not Out_{fmt}') for fmt in formats] | |
| ducks = [get_value(filtered_data, f'batting_Ducks_{fmt}') for fmt in formats] | |
| else: | |
| # Extract Bowling Data | |
| bowler_data = pd.DataFrame({ | |
| 'Format': ['IPL', 'Test', 'ODI', 'T20'], | |
| 'Matches': [get_value(filtered_data, f'Matches_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Innings': [get_value(filtered_data, f'bowling_{fmt}_Innings') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Balls': [get_value(filtered_data, f'bowling_{fmt}_Balls') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Runs': [get_value(filtered_data, f'bowling_{fmt}_Runs') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Maidens': [get_value(filtered_data, f'bowling_{fmt}_Maidens') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Wickets': [get_value(filtered_data, f'bowling_{fmt}_Wickets') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Average': [get_value(filtered_data, f'bowling_{fmt}_Avg') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Economy': [get_value(filtered_data, f'bowling_{fmt}_Eco') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'Strike Rate': [get_value(filtered_data, f'bowling_{fmt}_SR') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'BBI': [get_value(filtered_data, f'bowling_{fmt}_BBI') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| 'BBM': [get_value(filtered_data, f'bowling_{fmt}_BBM') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '4W': [get_value(filtered_data, f'bowling_{fmt}_4w') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '5W': [get_value(filtered_data, f'bowling_{fmt}_5w') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| '10W': [get_value(filtered_data, f'bowling_{fmt}_10w') for fmt in ['IPL', 'Test', 'ODI', 'T20']], | |
| }) | |
| st.subheader(f"Bowling Stats for {player_name} ({team_name})") | |
| st.dataframe(bowler_data) | |
| # Bar Chart for Matches Played | |
| matches_data = pd.DataFrame({ | |
| 'Format': ['IPL', 'Test', 'ODI', 'T20'], | |
| 'Matches': [get_value(filtered_data, f'Matches_{fmt}') for fmt in ['IPL', 'Test', 'ODI', 'T20']] | |
| }) | |
| fig_matches = px.bar(matches_data, x='Format', y='Matches', title=f'{player_name} - Matches Played Across Formats', color='Format') | |
| st.plotly_chart(fig_matches) | |
| # Extract the player data | |
| player_data = df[df['Player'] == player_name].iloc[0] | |
| wickets_columns = ['bowling_Test_Wickets','bowling_ODI_Wickets','bowling_T20_Wickets','bowling_IPL_Wickets'] | |
| runs_data = player_data[wickets_columns] | |
| # Create a DataFrame for Plotly | |
| plot_data = pd.DataFrame({'Format': ['Test', 'ODI', 'T20', 'IPL'], 'Runs': runs_data.values}) | |
| fig = px.pie(plot_data, names='Format', values='Runs', title=f'{player_name} - Distribution of Bowling Wickets Across all Formats') | |
| st.plotly_chart(fig) | |
| st.header("Author: L Sai Sreeja") |