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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") |