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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
def process_data(df):
# Clean and transform data
df = df[df['Project Category'].notna()]
# Create Week buckets
df['Start Date'] = pd.to_datetime(df['Date'].str.split(' to ').str[0], format='%d/%b/%y')
df['Week'] = df['Start Date'].apply(lambda x: 1 if x <= pd.Timestamp('2025-01-05') else 2)
# Aggregate utilization data
utilization = df.groupby(['Week', 'Project Category'])['Logged'].sum().unstack(fill_value=0)
# Calculate percentages
total_hours = utilization.sum(axis=1)
utilization_percent = utilization.div(total_hours, axis=0) * 100
# Select relevant categories
utilization_percent = utilization_percent[['Fixed Bid Projects - Billable',
'Non-Billable',
'Leaves']].rename(columns={
'Fixed Bid Projects - Billable': 'Billable',
'Non-Billable': 'Non-Billable',
'Leaves': 'Leaves'
})
return utilization_percent
def create_utilization_chart(week_data, week_number):
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(
week_data.values,
labels=week_data.index,
autopct='%1.1f%%',
colors=['#4CAF50', '#FFC107', '#9E9E9E']
)
plt.setp(autotexts, size=10, weight="bold", color='white')
ax.set_title(f'Week {week_number} Utilization', pad=20)
return fig
def main():
st.title('QA Team Utilization Dashboard')
uploaded_file = st.file_uploader("Upload Tempo Timesheet", type=['xls', 'xlsx'])
if uploaded_file:
df = pd.read_excel(uploaded_file, sheet_name='Report')
utilization_percent = process_data(df)
# Page 4 Visualization
st.header("Bi-Weekly Utilization Report")
col1, col2 = st.columns(2)
with col1:
week1 = utilization_percent.loc[1]
st.pyplot(create_utilization_chart(week1, 1))
with col2:
week2 = utilization_percent.loc[2]
st.pyplot(create_utilization_chart(week2, 2))
if __name__ == "__main__":
main() |