Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np

# Generate sample data
def generate_sample_data():
    np.random.seed(42)
    products = ['Laptop', 'Phone', 'Tablet', 'Watch', 'Headphones']
    months = pd.date_range(start='2023-01-01', end='2023-12-31', freq='M')
    
    data = []
    for month in months:
        for product in products:
            sales = np.random.randint(50, 500)
            data.append({
                'Month': month,
                'Product': product,
                'Sales': sales
            })
    
    return pd.DataFrame(data)

# Initialize session state
if 'df' not in st.session_state:
    st.session_state.df = generate_sample_data()
    st.session_state.selected_month = None

st.title('Monthly Sales Dashboard')

# Create pivot table for monthly totals
pivot_df = st.session_state.df.pivot_table(
    index='Product',
    columns=pd.Grouper(key='Month', freq='M'),
    values='Sales',
    aggfunc='sum'
).sum()

# Display total monthly sales
st.subheader('Monthly Sales')
monthly_sales = pd.DataFrame(pivot_df).T
monthly_sales.index = monthly_sales.index.strftime('%Y-%m')
st.dataframe(monthly_sales, use_container_width=True)

# Handle click on monthly sales
clicked = st.data_editor(
    monthly_sales,
    disabled=True,
    hide_index=False,
    use_container_width=True,
    key='clickable_table'
)

if clicked['edited_rows']:
    selected_month = list(clicked['edited_rows'].keys())[0]
    st.session_state.selected_month = pd.to_datetime(selected_month)

# Show drill-down if month is selected
if st.session_state.selected_month:
    st.subheader(f'Sales Drill-down for {st.session_state.selected_month.strftime("%Y-%m")}')
    
    # Get previous, current and next month data
    selected_date = st.session_state.selected_month
    prev_month = selected_date - pd.DateOffset(months=1)
    next_month = selected_date + pd.DateOffset(months=1)
    
    # Filter data for these months
    mask = (st.session_state.df['Month'].isin([prev_month, selected_date, next_month]))
    drill_down_data = st.session_state.df[mask].copy()
    
    # Create pivot table for drill-down
    drill_down_pivot = drill_down_data.pivot_table(
        index='Product',
        columns='Month',
        values='Sales',
        aggfunc='sum'
    )
    
    # Format column headers
    drill_down_pivot.columns = drill_down_pivot.columns.strftime('%Y-%m')
    
    # Display drill-down table
    st.table(drill_down_pivot)