Update app.py

app.py

@@ -326,3 +326,82 @@ if st.session_state['active_card'] == 'card3':
     )
 
     st.plotly_chart(fig, use_container_width=True)
+
+    #################### CARD-3 MONTHLY IMPLEMENTATION #########################
+    # Ensure 'Dt' column is in datetime format
+    df['Dt'] = pd.to_datetime(df['Dt'])
+
+    # Extract fiscal year and month from 'Dt' column
+    df['FY'] = df['Dt'].dt.year.astype(str)
+    df['Month'] = df['Dt'].dt.month.astype(str)
+
+    # Create FY_Month column
+    df['FY_Month'] = df['FY'] + '_' + df['Month']
+
+    # Filter data based on selected item type and selected regions
+    filtered_df = df[(df['Itemtype'] == selected_item_type) & (df['Region'].isin(selected_regions))]
+
+    # Group by Year, Region, Itemtype and aggregate SalesVolume and UnitPrice
+    agg_df = filtered_df.groupby(['FY_Month', 'Region', 'Itemtype']).agg({
+        'SalesVolume': 'sum',
+        'UnitPrice': 'mean'
+    }).reset_index()
+
+    # Split FY_Month again for correct sorting
+    agg_df[['FY', 'Month']] = agg_df['FY_Month'].str.split('_', expand=True)
+    agg_df['Month'] = agg_df['Month'].astype(int)
+    agg_df['FY'] = agg_df['FY'].astype(int)
+
+    # Combine FY and Month back into a datetime-like format for proper sorting
+    agg_df['FY_Month_dt'] = pd.to_datetime(agg_df['FY'].astype(str) + agg_df['Month'].astype(str).str.zfill(2), format='%Y%m')
+
+    # Sort values by Region, Itemtype, and FY_Month_dt
+    agg_df = agg_df.sort_values(by=['Region', 'Itemtype', 'FY_Month_dt'])
+
+    # Calculate YOY percentage changes in Sales Volume and Unit Price
+    agg_df['SalesVolume_pct_change'] = agg_df.groupby(['Region', 'Itemtype'])['SalesVolume'].pct_change().round(3) * 100
+    agg_df['UnitPrice_pct_change'] = agg_df.groupby(['Region', 'Itemtype'])['UnitPrice'].pct_change().round(3) * 100
+
+    # Calculate Price Elasticity Coefficient (PE)
+    agg_df['PE_Coeff'] = (agg_df['SalesVolume_pct_change'] / agg_df['UnitPrice_pct_change']).round(2)
+
+    # Exclude FY 2021 and FY 2025
+    agg_df_filtered = agg_df[~agg_df['FY'].astype(str).str.contains('2020|2021|2025')]
+
+    # Drop rows where PE_Coeff is NaN (optional)
+    agg_df_filtered = agg_df_filtered.dropna(subset=['PE_Coeff'])
+    agg_df_filtered = agg_df_filtered[(agg_df_filtered['PE_Coeff'] < 1000) & (agg_df_filtered['PE_Coeff'] > -1000)]
+
+    # Plot the PE Coefficient with Plotly
+    fig = go.Figure()
+
+    # Iterate through each selected region and plot separately
+    for region in selected_regions:
+        # Filter the DataFrame for the current region
+        region_df = agg_df_filtered[agg_df_filtered['Region'] == region]
+        
+        # Add a line trace for the region
+        fig.add_trace(go.Scatter(
+            x=region_df['FY_Month_dt'],  # Use the datetime-like column for correct sorting
+            y=region_df['PE_Coeff'], 
+            mode='lines+markers',
+            name=region,  # Set the name to the region to appear in the legend
+            line=dict(width=2),
+            marker=dict(size=6),
+        ))
+
+    # Customize layout
+    fig.update_layout(
+        title=f"Price Elasticity Coefficient (PE) by Year-Month for {selected_item_type}",
+        xaxis_title="Fiscal Year_Month",
+        yaxis_title="Price Elasticity Coefficient (PE)",
+        height=600,
+        width=1000,
+        legend_title="Region",
+        xaxis=dict(
+            tickformat='%Y-%m',  # Format X-axis ticks as Year-Month
+        )
+    )
+
+    # Show the plot in Streamlit
+    st.plotly_chart(fig, use_container_width=True)