Update app.py

app.py

@@ -88,7 +88,14 @@ if viz_type == "Complaints by Housing Block and Type":
     selected_block = st.sidebar.selectbox("Select Housing Block", options=block_options)
 
 # Filter data based on selected year
-filtered_data = data if selected_year == 'All Time' else data[data['Year Reported'] == selected_year]
+if selected_year != 'All Time':
+    filtered_data = data[data['Year Reported'] == selected_year]
+else:
+    filtered_data = data  
+
+# Further filter by Housing Block (if applicable)
+if viz_type == "Complaints by Housing Block and Type" and selected_block != 'All Blocks':
+    filtered_data = filtered_data[filtered_data['Housing Block'] == selected_block]
 
 # Filter data based on date range (only for Complaints Over Time visualization)
 if viz_type == "Complaints Over Time":
@@ -97,11 +104,7 @@ if viz_type == "Complaints Over Time":
         (filtered_data['Date Reported'] <= pd.to_datetime(end_date))
     ]
 else:
-    filtered_data_time = filtered_data
-
-# Filter data based on selected housing block (only for Complaints by Housing Block visualization)
-if viz_type == "Complaints by Housing Block and Type" and selected_block != 'All Blocks':
-    filtered_data_time = filtered_data_time[filtered_data_time['Housing Block'] == selected_block]
+    filtered_data_time = filtered_data  # Just use the filtered data without date range for other visualizations
 
 # Header for selected year
 st.header(f"Analysis for {'All Time' if selected_year == 'All Time' else selected_year}")
@@ -288,31 +291,31 @@ elif viz_type == "Complaints Over Time":
 elif viz_type == "Complaints by Housing Block and Type":
     st.subheader("Complaints by Housing Block and Type")
     
-    # Filter the data based on the selected block
+    # Filter the data based on the selected year and housing block
+    filtered_data_time = filtered_data  # Use filtered_data if date range is not needed
+    if selected_year != 'All Time':
+        filtered_data_time = filtered_data_time[filtered_data_time['Year Reported'] == selected_year]
+    
+    # Further filter by Housing Block (if applicable)
     if selected_block != 'All Blocks':
-        complaint_pivot = filtered_data_time[filtered_data_time['Housing Block'] == selected_block].pivot_table(
-            index='Housing Block',
-            columns='Type of Complaint',
-            values='Disposition',
-            aggfunc='count',
-            fill_value=0
-        )
-    else:
-        complaint_pivot = filtered_data_time.pivot_table(
-            index='Housing Block',
-            columns='Type of Complaint',
-            values='Disposition',
-            aggfunc='count',
-            fill_value=0
-        )
+        filtered_data_time = filtered_data_time[filtered_data_time['Housing Block'] == selected_block]
+    
+    # Pivot the data based on the filtered data
+    complaint_pivot = filtered_data_time.pivot_table(
+        index='Housing Block',
+        columns='Type of Complaint',
+        values='Disposition',
+        aggfunc='count',
+        fill_value=0
+    )
     
     # Ensure the pivoted data is numeric for plotting
     complaint_pivot = complaint_pivot.astype(float)
 
+    # Plot the data
     fig = complaint_pivot.plot(kind='bar', stacked=True, colormap='inferno', figsize=(10, 6)).get_figure()
     st.pyplot(fig)
 
-
     st.write("""
     **What this visualization shows:**  
     This line chart shows the trend of complaints over time, displaying the number of complaints reported for each day. It helps identify patterns, peaks, and trends in the complaints data.