Update app.py

app.py

@@ -494,9 +494,9 @@ elif viz_type == "Complaints by Housing Block and Type":
 #     """)
 
 elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestions Based on Professor's Feedback)":
-    st.subheader("Complaints by Housing Block and Type- Incorporating Suggestions Based on Professor's Feedback")
+    st.subheader("Complaints by Housing Block and Type - Incorporating Suggestions Based on Professor's Feedback")
     
-    # Define the blocks to be excluded
+    # Define blocks to be excluded
     excluded_blocks = [
         '3400 block', '3500 block', '3600 block', '3700 block', '3800 block', '3900 block', 
         '4000 block', '4100 block', '4200 block', '4300 block', '4400 block', '4500 block', 
@@ -514,19 +514,26 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
         '3200 block', '3300 block'
     ]
 
-    # Filtering the data based on the selected year and housing block
+    # Filtering the data based on selected year
     filtered_data_time = 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]
     
-    # Excluding blocks from the data (3400 block, 3500 block, 3600 block, ..., 5000 block)
+    # Exclude blocks from the data
     filtered_data_time = filtered_data_time[~filtered_data_time['Housing Block'].isin(excluded_blocks)]
     
-    # Further filtering by Housing Block (if applicable)
+    # Get the list of blocks excluding the unwanted ones
+    available_blocks = sorted(filtered_data_time['Housing Block'].unique().tolist())
+    
+    # Dropdown for Housing Block (excluding unwanted blocks)
+    block_options = ['All Blocks'] + available_blocks
+    selected_block = st.sidebar.selectbox("Select Housing Block", options=block_options, key="block_select")
+
+    # Further filtering by selected Housing Block (if applicable)
     if selected_block != 'All Blocks':
         filtered_data_time = filtered_data_time[filtered_data_time['Housing Block'] == selected_block]
     
-    # Pivoting the data based on the filtered data
+    # Pivoting the data
     complaint_pivot = filtered_data_time.pivot_table(
         index='Housing Block',
         columns='Type of Complaint',
@@ -535,10 +542,10 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
         fill_value=0
     )
     
-    # Ensuring the pivoted data is numeric for plotting
+    # Ensure the pivot data is numeric for plotting
     complaint_pivot = complaint_pivot.astype(float)
 
-    # Reordering the index of the pivot table according to the desired order, excluding the unwanted blocks
+    # Reordering the pivot table by the desired order (excluding unwanted blocks)
     complaint_pivot = complaint_pivot.reindex(desired_order)
 
     # If a specific block is selected, only show that block on the x-axis
@@ -585,8 +592,6 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
     The 'inferno' color palette is used to represent different complaint types, with darker shades indicating a higher frequency of complaints. The stacked bar chart makes it easy to compare the distribution of complaints by block and type.
     """)
 
-
-
 # Footer
 st.markdown("---")
 st.markdown("Dataset provided by the City of Urbana Open Data Portal.")