Update app.py

app.py

@@ -416,44 +416,35 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
     st.subheader("Complaints by Housing Block and Type- Incorporating Suggestions Based on Professor's Feedback")
     
     # Filtering the data based on the selected year and housing block
-    filtered_data_time = filtered_data  # Use filtered_data if date range is not needed
+    # Filtering the data based on the selected year and housing block
+    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]
     
-    # Further filtering by Housing Block 
+    # Further filtering by Housing Block (if applicable)
     if selected_block != 'All Blocks':
         filtered_data_time = filtered_data_time[filtered_data_time['Housing Block'] == selected_block]
     
-    # Grouping data and pivot
-    complaint_counts = filtered_data_time.groupby(['Housing Block', 'Type of Complaint']).size().reset_index(name='Count')
-    complaint_counts['Housing Block'] = pd.Categorical(
-        complaint_counts['Housing Block'], 
-        categories=desired_order, 
-        ordered=True
-    )
-    
-    complaint_pivot = complaint_counts.pivot_table(
-        index='Housing Block', 
-        columns='Type of Complaint', 
-        values='Count', 
-        aggfunc='sum', 
+    # Pivoting 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
     )
-    complaint_pivot = complaint_pivot.reindex(desired_order)
+    
+    # Ensuring the pivoted data is numeric for plotting
+    complaint_pivot = complaint_pivot.astype(float)
+
+    # Calculating percentages for each complaint type per housing block
     percentages = complaint_pivot.div(complaint_pivot.sum(axis=1), axis=0) * 100
 
-    # Creating the plot
-    fig, ax = plt.subplots(figsize=(10, 6))
-    bar_width = 0.8
-    complaint_pivot.plot(
-        kind='bar', 
-        stacked=True, 
-        cmap='inferno', 
-        width=bar_width, 
-        ax=ax
-    )
+    # Plotting the data
+    fig = complaint_pivot.plot(kind='bar', stacked=True, colormap='inferno', figsize=(10, 6)).get_figure()
 
-    # Adding percentage labels to the bars
+    # Adding percentage labels to the plot
+    ax = fig.gca()
     for idx, block in enumerate(complaint_pivot.index):
         cumulative_height = 0
         for i, complaint_type in enumerate(complaint_pivot.columns):
@@ -461,7 +452,7 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
             percent = percentages.iloc[idx, i]
             if count > 0:
                 # Compute the position for the percentage label
-                x_pos = idx - 0.4 + bar_width / 2
+                x_pos = idx - 0.4 + 0.8 / 2  # Adjusting the position of the label
                 y_pos = cumulative_height + count / 2
                 ax.text(
                     x_pos, y_pos, f"{percent:.1f}%", 
@@ -471,18 +462,20 @@ elif viz_type == "Complaints by Housing Block and Type (Incorporating Suggestion
                 )
             cumulative_height += count
 
+    # Display the plot in Streamlit
     st.pyplot(fig)
 
+    # Description of the visualization
     st.write("""
     **What this visualization shows:**  
-    This bar chart displays the distribution of complaints by Housing Block and Complaint Type. The data is stacked to show the total number of complaints per block, categorized by type, with percentage labels displayed for each section. This allows for a quick comparison of the most common complaint types across different housing blocks and the relative proportion of each type.
-
+    This bar chart displays the distribution of complaints by Housing Block and Complaint Type. The data is stacked to show the total number of complaints per block, categorized by type. This allows for a quick comparison of the most common complaint types across different housing blocks.
+    
     **Why it's interesting:**  
-    By analyzing the distribution of complaints by both block and type, organizations can identify specific areas where certain complaint types are more prevalent. This insight helps target interventions and allocate resources more efficiently based on the most common issues in different housing blocks. The percentage labels provide a clear view of the proportional representation of each complaint type within each block.
-
+    By analyzing the distribution of complaints by both block and type, organizations can identify specific areas where certain complaint types are more prevalent. This insight helps target interventions and allocate resources more efficiently based on the most common issues in different housing blocks.
+    
     **Color Scheme:**  
-    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, while the percentage labels offer a clearer understanding of the proportions.
-""")
+    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.
+    """)
 
 
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