Update app.py

app.py

@@ -138,11 +138,11 @@ with col2:
 if viz_type == "Complaint Types":
     st.subheader("Top 5 Complaint Types Pie Chart")
     
-    # Prepare data: Select the top 5 complaint types
+    # Preparing data: Select the top 5 complaint types
     top_complaints = filtered_data['Type of Complaint'].value_counts().nlargest(5).reset_index()
     top_complaints.columns = ['Complaint Type', 'Count']
     
-    # Create an interactive pie chart with the 'inferno' color scheme
+    # Creating an interactive pie chart with the 'inferno' color scheme
     fig = px.pie(
         top_complaints,
         names='Complaint Type',
@@ -154,10 +154,10 @@ if viz_type == "Complaint Types":
     )
     fig.update_traces(textinfo='percent+label', hovertemplate='<b>%{label}</b><br>Complaints: %{value}<br>Percentage: %{percent}')
     
-    # Display chart
+    # Displaying chart
     st.plotly_chart(fig, use_container_width=True)
     
-    # Updated write-up
+    # write-up
     st.write("""
     **What this visualization shows:**  
     This interactive pie chart displays the distribution of the top 5 complaint types by year.  
@@ -170,13 +170,13 @@ if viz_type == "Complaint Types":
 elif viz_type == "Geographic Distribution":
     st.subheader("Clustered Heatmap of Complaints")
     
-    # Generate the heatmap
+    # Generating the heatmap
     map_center = [filtered_data['Latitude'].mean(), filtered_data['Longitude'].mean()]
     m = folium.Map(location=map_center, zoom_start=12)
     heat_data = filtered_data[['Latitude', 'Longitude']].dropna().values.tolist()
     HeatMap(heat_data).add_to(m)
     
-    # Display the map
+    # Displaying the map
     st_folium(m, width=700, height=500)
     
     # Write-up
@@ -218,22 +218,22 @@ elif viz_type == "Geographic Distribution":
 elif viz_type == "Submission Methods":
     st.subheader("Submission Methods Analysis")
 
-    # Allow the user to select the type of chart (Bar or Pie)
+    # Allowing the user to select the type of chart (Bar or Pie)
     plot_type = st.selectbox("Select Plot Type", options=["Bar Chart", "Pie Chart"])
 
-    # Get the top 5 submission methods
+    # Getting the top 5 submission methods
     submission_counts = filtered_data['Method Submitted'].value_counts().nlargest(5)
     submission_data = submission_counts.reset_index()
     submission_data.columns = ['Submission Method', 'Count']
 
     if plot_type == "Bar Chart":
-        # Create a bar chart with Seaborn
+        # Creating a bar chart with Seaborn
         fig, ax = plt.subplots(figsize=(10, 6))
         sns.barplot(x=submission_data['Count'], y=submission_data['Submission Method'], palette='inferno', ax=ax)
         st.pyplot(fig)
     
     elif plot_type == "Pie Chart":
-        # Create an interactive pie chart with Plotly
+        # Creating an interactive pie chart with Plotly
         fig = px.pie(
             submission_data,
             names='Submission Method',
@@ -247,7 +247,7 @@ elif viz_type == "Submission Methods":
         fig.update_traces(textinfo='percent+label', hovertemplate='<b>%{label}</b><br>Complaints: %{value}<br>Percentage: %{percent}')
         st.plotly_chart(fig, use_container_width=True)
 
-    # Updated write-up
+    # Write-up
     st.write("""
     **What this visualization shows:**  
     This chart shows the number of complaints submitted via different methods, such as email, phone, online form, etc., with a focus on the top 5 submission methods.
@@ -281,10 +281,10 @@ elif viz_type == "Submission Methods":
 elif viz_type == "Complaints by Disposition":
     st.subheader("Complaints by Disposition")
     
-    # Group by Complaint Type and Disposition to make the analysis more complex
+    # Grouping by Complaint Type and Disposition to make the analysis more complex
     disposition_by_complaint_type = filtered_data.groupby(['Type of Complaint', 'Disposition']).size().unstack(fill_value=0)
     
-    # Plot the bar chart with the 'inferno' color scheme
+    # Plotting the bar chart with the 'inferno' color scheme
     fig, ax = plt.subplots(figsize=(12, 8))
     disposition_by_complaint_type.plot(kind='bar', stacked=True, colormap='inferno', ax=ax)
     
@@ -292,7 +292,7 @@ elif viz_type == "Complaints by Disposition":
     ax.set_ylabel("Number of Complaints")
     ax.set_xlabel("Complaint Type")
     
-    # Display the plot
+    # Displaying the plot
     st.pyplot(fig)
 
     st.write("""
@@ -372,16 +372,16 @@ 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 year and housing block
+    # 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
     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)
+    # 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]
     
-    # Pivot the data based on the filtered data
+    # Pivoting the data based on the filtered data
     complaint_pivot = filtered_data_time.pivot_table(
         index='Housing Block',
         columns='Type of Complaint',
@@ -390,10 +390,10 @@ elif viz_type == "Complaints by Housing Block and Type":
         fill_value=0
     )
     
-    # Ensure the pivoted data is numeric for plotting
+    # Ensuring the pivoted data is numeric for plotting
     complaint_pivot = complaint_pivot.astype(float)
 
-    # Plot the data
+    # Plotting the data
     fig = complaint_pivot.plot(kind='bar', stacked=True, colormap='inferno', figsize=(10, 6)).get_figure()
     st.pyplot(fig)