Update app.py

app.py

@@ -77,9 +77,28 @@ viz_type = st.sidebar.selectbox("Select Visualization", [
     "Top Complaint Types", "Complaints Over Time", "Complaints by Housing Block and Type"
 ])
 
-# Filter data based on year
+# Date Range Selector for Complaints Over Time
+start_date = st.sidebar.date_input("Start Date", pd.to_datetime("2020-01-01"))
+end_date = st.sidebar.date_input("End Date", pd.to_datetime("2024-12-31"))
+
+# Dropdown for Housing Block
+block_options = ['All Blocks'] + sorted(data['Housing Block'].unique().tolist())
+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]
 
+# Filter data based on date range
+filtered_data_time = filtered_data[
+    (filtered_data['Date Reported'] >= pd.to_datetime(start_date)) & 
+    (filtered_data['Date Reported'] <= pd.to_datetime(end_date))
+]
+
+# Filter data based on selected housing block
+if selected_block != 'All Blocks':
+    filtered_data_time = filtered_data_time[filtered_data_time['Housing Block'] == selected_block]
+
+# Header for selected year
 st.header(f"Analysis for {'All Time' if selected_year == 'All Time' else selected_year}")
 
 # Display metrics
@@ -247,12 +266,10 @@ elif viz_type == "Top Complaint Types":
 
 elif viz_type == "Complaints Over Time":
     st.subheader("Complaints Over Time")
-    complaints_over_time = filtered_data.groupby(filtered_data['Date Reported'].dt.date).size()
-    fig, ax = plt.subplots()
+    complaints_over_time = filtered_data_time.groupby(filtered_data_time['Date Reported'].dt.date).size()
     
-    # Apply color scheme to the plot
+    fig, ax = plt.subplots()
     ax.plot(complaints_over_time.index, complaints_over_time.values, marker='o', color='tab:blue')
-    
     ax.set_title("Complaints Over Time")
     st.pyplot(fig)
 
@@ -262,17 +279,29 @@ elif viz_type == "Complaints Over Time":
     The use of a blue color scheme highlights the flow and continuity of the data, providing a clear view of the patterns over time.
     """)
 
-elif viz_type == "Complaints by Housing Block and Type":
+elif iz_type == "Complaints by Housing Block and Type":
     st.subheader("Complaints by Housing Block and Type")
-    complaint_pivot = filtered_data.pivot_table(
-        index='Housing Block',
-        columns='Type of Complaint',
-        values='Disposition',
-        aggfunc='count',
-        fill_value=0
-    )
+    
+    if housing_block != 'All Blocks':
+        complaint_pivot = filtered_data_time.pivot_table(
+            index='Housing Block',
+            columns='Type of Complaint',
+            values='Disposition',
+            aggfunc='count',
+            fill_value=0
+        )
+    else:
+        complaint_pivot = filtered_data.pivot_table(
+            index='Housing Block',
+            columns='Type of Complaint',
+            values='Disposition',
+            aggfunc='count',
+            fill_value=0
+        )
+    
     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.