Update app.py

app.py

@@ -4,11 +4,11 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import altair as alt
+from datetime import datetime
 import folium
 from folium.plugins import HeatMap
 from streamlit_folium import st_folium
 import plotly.express as px
-from datetime import datetime
 
 # Set page config
 st.set_page_config(page_title="Nuisance Complaints Dashboard", layout="wide")
@@ -73,7 +73,8 @@ year_options = ['All Time'] + sorted(data['Year Reported'].unique().tolist())
 selected_year = st.sidebar.selectbox("Select Year", options=year_options)
 viz_type = st.sidebar.selectbox("Select Visualization", [
     "Complaint Types", "Geographic Distribution", "Resolution Status",
-    "Submission Methods", "Complaints by Disposition", "Monthly Trends by Complaint Type"
+    "Submission Methods", "Complaints by Disposition", "Monthly Trends by Complaint Type",
+    "Top Complaint Types", "Complaints Over Time", "Complaints by Housing Block and Type"
 ])
 
 # Filter data based on year
@@ -99,10 +100,6 @@ if viz_type == "Complaint Types":
     complaint_counts.columns = ['Complaint Type', 'Count']
     fig = px.pie(complaint_counts, names='Complaint Type', values='Count', hole=0.4)
     st.plotly_chart(fig, use_container_width=True)
-    st.write("""
-    **Write-up:** This visualization shows the distribution of complaint types as a donut chart. 
-    It provides a quick overview of the most common complaints. The warm color palette helps 
-    highlight differences between complaint categories.""")
 
 elif viz_type == "Geographic Distribution":
     st.subheader("Clustered Heatmap of Complaints")
@@ -111,68 +108,68 @@ elif viz_type == "Geographic Distribution":
     heat_data = filtered_data[['Latitude', 'Longitude']].dropna().values.tolist()
     HeatMap(heat_data).add_to(m)
     st_folium(m, width=700, height=500)
-    st.write("""
-    **Write-up:** This heatmap visualizes complaint hotspots geographically. Areas with 
-    higher complaint density are highlighted, helping policymakers focus resources effectively.""")
 
 elif viz_type == "Resolution Status":
     st.subheader("Interactive Complaint Resolution Status")
     resolution_counts = filtered_data['Disposition'].value_counts().reset_index()
     resolution_counts.columns = ['Disposition', 'Count']
-    resolution_counts['Percentage'] = (resolution_counts['Count'] / resolution_counts['Count'].sum()) * 100
     chart = alt.Chart(resolution_counts).mark_arc(innerRadius=50).encode(
         theta=alt.Theta(field="Count", type="quantitative"),
-        color=alt.Color(field="Disposition", type="nominal"),
-        tooltip=[
-            alt.Tooltip("Disposition", title="Resolution"),
-            alt.Tooltip("Count", title="Count"),
-            alt.Tooltip("Percentage", title="Percentage", format=".2f")
-        ]
-    )
-    st.altair_chart(chart, use_container_width=True)
-    st.write("""
-    **Write-up:** This chart visualizes resolution status using a donut chart. 
-    It provides insights into the efficiency of complaint resolutions.""")
-
-elif viz_type == "Monthly Trends by Complaint Type":
-    st.subheader("Monthly Trends Grouped by Complaint Types")
-    monthly_trends = (
-        filtered_data.groupby(['Month Reported', 'Type of Complaint'])
-        .size()
-        .reset_index(name='Count')
-    )
-    monthly_trends['Month'] = monthly_trends['Month Reported'].apply(
-        lambda x: datetime(2023, x, 1).strftime('%B')
-    )
-    chart = alt.Chart(monthly_trends).mark_line(point=True).encode(
-        x=alt.X('Month Reported:O', title='Month'),
-        y=alt.Y('Count:Q', title='Number of Complaints'),
-        color=alt.Color('Type of Complaint:N', title='Complaint Type'),
-        tooltip=["Type of Complaint:N", "Month:N", "Count:Q"]
+        color=alt.Color(field="Disposition", type="nominal")
     )
     st.altair_chart(chart, use_container_width=True)
-    st.write("""
-    **Write-up:** This line chart visualizes monthly trends in complaints grouped by type. 
-    The use of vibrant colors helps distinguish trends across different complaint types.""")
 
 elif viz_type == "Submission Methods":
     st.subheader("Submission Methods Analysis")
     submission_counts = filtered_data['Method Submitted'].value_counts()
     fig, ax = plt.subplots(figsize=(10, 6))
     sns.barplot(x=submission_counts.values, y=submission_counts.index, palette='inferno', ax=ax)
-    ax.set_title(f"Submission Methods in {selected_year}")
     st.pyplot(fig)
-    st.write("""
-    **Write-up:** This bar chart illustrates the preferred methods for complaint submission. 
-    The `inferno` color palette highlights differences across submission types.""")
 
 elif viz_type == "Complaints by Disposition":
     st.subheader("Complaints by Disposition")
     disposition_counts = filtered_data['Disposition'].value_counts()
     fig, ax = plt.subplots(figsize=(10, 6))
     sns.barplot(x=disposition_counts.values, y=disposition_counts.index, palette='viridis', ax=ax)
-    ax.set_title(f"Complaints by Disposition in {selected_year}")
     st.pyplot(fig)
-    st.write("""
-    **Write-up:** This bar chart shows the distribution of complaints across various dispositions. 
-    The `viridis` color palette effectively highlights disposition categories.""")
+
+elif viz_type == "Monthly Trends by Complaint Type":
+    st.subheader("Monthly Trends Grouped by Complaint Types")
+    monthly_trends = filtered_data.groupby(['Month Reported', 'Type of Complaint']).size().reset_index(name='Count')
+    chart = alt.Chart(monthly_trends).mark_line(point=True).encode(
+        x=alt.X('Month Reported:O', title='Month'),
+        y=alt.Y('Count:Q', title='Number of Complaints'),
+        color='Type of Complaint:N'
+    )
+    st.altair_chart(chart, use_container_width=True)
+
+elif viz_type == "Top Complaint Types":
+    st.subheader("Top Complaint Types")
+    complaint_counts = filtered_data['Type of Complaint'].value_counts().head(10)
+    fig, ax = plt.subplots()
+    sns.barplot(x=complaint_counts.values, y=complaint_counts.index, palette="inferno", ax=ax)
+    st.pyplot(fig)
+
+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()
+    ax.plot(complaints_over_time.index, complaints_over_time.values, marker='o')
+    ax.set_title("Complaints Over Time")
+    st.pyplot(fig)
+
+elif viz_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
+    )
+    fig = complaint_pivot.plot(kind='bar', stacked=True, colormap='inferno', figsize=(10, 6)).get_figure()
+    st.pyplot(fig)
+
+# Footer
+st.markdown("---")
+st.markdown("Dataset provided by the City of Urbana Open Data Portal.")