Update app.py

app.py

@@ -1,6 +1,9 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
 from datetime import datetime
 
 # Set page config
@@ -8,73 +11,99 @@ st.set_page_config(page_title="Nuisance Complaints Dashboard", layout="wide")
 
 # Title and introduction
 st.title("Nuisance Complaints Analysis Dashboard")
-st.markdown("**Team Members:** Shreyas Kulkarni (ssk16@illinois.edu), Vishal Devulapalli (nsd3@illinois.edu), Lu Chang (luchang2@illinois.edu), Li Qiming (qimingl4@illinois.edu), Ruchita Alate (ralate2@illinois.edu)")
+st.markdown("""
+**Team Members:**
+* Lu Chang (luchang2@illinois.edu)
+* Qiming Li (qimingl4@illinois.edu)
+* Ruchita Alate (ralate2@illinois.edu)
+* Shreyas Kulkarni (ssk16@illinois.edu)
+* Vishal Devulapalli (nsd3@illinois.edu)
+""")
 st.write("This dashboard analyzes nuisance complaints data from the City of Urbana.")
 
 # Load and clean data
 @st.cache_data
-def load_and_clean_data(file_path):
-    # Load data
-    data = pd.read_csv(file_path)
-    
-    # Drop rows with missing 'File Number'
-    data = data.dropna(subset=['File Number'])
-    
-    # Handle 'Date Notice Mailed or Given': Impute using median time from 'Date Reported'
-    data['Date Notice Mailed or Given'] = pd.to_datetime(data['Date Notice Mailed or Given'])
-    data['Date Reported'] = pd.to_datetime(data['Date Reported'])
-    median_delay = (data['Date Notice Mailed or Given'] - data['Date Reported']).dt.days.median()
-    data['Date Notice Mailed or Given'].fillna(data['Date Reported'] + pd.to_timedelta(median_delay, unit='D'), inplace=True)
-    
-    # Handle 'Type of Complaint': Fill missing with 'Unknown'
-    data['Type of Complaint'].fillna('Unknown', inplace=True)
-    
-    # Handle 'Disposition': Impute based on the most common value for the same complaint type
-    most_common_disposition = data.groupby('Type of Complaint')['Disposition'].apply(
-        lambda x: x.mode()[0] if not x.mode().empty else 'Pending')
-    data['Disposition'] = data.apply(
-        lambda row: most_common_disposition[row['Type of Complaint']] 
-        if pd.isnull(row['Disposition']) else row['Disposition'], axis=1)
-    
-    # Handle 'File Close Date': Fill missing with 'Unresolved'
-    data['File Close Date'] = pd.to_datetime(data['File Close Date'], errors='coerce')
-    data['File Close Date'].fillna('Unresolved', inplace=True)
-    
-    # Calculate processing time only for resolved cases
-    data['Processing Time'] = (data['File Close Date'] - data['Date Reported']).dt.days
-    
-    # Handle 'Method Submitted': Infer based on 'Submitted Online?'
-    data['Method Submitted'] = data.apply(
-        lambda row: 'Online' if row['Submitted Online?'] and pd.isnull(row['Method Submitted'])
-        else row['Method Submitted'], axis=1)
-    mode_method = data['Method Submitted'].mode()[0]
-    data['Method Submitted'].fillna(mode_method, inplace=True)
-    
-    # Drop rows with missing 'Submitted Online?'
-    data = data.dropna(subset=['Submitted Online?'])
-    
-    # Handle 'Mapped Location': Extract latitude and longitude
-    data['Latitude'] = data['Mapped Location'].str.extract(r'\(([^,]+),')[0].astype(float)
-    data['Longitude'] = data['Mapped Location'].str.extract(r', ([^,]+)\)').astype(float)
-    
-    return data
+def load_and_clean_data():
+    try:
+        # Load data
+        data = pd.read_csv('Nuisance_Complaints_20241204.csv')
+
+        # Drop rows with missing 'File Number'
+        data = data.dropna(subset=['File Number'])
+
+        # Convert dates and handle date-related columns
+        data['Date Reported'] = pd.to_datetime(data['Date Reported'])
+        data['Date Notice Mailed or Given'] = pd.to_datetime(data['Date Notice Mailed or Given'])
+        data['File Close Date'] = pd.to_datetime(data['File Close Date'], errors='coerce')
+
+        # Handle 'Date Notice Mailed or Given'
+        median_delay = (data['Date Notice Mailed or Given'] - data['Date Reported']).dt.days.median()
+        data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Notice Mailed or Given'] = \
+            data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Reported'] + pd.Timedelta(days=median_delay)
+
+        # Handle 'Type of Complaint'
+        data['Type of Complaint'] = data['Type of Complaint'].fillna('Unknown')
+
+        # Handle 'Disposition'
+        most_common_disposition = data.groupby('Type of Complaint')['Disposition'].agg(
+            lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else 'Pending'
+        )
+        data['Disposition'] = data.apply(
+            lambda row: most_common_disposition[row['Type of Complaint']]
+            if pd.isna(row['Disposition']) else row['Disposition'],
+            axis=1
+        )
+
+        # Calculate processing time for resolved cases
+        data['Processing Time'] = np.where(
+            data['File Close Date'].notna(),
+            (data['File Close Date'] - data['Date Reported']).dt.days,
+            np.nan
+        )
+
+        # Handle 'Method Submitted'
+        data.loc[
+            (data['Submitted Online?']) & (data['Method Submitted'].isna()),
+            'Method Submitted'
+        ] = 'Online'
+        data['Method Submitted'] = data['Method Submitted'].fillna(data['Method Submitted'].mode()[0])
+
+        # Drop rows with missing critical values
+        data = data.dropna(subset=['Submitted Online?', 'Mapped Location'])
+
+        # Extract and clean location data
+        data['Latitude'] = data['Mapped Location'].str.extract(r'\(([^,]+),')[0].astype(float)
+        data['Longitude'] = data['Mapped Location'].str.extract(r', ([^,]+)\)')[0].astype(float)
+
+        # Ensure Year Reported is integer
+        data['Year Reported'] = data['Year Reported'].astype(int)
+
+        return data
+
+    except Exception as e:
+        st.error(f"Error in data preprocessing: {str(e)}")
+        raise e
 
 # Load the data
-file_path = "Nuisance_Complaints_20241130.csv"
 try:
-    data = load_and_clean_data(file_path)
+    data = load_and_clean_data()
     st.success("Data successfully loaded and cleaned!")
 except Exception as e:
     st.error(f"Error loading data: {str(e)}")
     st.stop()
 
 # Create sidebar
+
 st.sidebar.header("Dashboard Controls")
+
+# Get unique years and convert to list for selectbox
+year_list = sorted(data['Year Reported'].unique().tolist())
+year_options = ['All Time'] + [int(year) for year in year_list]  # Convert years to integers
+
 selected_year = st.sidebar.selectbox(
     "Select Year",
-    options=sorted(data['Year Reported'].unique()),
+    options=year_options,
 )
-
 # Add visualization type selector
 viz_type = st.sidebar.selectbox(
     "Select Visualization",
@@ -82,35 +111,110 @@ viz_type = st.sidebar.selectbox(
      "Submission Methods", "Processing Time"]
 )
 
+
 # Filter data based on selected year
-filtered_data = data[data['Year Reported'] == selected_year]
+if selected_year == 'All Time':
+    filtered_data = data  # Use complete dataset when 'All Time' is selected
+else:
+    filtered_data = data[data['Year Reported'] == selected_year]
 
+# Update header text
+if selected_year == 'All Time':
+    st.header("Analysis for All Time")
+else:
+    st.header(f"Analysis for Year {selected_year}")
 # Main content
-st.header(f"Analysis for Year {selected_year}")
 
+# Create metrics
+# Create metrics
+# Create metrics
+# Create metrics
 # Create metrics
 col1, col2, col3 = st.columns(3)
 with col1:
     st.metric("Total Complaints", len(filtered_data))
 with col2:
-    # Calculate average processing time only for resolved cases
-    resolved_cases = filtered_data[filtered_data['File Close Date'] != 'Unresolved']
-    if len(resolved_cases) > 0:
-        avg_process_time = resolved_cases['Processing Time'].mean()
-        st.metric("Average Processing Time", f"{avg_process_time:.1f} days")
-    else:
-        st.metric("Average Processing Time", "N/A")
+    avg_time = filtered_data['Processing Time'].mean()
+    st.metric("Average Processing Time", f"{avg_time:.1f} days" if pd.notna(avg_time) else "N/A")
 with col3:
-    st.metric("Most Common Type", filtered_data['Type of Complaint'].mode()[0])
-
-# Add additional visualizations or tables based on `viz_type` here
+    if not filtered_data.empty:
+        most_common = filtered_data['Type of Complaint'].value_counts().index[0]
+        st.metric("Most Common Type", most_common)
+    else:
+        st.metric("Most Common Type", "N/A")
 if viz_type == "Complaint Types":
-    st.write("Visualization for Complaint Types will go here.")
+    # Interactive Pie Chart
+    st.subheader("Interactive Complaint Types Pie Chart")
+    complaint_counts = filtered_data['Type of Complaint'].value_counts().reset_index()
+    complaint_counts.columns = ['Complaint Type', 'Count']
+
+    fig = px.pie(
+        complaint_counts,
+        names='Complaint Type',
+        values='Count',
+        title=f'Complaint Types Distribution in {selected_year}',
+        hole=0.4  # Donut style
+    )
+    fig.update_traces(textinfo='percent+label')
+    st.plotly_chart(fig, use_container_width=True)
+
 elif viz_type == "Geographic Distribution":
-    st.write("Visualization for Geographic Distribution will go here.")
+    # Clustered Heatmap
+    st.subheader("Clustered Heatmap of Complaints")
+    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)
+
+    st_data = st_folium(m, width=700, height=500)
+
+
 elif viz_type == "Resolution Status":
-    st.write("Visualization for Resolution Status will go here.")
+    st.subheader("Complaint Resolution Status")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    resolution_counts = filtered_data['Disposition'].value_counts()
+    sns.barplot(x=resolution_counts.values, y=resolution_counts.index)
+    plt.title(f'Resolution Status Distribution in {selected_year}')
+    st.pyplot(fig)
+
 elif viz_type == "Submission Methods":
-    st.write("Visualization for Submission Methods will go here.")
+    st.subheader("Submission Methods Analysis")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    submission_counts = filtered_data['Method Submitted'].value_counts()
+    sns.barplot(x=submission_counts.values, y=submission_counts.index)
+    plt.title(f'Submission Methods in {selected_year}')
+    st.pyplot(fig)
+
+
 elif viz_type == "Processing Time":
-    st.write("Visualization for Processing Time will go here.")
+    st.subheader("Processing Time Analysis")
+    # Filter for resolved cases only
+    resolved_data = filtered_data[filtered_data['File Close Date'].notna()]
+    if len(resolved_data) > 0:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.histplot(data=resolved_data, x='Processing Time', bins=30)
+        plt.title(f'Distribution of Processing Times in {selected_year}')
+        plt.xlabel('Processing Time (Days)')
+        st.pyplot(fig)
+    else:
+        st.write("No resolved cases in this period")
+
+# Additional insights
+st.header("Key Insights")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Top 3 Complaint Types")
+    top_complaints = filtered_data['Type of Complaint'].value_counts().head(3)
+    st.write(top_complaints)
+
+with col2:
+    st.subheader("Resolution Efficiency")
+    resolution_rate = (filtered_data['Disposition'].value_counts() /
+                      len(filtered_data) * 100).round(2)
+    st.write(resolution_rate)
+
+# Footer
+st.markdown("---")
+st.markdown("Dataset provided by the City of Urbana Open Data Portal")