Createapp/py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import geopandas as gpd
+
+st.set_page_config(layout="wide", page_title="Singapore Housing Data Dashboard", page_icon=":house:")
+
+@st.cache_data
+def load_data():
+    # Use relative paths for deployment
+    df = pd.read_csv("Adjusted_Resale_Prices_2025_with_coords.csv")
+    gdf = gpd.read_file("planning_area_boundaries.geojson")
+    return df, gdf
+
+try:
+    df, gdf = load_data()
+except FileNotFoundError as e:
+    st.error(f"Data file not found: {e}")
+    st.stop()
+
+st.title("🏠 Singapore HDB Resale Price Dashboard")
+st.markdown("---")
+
+# Sidebar Filters
+st.sidebar.header("🔍 Filters")
+
+# Check if required columns exist
+required_columns = ["planning_area", "flat_type", "storey_range", "lease_commence_year", "resale_price"]
+missing_columns = [col for col in required_columns if col not in df.columns]
+
+if missing_columns:
+    st.error(f"Missing required columns: {missing_columns}")
+    st.write("Available columns:", list(df.columns))
+    st.stop()
+
+all_planning_areas = sorted(df["planning_area"].unique())
+selected_planning_area = st.sidebar.selectbox(
+    "Planning Area", 
+    ["All"] + all_planning_areas
+)
+
+all_flat_types = sorted(df["flat_type"].unique())
+selected_flat_types = st.sidebar.multiselect(
+    "Flat Type", 
+    all_flat_types,
+    default=all_flat_types
+)
+
+all_storey_ranges = sorted(df["storey_range"].unique())
+selected_storey_ranges = st.sidebar.multiselect(
+    "Storey Range", 
+    all_storey_ranges,
+    default=all_storey_ranges
+)
+
+min_year = int(df["lease_commence_year"].min())
+max_year = int(df["lease_commence_year"].max())
+selected_year_range = st.sidebar.slider(
+    "Lease Commencement Year", 
+    min_year, max_year, (min_year, max_year)
+)
+
+# Apply filters
+filtered_df = df[
+    (df["flat_type"].isin(selected_flat_types)) &
+    (df["storey_range"].isin(selected_storey_ranges)) &
+    (df["lease_commence_year"] >= selected_year_range[0]) &
+    (df["lease_commence_year"] <= selected_year_range[1])
+]
+
+if selected_planning_area != "All":
+    filtered_df = filtered_df[filtered_df["planning_area"] == selected_planning_area]
+
+# Display filter summary
+st.sidebar.markdown("---")
+st.sidebar.write(f"**Records shown:** {len(filtered_df):,}")
+st.sidebar.write(f"**Total records:** {len(df):,}")
+
+# Main content
+col1, col2 = st.columns([2, 1])
+
+with col2:
+    if not filtered_df.empty:
+        avg_price = filtered_df["resale_price"].mean()
+        median_price = filtered_df["resale_price"].median()
+        max_price = filtered_df["resale_price"].max()
+        min_price = filtered_df["resale_price"].min()
+        
+        st.metric("Average Price", f"${avg_price:,.0f}")
+        st.metric("Median Price", f"${median_price:,.0f}")
+        st.metric("Price Range", f"${min_price:,.0f} - ${max_price:,.0f}")
+
+with col1:
+    st.header("📊 Key Statistics")
+
+# Choropleth Map
+st.header("🗺️ Average Resale Price by Planning Area")
+if not filtered_df.empty:
+    avg_price_by_planning_area = filtered_df.groupby("planning_area")["resale_price"].mean().reset_index()
+    
+    # Try to merge with GeoDataFrame
+    try:
+        # Check if the GeoDataFrame has the expected column
+        if "PLN_AREA_N" in gdf.columns:
+            gdf_merged = gdf.merge(avg_price_by_planning_area, left_on="PLN_AREA_N", right_on="planning_area", how="left")
+        else:
+            # Try other common column names
+            possible_columns = [col for col in gdf.columns if "area" in col.lower() or "name" in col.lower()]
+            if possible_columns:
+                gdf_merged = gdf.merge(avg_price_by_planning_area, left_on=possible_columns[0], right_on="planning_area", how="left")
+            else:
+                st.error("Could not find matching column in GeoJSON for planning areas")
+                st.write("GeoJSON columns:", list(gdf.columns))
+                gdf_merged = None
+        
+        if gdf_merged is not None:
+            fig_map = px.choropleth_mapbox(
+                gdf_merged,
+                geojson=gdf_merged.geometry,
+                locations=gdf_merged.index,
+                color="resale_price",
+                color_continuous_scale="Viridis",
+                mapbox_style="carto-positron",
+                zoom=9.5,
+                center={"lat": 1.3521, "lon": 103.8198},
+                opacity=0.7,
+                labels={
+                    "resale_price": "Avg Resale Price (SGD)",
+                },
+                hover_name=gdf_merged.columns[0] if "PLN_AREA_N" not in gdf_merged.columns else "PLN_AREA_N",
+                hover_data={
+                    "resale_price": ":$,.0f",
+                }
+            )
+            fig_map.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, height=500)
+            st.plotly_chart(fig_map, use_container_width=True)
+        else:
+            st.warning("Could not create choropleth map due to data structure mismatch.")
+    except Exception as e:
+        st.error(f"Error creating map: {e}")
+        st.write("Showing data table instead:")
+        st.dataframe(avg_price_by_planning_area)
+else:
+    st.warning("No data to display for the selected filters on the map.")
+
+# Line Chart
+st.header("📈 Resale Price Trends Over Lease Commencement Year")
+if not filtered_df.empty:
+    avg_price_by_year = filtered_df.groupby("lease_commence_year")["resale_price"].mean().reset_index()
+    fig_line = px.line(
+        avg_price_by_year,
+        x="lease_commence_year",
+        y="resale_price",
+        title="Average Resale Price by Lease Commencement Year",
+        labels={
+            "lease_commence_year": "Lease Commencement Year",
+            "resale_price": "Average Resale Price (SGD)"
+        },
+        markers=True
+    )
+    fig_line.update_traces(mode="lines+markers", line=dict(width=3))
+    fig_line.update_layout(
+        hovermode="x unified",
+        height=400,
+        xaxis_title="Lease Commencement Year",
+        yaxis_title="Average Resale Price (SGD)"
+    )
+    st.plotly_chart(fig_line, use_container_width=True)
+else:
+    st.warning("No data to display for the selected filters on the line chart.")
+
+# Additional Charts
+if not filtered_df.empty:
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("📊 Price Distribution by Flat Type")
+        fig_box = px.box(
+            filtered_df,
+            x="flat_type",
+            y="resale_price",
+            title="Price Distribution by Flat Type"
+        )
+        fig_box.update_layout(height=400)
+        st.plotly_chart(fig_box, use_container_width=True)
+    
+    with col2:
+        st.subheader("🏢 Average Price by Storey Range")
+        avg_by_storey = filtered_df.groupby("storey_range")["resale_price"].mean().reset_index()
+        fig_bar = px.bar(
+            avg_by_storey,
+            x="storey_range",
+            y="resale_price",
+            title="Average Price by Storey Range"
+        )
+        fig_bar.update_layout(height=400)
+        st.plotly_chart(fig_bar, use_container_width=True)
+
+# Toggle for Data Table
+st.header("📋 Filtered Data Table")
+show_data_table = st.checkbox("Show filtered data table")
+if show_data_table:
+    if not filtered_df.empty:
+        st.dataframe(filtered_df, use_container_width=True)
+    else:
+        st.info("No data to display in the table for the selected filters.")
+
+# Footer
+st.markdown("---")
+st.markdown("*Data source: Singapore HDB Resale Prices*")