Delet Src

src/Adjusted_Resale_Prices_2025_with_coords.csv

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-version https://git-lfs.github.com/spec/v1
-oid sha256:a5d0cfae8fa43b5c33f94d73a0b4aa93b24f422322608b3b666103631a163aac
-size 40738143

src/streamlit1_app.py

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-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

src/streamlit_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*")