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app.py

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+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd
+import plotly.express as px
+from faicons import icon_svg
+from shinywidgets import render_plotly
+from state_choices import STATE_CHOICES
+
+from shiny import reactive
+from shiny.express import input, render, ui
+
+# ---------------------------------------------------------------------
+# Reading in Files
+# ---------------------------------------------------------------------
+new_listings_df = pd.read_csv(Path(__file__).parent / "Metro_new_listings_uc_sfrcondo_sm_month.csv")
+median_listing_price_df = pd.read_csv(Path(__file__).parent / "Metro_mlp_uc_sfrcondo_sm_month.csv")
+for_sale_inventory_df = pd.read_csv(Path(__file__).parent / "Metro_invt_fs_uc_sfrcondo_sm_month.csv")
+
+
+# ---------------------------------------------------------------------
+# Helper functions - converting to DateTime
+# ---------------------------------------------------------------------
+def string_to_date(date_str):
+    return datetime.strptime(date_str, "%Y-%m-%d").date()
+
+
+def filter_by_date(df: pd.DataFrame,date_range: tuple):
+    rng = sorted(date_range)
+    dates = pd.to_datetime(df["Date"], format="%Y-%m-%d").dt.date
+    return df[(dates >= rng[0]) & (dates <= rng[1])]
+
+
+# ---------------------------------------------------------------------
+# Visualizations
+# ---------------------------------------------------------------------
+
+#for_sale_inventory_df2 = for_sale_inventory_df["StateName"].fillna("United States")
+#for_sale_inventory_df2 = for_sale_inventory_df["StateName"].drop_duplicates()
+#for_sale_inventory_df2 = for_sale_inventory_df2.sort_values().tolist()
+
+
+ui.page_opts(title= "US Housing App")
+
+
+
+
+
+with ui.sidebar():
+    ui.input_select("state","Filter by State", choices=STATE_CHOICES),
+    ui.input_slider("date_range","Filter by Date Range",
+                min = string_to_date("2018-3-31"),
+                max = string_to_date("2024-4-30"),
+                value = [string_to_date(x) for x in ["2018-3-31","2024-4-30"]])
+
+with ui.layout_column_wrap():
+    with ui.value_box(showcase = icon_svg("dollar-sign")):
+        "Current Median List Price"
+
+        @render.ui
+        def price():
+            date_columns = median_listing_price_df.columns[6:]
+            states = median_listing_price_df.groupby("StateName").mean(numeric_only=True)
+            dates = states[date_columns].reset_index()
+            states = dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+            country = median_listing_price_df[median_listing_price_df["RegionType"] == "country"]
+            country_dates = country[date_columns].reset_index()
+            country_dates["StateName"] = "United States"
+            country = country_dates.melt(
+                id_vars=["StateName"], var_name="Date", value_name="Value"
+            )
+
+            res = pd.concat([states, country])
+
+            res = res[res["Date"] != "index"]
+
+            df = res[res["StateName"] == input.state()]
+
+            last_value = df.iloc[-1,-1]
+            return f"${last_value:,.0f}"
+
+    with ui.value_box(showcase = icon_svg("house")):
+        "Home Inventory % Change"
+        @render.ui
+        def change():
+            date_columns = median_listing_price_df.columns[6:]
+            states = median_listing_price_df.groupby("StateName").mean(numeric_only=True)
+            dates = states[date_columns].reset_index()
+            states = dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+            country = median_listing_price_df[median_listing_price_df["RegionType"] == "country"]
+            country_dates = country[date_columns].reset_index()
+            country_dates["StateName"] = "United States"
+            country = country_dates.melt(
+                id_vars=["StateName"], var_name="Date", value_name="Value"
+            )
+
+            res = pd.concat([states, country])
+
+            res = res[res["Date"] != "index"]
+
+            df = res[res["StateName"] == input.state()]
+
+            last_value = df.iloc[-1,-1]
+            second_last_value = df.iloc[-2,-1]
+
+            percentage_change = ((last_value - second_last_value)/second_last_value *100)
+            sign = "+" if percentage_change > 0 else "-"
+            return f"{sign}{percentage_change:.2f}%"
+
+# Plotly visualization of Median Home Price Per State
+
+with ui.navset_card_underline(title = "Median List Price"):
+
+    with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
+
+        @render_plotly
+        def list_price_plot():
+            # Grouping by State Name and specifying the Date Columns
+            price_grouped = median_listing_price_df.groupby('StateName').mean(numeric_only=True)     
+            date_columns = median_listing_price_df.columns[6:]
+            price_grouped_dates = price_grouped[date_columns].reset_index()   
+            price_df_for_viz = price_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+
+            price_df_for_viz = filter_by_date(price_df_for_viz, input.date_range())
+
+            if input.state() == "United States":
+                df = price_df_for_viz
+            else:
+                df = price_df_for_viz[price_df_for_viz["StateName"] == input.state()]
+
+
+            # Creating Visualization using Ployly
+            fig = px.line(df, x="Date", y="Value", color="StateName")
+            fig.update_xaxes(title_text="")
+            fig.update_yaxes(title_text="")
+            return fig
+    with ui.nav_panel("Table", icon = icon_svg("table")):
+        @render.data_frame
+        def list_price_data():
+            if input.state() == "United States":
+                df = median_listing_price_df
+            else:
+                df = median_listing_price_df[median_listing_price_df["StateName"] == input.state()]
+            return render.DataGrid(df)
+
+# Plotly visualization of Homes For Sale Per State
+
+with ui.navset_card_underline(title = "Home Inventory"):
+
+    with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
+        @render_plotly
+        def for_sale_plot():
+            # Grouping by State Name and specifying the Date Columns
+            for_sale_grouped = for_sale_inventory_df.groupby('StateName').sum(numeric_only=True)
+            date_columns = for_sale_inventory_df.columns[6:]
+            for_sale_grouped_grouped_dates = for_sale_grouped[date_columns].reset_index()
+            for_sale_df_for_viz = for_sale_grouped_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+
+
+            for_sale_df_for_viz = filter_by_date(for_sale_df_for_viz, input.date_range())
+
+            if input.state() == "United States":
+                df = for_sale_df_for_viz
+            else:
+                df = for_sale_df_for_viz[for_sale_df_for_viz["StateName"] == input.state()]
+
+            # Creating Visualization using Ployly
+            fig = px.line(df, x="Date", y="Value", color="StateName")
+            fig.update_xaxes(title_text="")
+            fig.update_yaxes(title_text="")
+            return fig
+    with ui.nav_panel("Table", icon = icon_svg("table")):
+        @render.data_frame
+        def for_sale_data():
+            if input.state() == "United States":
+                df = for_sale_inventory_df
+            else:
+                df = for_sale_inventory_df[for_sale_inventory_df["StateName"] == input.state()]
+            return render.DataGrid(df)
+
+# Plotly visualization of Listings Per State
+
+with ui.navset_card_underline(title = "New Listings"):
+
+    with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
+
+        @render_plotly
+        def listings_plot():
+            # Grouping by State Name and specifying the Date Columns
+            new_listings_grouped = new_listings_df.groupby('StateName').sum(numeric_only=True)
+            date_columns = new_listings_df.columns[6:]
+            new_listings_grouped_dates = new_listings_grouped[date_columns].reset_index()
+            new_listings_df_for_viz = new_listings_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+
+            new_listings_df_for_viz = filter_by_date(new_listings_df_for_viz, input.date_range())
+
+            if input.state() == "United States":
+                df = new_listings_df_for_viz
+            else:
+                df = new_listings_df_for_viz[new_listings_df_for_viz["StateName"] == input.state()]
+
+
+            # Creating Visualization using Ployly
+            fig = px.line(df, x="Date", y="Value", color="StateName")
+            fig.update_xaxes(title_text="")
+            fig.update_yaxes(title_text="")
+            return fig
+
+    with ui.nav_panel("Table", icon = icon_svg("table")):
+        @render.data_frame
+        def listings_data():
+            if input.state() == "United States":
+                df = new_listings_df
+            else:
+                df = new_listings_df[new_listings_df["StateName"] == input.state()]
+            return render.DataGrid(df)

app_starting_code.py

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+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd
+import plotly.express as px
+from faicons import icon_svg
+from shinywidgets import render_plotly
+from state_choices import STATE_CHOICES
+
+from shiny import reactive
+from shiny.express import input, render, ui
+
+# ---------------------------------------------------------------------
+# Reading in Files
+# ---------------------------------------------------------------------
+new_listings_df = pd.read_csv(Path(__file__).parent / "Metro_new_listings_uc_sfrcondo_sm_month.csv")
+median_listing_price_df = pd.read_csv(Path(__file__).parent / "Metro_mlp_uc_sfrcondo_sm_month.csv")
+for_sale_inventory_df = pd.read_csv(Path(__file__).parent / "Metro_invt_fs_uc_sfrcondo_sm_month.csv")
+
+
+# ---------------------------------------------------------------------
+# Helper functions - converting to DateTime
+# ---------------------------------------------------------------------
+def string_to_date(date_str):
+    return datetime.strptime(date_str, "%Y-%m-%d").date()
+
+
+def filter_by_date(df: pd.DataFrame, date_range: tuple):
+    rng = sorted(date_range)
+    dates = pd.to_datetime(df["Date"], format="%Y-%m-%d").dt.date
+    return df[(dates >= rng[0]) & (dates <= rng[1])]
+
+
+# ---------------------------------------------------------------------
+# Visualizations
+# ---------------------------------------------------------------------
+
+# Plotly visualization of Median Home Price Per State
+def list_price_plot():
+    # Grouping by State Name and specifying the Date Columns
+    price_grouped = median_listing_price_df.groupby('StateName').mean(numeric_only=True)     
+    date_columns = median_listing_price_df.columns[6:]
+    price_grouped_dates = price_grouped[date_columns].reset_index()   
+    price_df_for_viz = price_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+    # Creating Visualization using Ployly
+    fig = px.line(price_df_for_viz, x="Date", y="Value", color="StateName")
+    fig.update_xaxes(title_text="")
+    fig.update_yaxes(title_text="")
+    return fig
+
+
+# Plotly visualization of Homes For Sale Per State
+def for_sale_plot():
+    # Grouping by State Name and specifying the Date Columns
+    df2_grouped = for_sale_inventory_df.groupby('StateName').sum(numeric_only=True)
+    date_columns = for_sale_inventory_df.columns[6:]
+    df2_grouped_dates = df2_grouped[date_columns].reset_index()
+    df2_melted = df2_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+    # Creating Visualization using Ployly
+    df = for_sale_filtered()
+    fig = px.line(df, x="Date", y="Value", color="StateName")
+    fig.update_xaxes(title_text="")
+    fig.update_yaxes(title_text="")
+    return fig
+
+# Plotly visualization of Listings Per State
+def listings_plot():
+    # Grouping by State Name and specifying the Date Columns
+    df3_grouped = new_listings_df.groupby('StateName').sum(numeric_only=True)
+    date_columns = new_listings_df.columns[6:]
+    df3_grouped_dates = df3_grouped[date_columns].reset_index()
+    df3_melted = df3_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
+    # Creating Visualization using Ployly
+    df = listings_filtered()
+    fig = px.line(df, x="Date", y="Value", color="StateName")
+    fig.update_xaxes(title_text="")
+    fig.update_yaxes(title_text="")
+    return fig
+
+
+