^^

Dockerfile

@@ -23,7 +23,7 @@ COPY --chown=user .streamlit/ ./.streamlit/
 COPY --chown=user src/ ./src/
 COPY --chown=user src/pages/1_Historical_Prices.py ./src/pages/1_Historical_Prices.py
 COPY --chown=user src/pages/2_Prediction_Prices.py ./src/pages/2_Prediction_Prices.py
-COPY --chown=user src/pages/3_Historical_Risk_Score_Fire.py ./src/pages/3_Historical_Risk_Score_Fire.py
+COPY --chown=user src/pages/4_Historical_Risk_Score_Fire.py ./src/pages/4_Historical_Risk_Score_Fire.py
 
 EXPOSE 8501
 

src/pages/0_Summary.py

@@ -0,0 +1,636 @@
+import streamlit as st
+import asyncio
+import pandas as pd
+from functools import reduce
+import plotly.express as px
+
+from pages.utils.utils import (
+    async_load_file_s3,
+    async_load_geojson_from_s3,
+    _format_department_code,
+    async_load_file_s3_gzip,
+)
+
+from pages.utils.graphs import (
+    display_choropleth_map_country,
+    display_choropleth_map_for_department,
+)
+
+
+# Internal async function to gather all data loading tasks
+async def _load_all_data_async_internal_departements():
+    departements_df_task = async_load_file_s3(
+        "processed/housing/dataset_departements_housing_prices.csv"
+    )
+    departements_geojson_task = async_load_geojson_from_s3(
+        "processed/referentiel/departements.geojson"
+    )
+    insee_task = async_load_file_s3("processed/referentiel/ref_espace_communes.csv")
+    risks_df_task = async_load_file_s3_gzip(
+        "processed/risk-scores/risk-scores-departements-final.csv.gz"
+    )
+
+    return await asyncio.gather(
+        departements_df_task, departements_geojson_task, insee_task, risks_df_task
+    )
+
+
+# Internal async function to gather all data loading tasks
+async def _load_all_data_async_internal_communes():
+    communes_df_task = async_load_file_s3(
+        "processed/housing/dataset_housing_prices.csv"
+    )
+    communes_geojson_task = async_load_geojson_from_s3(
+        "processed/referentiel/communes.geojson"
+    )
+    risks_df_task = async_load_file_s3_gzip(
+        "processed/risk-scores/risk-scores-final.csv.gz"
+    )
+    return await asyncio.gather(communes_df_task, communes_geojson_task, risks_df_task)
+
+
+@st.cache_resource
+def load_all_data_wrapper_summary_country():
+    return asyncio.run(_load_all_data_async_internal_departements())
+
+
+@st.cache_resource
+def load_all_data_wrapper_summary_region():
+    return asyncio.run(_load_all_data_async_internal_communes())
+
+
+# --- Streamlit App Layout ---
+st.set_page_config(page_title="Oasis - Summary", page_icon=":money_with_wings:", layout="wide")
+
+###############################################################################
+
+st.header("Summary of Historical \"Good places\"")
+
+with st.spinner("Loading data and preparing maps..."):
+    (
+        dataset_departements_housing_prices,
+        departements_geojson,
+        insee_df,
+        dataset_departements_risks,
+    ) = load_all_data_wrapper_summary_country()
+
+# merge risks with housing prices (code_departement and annee)
+dataset_departements_risks = _format_department_code(dataset_departements_risks)
+dataset_departements_housing_prices = dataset_departements_housing_prices.merge(
+    dataset_departements_risks[["code_departement", "annee", "avg_risk_score"]],
+    on=["code_departement", "annee"],
+    how="left",
+)
+# scale the prixm2moyen to a range of 0-1 for better visualization
+dataset_departements_housing_prices["prixm2moyen"] = (
+    dataset_departements_housing_prices["prixm2moyen"] - dataset_departements_housing_prices["prixm2moyen"].min()
+) / (
+    dataset_departements_housing_prices["prixm2moyen"].max()
+    - dataset_departements_housing_prices["prixm2moyen"].min()
+)
+
+# scale the avg_risk_score to a range of 0-1 for better visualization
+dataset_departements_housing_prices["avg_risk_score"] = (
+    dataset_departements_housing_prices["avg_risk_score"] - dataset_departements_housing_prices["avg_risk_score"].min()
+) / (
+    dataset_departements_housing_prices["avg_risk_score"].max()
+    - dataset_departements_housing_prices["avg_risk_score"].min()
+)
+# combine prixm2moyen and avg_risk_score into a single column for visualization
+dataset_departements_housing_prices["combined_score"] = (
+    dataset_departements_housing_prices["prixm2moyen"]
+    * dataset_departements_housing_prices["avg_risk_score"]
+)
+
+st.subheader(
+    "The summary between prices and global risks in France", divider=True
+)
+st.write(
+    "This map summarizes the relationship between real estate prices and global risks in French departments."
+)
+
+fig_france = display_choropleth_map_country(
+    dataset_departements_housing_prices,
+    departements_geojson,
+    metric_name="combined_score",
+    metric_description="Summary of the relationship between prices and risks",
+    red_gradient=True,
+)
+st.plotly_chart(fig_france, use_container_width=False)
+st.write(
+    "Missing values are represented in light grey, while actual data is shown in a gradient from red (high prices) to green (low prices)."
+)
+
+###############################################################################
+
+st.subheader("Top & Bottom 5 Departments", divider=True)
+
+st.write("Select a year to view the top and bottom departments by combined score.")
+
+selected_year = st.selectbox(
+    "Select a Year",
+    options=dataset_departements_housing_prices["annee"].unique(),
+    format_func=lambda x: f"{x}",
+    index=list(dataset_departements_housing_prices["annee"].unique()).index(2024) if 2024 in dataset_departements_housing_prices["annee"].unique() else 0
+)
+
+top_departements = (
+    dataset_departements_housing_prices[
+        dataset_departements_housing_prices["annee"] == selected_year
+    ]
+    .groupby("code_departement")["combined_score"]
+    .mean()
+    .reset_index()
+    .sort_values(by="combined_score", ascending=False)
+    .rename(
+        columns={
+            "code_departement": "Department Code",
+            "combined_score": "Combined score (price and global risk)",
+        }
+    )
+    .assign(
+        **{
+            "Department Name": lambda x: x["Department Code"].apply(
+                lambda code: insee_df[
+                    _format_department_code(insee_df)["code_departement"] == code
+                ]["nom_departement"].values[0]
+                if code in _format_department_code(insee_df)["code_departement"].values
+                else "Unknown"
+            )
+        }
+    )
+    .head(5)
+)
+
+bottom_departements = (
+    dataset_departements_housing_prices[
+        (dataset_departements_housing_prices["annee"] == selected_year) & (dataset_departements_housing_prices["combined_score"] > 0)
+    ]
+    .groupby("code_departement")["combined_score"]
+    .mean()
+    .reset_index()
+    .sort_values(by="combined_score", ascending=True)
+    .rename(
+        columns={
+            "code_departement": "Department Code",
+            "combined_score": "Combined score (price and global risk)",
+        }
+    )
+    .assign(
+        **{
+            "Department Name": lambda x: x["Department Code"].apply(
+                lambda code: insee_df[
+                    _format_department_code(insee_df)["code_departement"] == code
+                ]["nom_departement"].values[0]
+                if code in _format_department_code(insee_df)["code_departement"].values
+                else "Unknown"
+            )
+        }
+    )
+    .head(5)
+)
+left_co, right_co = st.columns(2)
+
+with left_co:
+    fig = px.bar(
+        bottom_departements,
+        x="Department Name",
+        y="Combined score (price and global risk)",
+        title="Bottom 5 Departments",
+    )
+    st.plotly_chart(fig)
+    st.dataframe(
+        bottom_departements,
+        hide_index=True,
+        column_order=("Department Code", "Department Name", "Combined score (price and global risk)",),
+    )
+
+with right_co:
+    # display a bar chart of the top_departements
+    fig = px.bar(
+        top_departements.sort_values(by="Combined score (price and global risk)", ascending=True),
+        x="Department Name",
+        y="Combined score (price and global risk)",
+        title="Top 5 Departments",
+    )
+    st.plotly_chart(fig)
+    st.dataframe(
+        top_departements,
+        hide_index=True,
+        column_order=("Department Code", "Department Name", "Combined score (price and global risk)",),
+    )
+
+###############################################################################
+
+# st.subheader("Select Department(s) to View Historical Combined Scores", divider=True)
+
+# with st.spinner("Loading data and preparing maps..."):
+#     (
+#         dataset_housing_prices,
+#         communes_geojson,
+#         dataset_risks,
+#     ) = load_all_data_wrapper_summary_region()
+
+
+#     # merge risks with housing prices (code_departement and annee)
+#     dataset_risks = _format_department_code(dataset_risks)
+#     dataset_housing_prices = dataset_housing_prices.merge(
+#         dataset_risks[["code_departement", "annee", "avg_risk_score"]],
+#         on=["code_departement", "annee"],
+#         how="left",
+#     )
+#     # scale the prixm2moyen to a range of 0-1 for better visualization
+#     dataset_housing_prices["prixm2moyen"] = (
+#         dataset_housing_prices["prixm2moyen"]
+#         - dataset_housing_prices["prixm2moyen"].min()
+#     ) / (
+#         dataset_housing_prices["prixm2moyen"].max()
+#         - dataset_housing_prices["prixm2moyen"].min()
+#     )
+
+#     # scale the avg_risk_score to a range of 0-1 for better visualization
+#     dataset_housing_prices["avg_risk_score"] = (
+#         dataset_housing_prices["avg_risk_score"]
+#         - dataset_housing_prices["avg_risk_score"].min()
+#     ) / (
+#         dataset_housing_prices["avg_risk_score"].max()
+#         - dataset_housing_prices["avg_risk_score"].min()
+#     )
+#     # combine prixm2moyen and avg_risk_score into a single column for visualization
+#     dataset_housing_prices["combined_score"] = (
+#         dataset_housing_prices["prixm2moyen"]
+#         * dataset_housing_prices["avg_risk_score"]
+#     )
+
+#     min_global_commune_avg_risk_score = dataset_housing_prices[
+#         dataset_housing_prices["combined_score"] > 0
+#     ]["combined_score"].min()
+#     max_global_commune_avg_risk_score = dataset_housing_prices["combined_score"].max()
+
+
+# # Get all unique department codes for selectbox options
+# all_departement_codes = _format_department_code(insee_df)["code_departement"].unique()
+
+# # --- Department Selection 1 & 2 ---
+# col_dept1, col_dept2 = st.columns(2)
+
+# # Set default department values
+# default_dept_1 = all_departement_codes[0] if len(all_departement_codes) > 0 else None
+# default_dept_2 = all_departement_codes[1] if len(all_departement_codes) > 1 else None
+
+# with col_dept1:
+#     selected_departement = st.selectbox(
+#         "Select the first Department",
+#         options=all_departement_codes,
+#         format_func=lambda x: f"{x} - {insee_df[insee_df['code_departement'] == x]['nom_departement'].values[0]}"
+#         if x in insee_df["code_departement"].values
+#         else x,
+#         key="departement_select_1",
+#         index=list(all_departement_codes).index(default_dept_1) if default_dept_1 else 0
+#     )
+
+# with col_dept2:
+#     selected_departement_2 = st.selectbox(
+#         "Select the second Department (Optional for comparison)",
+#         options=[None] + list(all_departement_codes), # Add None option
+#         format_func=lambda x: f"{x} - {insee_df[insee_df['code_departement'] == x]['nom_departement'].values[0]}"
+#         if x is not None and x in insee_df["code_departement"].values
+#         else "None (Only show Department 1)",
+#         key="departement_select_2",
+#         index=list([None] + list(all_departement_codes)).index(default_dept_2) if default_dept_2 else 0
+#     )
+
+# st.write("This chart shows the average price per square meter in the selected department(s) over the years, with a focus on climatic events.")
+
+# # --- Data Preparation for Department Chart ---
+# all_departments_to_plot = []
+
+# # Process Department 1 data
+# if selected_departement: # Ensure a department is selected
+#     department_data_1 = dataset_housing_prices[
+#         dataset_housing_prices["code_departement"] == selected_departement
+#     ].copy()
+#     if not department_data_1.empty:
+#         department_data_1["annee"] = department_data_1["annee"].astype(str)
+#         # Group by year and calculate mean for the department
+#         department_data_1 = department_data_1.groupby("annee")["combined_score"].mean().reset_index()
+#         # Get the department name for the legend
+#         departement_name_1 = (
+#             insee_df[insee_df["code_departement"] == selected_departement][
+#                 "nom_departement"
+#             ].values[0]
+#             if selected_departement in insee_df["code_departement"].values
+#             else selected_departement
+#         )
+#         department_data_1.rename(columns={"combined_score": departement_name_1}, inplace=True)
+#         all_departments_to_plot.append(department_data_1)
+#     else:
+#         st.warning(f"No data available for Department 1: {selected_departement}")
+
+# # Process Department 2 data if selected
+# if (
+#     selected_departement_2 and selected_departement_2 != selected_departement
+# ):  # Ensure a valid second department is chosen and it's not the same as the first
+#     department_data_2 = dataset_housing_prices[
+#         dataset_housing_prices["code_departement"] == selected_departement_2
+#     ].copy()
+#     if not department_data_2.empty:
+#         department_data_2["annee"] = department_data_2["annee"].astype(str)
+#         # Group by year and calculate mean for the department
+#         department_data_2 = department_data_2.groupby("annee")["combined_score"].mean().reset_index()
+#         # Get the department name for the legend
+#         departement_name_2 = (
+#             insee_df[insee_df["code_departement"] == selected_departement_2][
+#                 "nom_departement"
+#             ].values[0]
+#             if selected_departement_2 in insee_df["code_departement"].values
+#             else selected_departement_2
+#         )
+#         department_data_2.rename(columns={"combined_score": departement_name_2}, inplace=True)
+#         all_departments_to_plot.append(department_data_2)
+#     else:
+#         st.warning(f"No data available for Department 2: {selected_departement_2}")
+# elif selected_departement_2 == selected_departement and selected_departement_2 is not None:
+#     st.info("You've selected the same department for both. Showing only one line.")
+
+
+# # Combine dataframes for plotting the department comparison chart
+# if all_departments_to_plot:
+#     combined_dept_df = reduce(
+#         lambda left, right: pd.merge(left, right, on="annee", how="outer"),
+#         all_departments_to_plot,
+#     )
+#     combined_dept_df.set_index("annee", inplace=True)
+
+#     fig_dept = px.line(
+#         combined_dept_df.reset_index(),
+#         x="annee",
+#         y=combined_dept_df.columns,
+#         title="Combined score (price and global risk)",
+#         labels={"annee": "Year", "value": "Combined score (price and global risk)"},
+#     )
+#     fig_dept.update_layout(
+#         xaxis_title="Year",
+#         yaxis_title="Combined score (price and global risk)",
+#         legend_title_text="Department",
+#     )
+#     st.plotly_chart(fig_dept, use_container_width=True)
+# else:
+#     st.info("Please select at least one department to display data.")
+
+# selected_departement_label = (f"{insee_df[insee_df['code_departement'] == selected_departement]['nom_departement'].values[0]}"
+#     if selected_departement in insee_df["code_departement"].values
+#     else selected_departement
+# )
+
+# # Prepare data for box plot
+# box_plot_data = dataset_housing_prices[
+#     (dataset_housing_prices["code_departement"] == selected_departement)
+#     | (dataset_housing_prices["code_departement"] == selected_departement_2)
+# ].copy()
+# box_plot_data["annee"] = box_plot_data["annee"].astype(
+#     str
+# )  # Ensure 'annee' is string for categorical x-axis
+# # Create the box plot
+# fig_box = px.box(
+#     box_plot_data,
+#     x="annee",
+#     y="combined_score",
+#     color="code_departement",
+#     title=f"Distribution of Prices in Department {selected_departement_label} by Year",
+# )
+# fig_box.update_layout(
+#     xaxis_title="Year",
+#     yaxis_title="Combined score (price and global risk)",
+# )
+# st.plotly_chart(fig_box, use_container_width=True)
+
+# ###############################################################################
+
+# # display the top 5 communes in the selected department
+# st.subheader(
+#     f"Top and Bottom 5 Communes in Department {selected_departement_label}",
+#     divider=True,
+# )
+
+# # selected year
+# selected_year_communes = st.selectbox(
+#     "Select a Year",
+#     options=dataset_housing_prices["annee"].unique(),
+#     format_func=lambda x: str(x),
+#     key="year_communes_selectbox",
+#     index=list(dataset_housing_prices["annee"].unique()).index(2024) if 2024 in dataset_housing_prices["annee"].unique() else 0
+# )
+
+# top_communes = (
+#     dataset_housing_prices[
+#         (dataset_housing_prices["code_departement"] == selected_departement)
+#         & (dataset_housing_prices["annee"] == selected_year_communes)
+#     ]
+#     .groupby("code_commune_insee")["combined_score"]
+#     .mean()
+#     .reset_index()
+#     .sort_values(by="combined_score", ascending=False)
+#     .rename(
+#         columns={
+#             "code_commune_insee": "Commune Code",
+#             "combined_score": "Combined score (price and global risk)",
+#         }
+#     )
+#     .assign(
+#         **{
+#             "Commune Name": lambda x: x["Commune Code"].apply(
+#                 lambda code: insee_df[
+#                     _format_department_code(insee_df)["code_commune_INSEE"] == code
+#                 ]["nom_commune_complet"].values[0]
+#                 if code
+#                 in _format_department_code(insee_df)["code_commune_INSEE"].values
+#                 else "Unknown"
+#             )
+#         }
+#     )
+#     .head(5)
+# )
+# bottom_communes = (
+#     dataset_housing_prices[
+#         (dataset_housing_prices["code_departement"] == selected_departement)
+#         & (dataset_housing_prices["annee"] == selected_year_communes)
+#         & (dataset_housing_prices["combined_score"] > 0)
+#     ]
+#     .groupby("code_commune_insee")["combined_score"]
+#     .mean()
+#     .reset_index()
+#     .sort_values(by="combined_score", ascending=True)
+#     .rename(
+#         columns={
+#             "code_commune_insee": "Commune Code",
+#             "combined_score": "Combined score (price and global risk)",
+#         }
+#     )
+#     .assign(
+#         **{
+#             "Commune Name": lambda x: x["Commune Code"].apply(
+#                 lambda code: insee_df[
+#                     _format_department_code(insee_df)["code_commune_INSEE"] == code
+#                 ]["nom_commune_complet"].values[0]
+#                 if code
+#                 in _format_department_code(insee_df)["code_commune_INSEE"].values
+#                 else "Unknown"
+#             )
+#         }
+#     )
+#     .head(5)
+# )
+# left_co, right_co = st.columns(2)
+# with left_co:
+#     fig = px.bar(
+#         bottom_communes.sort_values(by="Combined score (price and global risk)", ascending=True),
+#         x="Commune Name",
+#         y="Combined score (price and global risk)",
+#         title="Bottom 5 Communes",
+#     )
+#     st.plotly_chart(fig)
+#     st.dataframe(
+#         bottom_communes,
+#         hide_index=True,
+#         column_order=("Commune Code", "Commune Name", "Combined score (price and global risk)"),
+#     )
+
+# with right_co:
+#     fig = px.bar(
+#         top_communes.sort_values(by="Combined score (price and global risk)", ascending=True),
+#         x="Commune Name",
+#         y="Combined score (price and global risk)",
+#         title="Top 5 Communes",
+#     )
+#     st.plotly_chart(fig)
+#     st.dataframe(
+#         top_communes,
+#         hide_index=True,
+#         column_order=("Commune Code", "Commune Name", "Combined score (price and global risk)"),
+#     )
+
+# ###############################################################################
+
+# st.subheader(
+#     "Average Price per Square Meter in French Communes", divider=True
+# )
+# fig_department = display_choropleth_map_for_department(
+#     dataset_housing_prices,
+#     selected_departement,
+#     communes_geojson,
+#     min_global_commune_avg_risk_score,
+#     max_global_commune_avg_risk_score,
+#     title=f"Average Price per Square Meter in Department {selected_departement_label} (Animated by Year)",
+#     height_graph=1000,
+#     width_graph=1400,
+# )
+# st.plotly_chart(fig_department, use_container_width=False)
+# st.write(
+#     "Missing values are represented in light grey, while actual data is shown in a gradient from red (high prices) to green (low prices)."
+# )
+
+# ###############################################################################
+
+# st.subheader(f"Historical Price comparaison in Selected Commune in Departement {selected_departement_label}", divider=True)
+
+# available_communes = dataset_housing_prices[
+#     dataset_housing_prices["code_departement"] == selected_departement
+# ]["code_commune_insee"].unique()
+
+# let_col1, right_col2 = st.columns(2)
+# with let_col1:
+#     # --- Commune Selection 1 ---
+#     selected_commune_1 = st.selectbox(
+#         "Select the first Commune",
+#         options=available_communes,
+#         format_func=lambda x: f"{x} - {insee_df[insee_df['code_commune_INSEE'] == x]['nom_commune_complet'].values[0]}"
+#         if x in insee_df["code_commune_INSEE"].values
+#         else x,
+#         key="commune_select_1", 
+#         index=0 if len(available_communes) > 1 else 0
+#     )
+# with right_col2:
+#     # --- Commune Selection 2 ---
+#     selected_commune_2 = st.selectbox(
+#         "Select the second Commune (Optional for comparison)",
+#         options=[None]
+#         + list(available_communes),  # Add None as an option to not select a second commune
+#         format_func=lambda x: f"{x} - {insee_df[insee_df['code_commune_INSEE'] == x]['nom_commune_complet'].values[0]}"
+#         if x is not None and x in insee_df["code_commune_INSEE"].values
+#         else "None (Only show Commune 1)",
+#         key="commune_select_2", 
+#         index=2 if len(available_communes) > 2 else 0
+#     )
+
+# # --- Data Preparation for Chart ---
+# all_communes_to_plot = []
+
+# # Process Commune 1 data
+# commune_data_1 = dataset_housing_prices[
+#     dataset_housing_prices["code_commune_insee"] == selected_commune_1
+# ].copy()
+# if not commune_data_1.empty:
+#     commune_data_1["annee"] = commune_data_1["annee"].astype(str)
+#     commune_data_1 = commune_data_1.groupby("annee")["combined_score"].mean().reset_index()
+#     # Rename the price column to reflect the commune for the legend
+#     commune_name_1 = (
+#         insee_df[insee_df["code_commune_INSEE"] == selected_commune_1][
+#             "nom_commune_complet"
+#         ].values[0]
+#         if selected_commune_1 in insee_df["code_commune_INSEE"].values
+#         else selected_commune_1
+#     )
+#     commune_data_1.rename(columns={"combined_score": commune_name_1}, inplace=True)
+#     all_communes_to_plot.append(commune_data_1)
+# else:
+#     st.warning(f"No data available for Commune 1: {selected_commune_1}")
+
+
+# # Process Commune 2 data if selected
+# if (
+#     selected_commune_2 and selected_commune_2 != selected_commune_1
+# ):  # Ensure a valid second commune is chosen and it's not the same as the first
+#     commune_data_2 = dataset_housing_prices[
+#         dataset_housing_prices["code_commune_insee"] == selected_commune_2
+#     ].copy()
+#     if not commune_data_2.empty:
+#         commune_data_2["annee"] = commune_data_2["annee"].astype(str)
+#         commune_data_2 = (
+#             commune_data_2.groupby("annee")["combined_score"].mean().reset_index()
+#         )
+#         # Rename the price column for the second commune
+#         commune_name_2 = (
+#             insee_df[insee_df["code_commune_INSEE"] == selected_commune_2][
+#                 "nom_commune_complet"
+#             ].values[0]
+#             if selected_commune_2 in insee_df["code_commune_INSEE"].values
+#             else selected_commune_2
+#         )
+#         commune_data_2.rename(columns={"combined_score": commune_name_2}, inplace=True)
+#         all_communes_to_plot.append(commune_data_2)
+#     else:
+#         st.warning(f"No data available for Commune 2: {selected_commune_2}")
+# elif selected_commune_2 == selected_commune_1 and selected_commune_2 is not None:
+#     st.info("You've selected the same commune for both. Showing only one line.")
+
+# # Combine dataframes for plotting
+# if all_communes_to_plot:
+#     # Use reduce or pd.merge to combine, ensuring 'annee' is the common key
+
+#     # Start with the first dataframe, then merge others
+#     combined_df = reduce(
+#         lambda left, right: pd.merge(left, right, on="annee", how="outer"),
+#         all_communes_to_plot,
+#     )
+#     combined_df.set_index("annee", inplace=True)
+
+#     st.line_chart(
+#         combined_df,
+#         use_container_width=True,
+#         height=400,
+#         x_label="Year",
+#         y_label="Combined score (price and global risk)",
+#     )
+# else:
+#     st.info("Please select at least one commune to display data.")

src/pages/1_Historical_Prices.py

@@ -26,12 +26,9 @@ async def _load_all_data_async_internal_departements():
         "processed/referentiel/departements.geojson"
     )
     insee_task = async_load_file_s3("processed/referentiel/ref_espace_communes.csv")
-    risks_df_task = async_load_file_s3_gzip(
-        "processed/risk-scores/risk-scores-departements-final.csv.gz"
-    )
 
     return await asyncio.gather(
-        departements_df_task, departements_geojson_task, insee_task, risks_df_task
+        departements_df_task, departements_geojson_task, insee_task
     )
 
 
@@ -43,10 +40,7 @@ async def _load_all_data_async_internal_communes():
     communes_geojson_task = async_load_geojson_from_s3(
         "processed/referentiel/communes.geojson"
     )
-    risks_df_task = async_load_file_s3_gzip(
-        "processed/risk-scores/risk-scores-final.csv.gz"
-    )
-    return await asyncio.gather(communes_df_task, communes_geojson_task, risks_df_task)
+    return await asyncio.gather(communes_df_task, communes_geojson_task)
 
 
 @st.cache_resource
@@ -71,7 +65,6 @@ with st.spinner("Loading data and preparing maps..."):
         dataset_departements_housing_prices,
         departements_geojson,
         insee_df,
-        dataset_departements_risks,
     ) = load_all_data_wrapper_historical_prices_country()
 
 st.subheader(
@@ -339,7 +332,6 @@ with st.spinner("Loading data and preparing maps..."):
     (
         dataset_housing_prices,
         communes_geojson,
-        dataset_risks,
     ) = load_all_data_wrapper_historical_prices_region()
 
     min_global_commune_prixm2moyen = dataset_housing_prices[

src/pages/utils/graphs.py

@@ -65,7 +65,7 @@ def display_choropleth_map_country(
         zoom=5,
         opacity=0.75,
         hover_name="code_departement",
-        hover_data={metric_name: ":.0f", "annee": True},
+        hover_data={metric_name: ":.1f", "annee": True},
         title=title,
         height=height,
         width=width,