Merge branch 'feature/28_vue_acheteur' into dev

README.md

@@ -36,7 +36,12 @@ python run.py
 
 ## Notes de version
 
-#### 2.0.1 (23 septembre 2025)
+#### 2.1.0
+
+- Ajout de la vue acheteur
+- Variables globales uniquement en lecture
+
+##### 2.0.1 (23 septembre 2025)
 
 - Bloquage du bouton de téléchargement si trop de lignes (+ 65000) [#38](https://github.com/ColinMaudry/decp.info/issues/38)
 - Amélioration du script de déploiement (deploy.sh)

src/app.py

@@ -74,6 +74,7 @@ app.layout = html.Div(
                             page["name"], href=page["relative_path"], className="nav"
                         )
                         for page in page_registry.values()
+                        if page["name"] not in ["Acheteur", "Fournisseur"]
                     ]
                 ),
             ],

src/assets/style.css

@@ -71,8 +71,12 @@ td[data-dash-column="objet"] {
 }
 
 /* Alternance des couleurs pour les lignes */
-#table tr:nth-child(even) td {
+.marches_table {
+  font-family: sans-serif;
+}
+.marches_table .cell-table tr:nth-child(even) td {
   background-color: #feeeee;
+  font-family: sans-serif;
 }
 
 #header > *,
@@ -102,9 +106,51 @@ a.nav {
 }
 
 h3 {
+  margin: 36px 0 24px 0;
+}
+
+summary > h3 {
+  margin: 0;
   display: inline;
 }
 
 #_pages_content {
   padding-top: 28px;
 }
+
+/* Vue acheteur/fournisseur */
+.wrapper {
+  display: grid;
+  grid-gap: 10px;
+  margin-bottom: 50px;
+  justify-content: space-between;
+}
+
+.org_title {
+  grid-column: 1 / 3;
+  grid-row: 1;
+}
+
+.org_year {
+  grid-column: 3;
+  grid-row: 1;
+}
+
+.org_infos {
+  grid-column: 1;
+  grid-row: 2;
+}
+
+.org_infos > p {
+  margin: 8px 0;
+}
+
+.org_stats {
+  grid-column: 2;
+  grid-row: 2;
+}
+
+.org_map {
+  grid-column: 3;
+  grid-row: 2;
+}

src/figures.py

@@ -2,7 +2,7 @@ import json
 
 import plotly.express as px
 import polars as pl
-from dash import dash_table, html
+from dash import dash_table, dcc, html
 
 
 def get_map_count_marches(lf: pl.LazyFrame):
@@ -157,3 +157,36 @@ def get_sources_tables(source_path) -> html.Div:
     datatable.data = df.to_dicts()
 
     return html.Div(children=datatable)
+
+
+def point_on_map(lat, lon):
+    lat = float(lat)
+    lon = float(lon)
+
+    # Create a scatter mapbox or choropleth map
+    fig = px.scatter_map(
+        lat=[lat], lon=[lon], height=300, width=400, color=[1], size=[1]
+    )
+
+    fig.update_coloraxes(showscale=False)
+
+    # Set map style (you can use 'open-street-map', 'carto-positron', etc.)
+    fig.update_layout(
+        mapbox_style="light",  # Light, clean background
+        margin={"r": 0, "t": 0, "l": 0, "b": 0},
+    )
+
+    # Optionally, center the map on France
+    fig.update_geos(
+        center=dict(lat=46.603354, lon=1.888334),  # Center of France
+        lataxis_range=[41, 51.5],  # Latitude range for France
+        lonaxis_range=[-5, 10],  # Longitude range for France
+    )
+
+    # But scatter_mapbox doesn't use geos, so better to control via zoom/center manually
+    # Let's reset and use proper centering in scatter_mapbox instead:
+
+    fig.update_layout(map_center={"lat": 46.6, "lon": 1.89}, map_zoom=4)
+
+    graph = dcc.Graph(id="map", figure=fig)
+    return graph

src/pages/acheteur.py

@@ -0,0 +1,268 @@
+import datetime
+
+import polars as pl
+from dash import Input, Output, State, callback, dash_table, dcc, html, register_page
+
+from src.figures import point_on_map
+from src.utils import format_number, get_annuaire_data, get_departement_region, lf
+
+register_page(
+    __name__,
+    path_template="/acheteur/<acheteur_id>",
+    title="decp.info - acheteur",
+    name="Acheteur",
+    order=5,
+)
+
+# 21690123100011
+
+layout = [
+    dcc.Store(id="acheteur_data", storage_type="memory"),
+    dcc.Location(id="url", refresh="callback-nav"),
+    html.Div(
+        className="container",
+        children=[
+            html.Div(
+                className="wrapper",
+                children=[
+                    html.H2(
+                        className="org_title",
+                        children=[
+                            html.Span(id="acheteur_siret"),
+                            " - ",
+                            html.Span(id="acheteur_nom"),
+                        ],
+                    ),
+                    html.Div(
+                        className="org_year",
+                        children=dcc.Dropdown(
+                            id="acheteur_year",
+                            options=["Toutes"]
+                            + [
+                                str(year)
+                                for year in range(
+                                    2018, int(datetime.date.today().year) + 1
+                                )
+                            ],
+                            placeholder="Année",
+                        ),
+                    ),
+                    html.Div(
+                        className="org_infos",
+                        children=[
+                            # TODO: ajouter le type d'acheteur : commune, CD, CR, etc.
+                            html.P(["Commune : ", html.Strong(id="acheteur_commune")]),
+                            html.P(
+                                [
+                                    "Département : ",
+                                    html.Strong(id="acheteur_departement"),
+                                ]
+                            ),
+                            html.P(["Région : ", html.Strong(id="acheteur_region")]),
+                            html.A(
+                                id="acheteur_lien_annuaire",
+                                children="Plus de détails sur l'Annuaire des entreprises",
+                                target="_blank",
+                            ),
+                        ],
+                    ),
+                    html.Div(
+                        className="org_stats",
+                        children=[
+                            html.P(id="acheteur_titre_stats"),
+                            html.P(id="acheteur_marches_attribues"),
+                            html.P(id="acheteur_fournisseurs_differents"),
+                            html.Button(
+                                "Téléchargement au format Excel",
+                                id="btn-download-acheteur-data",
+                            ),
+                            dcc.Download(id="download-acheteur-data"),
+                        ],
+                    ),
+                    html.Div(className="org_map", id="acheteur_map"),
+                ],
+            ),
+            # récupérer les données de l'acheteur sur l'api annuaire
+            html.H3("Derniers marchés publics attribués"),
+            html.Div(id="acheteur_last_marches", children=""),
+        ],
+    ),
+]
+
+
+@callback(
+    Output(component_id="acheteur_siret", component_property="children"),
+    Output(component_id="acheteur_nom", component_property="children"),
+    Output(component_id="acheteur_commune", component_property="children"),
+    Output(component_id="acheteur_map", component_property="children"),
+    Output(component_id="acheteur_departement", component_property="children"),
+    Output(component_id="acheteur_region", component_property="children"),
+    Output(component_id="acheteur_lien_annuaire", component_property="href"),
+    Input(component_id="url", component_property="pathname"),
+)
+def update_acheteur_infos(url):
+    acheteur_siret = url.split("/")[-1]
+    if len(acheteur_siret) != 14:
+        acheteur_siret = (
+            f"Le SIRET renseigné doit faire 14 caractères ({acheteur_siret})"
+        )
+    data = get_annuaire_data(acheteur_siret)
+    data_etablissement = data["matching_etablissements"][0]
+    acheteur_map = point_on_map(
+        data_etablissement["latitude"], data_etablissement["longitude"]
+    )
+    code_departement, nom_departement, nom_region = get_departement_region(
+        data_etablissement["code_postal"]
+    )
+    departement = f"{nom_departement} ({code_departement})"
+    lien_annuaire = (
+        f"https://annuaire-entreprises.data.gouv.fr/etablissement/{acheteur_siret}"
+    )
+    return (
+        acheteur_siret,
+        data["nom_raison_sociale"],
+        data_etablissement["libelle_commune"],
+        acheteur_map,
+        departement,
+        nom_region,
+        lien_annuaire,
+    )
+
+
+@callback(
+    Output(component_id="acheteur_marches_attribues", component_property="children"),
+    Output(
+        component_id="acheteur_fournisseurs_differents", component_property="children"
+    ),
+    Input(component_id="acheteur_data", component_property="data"),
+)
+def update_acheteur_stats(data):
+    df = pl.DataFrame(data)
+    if df.height == 0:
+        df = pl.DataFrame(schema=lf.collect_schema())
+    df_marches = df.unique("id")
+    nb_marches = format_number(df_marches.height)
+    # somme_marches = format_number(int(df_marches.select(pl.sum("montant")).item()))
+    marches_attribues = [html.Strong(nb_marches), " marchés et accord-cadres attribués"]
+    # + ", pour un total de ", html.Strong(somme_marches + " €")]
+    del df_marches
+
+    nb_fournisseurs = df.unique("titulaire_id").height
+    nb_fournisseurs = [
+        html.Strong(format_number(nb_fournisseurs)),
+        " fournisseurs (SIRET) différents",
+    ]
+    del df
+
+    return marches_attribues, nb_fournisseurs
+
+
+@callback(
+    Output(component_id="acheteur_data", component_property="data"),
+    Input(component_id="url", component_property="pathname"),
+    Input(component_id="acheteur_year", component_property="value"),
+)
+def get_acheteur_marches_data(url, acheteur_year: str) -> pl.LazyFrame:
+    acheteur_siret = url.split("/")[-1]
+    lff = lf.filter(pl.col("acheteur_id") == acheteur_siret)
+    lff = lff.fill_null("")
+    lff = lff.select(
+        "id",
+        "objet",
+        "dateNotification",
+        "titulaire_id",
+        "titulaire_nom",
+        "montant",
+        "codeCPV",
+        "dureeMois",
+    )
+    if acheteur_year and acheteur_year != "Toutes":
+        lff = lff.filter(
+            pl.col("dateNotification").cast(pl.String).str.starts_with(acheteur_year)
+        )
+    lff = lff.sort(["dateNotification", "id"], descending=True, nulls_last=True)
+
+    data = lff.collect(engine="streaming").to_dicts()
+    return data
+
+
+@callback(
+    Output(component_id="acheteur_last_marches", component_property="children"),
+    Input(component_id="acheteur_data", component_property="data"),
+)
+def get_last_marches_table(data) -> html.Div:
+    columns = [
+        "id",
+        "objet",
+        "dateNotification",
+        "titulaire_nom",
+        "montant",
+        "codeCPV",
+        "dureeMois",
+    ]
+
+    table = html.Div(
+        className="marches_table",
+        id="marches_datatable",
+        children=dash_table.DataTable(
+            data=data,
+            page_action="native",
+            columns=[
+                {
+                    "name": i,
+                    "id": i,
+                    "presentation": "markdown",
+                    "type": "text",
+                    "format": {"nully": "N/A"},
+                    "hideable": False,
+                }
+                for i in columns
+                if i not in ["titulaire_id"]
+            ],
+            page_size=10,
+            style_cell_conditional=[
+                {
+                    "if": {"column_id": "objet"},
+                    "minWidth": "300px",
+                    "textAlign": "left",
+                    "overflow": "hidden",
+                    "lineHeight": "14px",
+                    "whiteSpace": "normal",
+                },
+                {
+                    "if": {"column_id": "titulaire_nom"},
+                    "minWidth": "200px",
+                    "textAlign": "left",
+                    "overflow": "hidden",
+                    "lineHeight": "14px",
+                    "whiteSpace": "normal",
+                },
+            ],
+        ),
+    )
+    return table
+
+
+@callback(
+    Output("download-acheteur-data", "data"),
+    Input("btn-download-acheteur-data", "n_clicks"),
+    State(component_id="acheteur_data", component_property="data"),
+    State(component_id="acheteur_nom", component_property="children"),
+    State(component_id="acheteur_year", component_property="value"),
+    prevent_initial_call=True,
+)
+def download_acheteur_data(
+    n_clicks,
+    data: [dict],
+    acheteur_nom: str,
+    annee: str,
+):
+    df_to_download = pl.DataFrame(data)
+
+    def to_bytes(buffer):
+        df_to_download.write_excel(
+            buffer, worksheet="DECP" if annee in ["Toutes", None] else annee
+        )
+
+    date = datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+    return dcc.send_bytes(to_bytes, filename=f"decp_{acheteur_nom}_{date}.xlsx")

src/pages/home.py

@@ -6,44 +6,18 @@ from dash import Input, Output, State, callback, dash_table, dcc, html, register
 from dotenv import load_dotenv
 
 from src.utils import (
-    add_annuaire_link,
+    add_org_links,
     add_resource_link,
-    booleans_to_strings,
+    filter_table_data,
     format_number,
     lf,
-    logger,
+    sort_table_data,
-    split_filter_part,
 )
 
 load_dotenv()
 
 update_date = os.path.getmtime(os.getenv("DATA_FILE_PARQUET_PATH"))
 update_date = datetime.fromtimestamp(update_date).strftime("%d/%m/%Y")
-df_filtered = pl.DataFrame()
-
-# Unique les données actuelles, pas les anciennes versions de marchés
-
-lf = lf.filter(pl.col("donneesActuelles"))
-
-# Suppression des colonnes inutiles
-lf = lf.drop(
-    [
-        "donneesActuelles",
-    ]
-)
-
-# Convertir les colonnes booléennes en chaînes de caractères
-lf = booleans_to_strings(lf)
-
-# Remplacer les valeurs manquantes par des chaînes vides
-lf = lf.fill_null("")
-
-
-# Ajout des liens vers l'annuaire
-lf = add_annuaire_link(lf)
-
-# Ajout des liens open data
-lf = add_resource_link(lf)
 
 schema = lf.collect_schema()
 
@@ -51,7 +25,9 @@ schema = lf.collect_schema()
 title = "Tableau"
 register_page(__name__, path="/", title="decp.info", name=title, order=1)
 
-datatable = dash_table.DataTable(
+datatable = html.Div(
+    className="marches_table",
+    children=dash_table.DataTable(
         cell_selectable=False,
         id="table",
         page_size=20,
@@ -59,17 +35,6 @@ datatable = dash_table.DataTable(
         page_action="custom",
         filter_action="custom",
         filter_options={"case": "insensitive", "placeholder_text": "Filtrer..."},
-    columns=[
-        {
-            "name": i,
-            "id": i,
-            "presentation": "markdown",
-            "type": "text",
-            "format": {"nully": "N/A"},
-            "hideable": True,
-        }
-        for i in lf.collect_schema().names()
-    ],
         sort_action="custom",
         sort_mode="multi",
         sort_by=[],
@@ -94,6 +59,7 @@ datatable = dash_table.DataTable(
         ],
         data_timestamp=0,
         markdown_options={"html": True},
+    ),
 )
 
 layout = [
@@ -147,6 +113,7 @@ layout = [
                         disabled=True,
                     ),
                     dcc.Download(id="download-data"),
+                    dcc.Store(id="filtered_data", storage_type="memory"),
                     html.P("Données mises à jour le " + str(update_date)),
                 ],
                 className="table-menu",
@@ -159,6 +126,8 @@ layout = [
 
 @callback(
     Output("table", "data"),
+    Output("table", "columns"),
+    # Output("filtered_data", "data"),
     Output("table", "data_timestamp"),
     Output("nb_rows", "children"),
     Output("btn-download-data", "disabled"),
@@ -172,56 +141,21 @@ layout = [
 )
 def update_table(page_current, page_size, filter_query, sort_by, data_timestamp):
     print(" + + + + + + + + + + + + + + + + + + ")
-    global df_filtered
 
     # Application des filtres
     lff: pl.LazyFrame = lf  # start from the original data
     if filter_query:
-        filtering_expressions = filter_query.split(" && ")
+        lff = filter_table_data(lff, filter_query)
-        for filter_part in filtering_expressions:
-            col_name, operator, filter_value = split_filter_part(filter_part)
-            col_type = str(schema[col_name])
-            print("filter_value:", filter_value)
-            print("filter_value_type:", type(filter_value))
-            print("col_type:", col_type)
-
-            if operator in ("<", "<=", ">", ">="):
-                filter_value = int(filter_value)
-                if operator == "<":
-                    lff = lff.filter(pl.col(col_name) < filter_value)
-                elif operator == ">":
-                    lff = lff.filter(pl.col(col_name) > filter_value)
-                elif operator == ">=":
-                    lff = lff.filter(pl.col(col_name) >= filter_value)
-                elif operator == "<=":
-                    lff = lff.filter(pl.col(col_name) <= filter_value)
-
-            elif col_type.startswith("Int") or col_type.startswith("Float"):
-                try:
-                    filter_value = int(filter_value)
-                except ValueError:
-                    logger.error(f"Invalid numeric filter value: {filter_value}")
-                    continue
-                lff = lff.filter(pl.col(col_name) == filter_value)
-
-            elif operator == "contains" and col_type == "String":
-                lff = lff.filter(pl.col(col_name).str.contains("(?i)" + filter_value))
-
-            # elif operator == 'datestartswith':
-            # lff = lff.filter(pl.col(col_name).str.startswith(filter_value)")
 
     if len(sort_by) > 0:
-        lff = lff.sort(
+        lff = sort_table_data(lff, sort_by)
-            [col["column_id"] for col in sort_by],
-            descending=[col["direction"] == "desc" for col in sort_by],
-            nulls_last=True,
-        )
-        print(sort_by)
 
+    # Remplace les strings null par "", mais pas les numeric null
+    lff = lff.fill_null("")
+
+    # Matérialisation des filtres
     dff: pl.DataFrame = lff.collect()
 
-    df_filtered = dff.clone()
-
     height = dff.height
 
     nb_rows = f"{format_number(height)} lignes"
@@ -230,6 +164,26 @@ def update_table(page_current, page_size, filter_query, sort_by, data_timestamp)
     start_row = page_current * page_size
     # end_row = (page_current + 1) * page_size
     dff = dff.slice(start_row, page_size)
+
+    # Ajout des liens vers l'annuaire des entreprises
+    dff = add_org_links(dff)
+
+    # Ajout des liens vers les fichiers Open Data
+    dff = add_resource_link(dff)
+
+    # Liste finale de colonnes
+    columns = [
+        {
+            "name": column,
+            "id": column,
+            "presentation": "markdown",
+            "type": "text",
+            "format": {"nully": "N/A"},
+            "hideable": True,
+        }
+        for column in dff.columns
+    ]
+
     dicts = dff.to_dicts()
 
     if height > 65000:
@@ -243,6 +197,7 @@ def update_table(page_current, page_size, filter_query, sort_by, data_timestamp)
 
     return (
         dicts,
+        columns,
         data_timestamp + 1,
         nb_rows,
         download_disabled,
@@ -254,26 +209,26 @@ def update_table(page_current, page_size, filter_query, sort_by, data_timestamp)
 @callback(
     Output("download-data", "data"),
     Input("btn-download-data", "n_clicks"),
+    State("table", "filter_query"),
+    State("table", "sort_by"),
     State("table", "hidden_columns"),
     prevent_initial_call=True,
 )
-def download_data(n_clicks, hidden_columns: list = None):
+def download_data(n_clicks, filter_query, sort_by, hidden_columns: list = None):
-    df_to_download = df_filtered.clone()
+    lff: pl.LazyFrame = lf  # start from the original data
-
-    print(df_to_download.columns)
-
-    # Rétablissement des colonnes source et sourceOpenData (voir add_resource_link)
-    df_to_download = df_to_download.with_columns(
-        pl.col("source").str.extract(r'href="(.*?)"').alias("sourceFile"),
-        pl.col("source").str.extract(r'">(.*?)<').alias("sourceDataset"),
-    )
 
     # Les colonnes masquées sont supprimées
     if hidden_columns:
-        df_to_download = df_to_download.drop(hidden_columns)
+        lff = lff.drop(hidden_columns)
+
+    if filter_query:
+        lff = filter_table_data(lff, filter_query)
+
+    if len(sort_by) > 0:
+        lff = sort_table_data(lff, sort_by)
 
     def to_bytes(buffer):
-        df_to_download.write_excel(buffer, worksheet="DECP")
+        lff.collect(engine="streaming").write_excel(buffer, worksheet="DECP")
 
     date = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     return dcc.send_bytes(to_bytes, filename=f"decp_{date}.xlsx")

src/utils.py

@@ -1,3 +1,4 @@
+import json
 import logging
 import os
 from time import sleep
@@ -5,6 +6,7 @@ from time import sleep
 import polars as pl
 import polars.selectors as cs
 from dotenv import load_dotenv
+from httpx import get
 from polars.exceptions import ComputeError
 
 load_dotenv()
@@ -40,18 +42,18 @@ def split_filter_part(filter_part):
     return [None] * 3
 
 
-def add_resource_link(lff: pl.LazyFrame) -> pl.LazyFrame:
+def add_resource_link(dff: pl.DataFrame) -> pl.DataFrame:
-    lff = lff.with_columns(
+    dff = dff.with_columns(
         (
             '<a href="' + pl.col("sourceFile") + '">' + pl.col("sourceDataset") + "</a>"
         ).alias("source")
     )
-    lff = lff.drop(["sourceFile", "sourceDataset"])
+    dff = dff.drop(["sourceFile", "sourceDataset"])
-    return lff
+    return dff
 
 
-def add_annuaire_link(lff: pl.LazyFrame):
+def add_org_links(dff: pl.DataFrame):
-    lff = lff.with_columns(
+    dff = dff.with_columns(
         pl.when(pl.col("titulaire_typeIdentifiant") == "SIRET")
         .then(
             '<a href = "https://annuaire-entreprises.data.gouv.fr/etablissement/'
@@ -63,16 +65,16 @@ def add_annuaire_link(lff: pl.LazyFrame):
         .otherwise(pl.col("titulaire_id"))
         .alias("titulaire_id")
     )
-    lff = lff.with_columns(
+    dff = dff.with_columns(
         (
-            '<a href = "https://annuaire-entreprises.data.gouv.fr/etablissement/'
+            '<a href = "/acheteur/'
             + pl.col("acheteur_id")
-            + '">'
+            + '" target="_blank">'
             + pl.col("acheteur_id")
             + "</a>"
         ).alias("acheteur_id")
     )
-    return lff
+    return dff
 
 
 def booleans_to_strings(lff: pl.LazyFrame) -> pl.LazyFrame:
@@ -101,6 +103,12 @@ def format_number(number) -> str:
     return number
 
 
+def get_annuaire_data(siret: str) -> dict:
+    url = f"https://recherche-entreprises.api.gouv.fr/search?q={siret}"
+    response = get(url)
+    return response.json()["results"][0]
+
+
 def get_decp_data() -> pl.LazyFrame:
     # Chargement du fichier parquet
     # Le fichier est chargé en mémoire, ce qui est plus rapide qu'une base de données pour le moment.
@@ -117,13 +125,85 @@ def get_decp_data() -> pl.LazyFrame:
         sleep(10)
         lff: pl.LazyFrame = pl.scan_parquet(os.getenv("DATA_FILE_PARQUET_PATH"))
 
-    # Remplacement des valeurs numériques par des chaînes de caractères
-    # lff = numbers_to_strings(lff)
-
     # Tri des marchés par date de notification
-    lff = lff.sort(by=["datePublicationDonnees"], descending=True, nulls_last=True)
+    lff = lff.sort(by=["dateNotification", "uid"], descending=True, nulls_last=True)
+
+    # Uniquement les données actuelles, pas les anciennes versions de marchés
+    lff = lff.filter(pl.col("donneesActuelles")).drop("donneesActuelles")
+
+    # Convertir les colonnes booléennes en chaînes de caractères
+    lff = booleans_to_strings(lff)
+
+    # Bizarrement je ne peux pas faire lff = lff.fill_null("") ici
+    # ça génère une erreur dans la page acheteur (acheteur_data.table) :
+    # AttributeError: partially initialized module 'pandas' has no attribute 'NaT' (most likely due to a circular import)
 
     return lff
 
 
+def get_departements() -> dict:
+    with open("data/departements.json", "rb") as f:
+        data = json.load(f)
+        return data
+
+
+def get_departement_region(code_postal):
+    if code_postal > "97000":
+        code_departement = code_postal[:3]
+    else:
+        code_departement = code_postal[:2]
+    nom_departement = departements[code_departement]["departement"]
+    nom_region = departements[code_departement]["region"]
+    return code_departement, nom_departement, nom_region
+
+
+def filter_table_data(lff: pl.LazyFrame, filter_query: str) -> pl.LazyFrame:
+    schema = lff.collect_schema()
+    filtering_expressions = filter_query.split(" && ")
+    for filter_part in filtering_expressions:
+        col_name, operator, filter_value = split_filter_part(filter_part)
+        col_type = str(schema[col_name])
+        print("filter_value:", filter_value)
+        print("filter_value_type:", type(filter_value))
+        print("col_type:", col_type)
+
+        if operator in ("<", "<=", ">", ">="):
+            filter_value = int(filter_value)
+            if operator == "<":
+                lff = lff.filter(pl.col(col_name) < filter_value)
+            elif operator == ">":
+                lff = lff.filter(pl.col(col_name) > filter_value)
+            elif operator == ">=":
+                lff = lff.filter(pl.col(col_name) >= filter_value)
+            elif operator == "<=":
+                lff = lff.filter(pl.col(col_name) <= filter_value)
+
+        elif col_type.startswith("Int") or col_type.startswith("Float"):
+            try:
+                filter_value = int(filter_value)
+            except ValueError:
+                logger.error(f"Invalid numeric filter value: {filter_value}")
+                continue
+            lff = lff.filter(pl.col(col_name) == filter_value)
+
+        elif operator == "contains" and col_type == "String":
+            lff = lff.filter(pl.col(col_name).str.contains("(?i)" + filter_value))
+
+        # elif operator == 'datestartswith':
+        # lff = lff.filter(pl.col(col_name).str.startswith(filter_value)")
+
+    return lff
+
+
+def sort_table_data(lff: pl.LazyFrame, sort_by: list) -> pl.LazyFrame:
+    lff = lff.sort(
+        [col["column_id"] for col in sort_by],
+        descending=[col["direction"] == "desc" for col in sort_by],
+        nulls_last=True,
+    )
+    print(sort_by)
+    return lff
+
+
 lf = get_decp_data()
+departements = get_departements()