Added Datatable

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -24,6 +24,7 @@ from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
     find_top_companies,
     get_all_company_relations,
     get_all_person_relations,
+    return_metric_table_df
 )
 
 
@@ -73,7 +74,8 @@ network = create_3d_graph(graph, nodes, edges, metrics, metric, layout, switch_n
 company_relation_type_filter = get_all_person_relations()["relation_type"].unique()
 person_relation_type_filter = get_all_company_relations()["relation_type"].unique()
 
-top_companies_df = find_top_companies()
+
+top_companies_df =  return_metric_table_df(metrics, nodes, "closeness") #find_top_companies()
 # with open("src/aki_prj23_transparenzregister/ui/assets/network_graph.html") as file:
 #     html_content = file.read()
 
@@ -83,10 +85,31 @@ layout = html.Div(
             html.Div(
                 className="top_companytable_style",
                 children=[
-                    html.Title(title="Top Ten Unternehmen", style={"align": "mid"}),
+                    html.H1(title="Top Ten Nodes in Graph by Metric", style={"align": "mid"}),
+                    html.Div(
+                                className="filter-wrapper-item",
+                                children=[
+                                    html.H5(
+                                        className="filter-description",
+                                        children=["Filter Metric:"],
+                                    ),
+                                    dcc.Dropdown(
+                                        [                                            
+                                            "eigenvector",
+                                            "degree",
+                                            "betweeness",
+                                            "closeness",
+                                        ],
+                                        "closeness",
+                                        id="dropdown_table_metric",
+                                        className="dropdown_style",
+                                    ),
+                                ],
+                            ),
                     dash_table.DataTable(                        
                         top_companies_df.to_dict("records"),
                         [{"name": i, "id": i} for i in top_companies_df.columns],
+                        id="metric_table",
                     ),
                 ],
             ),
@@ -337,4 +360,17 @@ def update_figure(
         return create_3d_graph(graph, nodes, edges, metrics, selected_metric, layout, switch_node_annotaion_value, switch_edge_annotaion_value, slider_value)
 
 
+@callback(
+    Output("metric_table", "data"),
+    [
+        Input("dropdown_table_metric", "value"),
+    ],
+)
+def update_table(metric_dropdown_value: str) -> dict:
+    table_df = return_metric_table_df(metrics, nodes, metric_dropdown_value)
+    table_df.to_dict("records")
+    # print(table_df.to_dict("records"))
+    columns =[{"name": i, "id": i} for i in table_df.columns]
+    # print(columns)
     
+    return table_df.to_dict("records")

src/aki_prj23_transparenzregister/utils/networkx/networkx_data.py

@@ -353,3 +353,26 @@ def create_edge_and_node_list_for_company(
             }
         )
     return nodes, edges
+
+
+def return_metric_table_df(metrics: pd.DataFrame, nodes: dict, metric: str)-> pd.DataFrame:
+    # tmp = pd.DataFrame(columns=["Platzierung", "company_name", "Umsatz M€"])
+    
+    tmp_list = []
+    category = []
+    for key, values in nodes.items():
+        # print(values["id"])
+        
+        if str(values["id"]).split("_")[0] == "c":
+            tmp_list.append(values["name"])
+            category.append("company")
+        if str(values["id"]).split("_")[0] == "p":
+            tmp_list.append(str(values["firstname"]) + " " + str(values["lastname"]))
+            category.append("person")
+    metrics["designation"] = tmp_list
+    metrics["category"] = category
+    tmp_df = metrics.sort_values(metric, ascending=False)
+    tmp_df = tmp_df[["designation", metric, "category"]].head(10)
+    tmp_df.rename(columns={metric: "Metric"}, inplace=True)
+    # print(tmp_df[["designation", metric, "category"]].head(10))
+    return tmp_df