In app.py Exports fuer CSV und XLSX hinzugefuegt

analytics/Dockerfile

@@ -4,7 +4,7 @@ WORKDIR /app
 
 # Erfordert keine schweren Systempakete, das beschleunigt auch den Build!
 # Wir installieren direkt die benötigten Python-Bibliotheken
-RUN pip3 install --no-cache-dir streamlit pandas plotly
+RUN pip3 install --no-cache-dir streamlit pandas plotly openpyxl
 
 # Port für Streamlit öffnen
 EXPOSE 8501

analytics/app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 import numpy as np
+import io
 
 # --- Konstanten ---
 TB2B = 1024**4  # TB in Bytes
@@ -89,6 +90,32 @@ if auswahl == "Übersicht & Metriken":
         })
     )
 
+    # --- Export-Buttons ---
+    export_col1, export_col2 = col2.columns(2)
+
+    # CSV Export
+    csv_data = df_department_overview.to_csv(index=True).encode('utf-8')
+    export_col1.download_button(
+        label="Export als CSV",
+        data=csv_data,
+        file_name="department_overview.csv",
+        mime="text/csv",
+    )
+
+    # Excel Export
+    excel_buffer = io.BytesIO()
+    with pd.ExcelWriter(excel_buffer, engine='openpyxl') as writer:
+        df_department_overview.to_excel(writer, index=True, sheet_name='Department Overview')
+    excel_data = excel_buffer.getvalue()
+    
+    export_col2.download_button(
+        label="Export als XLSX",
+        data=excel_data,
+        file_name="department_overview.xlsx",
+        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+    )
+
+
     if "department" in df:
         fig = px.pie(
             df,