diff --git a/app/app.py b/app/app.py
index 2e2f710..3689ca0 100644
--- a/app/app.py
+++ b/app/app.py
@@ -6,12 +6,15 @@ from flask_session import Session
 
 app = Flask(__name__)
 app.secret_key = "gfbierpf934hftrntr45otgß45890tfh34gft45rw"  # replace with a secure random key
+app.secret_key = "gfbierpf934hftrntr45otgß45890tfh34gft45rw"  # replace with a secure random key
 app.config['SESSION_TYPE'] = 'filesystem'
 app.config['SESSION_FILE_DIR'] = './.flask_session/'
 Session(app)
 
 STRIPE_COLS = ['Type', 'ID', 'Created', 'Description', 'Amount', 'Currency', 'Converted Amount', 'Fees', 'Net', 'Converted Currency', 'Details']
 RAISENOW_COLS = ['Identifikationsnummer', 'Erstellt', 'UTC-Offset', 'Status', 'Betrag', 'Währung', 'Übernommene Gebühren - Betrag', 'Übernommene Gebühren - Währung', 'Zahlungsmethode', 'Zahlungsanbieter', 'Nettobetrag', 'Auszahlungswährung']
+STRIPE_COLS = ['Type', 'ID', 'Created', 'Description', 'Amount', 'Currency', 'Converted Amount', 'Fees', 'Net', 'Converted Currency', 'Details']
+RAISENOW_COLS = ['Identifikationsnummer', 'Erstellt', 'UTC-Offset', 'Status', 'Betrag', 'Währung', 'Übernommene Gebühren - Betrag', 'Übernommene Gebühren - Währung', 'Zahlungsmethode', 'Zahlungsanbieter', 'Nettobetrag', 'Auszahlungswährung']
 
 
 def get_dataframe(key):
@@ -86,6 +89,18 @@ def get_merged_df(table_name):
             .str.extract(r'https?://[^/]+/([^/?#]+)')[0]
     )
 
+    # --- return raw tables if requested ---
+    if table_name == 'stripe_import':
+        return stripe.dropna(axis=1, how='all')
+    if table_name == 'raiseNow_import':
+        return raisenow.dropna(axis=1, how='all')
+    # additional assignment: build a mask of rows where norm_zweck is still empty/NaN
+    mask = raisenow['norm_zweck'].isna() | (raisenow['norm_zweck'] == '')
+    raisenow.loc[mask, 'norm_zweck'] = (
+        raisenow.loc[mask, 'raisenow_parameters.product.source_url']
+            .str.extract(r'https?://[^/]+/([^/?#]+)')[0]
+    )
+
     # --- return raw tables if requested ---
     if table_name == 'stripe_import':
         return stripe.dropna(axis=1, how='all')
@@ -96,6 +111,26 @@ def get_merged_df(table_name):
     pairs = []
     # index Raisenow rows for fast lookup + dropping
     rr = raisenow.set_index('idx_raisenow')
+    for _, s in stripe.iterrows():
+        # filter candidates by amount & name
+        cand = rr[
+            (rr['norm_amount'] == s['norm_amount']) &
+            (rr['norm_name']   == s['norm_name'])
+        ].copy()
+        if cand.empty:
+            continue
+        # compute absolute date difference (days only)
+        date_diff = (cand['norm_date'].dt.normalize() - s['norm_date'].normalize()).abs()
+        exact_cand = cand[date_diff == pd.Timedelta(0)]
+        if not exact_cand.empty:
+            # pick the first exact match
+            best = exact_cand.index[0]
+            pairs.append((int(s['idx_stripe']), int(best)))
+            rr = rr.drop(best)
+    # --- 1) Greedy exact same-day matches ---
+    pairs = []
+    # index Raisenow rows for fast lookup + dropping
+    rr = raisenow.set_index('idx_raisenow')
     for _, s in stripe.iterrows():
         # filter candidates by amount & name
         cand = rr[
@@ -146,6 +181,40 @@ def get_merged_df(table_name):
 
     combined = pd.DataFrame(merged_rows)
 
+    # --- slice out the requested view ---
+    # --- 2) Greedy fuzzy ±1-day matches on remaining rows ---
+    used_stripe = {s for s, _ in pairs}
+    stripe_left = stripe[~stripe['idx_stripe'].isin(used_stripe)].copy()
+    for _, s in stripe_left.iterrows():
+        cand = rr[
+            (rr['norm_amount'] == s['norm_amount']) &
+            (rr['norm_name']   == s['norm_name'])
+        ].copy()
+        if cand.empty:
+            continue
+        date_diff = (cand['norm_date'].dt.normalize() - s['norm_date'].normalize()).abs()
+        cand = cand[date_diff <= pd.Timedelta(days=1)]
+        if cand.empty:
+            continue
+        # pick the one with the smallest gap
+        best = date_diff.idxmin()
+        pairs.append((int(s['idx_stripe']), int(best)))
+        rr = rr.drop(best)
+
+    # --- build the merged DataFrame without suffixes ---
+    merged_rows = []
+    for s_idx, r_idx in pairs:
+        srow = stripe.loc[s_idx].to_dict()
+        rrow = raisenow.loc[r_idx].to_dict()
+        # drop any overlapping keys so we never get suffixes
+        for k in ['norm_amount','norm_name','norm_date','norm_email','idx_stripe']:
+            rrow.pop(k, None)
+        # now combine so stripe values win for those keys, and raisenow adds its own columns
+        merged = {**srow, **rrow}
+        merged_rows.append(merged)
+
+    combined = pd.DataFrame(merged_rows)
+
     # --- slice out the requested view ---
     if table_name == 'merged':
         result = combined
@@ -224,6 +293,16 @@ def download():
             'raisenow_only'
         ]
     }
+    sheets = {
+        name: get_merged_df(name)
+        for name in [
+            'stripe_import',
+            'raiseNow_import',
+            'merged',
+            'stripe_only',
+            'raisenow_only'
+        ]
+    }
 
     output = BytesIO()
     with pd.ExcelWriter(output, engine='xlsxwriter') as writer: