diff --git a/etl/src/data/etl_region_capacities.py b/etl/src/data/etl_region_capacities.py
index f4534cc..01759f9 100644
--- a/etl/src/data/etl_region_capacities.py
+++ b/etl/src/data/etl_region_capacities.py
@@ -23,10 +23,8 @@ def region_capacities(id: int):
     # Merge Dataframe to generate indices
     df = df.join(datesDF, on='ScrapeDate')
     df = df.join(propIdDF, on='property_id')
-
     # Drop now useless columns ScrapeDate and property_id
-    df = df[['calendarBody', 'date_index', 'prop_index']]
-
+    df = df[['ScrapeDate', 'calendarBody', 'date_index', 'prop_index']]
     # Calculate grid values
     gridData = []
     for row in df.rows(named=True):
@@ -36,13 +34,18 @@ def region_capacities(id: int):
             sum_hor = calDF.sum_horizontal()[0]
         else:
             sum_hor = 0
-        gridData.append([row['prop_index'], row['date_index'], sum_hor])
-    gridData = np.array(gridData)
+        # With Index
+        # gridData.append([row['prop_index'], row['date_index'], sum_hor])
+        # With ScrapeDate
+        gridData.append([row['ScrapeDate'], row['date_index'], sum_hor])
 
+    gridData = np.array(gridData)
     # get all values to calculate Max
-    allValues = gridData[:, 2]
+    allValues = gridData[:, 2].astype(int)
+    print(allValues)
     maxValue = np.max(allValues)
-    gridData[:, 2] = (gridData[:, 2]*100)/maxValue
+    print(maxValue)
+    gridData[:, 2] = (allValues*100)/maxValue
 
     # Return back to list
     gridData = gridData.tolist()
@@ -55,3 +58,6 @@ def region_capacities(id: int):
     outDict = {'scrapeDates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': gridData}
 
     return outDict
+
+out = region_capacities(1)
+print(out)
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