diff --git a/etl/src/data/etl_region_capacities.py b/etl/src/data/etl_region_capacities.py
index 989cf5b..39b0b0b 100644
--- a/etl/src/data/etl_region_capacities.py
+++ b/etl/src/data/etl_region_capacities.py
@@ -1,5 +1,5 @@
-from io import StringIO
 from datetime import date
+from io import StringIO
 
 import polars as pl
 
@@ -21,7 +21,7 @@ def region_capacities(id: int):
 	df_dates = pl.DataFrame()
 
 	# Get Data from JSON
-	gridData = []
+	gridData = pl.DataFrame(schema=[("scrape_date", pl.String), ("sum_hor", pl.Int64), ("calendar_width", pl.Int64)])
 	dayCounts = []
 	for row in extractions.rows(named=True):
 		# Return 0 for sum if calendar is null
@@ -30,24 +30,21 @@ def region_capacities(id: int):
 			sum_hor = calDF.sum_horizontal()[0]
 		else:
 			sum_hor = 0
-		gridData.append([row['ScrapeDate'], sum_hor, calDF.width])
+		gridData = gridData.vstack(pl.DataFrame({"scrape_date" : row['ScrapeDate'], "sum_hor": sum_hor, "calendar_width": calDF.width}))
 
 	# Create Aggregates of values
-	df = pl.DataFrame(gridData, strict=False)
-	df_count = df.group_by("column_0").agg(pl.col("column_1").count())
-	df_sum = df.group_by("column_0").agg(pl.col("column_1").sum())
-	df_numDays = df.group_by("column_0").agg(pl.col("column_2").max())
+	df_count = gridData.group_by("scrape_date").agg(pl.col("sum_hor").count())
+	df_sum = gridData.group_by("scrape_date").agg(pl.col("sum_hor").sum())
+	df_numDays = gridData.group_by("scrape_date").agg(pl.col("calendar_width").max())
 
 	# Join and rename DF's
-	df = df_sum.join(df_count, on= 'column_0').join(df_numDays, on= 'column_0')
-	df = df.rename({"column_0": "ScrapeDate", "column_1": "Sum", "column_1_right": "num_properties", "column_2": "max_value", })
+	df = df_sum.join(df_count, on= 'scrape_date').join(df_numDays, on= 'scrape_date')
 
 	# Calculate normed capacities for each scrapeDate
-	df = df.with_columns((pl.col("Sum") / pl.col("num_properties") / (pl.col("max_value")*2) * 100).alias("capacity"))
+	df = df.with_columns((pl.col("sum_hor") / pl.col("sum_hor_right") / (pl.col("calendar_width")*2) * 100).alias("capacity"))
 
 	# Sort the date column
-	df = df.cast({"ScrapeDate": date})
-	df = df.sort('ScrapeDate')
+	df = df.cast({"scrape_date": date}).sort('scrape_date')
 
-	result = {"capacities": df['capacity'].to_list(), "dates": df['ScrapeDate'].to_list()}
+	result = {"capacities": df['capacity'].to_list(), "dates": df['scrape_date'].to_list()}
 	return result
\ No newline at end of file