Linting

Author: timsaucer

examples/tpch/q02_minimum_cost_supplier.py

@@ -98,7 +98,13 @@
 # We want to evaluate the entire data frame, so we specify this.
 window_frame = datafusion.WindowFrame("rows", None, None)
 df = df.with_column(
-    "min_cost", F.window("min", [col("ps_supplycost")], partition_by=[col("ps_partkey")], window_frame=window_frame)
+    "min_cost",
+    F.window(
+        "min",
+        [col("ps_supplycost")],
+        partition_by=[col("ps_partkey")],
+        window_frame=window_frame,
+    ),
 )
 
 df = df.filter(col("min_cost") == col("ps_supplycost"))

examples/tpch/q19_discounted_revenue.py

@@ -74,6 +74,7 @@
 
 df = df.join(df_part, (["l_partkey"], ["p_partkey"]), "inner")
 
+
 # Create the user defined function (UDF) definition that does the work
 def is_of_interest(
     brand_arr: pa.Array,
@@ -112,6 +113,7 @@ def is_of_interest(
 
     return pa.array(result)
 
+
 # Turn the above function into a UDF that DataFusion can understand
 is_of_interest_udf = udf(
     is_of_interest,