apply black update
diff --git a/datafusion/__init__.py b/datafusion/__init__.py index b2e1028..bc36cef 100644 --- a/datafusion/__init__.py +++ b/datafusion/__init__.py
@@ -104,9 +104,7 @@ Create a new User Defined Aggregate Function """ if not issubclass(accum, Accumulator): - raise TypeError( - "`accum` must implement the abstract base class Accumulator" - ) + raise TypeError("`accum` must implement the abstract base class Accumulator") if name is None: name = accum.__qualname__ return AggregateUDF(
diff --git a/datafusion/tests/generic.py b/datafusion/tests/generic.py index 1f984a4..0739979 100644 --- a/datafusion/tests/generic.py +++ b/datafusion/tests/generic.py
@@ -50,9 +50,7 @@ datetime.datetime.now() - datetime.timedelta(days=1), datetime.datetime.now() + datetime.timedelta(days=1), ] - return pa.array( - data, type=pa.timestamp(f), mask=np.array([False, True, False]) - ) + return pa.array(data, type=pa.timestamp(f), mask=np.array([False, True, False])) def data_date32(): @@ -61,9 +59,7 @@ datetime.date(1980, 1, 1), datetime.date(2030, 1, 1), ] - return pa.array( - data, type=pa.date32(), mask=np.array([False, True, False]) - ) + return pa.array(data, type=pa.date32(), mask=np.array([False, True, False])) def data_timedelta(f): @@ -72,9 +68,7 @@ datetime.timedelta(days=1), datetime.timedelta(seconds=1), ] - return pa.array( - data, type=pa.duration(f), mask=np.array([False, True, False]) - ) + return pa.array(data, type=pa.duration(f), mask=np.array([False, True, False])) def data_binary_other():
diff --git a/datafusion/tests/test_dataframe.py b/datafusion/tests/test_dataframe.py index 51f7c22..7c75466 100644 --- a/datafusion/tests/test_dataframe.py +++ b/datafusion/tests/test_dataframe.py
@@ -124,9 +124,7 @@ def test_with_column_renamed(df): - df = df.with_column("c", column("a") + column("b")).with_column_renamed( - "c", "sum" - ) + df = df.with_column("c", column("a") + column("b")).with_column_renamed("c", "sum") result = df.collect()[0] @@ -190,9 +188,7 @@ [pa.array([1, 2, 3]), pa.array([4, 5, 6])], names=["a", "b"], ) - df_b = ctx.create_dataframe([[batch]]).sort( - column("a").sort(ascending=True) - ) + df_b = ctx.create_dataframe([[batch]]).sort(column("a").sort(ascending=True)) assert df_a.collect() == df_b.collect() @@ -201,9 +197,7 @@ df = df.select( column("a"), f.alias( - f.window( - "lead", [column("b")], order_by=[f.order_by(column("b"))] - ), + f.window("lead", [column("b")], order_by=[f.order_by(column("b"))]), "a_next", ), ) @@ -282,9 +276,7 @@ [pa.array([3]), pa.array([6])], names=["a", "b"], ) - df_c = ctx.create_dataframe([[batch]]).sort( - column("a").sort(ascending=True) - ) + df_c = ctx.create_dataframe([[batch]]).sort(column("a").sort(ascending=True)) df_a_i_b = df_a.intersect(df_b).sort(column("a").sort(ascending=True)) @@ -310,9 +302,7 @@ [pa.array([1, 2]), pa.array([4, 5])], names=["a", "b"], ) - df_c = ctx.create_dataframe([[batch]]).sort( - column("a").sort(ascending=True) - ) + df_c = ctx.create_dataframe([[batch]]).sort(column("a").sort(ascending=True)) df_a_e_b = df_a.except_all(df_b).sort(column("a").sort(ascending=True)) @@ -347,9 +337,7 @@ [pa.array([1, 2, 3, 3, 4, 5]), pa.array([4, 5, 6, 6, 7, 8])], names=["a", "b"], ) - df_c = ctx.create_dataframe([[batch]]).sort( - column("a").sort(ascending=True) - ) + df_c = ctx.create_dataframe([[batch]]).sort(column("a").sort(ascending=True)) df_a_u_b = df_a.union(df_b).sort(column("a").sort(ascending=True)) @@ -373,9 +361,7 @@ [pa.array([1, 2, 3, 4, 5]), pa.array([4, 5, 6, 7, 8])], names=["a", "b"], ) - df_c = ctx.create_dataframe([[batch]]).sort( - column("a").sort(ascending=True) - ) + df_c = ctx.create_dataframe([[batch]]).sort(column("a").sort(ascending=True)) df_a_u_b = df_a.union(df_b, True).sort(column("a").sort(ascending=True))
diff --git a/datafusion/tests/test_functions.py b/datafusion/tests/test_functions.py index daa2f19..93ec3cf 100644 --- a/datafusion/tests/test_functions.py +++ b/datafusion/tests/test_functions.py
@@ -59,9 +59,7 @@ """ Test literals with arithmetic operations """ - df = df.select( - literal(1) + column("b"), f.concat(column("a"), literal("!")) - ) + df = df.select(literal(1) + column("b"), f.concat(column("a"), literal("!"))) result = df.collect() assert len(result) == 1 result = result[0] @@ -72,9 +70,7 @@ def test_math_functions(): ctx = SessionContext() # create a RecordBatch and a new DataFrame from it - batch = pa.RecordBatch.from_arrays( - [pa.array([0.1, -0.7, 0.55])], names=["value"] - ) + batch = pa.RecordBatch.from_arrays([pa.array([0.1, -0.7, 0.55])], names=["value"]) df = ctx.create_dataframe([[batch]]) values = np.array([0.1, -0.7, 0.55]) @@ -103,15 +99,9 @@ np.testing.assert_array_almost_equal(result.column(4), np.arcsin(values)) np.testing.assert_array_almost_equal(result.column(5), np.arccos(values)) np.testing.assert_array_almost_equal(result.column(6), np.exp(values)) - np.testing.assert_array_almost_equal( - result.column(7), np.log(values + 1.0) - ) - np.testing.assert_array_almost_equal( - result.column(8), np.log2(values + 1.0) - ) - np.testing.assert_array_almost_equal( - result.column(9), np.log10(values + 1.0) - ) + np.testing.assert_array_almost_equal(result.column(7), np.log(values + 1.0)) + np.testing.assert_array_almost_equal(result.column(8), np.log2(values + 1.0)) + np.testing.assert_array_almost_equal(result.column(9), np.log10(values + 1.0)) np.testing.assert_array_less(result.column(10), np.ones_like(values)) @@ -149,18 +139,9 @@ ) assert result.column(1) == pa.array( [ - b( - "185F8DB32271FE25F561A6FC938B2E26" - "4306EC304EDA518007D1764826381969" - ), - b( - "78AE647DC5544D227130A0682A51E30B" - "C7777FBB6D8A8F17007463A3ECD1D524" - ), - b( - "BB7208BC9B5D7C04F1236A82A0093A5E" - "33F40423D5BA8D4266F7092C3BA43B62" - ), + b("185F8DB32271FE25F561A6FC938B2E26" "4306EC304EDA518007D1764826381969"), + b("78AE647DC5544D227130A0682A51E30B" "C7777FBB6D8A8F17007463A3ECD1D524"), + b("BB7208BC9B5D7C04F1236A82A0093A5E" "33F40423D5BA8D4266F7092C3BA43B62"), ] ) assert result.column(2) == pa.array( @@ -187,33 +168,15 @@ ) assert result.column(3) == pa.array( [ - b( - "F73A5FBF881F89B814871F46E26AD3FA" - "37CB2921C5E8561618639015B3CCBB71" - ), - b( - "B792A0383FB9E7A189EC150686579532" - "854E44B71AC394831DAED169BA85CCC5" - ), - b( - "27988A0E51812297C77A433F63523334" - "6AEE29A829DCF4F46E0F58F402C6CFCB" - ), + b("F73A5FBF881F89B814871F46E26AD3FA" "37CB2921C5E8561618639015B3CCBB71"), + b("B792A0383FB9E7A189EC150686579532" "854E44B71AC394831DAED169BA85CCC5"), + b("27988A0E51812297C77A433F63523334" "6AEE29A829DCF4F46E0F58F402C6CFCB"), ] ) assert result.column(4) == pa.array( [ - b( - "FBC2B0516EE8744D293B980779178A35" - "08850FDCFE965985782C39601B65794F" - ), - b( - "BF73D18575A736E4037D45F9E316085B" - "86C19BE6363DE6AA789E13DEAACC1C4E" - ), - b( - "C8D11B9F7237E4034ADBCD2005735F9B" - "C4C597C75AD89F4492BEC8F77D15F7EB" - ), + b("FBC2B0516EE8744D293B980779178A35" "08850FDCFE965985782C39601B65794F"), + b("BF73D18575A736E4037D45F9E316085B" "86C19BE6363DE6AA789E13DEAACC1C4E"), + b("C8D11B9F7237E4034ADBCD2005735F9B" "C4C597C75AD89F4492BEC8F77D15F7EB"), ] )
diff --git a/datafusion/tests/test_sql.py b/datafusion/tests/test_sql.py index 19c2766..f430f32 100644 --- a/datafusion/tests/test_sql.py +++ b/datafusion/tests/test_sql.py
@@ -72,9 +72,7 @@ result = pa.Table.from_batches(result) assert result.schema == alternative_schema - with pytest.raises( - ValueError, match="Delimiter must be a single character" - ): + with pytest.raises(ValueError, match="Delimiter must be a single character"): ctx.register_csv("csv4", path, delimiter="wrong") @@ -114,9 +112,7 @@ ) assert ctx.tables() == {"datapp"} - result = ctx.sql( - "SELECT grp, COUNT(*) AS cnt FROM datapp GROUP BY grp" - ).collect() + result = ctx.sql("SELECT grp, COUNT(*) AS cnt FROM datapp GROUP BY grp").collect() result = pa.Table.from_batches(result) rd = result.to_pydict() @@ -183,9 +179,7 @@ ).collect() expected_a = pa.array([50.0219, 50.0152], pa.float64()) expected_cast = pa.array([50, 50], pa.int32()) - expected = [ - pa.RecordBatch.from_arrays([expected_a, expected_cast], ["a", "a_int"]) - ] + expected = [pa.RecordBatch.from_arrays([expected_a, expected_cast], ["a", "a_int"])] np.testing.assert_equal(expected[0].column(1), expected[0].column(1)) @@ -205,9 +199,7 @@ "float", ] - select = ", ".join( - [f"CAST(9 AS {t}) AS A{i}" for i, t in enumerate(valid_types)] - ) + select = ", ".join([f"CAST(9 AS {t}) AS A{i}" for i, t in enumerate(valid_types)]) # can execute, which implies that we can cast ctx.sql(f"SELECT {select} FROM t").collect() @@ -236,14 +228,10 @@ ctx, tmp_path, fn, input_types, output_type, input_values, expected_values ): # write to disk - path = helpers.write_parquet( - tmp_path / "a.parquet", pa.array(input_values) - ) + path = helpers.write_parquet(tmp_path / "a.parquet", pa.array(input_values)) ctx.register_parquet("t", path) - func = udf( - fn, input_types, output_type, name="func", volatility="immutable" - ) + func = udf(fn, input_types, output_type, name="func", volatility="immutable") ctx.register_udf(func) batches = ctx.sql("SELECT func(a) AS tt FROM t").collect()
diff --git a/dev/release/check-rat-report.py b/dev/release/check-rat-report.py index 30a0111..d3dd7c5 100644 --- a/dev/release/check-rat-report.py +++ b/dev/release/check-rat-report.py
@@ -23,9 +23,7 @@ import xml.etree.ElementTree as ET if len(sys.argv) != 3: - sys.stderr.write( - "Usage: %s exclude_globs.lst rat_report.xml\n" % sys.argv[0] - ) + sys.stderr.write("Usage: %s exclude_globs.lst rat_report.xml\n" % sys.argv[0]) sys.exit(1) exclude_globs_filename = sys.argv[1]