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test_expr.py
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826 lines (740 loc) · 26.3 KB
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from datetime import datetime, timezone
import pyarrow as pa
import pytest
from datafusion import SessionContext, col, functions, lit
from datafusion.expr import (
Aggregate,
AggregateFunction,
BinaryExpr,
Column,
CopyTo,
CreateIndex,
DescribeTable,
DmlStatement,
DropCatalogSchema,
Filter,
Limit,
Literal,
Projection,
RecursiveQuery,
Sort,
TableScan,
TransactionEnd,
TransactionStart,
Values,
)
@pytest.fixture
def test_ctx():
ctx = SessionContext()
ctx.register_csv("test", "testing/data/csv/aggregate_test_100.csv")
return ctx
def test_projection(test_ctx):
df = test_ctx.sql("select c1, 123, c1 < 123 from test")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Projection)
expr = plan.projections()
col1 = expr[0].to_variant()
assert isinstance(col1, Column)
assert col1.name() == "c1"
assert col1.qualified_name() == "test.c1"
col2 = expr[1].to_variant()
assert isinstance(col2, Literal)
assert col2.data_type() == "Int64"
assert col2.value_i64() == 123
col3 = expr[2].to_variant()
assert isinstance(col3, BinaryExpr)
assert isinstance(col3.left().to_variant(), Column)
assert col3.op() == "<"
assert isinstance(col3.right().to_variant(), Literal)
plan = plan.input()[0].to_variant()
assert isinstance(plan, TableScan)
def test_filter(test_ctx):
df = test_ctx.sql("select c1 from test WHERE c1 > 5")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Projection)
plan = plan.input()[0].to_variant()
assert isinstance(plan, Filter)
def test_limit(test_ctx):
df = test_ctx.sql("select c1 from test LIMIT 10")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Limit)
assert "Skip: None" in str(plan)
df = test_ctx.sql("select c1 from test LIMIT 10 OFFSET 5")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Limit)
assert "Skip: Some(Literal(Int64(5)))" in str(plan)
def test_aggregate_query(test_ctx):
df = test_ctx.sql("select c1, count(*) from test group by c1")
plan = df.logical_plan()
projection = plan.to_variant()
assert isinstance(projection, Projection)
aggregate = projection.input()[0].to_variant()
assert isinstance(aggregate, Aggregate)
col1 = aggregate.group_by_exprs()[0].to_variant()
assert isinstance(col1, Column)
assert col1.name() == "c1"
assert col1.qualified_name() == "test.c1"
col2 = aggregate.aggregate_exprs()[0].to_variant()
assert isinstance(col2, AggregateFunction)
def test_sort(test_ctx):
df = test_ctx.sql("select c1 from test order by c1")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Sort)
def test_relational_expr(test_ctx):
ctx = SessionContext()
batch = pa.RecordBatch.from_arrays(
[
pa.array([1, 2, 3]),
pa.array(["alpha", "beta", "gamma"], type=pa.string_view()),
],
names=["a", "b"],
)
df = ctx.create_dataframe([[batch]], name="batch_array")
assert df.filter(col("a") == 1).count() == 1
assert df.filter(col("a") != 1).count() == 2
assert df.filter(col("a") >= 1).count() == 3
assert df.filter(col("a") > 1).count() == 2
assert df.filter(col("a") <= 3).count() == 3
assert df.filter(col("a") < 3).count() == 2
assert df.filter(col("b") == "beta").count() == 1
assert df.filter(col("b") != "beta").count() == 2
assert df.filter(col("a") == "beta").count() == 0
def test_expr_to_variant():
# Taken from https://github.com/apache/datafusion-python/issues/781
from datafusion import SessionContext
from datafusion.expr import Filter
def traverse_logical_plan(plan):
cur_node = plan.to_variant()
if isinstance(cur_node, Filter):
return cur_node.predicate().to_variant()
if hasattr(plan, "inputs"):
for input_plan in plan.inputs():
res = traverse_logical_plan(input_plan)
if res is not None:
return res
return None
ctx = SessionContext()
data = {"id": [1, 2, 3], "name": ["Alice", "Bob", "Charlie"]}
ctx.from_pydict(data, name="table1")
query = "SELECT * FROM table1 t1 WHERE t1.name IN ('dfa', 'ad', 'dfre', 'vsa')"
logical_plan = ctx.sql(query).optimized_logical_plan()
variant = traverse_logical_plan(logical_plan)
assert variant is not None
assert variant.expr().to_variant().qualified_name() == "table1.name"
assert (
str(variant.list())
== '[Expr(Utf8("dfa")), Expr(Utf8("ad")), Expr(Utf8("dfre")), Expr(Utf8("vsa"))]' # noqa: E501
)
assert not variant.negated()
def test_expr_getitem() -> None:
ctx = SessionContext()
data = {
"array_values": [[1, 2, 3], [4, 5], [6], []],
"struct_values": [
{"name": "Alice", "age": 15},
{"name": "Bob", "age": 14},
{"name": "Charlie", "age": 13},
{"name": None, "age": 12},
],
}
df = ctx.from_pydict(data, name="table1")
names = df.select(col("struct_values")["name"].alias("name")).collect()
names = [r.as_py() for rs in names for r in rs["name"]]
array_values = df.select(col("array_values")[1].alias("value")).collect()
array_values = [r.as_py() for rs in array_values for r in rs["value"]]
assert names == ["Alice", "Bob", "Charlie", None]
assert array_values == [2, 5, None, None]
def test_display_name_deprecation():
import warnings
expr = col("foo")
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered
warnings.simplefilter("always")
# should trigger warning
name = expr.display_name()
# Verify some things
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated" in str(w[-1].message)
# returns appropriate result
assert name == expr.schema_name()
assert name == "foo"
@pytest.fixture
def df():
ctx = SessionContext()
# create a RecordBatch and a new DataFrame from it
batch = pa.RecordBatch.from_arrays(
[pa.array([1, 2, None]), pa.array([4, None, 6]), pa.array([None, None, 8])],
names=["a", "b", "c"],
)
return ctx.from_arrow(batch)
def test_fill_null(df):
df = df.select(
col("a").fill_null(100).alias("a"),
col("b").fill_null(25).alias("b"),
col("c").fill_null(1234).alias("c"),
)
df.show()
result = df.collect()[0]
assert result.column(0) == pa.array([1, 2, 100])
assert result.column(1) == pa.array([4, 25, 6])
assert result.column(2) == pa.array([1234, 1234, 8])
def test_copy_to():
ctx = SessionContext()
ctx.sql("CREATE TABLE foo (a int, b int)").collect()
df = ctx.sql("COPY foo TO bar STORED AS CSV")
plan = df.logical_plan()
plan = plan.to_variant()
assert isinstance(plan, CopyTo)
def test_create_index():
ctx = SessionContext()
ctx.sql("CREATE TABLE foo (a int, b int)").collect()
plan = ctx.sql("create index idx on foo (a)").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, CreateIndex)
def test_describe_table():
ctx = SessionContext()
ctx.sql("CREATE TABLE foo (a int, b int)").collect()
plan = ctx.sql("describe foo").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, DescribeTable)
def test_dml_statement():
ctx = SessionContext()
ctx.sql("CREATE TABLE foo (a int, b int)").collect()
plan = ctx.sql("insert into foo values (1, 2)").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, DmlStatement)
def drop_catalog_schema():
ctx = SessionContext()
plan = ctx.sql("drop schema cat").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, DropCatalogSchema)
def test_recursive_query():
ctx = SessionContext()
plan = ctx.sql(
"""
WITH RECURSIVE cte AS (
SELECT 1 as n
UNION ALL
SELECT n + 1 FROM cte WHERE n < 5
)
SELECT * FROM cte;
"""
).logical_plan()
plan = plan.inputs()[0].inputs()[0].to_variant()
assert isinstance(plan, RecursiveQuery)
def test_values():
ctx = SessionContext()
plan = ctx.sql("values (1, 'foo'), (2, 'bar')").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, Values)
def test_transaction_start():
ctx = SessionContext()
plan = ctx.sql("START TRANSACTION").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, TransactionStart)
def test_transaction_end():
ctx = SessionContext()
plan = ctx.sql("COMMIT").logical_plan()
plan = plan.to_variant()
assert isinstance(plan, TransactionEnd)
def test_col_getattr():
ctx = SessionContext()
data = {
"array_values": [[1, 2, 3], [4, 5], [6], []],
"struct_values": [
{"name": "Alice", "age": 15},
{"name": "Bob", "age": 14},
{"name": "Charlie", "age": 13},
{"name": None, "age": 12},
],
}
df = ctx.from_pydict(data, name="table1")
names = df.select(col.struct_values["name"].alias("name")).collect()
names = [r.as_py() for rs in names for r in rs["name"]]
array_values = df.select(col.array_values[1].alias("value")).collect()
array_values = [r.as_py() for rs in array_values for r in rs["value"]]
assert names == ["Alice", "Bob", "Charlie", None]
assert array_values == [2, 5, None, None]
def test_alias_with_metadata(df):
df = df.select(col("a").alias("b", {"key": "value"}))
assert df.schema().field("b").metadata == {b"key": b"value"}
# These unit tests are to ensure the expression functions do not regress
# For the math functions we will use `functions.round` so we can more
# easily test for equivalence and not worry about floating point precision
@pytest.mark.parametrize(
("function", "expected_result"),
[
# Math Functions
pytest.param(
functions.round(col("a").asin(), lit(4)),
pa.array([-0.8481, 0.5236, 0.0, None], type=pa.float64()),
id="asin",
),
pytest.param(
functions.round(col("a").sin(), lit(4)),
pa.array([-0.6816, 0.4794, 0.0, None], type=pa.float64()),
id="sin",
),
pytest.param(
# Since log10 of negative returns NaN and you can't test NaN for
# equivalence, also do an abs() here.
functions.round(col("a").abs().log10(), lit(4)),
pa.array([-0.1249, -0.301, -float("inf"), None], type=pa.float64()),
id="log10",
),
pytest.param(
col("a").iszero(),
pa.array([False, False, True, None], type=pa.bool_()),
id="iszero",
),
pytest.param(
functions.round(col("a").acos(), lit(4)),
pa.array([2.4189, 1.0472, 1.5708, None], type=pa.float64()),
id="acos",
),
pytest.param(
col("e").isnan(),
pa.array([False, True, False, None], type=pa.bool_()),
id="isnan",
),
pytest.param(
functions.round(col("a").degrees(), lit(4)),
pa.array([-42.9718, 28.6479, 0.0, None], type=pa.float64()),
id="degrees",
),
pytest.param(
functions.round(col("a").asinh(), lit(4)),
pa.array([-0.6931, 0.4812, 0.0, None], type=pa.float64()),
id="asinh",
),
pytest.param(
col("a").abs(),
pa.array([0.75, 0.5, 0.0, None], type=pa.float64()),
id="abs",
),
pytest.param(
functions.round(col("a").exp(), lit(4)),
pa.array([0.4724, 1.6487, 1.0, None], type=pa.float64()),
id="exp",
),
pytest.param(
functions.round(col("a").cosh(), lit(4)),
pa.array([1.2947, 1.1276, 1.0, None], type=pa.float64()),
id="cosh",
),
pytest.param(
functions.round(col("a").radians(), lit(4)),
pa.array([-0.0131, 0.0087, 0.0, None], type=pa.float64()),
id="radians",
),
pytest.param(
functions.round(col("a").abs().sqrt(), lit(4)),
pa.array([0.866, 0.7071, 0.0, None], type=pa.float64()),
id="sqrt",
),
pytest.param(
functions.round(col("a").tanh(), lit(4)),
pa.array([-0.6351, 0.4621, 0.0, None], type=pa.float64()),
id="tanh",
),
pytest.param(
functions.round(col("a").atan(), lit(4)),
pa.array([-0.6435, 0.4636, 0.0, None], type=pa.float64()),
id="atan",
),
pytest.param(
functions.round(col("a").atanh(), lit(4)),
pa.array([-0.973, 0.5493, 0.0, None], type=pa.float64()),
id="atanh",
),
pytest.param(
# large numbers cause an integer overflow so divid to make smaller
(col("b") / lit(4)).factorial(),
pa.array([1, 3628800, 1, None], type=pa.int64()),
id="factorial",
),
pytest.param(
# Valid values of acosh must be >= 1.0
functions.round((col("a").abs() + lit(1.0)).acosh(), lit(4)),
pa.array([1.1588, 0.9624, 0.0, None], type=pa.float64()),
id="acosh",
),
pytest.param(
col("a").floor(),
pa.array([-1.0, 0.0, 0.0, None], type=pa.float64()),
id="floor",
),
pytest.param(
col("a").ceil(),
pa.array([-0.0, 1.0, 0.0, None], type=pa.float64()),
id="ceil",
),
pytest.param(
functions.round(col("a").abs().ln(), lit(4)),
pa.array([-0.2877, -0.6931, float("-inf"), None], type=pa.float64()),
id="ln",
),
pytest.param(
functions.round(col("a").tan(), lit(4)),
pa.array([-0.9316, 0.5463, 0.0, None], type=pa.float64()),
id="tan",
),
pytest.param(
functions.round(col("a").cbrt(), lit(4)),
pa.array([-0.9086, 0.7937, 0.0, None], type=pa.float64()),
id="cbrt",
),
pytest.param(
functions.round(col("a").cos(), lit(4)),
pa.array([0.7317, 0.8776, 1.0, None], type=pa.float64()),
id="cos",
),
pytest.param(
functions.round(col("a").sinh(), lit(4)),
pa.array([-0.8223, 0.5211, 0.0, None], type=pa.float64()),
id="sinh",
),
pytest.param(
col("a").signum(),
pa.array([-1.0, 1.0, 0.0, None], type=pa.float64()),
id="signum",
),
pytest.param(
functions.round(col("a").abs().log2(), lit(4)),
pa.array([-0.415, -1.0, float("-inf"), None], type=pa.float64()),
id="log2",
),
pytest.param(
functions.round(col("a").cot(), lit(4)),
pa.array([-1.0734, 1.8305, float("inf"), None], type=pa.float64()),
id="cot",
),
#
# String Functions
#
pytest.param(
col("c").reverse(),
pa.array(["olleH", " dlrow ", "!", None], type=pa.string()),
id="reverse",
),
pytest.param(
col("c").bit_length(),
pa.array([40, 56, 8, None], type=pa.int32()),
id="bit_length",
),
pytest.param(
col("b").to_hex(),
pa.array(["ffffffffffffffe2", "2a", "0", None], type=pa.string()),
id="to_hex",
),
pytest.param(
col("c").length(),
pa.array([5, 7, 1, None], type=pa.int32()),
id="length",
),
pytest.param(
col("c").lower(),
pa.array(["hello", " world ", "!", None], type=pa.string()),
id="lower",
),
pytest.param(
col("c").ascii(),
pa.array([72, 32, 33, None], type=pa.int32()),
id="ascii",
),
pytest.param(
col("c").sha512(),
pa.array(
[
bytes.fromhex(
"3615F80C9D293ED7402687F94B22D58E529B8CC7916F8FAC7FDDF7FBD5AF4CF777D3D795A7A00A16BF7E7F3FB9561EE9BAAE480DA9FE7A18769E71886B03F315"
),
bytes.fromhex(
"A6758FDA3C2F0B554084E18308EA99B94B54EEE8FDA72697CEA7844E524CC2F2F2EE4CC8BAC87D2E3E7222959FE3D0CA1A841761FDC0D1780F6FE9E39E369500"
),
bytes.fromhex(
"3831A6A6155E509DEE59A7F451EB35324D8F8F2DF6E3708894740F98FDEE23889F4DE5ADB0C5010DFB555CDA77C8AB5DC902094C52DE3278F35A75EBC25F093A"
),
None,
],
type=pa.binary(),
),
id="sha512",
),
pytest.param(
col("c").sha384(),
pa.array(
[
bytes.fromhex(
"3519FE5AD2C596EFE3E276A6F351B8FC0B03DB861782490D45F7598EBD0AB5FD5520ED102F38C4A5EC834E98668035FC"
),
bytes.fromhex(
"A6A38A9AE2CFD0D67F49989AD584632BF7D7A07DAD2277E92326A6A0B37F884A871D6173FB342CFE258E375258ACAAEC"
),
bytes.fromhex(
"1D0EC8C84EE9521E21F06774DE232367B64DE628474CB5B2E372B699A1F55AE335CC37193EF823E33324DFD9A70738A6"
),
None,
],
type=pa.binary(),
),
id="sha384",
),
pytest.param(
col("c").sha256(),
pa.array(
[
bytes.fromhex(
"185F8DB32271FE25F561A6FC938B2E264306EC304EDA518007D1764826381969"
),
bytes.fromhex(
"DE2EF0D77D456EC1CDE2C52F75996F6636A64079297213D548D875A488B03A75"
),
bytes.fromhex(
"BB7208BC9B5D7C04F1236A82A0093A5E33F40423D5BA8D4266F7092C3BA43B62"
),
None,
],
type=pa.binary(),
),
id="sha256",
),
pytest.param(
col("c").sha224(),
pa.array(
[
bytes.fromhex(
"4149DA18AA8BFC2B1E382C6C26556D01A92C261B6436DAD5E3BE3FCC"
),
bytes.fromhex(
"AD6DF6D9ECDDF50AF2A72D5E3144BA813EE954537572C0E8AB3066BE"
),
bytes.fromhex(
"6641A7E8278BCD49E476E7ACAE158F4105B2952D22AEB2E0B9A231A0"
),
None,
],
type=pa.binary(),
),
id="sha224",
),
pytest.param(
col("c").btrim(),
pa.array(["Hello", "world", "!", None], type=pa.string_view()),
id="btrim",
),
pytest.param(
col("c").trim(),
pa.array(["Hello", "world", "!", None], type=pa.string_view()),
id="trim",
),
pytest.param(
col("c").md5(),
pa.array(
[
"8b1a9953c4611296a827abf8c47804d7",
"de802497c24568d9a85d4eb8c2b6e8fe",
"9033e0e305f247c0c3c80d0c7848c8b3",
None,
],
type=pa.string(),
),
id="md5",
),
pytest.param(
col("c").octet_length(),
pa.array([5, 7, 1, None], type=pa.int32()),
id="octet_length",
),
pytest.param(
col("c").character_length(),
pa.array([5, 7, 1, None], type=pa.int32()),
id="character_length",
),
pytest.param(
col("c").char_length(),
pa.array([5, 7, 1, None], type=pa.int32()),
id="char_length",
),
pytest.param(
col("c").rtrim(),
pa.array(["Hello", " world", "!", None], type=pa.string_view()),
id="rtrim",
),
pytest.param(
col("c").ltrim(),
pa.array(["Hello", "world ", "!", None], type=pa.string_view()),
id="ltrim",
),
pytest.param(
col("c").upper(),
pa.array(["HELLO", " WORLD ", "!", None], type=pa.string()),
id="upper",
),
pytest.param(
lit(65).chr(),
pa.array(["A", "A", "A", "A"], type=pa.string()),
id="chr",
),
#
# Time Functions
#
pytest.param(
col("b").from_unixtime(),
pa.array(
[
datetime(1969, 12, 31, 23, 59, 30, tzinfo=timezone.utc),
datetime(1970, 1, 1, 0, 0, 42, tzinfo=timezone.utc),
datetime(1970, 1, 1, 0, 0, 0, tzinfo=timezone.utc),
None,
],
type=pa.timestamp("s"),
),
id="from_unixtime",
),
pytest.param(
col("c").initcap(),
pa.array(["Hello", " World ", "!", None], type=pa.string_view()),
id="initcap",
),
#
# Array Functions
#
pytest.param(
col("d").array_pop_back(),
pa.array([[-1, 1], [5, 10, 15], [], None], type=pa.list_(pa.int64())),
id="array_pop_back",
),
pytest.param(
col("d").array_pop_front(),
pa.array([[1, 0], [10, 15, 20], [], None], type=pa.list_(pa.int64())),
id="array_pop_front",
),
pytest.param(
col("d").array_length(),
pa.array([3, 4, 0, None], type=pa.uint64()),
id="array_length",
),
pytest.param(
col("d").list_length(),
pa.array([3, 4, 0, None], type=pa.uint64()),
id="list_length",
),
pytest.param(
col("d").array_ndims(),
pa.array([1, 1, 1, None], type=pa.uint64()),
id="array_ndims",
),
pytest.param(
col("d").list_ndims(),
pa.array([1, 1, 1, None], type=pa.uint64()),
id="list_ndims",
),
pytest.param(
col("d").array_dims(),
pa.array([[3], [4], None, None], type=pa.list_(pa.uint64())),
id="array_dims",
),
pytest.param(
col("d").array_empty(),
pa.array([False, False, True, None], type=pa.bool_()),
id="array_empty",
),
pytest.param(
col("d").list_distinct(),
pa.array(
[[-1, 0, 1], [5, 10, 15, 20], [], None], type=pa.list_(pa.int64())
),
id="list_distinct",
),
pytest.param(
col("d").array_distinct(),
pa.array(
[[-1, 0, 1], [5, 10, 15, 20], [], None], type=pa.list_(pa.int64())
),
id="array_distinct",
),
pytest.param(
col("d").cardinality(),
pa.array([3, 4, None, None], type=pa.uint64()),
id="cardinality",
),
pytest.param(
col("f").flatten(),
pa.array(
[[-1, 1, 0, 4, 4], [5, 10, 15, 20, 3], [], []],
type=pa.list_(pa.int64()),
),
id="flatten",
),
pytest.param(
col("d").list_dims(),
pa.array([[3], [4], None, None], type=pa.list_(pa.uint64())),
id="list_dims",
),
pytest.param(
col("d").empty(),
pa.array([False, False, True, None], type=pa.bool_()),
id="empty",
),
#
# Other Tests
#
pytest.param(
col("d").arrow_typeof(),
pa.array(
[
'List(Field { name: "item", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })', # noqa: E501
'List(Field { name: "item", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })', # noqa: E501
'List(Field { name: "item", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })', # noqa: E501
'List(Field { name: "item", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })', # noqa: E501
],
type=pa.string(),
),
id="arrow_typeof",
),
],
)
def test_expr_functions(ctx, function, expected_result):
batch = pa.RecordBatch.from_arrays(
[
pa.array([-0.75, 0.5, 0.0, None], type=pa.float64()),
pa.array([-30, 42, 0, None], type=pa.int64()),
pa.array(["Hello", " world ", "!", None], type=pa.string_view()),
pa.array(
[[-1, 1, 0], [5, 10, 15, 20], [], None], type=pa.list_(pa.int64())
),
pa.array([-0.75, float("nan"), 0.0, None], type=pa.float64()),
pa.array(
[[[-1, 1, 0], [4, 4]], [[5, 10, 15, 20], [3]], [[]], [None]],
type=pa.list_(pa.list_(pa.int64())),
),
],
names=["a", "b", "c", "d", "e", "f"],
)
df = ctx.create_dataframe([[batch]]).select(function)
result = df.collect()
assert len(result) == 1
assert result[0].column(0).equals(expected_result)