Copilot commented on code in PR #1464:
URL:
https://github.com/apache/datafusion-python/pull/1464#discussion_r3010872075
##########
python/tests/test_functions.py:
##########
@@ -1435,3 +1435,165 @@ def test_coalesce(df):
assert result.column(0) == pa.array(
["Hello", "fallback", "!"], type=pa.string_view()
)
+
+
+def test_greatest(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 5, None]),
+ pa.array([3, 2, None]),
+ pa.array([2, 8, None]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # Test greatest with two columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b")).alias("greatest_ab")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 5, None], type=pa.int64())
+
+ # Test greatest with three columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b"), column("c")).alias("greatest_abc")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 8, None], type=pa.int64())
+
+ # Test greatest with nulls mixed in (partial nulls)
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 10]),
+ pa.array([5, None]),
+ ],
+ names=["x", "y"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(f.greatest(column("x"),
column("y")).alias("g")).collect()[
+ 0
+ ]
+ assert result.column(0) == pa.array([5, 10], type=pa.int64())
+
+ # Test greatest with string columns
+ batch3 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["apple", "cherry"]),
+ pa.array(["banana", "apricot"]),
+ ],
+ names=["s1", "s2"],
+ )
+ df_test3 = ctx.create_dataframe([[batch3]])
+ result = df_test3.select(
+ f.greatest(column("s1"), column("s2")).alias("g")
+ ).collect()[0]
+ assert result.column(0).to_pylist() == ["banana", "cherry"]
+
+
+def test_least(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 5, None]),
+ pa.array([3, 2, None]),
+ pa.array([2, 8, None]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # Test least with two columns
+ result = df_test.select(
+ f.least(column("a"), column("b")).alias("least_ab")
+ ).collect()[0]
+ assert result.column(0) == pa.array([1, 2, None], type=pa.int64())
+
+ # Test least with three columns
+ result = df_test.select(
+ f.least(column("a"), column("b"), column("c")).alias("least_abc")
+ ).collect()[0]
+ assert result.column(0) == pa.array([1, 2, None], type=pa.int64())
+
+ # Test least with partial nulls
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 10]),
+ pa.array([5, None]),
+ ],
+ names=["x", "y"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(f.least(column("x"),
column("y")).alias("l")).collect()[0]
+ assert result.column(0) == pa.array([5, 10], type=pa.int64())
+
+ # Test least with string columns
+ batch3 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["apple", "cherry"]),
+ pa.array(["banana", "apricot"]),
+ ],
+ names=["s1", "s2"],
+ )
+ df_test3 = ctx.create_dataframe([[batch3]])
+ result = df_test3.select(f.least(column("s1"),
column("s2")).alias("l")).collect()[
+ 0
+ ]
+ assert result.column(0).to_pylist() == ["apple", "apricot"]
+
+
+def test_nvl2(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
Review Comment:
`df` fixture is accepted but never used in this test. Because pytest will
still construct the fixture, this adds unnecessary setup cost and can slow the
suite. Either remove the `df` parameter or refactor the test to reuse the
provided fixture/context instead of creating a new `SessionContext`.
##########
python/tests/test_functions.py:
##########
@@ -1435,3 +1435,165 @@ def test_coalesce(df):
assert result.column(0) == pa.array(
["Hello", "fallback", "!"], type=pa.string_view()
)
+
+
+def test_greatest(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 5, None]),
+ pa.array([3, 2, None]),
+ pa.array([2, 8, None]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # Test greatest with two columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b")).alias("greatest_ab")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 5, None], type=pa.int64())
+
+ # Test greatest with three columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b"), column("c")).alias("greatest_abc")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 8, None], type=pa.int64())
+
+ # Test greatest with nulls mixed in (partial nulls)
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 10]),
+ pa.array([5, None]),
+ ],
+ names=["x", "y"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(f.greatest(column("x"),
column("y")).alias("g")).collect()[
+ 0
+ ]
+ assert result.column(0) == pa.array([5, 10], type=pa.int64())
+
+ # Test greatest with string columns
+ batch3 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["apple", "cherry"]),
+ pa.array(["banana", "apricot"]),
+ ],
+ names=["s1", "s2"],
+ )
+ df_test3 = ctx.create_dataframe([[batch3]])
+ result = df_test3.select(
+ f.greatest(column("s1"), column("s2")).alias("g")
+ ).collect()[0]
+ assert result.column(0).to_pylist() == ["banana", "cherry"]
+
+
+def test_least(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 5, None]),
+ pa.array([3, 2, None]),
+ pa.array([2, 8, None]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # Test least with two columns
+ result = df_test.select(
+ f.least(column("a"), column("b")).alias("least_ab")
+ ).collect()[0]
+ assert result.column(0) == pa.array([1, 2, None], type=pa.int64())
+
+ # Test least with three columns
+ result = df_test.select(
+ f.least(column("a"), column("b"), column("c")).alias("least_abc")
+ ).collect()[0]
+ assert result.column(0) == pa.array([1, 2, None], type=pa.int64())
+
+ # Test least with partial nulls
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 10]),
+ pa.array([5, None]),
+ ],
+ names=["x", "y"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(f.least(column("x"),
column("y")).alias("l")).collect()[0]
+ assert result.column(0) == pa.array([5, 10], type=pa.int64())
+
+ # Test least with string columns
+ batch3 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["apple", "cherry"]),
+ pa.array(["banana", "apricot"]),
+ ],
+ names=["s1", "s2"],
+ )
+ df_test3 = ctx.create_dataframe([[batch3]])
+ result = df_test3.select(f.least(column("s1"),
column("s2")).alias("l")).collect()[
+ 0
+ ]
+ assert result.column(0).to_pylist() == ["apple", "apricot"]
+
+
+def test_nvl2(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 1, None, 4]),
+ pa.array([10, 20, 30, 40]),
+ pa.array([100, 200, 300, 400]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # nvl2 returns b when a is not null, c when a is null
+ result = df_test.select(
+ f.nvl2(column("a"), column("b"), column("c")).alias("result")
+ ).collect()[0]
+ assert result.column(0) == pa.array([100, 20, 300, 40], type=pa.int64())
+
+ # Test with string columns
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["x", None]),
+ pa.array(["not_null", "not_null"]),
+ pa.array(["is_null", "is_null"]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(
+ f.nvl2(column("a"), column("b"), column("c")).alias("result")
+ ).collect()[0]
+ assert result.column(0).to_pylist() == ["not_null", "is_null"]
+
+
+def test_ifnull(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
Review Comment:
`df` fixture is accepted but never used in this test. Because pytest will
still construct the fixture, this adds unnecessary setup cost and can slow the
suite. Either remove the `df` parameter or refactor the test to reuse the
provided fixture/context instead of creating a new `SessionContext`.
##########
python/tests/test_functions.py:
##########
@@ -1435,3 +1435,165 @@ def test_coalesce(df):
assert result.column(0) == pa.array(
["Hello", "fallback", "!"], type=pa.string_view()
)
+
+
+def test_greatest(df):
+ ctx = SessionContext()
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 5, None]),
+ pa.array([3, 2, None]),
+ pa.array([2, 8, None]),
+ ],
+ names=["a", "b", "c"],
+ )
+ df_test = ctx.create_dataframe([[batch]])
+
+ # Test greatest with two columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b")).alias("greatest_ab")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 5, None], type=pa.int64())
+
+ # Test greatest with three columns
+ result = df_test.select(
+ f.greatest(column("a"), column("b"), column("c")).alias("greatest_abc")
+ ).collect()[0]
+ assert result.column(0) == pa.array([3, 8, None], type=pa.int64())
+
+ # Test greatest with nulls mixed in (partial nulls)
+ batch2 = pa.RecordBatch.from_arrays(
+ [
+ pa.array([None, 10]),
+ pa.array([5, None]),
+ ],
+ names=["x", "y"],
+ )
+ df_test2 = ctx.create_dataframe([[batch2]])
+ result = df_test2.select(f.greatest(column("x"),
column("y")).alias("g")).collect()[
+ 0
+ ]
+ assert result.column(0) == pa.array([5, 10], type=pa.int64())
+
+ # Test greatest with string columns
+ batch3 = pa.RecordBatch.from_arrays(
+ [
+ pa.array(["apple", "cherry"]),
+ pa.array(["banana", "apricot"]),
+ ],
+ names=["s1", "s2"],
+ )
+ df_test3 = ctx.create_dataframe([[batch3]])
+ result = df_test3.select(
+ f.greatest(column("s1"), column("s2")).alias("g")
+ ).collect()[0]
+ assert result.column(0).to_pylist() == ["banana", "cherry"]
+
+
+def test_least(df):
Review Comment:
`df` fixture is accepted but never used in this test. Because pytest will
still construct the fixture, this adds unnecessary setup cost and can slow the
suite. Either remove the `df` parameter or refactor the test to reuse the
provided fixture/context instead of creating a new `SessionContext`.
```suggestion
def test_least():
```
##########
python/tests/test_functions.py:
##########
@@ -1435,3 +1435,165 @@ def test_coalesce(df):
assert result.column(0) == pa.array(
["Hello", "fallback", "!"], type=pa.string_view()
)
+
+
+def test_greatest(df):
Review Comment:
`df` fixture is accepted but never used in this test. Because pytest will
still construct the fixture, this adds unnecessary setup cost and can slow the
suite. Either remove the `df` parameter or refactor the test to reuse the
provided fixture/context instead of creating a new `SessionContext`.
```suggestion
def test_greatest():
```
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