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Merged
shobsi merged 8 commits into from
Apr 25, 2024
Merged

test: test most relevant dtype for aggregates #595

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728a4de
fix: keep most relevant dtype for aggregates
shobsi Apr 7, 2024
257ef8a
add aggregate tests for bool result
shobsi Apr 8, 2024
aab575c
Merge remote-tracking branch 'refs/remotes/github/main' into shobs-ag…
shobsi Apr 8, 2024
1d644a1
Merge remote-tracking branch 'refs/remotes/github/main' into shobs-ag…
shobsi Apr 10, 2024
e7c30cd
refactor and reuse dtypes.lcd_dtype
shobsi Apr 10, 2024
7d5561b
check_dtype=False
shobsi Apr 11, 2024
161b9e1
Merge remote-tracking branch 'refs/remotes/github/main' into shobs-ag…
shobsi Apr 11, 2024
5baa3a6
Merge remote-tracking branch 'refs/remotes/github/main' into shobs-ag…
shobsi Apr 22, 2024
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16 changes: 10 additions & 6 deletions bigframes/dtypes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -658,10 +658,14 @@ def is_compatible(scalar: typing.Any, dtype: Dtype) -> typing.Optional[Dtype]:
return None


def lcd_type(dtype1: Dtype, dtype2: Dtype) -> Dtype:
"""Get the supertype of the two types."""
if dtype1 == dtype2:
return dtype1
def lcd_type(*dtypes: Dtype) -> Dtype:
if len(dtypes) < 1:
raise ValueError("at least one dypes should be provided")
if len(dtypes) == 1:
return dtypes[0]
unique_dtypes = set(dtypes)
if len(unique_dtypes) == 1:
return unique_dtypes.pop()
# Implicit conversion currently only supported for numeric types
hierarchy: list[Dtype] = [
pd.BooleanDtype(),
Expand All @@ -670,9 +674,9 @@ def lcd_type(dtype1: Dtype, dtype2: Dtype) -> Dtype:
pd.ArrowDtype(pa.decimal256(76, 38)),
pd.Float64Dtype(),
]
if (dtype1 not in hierarchy) or (dtype2 not in hierarchy):
if any([dtype not in hierarchy for dtype in dtypes]):
return None
lcd_index = max(hierarchy.index(dtype1), hierarchy.index(dtype2))
lcd_index = max([hierarchy.index(dtype) for dtype in dtypes])
return hierarchy[lcd_index]


Expand Down
126 changes: 109 additions & 17 deletions tests/system/small/test_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2390,12 +2390,27 @@ def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods
def test_dataframe_agg_single_string(scalars_dfs):
numeric_cols = ["int64_col", "int64_too", "float64_col"]
scalars_df, scalars_pandas_df = scalars_dfs

bf_result = scalars_df[numeric_cols].agg("sum").to_pandas()
pd_result = scalars_pandas_df[numeric_cols].agg("sum")

# Pandas may produce narrower numeric types, but bigframes always produces Float64
pd_result = pd_result.astype("Float64")
pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)
assert bf_result.dtype == "Float64"
pd.testing.assert_series_equal(
pd_result, bf_result, check_dtype=False, check_index_type=False
)


def test_dataframe_agg_int_single_string(scalars_dfs):
numeric_cols = ["int64_col", "int64_too", "bool_col"]
scalars_df, scalars_pandas_df = scalars_dfs

bf_result = scalars_df[numeric_cols].agg("sum").to_pandas()
pd_result = scalars_pandas_df[numeric_cols].agg("sum")

assert bf_result.dtype == "Int64"
pd.testing.assert_series_equal(
pd_result, bf_result, check_dtype=False, check_index_type=False
)


def test_dataframe_agg_multi_string(scalars_dfs):
Expand Down Expand Up @@ -2431,6 +2446,27 @@ def test_dataframe_agg_multi_string(scalars_dfs):
).all()


def test_dataframe_agg_int_multi_string(scalars_dfs):
numeric_cols = ["int64_col", "int64_too", "bool_col"]
aggregations = [
"sum",
"nunique",
"count",
]
scalars_df, scalars_pandas_df = scalars_dfs
bf_result = scalars_df[numeric_cols].agg(aggregations).to_pandas()
pd_result = scalars_pandas_df[numeric_cols].agg(aggregations)

for dtype in bf_result.dtypes:
assert dtype == "Int64"

# Pandas may produce narrower numeric types
# Pandas has object index type
pd.testing.assert_frame_equal(
pd_result, bf_result, check_dtype=False, check_index_type=False
)


@skip_legacy_pandas
def test_df_describe(scalars_dfs):
scalars_df, scalars_pandas_df = scalars_dfs
Expand Down Expand Up @@ -2959,6 +2995,58 @@ def test_loc_setitem_bool_series_scalar_error(scalars_dfs):
pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = 99


@pytest.mark.parametrize(
("col", "op"),
[
# Int aggregates
pytest.param("int64_col", lambda x: x.sum(), id="int-sum"),
pytest.param("int64_col", lambda x: x.min(), id="int-min"),
pytest.param("int64_col", lambda x: x.max(), id="int-max"),
pytest.param("int64_col", lambda x: x.count(), id="int-count"),
pytest.param("int64_col", lambda x: x.nunique(), id="int-nunique"),
# Float aggregates
pytest.param("float64_col", lambda x: x.count(), id="float-count"),
pytest.param("float64_col", lambda x: x.nunique(), id="float-nunique"),
# Bool aggregates
pytest.param("bool_col", lambda x: x.sum(), id="bool-sum"),
pytest.param("bool_col", lambda x: x.count(), id="bool-count"),
pytest.param("bool_col", lambda x: x.nunique(), id="bool-nunique"),
# String aggregates
pytest.param("string_col", lambda x: x.count(), id="string-count"),
pytest.param("string_col", lambda x: x.nunique(), id="string-nunique"),
],
)
def test_dataframe_aggregate_int(scalars_df_index, scalars_pandas_df_index, col, op):
bf_result = op(scalars_df_index[[col]]).to_pandas()
pd_result = op(scalars_pandas_df_index[[col]])

# Check dtype separately
assert bf_result.dtype == "Int64"

# Pandas may produce narrower numeric types
# Pandas has object index type
assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False)


@pytest.mark.parametrize(
("col", "op"),
[
pytest.param("bool_col", lambda x: x.min(), id="bool-min"),
pytest.param("bool_col", lambda x: x.max(), id="bool-max"),
],
)
def test_dataframe_aggregate_bool(scalars_df_index, scalars_pandas_df_index, col, op):
bf_result = op(scalars_df_index[[col]]).to_pandas()
pd_result = op(scalars_pandas_df_index[[col]])

# Check dtype separately
assert bf_result.dtype == "boolean"

# Pandas may produce narrower numeric types
# Pandas has object index type
assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False)


@pytest.mark.parametrize(
("ordered"),
[
Expand All @@ -2967,34 +3055,38 @@ def test_loc_setitem_bool_series_scalar_error(scalars_dfs):
],
)
@pytest.mark.parametrize(
("op"),
("op", "bf_dtype"),
[
(lambda x: x.sum(numeric_only=True)),
(lambda x: x.mean(numeric_only=True)),
(lambda x: x.min(numeric_only=True)),
(lambda x: x.max(numeric_only=True)),
(lambda x: x.std(numeric_only=True)),
(lambda x: x.var(numeric_only=True)),
(lambda x: x.count(numeric_only=False)),
(lambda x: x.nunique()),
(lambda x: x.sum(numeric_only=True), "Float64"),
(lambda x: x.mean(numeric_only=True), "Float64"),
(lambda x: x.min(numeric_only=True), "Float64"),
(lambda x: x.max(numeric_only=True), "Float64"),
(lambda x: x.std(numeric_only=True), "Float64"),
(lambda x: x.var(numeric_only=True), "Float64"),
(lambda x: x.count(numeric_only=False), "Int64"),
(lambda x: x.nunique(), "Int64"),
],
ids=["sum", "mean", "min", "max", "std", "var", "count", "nunique"],
)
def test_dataframe_aggregates(scalars_df_index, scalars_pandas_df_index, op, ordered):
def test_dataframe_aggregates(
scalars_df_index, scalars_pandas_df_index, op, bf_dtype, ordered
):
col_names = ["int64_too", "float64_col", "string_col", "int64_col", "bool_col"]
bf_series = op(scalars_df_index[col_names])
pd_series = op(scalars_pandas_df_index[col_names])
bf_result = bf_series.to_pandas(ordered=ordered)
pd_result = op(scalars_pandas_df_index[col_names])

# Check dtype separately
assert bf_result.dtype == bf_dtype

# Pandas may produce narrower numeric types, but bigframes always produces Float64
# Pandas has object index type
pd_series.index = pd_series.index.astype(pd.StringDtype(storage="pyarrow"))
assert_series_equal(
pd_series,
pd_result,
bf_result,
check_dtype=False,
check_index_type=False,
ignore_order=not ordered,
check_dtype=False,
)


Expand Down