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Dec 4, 2020
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TST: fixturize unfocused test #38284

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246 changes: 132 additions & 114 deletions pandas/tests/groupby/test_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,140 +145,158 @@ def test_builtins_apply(keys, f):
tm.assert_series_equal(getattr(result, fname)(), getattr(df, fname)())


def test_arg_passthru():
# make sure that we are passing thru kwargs
# to our agg functions
class TestNumericOnly:
# make sure that we are passing thru kwargs to our agg functions

# GH3668
# GH5724
df = DataFrame(
{
"group": [1, 1, 2],
"int": [1, 2, 3],
"float": [4.0, 5.0, 6.0],
"string": list("abc"),
"category_string": Series(list("abc")).astype("category"),
"category_int": [7, 8, 9],
"datetime": pd.date_range("20130101", periods=3),
"datetimetz": pd.date_range("20130101", periods=3, tz="US/Eastern"),
"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
},
columns=[
"group",
"int",
"float",
"string",
"category_string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
],
)
@pytest.fixture
def df(self):
# GH3668
# GH5724
df = DataFrame(
{
"group": [1, 1, 2],
"int": [1, 2, 3],
"float": [4.0, 5.0, 6.0],
"string": list("abc"),
"category_string": Series(list("abc")).astype("category"),
"category_int": [7, 8, 9],
"datetime": date_range("20130101", periods=3),
"datetimetz": date_range("20130101", periods=3, tz="US/Eastern"),
"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
},
columns=[
"group",
"int",
"float",
"string",
"category_string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
],
)
return df

expected_columns_numeric = Index(["int", "float", "category_int"])
@pytest.mark.parametrize("method", ["mean", "median"])
def test_averages(self, df, method):
# mean / median
expected_columns_numeric = Index(["int", "float", "category_int"])

# mean / median
expected = DataFrame(
{
"category_int": [7.5, 9],
"float": [4.5, 6.0],
"timedelta": [pd.Timedelta("1.5s"), pd.Timedelta("3s")],
"int": [1.5, 3],
"datetime": [
Timestamp("2013-01-01 12:00:00"),
Timestamp("2013-01-03 00:00:00"),
],
"datetimetz": [
Timestamp("2013-01-01 12:00:00", tz="US/Eastern"),
Timestamp("2013-01-03 00:00:00", tz="US/Eastern"),
gb = df.groupby("group")
expected = DataFrame(
{
"category_int": [7.5, 9],
"float": [4.5, 6.0],
"timedelta": [pd.Timedelta("1.5s"), pd.Timedelta("3s")],
"int": [1.5, 3],
"datetime": [
Timestamp("2013-01-01 12:00:00"),
Timestamp("2013-01-03 00:00:00"),
],
"datetimetz": [
Timestamp("2013-01-01 12:00:00", tz="US/Eastern"),
Timestamp("2013-01-03 00:00:00", tz="US/Eastern"),
],
},
index=Index([1, 2], name="group"),
columns=[
"int",
"float",
"category_int",
"datetime",
"datetimetz",
"timedelta",
],
},
index=Index([1, 2], name="group"),
columns=["int", "float", "category_int", "datetime", "datetimetz", "timedelta"],
)

for attr in ["mean", "median"]:
result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns_numeric)
)

result = getattr(df.groupby("group"), attr)(numeric_only=False)
result = getattr(gb, method)(numeric_only=False)
tm.assert_frame_equal(result.reindex_like(expected), expected)

# TODO: min, max *should* handle
# categorical (ordered) dtype
expected_columns = Index(
[
"int",
"float",
"string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
]
)
for attr in ["min", "max"]:
result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = expected.columns

result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
self._check(df, method, expected_columns, expected_columns_numeric)

expected_columns = Index(
[
"int",
"float",
"string",
"category_string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
]
)
for attr in ["first", "last"]:
result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns)
@pytest.mark.parametrize("method", ["min", "max"])
def test_extrema(self, df, method):
# TODO: min, max *should* handle
# categorical (ordered) dtype

result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(
[
"int",
"float",
"string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
]
)
expected_columns_numeric = expected_columns

expected_columns = Index(["int", "float", "string", "category_int", "timedelta"])
self._check(df, method, expected_columns, expected_columns_numeric)

result = df.groupby("group").sum()
tm.assert_index_equal(result.columns, expected_columns_numeric)
@pytest.mark.parametrize("method", ["first", "last"])
def test_first_last(self, df, method):

result = df.groupby("group").sum(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(
[
"int",
"float",
"string",
"category_string",
"category_int",
"datetime",
"datetimetz",
"timedelta",
]
)
expected_columns_numeric = expected_columns

expected_columns = Index(["int", "float", "category_int"])
for attr in ["prod", "cumprod"]:
result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns_numeric)
self._check(df, method, expected_columns, expected_columns_numeric)

result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
@pytest.mark.parametrize("method", ["sum", "cumsum"])
def test_sum_cumsum(self, df, method):

# like min, max, but don't include strings
expected_columns = Index(
["int", "float", "category_int", "datetime", "datetimetz", "timedelta"]
)
for attr in ["cummin", "cummax"]:
result = getattr(df.groupby("group"), attr)()
# GH 15561: numeric_only=False set by default like min/max
tm.assert_index_equal(result.columns, expected_columns)
expected_columns_numeric = Index(["int", "float", "category_int"])
expected_columns = Index(
["int", "float", "string", "category_int", "timedelta"]
)
if method == "cumsum":
# cumsum loses string
expected_columns = Index(["int", "float", "category_int", "timedelta"])

result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
self._check(df, method, expected_columns, expected_columns_numeric)

@pytest.mark.parametrize("method", ["prod", "cumprod"])
def test_prod_cumprod(self, df, method):

expected_columns = Index(["int", "float", "category_int"])
expected_columns_numeric = expected_columns

self._check(df, method, expected_columns, expected_columns_numeric)

expected_columns = Index(["int", "float", "category_int", "timedelta"])
@pytest.mark.parametrize("method", ["cummin", "cummax"])
def test_cummin_cummax(self, df, method):
# like min, max, but don't include strings
expected_columns = Index(
["int", "float", "category_int", "datetime", "datetimetz", "timedelta"]
)

# GH#15561: numeric_only=False set by default like min/max
expected_columns_numeric = expected_columns

self._check(df, method, expected_columns, expected_columns_numeric)

result = getattr(df.groupby("group"), "cumsum")()
tm.assert_index_equal(result.columns, expected_columns_numeric)
def _check(self, df, method, expected_columns, expected_columns_numeric):
gb = df.groupby("group")

result = getattr(df.groupby("group"), "cumsum")(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
result = getattr(gb, method)()
tm.assert_index_equal(result.columns, expected_columns_numeric)

result = getattr(gb, method)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)


class TestGroupByNonCythonPaths:
Expand Down