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CLN: Consistent pandas.util.testing imports in pandas/tests/groupby #29287

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Oct 31, 2019
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CLN: Consistent pandas.util.testing imports in pandas/tests/groupby #29287

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67 changes: 36 additions & 31 deletions pandas/tests/groupby/test_categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@
qcut,
)
import pandas.util.testing as tm
from pandas.util.testing import assert_equal, assert_frame_equal, assert_series_equal


def cartesian_product_for_groupers(result, args, names):
Expand Down Expand Up @@ -159,7 +158,7 @@ def f(x):
exp_idx = CategoricalIndex(levels, categories=cats.categories, ordered=True)
expected = expected.reindex(exp_idx)

assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

grouped = data.groupby(cats, observed=False)
desc_result = grouped.describe()
Expand All @@ -172,7 +171,7 @@ def f(x):
ord_labels, ordered=True, categories=["foo", "bar", "baz", "qux"]
)
expected = ord_data.groupby(exp_cats, sort=False, observed=False).describe()
assert_frame_equal(desc_result, expected)
tm.assert_frame_equal(desc_result, expected)

# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
Expand Down Expand Up @@ -206,7 +205,7 @@ def test_level_get_group(observed):
)
result = g.get_group("a")

assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)


# GH#21636 flaky on py37; may be related to older numpy, see discussion
Expand All @@ -232,21 +231,21 @@ def test_apply(ordered):
# is coming back as Series([0., 1., 0.], index=["missing", "dense", "values"])
# when we expect Series(0., index=["values"])
result = grouped.apply(lambda x: np.mean(x))
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# we coerce back to ints
expected = expected.astype("int")
result = grouped.mean()
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

result = grouped.agg(np.mean)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# but for transform we should still get back the original index
idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"])
expected = Series(1, index=idx)
result = grouped.apply(lambda x: 1)
assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)


def test_observed(observed):
Expand Down Expand Up @@ -335,7 +334,7 @@ def test_observed(observed):
c, i = key
result = groups_double_key.get_group(key)
expected = df[(df.cat == c) & (df.ints == i)]
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# gh-8869
# with as_index
Expand Down Expand Up @@ -522,7 +521,7 @@ def test_datetime():
expected.index, categories=expected.index, ordered=True
)

assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

grouped = data.groupby(cats, observed=False)
desc_result = grouped.describe()
Expand All @@ -531,7 +530,7 @@ def test_datetime():
ord_labels = cats.take(idx)
ord_data = data.take(idx)
expected = ord_data.groupby(ord_labels, observed=False).describe()
assert_frame_equal(desc_result, expected)
tm.assert_frame_equal(desc_result, expected)
tm.assert_index_equal(desc_result.index, expected.index)
tm.assert_index_equal(
desc_result.index.get_level_values(0), expected.index.get_level_values(0)
Expand Down Expand Up @@ -560,15 +559,15 @@ def test_categorical_index():
expected.index = CategoricalIndex(
Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats"
)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# with a cat column, should produce a cat index
result = df.groupby("cats", observed=False).sum()
expected = df[list("abcd")].groupby(cats.codes, observed=False).sum()
expected.index = CategoricalIndex(
Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats"
)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)


def test_describe_categorical_columns():
Expand Down Expand Up @@ -757,7 +756,7 @@ def test_categorical_no_compress():
exp.index = CategoricalIndex(
exp.index, categories=cats.categories, ordered=cats.ordered
)
assert_series_equal(result, exp)
tm.assert_series_equal(result, exp)

codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3])
cats = Categorical.from_codes(codes, [0, 1, 2, 3], ordered=True)
Expand All @@ -767,7 +766,7 @@ def test_categorical_no_compress():
exp.index = CategoricalIndex(
exp.index, categories=cats.categories, ordered=cats.ordered
)
assert_series_equal(result, exp)
tm.assert_series_equal(result, exp)

cats = Categorical(
["a", "a", "a", "b", "b", "b", "c", "c", "c"],
Expand Down Expand Up @@ -829,12 +828,12 @@ def test_sort2():

col = "range"
result_sort = df.groupby(col, sort=True, observed=False).first()
assert_frame_equal(result_sort, expected_sort)
tm.assert_frame_equal(result_sort, expected_sort)

# when categories is ordered, group is ordered by category's order
expected_sort = result_sort
result_sort = df.groupby(col, sort=False, observed=False).first()
assert_frame_equal(result_sort, expected_sort)
tm.assert_frame_equal(result_sort, expected_sort)

df["range"] = Categorical(df["range"], ordered=False)
index = CategoricalIndex(
Expand All @@ -857,10 +856,10 @@ def test_sort2():

# this is an unordered categorical, but we allow this ####
result_sort = df.groupby(col, sort=True, observed=False).first()
assert_frame_equal(result_sort, expected_sort)
tm.assert_frame_equal(result_sort, expected_sort)

result_nosort = df.groupby(col, sort=False, observed=False).first()
assert_frame_equal(result_nosort, expected_nosort)
tm.assert_frame_equal(result_nosort, expected_nosort)


def test_sort_datetimelike():
Expand Down Expand Up @@ -912,10 +911,14 @@ def test_sort_datetimelike():
)

col = "dt"
assert_frame_equal(result_sort, df.groupby(col, sort=True, observed=False).first())
tm.assert_frame_equal(
result_sort, df.groupby(col, sort=True, observed=False).first()
)

# when categories is ordered, group is ordered by category's order
assert_frame_equal(result_sort, df.groupby(col, sort=False, observed=False).first())
tm.assert_frame_equal(
result_sort, df.groupby(col, sort=False, observed=False).first()
)

# ordered = False
df["dt"] = Categorical(df["dt"], ordered=False)
Expand All @@ -942,8 +945,10 @@ def test_sort_datetimelike():
result_nosort.index = CategoricalIndex(index, categories=index, name="dt")

col = "dt"
assert_frame_equal(result_sort, df.groupby(col, sort=True, observed=False).first())
assert_frame_equal(
tm.assert_frame_equal(
result_sort, df.groupby(col, sort=True, observed=False).first()
)
tm.assert_frame_equal(
result_nosort, df.groupby(col, sort=False, observed=False).first()
)

Expand Down Expand Up @@ -1022,7 +1027,7 @@ def test_groupby_multiindex_categorical_datetime():
names=["key1", "key2"],
)
expected = DataFrame({"values": [0, 4, 8, 3, 4, 5, 6, np.nan, 2]}, index=idx)
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
Expand Down Expand Up @@ -1058,7 +1063,7 @@ def test_groupby_agg_observed_true_single_column(as_index, expected):

result = df.groupby(["a", "b"], as_index=as_index, observed=True)["x"].sum()

assert_equal(result, expected)
tm.assert_equal(result, expected)


@pytest.mark.parametrize("fill_value", [None, np.nan, pd.NaT])
Expand All @@ -1070,7 +1075,7 @@ def test_shift(fill_value):
[None, "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False
)
res = ct.shift(1, fill_value=fill_value)
assert_equal(res, expected)
tm.assert_equal(res, expected)


@pytest.fixture
Expand Down Expand Up @@ -1112,7 +1117,7 @@ def test_seriesgroupby_observed_true(df_cat, operation, kwargs):
expected = Series(data=[1, 3, 2, 4], index=index, name="C")
grouped = df_cat.groupby(["A", "B"], observed=True)["C"]
result = getattr(grouped, operation)(sum)
assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("operation", ["agg", "apply"])
Expand All @@ -1130,7 +1135,7 @@ def test_seriesgroupby_observed_false_or_none(df_cat, observed, operation):
expected = Series(data=[2, 4, np.nan, 1, np.nan, 3], index=index, name="C")
grouped = df_cat.groupby(["A", "B"], observed=observed)["C"]
result = getattr(grouped, operation)(sum)
assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
Expand Down Expand Up @@ -1185,7 +1190,7 @@ def test_seriesgroupby_observed_apply_dict(df_cat, observed, index, data):
result = df_cat.groupby(["A", "B"], observed=observed)["C"].apply(
lambda x: OrderedDict([("min", x.min()), ("max", x.max())])
)
assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("code", [([1, 0, 0]), ([0, 0, 0])])
Expand All @@ -1195,7 +1200,7 @@ def test_groupby_categorical_axis_1(code):
cat = pd.Categorical.from_codes(code, categories=list("abc"))
result = df.groupby(cat, axis=1).mean()
expected = df.T.groupby(cat, axis=0).mean().T
assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)


def test_groupby_cat_preserves_structure(observed, ordered_fixture):
Expand All @@ -1212,7 +1217,7 @@ def test_groupby_cat_preserves_structure(observed, ordered_fixture):
.reset_index()
)

assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)


def test_get_nonexistent_category():
Expand Down
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