def test_groupby_categorical_no_compress(self):
data = Series(np.random.randn(9))
codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean()
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
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)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean().reindex(cats.categories)
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
cats = Categorical(["a", "a", "a", "b", "b", "b", "c", "c", "c"],
categories=["a", "b", "c", "d"], ordered=True)
data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
result = data.groupby("b").mean()
result = result["a"].values
exp = np.array([1, 2, 4, np.nan])
self.assert_numpy_array_equal(result, exp)
def test_groupby_categorical_no_compress(self):
data = Series(np.random.randn(9))
codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean()
exp.index = CategoricalIndex(exp.index,
categories=cats.categories,
ordered=cats.ordered)
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)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean().reindex(cats.categories)
exp.index = CategoricalIndex(exp.index,
categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
cats = Categorical(["a", "a", "a", "b", "b", "b", "c", "c", "c"],
categories=["a", "b", "c", "d"],
ordered=True)
data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
result = data.groupby("b").mean()
result = result["a"].values
exp = np.array([1, 2, 4, np.nan])
self.assert_numpy_array_equal(result, exp)
def test_groupby_categorical_index(self):
levels = ['foo', 'bar', 'baz', 'qux']
codes = np.random.randint(0, 4, size=20)
cats = Categorical.from_codes(codes, levels, ordered=True)
df = DataFrame(
np.repeat(
np.arange(20), 4).reshape(-1, 4), columns=list('abcd'))
df['cats'] = cats
# with a cat index
result = df.set_index('cats').groupby(level=0).sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(
Categorical.from_codes(
[0, 1, 2, 3], levels, ordered=True), name='cats')
assert_frame_equal(result, expected)
# with a cat column, should produce a cat index
result = df.groupby('cats').sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(
Categorical.from_codes(
[0, 1, 2, 3], levels, ordered=True), name='cats')
assert_frame_equal(result, expected)
def test_groupby_categorical_index(self):
levels = ['foo', 'bar', 'baz', 'qux']
codes = np.random.randint(0, 4, size=20)
cats = Categorical.from_codes(codes, levels, ordered=True)
df = DataFrame(np.repeat(np.arange(20), 4).reshape(-1, 4),
columns=list('abcd'))
df['cats'] = cats
# with a cat index
result = df.set_index('cats').groupby(level=0).sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(Categorical.from_codes([0, 1, 2, 3],
levels,
ordered=True),
name='cats')
assert_frame_equal(result, expected)
# with a cat column, should produce a cat index
result = df.groupby('cats').sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(Categorical.from_codes([0, 1, 2, 3],
levels,
ordered=True),
name='cats')
assert_frame_equal(result, expected)
def test_groupby_datetime_categorical(self):
# GH9049: ensure backward compatibility
levels = pd.date_range("2014-01-01", periods=4)
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
expected = expected.reindex(levels)
expected.index = CategoricalIndex(expected.index, categories=expected.index, ordered=True)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = cats.take_nd(idx)
ord_data = data.take(idx)
expected = ord_data.groupby(ord_labels).describe()
expected.index.names = [None, None]
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))
# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
exp = CategoricalIndex(expc)
self.assert_index_equal(desc_result.index.get_level_values(0), exp)
exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
self.assert_index_equal(desc_result.index.get_level_values(1), exp)
def test_groupby_categorical(self):
levels = ["foo", "bar", "baz", "qux"]
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
exp_idx = CategoricalIndex(levels, categories=cats.categories, ordered=True)
expected = expected.reindex(exp_idx)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = np.asarray(cats).take(idx)
ord_data = data.take(idx)
exp_cats = Categorical(ord_labels, ordered=True, categories=["foo", "bar", "baz", "qux"])
expected = ord_data.groupby(exp_cats, sort=False).describe()
expected.index.names = [None, None]
assert_frame_equal(desc_result, expected)
# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
exp = CategoricalIndex(expc)
self.assert_index_equal(desc_result.index.get_level_values(0), exp)
exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
self.assert_index_equal(desc_result.index.get_level_values(1), exp)
def test_groupby_categorical(self):
levels = ['foo', 'bar', 'baz', 'qux']
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
exp_idx = CategoricalIndex(levels,
categories=cats.categories,
ordered=True)
expected = expected.reindex(exp_idx)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = np.asarray(cats).take(idx)
ord_data = data.take(idx)
exp_cats = Categorical(ord_labels,
ordered=True,
categories=['foo', 'bar', 'baz', 'qux'])
expected = ord_data.groupby(exp_cats, sort=False).describe()
expected.index.names = [None, None]
assert_frame_equal(desc_result, expected)
# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8),
levels,
ordered=True)
exp = CategoricalIndex(expc)
self.assert_index_equal(desc_result.index.get_level_values(0), exp)
exp = Index(
['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'] * 4)
self.assert_index_equal(desc_result.index.get_level_values(1), exp)
def test_groupby_datetime_categorical(self):
# GH9049: ensure backward compatibility
levels = pd.date_range('2014-01-01', periods=4)
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
expected = expected.reindex(levels)
expected.index = CategoricalIndex(expected.index,
categories=expected.index,
ordered=True)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = cats.take_nd(idx)
ord_data = data.take(idx)
expected = ord_data.groupby(ord_labels).describe()
expected.index.names = [None, None]
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))
# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8),
levels,
ordered=True)
exp = CategoricalIndex(expc)
self.assert_index_equal(desc_result.index.get_level_values(0), exp)
exp = Index(
['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'] * 4)
self.assert_index_equal(desc_result.index.get_level_values(1), exp)
