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_apply_categorical_data(self):
# GH 10138
for ordered in [True, False]:
dense = Categorical(list('abc'), ordered=ordered)
# 'b' is in the categories but not in the list
missing = Categorical(list('aaa'),
categories=['a', 'b'],
ordered=ordered)
values = np.arange(len(dense))
df = DataFrame({
'missing': missing,
'dense': dense,
'values': values
})
grouped = df.groupby(['missing', 'dense'])
# missing category 'b' should still exist in the output index
idx = MultiIndex.from_product([
Categorical(['a', 'b'], ordered=ordered),
Categorical(['a', 'b', 'c'], ordered=ordered)
],
names=['missing', 'dense'])
expected = DataFrame([0, 1, 2, np.nan, np.nan, np.nan],
index=idx,
columns=['values'])
assert_frame_equal(grouped.apply(lambda x: np.mean(x)), expected)
assert_frame_equal(grouped.mean(), expected)
assert_frame_equal(grouped.agg(np.mean), expected)
# but for transform we should still get back the original index
idx = MultiIndex.from_product([['a'], ['a', 'b', 'c']],
names=['missing', 'dense'])
expected = Series(1, index=idx)
assert_series_equal(grouped.apply(lambda x: 1), 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)
def test_groupby_preserve_categorical_dtype(self):
# GH13743, GH13854
df = DataFrame({
'A': [1, 2, 1, 1, 2],
'B': [10, 16, 22, 28, 34],
'C1':
Categorical(list("abaab"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("abaab"), categories=list("bac"), ordered=True)
})
# single grouper
exp_full = DataFrame({
'A': [2.0, 1.0, np.nan],
'B': [25.0, 20.0, np.nan],
'C1':
Categorical(list("bac"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("bac"), categories=list("bac"), ordered=True)
})
for col in ['C1', 'C2']:
result1 = df.groupby(by=col, as_index=False).mean()
result2 = df.groupby(by=col, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
# multiple grouper
exp_full = DataFrame({
'A': [1, 1, 1, 2, 2, 2],
'B': [np.nan, 20.0, np.nan, 25.0, np.nan, np.nan],
'C1':
Categorical(list("bacbac"), categories=list("bac"), ordered=False),
'C2':
Categorical(list("bacbac"), categories=list("bac"), ordered=True)
})
for cols in [['A', 'C1'], ['A', 'C2']]:
result1 = df.groupby(by=cols, as_index=False).mean()
result2 = df.groupby(by=cols, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
def test_groupby_multi_categorical_as_index(self):
# GH13204
df = DataFrame({
'cat': Categorical([1, 2, 2], [1, 2, 3]),
'A': [10, 11, 11],
'B': [101, 102, 103]
})
result = df.groupby(['cat', 'A'], as_index=False).sum()
expected = DataFrame(
{
'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10, 11, 10, 11, 10, 11],
'B': [101.0, nan, nan, 205.0, nan, nan]
},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# function grouper
f = lambda r: df.loc[r, 'A']
result = df.groupby(['cat', f], as_index=False).sum()
expected = DataFrame(
{
'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10.0, nan, nan, 22.0, nan, nan],
'B': [101.0, nan, nan, 205.0, nan, nan]
},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# another not in-axis grouper (conflicting names in index)
s = Series(['a', 'b', 'b'], name='cat')
result = df.groupby(['cat', s], as_index=False).sum()
expected = DataFrame(
{
'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10.0, nan, nan, 22.0, nan, nan],
'B': [101.0, nan, nan, 205.0, nan, nan]
},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# is original index dropped?
expected = DataFrame(
{
'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10, 11, 10, 11, 10, 11],
'B': [101.0, nan, nan, 205.0, nan, nan]
},
columns=['cat', 'A', 'B'])
group_columns = ['cat', 'A']
for name in [None, 'X', 'B', 'cat']:
df.index = Index(list("abc"), name=name)
if name in group_columns and name in df.index.names:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = df.groupby(group_columns, as_index=False).sum()
else:
result = df.groupby(group_columns, as_index=False).sum()
tm.assert_frame_equal(result, expected, check_index_type=True)
def test_groupby_categorical_two_columns(self):
# https://github.com/pandas-dev/pandas/issues/8138
d = {
'cat':
pd.Categorical(["a", "b", "a", "b"],
categories=["a", "b", "c"],
ordered=True),
'ints': [1, 1, 2, 2],
'val': [10, 20, 30, 40]
}
test = pd.DataFrame(d)
# Grouping on a single column
groups_single_key = test.groupby("cat")
res = groups_single_key.agg('mean')
exp_index = pd.CategoricalIndex(["a", "b", "c"],
name="cat",
ordered=True)
exp = DataFrame({
"ints": [1.5, 1.5, np.nan],
"val": [20, 30, np.nan]
},
index=exp_index)
tm.assert_frame_equal(res, exp)
# Grouping on two columns
groups_double_key = test.groupby(["cat", "ints"])
res = groups_double_key.agg('mean')
exp = DataFrame({
"val": [10, 30, 20, 40, np.nan, np.nan],
"cat":
pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
"ints": [1, 2, 1, 2, 1, 2]
}).set_index(["cat", "ints"])
tm.assert_frame_equal(res, exp)
# GH 10132
for key in [('a', 1), ('b', 2), ('b', 1), ('a', 2)]:
c, i = key
result = groups_double_key.get_group(key)
expected = test[(test.cat == c) & (test.ints == i)]
assert_frame_equal(result, expected)
d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]}
test = pd.DataFrame(d)
values = pd.cut(test['C1'], [1, 2, 3, 6])
values.name = "cat"
groups_double_key = test.groupby([values, 'C2'])
res = groups_double_key.agg('mean')
nan = np.nan
idx = MultiIndex.from_product([
Categorical(["(1, 2]", "(2, 3]", "(3, 6]"], ordered=True),
[1, 2, 3, 4]
],
names=["cat", "C2"])
exp = DataFrame(
{
"C1": [nan, nan, nan, nan, 3, 3, nan, nan, nan, nan, 4, 5],
"C3":
[nan, nan, nan, nan, 10, 100, nan, nan, nan, nan, 200, 34]
},
index=idx)
tm.assert_frame_equal(res, exp)
