def test_method_delegation(self):
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.set_categories(list('cab'))
tm.assert_index_equal(
result, CategoricalIndex(list('aabbca'), categories=list('cab')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.rename_categories(list('efg'))
tm.assert_index_equal(
result, CategoricalIndex(list('ffggef'), categories=list('efg')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.add_categories(['d'])
tm.assert_index_equal(
result, CategoricalIndex(list('aabbca'), categories=list('cabd')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.remove_categories(['c'])
tm.assert_index_equal(
result,
CategoricalIndex(list('aabb') + [np.nan] + ['a'],
categories=list('ab')))
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.as_unordered()
tm.assert_index_equal(result, ci)
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.as_ordered()
tm.assert_index_equal(
result,
CategoricalIndex(list('aabbca'),
categories=list('cabdef'),
ordered=True))
# invalid
pytest.raises(ValueError,
lambda: ci.set_categories(list('cab'), inplace=True))
def test_format_different_scalar_lengths(self):
# GH35439
idx = CategoricalIndex(["aaaaaaaaa", "b"])
expected = ["aaaaaaaaa", "b"]
assert idx.format() == expected
def test_method_delegation(self):
ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
result = ci.set_categories(list("cab"))
tm.assert_index_equal(
result, CategoricalIndex(list("aabbca"), categories=list("cab")))
ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
result = ci.rename_categories(list("efg"))
tm.assert_index_equal(
result, CategoricalIndex(list("ffggef"), categories=list("efg")))
# GH18862 (let rename_categories take callables)
result = ci.rename_categories(lambda x: x.upper())
tm.assert_index_equal(
result, CategoricalIndex(list("AABBCA"), categories=list("CAB")))
ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
result = ci.add_categories(["d"])
tm.assert_index_equal(
result, CategoricalIndex(list("aabbca"), categories=list("cabd")))
ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
result = ci.remove_categories(["c"])
tm.assert_index_equal(
result,
CategoricalIndex(list("aabb") + [np.nan] + ["a"],
categories=list("ab")),
)
ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
result = ci.as_unordered()
tm.assert_index_equal(result, ci)
ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
result = ci.as_ordered()
tm.assert_index_equal(
result,
CategoricalIndex(list("aabbca"),
categories=list("cabdef"),
ordered=True),
)
# invalid
msg = "cannot use inplace with CategoricalIndex"
with pytest.raises(ValueError, match=msg):
ci.set_categories(list("cab"), inplace=True)
def test_insert_na_mismatched_dtype(self):
ci = CategoricalIndex([0, 1, 1])
msg = "'fill_value=NaT' is not present in this Categorical's categories"
with pytest.raises(TypeError, match=msg):
ci.insert(0, pd.NaT)
def test_frame_repr(self):
df = pd.DataFrame({"A": [1, 2, 3]},
index=CategoricalIndex(["a", "b", "c"]))
result = repr(df)
expected = " A\na 1\nb 2\nc 3"
assert result == expected
def __init__(
self,
index: Index,
grouper=None,
obj: Optional[FrameOrSeries] = None,
name=None,
level=None,
sort: bool = True,
observed: bool = False,
in_axis: bool = False,
):
self.name = name
self.level = level
self.grouper = _convert_grouper(index, grouper)
self.all_grouper = None
self.index = index
self.sort = sort
self.obj = obj
self.observed = observed
self.in_axis = in_axis
# right place for this?
if isinstance(grouper, (Series, Index)) and name is None:
self.name = grouper.name
if isinstance(grouper, MultiIndex):
self.grouper = grouper.values
# we have a single grouper which may be a myriad of things,
# some of which are dependent on the passing in level
if level is not None:
if not isinstance(level, int):
if level not in index.names:
raise AssertionError(f"Level {level} not in index")
level = index.names.index(level)
if self.name is None:
self.name = index.names[level]
(
self.grouper,
self._codes,
self._group_index,
) = index._get_grouper_for_level(self.grouper, level)
# a passed Grouper like, directly get the grouper in the same way
# as single grouper groupby, use the group_info to get codes
elif isinstance(self.grouper, Grouper):
# get the new grouper; we already have disambiguated
# what key/level refer to exactly, don't need to
# check again as we have by this point converted these
# to an actual value (rather than a pd.Grouper)
_, grouper, _ = self.grouper._get_grouper(self.obj, validate=False)
if self.name is None:
self.name = grouper.result_index.name
self.obj = self.grouper.obj
self.grouper = grouper._get_grouper()
else:
if self.grouper is None and self.name is not None and self.obj is not None:
self.grouper = self.obj[self.name]
elif isinstance(self.grouper, (list, tuple)):
self.grouper = com.asarray_tuplesafe(self.grouper)
# a passed Categorical
elif is_categorical_dtype(self.grouper):
self.grouper, self.all_grouper = recode_for_groupby(
self.grouper, self.sort, observed)
categories = self.grouper.categories
# we make a CategoricalIndex out of the cat grouper
# preserving the categories / ordered attributes
self._codes = self.grouper.codes
if observed:
codes = algorithms.unique1d(self.grouper.codes)
codes = codes[codes != -1]
if sort or self.grouper.ordered:
codes = np.sort(codes)
else:
codes = np.arange(len(categories))
self._group_index = CategoricalIndex(
Categorical.from_codes(codes=codes,
categories=categories,
ordered=self.grouper.ordered),
name=self.name,
)
# we are done
if isinstance(self.grouper, Grouping):
self.grouper = self.grouper.grouper
# no level passed
elif not isinstance(self.grouper,
(Series, Index, ExtensionArray, np.ndarray)):
if getattr(self.grouper, "ndim", 1) != 1:
t = self.name or str(type(self.grouper))
raise ValueError(f"Grouper for '{t}' not 1-dimensional")
self.grouper = self.index.map(self.grouper)
if not (hasattr(self.grouper, "__len__")
and len(self.grouper) == len(self.index)):
grper = pprint_thing(self.grouper)
errmsg = ("Grouper result violates len(labels) == "
f"len(data)\nresult: {grper}")
self.grouper = None # Try for sanity
raise AssertionError(errmsg)
# if we have a date/time-like grouper, make sure that we have
# Timestamps like
if getattr(self.grouper, "dtype", None) is not None:
if is_datetime64_dtype(self.grouper):
self.grouper = self.grouper.astype("datetime64[ns]")
elif is_timedelta64_dtype(self.grouper):
self.grouper = self.grouper.astype("timedelta64[ns]")
def test_method_delegation(self):
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.set_categories(list('cab'))
tm.assert_index_equal(
result, CategoricalIndex(list('aabbca'), categories=list('cab')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.rename_categories(list('efg'))
tm.assert_index_equal(
result, CategoricalIndex(list('ffggef'), categories=list('efg')))
# GH18862 (let rename_categories take callables)
result = ci.rename_categories(lambda x: x.upper())
tm.assert_index_equal(
result, CategoricalIndex(list('AABBCA'), categories=list('CAB')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.add_categories(['d'])
tm.assert_index_equal(
result, CategoricalIndex(list('aabbca'), categories=list('cabd')))
ci = CategoricalIndex(list('aabbca'), categories=list('cab'))
result = ci.remove_categories(['c'])
tm.assert_index_equal(
result,
CategoricalIndex(list('aabb') + [np.nan] + ['a'],
categories=list('ab')))
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.as_unordered()
tm.assert_index_equal(result, ci)
ci = CategoricalIndex(list('aabbca'), categories=list('cabdef'))
result = ci.as_ordered()
tm.assert_index_equal(
result,
CategoricalIndex(list('aabbca'),
categories=list('cabdef'),
ordered=True))
# invalid
msg = "cannot use inplace with CategoricalIndex"
with pytest.raises(ValueError, match=msg):
ci.set_categories(list('cab'), inplace=True)
def test_reindex_empty_index(self):
# See GH16770
c = CategoricalIndex([])
res, indexer = c.reindex(["a", "b"])
tm.assert_index_equal(res, Index(["a", "b"]), exact=True)
tm.assert_numpy_array_equal(indexer, np.array([-1, -1], dtype=np.intp))
def test_has_duplicates(self):
idx = CategoricalIndex([0, 0, 0], name="foo")
assert idx.is_unique is False
assert idx.has_duplicates is True
def create_index(self, *, categories=None, ordered=False):
if categories is None:
categories = list("cab")
return CategoricalIndex(list("aabbca"),
categories=categories,
ordered=ordered)
def test_insert_na_mismatched_dtype(self):
ci = CategoricalIndex([0, 1, 1])
result = ci.insert(0, pd.NaT)
expected = Index([pd.NaT, 0, 1, 1], dtype=object)
tm.assert_index_equal(result, expected)
def test_unique(self, data, categories, expected_data, ordered):
dtype = CategoricalDtype(categories, ordered=ordered)
idx = CategoricalIndex(data, dtype=dtype)
expected = CategoricalIndex(expected_data, dtype=dtype)
tm.assert_index_equal(idx.unique(), expected)
def test_has_duplicates(self):
idx = CategoricalIndex([0, 0, 0], name='foo')
assert not idx.is_unique
assert idx.has_duplicates
def test_is_unique(self, values, expected):
ci = CategoricalIndex(values)
assert ci.is_unique is expected
def create_index(self, categories=None, ordered=False):
if categories is None:
categories = list('cab')
return CategoricalIndex(list('aabbca'),
categories=categories,
ordered=ordered)
def test_drop_duplicates(self):
idx = CategoricalIndex([0, 0, 0], name="foo")
expected = CategoricalIndex([0], name="foo")
tm.assert_index_equal(idx.drop_duplicates(), expected)
tm.assert_index_equal(idx.unique(), expected)
def test_construction(self):
ci = self.create_index(categories=list('abcd'))
categories = ci.categories
result = Index(ci)
tm.assert_index_equal(result, ci, exact=True)
assert not result.ordered
result = Index(ci.values)
tm.assert_index_equal(result, ci, exact=True)
assert not result.ordered
# empty
result = CategoricalIndex(categories=categories)
tm.assert_index_equal(result.categories, Index(categories))
tm.assert_numpy_array_equal(result.codes, np.array([], dtype='int8'))
assert not result.ordered
# passing categories
result = CategoricalIndex(list('aabbca'), categories=categories)
tm.assert_index_equal(result.categories, Index(categories))
tm.assert_numpy_array_equal(result.codes,
np.array([0, 0, 1, 1, 2, 0], dtype='int8'))
c = pd.Categorical(list('aabbca'))
result = CategoricalIndex(c)
tm.assert_index_equal(result.categories, Index(list('abc')))
tm.assert_numpy_array_equal(result.codes,
np.array([0, 0, 1, 1, 2, 0], dtype='int8'))
assert not result.ordered
result = CategoricalIndex(c, categories=categories)
tm.assert_index_equal(result.categories, Index(categories))
tm.assert_numpy_array_equal(result.codes,
np.array([0, 0, 1, 1, 2, 0], dtype='int8'))
assert not result.ordered
ci = CategoricalIndex(c, categories=list('abcd'))
result = CategoricalIndex(ci)
tm.assert_index_equal(result.categories, Index(categories))
tm.assert_numpy_array_equal(result.codes,
np.array([0, 0, 1, 1, 2, 0], dtype='int8'))
assert not result.ordered
result = CategoricalIndex(ci, categories=list('ab'))
tm.assert_index_equal(result.categories, Index(list('ab')))
tm.assert_numpy_array_equal(
result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8'))
assert not result.ordered
result = CategoricalIndex(ci, categories=list('ab'), ordered=True)
tm.assert_index_equal(result.categories, Index(list('ab')))
tm.assert_numpy_array_equal(
result.codes, np.array([0, 0, 1, 1, -1, 0], dtype='int8'))
assert result.ordered
# turn me to an Index
result = Index(np.array(ci))
assert isinstance(result, Index)
assert not isinstance(result, CategoricalIndex)
def test_equals_categorical(self):
ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
ci2 = CategoricalIndex(["a", "b"],
categories=["a", "b", "c"],
ordered=True)
assert ci1.equals(ci1)
assert not ci1.equals(ci2)
assert ci1.equals(ci1.astype(object))
assert ci1.astype(object).equals(ci1)
assert (ci1 == ci1).all()
assert not (ci1 != ci1).all()
assert not (ci1 > ci1).all()
assert not (ci1 < ci1).all()
assert (ci1 <= ci1).all()
assert (ci1 >= ci1).all()
assert not (ci1 == 1).all()
assert (ci1 == Index(["a", "b"])).all()
assert (ci1 == ci1.values).all()
# invalid comparisons
with pytest.raises(ValueError, match="Lengths must match"):
ci1 == Index(["a", "b", "c"])
msg = (
"categorical index comparisons must have the same categories"
" and ordered attributes"
"|"
"Categoricals can only be compared if 'categories' are the same. "
"Categories are different lengths"
"|"
"Categoricals can only be compared if 'ordered' is the same")
with pytest.raises(TypeError, match=msg):
ci1 == ci2
with pytest.raises(TypeError, match=msg):
ci1 == Categorical(ci1.values, ordered=False)
with pytest.raises(TypeError, match=msg):
ci1 == Categorical(ci1.values, categories=list("abc"))
# tests
# make sure that we are testing for category inclusion properly
ci = CategoricalIndex(list("aabca"), categories=["c", "a", "b"])
assert not ci.equals(list("aabca"))
# Same categories, but different order
# Unordered
assert ci.equals(CategoricalIndex(list("aabca")))
# Ordered
assert not ci.equals(CategoricalIndex(list("aabca"), ordered=True))
assert ci.equals(ci.copy())
ci = CategoricalIndex(list("aabca") + [np.nan],
categories=["c", "a", "b"])
assert not ci.equals(list("aabca"))
assert not ci.equals(CategoricalIndex(list("aabca")))
assert ci.equals(ci.copy())
ci = CategoricalIndex(list("aabca") + [np.nan],
categories=["c", "a", "b"])
assert not ci.equals(list("aabca") + [np.nan])
assert ci.equals(CategoricalIndex(list("aabca") + [np.nan]))
assert not ci.equals(
CategoricalIndex(list("aabca") + [np.nan], ordered=True))
assert ci.equals(ci.copy())
def test_contains_interval(self, item, expected):
# GH 23705
ci = CategoricalIndex(IntervalIndex.from_breaks(range(3)))
result = item in ci
assert result is expected
def test_unique(self, data, categories, expected_data, expected_categories):
idx = CategoricalIndex(data, categories=categories)
expected = CategoricalIndex(expected_data, categories=expected_categories)
tm.assert_index_equal(idx.unique(), expected)
