test changes

pandas/core/algorithms.py

@@ -937,24 +937,12 @@ def value_counts_internal(
  and not sort
  and isinstance(values, (DatetimeIndex, TimedeltaIndex))
  and values.inferred_freq is not None
+ and len(idx) == len(values)
+ and idx.equals(values)
  ):
  # freq preservation
- # Check if the result would be the same as input
- if len(idx) == len(values) and idx.equals(values):
- # Rebuild idx with the correct type and inferred frequency
- if isinstance(values, DatetimeIndex):
- idx = DatetimeIndex(
- idx._data if hasattr(idx, "_data") else idx.values,
- freq=values.inferred_freq,
- name=idx.name,
- )
-
- elif isinstance(values, TimedeltaIndex):
- idx = TimedeltaIndex(
- idx._data if hasattr(idx, "_data") else idx.values,
- freq=values.inferred_freq,
- name=idx.name,
- )
+ # Rebuild idx with the correct type and inferred frequency
+ idx.freq = values.inferred_freq
 
  result = Series(counts, index=idx, name=name, copy=False)
 

pandas/tests/base/test_value_counts.py

@@ -341,151 +341,79 @@ def test_value_counts_object_inference_deprecated():
  tm.assert_series_equal(res, exp)
 
 
-def _vc_make_index(kind: str, periods=5, freq="D"):
- if kind == "dt":
- return pd.date_range("2016-01-01", periods=periods, freq=freq)
- if kind == "td":
- return pd.timedelta_range(Timedelta(0), periods=periods, freq=freq)
- raise ValueError("kind must be 'dt' or 'td'")
-
-
-@pytest.mark.parametrize(
- "kind,freq,normalize",
- [
- ("dt", "D", False),
- ("dt", "D", True),
- ("td", "D", False),
- ("td", "D", True),
- ("td", Timedelta(hours=1), False),
- ("td", Timedelta(hours=1), True),
- ],
-)
-def test_value_counts_freq_preserved_datetimelike_no_sort(kind, freq, normalize):
- idx = _vc_make_index(kind, periods=5, freq=freq)
- vc = idx.value_counts(sort=False, normalize=normalize)
- assert vc.index.freq == idx.freq
- if normalize:
- assert np.isclose(vc.values, 1 / len(idx)).all()
-
-
 @pytest.mark.parametrize(
- "kind,freq",
+ "index",
  [
- ("dt", "D"),
- ("td", "D"),
- ("td", Timedelta(hours=1)),
+ pd.date_range("2016-01-01", periods=5, freq="D"),
+ pd.timedelta_range(Timedelta(0), periods=5, freq="h"),
  ],
+ ids=["DatetimeIndex[D]", "TimedeltaIndex[h]"],
 )
-def test_value_counts_freq_drops_datetimelike_when_sorted(kind, freq):
- idx = _vc_make_index(kind, periods=5, freq=freq)
- vc = idx.value_counts() # default sort=True (reorders)
- assert vc.index.freq is None
-
-
 @pytest.mark.parametrize(
- "kind,freq",
+ "build,kwargs,exp_preserve,exp_hasnans,exp_index_fn",
  [
- ("dt", "D"),
- ("td", "D"),
- ("td", Timedelta(hours=1)),
+ (lambda idx: idx, {"sort": False}, True, False, lambda idx, obj: idx),
+ (
+ lambda idx: idx,
+ {"sort": False, "normalize": True},
+ True,
+ False,
+ lambda idx, obj: idx,
+ ),
+ (lambda idx: idx, {}, False, False, None),
+ (
+ lambda idx: idx.insert(1, idx[1]),
+ {"sort": False},
+ False,
+ False,
+ lambda idx, obj: type(idx)(idx, freq=None),
+ ),
+ (
+ lambda idx: idx.delete(2),
+ {"sort": False},
+ False,
+ False,
+ lambda idx, obj: type(idx)(obj, freq=None),
+ ),
+ (
+ lambda idx: idx.insert(1, pd.NaT),
+ {"sort": False, "dropna": False},
+ False,
+ True,
+ lambda idx, obj: type(idx)(
+ list(idx[:1]) + [pd.NaT] + list(idx[1:]), freq=None
+ ),
+ ),
+ (
+ lambda idx: idx.insert(1, pd.NaT),
+ {"sort": False, "dropna": True},
+ False,
+ False,
+ lambda idx, obj: type(idx)(idx, freq=None),
+ ),
  ],
 )
-def test_value_counts_freq_drops_datetimelike_with_duplicates(kind, freq):
- base = _vc_make_index(kind, periods=5, freq=freq)
- obj = base.insert(1, base[1]) # duplicate one label
- vc = obj.value_counts(sort=False)
- assert vc.index.freq is None
-
-
-@pytest.mark.parametrize(
- "kind,freq",
- [
- ("dt", "D"),
- ("td", "D"),
- ("td", Timedelta(hours=1)),
- ],
-)
-def test_value_counts_freq_drops_datetimelike_with_gap(kind, freq):
- base = _vc_make_index(kind, periods=5, freq=freq)
- obj = base.delete(2) # remove one step to break contiguity
- vc = obj.value_counts(sort=False)
- assert vc.index.freq is None
+def test_value_counts_freq_datetimelike(
+ index, build, kwargs, exp_preserve, exp_hasnans, exp_index_fn
+):
+ obj = build(index)
+ vc = obj.value_counts(**kwargs)
 
+ # without sort
+ if exp_index_fn is not None:
+ expected_idx = exp_index_fn(index, obj)
+ tm.assert_index_equal(vc.index, expected_idx)
 
-@pytest.mark.parametrize(
- "kind,freq,dropna,expect_hasnans",
- [
- ("dt", "D", False, True), # keep NaT
- ("dt", "D", True, False), # drop NaT
- ("td", "D", False, True),
- ("td", "D", True, False),
- ("td", Timedelta(hours=1), False, True),
- ("td", Timedelta(hours=1), True, False),
- ],
-)
-def test_value_counts_freq_drops_datetimelike_with_nat(
- kind, freq, dropna, expect_hasnans
-):
- base = _vc_make_index(kind, periods=3, freq=freq)
- obj = base.insert(1, pd.NaT)
- vc = obj.value_counts(dropna=dropna, sort=False)
- assert vc.index.freq is None
- assert vc.index.hasnans is expect_hasnans
+ # freq preservation / drop
+ if exp_preserve:
+ assert vc.index.freq == index.freq
+ else:
+ assert vc.index.freq is None
 
+ # NaT presence
+ assert vc.index.hasnans is exp_hasnans
 
-@pytest.mark.parametrize(
- "freq,start,periods,sort",
- [
- ("D", "2016-01-01", 5, False),
- ("D", "2016-01-01", 5, True),
- ("M", "2016-01", 6, False), # MonthEnd
- ("M", "2016-01", 6, True),
- ("Q-DEC", "2016Q1", 4, False), # QuarterEnd (Dec anchored)
- ("Q-DEC", "2016Q1", 4, True),
- ("Y-DEC", "2014", 3, False), # YearEnd (Dec anchored)
- ("Y-DEC", "2014", 3, True),
- ],
-)
-def test_value_counts_period_freq_preserved_sort_and_nosort(freq, start, periods, sort):
- pi = pd.period_range(start=start, periods=periods, freq=freq)
- vc = pi.value_counts(sort=sort)
- assert isinstance(vc.index, pd.PeriodIndex)
- assert vc.index.dtype == pi.dtype
- assert vc.index.freq == pi.freq
-
-
-def test_value_counts_period_freq_preserved_with_duplicates():
- pi = pd.period_range("2016-01", periods=5, freq="M")
- obj = pi.insert(1, pi[1]) # duplicate one label
- vc = obj.value_counts(sort=False)
- assert isinstance(vc.index, pd.PeriodIndex)
- assert vc.index.dtype == pi.dtype
- assert vc.index.freq == pi.freq
-
-
-def test_value_counts_period_freq_preserved_with_gap():
- pi = pd.period_range("2016-01", periods=5, freq="M")
- obj = pi.delete(2) # remove one element
- vc = obj.value_counts(sort=False)
- assert isinstance(vc.index, pd.PeriodIndex)
- assert vc.index.dtype == pi.dtype
- assert vc.index.freq == pi.freq
-
-
-def test_value_counts_period_freq_preserved_with_normalize():
- pi = pd.period_range("2016-01", periods=4, freq="M")
- vc = pi.value_counts(normalize=True, sort=False)
- assert isinstance(vc.index, pd.PeriodIndex)
- assert vc.index.dtype == pi.dtype
- assert vc.index.freq == pi.freq
- assert np.isclose(vc.values, 1 / len(pi)).all()
-
-
-def test_value_counts_period_freq_preserved_with_nat_dropna_true():
- pi = pd.period_range("2016-01", periods=5, freq="M")
- obj = pi.insert(1, pd.NaT)
- vc = obj.value_counts(dropna=True, sort=False)
- assert not vc.index.hasnans
- assert isinstance(vc.index, pd.PeriodIndex)
- assert vc.index.dtype == pi.dtype
- assert vc.index.freq == pi.freq
+ # without normalize
+ if kwargs.get("normalize", False):
+ expected_val = 1.0 / len(index)
+ assert np.isclose(vc.to_numpy(), expected_val).all()