diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py index cf6b2d372657d..2d7dddbc5ea42 100644 --- a/pandas/tests/reductions/test_reductions.py +++ b/pandas/tests/reductions/test_reductions.py @@ -83,16 +83,16 @@ def test_nanminmax(self, opname, dtype, val, index_or_series): # GH#7261 klass = index_or_series - if dtype in ["Int64", "boolean"] and klass == pd.Index: + if dtype in ["Int64", "boolean"] and klass == Index: pytest.skip("EAs can't yet be stored in an index") def check_missing(res): if dtype == "datetime64[ns]": - return res is pd.NaT + return res is NaT elif dtype == "Int64": return res is pd.NA else: - return pd.isna(res) + return isna(res) obj = klass([None], dtype=dtype) assert check_missing(getattr(obj, opname)()) @@ -120,7 +120,7 @@ def test_nanargminmax(self, opname, index_or_series): klass = index_or_series arg_op = "arg" + opname if klass is Index else "idx" + opname - obj = klass([pd.NaT, datetime(2011, 11, 1)]) + obj = klass([NaT, datetime(2011, 11, 1)]) assert getattr(obj, arg_op)() == 1 result = getattr(obj, arg_op)(skipna=False) if klass is Series: @@ -128,7 +128,7 @@ def test_nanargminmax(self, opname, index_or_series): else: assert result == -1 - obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT]) + obj = klass([NaT, datetime(2011, 11, 1), NaT]) # check DatetimeIndex non-monotonic path assert getattr(obj, arg_op)() == 1 result = getattr(obj, arg_op)(skipna=False) @@ -145,8 +145,8 @@ def test_nanops_empty_object(self, opname, index_or_series, dtype): obj = klass([], dtype=dtype) - assert getattr(obj, opname)() is pd.NaT - assert getattr(obj, opname)(skipna=False) is pd.NaT + assert getattr(obj, opname)() is NaT + assert getattr(obj, opname)(skipna=False) is NaT with pytest.raises(ValueError, match="empty sequence"): getattr(obj, arg_op)() @@ -170,13 +170,13 @@ def test_argminmax(self): assert obj.argmin(skipna=False) == -1 assert obj.argmax(skipna=False) == -1 - obj = Index([pd.NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), pd.NaT]) + obj = Index([NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), NaT]) assert obj.argmin() == 1 assert obj.argmax() == 2 assert obj.argmin(skipna=False) == -1 assert obj.argmax(skipna=False) == -1 - obj = Index([pd.NaT]) + obj = Index([NaT]) assert obj.argmin() == -1 assert obj.argmax() == -1 assert obj.argmin(skipna=False) == -1 @@ -186,7 +186,7 @@ def test_argminmax(self): def test_same_tz_min_max_axis_1(self, op, expected_col): # GH 10390 df = DataFrame( - pd.date_range("2016-01-01 00:00:00", periods=3, tz="UTC"), columns=["a"] + date_range("2016-01-01 00:00:00", periods=3, tz="UTC"), columns=["a"] ) df["b"] = df.a.subtract(Timedelta(seconds=3600)) result = getattr(df, op)(axis=1) @@ -262,13 +262,13 @@ def test_minmax_timedelta64(self): def test_minmax_timedelta_empty_or_na(self, op): # Return NaT obj = TimedeltaIndex([]) - assert getattr(obj, op)() is pd.NaT + assert getattr(obj, op)() is NaT - obj = TimedeltaIndex([pd.NaT]) - assert getattr(obj, op)() is pd.NaT + obj = TimedeltaIndex([NaT]) + assert getattr(obj, op)() is NaT - obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT]) - assert getattr(obj, op)() is pd.NaT + obj = TimedeltaIndex([NaT, NaT, NaT]) + assert getattr(obj, op)() is NaT def test_numpy_minmax_timedelta64(self): td = timedelta_range("16815 days", "16820 days", freq="D") @@ -373,7 +373,7 @@ def test_minmax_tz(self, tz_naive_fixture): # non-monotonic idx2 = DatetimeIndex( - ["2011-01-01", pd.NaT, "2011-01-03", "2011-01-02", pd.NaT], tz=tz + ["2011-01-01", NaT, "2011-01-03", "2011-01-02", NaT], tz=tz ) assert not idx2.is_monotonic @@ -387,13 +387,13 @@ def test_minmax_tz(self, tz_naive_fixture): def test_minmax_nat_datetime64(self, op): # Return NaT obj = DatetimeIndex([]) - assert pd.isna(getattr(obj, op)()) + assert isna(getattr(obj, op)()) - obj = DatetimeIndex([pd.NaT]) - assert pd.isna(getattr(obj, op)()) + obj = DatetimeIndex([NaT]) + assert isna(getattr(obj, op)()) - obj = DatetimeIndex([pd.NaT, pd.NaT, pd.NaT]) - assert pd.isna(getattr(obj, op)()) + obj = DatetimeIndex([NaT, NaT, NaT]) + assert isna(getattr(obj, op)()) def test_numpy_minmax_integer(self): # GH#26125 @@ -449,7 +449,7 @@ def test_numpy_minmax_range(self): # is the same as basic integer index def test_numpy_minmax_datetime64(self): - dr = pd.date_range(start="2016-01-15", end="2016-01-20") + dr = date_range(start="2016-01-15", end="2016-01-20") assert np.min(dr) == Timestamp("2016-01-15 00:00:00", freq="D") assert np.max(dr) == Timestamp("2016-01-20 00:00:00", freq="D") @@ -588,7 +588,7 @@ def test_empty(self, method, unit, use_bottleneck, dtype): assert result == unit result = getattr(s, method)(min_count=1) - assert pd.isna(result) + assert isna(result) # Skipna, default result = getattr(s, method)(skipna=True) @@ -599,13 +599,13 @@ def test_empty(self, method, unit, use_bottleneck, dtype): assert result == unit result = getattr(s, method)(skipna=True, min_count=1) - assert pd.isna(result) + assert isna(result) result = getattr(s, method)(skipna=False, min_count=0) assert result == unit result = getattr(s, method)(skipna=False, min_count=1) - assert pd.isna(result) + assert isna(result) # All-NA s = Series([np.nan], dtype=dtype) @@ -618,7 +618,7 @@ def test_empty(self, method, unit, use_bottleneck, dtype): assert result == unit result = getattr(s, method)(min_count=1) - assert pd.isna(result) + assert isna(result) # Skipna, default result = getattr(s, method)(skipna=True) @@ -629,7 +629,7 @@ def test_empty(self, method, unit, use_bottleneck, dtype): assert result == unit result = getattr(s, method)(skipna=True, min_count=1) - assert pd.isna(result) + assert isna(result) # Mix of valid, empty s = Series([np.nan, 1], dtype=dtype) @@ -657,18 +657,18 @@ def test_empty(self, method, unit, use_bottleneck, dtype): s = Series([1], dtype=dtype) result = getattr(s, method)(min_count=2) - assert pd.isna(result) + assert isna(result) result = getattr(s, method)(skipna=False, min_count=2) - assert pd.isna(result) + assert isna(result) s = Series([np.nan], dtype=dtype) result = getattr(s, method)(min_count=2) - assert pd.isna(result) + assert isna(result) s = Series([np.nan, 1], dtype=dtype) result = getattr(s, method)(min_count=2) - assert pd.isna(result) + assert isna(result) @pytest.mark.parametrize("method, unit", [("sum", 0.0), ("prod", 1.0)]) def test_empty_multi(self, method, unit): @@ -716,7 +716,7 @@ def test_ops_consistency_on_empty(self, method): # float result = getattr(Series(dtype=float), method)() - assert pd.isna(result) + assert isna(result) # timedelta64[ns] tdser = Series([], dtype="m8[ns]") @@ -732,7 +732,7 @@ def test_ops_consistency_on_empty(self, method): getattr(tdser, method)() else: result = getattr(tdser, method)() - assert result is pd.NaT + assert result is NaT def test_nansum_buglet(self): ser = Series([1.0, np.nan], index=[0, 1]) @@ -770,10 +770,10 @@ def test_sum_overflow(self, use_bottleneck): def test_empty_timeseries_reductions_return_nat(self): # covers GH#11245 for dtype in ("m8[ns]", "m8[ns]", "M8[ns]", "M8[ns, UTC]"): - assert Series([], dtype=dtype).min() is pd.NaT - assert Series([], dtype=dtype).max() is pd.NaT - assert Series([], dtype=dtype).min(skipna=False) is pd.NaT - assert Series([], dtype=dtype).max(skipna=False) is pd.NaT + assert Series([], dtype=dtype).min() is NaT + assert Series([], dtype=dtype).max() is NaT + assert Series([], dtype=dtype).min(skipna=False) is NaT + assert Series([], dtype=dtype).max(skipna=False) is NaT def test_numpy_argmin(self): # See GH#16830 @@ -820,7 +820,7 @@ def test_idxmin(self): # skipna or no assert string_series[string_series.idxmin()] == string_series.min() - assert pd.isna(string_series.idxmin(skipna=False)) + assert isna(string_series.idxmin(skipna=False)) # no NaNs nona = string_series.dropna() @@ -829,10 +829,10 @@ def test_idxmin(self): # all NaNs allna = string_series * np.nan - assert pd.isna(allna.idxmin()) + assert isna(allna.idxmin()) # datetime64[ns] - s = Series(pd.date_range("20130102", periods=6)) + s = Series(date_range("20130102", periods=6)) result = s.idxmin() assert result == 0 @@ -850,7 +850,7 @@ def test_idxmax(self): # skipna or no assert string_series[string_series.idxmax()] == string_series.max() - assert pd.isna(string_series.idxmax(skipna=False)) + assert isna(string_series.idxmax(skipna=False)) # no NaNs nona = string_series.dropna() @@ -859,7 +859,7 @@ def test_idxmax(self): # all NaNs allna = string_series * np.nan - assert pd.isna(allna.idxmax()) + assert isna(allna.idxmax()) from pandas import date_range @@ -1010,7 +1010,7 @@ def test_any_all_datetimelike(self): def test_timedelta64_analytics(self): # index min/max - dti = pd.date_range("2012-1-1", periods=3, freq="D") + dti = date_range("2012-1-1", periods=3, freq="D") td = Series(dti) - Timestamp("20120101") result = td.idxmin() @@ -1030,8 +1030,8 @@ def test_timedelta64_analytics(self): assert result == 2 # abs - s1 = Series(pd.date_range("20120101", periods=3)) - s2 = Series(pd.date_range("20120102", periods=3)) + s1 = Series(date_range("20120101", periods=3)) + s2 = Series(date_range("20120102", periods=3)) expected = Series(s2 - s1) result = np.abs(s1 - s2) @@ -1108,35 +1108,35 @@ class TestDatetime64SeriesReductions: @pytest.mark.parametrize( "nat_ser", [ - Series([pd.NaT, pd.NaT]), - Series([pd.NaT, Timedelta("nat")]), + Series([NaT, NaT]), + Series([NaT, Timedelta("nat")]), Series([Timedelta("nat"), Timedelta("nat")]), ], ) def test_minmax_nat_series(self, nat_ser): # GH#23282 - assert nat_ser.min() is pd.NaT - assert nat_ser.max() is pd.NaT - assert nat_ser.min(skipna=False) is pd.NaT - assert nat_ser.max(skipna=False) is pd.NaT + assert nat_ser.min() is NaT + assert nat_ser.max() is NaT + assert nat_ser.min(skipna=False) is NaT + assert nat_ser.max(skipna=False) is NaT @pytest.mark.parametrize( "nat_df", [ - DataFrame([pd.NaT, pd.NaT]), - DataFrame([pd.NaT, Timedelta("nat")]), + DataFrame([NaT, NaT]), + DataFrame([NaT, Timedelta("nat")]), DataFrame([Timedelta("nat"), Timedelta("nat")]), ], ) def test_minmax_nat_dataframe(self, nat_df): # GH#23282 - assert nat_df.min()[0] is pd.NaT - assert nat_df.max()[0] is pd.NaT - assert nat_df.min(skipna=False)[0] is pd.NaT - assert nat_df.max(skipna=False)[0] is pd.NaT + assert nat_df.min()[0] is NaT + assert nat_df.max()[0] is NaT + assert nat_df.min(skipna=False)[0] is NaT + assert nat_df.max(skipna=False)[0] is NaT def test_min_max(self): - rng = pd.date_range("1/1/2000", "12/31/2000") + rng = date_range("1/1/2000", "12/31/2000") rng2 = rng.take(np.random.permutation(len(rng))) the_min = rng2.min() @@ -1150,7 +1150,7 @@ def test_min_max(self): assert rng.max() == rng[-1] def test_min_max_series(self): - rng = pd.date_range("1/1/2000", periods=10, freq="4h") + rng = date_range("1/1/2000", periods=10, freq="4h") lvls = ["A", "A", "A", "B", "B", "B", "C", "C", "C", "C"] df = DataFrame({"TS": rng, "V": np.random.randn(len(rng)), "L": lvls})