diff --git a/pandas/tests/series/accessors/test_dt_accessor.py b/pandas/tests/series/accessors/test_dt_accessor.py index 6199e77e10166..5db159e1abb80 100644 --- a/pandas/tests/series/accessors/test_dt_accessor.py +++ b/pandas/tests/series/accessors/test_dt_accessor.py @@ -74,7 +74,7 @@ def get_expected(s, name): if isinstance(result, np.ndarray): if is_integer_dtype(result): result = result.astype("int64") - elif not is_list_like(result) or isinstance(result, pd.DataFrame): + elif not is_list_like(result) or isinstance(result, DataFrame): return result return Series(result, index=s.index, name=s.name) @@ -83,7 +83,7 @@ def compare(s, name): b = get_expected(s, prop) if not (is_list_like(a) and is_list_like(b)): assert a == b - elif isinstance(a, pd.DataFrame): + elif isinstance(a, DataFrame): tm.assert_frame_equal(a, b) else: tm.assert_series_equal(a, b) @@ -180,7 +180,7 @@ def compare(s, name): assert result.dtype == object result = s.dt.total_seconds() - assert isinstance(result, pd.Series) + assert isinstance(result, Series) assert result.dtype == "float64" freq_result = s.dt.freq @@ -236,11 +236,11 @@ def get_dir(s): # 11295 # ambiguous time error on the conversions - s = Series(pd.date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx") + s = Series(date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx") s = s.dt.tz_localize("UTC").dt.tz_convert("America/Chicago") results = get_dir(s) tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods))) - exp_values = pd.date_range( + exp_values = date_range( "2015-01-01", "2016-01-01", freq="T", tz="UTC" ).tz_convert("America/Chicago") # freq not preserved by tz_localize above @@ -297,7 +297,7 @@ def test_dt_round_tz(self): @pytest.mark.parametrize("method", ["ceil", "round", "floor"]) def test_dt_round_tz_ambiguous(self, method): # GH 18946 round near "fall back" DST - df1 = pd.DataFrame( + df1 = DataFrame( [ pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True), pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True), @@ -634,7 +634,7 @@ def test_dt_accessor_invalid(self, ser): assert not hasattr(ser, "dt") def test_dt_accessor_updates_on_inplace(self): - s = Series(pd.date_range("2018-01-01", periods=10)) + s = Series(date_range("2018-01-01", periods=10)) s[2] = None return_value = s.fillna(pd.Timestamp("2018-01-01"), inplace=True) assert return_value is None @@ -680,7 +680,7 @@ def test_dt_timetz_accessor(self, tz_naive_fixture): ) def test_isocalendar(self, input_series, expected_output): result = pd.to_datetime(Series(input_series)).dt.isocalendar() - expected_frame = pd.DataFrame( + expected_frame = DataFrame( expected_output, columns=["year", "week", "day"], dtype="UInt32" ) tm.assert_frame_equal(result, expected_frame) diff --git a/pandas/tests/series/indexing/test_datetime.py b/pandas/tests/series/indexing/test_datetime.py index 1de6540217655..e4ba530d0741c 100644 --- a/pandas/tests/series/indexing/test_datetime.py +++ b/pandas/tests/series/indexing/test_datetime.py @@ -352,7 +352,7 @@ def test_indexing_over_size_cutoff_period_index(monkeypatch): monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000) n = 1100 - idx = pd.period_range("1/1/2000", freq="T", periods=n) + idx = period_range("1/1/2000", freq="T", periods=n) assert idx._engine.over_size_threshold s = Series(np.random.randn(len(idx)), index=idx) diff --git a/pandas/tests/series/indexing/test_getitem.py b/pandas/tests/series/indexing/test_getitem.py index d57a4c271680b..7642ccff31c6a 100644 --- a/pandas/tests/series/indexing/test_getitem.py +++ b/pandas/tests/series/indexing/test_getitem.py @@ -309,7 +309,7 @@ def test_getitem_slice_integers(self): class TestSeriesGetitemListLike: - @pytest.mark.parametrize("box", [list, np.array, Index, pd.Series]) + @pytest.mark.parametrize("box", [list, np.array, Index, Series]) def test_getitem_no_matches(self, box): # GH#33462 we expect the same behavior for list/ndarray/Index/Series ser = Series(["A", "B"]) diff --git a/pandas/tests/series/indexing/test_indexing.py b/pandas/tests/series/indexing/test_indexing.py index cd5a7af1d5ec0..30c37113f6b8f 100644 --- a/pandas/tests/series/indexing/test_indexing.py +++ b/pandas/tests/series/indexing/test_indexing.py @@ -5,7 +5,6 @@ import numpy as np import pytest -import pandas as pd from pandas import ( DataFrame, IndexSlice, @@ -58,7 +57,7 @@ def test_basic_getitem_dt64tz_values(): # GH12089 # with tz for values ser = Series( - pd.date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"] + date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"] ) expected = Timestamp("2011-01-01", tz="US/Eastern") result = ser.loc["a"] @@ -114,7 +113,7 @@ def test_getitem_setitem_integers(): def test_series_box_timestamp(): - rng = pd.date_range("20090415", "20090519", freq="B") + rng = date_range("20090415", "20090519", freq="B") ser = Series(rng) assert isinstance(ser[0], Timestamp) assert isinstance(ser.at[1], Timestamp) @@ -131,7 +130,7 @@ def test_series_box_timestamp(): def test_series_box_timedelta(): - rng = pd.timedelta_range("1 day 1 s", periods=5, freq="h") + rng = timedelta_range("1 day 1 s", periods=5, freq="h") ser = Series(rng) assert isinstance(ser[0], Timedelta) assert isinstance(ser.at[1], Timedelta) diff --git a/pandas/tests/series/indexing/test_where.py b/pandas/tests/series/indexing/test_where.py index 1e50fef55b4ec..799f3d257434d 100644 --- a/pandas/tests/series/indexing/test_where.py +++ b/pandas/tests/series/indexing/test_where.py @@ -475,7 +475,7 @@ def test_where_datetimelike_categorical(tz_naive_fixture): # GH#37682 tz = tz_naive_fixture - dr = pd.date_range("2001-01-01", periods=3, tz=tz)._with_freq(None) + dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None) lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT]) rvals = pd.Categorical([dr[0], pd.NaT, dr[2]]) diff --git a/pandas/tests/series/methods/test_interpolate.py b/pandas/tests/series/methods/test_interpolate.py index cad5476d4861c..5686e6478772d 100644 --- a/pandas/tests/series/methods/test_interpolate.py +++ b/pandas/tests/series/methods/test_interpolate.py @@ -642,7 +642,7 @@ def test_interp_datetime64(self, method, tz_naive_fixture): def test_interp_pad_datetime64tz_values(self): # GH#27628 missing.interpolate_2d should handle datetimetz values - dti = pd.date_range("2015-04-05", periods=3, tz="US/Central") + dti = date_range("2015-04-05", periods=3, tz="US/Central") ser = Series(dti) ser[1] = pd.NaT result = ser.interpolate(method="pad") @@ -735,13 +735,13 @@ def test_series_interpolate_method_values(self): def test_series_interpolate_intraday(self): # #1698 - index = pd.date_range("1/1/2012", periods=4, freq="12D") + index = date_range("1/1/2012", periods=4, freq="12D") ts = Series([0, 12, 24, 36], index) new_index = index.append(index + pd.DateOffset(days=1)).sort_values() exp = ts.reindex(new_index).interpolate(method="time") - index = pd.date_range("1/1/2012", periods=4, freq="12H") + index = date_range("1/1/2012", periods=4, freq="12H") ts = Series([0, 12, 24, 36], index) new_index = index.append(index + pd.DateOffset(hours=1)).sort_values() result = ts.reindex(new_index).interpolate(method="time") diff --git a/pandas/tests/series/methods/test_shift.py b/pandas/tests/series/methods/test_shift.py index 60ec0a90e906f..73684e300ed77 100644 --- a/pandas/tests/series/methods/test_shift.py +++ b/pandas/tests/series/methods/test_shift.py @@ -353,14 +353,14 @@ def test_shift_preserve_freqstr(self, periods): # GH#21275 ser = Series( range(periods), - index=pd.date_range("2016-1-1 00:00:00", periods=periods, freq="H"), + index=date_range("2016-1-1 00:00:00", periods=periods, freq="H"), ) result = ser.shift(1, "2H") expected = Series( range(periods), - index=pd.date_range("2016-1-1 02:00:00", periods=periods, freq="H"), + index=date_range("2016-1-1 02:00:00", periods=periods, freq="H"), ) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 63c9b4d899622..aec1e65cbb4c0 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -689,16 +689,16 @@ def test_constructor_pass_nan_nat(self): tm.assert_series_equal(Series([np.nan, np.nan]), exp) tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp) - exp = Series([pd.NaT, pd.NaT]) + exp = Series([NaT, NaT]) assert exp.dtype == "datetime64[ns]" - tm.assert_series_equal(Series([pd.NaT, pd.NaT]), exp) - tm.assert_series_equal(Series(np.array([pd.NaT, pd.NaT])), exp) + tm.assert_series_equal(Series([NaT, NaT]), exp) + tm.assert_series_equal(Series(np.array([NaT, NaT])), exp) - tm.assert_series_equal(Series([pd.NaT, np.nan]), exp) - tm.assert_series_equal(Series(np.array([pd.NaT, np.nan])), exp) + tm.assert_series_equal(Series([NaT, np.nan]), exp) + tm.assert_series_equal(Series(np.array([NaT, np.nan])), exp) - tm.assert_series_equal(Series([np.nan, pd.NaT]), exp) - tm.assert_series_equal(Series(np.array([np.nan, pd.NaT])), exp) + tm.assert_series_equal(Series([np.nan, NaT]), exp) + tm.assert_series_equal(Series(np.array([np.nan, NaT])), exp) def test_constructor_cast(self): msg = "could not convert string to float" @@ -824,7 +824,7 @@ def test_constructor_dtype_datetime64(self): tm.assert_series_equal(result, expected) expected = Series( - [pd.NaT, datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]" + [NaT, datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]" ) result = Series([np.nan] + dates[1:], dtype="datetime64[ns]") tm.assert_series_equal(result, expected) @@ -888,13 +888,13 @@ def test_constructor_dtype_datetime64(self): assert series1.dtype == object # these will correctly infer a datetime - s = Series([None, pd.NaT, "2013-08-05 15:30:00.000001"]) + s = Series([None, NaT, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" - s = Series([np.nan, pd.NaT, "2013-08-05 15:30:00.000001"]) + s = Series([np.nan, NaT, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" - s = Series([pd.NaT, None, "2013-08-05 15:30:00.000001"]) + s = Series([NaT, None, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" - s = Series([pd.NaT, np.nan, "2013-08-05 15:30:00.000001"]) + s = Series([NaT, np.nan, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" # tz-aware (UTC and other tz's) @@ -907,15 +907,15 @@ def test_constructor_dtype_datetime64(self): assert str(Series(dr).iloc[0].tz) == "US/Eastern" # non-convertible - s = Series([1479596223000, -1479590, pd.NaT]) + s = Series([1479596223000, -1479590, NaT]) assert s.dtype == "object" - assert s[2] is pd.NaT + assert s[2] is NaT assert "NaT" in str(s) # if we passed a NaT it remains - s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), pd.NaT]) + s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), NaT]) assert s.dtype == "object" - assert s[2] is pd.NaT + assert s[2] is NaT assert "NaT" in str(s) # if we passed a nan it remains @@ -941,7 +941,7 @@ def test_constructor_with_datetime_tz(self): assert isinstance(result, np.ndarray) assert result.dtype == "datetime64[ns]" - exp = pd.DatetimeIndex(result) + exp = DatetimeIndex(result) exp = exp.tz_localize("UTC").tz_convert(tz=s.dt.tz) tm.assert_index_equal(dr, exp) @@ -977,7 +977,7 @@ def test_constructor_with_datetime_tz(self): t = Series(date_range("20130101", periods=1000, tz="US/Eastern")) assert "datetime64[ns, US/Eastern]" in str(t) - result = pd.DatetimeIndex(s, freq="infer") + result = DatetimeIndex(s, freq="infer") tm.assert_index_equal(result, dr) # inference @@ -1000,8 +1000,8 @@ def test_constructor_with_datetime_tz(self): assert lib.infer_dtype(s, skipna=True) == "datetime" # with all NaT - s = Series(pd.NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]") - expected = Series(pd.DatetimeIndex(["NaT", "NaT"], tz="US/Eastern")) + s = Series(NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]") + expected = Series(DatetimeIndex(["NaT", "NaT"], tz="US/Eastern")) tm.assert_series_equal(s, expected) @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64]) @@ -1018,7 +1018,7 @@ def test_construction_to_datetimelike_unit(self, arr_dtype, dtype, unit): tm.assert_series_equal(result, expected) - @pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", pd.NaT, np.nan, None]) + @pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", NaT, np.nan, None]) def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg): # GH 17415: With naive string result = Series([arg], dtype="datetime64[ns, CET]") @@ -1302,7 +1302,7 @@ def test_constructor_dtype_timedelta64(self): td = Series([timedelta(days=1), np.nan], dtype="m8[ns]") assert td.dtype == "timedelta64[ns]" - td = Series([np.timedelta64(300000000), pd.NaT], dtype="m8[ns]") + td = Series([np.timedelta64(300000000), NaT], dtype="m8[ns]") assert td.dtype == "timedelta64[ns]" # improved inference @@ -1317,7 +1317,7 @@ def test_constructor_dtype_timedelta64(self): td = Series([np.timedelta64(300000000), np.nan]) assert td.dtype == "timedelta64[ns]" - td = Series([pd.NaT, np.timedelta64(300000000)]) + td = Series([NaT, np.timedelta64(300000000)]) assert td.dtype == "timedelta64[ns]" td = Series([np.timedelta64(1, "s")]) @@ -1349,13 +1349,13 @@ def test_constructor_dtype_timedelta64(self): assert td.dtype == "object" # these will correctly infer a timedelta - s = Series([None, pd.NaT, "1 Day"]) + s = Series([None, NaT, "1 Day"]) assert s.dtype == "timedelta64[ns]" - s = Series([np.nan, pd.NaT, "1 Day"]) + s = Series([np.nan, NaT, "1 Day"]) assert s.dtype == "timedelta64[ns]" - s = Series([pd.NaT, None, "1 Day"]) + s = Series([NaT, None, "1 Day"]) assert s.dtype == "timedelta64[ns]" - s = Series([pd.NaT, np.nan, "1 Day"]) + s = Series([NaT, np.nan, "1 Day"]) assert s.dtype == "timedelta64[ns]" # GH 16406 @@ -1606,7 +1606,7 @@ def test_constructor_dict_multiindex(self): _d = sorted(d.items()) result = Series(d) expected = Series( - [x[1] for x in _d], index=pd.MultiIndex.from_tuples([x[0] for x in _d]) + [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d]) ) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/series/test_repr.py b/pandas/tests/series/test_repr.py index a91908f7fba52..96a69476ccbef 100644 --- a/pandas/tests/series/test_repr.py +++ b/pandas/tests/series/test_repr.py @@ -169,7 +169,7 @@ def test_repr_should_return_str(self): def test_repr_max_rows(self): # GH 6863 - with pd.option_context("max_rows", None): + with option_context("max_rows", None): str(Series(range(1001))) # should not raise exception def test_unicode_string_with_unicode(self):