import numpy as np import pytest import pandas.util._test_decorators as td import pandas as pd from pandas import ( CategoricalIndex, DataFrame, Index, NaT, Series, date_range, offsets, ) import pandas._testing as tm class TestDataFrameShift: @pytest.mark.parametrize( "input_data, output_data", [(np.empty(shape=(0,)), []), (np.ones(shape=(2,)), [np.nan, 1.0])], ) def test_shift_non_writable_array(self, input_data, output_data, frame_or_series): # GH21049 Verify whether non writable numpy array is shiftable input_data.setflags(write=False) result = frame_or_series(input_data).shift(1) if frame_or_series is not Series: # need to explicitly specify columns in the empty case expected = frame_or_series( output_data, index=range(len(output_data)), columns=range(1), dtype="float64", ) else: expected = frame_or_series(output_data, dtype="float64") tm.assert_equal(result, expected) def test_shift_mismatched_freq(self, frame_or_series): ts = frame_or_series( np.random.randn(5), index=date_range("1/1/2000", periods=5, freq="H") ) result = ts.shift(1, freq="5T") exp_index = ts.index.shift(1, freq="5T") tm.assert_index_equal(result.index, exp_index) # GH#1063, multiple of same base result = ts.shift(1, freq="4H") exp_index = ts.index + offsets.Hour(4) tm.assert_index_equal(result.index, exp_index) @pytest.mark.parametrize( "obj", [ Series([np.arange(5)]), date_range("1/1/2011", periods=24, freq="H"), Series(range(5), index=date_range("2017", periods=5)), ], ) @pytest.mark.parametrize("shift_size", [0, 1, 2]) def test_shift_always_copy(self, obj, shift_size, frame_or_series): # GH#22397 if frame_or_series is not Series: obj = obj.to_frame() assert obj.shift(shift_size) is not obj def test_shift_object_non_scalar_fill(self): # shift requires scalar fill_value except for object dtype ser = Series(range(3)) with pytest.raises(ValueError, match="fill_value must be a scalar"): ser.shift(1, fill_value=[]) df = ser.to_frame() with pytest.raises(ValueError, match="fill_value must be a scalar"): df.shift(1, fill_value=np.arange(3)) obj_ser = ser.astype(object) result = obj_ser.shift(1, fill_value={}) assert result[0] == {} obj_df = obj_ser.to_frame() result = obj_df.shift(1, fill_value={}) assert result.iloc[0, 0] == {} def test_shift_int(self, datetime_frame, frame_or_series): ts = tm.get_obj(datetime_frame, frame_or_series).astype(int) shifted = ts.shift(1) expected = ts.astype(float).shift(1) tm.assert_equal(shifted, expected) @pytest.mark.parametrize("dtype", ["int32", "int64"]) def test_shift_32bit_take(self, frame_or_series, dtype): # 32-bit taking # GH#8129 index = date_range("2000-01-01", periods=5) arr = np.arange(5, dtype=dtype) s1 = frame_or_series(arr, index=index) p = arr[1] result = s1.shift(periods=p) expected = frame_or_series([np.nan, 0, 1, 2, 3], index=index) tm.assert_equal(result, expected) @pytest.mark.parametrize("periods", [1, 2, 3, 4]) def test_shift_preserve_freqstr(self, periods, frame_or_series): # GH#21275 obj = frame_or_series( range(periods), index=date_range("2016-1-1 00:00:00", periods=periods, freq="H"), ) result = obj.shift(1, "2H") expected = frame_or_series( range(periods), index=date_range("2016-1-1 02:00:00", periods=periods, freq="H"), ) tm.assert_equal(result, expected) def test_shift_dst(self, frame_or_series): # GH#13926 dates = date_range("2016-11-06", freq="H", periods=10, tz="US/Eastern") obj = frame_or_series(dates) res = obj.shift(0) tm.assert_equal(res, obj) assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" res = obj.shift(1) exp_vals = [NaT] + dates.astype(object).values.tolist()[:9] exp = frame_or_series(exp_vals) tm.assert_equal(res, exp) assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" res = obj.shift(-2) exp_vals = dates.astype(object).values.tolist()[2:] + [NaT, NaT] exp = frame_or_series(exp_vals) tm.assert_equal(res, exp) assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" @pytest.mark.parametrize("ex", [10, -10, 20, -20]) def test_shift_dst_beyond(self, frame_or_series, ex): # GH#13926 dates = date_range("2016-11-06", freq="H", periods=10, tz="US/Eastern") obj = frame_or_series(dates) res = obj.shift(ex) exp = frame_or_series([NaT] * 10, dtype="datetime64[ns, US/Eastern]") tm.assert_equal(res, exp) assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" def test_shift_by_zero(self, datetime_frame, frame_or_series): # shift by 0 obj = tm.get_obj(datetime_frame, frame_or_series) unshifted = obj.shift(0) tm.assert_equal(unshifted, obj) def test_shift(self, datetime_frame): # naive shift ser = datetime_frame["A"] shifted = datetime_frame.shift(5) tm.assert_index_equal(shifted.index, datetime_frame.index) shifted_ser = ser.shift(5) tm.assert_series_equal(shifted["A"], shifted_ser) shifted = datetime_frame.shift(-5) tm.assert_index_equal(shifted.index, datetime_frame.index) shifted_ser = ser.shift(-5) tm.assert_series_equal(shifted["A"], shifted_ser) unshifted = datetime_frame.shift(5).shift(-5) tm.assert_numpy_array_equal( unshifted.dropna().values, datetime_frame.values[:-5] ) unshifted_ser = ser.shift(5).shift(-5) tm.assert_numpy_array_equal(unshifted_ser.dropna().values, ser.values[:-5]) def test_shift_by_offset(self, datetime_frame, frame_or_series): # shift by DateOffset obj = tm.get_obj(datetime_frame, frame_or_series) offset = offsets.BDay() shifted = obj.shift(5, freq=offset) assert len(shifted) == len(obj) unshifted = shifted.shift(-5, freq=offset) tm.assert_equal(unshifted, obj) shifted2 = obj.shift(5, freq="B") tm.assert_equal(shifted, shifted2) unshifted = obj.shift(0, freq=offset) tm.assert_equal(unshifted, obj) d = obj.index[0] shifted_d = d + offset * 5 if frame_or_series is DataFrame: tm.assert_series_equal(obj.xs(d), shifted.xs(shifted_d), check_names=False) else: tm.assert_almost_equal(obj.at[d], shifted.at[shifted_d]) def test_shift_with_periodindex(self, frame_or_series): # Shifting with PeriodIndex ps = tm.makePeriodFrame() ps = tm.get_obj(ps, frame_or_series) shifted = ps.shift(1) unshifted = shifted.shift(-1) tm.assert_index_equal(shifted.index, ps.index) tm.assert_index_equal(unshifted.index, ps.index) if frame_or_series is DataFrame: tm.assert_numpy_array_equal( unshifted.iloc[:, 0].dropna().values, ps.iloc[:-1, 0].values ) else: tm.assert_numpy_array_equal(unshifted.dropna().values, ps.values[:-1]) shifted2 = ps.shift(1, "B") shifted3 = ps.shift(1, offsets.BDay()) tm.assert_equal(shifted2, shifted3) tm.assert_equal(ps, shifted2.shift(-1, "B")) msg = "does not match PeriodIndex freq" with pytest.raises(ValueError, match=msg): ps.shift(freq="D") # legacy support shifted4 = ps.shift(1, freq="B") tm.assert_equal(shifted2, shifted4) shifted5 = ps.shift(1, freq=offsets.BDay()) tm.assert_equal(shifted5, shifted4) def test_shift_other_axis(self): # shift other axis # GH#6371 df = DataFrame(np.random.rand(10, 5)) expected = pd.concat( [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]], ignore_index=True, axis=1, ) result = df.shift(1, axis=1) tm.assert_frame_equal(result, expected) def test_shift_named_axis(self): # shift named axis df = DataFrame(np.random.rand(10, 5)) expected = pd.concat( [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]], ignore_index=True, axis=1, ) result = df.shift(1, axis="columns") tm.assert_frame_equal(result, expected) def test_shift_other_axis_with_freq(self, datetime_frame): obj = datetime_frame.T offset = offsets.BDay() # GH#47039 shifted = obj.shift(5, freq=offset, axis=1) assert len(shifted) == len(obj) unshifted = shifted.shift(-5, freq=offset, axis=1) tm.assert_equal(unshifted, obj) def test_shift_bool(self): df = DataFrame({"high": [True, False], "low": [False, False]}) rs = df.shift(1) xp = DataFrame( np.array([[np.nan, np.nan], [True, False]], dtype=object), columns=["high", "low"], ) tm.assert_frame_equal(rs, xp) def test_shift_categorical1(self, frame_or_series): # GH#9416 obj = frame_or_series(["a", "b", "c", "d"], dtype="category") rt = obj.shift(1).shift(-1) tm.assert_equal(obj.iloc[:-1], rt.dropna()) def get_cat_values(ndframe): # For Series we could just do ._values; for DataFrame # we may be able to do this if we ever have 2D Categoricals return ndframe._mgr.arrays[0] cat = get_cat_values(obj) sp1 = obj.shift(1) tm.assert_index_equal(obj.index, sp1.index) assert np.all(get_cat_values(sp1).codes[:1] == -1) assert np.all(cat.codes[:-1] == get_cat_values(sp1).codes[1:]) sn2 = obj.shift(-2) tm.assert_index_equal(obj.index, sn2.index) assert np.all(get_cat_values(sn2).codes[-2:] == -1) assert np.all(cat.codes[2:] == get_cat_values(sn2).codes[:-2]) tm.assert_index_equal(cat.categories, get_cat_values(sp1).categories) tm.assert_index_equal(cat.categories, get_cat_values(sn2).categories) def test_shift_categorical(self): # GH#9416 s1 = Series(["a", "b", "c"], dtype="category") s2 = Series(["A", "B", "C"], dtype="category") df = DataFrame({"one": s1, "two": s2}) rs = df.shift(1) xp = DataFrame({"one": s1.shift(1), "two": s2.shift(1)}) tm.assert_frame_equal(rs, xp) def test_shift_categorical_fill_value(self, frame_or_series): ts = frame_or_series(["a", "b", "c", "d"], dtype="category") res = ts.shift(1, fill_value="a") expected = frame_or_series( pd.Categorical( ["a", "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False ) ) tm.assert_equal(res, expected) # check for incorrect fill_value msg = r"Cannot setitem on a Categorical with a new category \(f\)" with pytest.raises(TypeError, match=msg): ts.shift(1, fill_value="f") def test_shift_fill_value(self, frame_or_series): # GH#24128 dti = date_range("1/1/2000", periods=5, freq="H") ts = frame_or_series([1.0, 2.0, 3.0, 4.0, 5.0], index=dti) exp = frame_or_series([0.0, 1.0, 2.0, 3.0, 4.0], index=dti) # check that fill value works result = ts.shift(1, fill_value=0.0) tm.assert_equal(result, exp) exp = frame_or_series([0.0, 0.0, 1.0, 2.0, 3.0], index=dti) result = ts.shift(2, fill_value=0.0) tm.assert_equal(result, exp) ts = frame_or_series([1, 2, 3]) res = ts.shift(2, fill_value=0) assert tm.get_dtype(res) == tm.get_dtype(ts) # retain integer dtype obj = frame_or_series([1, 2, 3, 4, 5], index=dti) exp = frame_or_series([0, 1, 2, 3, 4], index=dti) result = obj.shift(1, fill_value=0) tm.assert_equal(result, exp) exp = frame_or_series([0, 0, 1, 2, 3], index=dti) result = obj.shift(2, fill_value=0) tm.assert_equal(result, exp) def test_shift_empty(self): # Regression test for GH#8019 df = DataFrame({"foo": []}) rs = df.shift(-1) tm.assert_frame_equal(df, rs) def test_shift_duplicate_columns(self, using_array_manager): # GH#9092; verify that position-based shifting works # in the presence of duplicate columns column_lists = [list(range(5)), [1] * 5, [1, 1, 2, 2, 1]] data = np.random.randn(20, 5) warn = None if using_array_manager: warn = DeprecationWarning shifted = [] for columns in column_lists: df = DataFrame(data.copy(), columns=columns) for s in range(5): msg = "will attempt to set the values inplace" with tm.assert_produces_warning(warn, match=msg): df.iloc[:, s] = df.iloc[:, s].shift(s + 1) df.columns = range(5) shifted.append(df) # sanity check the base case nulls = shifted[0].isna().sum() tm.assert_series_equal(nulls, Series(range(1, 6), dtype="int64")) # check all answers are the same tm.assert_frame_equal(shifted[0], shifted[1]) tm.assert_frame_equal(shifted[0], shifted[2]) def test_shift_axis1_multiple_blocks(self, using_array_manager): # GH#35488 df1 = DataFrame(np.random.randint(1000, size=(5, 3))) df2 = DataFrame(np.random.randint(1000, size=(5, 2))) df3 = pd.concat([df1, df2], axis=1) if not using_array_manager: assert len(df3._mgr.blocks) == 2 result = df3.shift(2, axis=1) expected = df3.take([-1, -1, 0, 1, 2], axis=1) expected.iloc[:, :2] = np.nan expected.columns = df3.columns tm.assert_frame_equal(result, expected) # Case with periods < 0 # rebuild df3 because `take` call above consolidated df3 = pd.concat([df1, df2], axis=1) if not using_array_manager: assert len(df3._mgr.blocks) == 2 result = df3.shift(-2, axis=1) expected = df3.take([2, 3, 4, -1, -1], axis=1) expected.iloc[:, -2:] = np.nan expected.columns = df3.columns tm.assert_frame_equal(result, expected) @td.skip_array_manager_not_yet_implemented # TODO(ArrayManager) axis=1 support def test_shift_axis1_multiple_blocks_with_int_fill(self): # GH#42719 df1 = DataFrame(np.random.randint(1000, size=(5, 3))) df2 = DataFrame(np.random.randint(1000, size=(5, 2))) df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1) result = df3.shift(2, axis=1, fill_value=np.int_(0)) assert len(df3._mgr.blocks) == 2 expected = df3.take([-1, -1, 0, 1], axis=1) expected.iloc[:, :2] = np.int_(0) expected.columns = df3.columns tm.assert_frame_equal(result, expected) # Case with periods < 0 df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1) result = df3.shift(-2, axis=1, fill_value=np.int_(0)) assert len(df3._mgr.blocks) == 2 expected = df3.take([2, 3, -1, -1], axis=1) expected.iloc[:, -2:] = np.int_(0) expected.columns = df3.columns tm.assert_frame_equal(result, expected) @pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning") def test_tshift(self, datetime_frame, frame_or_series): # TODO(2.0): remove this test when tshift deprecation is enforced # PeriodIndex ps = tm.makePeriodFrame() ps = tm.get_obj(ps, frame_or_series) shifted = ps.tshift(1) unshifted = shifted.tshift(-1) tm.assert_equal(unshifted, ps) shifted2 = ps.tshift(freq="B") tm.assert_equal(shifted, shifted2) shifted3 = ps.tshift(freq=offsets.BDay()) tm.assert_equal(shifted, shifted3) msg = "Given freq M does not match PeriodIndex freq B" with pytest.raises(ValueError, match=msg): ps.tshift(freq="M") # DatetimeIndex dtobj = tm.get_obj(datetime_frame, frame_or_series) shifted = dtobj.tshift(1) unshifted = shifted.tshift(-1) tm.assert_equal(dtobj, unshifted) shifted2 = dtobj.tshift(freq=dtobj.index.freq) tm.assert_equal(shifted, shifted2) inferred_ts = DataFrame( datetime_frame.values, Index(np.asarray(datetime_frame.index)), columns=datetime_frame.columns, ) inferred_ts = tm.get_obj(inferred_ts, frame_or_series) shifted = inferred_ts.tshift(1) expected = dtobj.tshift(1) expected.index = expected.index._with_freq(None) tm.assert_equal(shifted, expected) unshifted = shifted.tshift(-1) tm.assert_equal(unshifted, inferred_ts) no_freq = dtobj.iloc[[0, 5, 7]] msg = "Freq was not set in the index hence cannot be inferred" with pytest.raises(ValueError, match=msg): no_freq.tshift() def test_tshift_deprecated(self, datetime_frame, frame_or_series): # GH#11631 dtobj = tm.get_obj(datetime_frame, frame_or_series) with tm.assert_produces_warning(FutureWarning): dtobj.tshift() def test_period_index_frame_shift_with_freq(self, frame_or_series): ps = tm.makePeriodFrame() ps = tm.get_obj(ps, frame_or_series) shifted = ps.shift(1, freq="infer") unshifted = shifted.shift(-1, freq="infer") tm.assert_equal(unshifted, ps) shifted2 = ps.shift(freq="B") tm.assert_equal(shifted, shifted2) shifted3 = ps.shift(freq=offsets.BDay()) tm.assert_equal(shifted, shifted3) def test_datetime_frame_shift_with_freq(self, datetime_frame, frame_or_series): dtobj = tm.get_obj(datetime_frame, frame_or_series) shifted = dtobj.shift(1, freq="infer") unshifted = shifted.shift(-1, freq="infer") tm.assert_equal(dtobj, unshifted) shifted2 = dtobj.shift(freq=dtobj.index.freq) tm.assert_equal(shifted, shifted2) inferred_ts = DataFrame( datetime_frame.values, Index(np.asarray(datetime_frame.index)), columns=datetime_frame.columns, ) inferred_ts = tm.get_obj(inferred_ts, frame_or_series) shifted = inferred_ts.shift(1, freq="infer") expected = dtobj.shift(1, freq="infer") expected.index = expected.index._with_freq(None) tm.assert_equal(shifted, expected) unshifted = shifted.shift(-1, freq="infer") tm.assert_equal(unshifted, inferred_ts) def test_period_index_frame_shift_with_freq_error(self, frame_or_series): ps = tm.makePeriodFrame() ps = tm.get_obj(ps, frame_or_series) msg = "Given freq M does not match PeriodIndex freq B" with pytest.raises(ValueError, match=msg): ps.shift(freq="M") def test_datetime_frame_shift_with_freq_error( self, datetime_frame, frame_or_series ): dtobj = tm.get_obj(datetime_frame, frame_or_series) no_freq = dtobj.iloc[[0, 5, 7]] msg = "Freq was not set in the index hence cannot be inferred" with pytest.raises(ValueError, match=msg): no_freq.shift(freq="infer") @td.skip_array_manager_not_yet_implemented # TODO(ArrayManager) axis=1 support def test_shift_dt64values_int_fill_deprecated(self): # GH#31971 ser = Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")]) with tm.assert_produces_warning(FutureWarning): result = ser.shift(1, fill_value=0) expected = Series([pd.Timestamp(0), ser[0]]) tm.assert_series_equal(result, expected) df = ser.to_frame() with tm.assert_produces_warning(FutureWarning): result = df.shift(1, fill_value=0) expected = expected.to_frame() tm.assert_frame_equal(result, expected) # axis = 1 df2 = DataFrame({"A": ser, "B": ser}) df2._consolidate_inplace() with tm.assert_produces_warning(FutureWarning): result = df2.shift(1, axis=1, fill_value=0) expected = DataFrame({"A": [pd.Timestamp(0), pd.Timestamp(0)], "B": df2["A"]}) tm.assert_frame_equal(result, expected) # same thing but not consolidated # This isn't great that we get different behavior, but # that will go away when the deprecation is enforced df3 = DataFrame({"A": ser}) df3["B"] = ser assert len(df3._mgr.arrays) == 2 result = df3.shift(1, axis=1, fill_value=0) expected = DataFrame({"A": [0, 0], "B": df2["A"]}) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "as_cat", [ pytest.param( True, marks=pytest.mark.xfail( reason="_can_hold_element incorrectly always returns True" ), ), False, ], ) @pytest.mark.parametrize( "vals", [ date_range("2020-01-01", periods=2), date_range("2020-01-01", periods=2, tz="US/Pacific"), pd.period_range("2020-01-01", periods=2, freq="D"), pd.timedelta_range("2020 Days", periods=2, freq="D"), pd.interval_range(0, 3, periods=2), pytest.param( pd.array([1, 2], dtype="Int64"), marks=pytest.mark.xfail( reason="_can_hold_element incorrectly always returns True" ), ), pytest.param( pd.array([1, 2], dtype="Float32"), marks=pytest.mark.xfail( reason="_can_hold_element incorrectly always returns True" ), ), ], ids=lambda x: str(x.dtype), ) # TODO(2.0): remove filtering @pytest.mark.filterwarnings("ignore:Index.ravel.*:FutureWarning") def test_shift_dt64values_axis1_invalid_fill(self, vals, as_cat): # GH#44564 ser = Series(vals) if as_cat: ser = ser.astype("category") df = DataFrame({"A": ser}) result = df.shift(-1, axis=1, fill_value="foo") expected = DataFrame({"A": ["foo", "foo"]}) tm.assert_frame_equal(result, expected) # same thing but multiple blocks df2 = DataFrame({"A": ser, "B": ser}) df2._consolidate_inplace() result = df2.shift(-1, axis=1, fill_value="foo") expected = DataFrame({"A": df2["B"], "B": ["foo", "foo"]}) tm.assert_frame_equal(result, expected) # same thing but not consolidated df3 = DataFrame({"A": ser}) df3["B"] = ser assert len(df3._mgr.arrays) == 2 result = df3.shift(-1, axis=1, fill_value="foo") tm.assert_frame_equal(result, expected) def test_shift_axis1_categorical_columns(self): # GH#38434 ci = CategoricalIndex(["a", "b", "c"]) df = DataFrame( {"a": [1, 3], "b": [2, 4], "c": [5, 6]}, index=ci[:-1], columns=ci ) result = df.shift(axis=1) expected = DataFrame( {"a": [np.nan, np.nan], "b": [1, 3], "c": [2, 4]}, index=ci[:-1], columns=ci ) tm.assert_frame_equal(result, expected) # periods != 1 result = df.shift(2, axis=1) expected = DataFrame( {"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 3]}, index=ci[:-1], columns=ci, ) tm.assert_frame_equal(result, expected) def test_shift_axis1_many_periods(self): # GH#44978 periods > len(columns) df = DataFrame(np.random.rand(5, 3)) shifted = df.shift(6, axis=1, fill_value=None) expected = df * np.nan tm.assert_frame_equal(shifted, expected) shifted2 = df.shift(-6, axis=1, fill_value=None) tm.assert_frame_equal(shifted2, expected)