import numpy as np import pytest import pandas.util._test_decorators as td from pandas import ( DataFrame, NaT, Series, date_range, ) import pandas._testing as tm class TestDataFrameInterpolate: def test_interpolate_datetimelike_values(self, frame_or_series): # GH#11312, GH#51005 orig = Series(date_range("2012-01-01", periods=5)) ser = orig.copy() ser[2] = NaT res = frame_or_series(ser).interpolate() expected = frame_or_series(orig) tm.assert_equal(res, expected) # datetime64tz cast ser_tz = ser.dt.tz_localize("US/Pacific") res_tz = frame_or_series(ser_tz).interpolate() expected_tz = frame_or_series(orig.dt.tz_localize("US/Pacific")) tm.assert_equal(res_tz, expected_tz) # timedelta64 cast ser_td = ser - ser[0] res_td = frame_or_series(ser_td).interpolate() expected_td = frame_or_series(orig - orig[0]) tm.assert_equal(res_td, expected_td) def test_interpolate_inplace(self, frame_or_series, using_array_manager, request): # GH#44749 if using_array_manager and frame_or_series is DataFrame: mark = pytest.mark.xfail(reason=".values-based in-place check is invalid") request.node.add_marker(mark) obj = frame_or_series([1, np.nan, 2]) orig = obj.values obj.interpolate(inplace=True) expected = frame_or_series([1, 1.5, 2]) tm.assert_equal(obj, expected) # check we operated *actually* inplace assert np.shares_memory(orig, obj.values) assert orig.squeeze()[1] == 1.5 def test_interp_basic(self, using_copy_on_write): df = DataFrame( { "A": [1, 2, np.nan, 4], "B": [1, 4, 9, np.nan], "C": [1, 2, 3, 5], "D": list("abcd"), } ) expected = DataFrame( { "A": [1.0, 2.0, 3.0, 4.0], "B": [1.0, 4.0, 9.0, 9.0], "C": [1, 2, 3, 5], "D": list("abcd"), } ) result = df.interpolate() tm.assert_frame_equal(result, expected) # check we didn't operate inplace GH#45791 cvalues = df["C"]._values dvalues = df["D"].values if using_copy_on_write: assert np.shares_memory(cvalues, result["C"]._values) assert np.shares_memory(dvalues, result["D"]._values) else: assert not np.shares_memory(cvalues, result["C"]._values) assert not np.shares_memory(dvalues, result["D"]._values) res = df.interpolate(inplace=True) assert res is None tm.assert_frame_equal(df, expected) # check we DID operate inplace assert np.shares_memory(df["C"]._values, cvalues) assert np.shares_memory(df["D"]._values, dvalues) def test_interp_basic_with_non_range_index(self): df = DataFrame( { "A": [1, 2, np.nan, 4], "B": [1, 4, 9, np.nan], "C": [1, 2, 3, 5], "D": list("abcd"), } ) result = df.set_index("C").interpolate() expected = df.set_index("C") expected.loc[3, "A"] = 3 expected.loc[5, "B"] = 9 tm.assert_frame_equal(result, expected) def test_interp_empty(self): # https://github.com/pandas-dev/pandas/issues/35598 df = DataFrame() result = df.interpolate() assert result is not df expected = df tm.assert_frame_equal(result, expected) def test_interp_bad_method(self): df = DataFrame( { "A": [1, 2, np.nan, 4], "B": [1, 4, 9, np.nan], "C": [1, 2, 3, 5], "D": list("abcd"), } ) msg = ( r"method must be one of \['linear', 'time', 'index', 'values', " r"'nearest', 'zero', 'slinear', 'quadratic', 'cubic', " r"'barycentric', 'krogh', 'spline', 'polynomial', " r"'from_derivatives', 'piecewise_polynomial', 'pchip', 'akima', " r"'cubicspline'\]. Got 'not_a_method' instead." ) with pytest.raises(ValueError, match=msg): df.interpolate(method="not_a_method") def test_interp_combo(self): df = DataFrame( { "A": [1.0, 2.0, np.nan, 4.0], "B": [1, 4, 9, np.nan], "C": [1, 2, 3, 5], "D": list("abcd"), } ) result = df["A"].interpolate() expected = Series([1.0, 2.0, 3.0, 4.0], name="A") tm.assert_series_equal(result, expected) result = df["A"].interpolate(downcast="infer") expected = Series([1, 2, 3, 4], name="A") tm.assert_series_equal(result, expected) def test_interp_nan_idx(self): df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]}) df = df.set_index("A") msg = ( "Interpolation with NaNs in the index has not been implemented. " "Try filling those NaNs before interpolating." ) with pytest.raises(NotImplementedError, match=msg): df.interpolate(method="values") @td.skip_if_no_scipy def test_interp_various(self): df = DataFrame( {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} ) df = df.set_index("C") expected = df.copy() result = df.interpolate(method="polynomial", order=1) expected.loc[3, "A"] = 2.66666667 expected.loc[13, "A"] = 5.76923076 tm.assert_frame_equal(result, expected) result = df.interpolate(method="cubic") # GH #15662. expected.loc[3, "A"] = 2.81547781 expected.loc[13, "A"] = 5.52964175 tm.assert_frame_equal(result, expected) result = df.interpolate(method="nearest") expected.loc[3, "A"] = 2 expected.loc[13, "A"] = 5 tm.assert_frame_equal(result, expected, check_dtype=False) result = df.interpolate(method="quadratic") expected.loc[3, "A"] = 2.82150771 expected.loc[13, "A"] = 6.12648668 tm.assert_frame_equal(result, expected) result = df.interpolate(method="slinear") expected.loc[3, "A"] = 2.66666667 expected.loc[13, "A"] = 5.76923077 tm.assert_frame_equal(result, expected) result = df.interpolate(method="zero") expected.loc[3, "A"] = 2.0 expected.loc[13, "A"] = 5 tm.assert_frame_equal(result, expected, check_dtype=False) @td.skip_if_no_scipy def test_interp_alt_scipy(self): df = DataFrame( {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} ) result = df.interpolate(method="barycentric") expected = df.copy() expected.loc[2, "A"] = 3 expected.loc[5, "A"] = 6 tm.assert_frame_equal(result, expected) result = df.interpolate(method="barycentric", downcast="infer") tm.assert_frame_equal(result, expected.astype(np.int64)) result = df.interpolate(method="krogh") expectedk = df.copy() expectedk["A"] = expected["A"] tm.assert_frame_equal(result, expectedk) result = df.interpolate(method="pchip") expected.loc[2, "A"] = 3 expected.loc[5, "A"] = 6.0 tm.assert_frame_equal(result, expected) def test_interp_rowwise(self): df = DataFrame( { 0: [1, 2, np.nan, 4], 1: [2, 3, 4, np.nan], 2: [np.nan, 4, 5, 6], 3: [4, np.nan, 6, 7], 4: [1, 2, 3, 4], } ) result = df.interpolate(axis=1) expected = df.copy() expected.loc[3, 1] = 5 expected.loc[0, 2] = 3 expected.loc[1, 3] = 3 expected[4] = expected[4].astype(np.float64) tm.assert_frame_equal(result, expected) result = df.interpolate(axis=1, method="values") tm.assert_frame_equal(result, expected) result = df.interpolate(axis=0) expected = df.interpolate() tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "axis_name, axis_number", [ pytest.param("rows", 0, id="rows_0"), pytest.param("index", 0, id="index_0"), pytest.param("columns", 1, id="columns_1"), ], ) def test_interp_axis_names(self, axis_name, axis_number): # GH 29132: test axis names data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]} df = DataFrame(data, dtype=np.float64) result = df.interpolate(axis=axis_name, method="linear") expected = df.interpolate(axis=axis_number, method="linear") tm.assert_frame_equal(result, expected) def test_rowwise_alt(self): df = DataFrame( { 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64], 1: [1, 2, 3, 4, 3, 2, 1, 0, -1], } ) df.interpolate(axis=0) # TODO: assert something? @pytest.mark.parametrize( "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)] ) def test_interp_leading_nans(self, check_scipy): df = DataFrame( {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]} ) result = df.interpolate() expected = df.copy() expected.loc[3, "B"] = -3.75 tm.assert_frame_equal(result, expected) if check_scipy: result = df.interpolate(method="polynomial", order=1) tm.assert_frame_equal(result, expected) def test_interp_raise_on_only_mixed(self, axis): df = DataFrame( { "A": [1, 2, np.nan, 4], "B": ["a", "b", "c", "d"], "C": [np.nan, 2, 5, 7], "D": [np.nan, np.nan, 9, 9], "E": [1, 2, 3, 4], } ) msg = ( "Cannot interpolate with all object-dtype columns " "in the DataFrame. Try setting at least one " "column to a numeric dtype." ) with pytest.raises(TypeError, match=msg): df.astype("object").interpolate(axis=axis) def test_interp_raise_on_all_object_dtype(self): # GH 22985 df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object") msg = ( "Cannot interpolate with all object-dtype columns " "in the DataFrame. Try setting at least one " "column to a numeric dtype." ) with pytest.raises(TypeError, match=msg): df.interpolate() def test_interp_inplace(self, using_copy_on_write): df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]}) expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]}) expected_cow = df.copy() result = df.copy() return_value = result["a"].interpolate(inplace=True) assert return_value is None if using_copy_on_write: tm.assert_frame_equal(result, expected_cow) else: tm.assert_frame_equal(result, expected) result = df.copy() return_value = result["a"].interpolate(inplace=True, downcast="infer") assert return_value is None if using_copy_on_write: tm.assert_frame_equal(result, expected_cow) else: tm.assert_frame_equal(result, expected.astype("int64")) def test_interp_inplace_row(self): # GH 10395 result = DataFrame( {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]} ) expected = result.interpolate(method="linear", axis=1, inplace=False) return_value = result.interpolate(method="linear", axis=1, inplace=True) assert return_value is None tm.assert_frame_equal(result, expected) def test_interp_ignore_all_good(self): # GH df = DataFrame( { "A": [1, 2, np.nan, 4], "B": [1, 2, 3, 4], "C": [1.0, 2.0, np.nan, 4.0], "D": [1.0, 2.0, 3.0, 4.0], } ) expected = DataFrame( { "A": np.array([1, 2, 3, 4], dtype="float64"), "B": np.array([1, 2, 3, 4], dtype="int64"), "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"), "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"), } ) result = df.interpolate(downcast=None) tm.assert_frame_equal(result, expected) # all good result = df[["B", "D"]].interpolate(downcast=None) tm.assert_frame_equal(result, df[["B", "D"]]) def test_interp_time_inplace_axis(self): # GH 9687 periods = 5 idx = date_range(start="2014-01-01", periods=periods) data = np.random.rand(periods, periods) data[data < 0.5] = np.nan expected = DataFrame(index=idx, columns=idx, data=data) result = expected.interpolate(axis=0, method="time") return_value = expected.interpolate(axis=0, method="time", inplace=True) assert return_value is None tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("axis_name, axis_number", [("index", 0), ("columns", 1)]) def test_interp_string_axis(self, axis_name, axis_number): # https://github.com/pandas-dev/pandas/issues/25190 x = np.linspace(0, 100, 1000) y = np.sin(x) df = DataFrame( data=np.tile(y, (10, 1)), index=np.arange(10), columns=x ).reindex(columns=x * 1.005) result = df.interpolate(method="linear", axis=axis_name) expected = df.interpolate(method="linear", axis=axis_number) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("method", ["ffill", "bfill", "pad"]) def test_interp_fillna_methods(self, request, axis, method, using_array_manager): # GH 12918 if using_array_manager and axis in (1, "columns"): # TODO(ArrayManager) support axis=1 td.mark_array_manager_not_yet_implemented(request) df = DataFrame( { "A": [1.0, 2.0, 3.0, 4.0, np.nan, 5.0], "B": [2.0, 4.0, 6.0, np.nan, 8.0, 10.0], "C": [3.0, 6.0, 9.0, np.nan, np.nan, 30.0], } ) expected = df.fillna(axis=axis, method=method) result = df.interpolate(method=method, axis=axis) tm.assert_frame_equal(result, expected)