import numpy as np
from numpy import nan
import pytest

import pandas as pd
from pandas import DataFrame, lreshape, melt, wide_to_long
import pandas.util.testing as tm


class TestMelt:
    def setup_method(self, method):
        self.df = tm.makeTimeDataFrame()[:10]
        self.df["id1"] = (self.df["A"] > 0).astype(np.int64)
        self.df["id2"] = (self.df["B"] > 0).astype(np.int64)

        self.var_name = "var"
        self.value_name = "val"

        self.df1 = pd.DataFrame(
            [
                [1.067683, -1.110463, 0.20867],
                [-1.321405, 0.368915, -1.055342],
                [-0.807333, 0.08298, -0.873361],
            ]
        )
        self.df1.columns = [list("ABC"), list("abc")]
        self.df1.columns.names = ["CAP", "low"]

    def test_top_level_method(self):
        result = melt(self.df)
        assert result.columns.tolist() == ["variable", "value"]

    def test_method_signatures(self):
        tm.assert_frame_equal(self.df.melt(), melt(self.df))

        tm.assert_frame_equal(
            self.df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"]),
            melt(self.df, id_vars=["id1", "id2"], value_vars=["A", "B"]),
        )

        tm.assert_frame_equal(
            self.df.melt(var_name=self.var_name, value_name=self.value_name),
            melt(self.df, var_name=self.var_name, value_name=self.value_name),
        )

        tm.assert_frame_equal(self.df1.melt(col_level=0), melt(self.df1, col_level=0))

    def test_default_col_names(self):
        result = self.df.melt()
        assert result.columns.tolist() == ["variable", "value"]

        result1 = self.df.melt(id_vars=["id1"])
        assert result1.columns.tolist() == ["id1", "variable", "value"]

        result2 = self.df.melt(id_vars=["id1", "id2"])
        assert result2.columns.tolist() == ["id1", "id2", "variable", "value"]

    def test_value_vars(self):
        result3 = self.df.melt(id_vars=["id1", "id2"], value_vars="A")
        assert len(result3) == 10

        result4 = self.df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"])
        expected4 = DataFrame(
            {
                "id1": self.df["id1"].tolist() * 2,
                "id2": self.df["id2"].tolist() * 2,
                "variable": ["A"] * 10 + ["B"] * 10,
                "value": (self.df["A"].tolist() + self.df["B"].tolist()),
            },
            columns=["id1", "id2", "variable", "value"],
        )
        tm.assert_frame_equal(result4, expected4)

    def test_value_vars_types(self):
        # GH 15348
        expected = DataFrame(
            {
                "id1": self.df["id1"].tolist() * 2,
                "id2": self.df["id2"].tolist() * 2,
                "variable": ["A"] * 10 + ["B"] * 10,
                "value": (self.df["A"].tolist() + self.df["B"].tolist()),
            },
            columns=["id1", "id2", "variable", "value"],
        )

        for type_ in (tuple, list, np.array):
            result = self.df.melt(id_vars=["id1", "id2"], value_vars=type_(("A", "B")))
            tm.assert_frame_equal(result, expected)

    def test_vars_work_with_multiindex(self):
        expected = DataFrame(
            {
                ("A", "a"): self.df1[("A", "a")],
                "CAP": ["B"] * len(self.df1),
                "low": ["b"] * len(self.df1),
                "value": self.df1[("B", "b")],
            },
            columns=[("A", "a"), "CAP", "low", "value"],
        )

        result = self.df1.melt(id_vars=[("A", "a")], value_vars=[("B", "b")])
        tm.assert_frame_equal(result, expected)

    def test_single_vars_work_with_multiindex(self):
        expected = DataFrame(
            {
                "A": {0: 1.067683, 1: -1.321405, 2: -0.807333},
                "CAP": {0: "B", 1: "B", 2: "B"},
                "value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
            }
        )
        result = self.df1.melt(["A"], ["B"], col_level=0)
        tm.assert_frame_equal(result, expected)

    def test_tuple_vars_fail_with_multiindex(self):
        # melt should fail with an informative error message if
        # the columns have a MultiIndex and a tuple is passed
        # for id_vars or value_vars.
        tuple_a = ("A", "a")
        list_a = [tuple_a]
        tuple_b = ("B", "b")
        list_b = [tuple_b]

        msg = r"(id|value)_vars must be a list of tuples when columns are a MultiIndex"
        for id_vars, value_vars in (
            (tuple_a, list_b),
            (list_a, tuple_b),
            (tuple_a, tuple_b),
        ):
            with pytest.raises(ValueError, match=msg):
                self.df1.melt(id_vars=id_vars, value_vars=value_vars)

    def test_custom_var_name(self):
        result5 = self.df.melt(var_name=self.var_name)
        assert result5.columns.tolist() == ["var", "value"]

        result6 = self.df.melt(id_vars=["id1"], var_name=self.var_name)
        assert result6.columns.tolist() == ["id1", "var", "value"]

        result7 = self.df.melt(id_vars=["id1", "id2"], var_name=self.var_name)
        assert result7.columns.tolist() == ["id1", "id2", "var", "value"]

        result8 = self.df.melt(
            id_vars=["id1", "id2"], value_vars="A", var_name=self.var_name
        )
        assert result8.columns.tolist() == ["id1", "id2", "var", "value"]

        result9 = self.df.melt(
            id_vars=["id1", "id2"], value_vars=["A", "B"], var_name=self.var_name
        )
        expected9 = DataFrame(
            {
                "id1": self.df["id1"].tolist() * 2,
                "id2": self.df["id2"].tolist() * 2,
                self.var_name: ["A"] * 10 + ["B"] * 10,
                "value": (self.df["A"].tolist() + self.df["B"].tolist()),
            },
            columns=["id1", "id2", self.var_name, "value"],
        )
        tm.assert_frame_equal(result9, expected9)

    def test_custom_value_name(self):
        result10 = self.df.melt(value_name=self.value_name)
        assert result10.columns.tolist() == ["variable", "val"]

        result11 = self.df.melt(id_vars=["id1"], value_name=self.value_name)
        assert result11.columns.tolist() == ["id1", "variable", "val"]

        result12 = self.df.melt(id_vars=["id1", "id2"], value_name=self.value_name)
        assert result12.columns.tolist() == ["id1", "id2", "variable", "val"]

        result13 = self.df.melt(
            id_vars=["id1", "id2"], value_vars="A", value_name=self.value_name
        )
        assert result13.columns.tolist() == ["id1", "id2", "variable", "val"]

        result14 = self.df.melt(
            id_vars=["id1", "id2"], value_vars=["A", "B"], value_name=self.value_name
        )
        expected14 = DataFrame(
            {
                "id1": self.df["id1"].tolist() * 2,
                "id2": self.df["id2"].tolist() * 2,
                "variable": ["A"] * 10 + ["B"] * 10,
                self.value_name: (self.df["A"].tolist() + self.df["B"].tolist()),
            },
            columns=["id1", "id2", "variable", self.value_name],
        )
        tm.assert_frame_equal(result14, expected14)

    def test_custom_var_and_value_name(self):

        result15 = self.df.melt(var_name=self.var_name, value_name=self.value_name)
        assert result15.columns.tolist() == ["var", "val"]

        result16 = self.df.melt(
            id_vars=["id1"], var_name=self.var_name, value_name=self.value_name
        )
        assert result16.columns.tolist() == ["id1", "var", "val"]

        result17 = self.df.melt(
            id_vars=["id1", "id2"], var_name=self.var_name, value_name=self.value_name
        )
        assert result17.columns.tolist() == ["id1", "id2", "var", "val"]

        result18 = self.df.melt(
            id_vars=["id1", "id2"],
            value_vars="A",
            var_name=self.var_name,
            value_name=self.value_name,
        )
        assert result18.columns.tolist() == ["id1", "id2", "var", "val"]

        result19 = self.df.melt(
            id_vars=["id1", "id2"],
            value_vars=["A", "B"],
            var_name=self.var_name,
            value_name=self.value_name,
        )
        expected19 = DataFrame(
            {
                "id1": self.df["id1"].tolist() * 2,
                "id2": self.df["id2"].tolist() * 2,
                self.var_name: ["A"] * 10 + ["B"] * 10,
                self.value_name: (self.df["A"].tolist() + self.df["B"].tolist()),
            },
            columns=["id1", "id2", self.var_name, self.value_name],
        )
        tm.assert_frame_equal(result19, expected19)

        df20 = self.df.copy()
        df20.columns.name = "foo"
        result20 = df20.melt()
        assert result20.columns.tolist() == ["foo", "value"]

    def test_col_level(self):
        res1 = self.df1.melt(col_level=0)
        res2 = self.df1.melt(col_level="CAP")
        assert res1.columns.tolist() == ["CAP", "value"]
        assert res2.columns.tolist() == ["CAP", "value"]

    def test_multiindex(self):
        res = self.df1.melt()
        assert res.columns.tolist() == ["CAP", "low", "value"]

    @pytest.mark.parametrize(
        "col",
        [
            pd.Series(pd.date_range("2010", periods=5, tz="US/Pacific")),
            pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
            pd.Series([0, 1, 0, 0, 0]),
        ],
    )
    def test_pandas_dtypes(self, col):
        # GH 15785
        df = DataFrame(
            {"klass": range(5), "col": col, "attr1": [1, 0, 0, 0, 0], "attr2": col}
        )
        expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col], ignore_index=True)
        result = melt(
            df, id_vars=["klass", "col"], var_name="attribute", value_name="value"
        )
        expected = DataFrame(
            {
                0: list(range(5)) * 2,
                1: pd.concat([col] * 2, ignore_index=True),
                2: ["attr1"] * 5 + ["attr2"] * 5,
                3: expected_value,
            }
        )
        expected.columns = ["klass", "col", "attribute", "value"]
        tm.assert_frame_equal(result, expected)

    def test_melt_missing_columns_raises(self):
        # GH-23575
        # This test is to ensure that pandas raises an error if melting is
        # attempted with column names absent from the dataframe

        # Generate data
        df = pd.DataFrame(np.random.randn(5, 4), columns=list("abcd"))

        # Try to melt with missing `value_vars` column name
        msg = "The following '{Var}' are not present in the DataFrame: {Col}"
        with pytest.raises(
            KeyError, match=msg.format(Var="value_vars", Col="\\['C'\\]")
        ):
            df.melt(["a", "b"], ["C", "d"])

        # Try to melt with missing `id_vars` column name
        with pytest.raises(KeyError, match=msg.format(Var="id_vars", Col="\\['A'\\]")):
            df.melt(["A", "b"], ["c", "d"])

        # Multiple missing
        with pytest.raises(
            KeyError,
            match=msg.format(Var="id_vars", Col="\\['not_here', 'or_there'\\]"),
        ):
            df.melt(["a", "b", "not_here", "or_there"], ["c", "d"])

        # Multiindex melt fails if column is missing from multilevel melt
        multi = df.copy()
        multi.columns = [list("ABCD"), list("abcd")]
        with pytest.raises(KeyError, match=msg.format(Var="id_vars", Col="\\['E'\\]")):
            multi.melt([("E", "a")], [("B", "b")])
        # Multiindex fails if column is missing from single level melt
        with pytest.raises(
            KeyError, match=msg.format(Var="value_vars", Col="\\['F'\\]")
        ):
            multi.melt(["A"], ["F"], col_level=0)


class TestLreshape:
    def test_pairs(self):
        data = {
            "birthdt": [
                "08jan2009",
                "20dec2008",
                "30dec2008",
                "21dec2008",
                "11jan2009",
            ],
            "birthwt": [1766, 3301, 1454, 3139, 4133],
            "id": [101, 102, 103, 104, 105],
            "sex": ["Male", "Female", "Female", "Female", "Female"],
            "visitdt1": [
                "11jan2009",
                "22dec2008",
                "04jan2009",
                "29dec2008",
                "20jan2009",
            ],
            "visitdt2": ["21jan2009", nan, "22jan2009", "31dec2008", "03feb2009"],
            "visitdt3": ["05feb2009", nan, nan, "02jan2009", "15feb2009"],
            "wt1": [1823, 3338, 1549, 3298, 4306],
            "wt2": [2011.0, nan, 1892.0, 3338.0, 4575.0],
            "wt3": [2293.0, nan, nan, 3377.0, 4805.0],
        }

        df = DataFrame(data)

        spec = {
            "visitdt": ["visitdt{i:d}".format(i=i) for i in range(1, 4)],
            "wt": ["wt{i:d}".format(i=i) for i in range(1, 4)],
        }
        result = lreshape(df, spec)

        exp_data = {
            "birthdt": [
                "08jan2009",
                "20dec2008",
                "30dec2008",
                "21dec2008",
                "11jan2009",
                "08jan2009",
                "30dec2008",
                "21dec2008",
                "11jan2009",
                "08jan2009",
                "21dec2008",
                "11jan2009",
            ],
            "birthwt": [
                1766,
                3301,
                1454,
                3139,
                4133,
                1766,
                1454,
                3139,
                4133,
                1766,
                3139,
                4133,
            ],
            "id": [101, 102, 103, 104, 105, 101, 103, 104, 105, 101, 104, 105],
            "sex": [
                "Male",
                "Female",
                "Female",
                "Female",
                "Female",
                "Male",
                "Female",
                "Female",
                "Female",
                "Male",
                "Female",
                "Female",
            ],
            "visitdt": [
                "11jan2009",
                "22dec2008",
                "04jan2009",
                "29dec2008",
                "20jan2009",
                "21jan2009",
                "22jan2009",
                "31dec2008",
                "03feb2009",
                "05feb2009",
                "02jan2009",
                "15feb2009",
            ],
            "wt": [
                1823.0,
                3338.0,
                1549.0,
                3298.0,
                4306.0,
                2011.0,
                1892.0,
                3338.0,
                4575.0,
                2293.0,
                3377.0,
                4805.0,
            ],
        }
        exp = DataFrame(exp_data, columns=result.columns)
        tm.assert_frame_equal(result, exp)

        result = lreshape(df, spec, dropna=False)
        exp_data = {
            "birthdt": [
                "08jan2009",
                "20dec2008",
                "30dec2008",
                "21dec2008",
                "11jan2009",
                "08jan2009",
                "20dec2008",
                "30dec2008",
                "21dec2008",
                "11jan2009",
                "08jan2009",
                "20dec2008",
                "30dec2008",
                "21dec2008",
                "11jan2009",
            ],
            "birthwt": [
                1766,
                3301,
                1454,
                3139,
                4133,
                1766,
                3301,
                1454,
                3139,
                4133,
                1766,
                3301,
                1454,
                3139,
                4133,
            ],
            "id": [
                101,
                102,
                103,
                104,
                105,
                101,
                102,
                103,
                104,
                105,
                101,
                102,
                103,
                104,
                105,
            ],
            "sex": [
                "Male",
                "Female",
                "Female",
                "Female",
                "Female",
                "Male",
                "Female",
                "Female",
                "Female",
                "Female",
                "Male",
                "Female",
                "Female",
                "Female",
                "Female",
            ],
            "visitdt": [
                "11jan2009",
                "22dec2008",
                "04jan2009",
                "29dec2008",
                "20jan2009",
                "21jan2009",
                nan,
                "22jan2009",
                "31dec2008",
                "03feb2009",
                "05feb2009",
                nan,
                nan,
                "02jan2009",
                "15feb2009",
            ],
            "wt": [
                1823.0,
                3338.0,
                1549.0,
                3298.0,
                4306.0,
                2011.0,
                nan,
                1892.0,
                3338.0,
                4575.0,
                2293.0,
                nan,
                nan,
                3377.0,
                4805.0,
            ],
        }
        exp = DataFrame(exp_data, columns=result.columns)
        tm.assert_frame_equal(result, exp)

        spec = {
            "visitdt": ["visitdt{i:d}".format(i=i) for i in range(1, 3)],
            "wt": ["wt{i:d}".format(i=i) for i in range(1, 4)],
        }
        msg = "All column lists must be same length"
        with pytest.raises(ValueError, match=msg):
            lreshape(df, spec)


class TestWideToLong:
    def test_simple(self):
        np.random.seed(123)
        x = np.random.randn(3)
        df = pd.DataFrame(
            {
                "A1970": {0: "a", 1: "b", 2: "c"},
                "A1980": {0: "d", 1: "e", 2: "f"},
                "B1970": {0: 2.5, 1: 1.2, 2: 0.7},
                "B1980": {0: 3.2, 1: 1.3, 2: 0.1},
                "X": dict(zip(range(3), x)),
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": x.tolist() + x.tolist(),
            "A": ["a", "b", "c", "d", "e", "f"],
            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
            "year": [1970, 1970, 1970, 1980, 1980, 1980],
            "id": [0, 1, 2, 0, 1, 2],
        }
        expected = DataFrame(exp_data)
        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
        result = wide_to_long(df, ["A", "B"], i="id", j="year")
        tm.assert_frame_equal(result, expected)

    def test_stubs(self):
        # GH9204
        df = pd.DataFrame([[0, 1, 2, 3, 8], [4, 5, 6, 7, 9]])
        df.columns = ["id", "inc1", "inc2", "edu1", "edu2"]
        stubs = ["inc", "edu"]

        # TODO: unused?
        df_long = pd.wide_to_long(df, stubs, i="id", j="age")  # noqa

        assert stubs == ["inc", "edu"]

    def test_separating_character(self):
        # GH14779
        np.random.seed(123)
        x = np.random.randn(3)
        df = pd.DataFrame(
            {
                "A.1970": {0: "a", 1: "b", 2: "c"},
                "A.1980": {0: "d", 1: "e", 2: "f"},
                "B.1970": {0: 2.5, 1: 1.2, 2: 0.7},
                "B.1980": {0: 3.2, 1: 1.3, 2: 0.1},
                "X": dict(zip(range(3), x)),
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": x.tolist() + x.tolist(),
            "A": ["a", "b", "c", "d", "e", "f"],
            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
            "year": [1970, 1970, 1970, 1980, 1980, 1980],
            "id": [0, 1, 2, 0, 1, 2],
        }
        expected = DataFrame(exp_data)
        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=".")
        tm.assert_frame_equal(result, expected)

    def test_escapable_characters(self):
        np.random.seed(123)
        x = np.random.randn(3)
        df = pd.DataFrame(
            {
                "A(quarterly)1970": {0: "a", 1: "b", 2: "c"},
                "A(quarterly)1980": {0: "d", 1: "e", 2: "f"},
                "B(quarterly)1970": {0: 2.5, 1: 1.2, 2: 0.7},
                "B(quarterly)1980": {0: 3.2, 1: 1.3, 2: 0.1},
                "X": dict(zip(range(3), x)),
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": x.tolist() + x.tolist(),
            "A(quarterly)": ["a", "b", "c", "d", "e", "f"],
            "B(quarterly)": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
            "year": [1970, 1970, 1970, 1980, 1980, 1980],
            "id": [0, 1, 2, 0, 1, 2],
        }
        expected = DataFrame(exp_data)
        expected = expected.set_index(["id", "year"])[
            ["X", "A(quarterly)", "B(quarterly)"]
        ]
        result = wide_to_long(df, ["A(quarterly)", "B(quarterly)"], i="id", j="year")
        tm.assert_frame_equal(result, expected)

    def test_unbalanced(self):
        # test that we can have a varying amount of time variables
        df = pd.DataFrame(
            {
                "A2010": [1.0, 2.0],
                "A2011": [3.0, 4.0],
                "B2010": [5.0, 6.0],
                "X": ["X1", "X2"],
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": ["X1", "X1", "X2", "X2"],
            "A": [1.0, 3.0, 2.0, 4.0],
            "B": [5.0, np.nan, 6.0, np.nan],
            "id": [0, 0, 1, 1],
            "year": [2010, 2011, 2010, 2011],
        }
        expected = pd.DataFrame(exp_data)
        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
        result = wide_to_long(df, ["A", "B"], i="id", j="year")
        tm.assert_frame_equal(result, expected)

    def test_character_overlap(self):
        # Test we handle overlapping characters in both id_vars and value_vars
        df = pd.DataFrame(
            {
                "A11": ["a11", "a22", "a33"],
                "A12": ["a21", "a22", "a23"],
                "B11": ["b11", "b12", "b13"],
                "B12": ["b21", "b22", "b23"],
                "BB11": [1, 2, 3],
                "BB12": [4, 5, 6],
                "BBBX": [91, 92, 93],
                "BBBZ": [91, 92, 93],
            }
        )
        df["id"] = df.index
        expected = pd.DataFrame(
            {
                "BBBX": [91, 92, 93, 91, 92, 93],
                "BBBZ": [91, 92, 93, 91, 92, 93],
                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
                "BB": [1, 2, 3, 4, 5, 6],
                "id": [0, 1, 2, 0, 1, 2],
                "year": [11, 11, 11, 12, 12, 12],
            }
        )
        expected = expected.set_index(["id", "year"])[["BBBX", "BBBZ", "A", "B", "BB"]]
        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))

    def test_invalid_separator(self):
        # if an invalid separator is supplied a empty data frame is returned
        sep = "nope!"
        df = pd.DataFrame(
            {
                "A2010": [1.0, 2.0],
                "A2011": [3.0, 4.0],
                "B2010": [5.0, 6.0],
                "X": ["X1", "X2"],
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": "",
            "A2010": [],
            "A2011": [],
            "B2010": [],
            "id": [],
            "year": [],
            "A": [],
            "B": [],
        }
        expected = pd.DataFrame(exp_data).astype({"year": "int"})
        expected = expected.set_index(["id", "year"])[
            ["X", "A2010", "A2011", "B2010", "A", "B"]
        ]
        expected.index.set_levels([0, 1], level=0, inplace=True)
        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=sep)
        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))

    def test_num_string_disambiguation(self):
        # Test that we can disambiguate number value_vars from
        # string value_vars
        df = pd.DataFrame(
            {
                "A11": ["a11", "a22", "a33"],
                "A12": ["a21", "a22", "a23"],
                "B11": ["b11", "b12", "b13"],
                "B12": ["b21", "b22", "b23"],
                "BB11": [1, 2, 3],
                "BB12": [4, 5, 6],
                "Arating": [91, 92, 93],
                "Arating_old": [91, 92, 93],
            }
        )
        df["id"] = df.index
        expected = pd.DataFrame(
            {
                "Arating": [91, 92, 93, 91, 92, 93],
                "Arating_old": [91, 92, 93, 91, 92, 93],
                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
                "BB": [1, 2, 3, 4, 5, 6],
                "id": [0, 1, 2, 0, 1, 2],
                "year": [11, 11, 11, 12, 12, 12],
            }
        )
        expected = expected.set_index(["id", "year"])[
            ["Arating", "Arating_old", "A", "B", "BB"]
        ]
        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))

    def test_invalid_suffixtype(self):
        # If all stubs names end with a string, but a numeric suffix is
        # assumed,  an empty data frame is returned
        df = pd.DataFrame(
            {
                "Aone": [1.0, 2.0],
                "Atwo": [3.0, 4.0],
                "Bone": [5.0, 6.0],
                "X": ["X1", "X2"],
            }
        )
        df["id"] = df.index
        exp_data = {
            "X": "",
            "Aone": [],
            "Atwo": [],
            "Bone": [],
            "id": [],
            "year": [],
            "A": [],
            "B": [],
        }
        expected = pd.DataFrame(exp_data).astype({"year": "int"})

        expected = expected.set_index(["id", "year"])
        expected.index.set_levels([0, 1], level=0, inplace=True)
        result = wide_to_long(df, ["A", "B"], i="id", j="year")
        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))

    def test_multiple_id_columns(self):
        # Taken from http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm
        df = pd.DataFrame(
            {
                "famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
                "birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
                "ht1": [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
                "ht2": [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9],
            }
        )
        expected = pd.DataFrame(
            {
                "ht": [
                    2.8,
                    3.4,
                    2.9,
                    3.8,
                    2.2,
                    2.9,
                    2.0,
                    3.2,
                    1.8,
                    2.8,
                    1.9,
                    2.4,
                    2.2,
                    3.3,
                    2.3,
                    3.4,
                    2.1,
                    2.9,
                ],
                "famid": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
                "birth": [1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3],
                "age": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
            }
        )
        expected = expected.set_index(["famid", "birth", "age"])[["ht"]]
        result = wide_to_long(df, "ht", i=["famid", "birth"], j="age")
        tm.assert_frame_equal(result, expected)

    def test_non_unique_idvars(self):
        # GH16382
        # Raise an error message if non unique id vars (i) are passed
        df = pd.DataFrame(
            {"A_A1": [1, 2, 3, 4, 5], "B_B1": [1, 2, 3, 4, 5], "x": [1, 1, 1, 1, 1]}
        )
        msg = "the id variables need to uniquely identify each row"
        with pytest.raises(ValueError, match=msg):
            wide_to_long(df, ["A_A", "B_B"], i="x", j="colname")

    def test_cast_j_int(self):
        df = pd.DataFrame(
            {
                "actor_1": ["CCH Pounder", "Johnny Depp", "Christoph Waltz"],
                "actor_2": ["Joel David Moore", "Orlando Bloom", "Rory Kinnear"],
                "actor_fb_likes_1": [1000.0, 40000.0, 11000.0],
                "actor_fb_likes_2": [936.0, 5000.0, 393.0],
                "title": ["Avatar", "Pirates of the Caribbean", "Spectre"],
            }
        )

        expected = pd.DataFrame(
            {
                "actor": [
                    "CCH Pounder",
                    "Johnny Depp",
                    "Christoph Waltz",
                    "Joel David Moore",
                    "Orlando Bloom",
                    "Rory Kinnear",
                ],
                "actor_fb_likes": [1000.0, 40000.0, 11000.0, 936.0, 5000.0, 393.0],
                "num": [1, 1, 1, 2, 2, 2],
                "title": [
                    "Avatar",
                    "Pirates of the Caribbean",
                    "Spectre",
                    "Avatar",
                    "Pirates of the Caribbean",
                    "Spectre",
                ],
            }
        ).set_index(["title", "num"])
        result = wide_to_long(
            df, ["actor", "actor_fb_likes"], i="title", j="num", sep="_"
        )

        tm.assert_frame_equal(result, expected)

    def test_identical_stubnames(self):
        df = pd.DataFrame(
            {
                "A2010": [1.0, 2.0],
                "A2011": [3.0, 4.0],
                "B2010": [5.0, 6.0],
                "A": ["X1", "X2"],
            }
        )
        msg = "stubname can't be identical to a column name"
        with pytest.raises(ValueError, match=msg):
            wide_to_long(df, ["A", "B"], i="A", j="colname")

    def test_nonnumeric_suffix(self):
        df = pd.DataFrame(
            {
                "treatment_placebo": [1.0, 2.0],
                "treatment_test": [3.0, 4.0],
                "result_placebo": [5.0, 6.0],
                "A": ["X1", "X2"],
            }
        )
        expected = pd.DataFrame(
            {
                "A": ["X1", "X1", "X2", "X2"],
                "colname": ["placebo", "test", "placebo", "test"],
                "result": [5.0, np.nan, 6.0, np.nan],
                "treatment": [1.0, 3.0, 2.0, 4.0],
            }
        )
        expected = expected.set_index(["A", "colname"])
        result = wide_to_long(
            df, ["result", "treatment"], i="A", j="colname", suffix="[a-z]+", sep="_"
        )
        tm.assert_frame_equal(result, expected)

    def test_mixed_type_suffix(self):
        df = pd.DataFrame(
            {
                "A": ["X1", "X2"],
                "result_1": [0, 9],
                "result_foo": [5.0, 6.0],
                "treatment_1": [1.0, 2.0],
                "treatment_foo": [3.0, 4.0],
            }
        )
        expected = pd.DataFrame(
            {
                "A": ["X1", "X2", "X1", "X2"],
                "colname": ["1", "1", "foo", "foo"],
                "result": [0.0, 9.0, 5.0, 6.0],
                "treatment": [1.0, 2.0, 3.0, 4.0],
            }
        ).set_index(["A", "colname"])
        result = wide_to_long(
            df, ["result", "treatment"], i="A", j="colname", suffix=".+", sep="_"
        )
        tm.assert_frame_equal(result, expected)

    def test_float_suffix(self):
        df = pd.DataFrame(
            {
                "treatment_1.1": [1.0, 2.0],
                "treatment_2.1": [3.0, 4.0],
                "result_1.2": [5.0, 6.0],
                "result_1": [0, 9],
                "A": ["X1", "X2"],
            }
        )
        expected = pd.DataFrame(
            {
                "A": ["X1", "X1", "X1", "X1", "X2", "X2", "X2", "X2"],
                "colname": [1, 1.1, 1.2, 2.1, 1, 1.1, 1.2, 2.1],
                "result": [0.0, np.nan, 5.0, np.nan, 9.0, np.nan, 6.0, np.nan],
                "treatment": [np.nan, 1.0, np.nan, 3.0, np.nan, 2.0, np.nan, 4.0],
            }
        )
        expected = expected.set_index(["A", "colname"])
        result = wide_to_long(
            df, ["result", "treatment"], i="A", j="colname", suffix="[0-9.]+", sep="_"
        )
        tm.assert_frame_equal(result, expected)

    def test_col_substring_of_stubname(self):
        # GH22468
        # Don't raise ValueError when a column name is a substring
        # of a stubname that's been passed as a string
        wide_data = {
            "node_id": {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
            "A": {0: 0.80, 1: 0.0, 2: 0.25, 3: 1.0, 4: 0.81},
            "PA0": {0: 0.74, 1: 0.56, 2: 0.56, 3: 0.98, 4: 0.6},
            "PA1": {0: 0.77, 1: 0.64, 2: 0.52, 3: 0.98, 4: 0.67},
            "PA3": {0: 0.34, 1: 0.70, 2: 0.52, 3: 0.98, 4: 0.67},
        }
        wide_df = pd.DataFrame.from_dict(wide_data)
        expected = pd.wide_to_long(
            wide_df, stubnames=["PA"], i=["node_id", "A"], j="time"
        )
        result = pd.wide_to_long(wide_df, stubnames="PA", i=["node_id", "A"], j="time")
        tm.assert_frame_equal(result, expected)
