text: # -*- coding: utf-8 -*-
  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%d' % i for i in range(1, 4)],
                  'wt': ['wt%d' % 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%d' % i for i in range(1, 3)],
                  'wt': ['wt%d' % 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: .7},
                             "B1980": {0: 3.2,
                                       1: 1.3,
                                       2: .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: .7},
                             "B.1980": {0: 3.2,
                                        1: 1.3,
                                        2: .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: .7},
                             "B(quarterly)1980": {0: 3.2,
                                                  1: 1.3,
                                                  2: .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)
  