code: import numpy as np
  import pytest
  
  import pandas as pd
  import pandas.util.testing as tm
  
  from .base import BaseExtensionTests
  
  
  class BaseMethodsTests(BaseExtensionTests):
      """Various Series and DataFrame methods."""
  
      @pytest.mark.parametrize('dropna', [True, False])
      def test_value_counts(self, all_data, dropna):
          all_data = all_data[:10]
          if dropna:
              other = np.array(all_data[~all_data.isna()])
          else:
              other = all_data
  
          result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
          expected = pd.Series(other).value_counts(
              dropna=dropna).sort_index()
  
          self.assert_series_equal(result, expected)
  
      def test_count(self, data_missing):
          df = pd.DataFrame({"A": data_missing})
          result = df.count(axis='columns')
          expected = pd.Series([0, 1])
          self.assert_series_equal(result, expected)
  
      def test_apply_simple_series(self, data):
          result = pd.Series(data).apply(id)
          assert isinstance(result, pd.Series)
  
      def test_argsort(self, data_for_sorting):
          result = pd.Series(data_for_sorting).argsort()
          expected = pd.Series(np.array([2, 0, 1], dtype=np.int64))
          self.assert_series_equal(result, expected)
  
      def test_argsort_missing(self, data_missing_for_sorting):
          result = pd.Series(data_missing_for_sorting).argsort()
          expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
          self.assert_series_equal(result, expected)
  
      @pytest.mark.parametrize('ascending', [True, False])
      def test_sort_values(self, data_for_sorting, ascending):
          ser = pd.Series(data_for_sorting)
          result = ser.sort_values(ascending=ascending)
          expected = ser.iloc[[2, 0, 1]]
          if not ascending:
              expected = expected[::-1]
  
          self.assert_series_equal(result, expected)
  
      @pytest.mark.parametrize('ascending', [True, False])
      def test_sort_values_missing(self, data_missing_for_sorting, ascending):
          ser = pd.Series(data_missing_for_sorting)
          result = ser.sort_values(ascending=ascending)
          if ascending:
              expected = ser.iloc[[2, 0, 1]]
          else:
              expected = ser.iloc[[0, 2, 1]]
          self.assert_series_equal(result, expected)
  
      @pytest.mark.parametrize('ascending', [True, False])
      def test_sort_values_frame(self, data_for_sorting, ascending):
          df = pd.DataFrame({"A": [1, 2, 1],
                             "B": data_for_sorting})
          result = df.sort_values(['A', 'B'])
          expected = pd.DataFrame({"A": [1, 1, 2],
                                   'B': data_for_sorting.take([2, 0, 1])},
                                  index=[2, 0, 1])
          self.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize('box', [pd.Series, lambda x: x])
      @pytest.mark.parametrize('method', [lambda x: x.unique(), pd.unique])
      def test_unique(self, data, box, method):
          duplicated = box(data._from_sequence([data[0], data[0]]))
  
          result = method(duplicated)
  
          assert len(result) == 1
          assert isinstance(result, type(data))
          assert result[0] == duplicated[0]
  
      @pytest.mark.parametrize('na_sentinel', [-1, -2])
      def test_factorize(self, data_for_grouping, na_sentinel):
          labels, uniques = pd.factorize(data_for_grouping,
                                         na_sentinel=na_sentinel)
          expected_labels = np.array([0, 0, na_sentinel,
                                     na_sentinel, 1, 1, 0, 2],
                                     dtype=np.intp)
          expected_uniques = data_for_grouping.take([0, 4, 7])
  
          tm.assert_numpy_array_equal(labels, expected_labels)
          self.assert_extension_array_equal(uniques, expected_uniques)
  
      @pytest.mark.parametrize('na_sentinel', [-1, -2])
      def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
          l1, u1 = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
          l2, u2 = data_for_grouping.factorize(na_sentinel=na_sentinel)
  
          tm.assert_numpy_array_equal(l1, l2)
          self.assert_extension_array_equal(u1, u2)
  
      def test_combine_le(self, data_repeated):
          # GH 20825
          # Test that combine works when doing a <= (le) comparison
          orig_data1, orig_data2 = data_repeated(2)
          s1 = pd.Series(orig_data1)
          s2 = pd.Series(orig_data2)
          result = s1.combine(s2, lambda x1, x2: x1 <= x2)
          expected = pd.Series([a <= b for (a, b) in
                                zip(list(orig_data1), list(orig_data2))])
          self.assert_series_equal(result, expected)
  
          val = s1.iloc[0]
          result = s1.combine(val, lambda x1, x2: x1 <= x2)
          expected = pd.Series([a <= val for a in list(orig_data1)])
          self.assert_series_equal(result, expected)
  
      def test_combine_add(self, data_repeated):
          # GH 20825
          orig_data1, orig_data2 = data_repeated(2)
          s1 = pd.Series(orig_data1)
          s2 = pd.Series(orig_data2)
          result = s1.combine(s2, lambda x1, x2: x1 + x2)
          with np.errstate(over='ignore'):
              expected = pd.Series(
                  orig_data1._from_sequence([a + b for (a, b) in
                                             zip(list(orig_data1),
                                                 list(orig_data2))]))
          self.assert_series_equal(result, expected)
  
          val = s1.iloc[0]
          result = s1.combine(val, lambda x1, x2: x1 + x2)
          expected = pd.Series(
              orig_data1._from_sequence([a + val for a in list(orig_data1)]))
          self.assert_series_equal(result, expected)
  
      @pytest.mark.parametrize('frame', [True, False])
      @pytest.mark.parametrize('periods, indices', [
          (-2, [2, 3, 4, -1, -1]),
          (0, [0, 1, 2, 3, 4]),
          (2, [-1, -1, 0, 1, 2]),
      ])
      def test_container_shift(self, data, frame, periods, indices):
          # https://github.com/pandas-dev/pandas/issues/22386
          subset = data[:5]
          data = pd.Series(subset, name='A')
          expected = pd.Series(subset.take(indices, allow_fill=True), name='A')
  
          if frame:
              result = data.to_frame(name='A').assign(B=1).shift(periods)
              expected = pd.concat([
                  expected,
                  pd.Series([1] * 5, name='B').shift(periods)
              ], axis=1)
              compare = self.assert_frame_equal
          else:
              result = data.shift(periods)
              compare = self.assert_series_equal
  
          compare(result, expected)
  
      @pytest.mark.parametrize("as_frame", [True, False])
      def test_hash_pandas_object_works(self, data, as_frame):
          # https://github.com/pandas-dev/pandas/issues/23066
          data = pd.Series(data)
          if as_frame:
              data = data.to_frame()
          a = pd.util.hash_pandas_object(data)
          b = pd.util.hash_pandas_object(data)
          self.assert_equal(a, b)
  
apis: pandas.util.hash_pandas_object pandas.Series pandas.util.testing.assert_numpy_array_equal pandas.DataFrame numpy.errstate numpy.array pandas.factorize