def test_offset_1part_0(self): # 0 parts in_val = [] expected = [] ind = FilterByRepeatIndexer(in_val) actual = ind.run() self.assertEqual(len(expected), len(actual)) # same number of parts?
def test_offset_1part_2(self): # input is expected output in_val = [pandas.Series(['a', 'b', 'c', 'd'], index=[0.0, 0.5, 1.0, 1.5])] expected = [pandas.Series(['a', 'b', 'c', 'd'], index=[0.0, 0.5, 1.0, 1.5])] ind = FilterByRepeatIndexer(in_val) actual = ind.run() self.assertEqual(len(expected), len(actual)) # same number of parts? for i in xrange(len(expected)): # compare each Series self.assertSequenceEqual(list(expected[i].index), list(actual[i].index)) self.assertSequenceEqual(list(expected[i].values), list(actual[i].values))
def test_offset_1part_1(self): # 0 length # NOTE: this requires much more extensive testing in the multi-part suite in_val = [pandas.Series()] expected = [pandas.Series()] ind = FilterByRepeatIndexer(in_val) actual = ind.run() self.assertEqual(len(expected), len(actual)) # same number of parts? for part_i in xrange(len(expected)): # same number of rows? self.assertEqual(len(expected[part_i].index), len(actual[part_i].index))
def test_offset_1part_6(self): # pseudo-random in_val = [pandas.Series(['d', 'd', 'a', 's', 's', 'd', 'f', 'a', 'f', 'f', 's', 'd', 'f', 's', 'f', 'd', 's', 's', 'a', 's'], index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])] expected = [pandas.Series(['d', 'a', 's', 'd', 'f', 'a', 'f', 's', 'd', 'f', 's', 'f', 'd', 's', 'a', 's'], index=[1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20])] ind = FilterByRepeatIndexer(in_val) actual = ind.run() self.assertEqual(len(expected), len(actual)) # same number of parts? for i in xrange(len(expected)): # compare each Series self.assertSequenceEqual(list(expected[i].index), list(actual[i].index)) self.assertSequenceEqual(list(expected[i].values), list(actual[i].values))