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))
