def test_from_pandas_multiindex(self):
pandas_index = pd.MultiIndex.from_product([[0, 1], [2., 3.]])
actual = MultiIndex.from_pandas(pandas_index)
expected = MultiIndex([[0, 0, 1, 1], [2., 3., 2., 3.]])
assert_multiindex_equal(actual, expected)
def test_to_pandas_multiindex(self):
data = [[0, 0, 1, 1], [2., 3., 2., 3.]]
mi = MultiIndex(data)
actual = mi.to_pandas()
expected = pd.MultiIndex.from_arrays(data)
assert actual.equals(expected)
def test_dropna(self):
mi = MultiIndex([[0, -999, 2, -999],
Index([1., -999., -999., 3.],
dtype=np.dtype(np.float64))])
actual = mi.dropna()
expected = MultiIndex([[0], [1.]])
assert_multiindex_equal(actual, expected)
def test_slice(self, data_f32, index_i64):
mi = MultiIndex([data_f32, index_i64])
actual = mi[1:3]
expected = MultiIndex([
Index(np.array([2, 3], dtype=np.float32), np.dtype(np.float32)),
Index(np.array([1, 2], dtype=np.int64), np.dtype(np.int64))
])
assert_multiindex_equal(actual, expected)
def test_filter(self, data_f32, index_i64):
mi = MultiIndex([data_f32, index_i64])
actual = mi[Index(np.array([False, True, True, False, False]))]
expected = MultiIndex([
Index(np.array([2, 3], dtype=np.float32), np.dtype(np.float32)),
Index(np.array([1, 2], dtype=np.int64), np.dtype(np.int64))
])
assert_multiindex_equal(actual, expected)
def test_len_raw(self, data_f32, data_i64):
ind = MultiIndex([data_f32, data_i64])
actual = len(ind)
expected = 5
assert actual == expected
def test_set_multi_index(self, df_small, data_f32, data_i64, data_str):
actual = df_small.set_index(['b', 'c'])
expected = DataFrame({'a': data_f32},
MultiIndex([
Index(data_i64, np.dtype(np.int64), 'b'),
Index(data_str, name='c')
], ['b', 'c']))
assert_dataframe_equal(actual, expected)
def test_groupby_multi(self, index_i64, series_i64, df_dupl):
actual = df_dupl.groupby(['a', 'b']).min()
expected = DataFrame(
OrderedDict({'c': [1, 2, 4, 5]}),
MultiIndex([
Index(np.array([0, 1, 2, 3], dtype=np.float32),
np.dtype(np.float32), 'a'),
Index([4, 5, 6, 6], np.dtype(np.int64), 'b')
], ['a', 'b']))
assert_dataframe_equal(actual, expected, sort=True)
def test_reset_multi_index(self, data_f32, data_i64, data_str, index_i64,
names, expected_names):
df = DataFrame(OrderedDict({'c': data_str}),
MultiIndex([data_f32, data_i64], names=names))
actual = df.reset_index()
expected = DataFrame(
OrderedDict(((expected_names[0], data_f32),
(expected_names[1], data_i64), ('c', data_str))),
index_i64)
assert_dataframe_equal(actual, expected)
def test_merge_sorted_unique_multi_on_inner(self, df1, df2):
actual = df1.merge(df2, on=['a', 'b'], is_on_sorted=True)
expected = DataFrame(
OrderedDict(
(('index', np.array([5])), ('d',
np.array(['def'],
dtype=np.dtype('|S4'))),
('c', np.array([5])))),
MultiIndex(
[np.array([3]), np.array([4], dtype=np.float32)], ['a', 'b']))
assert_dataframe_equal(actual, expected)
def test_evaluate(self, data_f32, index_i64):
actual = MultiIndex([data_f32, index_i64]).evaluate()
expected = MultiIndex(
[Index(data_f32, np.dtype(np.float32)), index_i64])
assert_multiindex_equal(actual, expected)
def df2():
return DataFrame(OrderedDict((('b', np.arange(3, 6, dtype=np.float32)), ('c', np.arange(4, 7)))),
MultiIndex([np.array([1, 3, 5]),
Index(np.array(['abc', 'def', 'efgh'], dtype=np.bytes_))],
['a', 'd']))