def test_hierarchy_from_labels_e(self) -> None:
index_constructors = (Index, IndexDate)
labels = (
('a', '2019-01-01'),
('a', '2019-02-01'),
('b', '2019-01-01'),
('b', '2019-02-01'),
)
from static_frame.core.exception import ErrorInitIndex
with self.assertRaises(ErrorInitIndex):
ih = IndexHierarchy.from_labels(labels,
index_constructors=(Index, ))
ih = IndexHierarchy.from_labels(labels,
index_constructors=index_constructors)
self.assertEqual(ih.loc[HLoc[:, '2019-02']].values.tolist(),
[['a', datetime.date(2019, 2, 1)],
['b', datetime.date(2019, 2, 1)]])
self.assertEqual(ih.loc[HLoc[:, '2019']].values.tolist(),
[['a', datetime.date(2019, 1, 1)],
['a', datetime.date(2019, 2, 1)],
['b', datetime.date(2019, 1, 1)],
['b', datetime.date(2019, 2, 1)]])
self.assertEqual(ih.loc[HLoc[:, '2019-02-01']].values.tolist(),
[['a', datetime.date(2019, 2, 1)],
['b', datetime.date(2019, 2, 1)]])
def test_index_many_concat_c(self) -> None:
from datetime import date
i1 = IndexHierarchy.from_labels(
[[1, date(2019, 1, 1)], [2, date(2019, 1, 2)]],
index_constructors=[Index, IndexDate])
i2 = IndexHierarchy.from_labels(
[[2, date(2019, 1, 3)], [3, date(2019, 1, 4)]],
index_constructors=[Index, IndexDate])
i3 = IndexHierarchy.from_labels(
[[4, date(2019, 1, 5)], [5, date(2019, 1, 6)]],
index_constructors=[Index, IndexDate])
i4 = IndexHierarchy.from_labels([[4, date(2019, 1, 5)],
[5, date(2019, 1, 6)]])
i5 = index_many_concat((i1, i2, i3), cls_default=Index)
assert isinstance(i5, IndexHierarchy)
self.assertEqual(i5.index_types.to_pairs(),
((0, Index), (1, IndexDate)))
self.assertEqual(i5.values.tolist(),
[[1, date(2019, 1, 1)], [2, date(2019, 1, 2)],
[2, date(2019, 1, 3)], [3, date(2019, 1, 4)],
[4, date(2019, 1, 5)], [5, date(2019, 1, 6)]])
# with unaligned index types we fall back in Index
i6 = index_many_concat((i1, i2, i4), cls_default=Index)
assert isinstance(i6, IndexHierarchy)
self.assertEqual(i6.index_types.to_pairs(), ((0, Index), (1, Index)))
def test_hierarchy_from_labels_a(self):
labels = (
('I', 'A', 1),
('I', 'A', 2),
('I', 'B', 1),
('I', 'B', 2),
('II', 'A', 1),
('II', 'A', 2),
('II', 'B', 1),
('II', 'B', 2),
)
ih = IndexHierarchy.from_labels(labels)
self.assertEqual(len(ih), 8)
self.assertEqual(ih.depth, 3)
self.assertEqual([ih.loc_to_iloc(x) for x in labels],
[0, 1, 2, 3, 4, 5, 6, 7])
labels = (
('I', 'A', 1),
('I', 'A', 2),
('I', 'B', 1),
('II', 'B', 2),
)
ih = IndexHierarchy.from_labels(labels)
self.assertEqual(len(ih), 4)
self.assertEqual(ih.depth, 3)
self.assertEqual([ih.loc_to_iloc(x) for x in labels], [0, 1, 2, 3])
def test_hierarchy_intersection_a(self):
labels = (
('I', 'A'),
('I', 'B'),
('II', 'A'),
('II', 'B'),
)
ih1 = IndexHierarchy.from_labels(labels)
labels = (
('II', 'A'),
('II', 'B'),
('III', 'A'),
('III', 'B'),
)
ih2 = IndexHierarchy.from_labels(labels)
post = ih1.intersection(ih2)
self.assertEqual(post.values.tolist(), [['II', 'A'], ['II', 'B']])
post = ih1.union(ih2)
self.assertEqual(post.values.tolist(),
[['I', 'A'], ['I', 'B'], ['II', 'A'], ['II', 'B'],
['III', 'A'], ['III', 'B']])
def test_index_display(self, ih: IndexHierarchy) -> None:
d1 = ih.display()
self.assertTrue(len(d1) > 0)
d2 = ih.display_tall()
self.assertTrue(len(d2) > 0)
d3 = ih.display_wide()
self.assertTrue(len(d3) > 0)
def test_hierarhcy_init_a(self):
labels = (('I', 'A'),
('I', 'B'),
)
ih1 = IndexHierarchy.from_labels(labels, name='foo')
ih2 = IndexHierarchy(ih1)
self.assertEqual(ih1.name, 'foo')
self.assertEqual(ih2.name, 'foo')
def test_index_levels_equals_b(self) -> None:
idx1 = Index(('a', 'b', 'c', 'd', 'e'))
idx2 = Index(range(10))
levels1 = IndexHierarchy.from_product(idx1, idx2)._levels
idx3 = Index(('a', 'b', 'c', 'd', 'e'))
idx4 = Index(range(10))
levels2 = IndexHierarchy.from_product(idx3, idx4)._levels
self.assertTrue(levels1.equals(levels2))
def test_index_hierarchy_pickle_a(self) -> None:
a = IndexHierarchy.from_product((10, 20), (3, 7))
b = IndexHierarchy.from_product(('a', 'b'), ('x', 'y'))
for index in (a, b):
# force creating of ._labels
self.assertTrue(len(index.values), len(index))
pbytes = pickle.dumps(index)
index_new = pickle.loads(pbytes)
for v in index: # iter labels (arrays here)
self.assertFalse(index_new._labels.flags.writeable)
self.assertEqual(index_new.loc[tuple(v)], index.loc[tuple(v)])
def from_pandas(cls, value, *, is_go: bool = False) -> 'IndexBase':
'''
Given a Pandas index, return the appropriate IndexBase derived class.
'''
import pandas
from static_frame import Index
from static_frame import IndexGO
from static_frame import IndexDate
from static_frame import IndexHierarchy
from static_frame import IndexHierarchyGO
if isinstance(value, pandas.MultiIndex):
# iterating over a hierarchucal index will iterate over labels
if is_go:
return IndexHierarchyGO.from_labels(value)
return IndexHierarchy.from_labels(value)
elif isinstance(value, pandas.DatetimeIndex):
if is_go:
raise NotImplementedError(
'No grow-only version of IndexDate yet exists')
return IndexDate(value)
if is_go:
return IndexGO(value)
return Index(value)
def test_hierarchy_frame_a(self):
OD = OrderedDict
tree = OD([
('I', OD([
('A', (1,)), ('B', (1, 2))
])
),
('II', OD([
('A', (1,)), ('B', (1, 2))
])
),
])
ih = IndexHierarchy.from_tree(tree)
data = np.arange(6*6).reshape(6, 6)
f1 = Frame(data, index=ih, columns=ih)
# self.assertEqual(len(f.to_pairs(0)), 8)
f2 = f1.assign.loc[('I', 'B', 2), ('II', 'A', 1)](200)
post = f2.to_pairs(0)
self.assertEqual(post,
((('I', 'A', 1), ((('I', 'A', 1), 0), (('I', 'B', 1), 6), (('I', 'B', 2), 12), (('II', 'A', 1), 18), (('II', 'B', 1), 24), (('II', 'B', 2), 30))), (('I', 'B', 1), ((('I', 'A', 1), 1), (('I', 'B', 1), 7), (('I', 'B', 2), 13), (('II', 'A', 1), 19), (('II', 'B', 1), 25), (('II', 'B', 2), 31))), (('I', 'B', 2), ((('I', 'A', 1), 2), (('I', 'B', 1), 8), (('I', 'B', 2), 14), (('II', 'A', 1), 20), (('II', 'B', 1), 26), (('II', 'B', 2), 32))), (('II', 'A', 1), ((('I', 'A', 1), 3), (('I', 'B', 1), 9), (('I', 'B', 2), 200), (('II', 'A', 1), 21), (('II', 'B', 1), 27), (('II', 'B', 2), 33))), (('II', 'B', 1), ((('I', 'A', 1), 4), (('I', 'B', 1), 10), (('I', 'B', 2), 16), (('II', 'A', 1), 22), (('II', 'B', 1), 28), (('II', 'B', 2), 34))), (('II', 'B', 2), ((('I', 'A', 1), 5), (('I', 'B', 1), 11), (('I', 'B', 2), 17), (('II', 'A', 1), 23), (('II', 'B', 1), 29), (('II', 'B', 2), 35))))
)
f3 = f1.assign.loc[('I', 'B', 2):, HLoc[:, :, 2]](200)
self.assertEqual(f3.to_pairs(0),
((('I', 'A', 1), ((('I', 'A', 1), 0), (('I', 'B', 1), 6), (('I', 'B', 2), 12), (('II', 'A', 1), 18), (('II', 'B', 1), 24), (('II', 'B', 2), 30))), (('I', 'B', 1), ((('I', 'A', 1), 1), (('I', 'B', 1), 7), (('I', 'B', 2), 13), (('II', 'A', 1), 19), (('II', 'B', 1), 25), (('II', 'B', 2), 31))), (('I', 'B', 2), ((('I', 'A', 1), 2), (('I', 'B', 1), 8), (('I', 'B', 2), 200), (('II', 'A', 1), 200), (('II', 'B', 1), 200), (('II', 'B', 2), 200))), (('II', 'A', 1), ((('I', 'A', 1), 3), (('I', 'B', 1), 9), (('I', 'B', 2), 15), (('II', 'A', 1), 21), (('II', 'B', 1), 27), (('II', 'B', 2), 33))), (('II', 'B', 1), ((('I', 'A', 1), 4), (('I', 'B', 1), 10), (('I', 'B', 2), 16), (('II', 'A', 1), 22), (('II', 'B', 1), 28), (('II', 'B', 2), 34))), (('II', 'B', 2), ((('I', 'A', 1), 5), (('I', 'B', 1), 11), (('I', 'B', 2), 200), (('II', 'A', 1), 200), (('II', 'B', 1), 200), (('II', 'B', 2), 200))))
)
def test_hierarchy_contains_a(self):
labels = (('I', 'A'),
('I', 'B'),
)
ih = IndexHierarchy.from_labels(labels)
self.assertTrue(('I', 'A') in ih)
def test_index_level_index_types_a(self) -> None:
idx1 = Index(('A', 'B'))
idx2 = IndexDate.from_date_range('2019-01-05', '2019-01-08')
idx3 = Index((1, 2))
hidx = IndexHierarchy.from_product(idx1, idx2, idx3)
self.assertEqual([it.__name__ for it in hidx._levels.index_types()],
['Index', 'IndexDate', 'Index'])
def from_pandas(cls,
value: 'pandas.DataFrame',
) -> 'IndexBase':
'''
Given a Pandas index, return the appropriate IndexBase derived class.
'''
import pandas
from static_frame.core.index_datetime import IndexDatetime
from static_frame import Index
from static_frame import IndexGO
from static_frame import IndexHierarchy
from static_frame import IndexHierarchyGO
if isinstance(value, pandas.MultiIndex):
# iterating over a hierarchucal index will iterate over labels
name = tuple(value.names)
if not cls.STATIC:
return IndexHierarchyGO.from_labels(value, name=name)
return IndexHierarchy.from_labels(value, name=name)
elif isinstance(value, pandas.DatetimeIndex):
# coming from a Pandas datetime index, in the absence of other information, the best match is a Nanosecond index
if not issubclass(cls, IndexDatetime):
raise ErrorInitIndex(f'cannot create a datetime Index from {cls}')
if not cls.STATIC:
return cls(value, name=value.name)
return cls(value, name=value.name)
if not cls.STATIC:
return IndexGO(value, name=value.name)
return Index(value, name=value.name)
def test_hierarchy_extract_a(self):
idx = IndexHierarchy.from_product(['A', 'B'], [1, 2])
self.assertEqual(idx.iloc[1], ('A', 2))
self.assertEqual(idx.loc[('B', 1)], ('B', 1))
self.assertEqual(idx[2], ('B', 1))
self.assertEqual(idx.loc[HLoc['B', 1]], ('B', 1))
def test_hierarchy_name_a(self):
idx1 = IndexHierarchy.from_product(list('ab'), list('xy'), name='q')
self.assertEqual(idx1.name, 'q')
idx2 = idx1.rename('w')
self.assertEqual(idx2.name, 'w')
def test_hierarchy_relabel_a(self):
labels = (
('I', 'A'),
('I', 'B'),
('II', 'A'),
('II', 'B'),
)
ih = IndexHierarchy.from_labels(labels)
ih.relabel({('I', 'B'): ('I', 'C')})
ih2 = ih.relabel({('I', 'B'): ('I', 'C')})
self.assertEqual(ih2.values.tolist(),
[['I', 'A'], ['I', 'C'], ['II', 'A'], ['II', 'B']])
with self.assertRaises(Exception):
ih3 = ih.relabel({('I', 'B'): ('I', 'C', 1)})
ih3 = ih.relabel(lambda x: tuple(e.lower() for e in x))
self.assertEqual(ih3.values.tolist(),
[['i', 'a'], ['i', 'b'], ['ii', 'a'], ['ii', 'b']])
def test_index_hierarchy_sort_a(self) -> None:
ih1 = IndexHierarchy.from_product((1, 2), (30, 70))
self.assertEqual(
ih1.sort(ascending=False).values.tolist(),
[[2, 70], [2, 30], [1, 70], [1, 30]])
def test_hierarchy_extract_a(self) -> None:
idx = IndexHierarchy.from_product(['A', 'B'], [1, 2])
self.assertEqual(idx.iloc[1], ('A', 2))
self.assertEqual(idx.loc[('B', 1)], ('B', 1))
self.assertEqual(idx[2], ('B', 1)) #pylint: disable=E1136
self.assertEqual(idx.loc[HLoc['B', 1]], ('B', 1))
def test_hierarchy_from_labels_delimited_a(self) -> None:
labels = ("'I' 'A'", "'I' 'B'")
ih = IndexHierarchy.from_labels_delimited(labels)
self.assertEqual(ih.values.tolist(), [['I', 'A'], ['I', 'B']])
def test_hierarchy_from_product_a(self):
groups = Index(('A', 'B', 'C'))
dates = IndexDate.from_date_range('2018-01-01', '2018-01-04')
observations = Index(('x', 'y'))
ih = IndexHierarchy.from_product(groups, dates, observations)
def test_frame_iter_element_c(self) -> None:
a2 = np.array([[None, None], [None, 1], [None, 5]], dtype=object)
a1 = np.array([True, False, True])
a3 = np.array([['a'], ['b'], ['c']])
tb1 = TypeBlocks.from_blocks((a3, a1, a2))
f1 = Frame(tb1,
index=self.get_letters(None, tb1.shape[0]),
columns=IndexHierarchy.from_product(('i', 'ii'),
('a', 'b')))
values = list(f1.iter_element())
self.assertEqual(
values,
['a', True, None, None, 'b', False, None, 1, 'c', True, None, 5])
f2 = f1.iter_element().apply(lambda x: str(x).lower().replace('e', ''))
self.assertEqual(
f1.columns.__class__,
f2.columns.__class__,
)
self.assertEqual(
f2.to_pairs(0),
((('i', 'a'), (('a', 'a'), ('b', 'b'), ('c', 'c'))),
(('i', 'b'), (('a', 'tru'), ('b', 'fals'), ('c', 'tru'))),
(('ii', 'a'), (('a', 'non'), ('b', 'non'), ('c', 'non'))),
(('ii', 'b'), (('a', 'non'), ('b', '1'), ('c', '5')))))
def test_build_key_indexers_from_key_a(self) -> None:
ih = IndexHierarchy.from_product(range(3), range(4, 7), tuple('ABC'))
hlmapA = ih._map
hlmapB = deepcopy(ih._map)
hlmapB.encoding_can_overflow = True
def check(
key: tuple, # type: ignore
expected: tp.List[tp.List[int]],
) -> None:
resultA = hlmapA.build_key_indexers(key, indices=ih._indices)
self.assertEqual(resultA.dtype, np.uint64)
self.assertListEqual(resultA.tolist(), expected)
resultB = hlmapB.build_key_indexers(key, indices=ih._indices)
self.assertEqual(resultB.dtype, object)
self.assertListEqual(resultB.tolist(), expected)
check((0, 5, 'A'), [0, 1, 0]) # type: ignore
check((0, 5, ['A']), [[0, 1, 0]])
check(([0, 1], 5, ['B']), [[0, 1, 1], [1, 1, 1]])
check(([0, 1], 5, 'A'), [[0, 1, 0], [1, 1, 0]])
check(
([0, 1], [4, 5, 6], 'C'),
[[0, 0, 2], [0, 1, 2], [0, 2, 2], [1, 0, 2], [1, 1, 2], [1, 2, 2]])
def test_index_level_label_widths_at_depth_a(self) -> None:
OD = OrderedDict
tree = OD([
('I', OD([
('A', (1, 2)), ('B', (1, 2, 3)), ('C', (2, 3))
])
),
('II', OD([
('A', (1,)), ('B', (1,))
])
),
('III', OD([
('A', (1, 2, 3)), ('B', (1,))
])
),
])
levels = IndexHierarchy._tree_to_index_level(tree)
post0 = tuple(levels.label_widths_at_depth(0))
post1 = tuple(levels.label_widths_at_depth(1))
post2 = tuple(levels.label_widths_at_depth(2))
self.assertEqual(post0, (('I', 7), ('II', 2), ('III', 4)))
self.assertEqual(post1,
(('A', 2), ('B', 3), ('C', 2), ('A', 1), ('B', 1), ('A', 3), ('B', 1))
)
self.assertEqual(post2,
(((1, 1), (2, 1), (1, 1), (2, 1), (3, 1), (2, 1), (3, 1), (1, 1), (1, 1), (1, 1), (2, 1), (3, 1), (1, 1)))
)
def test_hierarchy_reversed(self):
labels = (('a', 1), ('a', 2), ('b', 1), ('b', 2))
hier_idx = IndexHierarchy.from_labels(labels)
self.assertTrue(
all(
tuple(hidx_1) == hidx_2 for hidx_1, hidx_2 in zip(
reversed(hier_idx), reversed(labels))))
def test_index_many_concat_d(self) -> None:
from datetime import date
i1 = IndexHierarchy.from_labels(
[[1, date(2019, 1, 1)], [2, date(2019, 1, 2)]],
index_constructors=[Index, IndexDate])
i2 = IndexHierarchy.from_labels(
[[2, date(2019, 1, 3)], [3, date(2019, 1, 4)]],
index_constructors=[Index, IndexDate])
post1 = index_many_concat((i1, i2), cls_default=IndexGO)
self.assertEqual(post1.__class__, IndexHierarchyGO)
assert isinstance(post1, IndexHierarchy)
self.assertEqual(post1.values.tolist(),
[[1, date(2019, 1, 1)], [2, date(2019, 1, 2)],
[2, date(2019, 1, 3)], [3, date(2019, 1, 4)]])
def from_pandas(
cls,
value: 'pandas.DataFrame',
) -> 'IndexBase':
'''
Given a Pandas index, return the appropriate IndexBase derived class.
'''
import pandas
from static_frame import Index
from static_frame import IndexGO
from static_frame import IndexHierarchy
from static_frame import IndexHierarchyGO
from static_frame import IndexNanosecond
from static_frame import IndexNanosecondGO
from static_frame.core.index_datetime import IndexDatetime
if isinstance(value, pandas.MultiIndex):
# iterating over a hierarchucal index will iterate over labels
name = tuple(value.names)
if not cls.STATIC:
return IndexHierarchyGO.from_labels(value, name=name)
return IndexHierarchy.from_labels(value, name=name)
elif isinstance(value, pandas.DatetimeIndex):
# if IndexDatetime, use cls, else use IndexNanosecond
if issubclass(cls, IndexDatetime):
return cls(value, name=value.name)
else:
if not cls.STATIC:
return IndexNanosecondGO(value, name=value.name)
return IndexNanosecond(value, name=value.name)
if not cls.STATIC:
return IndexGO(value, name=value.name)
return Index(value, name=value.name)
def test_frame_iter_group_labels_b(self) -> None:
records = (
(2, 2, 'a', 'q', False, False),
(30, 34, 'b', 'c', True, False),
(2, 95, 'c', 'd', False, False),
)
f1 = Frame.from_records(records,
columns=IndexHierarchy.from_product(
(1, 2, 3), ('a', 'b')),
index=('x', 'y', 'z'))
# with axis 1, we are grouping based on columns while maintain the index
post_tuple = tuple(f1.iter_group_labels(1, axis=1))
self.assertEqual(len(post_tuple), 2)
post = f1[HLoc[f1.columns[0]]]
self.assertEqual(post.__class__, Series)
self.assertEqual(post.to_pairs(), (('x', 2), ('y', 30), ('z', 2)))
post = f1.loc[:, HLoc[f1.columns[0]]]
self.assertEqual(post.__class__, Series)
self.assertEqual(post.to_pairs(), (('x', 2), ('y', 30), ('z', 2)))
self.assertEqual(
f1.iter_group_labels(
1, axis=1).apply(lambda x: x.iloc[:, 0].sum()).to_pairs(),
(('a', 34), ('b', 131)))
def test_index_level_values_at_depth_a(self) -> None:
hidx = IndexHierarchy.from_labels(
(('a', 1, 'x'), ('a', 2, 'y'), ('b', 1, 'foo'), ('b', 1, 'bar')))
lvl = hidx._levels
self.assertEqual(
lvl.values_at_depth(2).tolist(), ['x', 'y', 'foo', 'bar'])
self.assertEqual(lvl.depth, next(lvl.depths()))
def test_index_init_b(self) -> None:
idx1 = IndexHierarchy.from_product(['A', 'B'], [1, 2])
idx2 = Index(idx1)
self.assertEqual(idx2.values.tolist(), [('A', 1), ('A', 2), ('B', 1),
('B', 2)])
def test_hierarchy_to_pandas_a(self):
idx1 = IndexHierarchy.from_product(list('ab'), list('xy'), name='q')
pdidx = idx1.to_pandas()
# NOTE: pandas .values on a hierarchical index returns an array of tuples
self.assertEqual(idx1.values.tolist(),
[list(x) for x in pdidx.values.tolist()])
