def test_join_right(self):
other = Int64Index([7, 12, 25, 1, 2, 5])
other_mono = Int64Index([1, 2, 5, 7, 12, 25])
# not monotonic
res, lidx, ridx = self.index.join(other,
how='right',
return_indexers=True)
eres = other
elidx = np.array([-1, 6, -1, -1, 1, -1], dtype='i4')
self.assert_(isinstance(other, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(ridx is None)
# monotonic
res, lidx, ridx = self.index.join(other_mono,
how='right',
return_indexers=True)
eres = other_mono
elidx = np.array([-1, 1, -1, -1, 6, -1], dtype='i4')
self.assert_(isinstance(other, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(ridx is None)
def test_constructor(self):
# pass list, coerce fine
index = Int64Index([-5, 0, 1, 2])
expected = np.array([-5, 0, 1, 2], dtype=np.int64)
self.assert_(np.array_equal(index, expected))
# from iterable
index = Int64Index(iter([-5, 0, 1, 2]))
self.assert_(np.array_equal(index, expected))
# scalar raise Exception
self.assertRaises(ValueError, Int64Index, 5)
def test_constructor_corner(self):
arr = np.array([1, 2, 3, 4], dtype=object)
index = Int64Index(arr)
self.assert_(index.values.dtype == np.int64)
self.assert_(index.equals(arr))
# preventing casting
arr = np.array([1, '2', 3, '4'], dtype=object)
self.assertRaises(TypeError, Int64Index, arr)
def test_ensure_platform_int():
# verify that when we create certain types of indices
# they remain the correct type under platform conversions
from pandas.core.index import Int64Index
# int64
x = Int64Index([1, 2, 3], dtype='int64')
assert (x.dtype == np.int64)
pi = com._ensure_platform_int(x)
assert (pi.dtype == np.int_)
# int32
x = Int64Index([1, 2, 3], dtype='int32')
assert (x.dtype == np.int32)
pi = com._ensure_platform_int(x)
assert (pi.dtype == np.int_)
def unique(self):
"""
Index.unique with handling for DatetimeIndex/PeriodIndex metadata
Returns
-------
result : DatetimeIndex or PeriodIndex
"""
from pandas.core.index import Int64Index
result = Int64Index.unique(self)
return self._simple_new(result, name=self.name, freq=self.freq,
tz=getattr(self, 'tz', None))
def unique(self):
"""
Index.unique with handling for DatetimeIndex/PeriodIndex metadata
Returns
-------
result : DatetimeIndex or PeriodIndex
"""
from pandas.core.index import Int64Index
result = Int64Index.unique(self)
return self._simple_new(result, name=self.name, freq=self.freq,
tz=getattr(self, 'tz', None))
def test_join_inner(self):
other = Int64Index([7, 12, 25, 1, 2, 5])
other_mono = Int64Index([1, 2, 5, 7, 12, 25])
# not monotonic
res, lidx, ridx = self.index.join(other,
how='inner',
return_indexers=True)
# no guarantee of sortedness, so sort for comparison purposes
ind = res.argsort()
res = res.take(ind)
lidx = lidx.take(ind)
ridx = ridx.take(ind)
eres = Int64Index([2, 12])
elidx = np.array([1, 6])
eridx = np.array([4, 1])
self.assert_(isinstance(res, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(np.array_equal(ridx, eridx))
# monotonic
res, lidx, ridx = self.index.join(other_mono,
how='inner',
return_indexers=True)
res2 = self.index.intersection(other_mono)
self.assert_(res.equals(res2))
eridx = np.array([1, 4])
self.assert_(isinstance(res, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(np.array_equal(ridx, eridx))
def test_join_outer(self):
other = Int64Index([7, 12, 25, 1, 2, 5])
other_mono = Int64Index([1, 2, 5, 7, 12, 25])
# not monotonic
# guarantee of sortedness
res, lidx, ridx = self.index.join(other,
how='outer',
return_indexers=True)
noidx_res = self.index.join(other, how='outer')
self.assert_(res.equals(noidx_res))
eres = Int64Index([0, 1, 2, 4, 5, 6, 7, 8, 10, 12, 14, 16, 18, 25])
elidx = np.array([0, -1, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, 9, -1],
dtype='i4')
eridx = np.array([-1, 3, 4, -1, 5, -1, 0, -1, -1, 1, -1, -1, -1, 2],
dtype='i4')
self.assert_(isinstance(res, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(np.array_equal(ridx, eridx))
# monotonic
res, lidx, ridx = self.index.join(other_mono,
how='outer',
return_indexers=True)
noidx_res = self.index.join(other_mono, how='outer')
self.assert_(res.equals(noidx_res))
eridx = np.array([-1, 0, 1, -1, 2, -1, 3, -1, -1, 4, -1, -1, -1, 5],
dtype='i4')
self.assert_(isinstance(res, Int64Index))
self.assert_(res.equals(eres))
self.assert_(np.array_equal(lidx, elidx))
self.assert_(np.array_equal(ridx, eridx))
def iter_slices(table, other, mode, keep_empty):
"""Yields indices to extract ranges from `table`.
Returns an iterable of integer arrays that can apply to Series objects,
i.e. columns of `table`. These indices are of the DataFrame/Series' Index,
not array coordinates -- so be sure to use DataFrame.loc, Series.loc, or
Series getitem, as opposed to .iloc or indexing directly into Numpy arrays.
"""
for _c, bin_rows, src_rows in by_shared_chroms(other, table, keep_empty):
if src_rows is None:
# Emit empty indices since 'table' is missing this chromosome
for _ in range(len(bin_rows)):
yield Int64Index([])
else:
for slc, _s, _e in idx_ranges(src_rows, None, bin_rows.start,
bin_rows.end, mode):
indices = src_rows.index[slc].values
if keep_empty or len(indices):
yield indices
def test_take_preserve_name(self):
index = Int64Index([1, 2, 3, 4], name='foo')
taken = index.take([3, 0, 1])
self.assertEqual(index.name, taken.name)
def test_get_indexer_backfill(self):
target = Int64Index(np.arange(10))
indexer = self.index.get_indexer(target, method='backfill')
expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5])
self.assert_(np.array_equal(indexer, expected))
def test_get_indexer(self):
target = Int64Index(np.arange(10))
indexer = self.index.get_indexer(target)
expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1])
self.assert_(np.array_equal(indexer, expected))
def test_is_monotonic(self):
self.assert_(self.index.is_monotonic)
index = Int64Index([4, 3, 2, 1])
self.assert_(not index.is_monotonic)
def setUp(self):
self.index = Int64Index(np.arange(0, 20, 2))
