Python pandas.UInt64Index() Examples
The following are 30
code examples of pandas.UInt64Index().
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Example #1
| Source File: common.py From vnpy_crypto with MIT License | 6 votes |
|
def test_map_dictlike(self, mapper):
index = self.create_index()
if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(index)))
identity = mapper(index.values, index)
# we don't infer to UInt64 for a dict
if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
expected = index.astype('int64')
else:
expected = index
result = index.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(index))
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
Example #2
| Source File: common.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_map_dictlike(self, mapper):
index = self.create_index()
if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(index)))
identity = mapper(index.values, index)
# we don't infer to UInt64 for a dict
if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
expected = index.astype('int64')
else:
expected = index
result = index.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(index))
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
Example #3
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_get_indexer(self):
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target)
expected = np.array([0, -1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='pad')
expected = np.array([0, 0, 1, 2, 3, 4,
4, 4, 4, 4], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='backfill')
expected = np.array([0, 1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
Example #4
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_constructor(self):
idx = UInt64Index([1, 2, 3])
res = Index([1, 2, 3], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63])
tm.assert_index_equal(res, idx)
idx = Index([-1, 2**63], dtype=object)
res = Index(np.array([-1, 2**63], dtype=object))
tm.assert_index_equal(res, idx)
Example #5
| Source File: test_analytics.py From coffeegrindsize with MIT License | 6 votes |
|
def test_map_dictlike(idx, mapper):
if isinstance(idx, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(idx)))
identity = mapper(idx.values, idx)
# we don't infer to UInt64 for a dict
if isinstance(idx, pd.UInt64Index) and isinstance(identity, dict):
expected = idx.astype('int64')
else:
expected = idx
result = idx.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(idx))
result = idx.map(mapper(expected, idx))
tm.assert_index_equal(result, expected)
Example #6
| Source File: test_numeric.py From recruit with Apache License 2.0 | 6 votes |
|
def test_constructor(self):
idx = UInt64Index([1, 2, 3])
res = Index([1, 2, 3], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63])
tm.assert_index_equal(res, idx)
idx = Index([-1, 2**63], dtype=object)
res = Index(np.array([-1, 2**63], dtype=object))
tm.assert_index_equal(res, idx)
Example #7
| Source File: test_numeric.py From recruit with Apache License 2.0 | 6 votes |
|
def test_get_indexer(self):
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target)
expected = np.array([0, -1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='pad')
expected = np.array([0, 0, 1, 2, 3, 4,
4, 4, 4, 4], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='backfill')
expected = np.array([0, 1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
Example #8
| Source File: test_analytics.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_map_dictlike(idx, mapper):
if isinstance(idx, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(idx)))
identity = mapper(idx.values, idx)
# we don't infer to UInt64 for a dict
if isinstance(idx, pd.UInt64Index) and isinstance(identity, dict):
expected = idx.astype('int64')
else:
expected = idx
result = idx.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(idx))
result = idx.map(mapper(expected, idx))
tm.assert_index_equal(result, expected)
Example #9
| Source File: test_numeric.py From coffeegrindsize with MIT License | 6 votes |
|
def test_constructor(self):
idx = UInt64Index([1, 2, 3])
res = Index([1, 2, 3], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63], dtype=np.uint64)
tm.assert_index_equal(res, idx)
idx = UInt64Index([1, 2**63])
res = Index([1, 2**63])
tm.assert_index_equal(res, idx)
idx = Index([-1, 2**63], dtype=object)
res = Index(np.array([-1, 2**63], dtype=object))
tm.assert_index_equal(res, idx)
Example #10
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
|
def test_map_dictlike(idx, mapper):
if isinstance(idx, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(idx)))
identity = mapper(idx.values, idx)
# we don't infer to UInt64 for a dict
if isinstance(idx, pd.UInt64Index) and isinstance(identity, dict):
expected = idx.astype('int64')
else:
expected = idx
result = idx.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(idx))
result = idx.map(mapper(expected, idx))
tm.assert_index_equal(result, expected)
Example #11
| Source File: test_numeric.py From coffeegrindsize with MIT License | 6 votes |
|
def test_get_indexer(self):
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target)
expected = np.array([0, -1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='pad')
expected = np.array([0, 0, 1, 2, 3, 4,
4, 4, 4, 4], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
indexer = self.index.get_indexer(target, method='backfill')
expected = np.array([0, 1, 1, 2, 3, 4,
-1, -1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(indexer, expected)
Example #12
| Source File: common.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_map(self):
# callable
index = self.create_index()
# we don't infer UInt64
if isinstance(index, pd.UInt64Index):
expected = index.astype('int64')
else:
expected = index
result = index.map(lambda x: x)
tm.assert_index_equal(result, expected)
Example #13
| Source File: test_generic.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
Example #14
| Source File: common.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_hasnans_isnans(self):
# GH 11343, added tests for hasnans / isnans
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
pass
else:
idx = index.copy()
# cases in indices doesn't include NaN
expected = np.array([False] * len(idx), dtype=bool)
tm.assert_numpy_array_equal(idx._isnan, expected)
assert not idx.hasnans
idx = index.copy()
values = idx.values
if len(index) == 0:
continue
elif isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
Example #15
| Source File: test_astype.py From coffeegrindsize with MIT License | 5 votes |
|
def test_astype_uint(self):
arr = date_range('2000', periods=2)
expected = pd.UInt64Index(
np.array([946684800000000000, 946771200000000000], dtype="uint64")
)
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
Example #16
| Source File: test_astype.py From coffeegrindsize with MIT License | 5 votes |
|
def test_astype_uint(self):
arr = period_range('2000', periods=2)
expected = pd.UInt64Index(np.array([10957, 10958], dtype='uint64'))
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
Example #17
| Source File: test_missing.py From coffeegrindsize with MIT License | 5 votes |
|
def test_fillna(idx):
# GH 11343
# TODO: Remove or Refactor. Not Implemented for MultiIndex
for name, index in [('idx', idx), ]:
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with pytest.raises(TypeError, match=msg):
idx.fillna([idx[0]])
idx = index.copy()
values = idx.values
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
Example #18
| Source File: common.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_fillna(self):
# GH 11343
for name, index in self.indices.items():
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with tm.assert_raises_regex(NotImplementedError, msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with tm.assert_raises_regex(TypeError, msg):
idx.fillna([idx[0]])
idx = index.copy()
values = idx.values
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
Example #19
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
Example #20
| Source File: test_numeric.py From coffeegrindsize with MIT License | 5 votes |
|
def test_join_outer(self):
other = UInt64Index(2**63 + np.array(
[7, 12, 25, 1, 2, 10], dtype='uint64'))
other_mono = UInt64Index(2**63 + np.array(
[1, 2, 7, 10, 12, 25], dtype='uint64'))
# 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')
tm.assert_index_equal(res, noidx_res)
eres = UInt64Index(2**63 + np.array(
[0, 1, 2, 7, 10, 12, 15, 20, 25], dtype='uint64'))
elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
eridx = np.array([-1, 3, 4, 0, 5, 1, -1, -1, 2], dtype=np.intp)
assert isinstance(res, UInt64Index)
tm.assert_index_equal(res, eres)
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_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')
tm.assert_index_equal(res, noidx_res)
elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
eridx = np.array([-1, 0, 1, 2, 3, 4, -1, -1, 5], dtype=np.intp)
assert isinstance(res, UInt64Index)
tm.assert_index_equal(res, eres)
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_array_equal(ridx, eridx)
Example #21
| Source File: test_numeric.py From coffeegrindsize with MIT License | 5 votes |
|
def setup_method(self, method):
vals = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
self.indices = dict(index=UInt64Index(vals),
index_dec=UInt64Index(reversed(vals)))
self.setup_indices()
Example #22
| Source File: forecasting.py From sktime with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def check_time_index(time_index):
"""Check time index.
Parameters
----------
time_index : pd.Index or np.array
Returns
-------
time_index : pd.Index
"""
if isinstance(time_index, np.ndarray):
time_index = pd.Index(time_index)
# period or datetime index are not support yet
supported_index_types = (pd.RangeIndex, pd.Int64Index, pd.UInt64Index)
if not isinstance(time_index, supported_index_types):
raise NotImplementedError(f"{type(time_index)} is not supported, "
f"please use one of "
f"{supported_index_types} instead.")
if not time_index.is_monotonic:
raise ValueError(
f"Time index must be sorted (monotonically increasing), "
f"but found: {time_index}")
return time_index
Example #23
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_mul_int_series(self, numeric_idx):
idx = numeric_idx
didx = idx * idx
arr_dtype = 'uint64' if isinstance(idx, pd.UInt64Index) else 'int64'
result = idx * Series(np.arange(5, dtype=arr_dtype))
tm.assert_series_equal(result, Series(didx))
Example #24
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_mul_int_array(self, numeric_idx):
idx = numeric_idx
didx = idx * idx
result = idx * np.array(5, dtype='int64')
tm.assert_index_equal(result, idx * 5)
arr_dtype = 'uint64' if isinstance(idx, pd.UInt64Index) else 'int64'
result = idx * np.arange(5, dtype=arr_dtype)
tm.assert_index_equal(result, didx)
Example #25
| Source File: test_dtypes.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_astype_column_metadata(self, dtype):
# GH 19920
columns = pd.UInt64Index([100, 200, 300], name='foo')
df = DataFrame(np.arange(15).reshape(5, 3), columns=columns)
df = df.astype(dtype)
tm.assert_index_equal(df.columns, columns)
Example #26
| Source File: test_indexing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_contains_with_float_index(self):
# GH#22085
integer_index = pd.Int64Index([0, 1, 2, 3])
uinteger_index = pd.UInt64Index([0, 1, 2, 3])
float_index = pd.Float64Index([0.1, 1.1, 2.2, 3.3])
for index in (integer_index, uinteger_index):
assert 1.1 not in index
assert 1.0 in index
assert 1 in index
assert 1.1 in float_index
assert 1.0 not in float_index
assert 1 not in float_index
Example #27
| Source File: test_missing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_fillna(idx):
# GH 11343
# TODO: Remove or Refactor. Not Implemented for MultiIndex
for name, index in [('idx', idx), ]:
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with pytest.raises(TypeError, match=msg):
idx.fillna([idx[0]])
idx = index.copy()
values = idx.values
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
Example #28
| Source File: test_astype.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_astype_uint(self):
arr = period_range('2000', periods=2)
expected = pd.UInt64Index(np.array([10957, 10958], dtype='uint64'))
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
Example #29
| Source File: test_astype.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_astype_uint(self):
arr = date_range('2000', periods=2)
expected = pd.UInt64Index(
np.array([946684800000000000, 946771200000000000], dtype="uint64")
)
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
Example #30
| Source File: test_numeric.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_join_outer(self):
other = UInt64Index(2**63 + np.array(
[7, 12, 25, 1, 2, 10], dtype='uint64'))
other_mono = UInt64Index(2**63 + np.array(
[1, 2, 7, 10, 12, 25], dtype='uint64'))
# 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')
tm.assert_index_equal(res, noidx_res)
eres = UInt64Index(2**63 + np.array(
[0, 1, 2, 7, 10, 12, 15, 20, 25], dtype='uint64'))
elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
eridx = np.array([-1, 3, 4, 0, 5, 1, -1, -1, 2], dtype=np.intp)
assert isinstance(res, UInt64Index)
tm.assert_index_equal(res, eres)
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_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')
tm.assert_index_equal(res, noidx_res)
elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
eridx = np.array([-1, 0, 1, 2, 3, 4, -1, -1, 5], dtype=np.intp)
assert isinstance(res, UInt64Index)
tm.assert_index_equal(res, eres)
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_array_equal(ridx, eridx)
