Python pandas.util.testing.makePeriodIndex() Examples
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code examples of pandas.util.testing.makePeriodIndex().
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Example #1
| Source File: test_reductions.py From recruit with Apache License 2.0 | 6 votes |
|
def get_objs():
indexes = [
tm.makeBoolIndex(10, name='a'),
tm.makeIntIndex(10, name='a'),
tm.makeFloatIndex(10, name='a'),
tm.makeDateIndex(10, name='a'),
tm.makeDateIndex(10, name='a').tz_localize(tz='US/Eastern'),
tm.makePeriodIndex(10, name='a'),
tm.makeStringIndex(10, name='a'),
tm.makeUnicodeIndex(10, name='a')
]
arr = np.random.randn(10)
series = [Series(arr, index=idx, name='a') for idx in indexes]
objs = indexes + series
return objs
Example #2
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def setup_method(self, method):
self.bool_index = tm.makeBoolIndex(10, name='a')
self.int_index = tm.makeIntIndex(10, name='a')
self.float_index = tm.makeFloatIndex(10, name='a')
self.dt_index = tm.makeDateIndex(10, name='a')
self.dt_tz_index = tm.makeDateIndex(10, name='a').tz_localize(
tz='US/Eastern')
self.period_index = tm.makePeriodIndex(10, name='a')
self.string_index = tm.makeStringIndex(10, name='a')
self.unicode_index = tm.makeUnicodeIndex(10, name='a')
arr = np.random.randn(10)
self.int_series = Series(arr, index=self.int_index, name='a')
self.float_series = Series(arr, index=self.float_index, name='a')
self.dt_series = Series(arr, index=self.dt_index, name='a')
self.dt_tz_series = self.dt_tz_index.to_series(keep_tz=True)
self.period_series = Series(arr, index=self.period_index, name='a')
self.string_series = Series(arr, index=self.string_index, name='a')
types = ['bool', 'int', 'float', 'dt', 'dt_tz', 'period', 'string',
'unicode']
fmts = ["{0}_{1}".format(t, f)
for t in types for f in ['index', 'series']]
self.objs = [getattr(self, f)
for f in fmts if getattr(self, f, None) is not None]
Example #3
| Source File: test_base.py From coffeegrindsize with MIT License | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeRangeIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #4
| Source File: test_base.py From vnpy_crypto with MIT License | 6 votes |
|
def setup_method(self, method):
self.bool_index = tm.makeBoolIndex(10, name='a')
self.int_index = tm.makeIntIndex(10, name='a')
self.float_index = tm.makeFloatIndex(10, name='a')
self.dt_index = tm.makeDateIndex(10, name='a')
self.dt_tz_index = tm.makeDateIndex(10, name='a').tz_localize(
tz='US/Eastern')
self.period_index = tm.makePeriodIndex(10, name='a')
self.string_index = tm.makeStringIndex(10, name='a')
self.unicode_index = tm.makeUnicodeIndex(10, name='a')
arr = np.random.randn(10)
self.int_series = Series(arr, index=self.int_index, name='a')
self.float_series = Series(arr, index=self.float_index, name='a')
self.dt_series = Series(arr, index=self.dt_index, name='a')
self.dt_tz_series = self.dt_tz_index.to_series(keep_tz=True)
self.period_series = Series(arr, index=self.period_index, name='a')
self.string_series = Series(arr, index=self.string_index, name='a')
types = ['bool', 'int', 'float', 'dt', 'dt_tz', 'period', 'string',
'unicode']
fmts = ["{0}_{1}".format(t, f)
for t in types for f in ['index', 'series']]
self.objs = [getattr(self, f)
for f in fmts if getattr(self, f, None) is not None]
Example #5
| Source File: test_pytables.py From Computable with MIT License | 6 votes |
|
def test_tseries_indices_series(self):
with ensure_clean_store(self.path) as store:
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
store['a'] = ser
result = store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
store['a'] = ser
result = store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
Example #6
| Source File: test_pytables.py From Computable with MIT License | 6 votes |
|
def test_tseries_indices_frame(self):
with ensure_clean_store(self.path) as store:
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
store['a'] = df
result = store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
store['a'] = df
result = store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
Example #7
| Source File: test_base.py From vnpy_crypto with MIT License | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeRangeIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #8
| Source File: test_base.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeRangeIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #9
| Source File: test_base.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeRangeIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #10
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeIntIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #11
| Source File: test_base.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def setup_method(self, method):
self.bool_index = tm.makeBoolIndex(10, name='a')
self.int_index = tm.makeIntIndex(10, name='a')
self.float_index = tm.makeFloatIndex(10, name='a')
self.dt_index = tm.makeDateIndex(10, name='a')
self.dt_tz_index = tm.makeDateIndex(10, name='a').tz_localize(
tz='US/Eastern')
self.period_index = tm.makePeriodIndex(10, name='a')
self.string_index = tm.makeStringIndex(10, name='a')
self.unicode_index = tm.makeUnicodeIndex(10, name='a')
arr = np.random.randn(10)
self.int_series = Series(arr, index=self.int_index, name='a')
self.float_series = Series(arr, index=self.float_index, name='a')
self.dt_series = Series(arr, index=self.dt_index, name='a')
self.dt_tz_series = self.dt_tz_index.to_series(keep_tz=True)
self.period_series = Series(arr, index=self.period_index, name='a')
self.string_series = Series(arr, index=self.string_index, name='a')
types = ['bool', 'int', 'float', 'dt', 'dt_tz', 'period', 'string',
'unicode']
fmts = ["{0}_{1}".format(t, f)
for t in types for f in ['index', 'series']]
self.objs = [getattr(self, f)
for f in fmts if getattr(self, f, None) is not None]
Example #12
| Source File: test_reductions.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def get_objs():
indexes = [
tm.makeBoolIndex(10, name='a'),
tm.makeIntIndex(10, name='a'),
tm.makeFloatIndex(10, name='a'),
tm.makeDateIndex(10, name='a'),
tm.makeDateIndex(10, name='a').tz_localize(tz='US/Eastern'),
tm.makePeriodIndex(10, name='a'),
tm.makeStringIndex(10, name='a'),
tm.makeUnicodeIndex(10, name='a')
]
arr = np.random.randn(10)
series = [Series(arr, index=idx, name='a') for idx in indexes]
objs = indexes + series
return objs
Example #13
| Source File: test_base.py From recruit with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
strIndex=tm.makeStringIndex(100),
dateIndex=tm.makeDateIndex(100),
periodIndex=tm.makePeriodIndex(100),
tdIndex=tm.makeTimedeltaIndex(100),
intIndex=tm.makeIntIndex(100),
uintIndex=tm.makeUIntIndex(100),
rangeIndex=tm.makeRangeIndex(100),
floatIndex=tm.makeFloatIndex(100),
boolIndex=Index([True, False]),
catIndex=tm.makeCategoricalIndex(100),
empty=Index([]),
tuples=MultiIndex.from_tuples(lzip(
['foo', 'bar', 'baz'], [1, 2, 3])),
repeats=Index([0, 0, 1, 1, 2, 2]))
self.setup_indices()
Example #14
| Source File: test_hashing.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_hash_pandas_object(self):
for obj in [Series([1, 2, 3]),
Series([1.0, 1.5, 3.2]),
Series([1.0, 1.5, np.nan]),
Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
Series(['a', 'b', 'c']),
Series(['a', np.nan, 'c']),
Series(['a', None, 'c']),
Series([True, False, True]),
Series(),
Index([1, 2, 3]),
Index([True, False, True]),
DataFrame({'x': ['a', 'b', 'c'], 'y': [1, 2, 3]}),
DataFrame(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeTimedeltaIndex(),
tm.makePeriodIndex(),
Series(tm.makePeriodIndex()),
Series(pd.date_range('20130101',
periods=3, tz='US/Eastern')),
MultiIndex.from_product(
[range(5),
['foo', 'bar', 'baz'],
pd.date_range('20130101', periods=2)]),
MultiIndex.from_product(
[pd.CategoricalIndex(list('aabc')),
range(3)])]:
self.check_equal(obj)
self.check_not_equal_with_index(obj)
Example #15
| Source File: test_grouping.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_grouper_index_types(self):
# related GH5375
# groupby misbehaving when using a Floatlike index
df = DataFrame(np.arange(10).reshape(5, 2), columns=list('AB'))
for index in [tm.makeFloatIndex, tm.makeStringIndex,
tm.makeUnicodeIndex, tm.makeIntIndex, tm.makeDateIndex,
tm.makePeriodIndex]:
df.index = index(len(df))
df.groupby(list('abcde')).apply(lambda x: x)
df.index = list(reversed(df.index.tolist()))
df.groupby(list('abcde')).apply(lambda x: x)
Example #16
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_map_tseries_indices_return_index(self):
date_index = tm.makeDateIndex(10)
exp = Index([1] * 10)
tm.assert_index_equal(exp, date_index.map(lambda x: 1))
period_index = tm.makePeriodIndex(10)
tm.assert_index_equal(exp, period_index.map(lambda x: 1))
tdelta_index = tm.makeTimedeltaIndex(10)
tm.assert_index_equal(exp, tdelta_index.map(lambda x: 1))
date_index = tm.makeDateIndex(24, freq='h', name='hourly')
exp = Index(range(24), name='hourly')
tm.assert_index_equal(exp, date_index.map(lambda x: x.hour))
Example #17
| Source File: test_period.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def setup_method(self, method):
self.indices = dict(index=tm.makePeriodIndex(10),
index_dec=period_range('20130101', periods=10,
freq='D')[::-1])
self.setup_indices()
Example #18
| Source File: test_frequencies.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_invalid_index_types(self):
# test all index types
for i in [tm.makeIntIndex(10), tm.makeFloatIndex(10),
tm.makePeriodIndex(10)]:
pytest.raises(TypeError, lambda: frequencies.infer_freq(i))
# GH 10822
# odd error message on conversions to datetime for unicode
if not is_platform_windows():
for i in [tm.makeStringIndex(10), tm.makeUnicodeIndex(10)]:
pytest.raises(ValueError, lambda: frequencies.infer_freq(i))
Example #19
| Source File: test_constructors.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_constructor_dict(self):
d = {'a': 0., 'b': 1., 'c': 2.}
result = Series(d, index=['b', 'c', 'd', 'a'])
expected = Series([1, 2, nan, 0], index=['b', 'c', 'd', 'a'])
assert_series_equal(result, expected)
pidx = tm.makePeriodIndex(100)
d = {pidx[0]: 0, pidx[1]: 1}
result = Series(d, index=pidx)
expected = Series(np.nan, pidx)
expected.iloc[0] = 0
expected.iloc[1] = 1
assert_series_equal(result, expected)
Example #20
| Source File: test_constructors.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_constructor_dict(self):
d = {'a': 0., 'b': 1., 'c': 2.}
result = Series(d, index=['b', 'c', 'd', 'a'])
expected = Series([1, 2, nan, 0], index=['b', 'c', 'd', 'a'])
assert_series_equal(result, expected)
pidx = tm.makePeriodIndex(100)
d = {pidx[0]: 0, pidx[1]: 1}
result = Series(d, index=pidx)
expected = Series(np.nan, pidx)
expected.iloc[0] = 0
expected.iloc[1] = 1
assert_series_equal(result, expected)
Example #21
| Source File: test_groupby.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_grouper_index_types(self):
# related GH5375
# groupby misbehaving when using a Floatlike index
df = DataFrame(np.arange(10).reshape(5, 2), columns=list('AB'))
for index in [tm.makeFloatIndex, tm.makeStringIndex,
tm.makeUnicodeIndex, tm.makeIntIndex, tm.makeDateIndex,
tm.makePeriodIndex]:
df.index = index(len(df))
df.groupby(list('abcde')).apply(lambda x: x)
df.index = list(reversed(df.index.tolist()))
df.groupby(list('abcde')).apply(lambda x: x)
Example #22
| Source File: test_generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_head_tail(self):
# GH5370
o = self._construct(shape=10)
# check all index types
for index in [tm.makeFloatIndex, tm.makeIntIndex, tm.makeStringIndex,
tm.makeUnicodeIndex, tm.makeDateIndex,
tm.makePeriodIndex]:
axis = o._get_axis_name(0)
setattr(o, axis, index(len(getattr(o, axis))))
# Panel + dims
try:
o.head()
except (NotImplementedError):
pytest.skip('not implemented on {0}'.format(
o.__class__.__name__))
self._compare(o.head(), o.iloc[:5])
self._compare(o.tail(), o.iloc[-5:])
# 0-len
self._compare(o.head(0), o.iloc[0:0])
self._compare(o.tail(0), o.iloc[0:0])
# bounded
self._compare(o.head(len(o) + 1), o)
self._compare(o.tail(len(o) + 1), o)
# neg index
self._compare(o.head(-3), o.head(7))
self._compare(o.tail(-3), o.tail(7))
Example #23
| Source File: test_period.py From coffeegrindsize with MIT License | 5 votes |
|
def setup_method(self, method):
self.indices = dict(index=tm.makePeriodIndex(10),
index_dec=period_range('20130101', periods=10,
freq='D')[::-1])
self.setup_indices()
Example #24
| Source File: test_frequencies.py From coffeegrindsize with MIT License | 5 votes |
|
def test_invalid_index_types(self):
# test all index types
for i in [tm.makeIntIndex(10), tm.makeFloatIndex(10),
tm.makePeriodIndex(10)]:
pytest.raises(TypeError, lambda: frequencies.infer_freq(i))
# GH 10822
# odd error message on conversions to datetime for unicode
if not is_platform_windows():
for i in [tm.makeStringIndex(10), tm.makeUnicodeIndex(10)]:
pytest.raises(ValueError, lambda: frequencies.infer_freq(i))
Example #25
| Source File: test_period.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def setup_method(self, method):
self.indices = dict(index=tm.makePeriodIndex(10),
index_dec=period_range('20130101', periods=10,
freq='D')[::-1])
self.setup_indices()
Example #26
| Source File: test_frequencies.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_invalid_index_types(self):
# test all index types
for i in [tm.makeIntIndex(10), tm.makeFloatIndex(10),
tm.makePeriodIndex(10)]:
pytest.raises(TypeError, lambda: frequencies.infer_freq(i))
# GH 10822
# odd error message on conversions to datetime for unicode
if not is_platform_windows():
for i in [tm.makeStringIndex(10), tm.makeUnicodeIndex(10)]:
pytest.raises(ValueError, lambda: frequencies.infer_freq(i))
Example #27
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_head_tail(self):
# GH5370
o = self._construct(shape=10)
# check all index types
for index in [tm.makeFloatIndex, tm.makeIntIndex, tm.makeStringIndex,
tm.makeUnicodeIndex, tm.makeDateIndex,
tm.makePeriodIndex]:
axis = o._get_axis_name(0)
setattr(o, axis, index(len(getattr(o, axis))))
# Panel + dims
try:
o.head()
except (NotImplementedError):
pytest.skip('not implemented on {0}'.format(
o.__class__.__name__))
self._compare(o.head(), o.iloc[:5])
self._compare(o.tail(), o.iloc[-5:])
# 0-len
self._compare(o.head(0), o.iloc[0:0])
self._compare(o.tail(0), o.iloc[0:0])
# bounded
self._compare(o.head(len(o) + 1), o)
self._compare(o.tail(len(o) + 1), o)
# neg index
self._compare(o.head(-3), o.head(7))
self._compare(o.tail(-3), o.tail(7))
Example #28
| Source File: test_constructors.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_constructor_dict(self):
d = {'a': 0., 'b': 1., 'c': 2.}
result = Series(d, index=['b', 'c', 'd', 'a'])
expected = Series([1, 2, nan, 0], index=['b', 'c', 'd', 'a'])
assert_series_equal(result, expected)
pidx = tm.makePeriodIndex(100)
d = {pidx[0]: 0, pidx[1]: 1}
result = Series(d, index=pidx)
expected = Series(np.nan, pidx)
expected.iloc[0] = 0
expected.iloc[1] = 1
assert_series_equal(result, expected)
Example #29
| Source File: test_grouping.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_grouper_index_types(self):
# related GH5375
# groupby misbehaving when using a Floatlike index
df = DataFrame(np.arange(10).reshape(5, 2), columns=list('AB'))
for index in [tm.makeFloatIndex, tm.makeStringIndex,
tm.makeUnicodeIndex, tm.makeIntIndex, tm.makeDateIndex,
tm.makePeriodIndex]:
df.index = index(len(df))
df.groupby(list('abcde')).apply(lambda x: x)
df.index = list(reversed(df.index.tolist()))
df.groupby(list('abcde')).apply(lambda x: x)
Example #30
| Source File: test_hashing.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_hash_pandas_object(self):
for obj in [Series([1, 2, 3]),
Series([1.0, 1.5, 3.2]),
Series([1.0, 1.5, np.nan]),
Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
Series(['a', 'b', 'c']),
Series(['a', np.nan, 'c']),
Series(['a', None, 'c']),
Series([True, False, True]),
Series(),
Index([1, 2, 3]),
Index([True, False, True]),
DataFrame({'x': ['a', 'b', 'c'], 'y': [1, 2, 3]}),
DataFrame(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeTimedeltaIndex(),
tm.makePeriodIndex(),
Series(tm.makePeriodIndex()),
Series(pd.date_range('20130101',
periods=3, tz='US/Eastern')),
MultiIndex.from_product(
[range(5),
['foo', 'bar', 'baz'],
pd.date_range('20130101', periods=2)]),
MultiIndex.from_product(
[pd.CategoricalIndex(list('aabc')),
range(3)])]:
self.check_equal(obj)
self.check_not_equal_with_index(obj)
