Python pandas.util.testing.makeObjectSeries() Examples
The following are 30
code examples of pandas.util.testing.makeObjectSeries().
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Example #1
| Source File: test_generic.py From vnpy_crypto with MIT License | 6 votes |
|
def test_take(self):
indices = [1, 5, -2, 6, 3, -1]
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
out = s.take(indices)
expected = Series(data=s.values.take(indices),
index=s.index.take(indices), dtype=s.dtype)
tm.assert_series_equal(out, expected)
for df in [tm.makeTimeDataFrame()]:
out = df.take(indices)
expected = DataFrame(data=df.values.take(indices, axis=0),
index=df.index.take(indices),
columns=df.columns)
tm.assert_frame_equal(out, expected)
indices = [-3, 2, 0, 1]
with catch_warnings(record=True):
for p in [tm.makePanel()]:
out = p.take(indices)
expected = Panel(data=p.values.take(indices, axis=0),
items=p.items.take(indices),
major_axis=p.major_axis,
minor_axis=p.minor_axis)
tm.assert_panel_equal(out, expected)
Example #2
| Source File: test_generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_take(self):
indices = [1, 5, -2, 6, 3, -1]
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
out = s.take(indices)
expected = Series(data=s.values.take(indices),
index=s.index.take(indices), dtype=s.dtype)
tm.assert_series_equal(out, expected)
for df in [tm.makeTimeDataFrame()]:
out = df.take(indices)
expected = DataFrame(data=df.values.take(indices, axis=0),
index=df.index.take(indices),
columns=df.columns)
tm.assert_frame_equal(out, expected)
indices = [-3, 2, 0, 1]
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
out = p.take(indices)
expected = Panel(data=p.values.take(indices, axis=0),
items=p.items.take(indices),
major_axis=p.major_axis,
minor_axis=p.minor_axis)
tm.assert_panel_equal(out, expected)
Example #3
| Source File: test_generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_transpose(self):
msg = (r"transpose\(\) got multiple values for "
r"keyword argument 'axes'")
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
# calls implementation in pandas/core/base.py
tm.assert_series_equal(s.transpose(), s)
for df in [tm.makeTimeDataFrame()]:
tm.assert_frame_equal(df.transpose().transpose(), df)
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.transpose(2, 0, 1)
.transpose(1, 2, 0), p)
with pytest.raises(TypeError, match=msg):
p.transpose(2, 0, 1, axes=(2, 0, 1))
Example #4
| Source File: test_pandas.py From Computable with MIT License | 6 votes |
|
def setUp(self):
self.dirpath = tm.get_data_path()
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
self.objSeries = tm.makeObjectSeries()
self.objSeries.name = 'objects'
self.empty_series = Series([], index=[])
self.empty_frame = DataFrame({})
self.frame = _frame.copy()
self.frame2 = _frame2.copy()
self.intframe = _intframe.copy()
self.tsframe = _tsframe.copy()
self.mixed_frame = _mixed_frame.copy()
Example #5
| Source File: test_generic.py From vnpy_crypto with MIT License | 6 votes |
|
def test_transpose(self):
msg = (r"transpose\(\) got multiple values for "
r"keyword argument 'axes'")
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
# calls implementation in pandas/core/base.py
tm.assert_series_equal(s.transpose(), s)
for df in [tm.makeTimeDataFrame()]:
tm.assert_frame_equal(df.transpose().transpose(), df)
with catch_warnings(record=True):
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.transpose(2, 0, 1)
.transpose(1, 2, 0), p)
tm.assert_raises_regex(TypeError, msg, p.transpose,
2, 0, 1, axes=(2, 0, 1))
Example #6
| Source File: test_pandas.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def setup_method(self, method):
self.dirpath = tm.get_data_path()
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
self.objSeries = tm.makeObjectSeries()
self.objSeries.name = 'objects'
self.empty_series = Series([], index=[])
self.empty_frame = DataFrame({})
self.frame = _frame.copy()
self.frame2 = _frame2.copy()
self.intframe = _intframe.copy()
self.tsframe = _tsframe.copy()
self.mixed_frame = _mixed_frame.copy()
self.categorical = _cat_frame.copy()
Example #7
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_transpose(self):
msg = (r"transpose\(\) got multiple values for "
r"keyword argument 'axes'")
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
# calls implementation in pandas/core/base.py
tm.assert_series_equal(s.transpose(), s)
for df in [tm.makeTimeDataFrame()]:
tm.assert_frame_equal(df.transpose().transpose(), df)
with catch_warnings(record=True):
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.transpose(2, 0, 1)
.transpose(1, 2, 0), p)
tm.assert_raises_regex(TypeError, msg, p.transpose,
2, 0, 1, axes=(2, 0, 1))
Example #8
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_take(self):
indices = [1, 5, -2, 6, 3, -1]
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
out = s.take(indices)
expected = Series(data=s.values.take(indices),
index=s.index.take(indices), dtype=s.dtype)
tm.assert_series_equal(out, expected)
for df in [tm.makeTimeDataFrame()]:
out = df.take(indices)
expected = DataFrame(data=df.values.take(indices, axis=0),
index=df.index.take(indices),
columns=df.columns)
tm.assert_frame_equal(out, expected)
indices = [-3, 2, 0, 1]
with catch_warnings(record=True):
for p in [tm.makePanel()]:
out = p.take(indices)
expected = Panel(data=p.values.take(indices, axis=0),
items=p.items.take(indices),
major_axis=p.major_axis,
minor_axis=p.minor_axis)
tm.assert_panel_equal(out, expected)
Example #9
| Source File: test_generic.py From recruit with Apache License 2.0 | 6 votes |
|
def test_take(self):
indices = [1, 5, -2, 6, 3, -1]
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
out = s.take(indices)
expected = Series(data=s.values.take(indices),
index=s.index.take(indices), dtype=s.dtype)
tm.assert_series_equal(out, expected)
for df in [tm.makeTimeDataFrame()]:
out = df.take(indices)
expected = DataFrame(data=df.values.take(indices, axis=0),
index=df.index.take(indices),
columns=df.columns)
tm.assert_frame_equal(out, expected)
indices = [-3, 2, 0, 1]
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
out = p.take(indices)
expected = Panel(data=p.values.take(indices, axis=0),
items=p.items.take(indices),
major_axis=p.major_axis,
minor_axis=p.minor_axis)
tm.assert_panel_equal(out, expected)
Example #10
| Source File: test_generic.py From recruit with Apache License 2.0 | 6 votes |
|
def test_transpose(self):
msg = (r"transpose\(\) got multiple values for "
r"keyword argument 'axes'")
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries()]:
# calls implementation in pandas/core/base.py
tm.assert_series_equal(s.transpose(), s)
for df in [tm.makeTimeDataFrame()]:
tm.assert_frame_equal(df.transpose().transpose(), df)
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [tm.makePanel()]:
tm.assert_panel_equal(p.transpose(2, 0, 1)
.transpose(1, 2, 0), p)
with pytest.raises(TypeError, match=msg):
p.transpose(2, 0, 1, axes=(2, 0, 1))
Example #11
| Source File: test_packers.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def setup_method(self, method):
super(TestSeries, self).setup_method(method)
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 2 +
[Timestamp('20130603', tz='CET')] * 3,
'G': [Timestamp('20130102', tz='US/Eastern')] * 5,
'H': Categorical([1, 2, 3, 4, 5]),
'I': Categorical([1, 2, 3, 4, 5], ordered=True),
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
self.d['dt_tz_mixed'] = Series(data['F'])
self.d['dt_tz'] = Series(data['G'])
self.d['cat_ordered'] = Series(data['H'])
self.d['cat_unordered'] = Series(data['I'])
Example #12
| Source File: test_missing.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_isna_isnull(self, isna_f):
assert not isna_f(1.)
assert isna_f(None)
assert isna_f(np.NaN)
assert float('nan')
assert not isna_f(np.inf)
assert not isna_f(-np.inf)
# series
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert isinstance(isna_f(s), Series)
# frame
for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(),
tm.makeMixedDataFrame()]:
result = isna_f(df)
expected = df.apply(isna_f)
tm.assert_frame_equal(result, expected)
# panel
with catch_warnings(record=True):
for p in [tm.makePanel(), tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel_equal(result, expected)
# panel 4d
with catch_warnings(record=True):
for p in [tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D())]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel4d_equal(result, expected)
Example #13
| Source File: common.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
Example #14
| Source File: test_missing.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_isna_isnull(self, isna_f):
assert not isna_f(1.)
assert isna_f(None)
assert isna_f(np.NaN)
assert float('nan')
assert not isna_f(np.inf)
assert not isna_f(-np.inf)
# series
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert isinstance(isna_f(s), Series)
# frame
for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(),
tm.makeMixedDataFrame()]:
result = isna_f(df)
expected = df.apply(isna_f)
tm.assert_frame_equal(result, expected)
# panel
with catch_warnings(record=True):
for p in [tm.makePanel(), tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel_equal(result, expected)
Example #15
| Source File: test_missing.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_notna_notnull(notna_f):
assert notna_f(1.)
assert not notna_f(None)
assert not notna_f(np.NaN)
with cf.option_context("mode.use_inf_as_na", False):
assert notna_f(np.inf)
assert notna_f(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notna_f(arr)
assert result.all()
with cf.option_context("mode.use_inf_as_na", True):
assert not notna_f(np.inf)
assert not notna_f(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notna_f(arr)
assert result.sum() == 2
with cf.option_context("mode.use_inf_as_na", False):
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert (isinstance(notna_f(s), Series))
Example #16
| Source File: common.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
Example #17
| Source File: test_packers.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def setup_method(self, method):
super(TestSeries, self).setup_method(method)
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 2 +
[Timestamp('20130603', tz='CET')] * 3,
'G': [Timestamp('20130102', tz='US/Eastern')] * 5,
'H': Categorical([1, 2, 3, 4, 5]),
'I': Categorical([1, 2, 3, 4, 5], ordered=True),
'J': (np.bool_(1), 2, 3, 4, 5),
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
self.d['dt_tz_mixed'] = Series(data['F'])
self.d['dt_tz'] = Series(data['G'])
self.d['cat_ordered'] = Series(data['H'])
self.d['cat_unordered'] = Series(data['I'])
self.d['numpy_bool_mixed'] = Series(data['J'])
Example #18
| Source File: test_pandas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def setup(self, datapath):
self.dirpath = datapath("io", "json", "data")
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
self.objSeries = tm.makeObjectSeries()
self.objSeries.name = 'objects'
self.empty_series = Series([], index=[])
self.empty_frame = DataFrame({})
self.frame = _frame.copy()
self.frame2 = _frame2.copy()
self.intframe = _intframe.copy()
self.tsframe = _tsframe.copy()
self.mixed_frame = _mixed_frame.copy()
self.categorical = _cat_frame.copy()
yield
del self.dirpath
del self.ts
del self.series
del self.objSeries
del self.empty_series
del self.empty_frame
del self.frame
del self.frame2
del self.intframe
del self.tsframe
del self.mixed_frame
Example #19
| Source File: test_missing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_isna_isnull(self, isna_f):
assert not isna_f(1.)
assert isna_f(None)
assert isna_f(np.NaN)
assert float('nan')
assert not isna_f(np.inf)
assert not isna_f(-np.inf)
# series
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert isinstance(isna_f(s), Series)
# frame
for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(),
tm.makeMixedDataFrame()]:
result = isna_f(df)
expected = df.apply(isna_f)
tm.assert_frame_equal(result, expected)
# panel
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [tm.makePanel(), tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel_equal(result, expected)
Example #20
| Source File: test_arithmetic.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_object_ser_add_invalid(self):
# invalid ops
obj_ser = tm.makeObjectSeries()
obj_ser.name = 'objects'
with pytest.raises(Exception):
obj_ser + 1
with pytest.raises(Exception):
obj_ser + np.array(1, dtype=np.int64)
with pytest.raises(Exception):
obj_ser - 1
with pytest.raises(Exception):
obj_ser - np.array(1, dtype=np.int64)
Example #21
| Source File: test_missing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_notna_notnull(notna_f):
assert notna_f(1.)
assert not notna_f(None)
assert not notna_f(np.NaN)
with cf.option_context("mode.use_inf_as_na", False):
assert notna_f(np.inf)
assert notna_f(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notna_f(arr)
assert result.all()
with cf.option_context("mode.use_inf_as_na", True):
assert not notna_f(np.inf)
assert not notna_f(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notna_f(arr)
assert result.sum() == 2
with cf.option_context("mode.use_inf_as_na", False):
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert (isinstance(notna_f(s), Series))
Example #22
| Source File: common.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
Example #23
| Source File: test_packers.py From Computable with MIT License | 5 votes |
|
def setUp(self):
super(TestSeries, self).setUp()
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(tslib.iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
Example #24
| Source File: test_packers.py From vnpy_crypto with MIT License | 5 votes |
|
def setup_method(self, method):
super(TestSeries, self).setup_method(method)
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 2 +
[Timestamp('20130603', tz='CET')] * 3,
'G': [Timestamp('20130102', tz='US/Eastern')] * 5,
'H': Categorical([1, 2, 3, 4, 5]),
'I': Categorical([1, 2, 3, 4, 5], ordered=True),
'J': (np.bool_(1), 2, 3, 4, 5),
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
self.d['dt_tz_mixed'] = Series(data['F'])
self.d['dt_tz'] = Series(data['G'])
self.d['cat_ordered'] = Series(data['H'])
self.d['cat_unordered'] = Series(data['I'])
self.d['numpy_bool_mixed'] = Series(data['J'])
Example #25
| Source File: test_pandas.py From vnpy_crypto with MIT License | 5 votes |
|
def setup(self, datapath):
self.dirpath = datapath("io", "json", "data")
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
self.objSeries = tm.makeObjectSeries()
self.objSeries.name = 'objects'
self.empty_series = Series([], index=[])
self.empty_frame = DataFrame({})
self.frame = _frame.copy()
self.frame2 = _frame2.copy()
self.intframe = _intframe.copy()
self.tsframe = _tsframe.copy()
self.mixed_frame = _mixed_frame.copy()
self.categorical = _cat_frame.copy()
yield
del self.dirpath
del self.ts
del self.series
del self.objSeries
del self.empty_series
del self.empty_frame
del self.frame
del self.frame2
del self.intframe
del self.tsframe
del self.mixed_frame
Example #26
| Source File: test_missing.py From vnpy_crypto with MIT License | 5 votes |
|
def test_isna_isnull(self, isna_f):
assert not isna_f(1.)
assert isna_f(None)
assert isna_f(np.NaN)
assert float('nan')
assert not isna_f(np.inf)
assert not isna_f(-np.inf)
# series
for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
tm.makeObjectSeries(), tm.makeTimeSeries(),
tm.makePeriodSeries()]:
assert isinstance(isna_f(s), Series)
# frame
for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(),
tm.makeMixedDataFrame()]:
result = isna_f(df)
expected = df.apply(isna_f)
tm.assert_frame_equal(result, expected)
# panel
with catch_warnings(record=True):
for p in [tm.makePanel(), tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel_equal(result, expected)
Example #27
| Source File: test_arithmetic.py From vnpy_crypto with MIT License | 5 votes |
|
def test_object_ser_add_invalid(self):
# invalid ops
obj_ser = tm.makeObjectSeries()
obj_ser.name = 'objects'
with pytest.raises(Exception):
obj_ser + 1
with pytest.raises(Exception):
obj_ser + np.array(1, dtype=np.int64)
with pytest.raises(Exception):
obj_ser - 1
with pytest.raises(Exception):
obj_ser - np.array(1, dtype=np.int64)
Example #28
| Source File: common.py From vnpy_crypto with MIT License | 5 votes |
|
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
Example #29
| Source File: conftest.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def object_series():
"""
Fixture for Series of dtype datetime64[ns] with Index of unique strings
"""
s = tm.makeObjectSeries()
s.name = 'objects'
return s
Example #30
| Source File: test_packers.py From recruit with Apache License 2.0 | 5 votes |
|
def setup_method(self, method):
super(TestSeries, self).setup_method(method)
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 2 +
[Timestamp('20130603', tz='CET')] * 3,
'G': [Timestamp('20130102', tz='US/Eastern')] * 5,
'H': Categorical([1, 2, 3, 4, 5]),
'I': Categorical([1, 2, 3, 4, 5], ordered=True),
'J': (np.bool_(1), 2, 3, 4, 5),
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
self.d['dt_tz_mixed'] = Series(data['F'])
self.d['dt_tz'] = Series(data['G'])
self.d['cat_ordered'] = Series(data['H'])
self.d['cat_unordered'] = Series(data['I'])
self.d['numpy_bool_mixed'] = Series(data['J'])
