Python pandas.util.testing.makeDateIndex() Examples
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code examples of pandas.util.testing.makeDateIndex().
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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_packers.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
super(TestIndex, self).setup_method(method)
self.d = {
'string': tm.makeStringIndex(100),
'date': tm.makeDateIndex(100),
'int': tm.makeIntIndex(100),
'rng': tm.makeRangeIndex(100),
'float': tm.makeFloatIndex(100),
'empty': Index([]),
'tuple': Index(zip(['foo', 'bar', 'baz'], [1, 2, 3])),
'period': Index(period_range('2012-1-1', freq='M', periods=3)),
'date2': Index(date_range('2013-01-1', periods=10)),
'bdate': Index(bdate_range('2013-01-02', periods=10)),
'cat': tm.makeCategoricalIndex(100),
'interval': tm.makeIntervalIndex(100),
'timedelta': tm.makeTimedeltaIndex(100, 'H')
}
self.mi = {
'reg': MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
('foo', 'two'),
('qux', 'one'), ('qux', 'two')],
names=['first', 'second']),
}
Example #3
| 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 #4
| Source File: test_setops.py From recruit with Apache License 2.0 | 6 votes |
|
def test_intersection2(self):
first = tm.makeDateIndex(10)
second = first[5:]
intersect = first.intersection(second)
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values) for klass in [np.array, Series, list]]
for case in cases:
result = first.intersection(case)
assert tm.equalContents(result, second)
third = Index(['a', 'b', 'c'])
result = first.intersection(third)
expected = pd.Index([], dtype=object)
tm.assert_index_equal(result, expected)
Example #5
| Source File: test_series.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_line_area_nan_series(self):
values = [1, 2, np.nan, 3]
s = Series(values)
ts = Series(values, index=tm.makeDateIndex(k=4))
for d in [s, ts]:
ax = _check_plot_works(d.plot)
masked = ax.lines[0].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
tm.assert_numpy_array_equal(
masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
Example #6
| Source File: test_series.py From recruit with Apache License 2.0 | 6 votes |
|
def test_line_area_nan_series(self):
values = [1, 2, np.nan, 3]
s = Series(values)
ts = Series(values, index=tm.makeDateIndex(k=4))
for d in [s, ts]:
ax = _check_plot_works(d.plot)
masked = ax.lines[0].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
tm.assert_numpy_array_equal(
masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
Example #7
| 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 #8
| Source File: test_packers.py From recruit with Apache License 2.0 | 6 votes |
|
def setup_method(self, method):
super(TestIndex, self).setup_method(method)
self.d = {
'string': tm.makeStringIndex(100),
'date': tm.makeDateIndex(100),
'int': tm.makeIntIndex(100),
'rng': tm.makeRangeIndex(100),
'float': tm.makeFloatIndex(100),
'empty': Index([]),
'tuple': Index(zip(['foo', 'bar', 'baz'], [1, 2, 3])),
'period': Index(period_range('2012-1-1', freq='M', periods=3)),
'date2': Index(date_range('2013-01-1', periods=10)),
'bdate': Index(bdate_range('2013-01-02', periods=10)),
'cat': tm.makeCategoricalIndex(100),
'interval': tm.makeIntervalIndex(100),
'timedelta': tm.makeTimedeltaIndex(100, 'H')
}
self.mi = {
'reg': MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
('foo', 'two'),
('qux', 'one'), ('qux', 'two')],
names=['first', 'second']),
}
Example #9
| 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 #10
| Source File: test_series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_line_area_nan_series(self):
values = [1, 2, np.nan, 3]
s = Series(values)
ts = Series(values, index=tm.makeDateIndex(k=4))
for d in [s, ts]:
ax = _check_plot_works(d.plot)
masked = ax.lines[0].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
tm.assert_numpy_array_equal(
masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
Example #11
| Source File: test_setops.py From vnpy_crypto with MIT License | 6 votes |
|
def test_intersection2(self):
first = tm.makeDateIndex(10)
second = first[5:]
intersect = first.intersection(second)
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values) for klass in [np.array, Series, list]]
for case in cases:
result = first.intersection(case)
assert tm.equalContents(result, second)
third = Index(['a', 'b', 'c'])
result = first.intersection(third)
expected = pd.Index([], dtype=object)
tm.assert_index_equal(result, expected)
Example #12
| Source File: test_setops.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_intersection2(self):
first = tm.makeDateIndex(10)
second = first[5:]
intersect = first.intersection(second)
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values) for klass in [np.array, Series, list]]
for case in cases:
result = first.intersection(case)
assert tm.equalContents(result, second)
third = Index(['a', 'b', 'c'])
result = first.intersection(third)
expected = pd.Index([], dtype=object)
tm.assert_index_equal(result, expected)
Example #13
| Source File: test_series.py From vnpy_crypto with MIT License | 6 votes |
|
def test_line_area_nan_series(self):
values = [1, 2, np.nan, 3]
s = Series(values)
ts = Series(values, index=tm.makeDateIndex(k=4))
for d in [s, ts]:
ax = _check_plot_works(d.plot)
masked = ax.lines[0].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
tm.assert_numpy_array_equal(
masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
Example #14
| 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 #15
| 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 #16
| 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 #17
| 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 #18
| 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 #19
| Source File: test_packers.py From vnpy_crypto with MIT License | 6 votes |
|
def setup_method(self, method):
super(TestIndex, self).setup_method(method)
self.d = {
'string': tm.makeStringIndex(100),
'date': tm.makeDateIndex(100),
'int': tm.makeIntIndex(100),
'rng': tm.makeRangeIndex(100),
'float': tm.makeFloatIndex(100),
'empty': Index([]),
'tuple': Index(zip(['foo', 'bar', 'baz'], [1, 2, 3])),
'period': Index(period_range('2012-1-1', freq='M', periods=3)),
'date2': Index(date_range('2013-01-1', periods=10)),
'bdate': Index(bdate_range('2013-01-02', periods=10)),
'cat': tm.makeCategoricalIndex(100),
'interval': tm.makeIntervalIndex(100),
'timedelta': tm.makeTimedeltaIndex(100, 'H')
}
self.mi = {
'reg': MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
('foo', 'two'),
('qux', 'one'), ('qux', 'two')],
names=['first', 'second']),
}
Example #20
| Source File: test_base.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_map_tseries_indices_accsr_return_index(self):
date_index = tm.makeDateIndex(24, freq='h', name='hourly')
expected = Index(range(24), name='hourly')
tm.assert_index_equal(expected, date_index.map(lambda x: x.hour))
Example #21
| Source File: test_datetime.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_isin(self):
index = tm.makeDateIndex(4)
result = index.isin(index)
assert result.all()
result = index.isin(list(index))
assert result.all()
assert_almost_equal(index.isin([index[2], 5]),
np.array([False, False, True, False]))
Example #22
| Source File: test_base.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_outer_join_sort(self):
left_index = Index(np.random.permutation(15))
right_index = tm.makeDateIndex(10)
with tm.assert_produces_warning(RuntimeWarning):
result = left_index.join(right_index, how='outer')
# right_index in this case because DatetimeIndex has join precedence
# over Int64Index
with tm.assert_produces_warning(RuntimeWarning):
expected = right_index.astype(object).union(
left_index.astype(object))
tm.assert_index_equal(result, expected)
Example #23
| Source File: test_pytables.py From Computable with MIT License | 5 votes |
|
def test_store_index_types(self):
# GH5386
# test storing various index types
with ensure_clean_store(self.path) as store:
def check(format,index):
df = DataFrame(np.random.randn(10,2),columns=list('AB'))
df.index = index(len(df))
_maybe_remove(store, 'df')
store.put('df',df,format=format)
assert_frame_equal(df,store['df'])
for index in [ tm.makeFloatIndex, tm.makeStringIndex, tm.makeIntIndex,
tm.makeDateIndex, tm.makePeriodIndex ]:
check('table',index)
check('fixed',index)
# unicode
index = tm.makeUnicodeIndex
if compat.PY3:
check('table',index)
check('fixed',index)
else:
# only support for fixed types (and they have a perf warning)
self.assertRaises(TypeError, check, 'table', index)
with tm.assert_produces_warning(expected_warning=PerformanceWarning):
check('fixed',index)
Example #24
| Source File: test_timeseries.py From Computable with MIT License | 5 votes |
|
def test_isin(self):
index = tm.makeDateIndex(4)
result = index.isin(index)
self.assert_(result.all())
result = index.isin(list(index))
self.assert_(result.all())
assert_almost_equal(index.isin([index[2], 5]),
[False, False, True, False])
Example #25
| Source File: test_frame.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_line_area_nan_df(self):
values1 = [1, 2, np.nan, 3]
values2 = [3, np.nan, 2, 1]
df = DataFrame({'a': values1, 'b': values2})
tdf = DataFrame({'a': values1,
'b': values2}, index=tm.makeDateIndex(k=4))
for d in [df, tdf]:
ax = _check_plot_works(d.plot)
masked1 = ax.lines[0].get_ydata()
masked2 = ax.lines[1].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked1.data, 2), exp)
exp = np.array([3, 2, 1], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked2.data, 1), exp)
tm.assert_numpy_array_equal(
masked1.mask, np.array([False, False, True, False]))
tm.assert_numpy_array_equal(
masked2.mask, np.array([False, True, False, False]))
expected1 = np.array([1, 2, 0, 3], dtype=np.float64)
expected2 = np.array([3, 0, 2, 1], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(),
expected1 + expected2)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(),
expected1 + expected2)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected2)
Example #26
| Source File: test_scalar_compat.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_dti_timestamp_fields(self, field):
# extra fields from DatetimeIndex like quarter and week
idx = tm.makeDateIndex(100)
expected = getattr(idx, field)[-1]
if field == 'weekday_name':
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = getattr(Timestamp(idx[-1]), field)
else:
result = getattr(Timestamp(idx[-1]), field)
assert result == expected
Example #27
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_outer_join_sort(self):
left_idx = Index(np.random.permutation(15))
right_idx = tm.makeDateIndex(10)
with tm.assert_produces_warning(RuntimeWarning):
joined = left_idx.join(right_idx, how='outer')
# right_idx in this case because DatetimeIndex has join precedence over
# Int64Index
with tm.assert_produces_warning(RuntimeWarning):
expected = right_idx.astype(object).union(left_idx.astype(object))
tm.assert_index_equal(joined, expected)
Example #28
| Source File: test_base.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_str_attribute(self):
# GH9068
methods = ['strip', 'rstrip', 'lstrip']
idx = Index([' jack', 'jill ', ' jesse ', 'frank'])
for method in methods:
expected = Index([getattr(str, method)(x) for x in idx.values])
tm.assert_index_equal(
getattr(Index.str, method)(idx.str), expected)
# create a few instances that are not able to use .str accessor
indices = [Index(range(5)), tm.makeDateIndex(10),
MultiIndex.from_tuples([('foo', '1'), ('bar', '3')]),
PeriodIndex(start='2000', end='2010', freq='A')]
for idx in indices:
with tm.assert_raises_regex(AttributeError,
'only use .str accessor'):
idx.str.repeat(2)
idx = Index(['a b c', 'd e', 'f'])
expected = Index([['a', 'b', 'c'], ['d', 'e'], ['f']])
tm.assert_index_equal(idx.str.split(), expected)
tm.assert_index_equal(idx.str.split(expand=False), expected)
expected = MultiIndex.from_tuples([('a', 'b', 'c'), ('d', 'e', np.nan),
('f', np.nan, np.nan)])
tm.assert_index_equal(idx.str.split(expand=True), expected)
# test boolean case, should return np.array instead of boolean Index
idx = Index(['a1', 'a2', 'b1', 'b2'])
expected = np.array([True, True, False, False])
tm.assert_numpy_array_equal(idx.str.startswith('a'), expected)
assert isinstance(idx.str.startswith('a'), np.ndarray)
s = Series(range(4), index=idx)
expected = Series(range(2), index=['a1', 'a2'])
tm.assert_series_equal(s[s.index.str.startswith('a')], expected)
Example #29
| Source File: test_converter.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_dateindex_conversion(self):
decimals = 9
for freq in ('B', 'L', 'S'):
dateindex = tm.makeDateIndex(k=10, freq=freq)
rs = self.dtc.convert(dateindex, None, None)
xp = converter.dates.date2num(dateindex._mpl_repr())
tm.assert_almost_equal(rs, xp, decimals)
Example #30
| Source File: test_scalar_compat.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_dti_timestamp_freq_fields(self):
# extra fields from DatetimeIndex like quarter and week
idx = tm.makeDateIndex(100)
assert idx.freq == Timestamp(idx[-1], idx.freq).freq
assert idx.freqstr == Timestamp(idx[-1], idx.freq).freqstr
# ----------------------------------------------------------------
# DatetimeIndex.round
