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Merged
merged 12 commits into from
Jan 10, 2018
Merged

Followup Cleanup DTI test_arithmetic, ASV #19149

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8b84d7b
remove asv for Timestamp.offset since it no longer exists
jbrockmendel Jan 9, 2018
a04fe29
parametrize test, extend Series test to DatetimeIndex, fix un-run cases
jbrockmendel Jan 9, 2018
c4b0a19
reindent
jbrockmendel Jan 9, 2018
72bc0a3
parametrize normalize, comment, prettify
jbrockmendel Jan 9, 2018
4dab645
Merge branch 'master' of https://github.com/pandas-dev/pandas into fo…
jbrockmendel Jan 10, 2018
1c0bb72
dont zip
jbrockmendel Jan 10, 2018
39bd287
remove unnecesary
jbrockmendel Jan 10, 2018
262c231
cleanup
jbrockmendel Jan 10, 2018
1bbfa4f
un-xfail Timedelta __rdiv__ test
jbrockmendel Jan 10, 2018
26f805a
break up and parametrize big tests
jbrockmendel Jan 10, 2018
0127f2a
add missing assert_series_equal
jbrockmendel Jan 10, 2018
40a7c6a
Separate and parametrize mul/div dt64 test
jbrockmendel Jan 10, 2018
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3 changes: 0 additions & 3 deletions asv_bench/benchmarks/timestamp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,9 +36,6 @@ def setup(self, tz, freq):
def time_tz(self, tz, freq):
self.ts.tz

def time_offset(self, tz, freq):
self.ts.offset

def time_dayofweek(self, tz, freq):
self.ts.dayofweek

Expand Down
186 changes: 105 additions & 81 deletions pandas/tests/indexes/datetimes/test_arithmetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -447,6 +447,111 @@ def test_dti_with_offset_series(self, tz, names):
tm.assert_series_equal(res3, expected_sub)


@pytest.mark.parametrize('klass,assert_func', [
(Series, tm.assert_series_equal),
(DatetimeIndex, tm.assert_index_equal)])
def test_dt64_with_offset_array(klass, assert_func):
# GH#10699
# array of offsets
box = Series if klass is Series else pd.Index
with tm.assert_produces_warning(PerformanceWarning):
s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')])
result = s + box([pd.offsets.DateOffset(years=1),
pd.offsets.MonthEnd()])
exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29')])
assert_func(result, exp)

# same offset
result = s + box([pd.offsets.DateOffset(years=1),
pd.offsets.DateOffset(years=1)])
exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')])
assert_func(result, exp)


@pytest.mark.parametrize('klass,assert_func', [
(Series, tm.assert_series_equal),
(DatetimeIndex, tm.assert_index_equal)])
def test_dt64_with_DateOffsets_relativedelta(klass, assert_func):
# GH#10699
vec = klass([Timestamp('2000-01-05 00:15:00'),
Timestamp('2000-01-31 00:23:00'),
Timestamp('2000-01-01'),
Timestamp('2000-03-31'),
Timestamp('2000-02-29'),
Timestamp('2000-12-31'),
Timestamp('2000-05-15'),
Timestamp('2001-06-15')])

# DateOffset relativedelta fastpath
relative_kwargs = [('years', 2), ('months', 5), ('days', 3),
('hours', 5), ('minutes', 10), ('seconds', 2),
('microseconds', 5)]
for i, kwd in enumerate(relative_kwargs):
op = pd.DateOffset(**dict([kwd]))
assert_func(klass([x + op for x in vec]), vec + op)
assert_func(klass([x - op for x in vec]), vec - op)
op = pd.DateOffset(**dict(relative_kwargs[:i + 1]))
assert_func(klass([x + op for x in vec]), vec + op)
assert_func(klass([x - op for x in vec]), vec - op)


@pytest.mark.parametrize('cls_name', [
'YearBegin', ('YearBegin', {'month': 5}),
'YearEnd', ('YearEnd', {'month': 5}),
'MonthBegin', 'MonthEnd',
'SemiMonthEnd', 'SemiMonthBegin',
'Week', ('Week', {'weekday': 3}),
'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
'CustomBusinessDay', 'CDay', 'CBMonthEnd',
'CBMonthBegin', 'BMonthBegin', 'BMonthEnd',
'BusinessHour', 'BYearBegin', 'BYearEnd',
'BQuarterBegin', ('LastWeekOfMonth', {'weekday': 2}),
('FY5253Quarter', {'qtr_with_extra_week': 1,
'startingMonth': 1,
'weekday': 2,
'variation': 'nearest'}),
('FY5253', {'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
('WeekOfMonth', {'weekday': 2, 'week': 2}),
'Easter', ('DateOffset', {'day': 4}),
('DateOffset', {'month': 5})])
@pytest.mark.parametrize('normalize', [True, False])
@pytest.mark.parametrize('klass,assert_func', [
(Series, tm.assert_series_equal),
(DatetimeIndex, tm.assert_index_equal)])
def test_dt64_with_DateOffsets(klass, assert_func, normalize, cls_name):
# GH#10699
# assert these are equal on a piecewise basis
vec = klass([Timestamp('2000-01-05 00:15:00'),
Timestamp('2000-01-31 00:23:00'),
Timestamp('2000-01-01'),
Timestamp('2000-03-31'),
Timestamp('2000-02-29'),
Timestamp('2000-12-31'),
Timestamp('2000-05-15'),
Timestamp('2001-06-15')])

if isinstance(cls_name, tuple):
# If cls_name param is a tuple, then 2nd entry is kwargs for
# the offset constructor
cls_name, kwargs = cls_name
else:
kwargs = {}

offset_cls = getattr(pd.offsets, cls_name)

with warnings.catch_warnings(record=True):
for n in [0, 5]:
if (cls_name in ['WeekOfMonth', 'LastWeekOfMonth',
'FY5253Quarter', 'FY5253'] and n == 0):
# passing n = 0 is invalid for these offset classes
continue

offset = offset_cls(n, normalize=normalize, **kwargs)
assert_func(klass([x + offset for x in vec]), vec + offset)
assert_func(klass([x - offset for x in vec]), vec - offset)
assert_func(klass([offset + x for x in vec]), offset + vec)


# GH 10699
@pytest.mark.parametrize('klass,assert_func', zip([Series, DatetimeIndex],
[tm.assert_series_equal,
Expand Down Expand Up @@ -480,84 +585,3 @@ def test_datetime64_with_DateOffset(klass, assert_func):
Timestamp('2000-02-29', tz='US/Central')], name='a')
assert_func(result, exp)
assert_func(result2, exp)

# array of offsets - valid for Series only
if klass is Series:
with tm.assert_produces_warning(PerformanceWarning):
s = klass([Timestamp('2000-1-1'), Timestamp('2000-2-1')])
result = s + Series([pd.offsets.DateOffset(years=1),
pd.offsets.MonthEnd()])
exp = klass([Timestamp('2001-1-1'), Timestamp('2000-2-29')
])
assert_func(result, exp)

# same offset
result = s + Series([pd.offsets.DateOffset(years=1),
pd.offsets.DateOffset(years=1)])
exp = klass([Timestamp('2001-1-1'), Timestamp('2001-2-1')])
assert_func(result, exp)

s = klass([Timestamp('2000-01-05 00:15:00'),
Timestamp('2000-01-31 00:23:00'),
Timestamp('2000-01-01'),
Timestamp('2000-03-31'),
Timestamp('2000-02-29'),
Timestamp('2000-12-31'),
Timestamp('2000-05-15'),
Timestamp('2001-06-15')])

# DateOffset relativedelta fastpath
relative_kwargs = [('years', 2), ('months', 5), ('days', 3),
('hours', 5), ('minutes', 10), ('seconds', 2),
('microseconds', 5)]
for i, kwd in enumerate(relative_kwargs):
op = pd.DateOffset(**dict([kwd]))
assert_func(klass([x + op for x in s]), s + op)
assert_func(klass([x - op for x in s]), s - op)
op = pd.DateOffset(**dict(relative_kwargs[:i + 1]))
assert_func(klass([x + op for x in s]), s + op)
assert_func(klass([x - op for x in s]), s - op)

# assert these are equal on a piecewise basis
offsets = ['YearBegin', ('YearBegin', {'month': 5}),
'YearEnd', ('YearEnd', {'month': 5}),
'MonthBegin', 'MonthEnd',
'SemiMonthEnd', 'SemiMonthBegin',
'Week', ('Week', {'weekday': 3}),
'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
'CustomBusinessDay', 'CDay', 'CBMonthEnd',
'CBMonthBegin', 'BMonthBegin', 'BMonthEnd',
'BusinessHour', 'BYearBegin', 'BYearEnd',
'BQuarterBegin', ('LastWeekOfMonth', {'weekday': 2}),
('FY5253Quarter', {'qtr_with_extra_week': 1,
'startingMonth': 1,
'weekday': 2,
'variation': 'nearest'}),
('FY5253', {'weekday': 0,
'startingMonth': 2,
'variation':
'nearest'}),
('WeekOfMonth', {'weekday': 2,
'week': 2}),
'Easter', ('DateOffset', {'day': 4}),
('DateOffset', {'month': 5})]

with warnings.catch_warnings(record=True):
for normalize in (True, False):
for do in offsets:
if isinstance(do, tuple):
do, kwargs = do
else:
do = do
kwargs = {}

for n in [0, 5]:
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@jbrockmendel jbrockmendel Jan 9, 2018 •
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I'm pretty sure that this for n in [0, 5]: down through line 563557 is intended to have one fewer level of indentation. That change is implemented up in test_dt64_with_DateOffsets.

if (do in ['WeekOfMonth', 'LastWeekOfMonth',
'FY5253Quarter', 'FY5253'] and n == 0):
continue
op = getattr(pd.offsets, do)(n,
normalize=normalize,
**kwargs)
assert_func(klass([x + op for x in s]), s + op)
assert_func(klass([x - op for x in s]), s - op)
assert_func(klass([op + x for x in s]), op + s)
149 changes: 75 additions & 74 deletions pandas/tests/series/test_operators.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1005,9 +1005,7 @@ def test_operators_timedelta64_with_timedelta_invalid(self, scalar_td):

@pytest.mark.parametrize('scalar_td', [
timedelta(minutes=5, seconds=4),
pytest.param(Timedelta('5m4s'),
marks=pytest.mark.xfail(reason="Timedelta.__floordiv__ "
"bug GH#18846")),
Timedelta('5m4s'),
Timedelta('5m4s').to_timedelta64()])
def test_timedelta_rfloordiv(self, scalar_td):
# GH#18831
Expand Down Expand Up @@ -1381,21 +1379,23 @@ def test_datetime64_ops_nat(self):
assert_series_equal(NaT + nat_series_dtype_timestamp,
nat_series_dtype_timestamp)

@pytest.mark.parametrize('dt64_series', [
Series([Timestamp('19900315'), Timestamp('19900315')]),
Series([NaT, Timestamp('19900315')]),
Series([NaT, NaT], dtype='datetime64[ns]')])
@pytest.mark.parametrize('one', [1, 1.0, np.array(1)])
def test_dt64_mul_div_numeric_invalid(self, one, dt64_series):
# multiplication
with pytest.raises(TypeError):
datetime_series * 1
with pytest.raises(TypeError):
nat_series_dtype_timestamp * 1
dt64_series * one
with pytest.raises(TypeError):
datetime_series * 1.0
with pytest.raises(TypeError):
nat_series_dtype_timestamp * 1.0
one * dt64_series

# division
with pytest.raises(TypeError):
nat_series_dtype_timestamp / 1.0
dt64_series / one
with pytest.raises(TypeError):
nat_series_dtype_timestamp / 1
one / dt64_series

def test_dt64series_arith_overflow(self):
# GH#12534, fixed by #19024
Expand Down Expand Up @@ -1574,6 +1574,7 @@ def test_timedelta64_conversions(self):
expected = s1.apply(
lambda x: Timedelta(np.timedelta64(m, unit)) / x)
result = np.timedelta64(m, unit) / s1
assert_series_equal(result, expected)

# astype
s = Series(date_range('20130101', periods=3))
Expand Down Expand Up @@ -1990,69 +1991,69 @@ def test_series_frame_radd_bug(self):
with pytest.raises(TypeError):
self.ts + datetime.now()

def test_series_radd_more(self):
data = [[1, 2, 3],
[1.1, 2.2, 3.3],
[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'),
pd.NaT],
['x', 'y', 1]]

for d in data:
for dtype in [None, object]:
s = Series(d, dtype=dtype)
with pytest.raises(TypeError):
'foo_' + s

for dtype in [None, object]:
res = 1 + pd.Series([1, 2, 3], dtype=dtype)
exp = pd.Series([2, 3, 4], dtype=dtype)
assert_series_equal(res, exp)
res = pd.Series([1, 2, 3], dtype=dtype) + 1
assert_series_equal(res, exp)

res = np.nan + pd.Series([1, 2, 3], dtype=dtype)
exp = pd.Series([np.nan, np.nan, np.nan], dtype=dtype)
assert_series_equal(res, exp)
res = pd.Series([1, 2, 3], dtype=dtype) + np.nan
assert_series_equal(res, exp)

s = pd.Series([pd.Timedelta('1 days'), pd.Timedelta('2 days'),
pd.Timedelta('3 days')], dtype=dtype)
exp = pd.Series([pd.Timedelta('4 days'), pd.Timedelta('5 days'),
pd.Timedelta('6 days')])
assert_series_equal(pd.Timedelta('3 days') + s, exp)
assert_series_equal(s + pd.Timedelta('3 days'), exp)

s = pd.Series(['x', np.nan, 'x'])
assert_series_equal('a' + s, pd.Series(['ax', np.nan, 'ax']))
assert_series_equal(s + 'a', pd.Series(['xa', np.nan, 'xa']))

def test_frame_radd_more(self):
data = [[1, 2, 3],
[1.1, 2.2, 3.3],
[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'),
pd.NaT],
['x', 'y', 1]]

for d in data:
for dtype in [None, object]:
s = DataFrame(d, dtype=dtype)
with pytest.raises(TypeError):
'foo_' + s

for dtype in [None, object]:
res = 1 + pd.DataFrame([1, 2, 3], dtype=dtype)
exp = pd.DataFrame([2, 3, 4], dtype=dtype)
assert_frame_equal(res, exp)
res = pd.DataFrame([1, 2, 3], dtype=dtype) + 1
assert_frame_equal(res, exp)

res = np.nan + pd.DataFrame([1, 2, 3], dtype=dtype)
exp = pd.DataFrame([np.nan, np.nan, np.nan], dtype=dtype)
assert_frame_equal(res, exp)
res = pd.DataFrame([1, 2, 3], dtype=dtype) + np.nan
assert_frame_equal(res, exp)

def test_series_radd_str(self):
ser = pd.Series(['x', np.nan, 'x'])
assert_series_equal('a' + ser, pd.Series(['ax', np.nan, 'ax']))
assert_series_equal(ser + 'a', pd.Series(['xa', np.nan, 'xa']))

@pytest.mark.parametrize('dtype', [None, object])
def test_series_radd_more(self, dtype):
res = 1 + pd.Series([1, 2, 3], dtype=dtype)
exp = pd.Series([2, 3, 4], dtype=dtype)
assert_series_equal(res, exp)
res = pd.Series([1, 2, 3], dtype=dtype) + 1
assert_series_equal(res, exp)

res = np.nan + pd.Series([1, 2, 3], dtype=dtype)
exp = pd.Series([np.nan, np.nan, np.nan], dtype=dtype)
assert_series_equal(res, exp)
res = pd.Series([1, 2, 3], dtype=dtype) + np.nan
assert_series_equal(res, exp)

s = pd.Series([pd.Timedelta('1 days'), pd.Timedelta('2 days'),
pd.Timedelta('3 days')], dtype=dtype)
exp = pd.Series([pd.Timedelta('4 days'), pd.Timedelta('5 days'),
pd.Timedelta('6 days')])
assert_series_equal(pd.Timedelta('3 days') + s, exp)
assert_series_equal(s + pd.Timedelta('3 days'), exp)

@pytest.mark.parametrize('data', [
[1, 2, 3],
[1.1, 2.2, 3.3],
[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), pd.NaT],
['x', 'y', 1]])
@pytest.mark.parametrize('dtype', [None, object])
def test_series_radd_str_invalid(self, dtype, data):
ser = Series(data, dtype=dtype)
with pytest.raises(TypeError):
'foo_' + ser

@pytest.mark.parametrize('data', [
[1, 2, 3],
[1.1, 2.2, 3.3],
[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), pd.NaT],
['x', 'y', 1]])
@pytest.mark.parametrize('dtype', [None, object])
def test_frame_radd_str_invalid(self, dtype, data):
df = DataFrame(data, dtype=dtype)
with pytest.raises(TypeError):
'foo_' + df

@pytest.mark.parametrize('dtype', [None, object])
def test_frame_radd_more(self, dtype):
res = 1 + pd.DataFrame([1, 2, 3], dtype=dtype)
exp = pd.DataFrame([2, 3, 4], dtype=dtype)
assert_frame_equal(res, exp)
res = pd.DataFrame([1, 2, 3], dtype=dtype) + 1
assert_frame_equal(res, exp)

res = np.nan + pd.DataFrame([1, 2, 3], dtype=dtype)
exp = pd.DataFrame([np.nan, np.nan, np.nan], dtype=dtype)
assert_frame_equal(res, exp)
res = pd.DataFrame([1, 2, 3], dtype=dtype) + np.nan
assert_frame_equal(res, exp)

def test_frame_radd_str(self):
df = pd.DataFrame(['x', np.nan, 'x'])
assert_frame_equal('a' + df, pd.DataFrame(['ax', np.nan, 'ax']))
assert_frame_equal(df + 'a', pd.DataFrame(['xa', np.nan, 'xa']))
Expand Down