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Validate Timestamp with invalid offset raises ValueError #13116

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.18.2.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ Other enhancements
- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`)



- Force ``offset`` validation during ``Timestamp`` construction (:issue:`13114`)

.. _whatsnew_0182.api:

Expand Down
4 changes: 4 additions & 0 deletions pandas/tseries/tests/test_timeseries.py
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Expand Up @@ -4291,6 +4291,10 @@ def test_unit_errors(self):
self.assertEqual(result, expected)
self.assertIsInstance(result, Timestamp)

def test_invalid_offset(self):
# GH 11630
self.assertRaises(ValueError, lambda: pd.Timestamp(2015, 11, 12))

def test_roundtrip(self):

# test value to string and back conversions
Expand Down
334 changes: 168 additions & 166 deletions pandas/tslib.pyx
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Expand Up @@ -214,6 +214,171 @@ cdef inline bint _is_fixed_offset(object tz):
return 0
return 1

@cython.wraparound(False)
@cython.boundscheck(False)
def get_date_field(ndarray[int64_t] dtindex, object field):
"""
Given a int64-based datetime index, extract the year, month, etc.,
field and return an array of these values.
"""
cdef:
_TSObject ts
Py_ssize_t i, count = 0
ndarray[int32_t] out
ndarray[int32_t, ndim=2] _month_offset
int isleap, isleap_prev
pandas_datetimestruct dts
int mo_off, doy, dow, woy

_month_offset = np.array(
[[ 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365 ],
[ 0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366 ]],
dtype=np.int32 )

count = len(dtindex)
out = np.empty(count, dtype='i4')

if field == 'Y':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.year
return out

elif field == 'M':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.month
return out

elif field == 'D':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.day
return out

elif field == 'h':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.hour
return out

elif field == 'm':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.min
return out

elif field == 's':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.sec
return out

elif field == 'us':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.us
return out

elif field == 'ns':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.ps / 1000
return out
elif field == 'doy':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
isleap = is_leapyear(dts.year)
out[i] = _month_offset[isleap, dts.month-1] + dts.day
return out

elif field == 'dow':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dayofweek(dts.year, dts.month, dts.day)
return out

elif field == 'woy':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
isleap = is_leapyear(dts.year)
isleap_prev = is_leapyear(dts.year - 1)
mo_off = _month_offset[isleap, dts.month - 1]
doy = mo_off + dts.day
dow = dayofweek(dts.year, dts.month, dts.day)

#estimate
woy = (doy - 1) - dow + 3
if woy >= 0:
woy = woy / 7 + 1

# verify
if woy < 0:
if (woy > -2) or (woy == -2 and isleap_prev):
woy = 53
else:
woy = 52
elif woy == 53:
if 31 - dts.day + dow < 3:
woy = 1

out[i] = woy
return out

elif field == 'q':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.month
out[i] = ((out[i] - 1) / 3) + 1
return out

elif field == 'dim':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = days_in_month(dts)
return out

raise ValueError("Field %s not supported" % field)


_zero_time = datetime_time(0, 0)

Expand Down Expand Up @@ -311,6 +476,9 @@ class Timestamp(_Timestamp):
ts_base.offset = offset
ts_base.nanosecond = ts.dts.ps / 1000

# force offset check
ts_base.isoformat()

return ts_base


Expand Down Expand Up @@ -4197,172 +4365,6 @@ def get_time_micros(ndarray[int64_t] dtindex):
return micros


@cython.wraparound(False)
@cython.boundscheck(False)
def get_date_field(ndarray[int64_t] dtindex, object field):
"""
Given a int64-based datetime index, extract the year, month, etc.,
field and return an array of these values.
"""
cdef:
_TSObject ts
Py_ssize_t i, count = 0
ndarray[int32_t] out
ndarray[int32_t, ndim=2] _month_offset
int isleap, isleap_prev
pandas_datetimestruct dts
int mo_off, doy, dow, woy

_month_offset = np.array(
[[ 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365 ],
[ 0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366 ]],
dtype=np.int32 )

count = len(dtindex)
out = np.empty(count, dtype='i4')

if field == 'Y':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.year
return out

elif field == 'M':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.month
return out

elif field == 'D':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.day
return out

elif field == 'h':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.hour
return out

elif field == 'm':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.min
return out

elif field == 's':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.sec
return out

elif field == 'us':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.us
return out

elif field == 'ns':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.ps / 1000
return out
elif field == 'doy':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
isleap = is_leapyear(dts.year)
out[i] = _month_offset[isleap, dts.month-1] + dts.day
return out

elif field == 'dow':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dayofweek(dts.year, dts.month, dts.day)
return out

elif field == 'woy':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
isleap = is_leapyear(dts.year)
isleap_prev = is_leapyear(dts.year - 1)
mo_off = _month_offset[isleap, dts.month - 1]
doy = mo_off + dts.day
dow = dayofweek(dts.year, dts.month, dts.day)

#estimate
woy = (doy - 1) - dow + 3
if woy >= 0:
woy = woy / 7 + 1

# verify
if woy < 0:
if (woy > -2) or (woy == -2 and isleap_prev):
woy = 53
else:
woy = 52
elif woy == 53:
if 31 - dts.day + dow < 3:
woy = 1

out[i] = woy
return out

elif field == 'q':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = dts.month
out[i] = ((out[i] - 1) / 3) + 1
return out

elif field == 'dim':
with nogil:
for i in range(count):
if dtindex[i] == NPY_NAT: out[i] = -1; continue

pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts)
out[i] = days_in_month(dts)
return out

raise ValueError("Field %s not supported" % field)


@cython.wraparound(False)
def get_start_end_field(ndarray[int64_t] dtindex, object field, object freqstr=None, int month_kw=12):
"""
Expand Down