def test_add(self, offset_types, tz_naive_fixture):
tz = tz_naive_fixture
dt = datetime(2011, 1, 1, 9, 0)
offset_s = self._get_offset(offset_types)
expected = self.expecteds[offset_types.__name__]
result_dt = dt + offset_s
result_ts = Timestamp(dt) + offset_s
for result in [result_dt, result_ts]:
assert isinstance(result, Timestamp)
assert result == expected
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
assert isinstance(result, Timestamp)
assert result == expected_localize
# normalize=True, disallowed for Tick subclasses GH#21427
if issubclass(offset_types, Tick):
return
offset_s = self._get_offset(offset_types, normalize=True)
expected = Timestamp(expected.date())
result_dt = dt + offset_s
result_ts = Timestamp(dt) + offset_s
for result in [result_dt, result_ts]:
assert isinstance(result, Timestamp)
assert result == expected
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
assert isinstance(result, Timestamp)
assert result == expected_localize
def describe_categorical_1d(data: "Series", is_series: bool) -> "Series":
"""Describe series containing categorical data.
Parameters
----------
data : Series
Series to be described.
is_series : bool
True if the original object is a Series.
False if the one column of the DataFrame is described.
"""
names = ["count", "unique"]
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
dtype = None
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_any_dtype(data.dtype):
if is_series:
stacklevel = 6
else:
stacklevel = 7
warnings.warn(
"Treating datetime data as categorical rather than numeric in "
"`.describe` is deprecated and will be removed in a future "
"version of pandas. Specify `datetime_is_numeric=True` to "
"silence this warning and adopt the future behavior now.",
FutureWarning,
stacklevel=stacklevel,
)
tz = data.dt.tz
asint = data.dropna().values.view("i8")
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ["top", "freq", "first", "last"]
result += [
top,
freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz),
]
else:
names += ["top", "freq"]
result += [top, freq]
# If the DataFrame is empty, set 'top' and 'freq' to None
# to maintain output shape consistency
else:
names += ["top", "freq"]
result += [np.nan, np.nan]
dtype = "object"
from pandas import Series
return Series(result, index=names, name=data.name, dtype=dtype)
def test_subtracting_different_timezones(self, tz_aware_fixture):
t_raw = Timestamp("20130101")
t_UTC = t_raw.tz_localize("UTC")
t_diff = t_UTC.tz_convert(tz_aware_fixture) + Timedelta("0 days 05:00:00")
result = t_diff - t_UTC
assert isinstance(result, Timedelta)
assert result == Timedelta("0 days 05:00:00")
def describe_timestamp_as_categorical_1d(
data: Series,
percentiles_ignored: Sequence[float],
) -> Series:
"""Describe series containing timestamp data treated as categorical.
Parameters
----------
data : Series
Series to be described.
percentiles_ignored : list-like of numbers
Ignored, but in place to unify interface.
"""
names = ["count", "unique"]
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
dtype = None
if count_unique > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
tz = data.dt.tz
asint = data.dropna().values.view("i8")
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ["top", "freq", "first", "last"]
result += [
top,
freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz),
]
# If the DataFrame is empty, set 'top' and 'freq' to None
# to maintain output shape consistency
else:
names += ["top", "freq"]
result += [np.nan, np.nan]
dtype = "object"
from pandas import Series
return Series(result, index=names, name=data.name, dtype=dtype)
def test_replace_tzinfo_equiv_tz_localize_none(self):
# GH#14621, GH#7825
# assert conversion to naive is the same as replacing tzinfo with None
ts = Timestamp("2013-11-03 01:59:59.999999-0400", tz="US/Eastern")
assert ts.tz_localize(None) == ts.replace(tzinfo=None)
