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837 lines
29 KiB
837 lines
29 KiB
1 year ago
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import calendar
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from datetime import (
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date,
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datetime,
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time,
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)
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import locale
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import unicodedata
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import numpy as np
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import pytest
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import pytz
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from pandas._libs.tslibs.timezones import maybe_get_tz
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from pandas.errors import SettingWithCopyError
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from pandas.core.dtypes.common import (
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is_integer_dtype,
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is_list_like,
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)
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import pandas as pd
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from pandas import (
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DataFrame,
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DatetimeIndex,
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Index,
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Period,
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PeriodIndex,
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Series,
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TimedeltaIndex,
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date_range,
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period_range,
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timedelta_range,
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)
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import pandas._testing as tm
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from pandas.core.arrays import (
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DatetimeArray,
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PeriodArray,
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TimedeltaArray,
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)
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ok_for_period = PeriodArray._datetimelike_ops
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ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"]
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ok_for_dt = DatetimeArray._datetimelike_ops
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ok_for_dt_methods = [
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"to_period",
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"to_pydatetime",
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"tz_localize",
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"tz_convert",
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"normalize",
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"strftime",
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"round",
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"floor",
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"ceil",
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"day_name",
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"month_name",
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"isocalendar",
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"as_unit",
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]
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ok_for_td = TimedeltaArray._datetimelike_ops
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ok_for_td_methods = [
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"components",
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"to_pytimedelta",
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"total_seconds",
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"round",
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"floor",
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"ceil",
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"as_unit",
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]
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def get_dir(ser):
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# check limited display api
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results = [r for r in ser.dt.__dir__() if not r.startswith("_")]
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return sorted(set(results))
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class TestSeriesDatetimeValues:
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def _compare(self, ser, name):
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# GH 7207, 11128
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# test .dt namespace accessor
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def get_expected(ser, prop):
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result = getattr(Index(ser._values), prop)
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if isinstance(result, np.ndarray):
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if is_integer_dtype(result):
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result = result.astype("int64")
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elif not is_list_like(result) or isinstance(result, DataFrame):
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return result
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return Series(result, index=ser.index, name=ser.name)
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left = getattr(ser.dt, name)
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right = get_expected(ser, name)
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if not (is_list_like(left) and is_list_like(right)):
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assert left == right
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elif isinstance(left, DataFrame):
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tm.assert_frame_equal(left, right)
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else:
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tm.assert_series_equal(left, right)
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@pytest.mark.parametrize("freq", ["D", "s", "ms"])
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def test_dt_namespace_accessor_datetime64(self, freq):
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# GH#7207, GH#11128
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# test .dt namespace accessor
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# datetimeindex
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dti = date_range("20130101", periods=5, freq=freq)
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ser = Series(dti, name="xxx")
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for prop in ok_for_dt:
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# we test freq below
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if prop != "freq":
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self._compare(ser, prop)
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for prop in ok_for_dt_methods:
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getattr(ser.dt, prop)
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msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = ser.dt.to_pydatetime()
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assert isinstance(result, np.ndarray)
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assert result.dtype == object
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result = ser.dt.tz_localize("US/Eastern")
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exp_values = DatetimeIndex(ser.values).tz_localize("US/Eastern")
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expected = Series(exp_values, index=ser.index, name="xxx")
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tm.assert_series_equal(result, expected)
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tz_result = result.dt.tz
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assert str(tz_result) == "US/Eastern"
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freq_result = ser.dt.freq
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assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
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# let's localize, then convert
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result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
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exp_values = (
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DatetimeIndex(ser.values).tz_localize("UTC").tz_convert("US/Eastern")
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)
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expected = Series(exp_values, index=ser.index, name="xxx")
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tm.assert_series_equal(result, expected)
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def test_dt_namespace_accessor_datetime64tz(self):
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# GH#7207, GH#11128
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# test .dt namespace accessor
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# datetimeindex with tz
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dti = date_range("20130101", periods=5, tz="US/Eastern")
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ser = Series(dti, name="xxx")
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for prop in ok_for_dt:
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# we test freq below
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if prop != "freq":
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self._compare(ser, prop)
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for prop in ok_for_dt_methods:
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getattr(ser.dt, prop)
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msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = ser.dt.to_pydatetime()
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assert isinstance(result, np.ndarray)
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assert result.dtype == object
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result = ser.dt.tz_convert("CET")
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expected = Series(ser._values.tz_convert("CET"), index=ser.index, name="xxx")
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tm.assert_series_equal(result, expected)
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tz_result = result.dt.tz
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assert str(tz_result) == "CET"
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freq_result = ser.dt.freq
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assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
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def test_dt_namespace_accessor_timedelta(self):
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# GH#7207, GH#11128
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# test .dt namespace accessor
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# timedelta index
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cases = [
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Series(
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timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx"
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),
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Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"),
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Series(
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timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"),
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name="xxx",
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),
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]
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for ser in cases:
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for prop in ok_for_td:
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# we test freq below
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if prop != "freq":
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self._compare(ser, prop)
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for prop in ok_for_td_methods:
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getattr(ser.dt, prop)
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result = ser.dt.components
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assert isinstance(result, DataFrame)
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tm.assert_index_equal(result.index, ser.index)
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result = ser.dt.to_pytimedelta()
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assert isinstance(result, np.ndarray)
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assert result.dtype == object
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result = ser.dt.total_seconds()
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assert isinstance(result, Series)
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assert result.dtype == "float64"
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freq_result = ser.dt.freq
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assert freq_result == TimedeltaIndex(ser.values, freq="infer").freq
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def test_dt_namespace_accessor_period(self):
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# GH#7207, GH#11128
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# test .dt namespace accessor
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# periodindex
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pi = period_range("20130101", periods=5, freq="D")
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ser = Series(pi, name="xxx")
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for prop in ok_for_period:
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# we test freq below
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if prop != "freq":
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self._compare(ser, prop)
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for prop in ok_for_period_methods:
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getattr(ser.dt, prop)
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freq_result = ser.dt.freq
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assert freq_result == PeriodIndex(ser.values).freq
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def test_dt_namespace_accessor_index_and_values(self):
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# both
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index = date_range("20130101", periods=3, freq="D")
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dti = date_range("20140204", periods=3, freq="s")
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ser = Series(dti, index=index, name="xxx")
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exp = Series(
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np.array([2014, 2014, 2014], dtype="int32"), index=index, name="xxx"
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)
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tm.assert_series_equal(ser.dt.year, exp)
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exp = Series(np.array([2, 2, 2], dtype="int32"), index=index, name="xxx")
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tm.assert_series_equal(ser.dt.month, exp)
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exp = Series(np.array([0, 1, 2], dtype="int32"), index=index, name="xxx")
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tm.assert_series_equal(ser.dt.second, exp)
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exp = Series([ser.iloc[0]] * 3, index=index, name="xxx")
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tm.assert_series_equal(ser.dt.normalize(), exp)
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def test_dt_accessor_limited_display_api(self):
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# tznaive
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ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
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results = get_dir(ser)
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tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
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# tzaware
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ser = Series(date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx")
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ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
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results = get_dir(ser)
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tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
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# Period
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ser = Series(
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period_range("20130101", periods=5, freq="D", name="xxx").astype(object)
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)
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results = get_dir(ser)
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tm.assert_almost_equal(
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results, sorted(set(ok_for_period + ok_for_period_methods))
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)
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def test_dt_accessor_ambiguous_freq_conversions(self):
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# GH#11295
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# ambiguous time error on the conversions
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ser = Series(date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx")
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ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
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exp_values = date_range(
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"2015-01-01", "2016-01-01", freq="T", tz="UTC"
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).tz_convert("America/Chicago")
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# freq not preserved by tz_localize above
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exp_values = exp_values._with_freq(None)
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expected = Series(exp_values, name="xxx")
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tm.assert_series_equal(ser, expected)
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def test_dt_accessor_not_writeable(self, using_copy_on_write):
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# no setting allowed
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ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
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with pytest.raises(ValueError, match="modifications"):
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ser.dt.hour = 5
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# trying to set a copy
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msg = "modifications to a property of a datetimelike.+not supported"
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with pd.option_context("chained_assignment", "raise"):
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if using_copy_on_write:
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with tm.raises_chained_assignment_error():
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ser.dt.hour[0] = 5
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else:
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with pytest.raises(SettingWithCopyError, match=msg):
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ser.dt.hour[0] = 5
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|
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@pytest.mark.parametrize(
|
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"method, dates",
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[
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["round", ["2012-01-02", "2012-01-02", "2012-01-01"]],
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["floor", ["2012-01-01", "2012-01-01", "2012-01-01"]],
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["ceil", ["2012-01-02", "2012-01-02", "2012-01-02"]],
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],
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)
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def test_dt_round(self, method, dates):
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# round
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ser = Series(
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pd.to_datetime(
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["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
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),
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name="xxx",
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)
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result = getattr(ser.dt, method)("D")
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expected = Series(pd.to_datetime(dates), name="xxx")
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tm.assert_series_equal(result, expected)
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def test_dt_round_tz(self):
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ser = Series(
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pd.to_datetime(
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["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
|
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),
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name="xxx",
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)
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result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern").dt.round("D")
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|
|
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exp_values = pd.to_datetime(
|
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["2012-01-01", "2012-01-01", "2012-01-01"]
|
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).tz_localize("US/Eastern")
|
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expected = Series(exp_values, name="xxx")
|
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tm.assert_series_equal(result, expected)
|
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|
|
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|
@pytest.mark.parametrize("method", ["ceil", "round", "floor"])
|
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def test_dt_round_tz_ambiguous(self, method):
|
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# GH 18946 round near "fall back" DST
|
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df1 = DataFrame(
|
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|
[
|
||
|
pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True),
|
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|
pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True),
|
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pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True),
|
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|
],
|
||
|
columns=["date"],
|
||
|
)
|
||
|
df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid")
|
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|
# infer
|
||
|
result = getattr(df1.date.dt, method)("H", ambiguous="infer")
|
||
|
expected = df1["date"]
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
# bool-array
|
||
|
result = getattr(df1.date.dt, method)("H", ambiguous=[True, False, False])
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
# NaT
|
||
|
result = getattr(df1.date.dt, method)("H", ambiguous="NaT")
|
||
|
expected = df1["date"].copy()
|
||
|
expected.iloc[0:2] = pd.NaT
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
# raise
|
||
|
with tm.external_error_raised(pytz.AmbiguousTimeError):
|
||
|
getattr(df1.date.dt, method)("H", ambiguous="raise")
|
||
|
|
||
|
@pytest.mark.parametrize(
|
||
|
"method, ts_str, freq",
|
||
|
[
|
||
|
["ceil", "2018-03-11 01:59:00-0600", "5min"],
|
||
|
["round", "2018-03-11 01:59:00-0600", "5min"],
|
||
|
["floor", "2018-03-11 03:01:00-0500", "2H"],
|
||
|
],
|
||
|
)
|
||
|
def test_dt_round_tz_nonexistent(self, method, ts_str, freq):
|
||
|
# GH 23324 round near "spring forward" DST
|
||
|
ser = Series([pd.Timestamp(ts_str, tz="America/Chicago")])
|
||
|
result = getattr(ser.dt, method)(freq, nonexistent="shift_forward")
|
||
|
expected = Series([pd.Timestamp("2018-03-11 03:00:00", tz="America/Chicago")])
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
result = getattr(ser.dt, method)(freq, nonexistent="NaT")
|
||
|
expected = Series([pd.NaT]).dt.tz_localize(result.dt.tz)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
with pytest.raises(pytz.NonExistentTimeError, match="2018-03-11 02:00:00"):
|
||
|
getattr(ser.dt, method)(freq, nonexistent="raise")
|
||
|
|
||
|
@pytest.mark.parametrize("freq", ["ns", "U", "1000U"])
|
||
|
def test_dt_round_nonnano_higher_resolution_no_op(self, freq):
|
||
|
# GH 52761
|
||
|
ser = Series(
|
||
|
["2020-05-31 08:00:00", "2000-12-31 04:00:05", "1800-03-14 07:30:20"],
|
||
|
dtype="datetime64[ms]",
|
||
|
)
|
||
|
expected = ser.copy()
|
||
|
result = ser.dt.round(freq)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
assert not np.shares_memory(ser.array._ndarray, result.array._ndarray)
|
||
|
|
||
|
def test_dt_namespace_accessor_categorical(self):
|
||
|
# GH 19468
|
||
|
dti = DatetimeIndex(["20171111", "20181212"]).repeat(2)
|
||
|
ser = Series(pd.Categorical(dti), name="foo")
|
||
|
result = ser.dt.year
|
||
|
expected = Series([2017, 2017, 2018, 2018], dtype="int32", name="foo")
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_dt_tz_localize_categorical(self, tz_aware_fixture):
|
||
|
# GH 27952
|
||
|
tz = tz_aware_fixture
|
||
|
datetimes = Series(
|
||
|
["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns]"
|
||
|
)
|
||
|
categorical = datetimes.astype("category")
|
||
|
result = categorical.dt.tz_localize(tz)
|
||
|
expected = datetimes.dt.tz_localize(tz)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_dt_tz_convert_categorical(self, tz_aware_fixture):
|
||
|
# GH 27952
|
||
|
tz = tz_aware_fixture
|
||
|
datetimes = Series(
|
||
|
["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns, MET]"
|
||
|
)
|
||
|
categorical = datetimes.astype("category")
|
||
|
result = categorical.dt.tz_convert(tz)
|
||
|
expected = datetimes.dt.tz_convert(tz)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
@pytest.mark.parametrize("accessor", ["year", "month", "day"])
|
||
|
def test_dt_other_accessors_categorical(self, accessor):
|
||
|
# GH 27952
|
||
|
datetimes = Series(
|
||
|
["2018-01-01", "2018-01-01", "2019-01-02"], dtype="datetime64[ns]"
|
||
|
)
|
||
|
categorical = datetimes.astype("category")
|
||
|
result = getattr(categorical.dt, accessor)
|
||
|
expected = getattr(datetimes.dt, accessor)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_dt_accessor_no_new_attributes(self):
|
||
|
# https://github.com/pandas-dev/pandas/issues/10673
|
||
|
ser = Series(date_range("20130101", periods=5, freq="D"))
|
||
|
with pytest.raises(AttributeError, match="You cannot add any new attribute"):
|
||
|
ser.dt.xlabel = "a"
|
||
|
|
||
|
# error: Unsupported operand types for + ("List[None]" and "List[str]")
|
||
|
@pytest.mark.parametrize(
|
||
|
"time_locale", [None] + tm.get_locales() # type: ignore[operator]
|
||
|
)
|
||
|
def test_dt_accessor_datetime_name_accessors(self, time_locale):
|
||
|
# Test Monday -> Sunday and January -> December, in that sequence
|
||
|
if time_locale is None:
|
||
|
# If the time_locale is None, day-name and month_name should
|
||
|
# return the english attributes
|
||
|
expected_days = [
|
||
|
"Monday",
|
||
|
"Tuesday",
|
||
|
"Wednesday",
|
||
|
"Thursday",
|
||
|
"Friday",
|
||
|
"Saturday",
|
||
|
"Sunday",
|
||
|
]
|
||
|
expected_months = [
|
||
|
"January",
|
||
|
"February",
|
||
|
"March",
|
||
|
"April",
|
||
|
"May",
|
||
|
"June",
|
||
|
"July",
|
||
|
"August",
|
||
|
"September",
|
||
|
"October",
|
||
|
"November",
|
||
|
"December",
|
||
|
]
|
||
|
else:
|
||
|
with tm.set_locale(time_locale, locale.LC_TIME):
|
||
|
expected_days = calendar.day_name[:]
|
||
|
expected_months = calendar.month_name[1:]
|
||
|
|
||
|
ser = Series(date_range(freq="D", start=datetime(1998, 1, 1), periods=365))
|
||
|
english_days = [
|
||
|
"Monday",
|
||
|
"Tuesday",
|
||
|
"Wednesday",
|
||
|
"Thursday",
|
||
|
"Friday",
|
||
|
"Saturday",
|
||
|
"Sunday",
|
||
|
]
|
||
|
for day, name, eng_name in zip(range(4, 11), expected_days, english_days):
|
||
|
name = name.capitalize()
|
||
|
assert ser.dt.day_name(locale=time_locale)[day] == name
|
||
|
assert ser.dt.day_name(locale=None)[day] == eng_name
|
||
|
ser = pd.concat([ser, Series([pd.NaT])])
|
||
|
assert np.isnan(ser.dt.day_name(locale=time_locale).iloc[-1])
|
||
|
|
||
|
ser = Series(date_range(freq="M", start="2012", end="2013"))
|
||
|
result = ser.dt.month_name(locale=time_locale)
|
||
|
expected = Series([month.capitalize() for month in expected_months])
|
||
|
|
||
|
# work around https://github.com/pandas-dev/pandas/issues/22342
|
||
|
result = result.str.normalize("NFD")
|
||
|
expected = expected.str.normalize("NFD")
|
||
|
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
for s_date, expected in zip(ser, expected_months):
|
||
|
result = s_date.month_name(locale=time_locale)
|
||
|
expected = expected.capitalize()
|
||
|
|
||
|
result = unicodedata.normalize("NFD", result)
|
||
|
expected = unicodedata.normalize("NFD", expected)
|
||
|
|
||
|
assert result == expected
|
||
|
|
||
|
ser = pd.concat([ser, Series([pd.NaT])])
|
||
|
assert np.isnan(ser.dt.month_name(locale=time_locale).iloc[-1])
|
||
|
|
||
|
def test_strftime(self):
|
||
|
# GH 10086
|
||
|
ser = Series(date_range("20130101", periods=5))
|
||
|
result = ser.dt.strftime("%Y/%m/%d")
|
||
|
expected = Series(
|
||
|
["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
ser = Series(date_range("2015-02-03 11:22:33.4567", periods=5))
|
||
|
result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
|
||
|
expected = Series(
|
||
|
[
|
||
|
"2015/02/03 11-22-33",
|
||
|
"2015/02/04 11-22-33",
|
||
|
"2015/02/05 11-22-33",
|
||
|
"2015/02/06 11-22-33",
|
||
|
"2015/02/07 11-22-33",
|
||
|
]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
ser = Series(period_range("20130101", periods=5))
|
||
|
result = ser.dt.strftime("%Y/%m/%d")
|
||
|
expected = Series(
|
||
|
["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
ser = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s"))
|
||
|
result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
|
||
|
expected = Series(
|
||
|
[
|
||
|
"2015/02/03 11-22-33",
|
||
|
"2015/02/03 11-22-34",
|
||
|
"2015/02/03 11-22-35",
|
||
|
"2015/02/03 11-22-36",
|
||
|
"2015/02/03 11-22-37",
|
||
|
]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_strftime_dt64_days(self):
|
||
|
ser = Series(date_range("20130101", periods=5))
|
||
|
ser.iloc[0] = pd.NaT
|
||
|
result = ser.dt.strftime("%Y/%m/%d")
|
||
|
expected = Series(
|
||
|
[np.nan, "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
datetime_index = date_range("20150301", periods=5)
|
||
|
result = datetime_index.strftime("%Y/%m/%d")
|
||
|
|
||
|
expected = Index(
|
||
|
["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
|
||
|
dtype=np.object_,
|
||
|
)
|
||
|
# dtype may be S10 or U10 depending on python version
|
||
|
tm.assert_index_equal(result, expected)
|
||
|
|
||
|
def test_strftime_period_days(self):
|
||
|
period_index = period_range("20150301", periods=5)
|
||
|
result = period_index.strftime("%Y/%m/%d")
|
||
|
expected = Index(
|
||
|
["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
|
||
|
dtype="=U10",
|
||
|
)
|
||
|
tm.assert_index_equal(result, expected)
|
||
|
|
||
|
def test_strftime_dt64_microsecond_resolution(self):
|
||
|
ser = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)])
|
||
|
result = ser.dt.strftime("%Y-%m-%d %H:%M:%S")
|
||
|
expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_strftime_period_hours(self):
|
||
|
ser = Series(period_range("20130101", periods=4, freq="H"))
|
||
|
result = ser.dt.strftime("%Y/%m/%d %H:%M:%S")
|
||
|
expected = Series(
|
||
|
[
|
||
|
"2013/01/01 00:00:00",
|
||
|
"2013/01/01 01:00:00",
|
||
|
"2013/01/01 02:00:00",
|
||
|
"2013/01/01 03:00:00",
|
||
|
]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_strftime_period_minutes(self):
|
||
|
ser = Series(period_range("20130101", periods=4, freq="L"))
|
||
|
result = ser.dt.strftime("%Y/%m/%d %H:%M:%S.%l")
|
||
|
expected = Series(
|
||
|
[
|
||
|
"2013/01/01 00:00:00.000",
|
||
|
"2013/01/01 00:00:00.001",
|
||
|
"2013/01/01 00:00:00.002",
|
||
|
"2013/01/01 00:00:00.003",
|
||
|
]
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
@pytest.mark.parametrize(
|
||
|
"data",
|
||
|
[
|
||
|
DatetimeIndex(["2019-01-01", pd.NaT]),
|
||
|
PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]"),
|
||
|
],
|
||
|
)
|
||
|
def test_strftime_nat(self, data):
|
||
|
# GH 29578
|
||
|
ser = Series(data)
|
||
|
result = ser.dt.strftime("%Y-%m-%d")
|
||
|
expected = Series(["2019-01-01", np.nan])
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
@pytest.mark.parametrize(
|
||
|
"data", [DatetimeIndex([pd.NaT]), PeriodIndex([pd.NaT], dtype="period[D]")]
|
||
|
)
|
||
|
def test_strftime_all_nat(self, data):
|
||
|
# https://github.com/pandas-dev/pandas/issues/45858
|
||
|
ser = Series(data)
|
||
|
with tm.assert_produces_warning(None):
|
||
|
result = ser.dt.strftime("%Y-%m-%d")
|
||
|
expected = Series([np.nan], dtype=object)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_valid_dt_with_missing_values(self):
|
||
|
# GH 8689
|
||
|
ser = Series(date_range("20130101", periods=5, freq="D"))
|
||
|
ser.iloc[2] = pd.NaT
|
||
|
|
||
|
for attr in ["microsecond", "nanosecond", "second", "minute", "hour", "day"]:
|
||
|
expected = getattr(ser.dt, attr).copy()
|
||
|
expected.iloc[2] = np.nan
|
||
|
result = getattr(ser.dt, attr)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
result = ser.dt.date
|
||
|
expected = Series(
|
||
|
[
|
||
|
date(2013, 1, 1),
|
||
|
date(2013, 1, 2),
|
||
|
pd.NaT,
|
||
|
date(2013, 1, 4),
|
||
|
date(2013, 1, 5),
|
||
|
],
|
||
|
dtype="object",
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
result = ser.dt.time
|
||
|
expected = Series([time(0), time(0), pd.NaT, time(0), time(0)], dtype="object")
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
def test_dt_accessor_api(self):
|
||
|
# GH 9322
|
||
|
from pandas.core.indexes.accessors import (
|
||
|
CombinedDatetimelikeProperties,
|
||
|
DatetimeProperties,
|
||
|
)
|
||
|
|
||
|
assert Series.dt is CombinedDatetimelikeProperties
|
||
|
|
||
|
ser = Series(date_range("2000-01-01", periods=3))
|
||
|
assert isinstance(ser.dt, DatetimeProperties)
|
||
|
|
||
|
@pytest.mark.parametrize(
|
||
|
"ser",
|
||
|
[
|
||
|
Series(np.arange(5)),
|
||
|
Series(list("abcde")),
|
||
|
Series(np.random.default_rng(2).standard_normal(5)),
|
||
|
],
|
||
|
)
|
||
|
def test_dt_accessor_invalid(self, ser):
|
||
|
# GH#9322 check that series with incorrect dtypes don't have attr
|
||
|
with pytest.raises(AttributeError, match="only use .dt accessor"):
|
||
|
ser.dt
|
||
|
assert not hasattr(ser, "dt")
|
||
|
|
||
|
def test_dt_accessor_updates_on_inplace(self):
|
||
|
ser = Series(date_range("2018-01-01", periods=10))
|
||
|
ser[2] = None
|
||
|
return_value = ser.fillna(pd.Timestamp("2018-01-01"), inplace=True)
|
||
|
assert return_value is None
|
||
|
result = ser.dt.date
|
||
|
assert result[0] == result[2]
|
||
|
|
||
|
def test_date_tz(self):
|
||
|
# GH11757
|
||
|
rng = DatetimeIndex(
|
||
|
["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"],
|
||
|
tz="US/Eastern",
|
||
|
)
|
||
|
ser = Series(rng)
|
||
|
expected = Series([date(2014, 4, 4), date(2014, 7, 18), date(2015, 11, 22)])
|
||
|
tm.assert_series_equal(ser.dt.date, expected)
|
||
|
tm.assert_series_equal(ser.apply(lambda x: x.date()), expected)
|
||
|
|
||
|
def test_dt_timetz_accessor(self, tz_naive_fixture):
|
||
|
# GH21358
|
||
|
tz = maybe_get_tz(tz_naive_fixture)
|
||
|
|
||
|
dtindex = DatetimeIndex(
|
||
|
["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"], tz=tz
|
||
|
)
|
||
|
ser = Series(dtindex)
|
||
|
expected = Series(
|
||
|
[time(23, 56, tzinfo=tz), time(21, 24, tzinfo=tz), time(22, 14, tzinfo=tz)]
|
||
|
)
|
||
|
result = ser.dt.timetz
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
@pytest.mark.parametrize(
|
||
|
"input_series, expected_output",
|
||
|
[
|
||
|
[["2020-01-01"], [[2020, 1, 3]]],
|
||
|
[[pd.NaT], [[np.nan, np.nan, np.nan]]],
|
||
|
[["2019-12-31", "2019-12-29"], [[2020, 1, 2], [2019, 52, 7]]],
|
||
|
[["2010-01-01", pd.NaT], [[2009, 53, 5], [np.nan, np.nan, np.nan]]],
|
||
|
# see GH#36032
|
||
|
[["2016-01-08", "2016-01-04"], [[2016, 1, 5], [2016, 1, 1]]],
|
||
|
[["2016-01-07", "2016-01-01"], [[2016, 1, 4], [2015, 53, 5]]],
|
||
|
],
|
||
|
)
|
||
|
def test_isocalendar(self, input_series, expected_output):
|
||
|
result = pd.to_datetime(Series(input_series)).dt.isocalendar()
|
||
|
expected_frame = DataFrame(
|
||
|
expected_output, columns=["year", "week", "day"], dtype="UInt32"
|
||
|
)
|
||
|
tm.assert_frame_equal(result, expected_frame)
|
||
|
|
||
|
def test_hour_index(self):
|
||
|
dt_series = Series(
|
||
|
date_range(start="2021-01-01", periods=5, freq="h"),
|
||
|
index=[2, 6, 7, 8, 11],
|
||
|
dtype="category",
|
||
|
)
|
||
|
result = dt_series.dt.hour
|
||
|
expected = Series(
|
||
|
[0, 1, 2, 3, 4],
|
||
|
dtype="int32",
|
||
|
index=[2, 6, 7, 8, 11],
|
||
|
)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
|
||
|
class TestSeriesPeriodValuesDtAccessor:
|
||
|
@pytest.mark.parametrize(
|
||
|
"input_vals",
|
||
|
[
|
||
|
[Period("2016-01", freq="M"), Period("2016-02", freq="M")],
|
||
|
[Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")],
|
||
|
[
|
||
|
Period("2016-01-01 00:00:00", freq="H"),
|
||
|
Period("2016-01-01 01:00:00", freq="H"),
|
||
|
],
|
||
|
[
|
||
|
Period("2016-01-01 00:00:00", freq="M"),
|
||
|
Period("2016-01-01 00:01:00", freq="M"),
|
||
|
],
|
||
|
[
|
||
|
Period("2016-01-01 00:00:00", freq="S"),
|
||
|
Period("2016-01-01 00:00:01", freq="S"),
|
||
|
],
|
||
|
],
|
||
|
)
|
||
|
def test_end_time_timevalues(self, input_vals):
|
||
|
# GH#17157
|
||
|
# Check that the time part of the Period is adjusted by end_time
|
||
|
# when using the dt accessor on a Series
|
||
|
input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
|
||
|
|
||
|
ser = Series(input_vals)
|
||
|
result = ser.dt.end_time
|
||
|
expected = ser.apply(lambda x: x.end_time)
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
@pytest.mark.parametrize("input_vals", [("2001"), ("NaT")])
|
||
|
def test_to_period(self, input_vals):
|
||
|
# GH#21205
|
||
|
expected = Series([input_vals], dtype="Period[D]")
|
||
|
result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D")
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
|
||
|
def test_normalize_pre_epoch_dates():
|
||
|
# GH: 36294
|
||
|
ser = pd.to_datetime(Series(["1969-01-01 09:00:00", "2016-01-01 09:00:00"]))
|
||
|
result = ser.dt.normalize()
|
||
|
expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"]))
|
||
|
tm.assert_series_equal(result, expected)
|
||
|
|
||
|
|
||
|
def test_day_attribute_non_nano_beyond_int32():
|
||
|
# GH 52386
|
||
|
data = np.array(
|
||
|
[
|
||
|
136457654736252,
|
||
|
134736784364431,
|
||
|
245345345545332,
|
||
|
223432411,
|
||
|
2343241,
|
||
|
3634548734,
|
||
|
23234,
|
||
|
],
|
||
|
dtype="timedelta64[s]",
|
||
|
)
|
||
|
ser = Series(data)
|
||
|
result = ser.dt.days
|
||
|
expected = Series([1579371003, 1559453522, 2839645203, 2586, 27, 42066, 0])
|
||
|
tm.assert_series_equal(result, expected)
|