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projektAI/venv/Lib/site-packages/pandas/tests/series/accessors/test_dt_accessor.py

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2021-06-06 22:13:05 +02:00
import calendar
from datetime import date, datetime, time
import locale
import unicodedata
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.timezones import maybe_get_tz
from pandas.core.dtypes.common import is_integer_dtype, is_list_like
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
Period,
PeriodIndex,
Series,
TimedeltaIndex,
date_range,
period_range,
timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays import PeriodArray
import pandas.core.common as com
class TestSeriesDatetimeValues:
def test_dt_namespace_accessor(self):
# GH 7207, 11128
# test .dt namespace accessor
ok_for_period = PeriodArray._datetimelike_ops
ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"]
ok_for_dt = DatetimeIndex._datetimelike_ops
ok_for_dt_methods = [
"to_period",
"to_pydatetime",
"tz_localize",
"tz_convert",
"normalize",
"strftime",
"round",
"floor",
"ceil",
"day_name",
"month_name",
"isocalendar",
]
ok_for_td = TimedeltaIndex._datetimelike_ops
ok_for_td_methods = [
"components",
"to_pytimedelta",
"total_seconds",
"round",
"floor",
"ceil",
]
def get_expected(s, name):
result = getattr(Index(s._values), prop)
if isinstance(result, np.ndarray):
if is_integer_dtype(result):
result = result.astype("int64")
elif not is_list_like(result) or isinstance(result, pd.DataFrame):
return result
return Series(result, index=s.index, name=s.name)
def compare(s, name):
a = getattr(s.dt, prop)
b = get_expected(s, prop)
if not (is_list_like(a) and is_list_like(b)):
assert a == b
elif isinstance(a, pd.DataFrame):
tm.assert_frame_equal(a, b)
else:
tm.assert_series_equal(a, b)
# datetimeindex
cases = [
Series(date_range("20130101", periods=5), name="xxx"),
Series(date_range("20130101", periods=5, freq="s"), name="xxx"),
Series(date_range("20130101 00:00:00", periods=5, freq="ms"), name="xxx"),
]
for s in cases:
for prop in ok_for_dt:
# we test freq below
# we ignore week and weekofyear because they are deprecated
if prop not in ["freq", "week", "weekofyear"]:
compare(s, prop)
for prop in ok_for_dt_methods:
getattr(s.dt, prop)
result = s.dt.to_pydatetime()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.tz_localize("US/Eastern")
exp_values = DatetimeIndex(s.values).tz_localize("US/Eastern")
expected = Series(exp_values, index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
tz_result = result.dt.tz
assert str(tz_result) == "US/Eastern"
freq_result = s.dt.freq
assert freq_result == DatetimeIndex(s.values, freq="infer").freq
# let's localize, then convert
result = s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
exp_values = (
DatetimeIndex(s.values).tz_localize("UTC").tz_convert("US/Eastern")
)
expected = Series(exp_values, index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
# datetimeindex with tz
s = Series(date_range("20130101", periods=5, tz="US/Eastern"), name="xxx")
for prop in ok_for_dt:
# we test freq below
# we ignore week and weekofyear because they are deprecated
if prop not in ["freq", "week", "weekofyear"]:
compare(s, prop)
for prop in ok_for_dt_methods:
getattr(s.dt, prop)
result = s.dt.to_pydatetime()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.tz_convert("CET")
expected = Series(s._values.tz_convert("CET"), index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
tz_result = result.dt.tz
assert str(tz_result) == "CET"
freq_result = s.dt.freq
assert freq_result == DatetimeIndex(s.values, freq="infer").freq
# timedelta index
cases = [
Series(
timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx"
),
Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"),
Series(
timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"),
name="xxx",
),
]
for s in cases:
for prop in ok_for_td:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_td_methods:
getattr(s.dt, prop)
result = s.dt.components
assert isinstance(result, DataFrame)
tm.assert_index_equal(result.index, s.index)
result = s.dt.to_pytimedelta()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.total_seconds()
assert isinstance(result, pd.Series)
assert result.dtype == "float64"
freq_result = s.dt.freq
assert freq_result == TimedeltaIndex(s.values, freq="infer").freq
# both
index = date_range("20130101", periods=3, freq="D")
s = Series(date_range("20140204", periods=3, freq="s"), index=index, name="xxx")
exp = Series(
np.array([2014, 2014, 2014], dtype="int64"), index=index, name="xxx"
)
tm.assert_series_equal(s.dt.year, exp)
exp = Series(np.array([2, 2, 2], dtype="int64"), index=index, name="xxx")
tm.assert_series_equal(s.dt.month, exp)
exp = Series(np.array([0, 1, 2], dtype="int64"), index=index, name="xxx")
tm.assert_series_equal(s.dt.second, exp)
exp = Series([s[0]] * 3, index=index, name="xxx")
tm.assert_series_equal(s.dt.normalize(), exp)
# periodindex
cases = [Series(period_range("20130101", periods=5, freq="D"), name="xxx")]
for s in cases:
for prop in ok_for_period:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_period_methods:
getattr(s.dt, prop)
freq_result = s.dt.freq
assert freq_result == PeriodIndex(s.values).freq
# test limited display api
def get_dir(s):
results = [r for r in s.dt.__dir__() if not r.startswith("_")]
return sorted(set(results))
s = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
results = get_dir(s)
tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
s = Series(
period_range("20130101", periods=5, freq="D", name="xxx").astype(object)
)
results = get_dir(s)
tm.assert_almost_equal(
results, sorted(set(ok_for_period + ok_for_period_methods))
)
# 11295
# ambiguous time error on the conversions
s = Series(pd.date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx")
s = s.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
results = get_dir(s)
tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
exp_values = pd.date_range(
"2015-01-01", "2016-01-01", freq="T", tz="UTC"
).tz_convert("America/Chicago")
# freq not preserved by tz_localize above
exp_values = exp_values._with_freq(None)
expected = Series(exp_values, name="xxx")
tm.assert_series_equal(s, expected)
# no setting allowed
s = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
with pytest.raises(ValueError, match="modifications"):
s.dt.hour = 5
# trying to set a copy
msg = "modifications to a property of a datetimelike.+not supported"
with pd.option_context("chained_assignment", "raise"):
with pytest.raises(com.SettingWithCopyError, match=msg):
s.dt.hour[0] = 5
@pytest.mark.parametrize(
"method, dates",
[
["round", ["2012-01-02", "2012-01-02", "2012-01-01"]],
["floor", ["2012-01-01", "2012-01-01", "2012-01-01"]],
["ceil", ["2012-01-02", "2012-01-02", "2012-01-02"]],
],
)
def test_dt_round(self, method, dates):
# round
s = Series(
pd.to_datetime(
["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
),
name="xxx",
)
result = getattr(s.dt, method)("D")
expected = Series(pd.to_datetime(dates), name="xxx")
tm.assert_series_equal(result, expected)
def test_dt_round_tz(self):
s = Series(
pd.to_datetime(
["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
),
name="xxx",
)
result = s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern").dt.round("D")
exp_values = pd.to_datetime(
["2012-01-01", "2012-01-01", "2012-01-01"]
).tz_localize("US/Eastern")
expected = Series(exp_values, name="xxx")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("method", ["ceil", "round", "floor"])
def test_dt_round_tz_ambiguous(self, method):
# GH 18946 round near "fall back" DST
df1 = pd.DataFrame(
[
pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True),
pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True),
pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True),
],
columns=["date"],
)
df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid")
# 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
s = Series([pd.Timestamp(ts_str, tz="America/Chicago")])
result = getattr(s.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(s.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(s.dt, method)(freq, nonexistent="raise")
def test_dt_namespace_accessor_categorical(self):
# GH 19468
dti = DatetimeIndex(["20171111", "20181212"]).repeat(2)
s = Series(pd.Categorical(dti), name="foo")
result = s.dt.year
expected = Series([2017, 2017, 2018, 2018], 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
s = Series(date_range("20130101", periods=5, freq="D"))
with pytest.raises(AttributeError, match="You cannot add any new attribute"):
s.dt.xlabel = "a"
@pytest.mark.parametrize(
"time_locale", [None] if tm.get_locales() is None else [None] + tm.get_locales()
)
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:]
s = 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 s.dt.day_name(locale=time_locale)[day] == name
s = s.append(Series([pd.NaT]))
assert np.isnan(s.dt.day_name(locale=time_locale).iloc[-1])
s = Series(date_range(freq="M", start="2012", end="2013"))
result = s.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(s, 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
s = s.append(Series([pd.NaT]))
assert np.isnan(s.dt.month_name(locale=time_locale).iloc[-1])
def test_strftime(self):
# GH 10086
s = Series(date_range("20130101", periods=5))
result = s.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)
s = Series(date_range("2015-02-03 11:22:33.4567", periods=5))
result = s.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)
s = Series(period_range("20130101", periods=5))
result = s.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)
s = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s"))
result = s.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)
s = Series(date_range("20130101", periods=5))
s.iloc[0] = pd.NaT
result = s.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)
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)
s = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)])
result = s.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)
s = Series(period_range("20130101", periods=4, freq="H"))
result = s.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",
]
)
s = Series(period_range("20130101", periods=4, freq="L"))
result = s.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
s = Series(data)
result = s.dt.strftime("%Y-%m-%d")
expected = Series(["2019-01-01", np.nan])
tm.assert_series_equal(result, expected)
def test_valid_dt_with_missing_values(self):
from datetime import date, time
# GH 8689
s = Series(date_range("20130101", periods=5, freq="D"))
s.iloc[2] = pd.NaT
for attr in ["microsecond", "nanosecond", "second", "minute", "hour", "day"]:
expected = getattr(s.dt, attr).copy()
expected.iloc[2] = np.nan
result = getattr(s.dt, attr)
tm.assert_series_equal(result, expected)
result = s.dt.date
expected = Series(
[
date(2013, 1, 1),
date(2013, 1, 2),
np.nan,
date(2013, 1, 4),
date(2013, 1, 5),
],
dtype="object",
)
tm.assert_series_equal(result, expected)
result = s.dt.time
expected = Series([time(0), time(0), np.nan, 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
s = Series(date_range("2000-01-01", periods=3))
assert isinstance(s.dt, DatetimeProperties)
@pytest.mark.parametrize(
"ser", [Series(np.arange(5)), Series(list("abcde")), Series(np.random.randn(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):
s = Series(pd.date_range("2018-01-01", periods=10))
s[2] = None
return_value = s.fillna(pd.Timestamp("2018-01-01"), inplace=True)
assert return_value is None
result = s.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",
)
s = Series(rng)
expected = Series([date(2014, 4, 4), date(2014, 7, 18), date(2015, 11, 22)])
tm.assert_series_equal(s.dt.date, expected)
tm.assert_series_equal(s.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
)
s = Series(dtindex)
expected = Series(
[time(23, 56, tzinfo=tz), time(21, 24, tzinfo=tz), time(22, 14, tzinfo=tz)]
)
result = s.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 = pd.DataFrame(
expected_output, columns=["year", "week", "day"], dtype="UInt32"
)
tm.assert_frame_equal(result, expected_frame)
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))
s = Series(input_vals)
result = s.dt.end_time
expected = s.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_week_and_weekofyear_are_deprecated():
# GH#33595 Deprecate week and weekofyear
series = pd.to_datetime(Series(["2020-01-01"]))
with tm.assert_produces_warning(FutureWarning):
series.dt.week
with tm.assert_produces_warning(FutureWarning):
series.dt.weekofyear
def test_normalize_pre_epoch_dates():
# GH: 36294
s = pd.to_datetime(Series(["1969-01-01 09:00:00", "2016-01-01 09:00:00"]))
result = s.dt.normalize()
expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"]))
tm.assert_series_equal(result, expected)
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