# coding=utf-8 # pylint: disable-msg=E1101,W0612 import locale import calendar import pytest from datetime import datetime, date import numpy as np import pandas as pd from pandas.core.dtypes.common import is_integer_dtype, is_list_like from pandas import (Index, Series, DataFrame, bdate_range, date_range, period_range, timedelta_range, PeriodIndex, DatetimeIndex, TimedeltaIndex) import pandas.core.common as com from pandas.util.testing import assert_series_equal import pandas.util.testing as tm from .common import TestData class TestSeriesDatetimeValues(TestData): def test_dt_namespace_accessor(self): # GH 7207, 11128 # test .dt namespace accessor ok_for_period = PeriodIndex._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'] 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): 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 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 if prop != 'freq': 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) # 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 = s.dt.round('D') expected = Series(pd.to_datetime(['2012-01-02', '2012-01-02', '2012-01-01']), name='xxx') tm.assert_series_equal(result, expected) # round with tz 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) # floor 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.floor('D') expected = Series(pd.to_datetime(['2012-01-01', '2012-01-01', '2012-01-01']), name='xxx') tm.assert_series_equal(result, expected) # ceil 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.ceil('D') expected = Series(pd.to_datetime(['2012-01-02', '2012-01-02', '2012-01-02']), 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 if prop != 'freq': 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 = pd.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 list(sorted(set(results))) s = Series(date_range('20130101', periods=5, freq='D'), name='xxx') results = get_dir(s) tm.assert_almost_equal( results, list(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, list(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, list(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') 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 tm.assert_raises_regex(ValueError, "modifications"): s.dt.hour = 5 # trying to set a copy with pd.option_context('chained_assignment', 'raise'): def f(): s.dt.hour[0] = 5 pytest.raises(com.SettingWithCopyError, f) 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_accessor_no_new_attributes(self): # https://github.com/pandas-dev/pandas/issues/10673 s = Series(date_range('20130101', periods=5, freq='D')) with tm.assert_raises_regex(AttributeError, "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(DatetimeIndex(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.weekday_name[day] == eng_name 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(DatetimeIndex(freq='M', start='2012', end='2013')) result = s.dt.month_name(locale=time_locale) expected = Series([month.capitalize() for month in expected_months]) tm.assert_series_equal(result, expected) for s_date, expected in zip(s, expected_months): result = s_date.month_name(locale=time_locale) assert result == expected.capitalize() 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(['NaT', '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) 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) for s in [Series(np.arange(5)), Series(list('abcde')), Series(np.random.randn(5))]: with tm.assert_raises_regex(AttributeError, "only use .dt accessor"): s.dt assert not hasattr(s, 'dt') def test_between(self): s = Series(bdate_range('1/1/2000', periods=20).astype(object)) s[::2] = np.nan result = s[s.between(s[3], s[17])] expected = s[3:18].dropna() assert_series_equal(result, expected) result = s[s.between(s[3], s[17], inclusive=False)] expected = s[5:16].dropna() assert_series_equal(result, expected) def test_date_tz(self): # GH11757 rng = pd.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)]) assert_series_equal(s.dt.date, expected) assert_series_equal(s.apply(lambda x: x.date()), expected) def test_datetime_understood(self): # Ensures it doesn't fail to create the right series # reported in issue#16726 series = pd.Series(pd.date_range("2012-01-01", periods=3)) offset = pd.offsets.DateOffset(days=6) result = series - offset expected = pd.Series(pd.to_datetime([ '2011-12-26', '2011-12-27', '2011-12-28'])) tm.assert_series_equal(result, expected)