""" test to_datetime """ import pytz import pytest import locale import calendar import dateutil import numpy as np from dateutil.parser import parse from datetime import datetime, date, time from distutils.version import LooseVersion import pandas as pd from pandas._libs import tslib from pandas._libs.tslibs import parsing from pandas.core.tools import datetimes as tools from pandas.errors import OutOfBoundsDatetime from pandas.compat import lmap, PY3 from pandas.core.dtypes.common import is_datetime64_ns_dtype from pandas.util import testing as tm import pandas.util._test_decorators as td from pandas.util.testing import assert_series_equal from pandas import (isna, to_datetime, Timestamp, Series, DataFrame, Index, DatetimeIndex, NaT, date_range, compat) class TestTimeConversionFormats(object): @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format(self, cache): values = ['1/1/2000', '1/2/2000', '1/3/2000'] results1 = [Timestamp('20000101'), Timestamp('20000201'), Timestamp('20000301')] results2 = [Timestamp('20000101'), Timestamp('20000102'), Timestamp('20000103')] for vals, expecteds in [(values, (Index(results1), Index(results2))), (Series(values), (Series(results1), Series(results2))), (values[0], (results1[0], results2[0])), (values[1], (results1[1], results2[1])), (values[2], (results1[2], results2[2]))]: for i, fmt in enumerate(['%d/%m/%Y', '%m/%d/%Y']): result = to_datetime(vals, format=fmt, cache=cache) expected = expecteds[i] if isinstance(expected, Series): assert_series_equal(result, Series(expected)) elif isinstance(expected, Timestamp): assert result == expected else: tm.assert_index_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format_YYYYMMDD(self, cache): s = Series([19801222, 19801222] + [19810105] * 5) expected = Series([Timestamp(x) for x in s.apply(str)]) result = to_datetime(s, format='%Y%m%d', cache=cache) assert_series_equal(result, expected) result = to_datetime(s.apply(str), format='%Y%m%d', cache=cache) assert_series_equal(result, expected) # with NaT expected = Series([Timestamp("19801222"), Timestamp("19801222")] + [Timestamp("19810105")] * 5) expected[2] = np.nan s[2] = np.nan result = to_datetime(s, format='%Y%m%d', cache=cache) assert_series_equal(result, expected) # string with NaT s = s.apply(str) s[2] = 'nat' result = to_datetime(s, format='%Y%m%d', cache=cache) assert_series_equal(result, expected) # coercion # GH 7930 s = Series([20121231, 20141231, 99991231]) result = pd.to_datetime(s, format='%Y%m%d', errors='ignore', cache=cache) expected = Series([datetime(2012, 12, 31), datetime(2014, 12, 31), datetime(9999, 12, 31)], dtype=object) tm.assert_series_equal(result, expected) result = pd.to_datetime(s, format='%Y%m%d', errors='coerce', cache=cache) expected = Series(['20121231', '20141231', 'NaT'], dtype='M8[ns]') assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format_integer(self, cache): # GH 10178 s = Series([2000, 2001, 2002]) expected = Series([Timestamp(x) for x in s.apply(str)]) result = to_datetime(s, format='%Y', cache=cache) assert_series_equal(result, expected) s = Series([200001, 200105, 200206]) expected = Series([Timestamp(x[:4] + '-' + x[4:]) for x in s.apply(str) ]) result = to_datetime(s, format='%Y%m', cache=cache) assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format_microsecond(self, cache): # these are locale dependent lang, _ = locale.getlocale() month_abbr = calendar.month_abbr[4] val = '01-{}-2011 00:00:01.978'.format(month_abbr) format = '%d-%b-%Y %H:%M:%S.%f' result = to_datetime(val, format=format, cache=cache) exp = datetime.strptime(val, format) assert result == exp @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format_time(self, cache): data = [ ['01/10/2010 15:20', '%m/%d/%Y %H:%M', Timestamp('2010-01-10 15:20')], ['01/10/2010 05:43', '%m/%d/%Y %I:%M', Timestamp('2010-01-10 05:43')], ['01/10/2010 13:56:01', '%m/%d/%Y %H:%M:%S', Timestamp('2010-01-10 13:56:01')] # , # ['01/10/2010 08:14 PM', '%m/%d/%Y %I:%M %p', # Timestamp('2010-01-10 20:14')], # ['01/10/2010 07:40 AM', '%m/%d/%Y %I:%M %p', # Timestamp('2010-01-10 07:40')], # ['01/10/2010 09:12:56 AM', '%m/%d/%Y %I:%M:%S %p', # Timestamp('2010-01-10 09:12:56')] ] for s, format, dt in data: assert to_datetime(s, format=format, cache=cache) == dt @td.skip_if_has_locale @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_with_non_exact(self, cache): # GH 10834 # 8904 # exact kw s = Series(['19MAY11', 'foobar19MAY11', '19MAY11:00:00:00', '19MAY11 00:00:00Z']) result = to_datetime(s, format='%d%b%y', exact=False, cache=cache) expected = to_datetime(s.str.extract(r'(\d+\w+\d+)', expand=False), format='%d%b%y', cache=cache) assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_parse_nanoseconds_with_formula(self, cache): # GH8989 # trunctaing the nanoseconds when a format was provided for v in ["2012-01-01 09:00:00.000000001", "2012-01-01 09:00:00.000001", "2012-01-01 09:00:00.001", "2012-01-01 09:00:00.001000", "2012-01-01 09:00:00.001000000", ]: expected = pd.to_datetime(v, cache=cache) result = pd.to_datetime(v, format="%Y-%m-%d %H:%M:%S.%f", cache=cache) assert result == expected @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_format_weeks(self, cache): data = [ ['2009324', '%Y%W%w', Timestamp('2009-08-13')], ['2013020', '%Y%U%w', Timestamp('2013-01-13')] ] for s, format, dt in data: assert to_datetime(s, format=format, cache=cache) == dt class TestToDatetime(object): def test_to_datetime_pydatetime(self): actual = pd.to_datetime(datetime(2008, 1, 15)) assert actual == datetime(2008, 1, 15) def test_to_datetime_YYYYMMDD(self): actual = pd.to_datetime('20080115') assert actual == datetime(2008, 1, 15) def test_to_datetime_unparseable_ignore(self): # unparseable s = 'Month 1, 1999' assert pd.to_datetime(s, errors='ignore') == s @td.skip_if_windows # `tm.set_timezone` does not work in windows def test_to_datetime_now(self): # See GH#18666 with tm.set_timezone('US/Eastern'): npnow = np.datetime64('now').astype('datetime64[ns]') pdnow = pd.to_datetime('now') pdnow2 = pd.to_datetime(['now'])[0] # These should all be equal with infinite perf; this gives # a generous margin of 10 seconds assert abs(pdnow.value - npnow.astype(np.int64)) < 1e10 assert abs(pdnow2.value - npnow.astype(np.int64)) < 1e10 assert pdnow.tzinfo is None assert pdnow2.tzinfo is None @td.skip_if_windows # `tm.set_timezone` does not work in windows def test_to_datetime_today(self): # See GH#18666 # Test with one timezone far ahead of UTC and another far behind, so # one of these will _almost_ alawys be in a different day from UTC. # Unfortunately this test between 12 and 1 AM Samoa time # this both of these timezones _and_ UTC will all be in the same day, # so this test will not detect the regression introduced in #18666. with tm.set_timezone('Pacific/Auckland'): # 12-13 hours ahead of UTC nptoday = np.datetime64('today')\ .astype('datetime64[ns]').astype(np.int64) pdtoday = pd.to_datetime('today') pdtoday2 = pd.to_datetime(['today'])[0] tstoday = pd.Timestamp('today') tstoday2 = pd.Timestamp.today() # These should all be equal with infinite perf; this gives # a generous margin of 10 seconds assert abs(pdtoday.normalize().value - nptoday) < 1e10 assert abs(pdtoday2.normalize().value - nptoday) < 1e10 assert abs(pdtoday.value - tstoday.value) < 1e10 assert abs(pdtoday.value - tstoday2.value) < 1e10 assert pdtoday.tzinfo is None assert pdtoday2.tzinfo is None with tm.set_timezone('US/Samoa'): # 11 hours behind UTC nptoday = np.datetime64('today')\ .astype('datetime64[ns]').astype(np.int64) pdtoday = pd.to_datetime('today') pdtoday2 = pd.to_datetime(['today'])[0] # These should all be equal with infinite perf; this gives # a generous margin of 10 seconds assert abs(pdtoday.normalize().value - nptoday) < 1e10 assert abs(pdtoday2.normalize().value - nptoday) < 1e10 assert pdtoday.tzinfo is None assert pdtoday2.tzinfo is None def test_to_datetime_today_now_unicode_bytes(self): to_datetime([u'now']) to_datetime([u'today']) if not PY3: to_datetime(['now']) to_datetime(['today']) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_dt64s(self, cache): in_bound_dts = [ np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] for dt in in_bound_dts: assert pd.to_datetime(dt, cache=cache) == Timestamp(dt) oob_dts = [np.datetime64('1000-01-01'), np.datetime64('5000-01-02'), ] for dt in oob_dts: pytest.raises(ValueError, pd.to_datetime, dt, errors='raise') pytest.raises(ValueError, Timestamp, dt) assert pd.to_datetime(dt, errors='coerce', cache=cache) is NaT @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_array_of_dt64s(self, cache): dts = [np.datetime64('2000-01-01'), np.datetime64('2000-01-02'), ] # Assuming all datetimes are in bounds, to_datetime() returns # an array that is equal to Timestamp() parsing tm.assert_numpy_array_equal( pd.to_datetime(dts, box=False, cache=cache), np.array([Timestamp(x).asm8 for x in dts]) ) # A list of datetimes where the last one is out of bounds dts_with_oob = dts + [np.datetime64('9999-01-01')] pytest.raises(ValueError, pd.to_datetime, dts_with_oob, errors='raise') tm.assert_numpy_array_equal( pd.to_datetime(dts_with_oob, box=False, errors='coerce', cache=cache), np.array( [ Timestamp(dts_with_oob[0]).asm8, Timestamp(dts_with_oob[1]).asm8, tslib.iNaT, ], dtype='M8' ) ) # With errors='ignore', out of bounds datetime64s # are converted to their .item(), which depending on the version of # numpy is either a python datetime.datetime or datetime.date tm.assert_numpy_array_equal( pd.to_datetime(dts_with_oob, box=False, errors='ignore', cache=cache), np.array( [dt.item() for dt in dts_with_oob], dtype='O' ) ) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_tz(self, cache): # xref 8260 # uniform returns a DatetimeIndex arr = [pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'), pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')] result = pd.to_datetime(arr, cache=cache) expected = DatetimeIndex( ['2013-01-01 13:00:00', '2013-01-02 14:00:00'], tz='US/Pacific') tm.assert_index_equal(result, expected) # mixed tzs will raise arr = [pd.Timestamp('2013-01-01 13:00:00', tz='US/Pacific'), pd.Timestamp('2013-01-02 14:00:00', tz='US/Eastern')] pytest.raises(ValueError, lambda: pd.to_datetime(arr, cache=cache)) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_tz_pytz(self, cache): # see gh-8260 us_eastern = pytz.timezone('US/Eastern') arr = np.array([us_eastern.localize(datetime(year=2000, month=1, day=1, hour=3, minute=0)), us_eastern.localize(datetime(year=2000, month=6, day=1, hour=3, minute=0))], dtype=object) result = pd.to_datetime(arr, utc=True, cache=cache) expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', '2000-06-01 07:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) tm.assert_index_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) @pytest.mark.parametrize("init_constructor, end_constructor, test_method", [(Index, DatetimeIndex, tm.assert_index_equal), (list, DatetimeIndex, tm.assert_index_equal), (np.array, DatetimeIndex, tm.assert_index_equal), (Series, Series, tm.assert_series_equal)]) def test_to_datetime_utc_true(self, cache, init_constructor, end_constructor, test_method): # See gh-11934 & gh-6415 data = ['20100102 121314', '20100102 121315'] expected_data = [pd.Timestamp('2010-01-02 12:13:14', tz='utc'), pd.Timestamp('2010-01-02 12:13:15', tz='utc')] result = pd.to_datetime(init_constructor(data), format='%Y%m%d %H%M%S', utc=True, cache=cache) expected = end_constructor(expected_data) test_method(result, expected) # Test scalar case as well for scalar, expected in zip(data, expected_data): result = pd.to_datetime(scalar, format='%Y%m%d %H%M%S', utc=True, cache=cache) assert result == expected @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_utc_true_with_series_single_value(self, cache): # GH 15760 UTC=True with Series ts = 1.5e18 result = pd.to_datetime(pd.Series([ts]), utc=True, cache=cache) expected = pd.Series([pd.Timestamp(ts, tz='utc')]) tm.assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_utc_true_with_series_tzaware_string(self, cache): ts = '2013-01-01 00:00:00-01:00' expected_ts = '2013-01-01 01:00:00' data = pd.Series([ts] * 3) result = pd.to_datetime(data, utc=True, cache=cache) expected = pd.Series([pd.Timestamp(expected_ts, tz='utc')] * 3) tm.assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) @pytest.mark.parametrize('date, dtype', [('2013-01-01 01:00:00', 'datetime64[ns]'), ('2013-01-01 01:00:00', 'datetime64[ns, UTC]')]) def test_to_datetime_utc_true_with_series_datetime_ns(self, cache, date, dtype): expected = pd.Series([pd.Timestamp('2013-01-01 01:00:00', tz='UTC')]) result = pd.to_datetime(pd.Series([date], dtype=dtype), utc=True, cache=cache) tm.assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_tz_psycopg2(self, cache): # xref 8260 try: import psycopg2 except ImportError: pytest.skip("no psycopg2 installed") # misc cases tz1 = psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None) tz2 = psycopg2.tz.FixedOffsetTimezone(offset=-240, name=None) arr = np.array([datetime(2000, 1, 1, 3, 0, tzinfo=tz1), datetime(2000, 6, 1, 3, 0, tzinfo=tz2)], dtype=object) result = pd.to_datetime(arr, errors='coerce', utc=True, cache=cache) expected = DatetimeIndex(['2000-01-01 08:00:00+00:00', '2000-06-01 07:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) tm.assert_index_equal(result, expected) # dtype coercion i = pd.DatetimeIndex([ '2000-01-01 08:00:00+00:00' ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) assert is_datetime64_ns_dtype(i) # tz coerceion result = pd.to_datetime(i, errors='coerce', cache=cache) tm.assert_index_equal(result, i) result = pd.to_datetime(i, errors='coerce', utc=True, cache=cache) expected = pd.DatetimeIndex(['2000-01-01 13:00:00'], dtype='datetime64[ns, UTC]') tm.assert_index_equal(result, expected) @pytest.mark.parametrize( 'cache', [pytest.param(True, marks=pytest.mark.skipif(True, reason="GH 18111")), False]) def test_datetime_bool(self, cache): # GH13176 with pytest.raises(TypeError): to_datetime(False) assert to_datetime(False, errors="coerce", cache=cache) is NaT assert to_datetime(False, errors="ignore", cache=cache) is False with pytest.raises(TypeError): to_datetime(True) assert to_datetime(True, errors="coerce", cache=cache) is NaT assert to_datetime(True, errors="ignore", cache=cache) is True with pytest.raises(TypeError): to_datetime([False, datetime.today()], cache=cache) with pytest.raises(TypeError): to_datetime(['20130101', True], cache=cache) tm.assert_index_equal(to_datetime([0, False, NaT, 0.0], errors="coerce", cache=cache), DatetimeIndex([to_datetime(0, cache=cache), NaT, NaT, to_datetime(0, cache=cache)])) def test_datetime_invalid_datatype(self): # GH13176 with pytest.raises(TypeError): pd.to_datetime(bool) with pytest.raises(TypeError): pd.to_datetime(pd.to_datetime) @pytest.mark.parametrize("utc", [True, None]) @pytest.mark.parametrize("format", ['%Y%m%d %H:%M:%S', None]) @pytest.mark.parametrize("box", [True, False]) @pytest.mark.parametrize("constructor", [list, tuple, np.array, pd.Index]) def test_to_datetime_cache(self, utc, format, box, constructor): date = '20130101 00:00:00' test_dates = [date] * 10**5 data = constructor(test_dates) result = pd.to_datetime(data, utc=utc, format=format, box=box, cache=True) expected = pd.to_datetime(data, utc=utc, format=format, box=box, cache=False) if box: tm.assert_index_equal(result, expected) else: tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("utc", [True, None]) @pytest.mark.parametrize("format", ['%Y%m%d %H:%M:%S', None]) def test_to_datetime_cache_series(self, utc, format): date = '20130101 00:00:00' test_dates = [date] * 10**5 data = pd.Series(test_dates) result = pd.to_datetime(data, utc=utc, format=format, cache=True) expected = pd.to_datetime(data, utc=utc, format=format, cache=False) tm.assert_series_equal(result, expected) def test_to_datetime_cache_scalar(self): date = '20130101 00:00:00' result = pd.to_datetime(date, cache=True) expected = pd.Timestamp('20130101 00:00:00') assert result == expected @pytest.mark.parametrize('date, format', [('2017-20', '%Y-%W'), ('20 Sunday', '%W %A'), ('20 Sun', '%W %a'), ('2017-21', '%Y-%U'), ('20 Sunday', '%U %A'), ('20 Sun', '%U %a')]) def test_week_without_day_and_calendar_year(self, date, format): # GH16774 msg = "Cannot use '%W' or '%U' without day and year" with tm.assert_raises_regex(ValueError, msg): pd.to_datetime(date, format=format) class TestToDatetimeUnit(object): @pytest.mark.parametrize('cache', [True, False]) def test_unit(self, cache): # GH 11758 # test proper behavior with erros with pytest.raises(ValueError): to_datetime([1], unit='D', format='%Y%m%d', cache=cache) values = [11111111, 1, 1.0, tslib.iNaT, NaT, np.nan, 'NaT', ''] result = to_datetime(values, unit='D', errors='ignore', cache=cache) expected = Index([11111111, Timestamp('1970-01-02'), Timestamp('1970-01-02'), NaT, NaT, NaT, NaT, NaT], dtype=object) tm.assert_index_equal(result, expected) result = to_datetime(values, unit='D', errors='coerce', cache=cache) expected = DatetimeIndex(['NaT', '1970-01-02', '1970-01-02', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT']) tm.assert_index_equal(result, expected) with pytest.raises(tslib.OutOfBoundsDatetime): to_datetime(values, unit='D', errors='raise', cache=cache) values = [1420043460000, tslib.iNaT, NaT, np.nan, 'NaT'] result = to_datetime(values, errors='ignore', unit='s', cache=cache) expected = Index([1420043460000, NaT, NaT, NaT, NaT], dtype=object) tm.assert_index_equal(result, expected) result = to_datetime(values, errors='coerce', unit='s', cache=cache) expected = DatetimeIndex(['NaT', 'NaT', 'NaT', 'NaT', 'NaT']) tm.assert_index_equal(result, expected) with pytest.raises(tslib.OutOfBoundsDatetime): to_datetime(values, errors='raise', unit='s', cache=cache) # if we have a string, then we raise a ValueError # and NOT an OutOfBoundsDatetime for val in ['foo', Timestamp('20130101')]: try: to_datetime(val, errors='raise', unit='s', cache=cache) except tslib.OutOfBoundsDatetime: raise AssertionError("incorrect exception raised") except ValueError: pass @pytest.mark.parametrize('cache', [True, False]) def test_unit_consistency(self, cache): # consistency of conversions expected = Timestamp('1970-05-09 14:25:11') result = pd.to_datetime(11111111, unit='s', errors='raise', cache=cache) assert result == expected assert isinstance(result, Timestamp) result = pd.to_datetime(11111111, unit='s', errors='coerce', cache=cache) assert result == expected assert isinstance(result, Timestamp) result = pd.to_datetime(11111111, unit='s', errors='ignore', cache=cache) assert result == expected assert isinstance(result, Timestamp) @pytest.mark.parametrize('cache', [True, False]) def test_unit_with_numeric(self, cache): # GH 13180 # coercions from floats/ints are ok expected = DatetimeIndex(['2015-06-19 05:33:20', '2015-05-27 22:33:20']) arr1 = [1.434692e+18, 1.432766e+18] arr2 = np.array(arr1).astype('int64') for errors in ['ignore', 'raise', 'coerce']: result = pd.to_datetime(arr1, errors=errors, cache=cache) tm.assert_index_equal(result, expected) result = pd.to_datetime(arr2, errors=errors, cache=cache) tm.assert_index_equal(result, expected) # but we want to make sure that we are coercing # if we have ints/strings expected = DatetimeIndex(['NaT', '2015-06-19 05:33:20', '2015-05-27 22:33:20']) arr = ['foo', 1.434692e+18, 1.432766e+18] result = pd.to_datetime(arr, errors='coerce', cache=cache) tm.assert_index_equal(result, expected) expected = DatetimeIndex(['2015-06-19 05:33:20', '2015-05-27 22:33:20', 'NaT', 'NaT']) arr = [1.434692e+18, 1.432766e+18, 'foo', 'NaT'] result = pd.to_datetime(arr, errors='coerce', cache=cache) tm.assert_index_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_unit_mixed(self, cache): # mixed integers/datetimes expected = DatetimeIndex(['2013-01-01', 'NaT', 'NaT']) arr = [pd.Timestamp('20130101'), 1.434692e+18, 1.432766e+18] result = pd.to_datetime(arr, errors='coerce', cache=cache) tm.assert_index_equal(result, expected) with pytest.raises(ValueError): pd.to_datetime(arr, errors='raise', cache=cache) expected = DatetimeIndex(['NaT', 'NaT', '2013-01-01']) arr = [1.434692e+18, 1.432766e+18, pd.Timestamp('20130101')] result = pd.to_datetime(arr, errors='coerce', cache=cache) tm.assert_index_equal(result, expected) with pytest.raises(ValueError): pd.to_datetime(arr, errors='raise', cache=cache) @pytest.mark.parametrize('cache', [True, False]) def test_unit_rounding(self, cache): # GH 14156: argument will incur floating point errors but no # premature rounding result = pd.to_datetime(1434743731.8770001, unit='s', cache=cache) expected = pd.Timestamp('2015-06-19 19:55:31.877000093') assert result == expected @pytest.mark.parametrize('cache', [True, False]) def test_dataframe(self, cache): df = DataFrame({'year': [2015, 2016], 'month': [2, 3], 'day': [4, 5], 'hour': [6, 7], 'minute': [58, 59], 'second': [10, 11], 'ms': [1, 1], 'us': [2, 2], 'ns': [3, 3]}) result = to_datetime({'year': df['year'], 'month': df['month'], 'day': df['day']}, cache=cache) expected = Series([Timestamp('20150204 00:00:00'), Timestamp('20160305 00:0:00')]) assert_series_equal(result, expected) # dict-like result = to_datetime(df[['year', 'month', 'day']].to_dict(), cache=cache) assert_series_equal(result, expected) # dict but with constructable df2 = df[['year', 'month', 'day']].to_dict() df2['month'] = 2 result = to_datetime(df2, cache=cache) expected2 = Series([Timestamp('20150204 00:00:00'), Timestamp('20160205 00:0:00')]) assert_series_equal(result, expected2) # unit mappings units = [{'year': 'years', 'month': 'months', 'day': 'days', 'hour': 'hours', 'minute': 'minutes', 'second': 'seconds'}, {'year': 'year', 'month': 'month', 'day': 'day', 'hour': 'hour', 'minute': 'minute', 'second': 'second'}, ] for d in units: result = to_datetime(df[list(d.keys())].rename(columns=d), cache=cache) expected = Series([Timestamp('20150204 06:58:10'), Timestamp('20160305 07:59:11')]) assert_series_equal(result, expected) d = {'year': 'year', 'month': 'month', 'day': 'day', 'hour': 'hour', 'minute': 'minute', 'second': 'second', 'ms': 'ms', 'us': 'us', 'ns': 'ns'} result = to_datetime(df.rename(columns=d), cache=cache) expected = Series([Timestamp('20150204 06:58:10.001002003'), Timestamp('20160305 07:59:11.001002003')]) assert_series_equal(result, expected) # coerce back to int result = to_datetime(df.astype(str), cache=cache) assert_series_equal(result, expected) # passing coerce df2 = DataFrame({'year': [2015, 2016], 'month': [2, 20], 'day': [4, 5]}) msg = ("cannot assemble the datetimes: time data .+ does not " r"match format '%Y%m%d' \(match\)") with tm.assert_raises_regex(ValueError, msg): to_datetime(df2, cache=cache) result = to_datetime(df2, errors='coerce', cache=cache) expected = Series([Timestamp('20150204 00:00:00'), NaT]) assert_series_equal(result, expected) # extra columns msg = ("extra keys have been passed to the datetime assemblage: " r"\[foo\]") with tm.assert_raises_regex(ValueError, msg): df2 = df.copy() df2['foo'] = 1 to_datetime(df2, cache=cache) # not enough msg = (r'to assemble mappings requires at least that \[year, month, ' r'day\] be specified: \[.+\] is missing') for c in [['year'], ['year', 'month'], ['year', 'month', 'second'], ['month', 'day'], ['year', 'day', 'second']]: with tm.assert_raises_regex(ValueError, msg): to_datetime(df[c], cache=cache) # duplicates msg = 'cannot assemble with duplicate keys' df2 = DataFrame({'year': [2015, 2016], 'month': [2, 20], 'day': [4, 5]}) df2.columns = ['year', 'year', 'day'] with tm.assert_raises_regex(ValueError, msg): to_datetime(df2, cache=cache) df2 = DataFrame({'year': [2015, 2016], 'month': [2, 20], 'day': [4, 5], 'hour': [4, 5]}) df2.columns = ['year', 'month', 'day', 'day'] with tm.assert_raises_regex(ValueError, msg): to_datetime(df2, cache=cache) @pytest.mark.parametrize('cache', [True, False]) def test_dataframe_dtypes(self, cache): # #13451 df = DataFrame({'year': [2015, 2016], 'month': [2, 3], 'day': [4, 5]}) # int16 result = to_datetime(df.astype('int16'), cache=cache) expected = Series([Timestamp('20150204 00:00:00'), Timestamp('20160305 00:00:00')]) assert_series_equal(result, expected) # mixed dtypes df['month'] = df['month'].astype('int8') df['day'] = df['day'].astype('int8') result = to_datetime(df, cache=cache) expected = Series([Timestamp('20150204 00:00:00'), Timestamp('20160305 00:00:00')]) assert_series_equal(result, expected) # float df = DataFrame({'year': [2000, 2001], 'month': [1.5, 1], 'day': [1, 1]}) with pytest.raises(ValueError): to_datetime(df, cache=cache) class TestToDatetimeMisc(object): def test_to_datetime_barely_out_of_bounds(self): # GH#19529 # GH#19382 close enough to bounds that dropping nanos would result # in an in-bounds datetime arr = np.array(['2262-04-11 23:47:16.854775808'], dtype=object) with pytest.raises(OutOfBoundsDatetime): to_datetime(arr) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_iso8601(self, cache): result = to_datetime(["2012-01-01 00:00:00"], cache=cache) exp = Timestamp("2012-01-01 00:00:00") assert result[0] == exp result = to_datetime(['20121001'], cache=cache) # bad iso 8601 exp = Timestamp('2012-10-01') assert result[0] == exp @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_default(self, cache): rs = to_datetime('2001', cache=cache) xp = datetime(2001, 1, 1) assert rs == xp # dayfirst is essentially broken # to_datetime('01-13-2012', dayfirst=True) # pytest.raises(ValueError, to_datetime('01-13-2012', # dayfirst=True)) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_on_datetime64_series(self, cache): # #2699 s = Series(date_range('1/1/2000', periods=10)) result = to_datetime(s, cache=cache) assert result[0] == s[0] @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_with_space_in_series(self, cache): # GH 6428 s = Series(['10/18/2006', '10/18/2008', ' ']) pytest.raises(ValueError, lambda: to_datetime(s, errors='raise', cache=cache)) result_coerce = to_datetime(s, errors='coerce', cache=cache) expected_coerce = Series([datetime(2006, 10, 18), datetime(2008, 10, 18), NaT]) tm.assert_series_equal(result_coerce, expected_coerce) result_ignore = to_datetime(s, errors='ignore', cache=cache) tm.assert_series_equal(result_ignore, s) @td.skip_if_has_locale @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_with_apply(self, cache): # this is only locale tested with US/None locales # GH 5195 # with a format and coerce a single item to_datetime fails td = Series(['May 04', 'Jun 02', 'Dec 11'], index=[1, 2, 3]) expected = pd.to_datetime(td, format='%b %y', cache=cache) result = td.apply(pd.to_datetime, format='%b %y', cache=cache) assert_series_equal(result, expected) td = pd.Series(['May 04', 'Jun 02', ''], index=[1, 2, 3]) pytest.raises(ValueError, lambda: pd.to_datetime(td, format='%b %y', errors='raise', cache=cache)) pytest.raises(ValueError, lambda: td.apply(pd.to_datetime, format='%b %y', errors='raise', cache=cache)) expected = pd.to_datetime(td, format='%b %y', errors='coerce', cache=cache) result = td.apply( lambda x: pd.to_datetime(x, format='%b %y', errors='coerce', cache=cache)) assert_series_equal(result, expected) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_types(self, cache): # empty string result = to_datetime('', cache=cache) assert result is NaT result = to_datetime(['', ''], cache=cache) assert isna(result).all() # ints result = Timestamp(0) expected = to_datetime(0, cache=cache) assert result == expected # GH 3888 (strings) expected = to_datetime(['2012'], cache=cache)[0] result = to_datetime('2012', cache=cache) assert result == expected # array = ['2012','20120101','20120101 12:01:01'] array = ['20120101', '20120101 12:01:01'] expected = list(to_datetime(array, cache=cache)) result = lmap(Timestamp, array) tm.assert_almost_equal(result, expected) # currently fails ### # result = Timestamp('2012') # expected = to_datetime('2012') # assert result == expected @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_unprocessable_input(self, cache): # GH 4928 tm.assert_numpy_array_equal( to_datetime([1, '1'], errors='ignore', cache=cache), np.array([1, '1'], dtype='O') ) pytest.raises(TypeError, to_datetime, [1, '1'], errors='raise', cache=cache) def test_to_datetime_other_datetime64_units(self): # 5/25/2012 scalar = np.int64(1337904000000000).view('M8[us]') as_obj = scalar.astype('O') index = DatetimeIndex([scalar]) assert index[0] == scalar.astype('O') value = Timestamp(scalar) assert value == as_obj def test_to_datetime_list_of_integers(self): rng = date_range('1/1/2000', periods=20) rng = DatetimeIndex(rng.values) ints = list(rng.asi8) result = DatetimeIndex(ints) tm.assert_index_equal(rng, result) def test_to_datetime_overflow(self): # gh-17637 # we are overflowing Timedelta range here with pytest.raises(OverflowError): date_range(start='1/1/1700', freq='B', periods=100000) @pytest.mark.parametrize('cache', [True, False]) def test_string_na_nat_conversion(self, cache): # GH #999, #858 from pandas.compat import parse_date strings = np.array(['1/1/2000', '1/2/2000', np.nan, '1/4/2000, 12:34:56'], dtype=object) expected = np.empty(4, dtype='M8[ns]') for i, val in enumerate(strings): if isna(val): expected[i] = tslib.iNaT else: expected[i] = parse_date(val) result = tslib.array_to_datetime(strings) tm.assert_almost_equal(result, expected) result2 = to_datetime(strings, cache=cache) assert isinstance(result2, DatetimeIndex) tm.assert_numpy_array_equal(result, result2.values) malformed = np.array(['1/100/2000', np.nan], dtype=object) # GH 10636, default is now 'raise' pytest.raises(ValueError, lambda: to_datetime(malformed, errors='raise', cache=cache)) result = to_datetime(malformed, errors='ignore', cache=cache) tm.assert_numpy_array_equal(result, malformed) pytest.raises(ValueError, to_datetime, malformed, errors='raise', cache=cache) idx = ['a', 'b', 'c', 'd', 'e'] series = Series(['1/1/2000', np.nan, '1/3/2000', np.nan, '1/5/2000'], index=idx, name='foo') dseries = Series([to_datetime('1/1/2000', cache=cache), np.nan, to_datetime('1/3/2000', cache=cache), np.nan, to_datetime('1/5/2000', cache=cache)], index=idx, name='foo') result = to_datetime(series, cache=cache) dresult = to_datetime(dseries, cache=cache) expected = Series(np.empty(5, dtype='M8[ns]'), index=idx) for i in range(5): x = series[i] if isna(x): expected[i] = tslib.iNaT else: expected[i] = to_datetime(x, cache=cache) assert_series_equal(result, expected, check_names=False) assert result.name == 'foo' assert_series_equal(dresult, expected, check_names=False) assert dresult.name == 'foo' @pytest.mark.parametrize('dtype', [ 'datetime64[h]', 'datetime64[m]', 'datetime64[s]', 'datetime64[ms]', 'datetime64[us]', 'datetime64[ns]']) @pytest.mark.parametrize('cache', [True, False]) def test_dti_constructor_numpy_timeunits(self, cache, dtype): # GH 9114 base = pd.to_datetime(['2000-01-01T00:00', '2000-01-02T00:00', 'NaT'], cache=cache) values = base.values.astype(dtype) tm.assert_index_equal(DatetimeIndex(values), base) tm.assert_index_equal(to_datetime(values, cache=cache), base) @pytest.mark.parametrize('cache', [True, False]) def test_dayfirst(self, cache): # GH 5917 arr = ['10/02/2014', '11/02/2014', '12/02/2014'] expected = DatetimeIndex([datetime(2014, 2, 10), datetime(2014, 2, 11), datetime(2014, 2, 12)]) idx1 = DatetimeIndex(arr, dayfirst=True) idx2 = DatetimeIndex(np.array(arr), dayfirst=True) idx3 = to_datetime(arr, dayfirst=True, cache=cache) idx4 = to_datetime(np.array(arr), dayfirst=True, cache=cache) idx5 = DatetimeIndex(Index(arr), dayfirst=True) idx6 = DatetimeIndex(Series(arr), dayfirst=True) tm.assert_index_equal(expected, idx1) tm.assert_index_equal(expected, idx2) tm.assert_index_equal(expected, idx3) tm.assert_index_equal(expected, idx4) tm.assert_index_equal(expected, idx5) tm.assert_index_equal(expected, idx6) class TestGuessDatetimeFormat(object): @td.skip_if_not_us_locale def test_guess_datetime_format_for_array(self): expected_format = '%Y-%m-%d %H:%M:%S.%f' dt_string = datetime(2011, 12, 30, 0, 0, 0).strftime(expected_format) test_arrays = [ np.array([dt_string, dt_string, dt_string], dtype='O'), np.array([np.nan, np.nan, dt_string], dtype='O'), np.array([dt_string, 'random_string'], dtype='O'), ] for test_array in test_arrays: assert tools._guess_datetime_format_for_array( test_array) == expected_format format_for_string_of_nans = tools._guess_datetime_format_for_array( np.array( [np.nan, np.nan, np.nan], dtype='O')) assert format_for_string_of_nans is None class TestToDatetimeInferFormat(object): @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_infer_datetime_format_consistent_format(self, cache): s = pd.Series(pd.date_range('20000101', periods=50, freq='H')) test_formats = ['%m-%d-%Y', '%m/%d/%Y %H:%M:%S.%f', '%Y-%m-%dT%H:%M:%S.%f'] for test_format in test_formats: s_as_dt_strings = s.apply(lambda x: x.strftime(test_format)) with_format = pd.to_datetime(s_as_dt_strings, format=test_format, cache=cache) no_infer = pd.to_datetime(s_as_dt_strings, infer_datetime_format=False, cache=cache) yes_infer = pd.to_datetime(s_as_dt_strings, infer_datetime_format=True, cache=cache) # Whether the format is explicitly passed, it is inferred, or # it is not inferred, the results should all be the same tm.assert_series_equal(with_format, no_infer) tm.assert_series_equal(no_infer, yes_infer) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_infer_datetime_format_inconsistent_format(self, cache): s = pd.Series(np.array(['01/01/2011 00:00:00', '01-02-2011 00:00:00', '2011-01-03T00:00:00'])) # When the format is inconsistent, infer_datetime_format should just # fallback to the default parsing tm.assert_series_equal(pd.to_datetime(s, infer_datetime_format=False, cache=cache), pd.to_datetime(s, infer_datetime_format=True, cache=cache)) s = pd.Series(np.array(['Jan/01/2011', 'Feb/01/2011', 'Mar/01/2011'])) tm.assert_series_equal(pd.to_datetime(s, infer_datetime_format=False, cache=cache), pd.to_datetime(s, infer_datetime_format=True, cache=cache)) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_infer_datetime_format_series_with_nans(self, cache): s = pd.Series(np.array(['01/01/2011 00:00:00', np.nan, '01/03/2011 00:00:00', np.nan])) tm.assert_series_equal(pd.to_datetime(s, infer_datetime_format=False, cache=cache), pd.to_datetime(s, infer_datetime_format=True, cache=cache)) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_infer_datetime_format_series_start_with_nans(self, cache): s = pd.Series(np.array([np.nan, np.nan, '01/01/2011 00:00:00', '01/02/2011 00:00:00', '01/03/2011 00:00:00'])) tm.assert_series_equal(pd.to_datetime(s, infer_datetime_format=False, cache=cache), pd.to_datetime(s, infer_datetime_format=True, cache=cache)) @pytest.mark.parametrize('cache', [True, False]) def test_to_datetime_iso8601_noleading_0s(self, cache): # GH 11871 s = pd.Series(['2014-1-1', '2014-2-2', '2015-3-3']) expected = pd.Series([pd.Timestamp('2014-01-01'), pd.Timestamp('2014-02-02'), pd.Timestamp('2015-03-03')]) tm.assert_series_equal(pd.to_datetime(s, cache=cache), expected) tm.assert_series_equal(pd.to_datetime(s, format='%Y-%m-%d', cache=cache), expected) class TestDaysInMonth(object): # tests for issue #10154 @pytest.mark.parametrize('cache', [True, False]) def test_day_not_in_month_coerce(self, cache): assert isna(to_datetime('2015-02-29', errors='coerce', cache=cache)) assert isna(to_datetime('2015-02-29', format="%Y-%m-%d", errors='coerce', cache=cache)) assert isna(to_datetime('2015-02-32', format="%Y-%m-%d", errors='coerce', cache=cache)) assert isna(to_datetime('2015-04-31', format="%Y-%m-%d", errors='coerce', cache=cache)) @pytest.mark.parametrize('cache', [True, False]) def test_day_not_in_month_raise(self, cache): pytest.raises(ValueError, to_datetime, '2015-02-29', errors='raise', cache=cache) pytest.raises(ValueError, to_datetime, '2015-02-29', errors='raise', format="%Y-%m-%d", cache=cache) pytest.raises(ValueError, to_datetime, '2015-02-32', errors='raise', format="%Y-%m-%d", cache=cache) pytest.raises(ValueError, to_datetime, '2015-04-31', errors='raise', format="%Y-%m-%d", cache=cache) @pytest.mark.parametrize('cache', [True, False]) def test_day_not_in_month_ignore(self, cache): assert to_datetime('2015-02-29', errors='ignore', cache=cache) == '2015-02-29' assert to_datetime('2015-02-29', errors='ignore', format="%Y-%m-%d", cache=cache) == '2015-02-29' assert to_datetime('2015-02-32', errors='ignore', format="%Y-%m-%d", cache=cache) == '2015-02-32' assert to_datetime('2015-04-31', errors='ignore', format="%Y-%m-%d", cache=cache) == '2015-04-31' class TestDatetimeParsingWrappers(object): @pytest.mark.parametrize('cache', [True, False]) def test_parsers(self, cache): # dateutil >= 2.5.0 defaults to yearfirst=True # https://github.com/dateutil/dateutil/issues/217 yearfirst = True cases = {'2011-01-01': datetime(2011, 1, 1), '2Q2005': datetime(2005, 4, 1), '2Q05': datetime(2005, 4, 1), '2005Q1': datetime(2005, 1, 1), '05Q1': datetime(2005, 1, 1), '2011Q3': datetime(2011, 7, 1), '11Q3': datetime(2011, 7, 1), '3Q2011': datetime(2011, 7, 1), '3Q11': datetime(2011, 7, 1), # quarterly without space '2000Q4': datetime(2000, 10, 1), '00Q4': datetime(2000, 10, 1), '4Q2000': datetime(2000, 10, 1), '4Q00': datetime(2000, 10, 1), '2000q4': datetime(2000, 10, 1), '2000-Q4': datetime(2000, 10, 1), '00-Q4': datetime(2000, 10, 1), '4Q-2000': datetime(2000, 10, 1), '4Q-00': datetime(2000, 10, 1), '00q4': datetime(2000, 10, 1), '2005': datetime(2005, 1, 1), '2005-11': datetime(2005, 11, 1), '2005 11': datetime(2005, 11, 1), '11-2005': datetime(2005, 11, 1), '11 2005': datetime(2005, 11, 1), '200511': datetime(2020, 5, 11), '20051109': datetime(2005, 11, 9), '20051109 10:15': datetime(2005, 11, 9, 10, 15), '20051109 08H': datetime(2005, 11, 9, 8, 0), '2005-11-09 10:15': datetime(2005, 11, 9, 10, 15), '2005-11-09 08H': datetime(2005, 11, 9, 8, 0), '2005/11/09 10:15': datetime(2005, 11, 9, 10, 15), '2005/11/09 08H': datetime(2005, 11, 9, 8, 0), "Thu Sep 25 10:36:28 2003": datetime(2003, 9, 25, 10, 36, 28), "Thu Sep 25 2003": datetime(2003, 9, 25), "Sep 25 2003": datetime(2003, 9, 25), "January 1 2014": datetime(2014, 1, 1), # GH 10537 '2014-06': datetime(2014, 6, 1), '06-2014': datetime(2014, 6, 1), '2014-6': datetime(2014, 6, 1), '6-2014': datetime(2014, 6, 1), '20010101 12': datetime(2001, 1, 1, 12), '20010101 1234': datetime(2001, 1, 1, 12, 34), '20010101 123456': datetime(2001, 1, 1, 12, 34, 56), } for date_str, expected in compat.iteritems(cases): result1, _, _ = parsing.parse_time_string(date_str, yearfirst=yearfirst) result2 = to_datetime(date_str, yearfirst=yearfirst) result3 = to_datetime([date_str], yearfirst=yearfirst) # result5 is used below result4 = to_datetime(np.array([date_str], dtype=object), yearfirst=yearfirst, cache=cache) result6 = DatetimeIndex([date_str], yearfirst=yearfirst) # result7 is used below result8 = DatetimeIndex(Index([date_str]), yearfirst=yearfirst) result9 = DatetimeIndex(Series([date_str]), yearfirst=yearfirst) for res in [result1, result2]: assert res == expected for res in [result3, result4, result6, result8, result9]: exp = DatetimeIndex([pd.Timestamp(expected)]) tm.assert_index_equal(res, exp) # these really need to have yearfirst, but we don't support if not yearfirst: result5 = Timestamp(date_str) assert result5 == expected result7 = date_range(date_str, freq='S', periods=1, yearfirst=yearfirst) assert result7 == expected # NaT result1, _, _ = parsing.parse_time_string('NaT') result2 = to_datetime('NaT') result3 = Timestamp('NaT') result4 = DatetimeIndex(['NaT'])[0] assert result1 is tslib.NaT assert result2 is tslib.NaT assert result3 is tslib.NaT assert result4 is tslib.NaT @pytest.mark.parametrize('cache', [True, False]) def test_parsers_dayfirst_yearfirst(self, cache): # OK # 2.5.1 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2012-10-11 00:00:00 # 2.5.3 10-11-12 [dayfirst=0, yearfirst=0] -> 2012-10-11 00:00:00 # OK # 2.5.1 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 # 2.5.2 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 # 2.5.3 10-11-12 [dayfirst=0, yearfirst=1] -> 2010-11-12 00:00:00 # bug fix in 2.5.2 # 2.5.1 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-11-12 00:00:00 # 2.5.2 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 # 2.5.3 10-11-12 [dayfirst=1, yearfirst=1] -> 2010-12-11 00:00:00 # OK # 2.5.1 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 # 2.5.2 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 # 2.5.3 10-11-12 [dayfirst=1, yearfirst=0] -> 2012-11-10 00:00:00 # OK # 2.5.1 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 # 2.5.2 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 # 2.5.3 20/12/21 [dayfirst=0, yearfirst=0] -> 2021-12-20 00:00:00 # OK # 2.5.1 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 # 2.5.2 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 # 2.5.3 20/12/21 [dayfirst=0, yearfirst=1] -> 2020-12-21 00:00:00 # revert of bug in 2.5.2 # 2.5.1 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 # 2.5.2 20/12/21 [dayfirst=1, yearfirst=1] -> month must be in 1..12 # 2.5.3 20/12/21 [dayfirst=1, yearfirst=1] -> 2020-12-21 00:00:00 # OK # 2.5.1 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 # 2.5.2 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 # 2.5.3 20/12/21 [dayfirst=1, yearfirst=0] -> 2021-12-20 00:00:00 is_lt_253 = LooseVersion(dateutil.__version__) < LooseVersion('2.5.3') # str : dayfirst, yearfirst, expected cases = {'10-11-12': [(False, False, datetime(2012, 10, 11)), (True, False, datetime(2012, 11, 10)), (False, True, datetime(2010, 11, 12)), (True, True, datetime(2010, 12, 11))], '20/12/21': [(False, False, datetime(2021, 12, 20)), (True, False, datetime(2021, 12, 20)), (False, True, datetime(2020, 12, 21)), (True, True, datetime(2020, 12, 21))]} for date_str, values in compat.iteritems(cases): for dayfirst, yearfirst, expected in values: # odd comparisons across version # let's just skip if dayfirst and yearfirst and is_lt_253: continue # compare with dateutil result dateutil_result = parse(date_str, dayfirst=dayfirst, yearfirst=yearfirst) assert dateutil_result == expected result1, _, _ = parsing.parse_time_string(date_str, dayfirst=dayfirst, yearfirst=yearfirst) # we don't support dayfirst/yearfirst here: if not dayfirst and not yearfirst: result2 = Timestamp(date_str) assert result2 == expected result3 = to_datetime(date_str, dayfirst=dayfirst, yearfirst=yearfirst, cache=cache) result4 = DatetimeIndex([date_str], dayfirst=dayfirst, yearfirst=yearfirst)[0] assert result1 == expected assert result3 == expected assert result4 == expected @pytest.mark.parametrize('cache', [True, False]) def test_parsers_timestring(self, cache): # must be the same as dateutil result cases = {'10:15': (parse('10:15'), datetime(1, 1, 1, 10, 15)), '9:05': (parse('9:05'), datetime(1, 1, 1, 9, 5))} for date_str, (exp_now, exp_def) in compat.iteritems(cases): result1, _, _ = parsing.parse_time_string(date_str) result2 = to_datetime(date_str) result3 = to_datetime([date_str]) result4 = Timestamp(date_str) result5 = DatetimeIndex([date_str])[0] # parse time string return time string based on default date # others are not, and can't be changed because it is used in # time series plot assert result1 == exp_def assert result2 == exp_now assert result3 == exp_now assert result4 == exp_now assert result5 == exp_now @td.skip_if_has_locale def test_parsers_time(self): # GH11818 strings = ["14:15", "1415", "2:15pm", "0215pm", "14:15:00", "141500", "2:15:00pm", "021500pm", time(14, 15)] expected = time(14, 15) for time_string in strings: assert tools.to_time(time_string) == expected new_string = "14.15" pytest.raises(ValueError, tools.to_time, new_string) assert tools.to_time(new_string, format="%H.%M") == expected arg = ["14:15", "20:20"] expected_arr = [time(14, 15), time(20, 20)] assert tools.to_time(arg) == expected_arr assert tools.to_time(arg, format="%H:%M") == expected_arr assert tools.to_time(arg, infer_time_format=True) == expected_arr assert tools.to_time(arg, format="%I:%M%p", errors="coerce") == [None, None] res = tools.to_time(arg, format="%I:%M%p", errors="ignore") tm.assert_numpy_array_equal(res, np.array(arg, dtype=np.object_)) with pytest.raises(ValueError): tools.to_time(arg, format="%I:%M%p", errors="raise") tm.assert_series_equal(tools.to_time(Series(arg, name="test")), Series(expected_arr, name="test")) res = tools.to_time(np.array(arg)) assert isinstance(res, list) assert res == expected_arr @pytest.mark.parametrize('cache', [True, False]) def test_parsers_timezone_minute_offsets_roundtrip(self, cache): # GH11708 base = to_datetime("2013-01-01 00:00:00", cache=cache) dt_strings = [ ('2013-01-01 05:45+0545', "Asia/Katmandu", "Timestamp('2013-01-01 05:45:00+0545', tz='Asia/Katmandu')"), ('2013-01-01 05:30+0530', "Asia/Kolkata", "Timestamp('2013-01-01 05:30:00+0530', tz='Asia/Kolkata')") ] for dt_string, tz, dt_string_repr in dt_strings: dt_time = to_datetime(dt_string, cache=cache) assert base == dt_time converted_time = dt_time.tz_localize('UTC').tz_convert(tz) assert dt_string_repr == repr(converted_time) def test_normalize_date(): value = date(2012, 9, 7) result = tslib.normalize_date(value) assert (result == datetime(2012, 9, 7)) value = datetime(2012, 9, 7, 12) result = tslib.normalize_date(value) assert (result == datetime(2012, 9, 7)) @pytest.fixture(params=['D', 's', 'ms', 'us', 'ns']) def units(request): return request.param @pytest.fixture def epoch_1960(): # for origin as 1960-01-01 return Timestamp('1960-01-01') @pytest.fixture def units_from_epochs(): return list(range(5)) @pytest.fixture(params=[epoch_1960(), epoch_1960().to_pydatetime(), epoch_1960().to_datetime64(), str(epoch_1960())]) def epochs(request): return request.param @pytest.fixture def julian_dates(): return pd.date_range('2014-1-1', periods=10).to_julian_date().values class TestOrigin(object): def test_to_basic(self, julian_dates): # gh-11276, gh-11745 # for origin as julian result = Series(pd.to_datetime( julian_dates, unit='D', origin='julian')) expected = Series(pd.to_datetime( julian_dates - pd.Timestamp(0).to_julian_date(), unit='D')) assert_series_equal(result, expected) result = Series(pd.to_datetime( [0, 1, 2], unit='D', origin='unix')) expected = Series([Timestamp('1970-01-01'), Timestamp('1970-01-02'), Timestamp('1970-01-03')]) assert_series_equal(result, expected) # default result = Series(pd.to_datetime( [0, 1, 2], unit='D')) expected = Series([Timestamp('1970-01-01'), Timestamp('1970-01-02'), Timestamp('1970-01-03')]) assert_series_equal(result, expected) def test_julian_round_trip(self): result = pd.to_datetime(2456658, origin='julian', unit='D') assert result.to_julian_date() == 2456658 # out-of-bounds with pytest.raises(ValueError): pd.to_datetime(1, origin="julian", unit='D') def test_invalid_unit(self, units, julian_dates): # checking for invalid combination of origin='julian' and unit != D if units != 'D': with pytest.raises(ValueError): pd.to_datetime(julian_dates, unit=units, origin='julian') def test_invalid_origin(self): # need to have a numeric specified with pytest.raises(ValueError): pd.to_datetime("2005-01-01", origin="1960-01-01") with pytest.raises(ValueError): pd.to_datetime("2005-01-01", origin="1960-01-01", unit='D') def test_epoch(self, units, epochs, epoch_1960, units_from_epochs): expected = Series( [pd.Timedelta(x, unit=units) + epoch_1960 for x in units_from_epochs]) result = Series(pd.to_datetime( units_from_epochs, unit=units, origin=epochs)) assert_series_equal(result, expected) @pytest.mark.parametrize("origin, exc", [('random_string', ValueError), ('epoch', ValueError), ('13-24-1990', ValueError), (datetime(1, 1, 1), tslib.OutOfBoundsDatetime)]) def test_invalid_origins(self, origin, exc, units, units_from_epochs): with pytest.raises(exc): pd.to_datetime(units_from_epochs, unit=units, origin=origin) def test_invalid_origins_tzinfo(self): # GH16842 with pytest.raises(ValueError): pd.to_datetime(1, unit='D', origin=datetime(2000, 1, 1, tzinfo=pytz.utc)) def test_processing_order(self): # make sure we handle out-of-bounds *before* # constructing the dates result = pd.to_datetime(200 * 365, unit='D') expected = Timestamp('2169-11-13 00:00:00') assert result == expected result = pd.to_datetime(200 * 365, unit='D', origin='1870-01-01') expected = Timestamp('2069-11-13 00:00:00') assert result == expected result = pd.to_datetime(300 * 365, unit='D', origin='1870-01-01') expected = Timestamp('2169-10-20 00:00:00') assert result == expected