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Version:
0.23.3 ▾
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""" 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