def test_coercing_dates_outside_of_datetime64_ns_bounds(self):
invalid_dates = [
datetime.date(1000, 1, 1),
datetime.datetime(1000, 1, 1),
'1000-01-01',
'Jan 1, 1000',
np.datetime64('1000-01-01'),
]
for invalid_date in invalid_dates:
self.assertRaises(
ValueError,
tslib.array_to_datetime,
np.array([invalid_date], dtype='object'),
errors='raise',
)
self.assert_numpy_array_equal(
tslib.array_to_datetime(
np.array([invalid_date], dtype='object'),
errors='coerce'),
np.array([tslib.iNaT], dtype='M8[ns]')
)
arr = np.array(['1/1/1000', '1/1/2000'], dtype=object)
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, errors='coerce'),
np.array(
[
tslib.iNaT,
'2000-01-01T00:00:00.000000000-0000'
],
dtype='M8[ns]'
)
)
def test_coercing_dates_outside_of_datetime64_ns_bounds(self):
invalid_dates = [
datetime.date(1000, 1, 1),
datetime.datetime(1000, 1, 1),
"1000-01-01",
"Jan 1, 1000",
np.datetime64("1000-01-01"),
]
for invalid_date in invalid_dates:
self.assertRaises(
ValueError, tslib.array_to_datetime, np.array([invalid_date], dtype="object"), coerce=False, raise_=True
)
self.assertTrue(
np.array_equal(
tslib.array_to_datetime(np.array([invalid_date], dtype="object"), coerce=True),
np.array([tslib.iNaT], dtype="M8[ns]"),
)
)
arr = np.array(["1/1/1000", "1/1/2000"], dtype=object)
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, coerce=True),
np.array([tslib.iNaT, "2000-01-01T00:00:00.000000000-0000"], dtype="M8[ns]"),
)
def test_parsing_valid_dates(self):
arr = np.array(['01-01-2013', '01-02-2013'], dtype=object)
self.assert_(
np.array_equal(
tslib.array_to_datetime(arr),
np.array(
[
'2013-01-01T00:00:00.000000000-0000',
'2013-01-02T00:00:00.000000000-0000'
],
dtype='M8[ns]'
)
)
)
arr = np.array(['Mon Sep 16 2013', 'Tue Sep 17 2013'], dtype=object)
self.assert_(
np.array_equal(
tslib.array_to_datetime(arr),
np.array(
[
'2013-09-16T00:00:00.000000000-0000',
'2013-09-17T00:00:00.000000000-0000'
],
dtype='M8[ns]'
)
)
)
def test_coercing_dates_outside_of_datetime64_ns_bounds(self):
invalid_dates = [
datetime.date(1000, 1, 1),
datetime.datetime(1000, 1, 1),
'1000-01-01',
'Jan 1, 1000',
np.datetime64('1000-01-01'),
]
for invalid_date in invalid_dates:
self.assertRaises(
ValueError,
tslib.array_to_datetime,
np.array([invalid_date], dtype='object'),
errors='raise',
)
self.assert_numpy_array_equal(
tslib.array_to_datetime(np.array([invalid_date],
dtype='object'),
errors='coerce'),
np.array([tslib.iNaT], dtype='M8[ns]'))
arr = np.array(['1/1/1000', '1/1/2000'], dtype=object)
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, errors='coerce'),
np_array_datetime64_compat(
[tslib.iNaT, '2000-01-01T00:00:00.000000000-0000'],
dtype='M8[ns]'))
def test_parsing_valid_dates(self):
arr = np.array(['01-01-2013', '01-02-2013'], dtype=object)
self.assert_(
np.array_equal(
tslib.array_to_datetime(arr),
np.array(
[
'2013-01-01T00:00:00.000000000-0000',
'2013-01-02T00:00:00.000000000-0000'
],
dtype='M8[ns]'
)
)
)
arr = np.array(['Mon Sep 16 2013', 'Tue Sep 17 2013'], dtype=object)
self.assert_(
np.array_equal(
tslib.array_to_datetime(arr),
np.array(
[
'2013-09-16T00:00:00.000000000-0000',
'2013-09-17T00:00:00.000000000-0000'
],
dtype='M8[ns]'
)
)
)
def test_dates_outside_of_datetime64_ns_bounds(self):
# These datetimes are outside of the bounds of the
# datetime64[ns] bounds, so they cannot be converted to
# datetimes
arr = np.array(['1/1/1676', '1/2/1676'], dtype=object)
self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr))
arr = np.array(['1/1/2263', '1/2/2263'], dtype=object)
self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr))
def test_dates_outside_of_datetime64_ns_bounds(self):
# These datetimes are outside of the bounds of the
# datetime64[ns] bounds, so they cannot be converted to
# datetimes
arr = np.array(['1/1/1676', '1/2/1676'], dtype=object)
self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr))
arr = np.array(['1/1/2263', '1/2/2263'], dtype=object)
self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr))
def test_number_looking_strings_not_into_datetime(self):
# #4601
# These strings don't look like datetimes so they shouldn't be
# attempted to be converted
arr = np.array(["-352.737091", "183.575577"], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
arr = np.array(["1", "2", "3", "4", "5"], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
def test_number_looking_strings_not_into_datetime(self):
# #4601
# These strings don't look like datetimes so they shouldn't be
# attempted to be converted
arr = np.array(['-352.737091', '183.575577'], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
arr = np.array(['1', '2', '3', '4', '5'], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
def test_number_looking_strings_not_into_datetime(self):
# #4601
# These strings don't look like datetimes so they shouldn't be
# attempted to be converted
arr = np.array(['-352.737091', '183.575577'], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
arr = np.array(['1', '2', '3', '4', '5'], dtype=object)
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
def test_parsing_valid_dates(self):
arr = np.array(["01-01-2013", "01-02-2013"], dtype=object)
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr),
np.array(["2013-01-01T00:00:00.000000000-0000", "2013-01-02T00:00:00.000000000-0000"], dtype="M8[ns]"),
)
arr = np.array(["Mon Sep 16 2013", "Tue Sep 17 2013"], dtype=object)
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr),
np.array(["2013-09-16T00:00:00.000000000-0000", "2013-09-17T00:00:00.000000000-0000"], dtype="M8[ns]"),
)
def test_coerce_of_invalid_datetimes(self):
arr = np.array(["01-01-2013", "not_a_date", "1"], dtype=object)
# Without coercing, the presence of any invalid dates prevents
# any values from being converted
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
# With coercing, the invalid dates becomes iNaT
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, coerce=True),
np.array(["2013-01-01T00:00:00.000000000-0000", tslib.iNaT, tslib.iNaT], dtype="M8[ns]"),
)
def test_coerce_of_invalid_datetimes(self):
arr = np.array(['01-01-2013', 'not_a_date', '1'], dtype=object)
# Without coercing, the presence of any invalid dates prevents
# any values from being converted
self.assert_numpy_array_equal(tslib.array_to_datetime(arr), arr)
# With coercing, the invalid dates becomes iNaT
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, coerce=True),
np.array(
['2013-01-01T00:00:00.000000000-0000', tslib.iNaT, tslib.iNaT],
dtype='M8[ns]'))
def test_parsing_timezone_offsets(self):
# All of these datetime strings with offsets are equivalent
# to the same datetime after the timezone offset is added
dt_strings = [
"01-01-2013 08:00:00+08:00",
"2013-01-01T08:00:00.000000000+0800",
"2012-12-31T16:00:00.000000000-0800",
"12-31-2012 23:00:00-01:00",
]
expected_output = tslib.array_to_datetime(np.array(["01-01-2013 00:00:00"], dtype=object))
for dt_string in dt_strings:
self.assert_numpy_array_equal(tslib.array_to_datetime(np.array([dt_string], dtype=object)), expected_output)
def test_parsing_timezone_offsets(self):
# All of these datetime strings with offsets are equivalent
# to the same datetime after the timezone offset is added
dt_strings = [
'01-01-2013 08:00:00+08:00', '2013-01-01T08:00:00.000000000+0800',
'2012-12-31T16:00:00.000000000-0800', '12-31-2012 23:00:00-01:00'
]
expected_output = tslib.array_to_datetime(
np.array(['01-01-2013 00:00:00'], dtype=object))
for dt_string in dt_strings:
self.assert_numpy_array_equal(
tslib.array_to_datetime(np.array([dt_string], dtype=object)),
expected_output)
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import to_timedelta
coerce = True
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (other is not None and other.dtype == 'timedelta64[ns]' and
all(isnull(v) for v in values)):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
# a datelike
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif not (isinstance(values, (np.ndarray, pd.Series)) and
is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = to_timedelta(values, coerce=coerce)
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif isinstance(values[0], pd.DateOffset):
# handle DateOffsets
os = np.array([getattr(v, 'delta', None) for v in values])
mask = isnull(os)
if mask.any():
raise TypeError("cannot use a non-absolute DateOffset in "
"datetime/timedelta operations [{0}]".format(
', '.join([com.pprint_thing(v)
for v in values[mask]])))
values = to_timedelta(os, coerce=coerce)
elif inferred_type == 'floating':
# all nan, so ok, use the other dtype (e.g. timedelta or datetime)
if isnull(values).all():
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
else:
raise TypeError(
'incompatible type [{0}] for a datetime/timedelta '
'operation'.format(np.array(values).dtype))
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
return values
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
parsed = lib.try_parse_year_month_day(carg / 10000,
carg / 100 % 100,
carg % 100)
return tslib.array_to_datetime(parsed, errors=errors)
def make_block(values, items, ref_items):
dtype = values.dtype
vtype = dtype.type
klass = None
if issubclass(vtype, np.floating):
klass = FloatBlock
elif issubclass(vtype, np.complexfloating):
klass = ComplexBlock
elif issubclass(vtype, np.datetime64):
klass = DatetimeBlock
elif issubclass(vtype, np.integer):
if vtype != np.int64:
values = values.astype("i8")
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
# try to infer a datetimeblock
if klass is None and np.prod(values.shape):
flat = values.flatten()
inferred_type = lib.infer_dtype(flat)
if inferred_type == "datetime":
# we have an object array that has been inferred as datetime, so convert it
try:
values = tslib.array_to_datetime(flat).reshape(values.shape)
klass = DatetimeBlock
except: # it already object, so leave it
pass
if klass is None:
klass = ObjectBlock
return klass(values, items, ref_items, ndim=values.ndim)
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import to_timedelta
coerce = True
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (other is not None and other.dtype == 'timedelta64[ns]'
and all(isnull(v) for v in values)):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = tslib.iNaT
# a datelike
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif not (isinstance(values, (np.ndarray, pd.Series))
and com.is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = to_timedelta(values, coerce=coerce)
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif isinstance(values[0], pd.DateOffset):
# handle DateOffsets
os = np.array([getattr(v, 'delta', None) for v in values])
mask = isnull(os)
if mask.any():
raise TypeError(
"cannot use a non-absolute DateOffset in "
"datetime/timedelta operations [{0}]".format(', '.join(
[com.pprint_thing(v) for v in values[mask]])))
values = to_timedelta(os, coerce=coerce)
elif inferred_type == 'floating':
# all nan, so ok, use the other dtype (e.g. timedelta or datetime)
if isnull(values).all():
values = np.empty(values.shape, dtype=other.dtype)
values[:] = tslib.iNaT
else:
raise TypeError(
'incompatible type [{0}] for a datetime/timedelta '
'operation'.format(np.array(values).dtype))
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
return values
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
parsed = lib.try_parse_year_month_day(carg / 10000,
carg / 100 % 100,
carg % 100)
return tslib.array_to_datetime(parsed, errors=errors)
def _convert_listlike(arg, box):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError as e:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
return arg
arg = com._ensure_object(arg)
try:
if format is not None:
result = tslib.array_strptime(arg, format)
else:
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst,
coerce=coerce, unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import to_timedelta
ovalues = values
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (other is not None and other.dtype == 'timedelta64[ns]' and
all(isnull(v) for v in values)):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
# a datelike
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
# datetime with tz
elif isinstance(ovalues, datetime.datetime) and hasattr(ovalues,'tz'):
values = pd.DatetimeIndex(values)
# datetime array with tz
elif com.is_datetimetz(values):
if isinstance(values, pd.Series):
values = values._values
elif not (isinstance(values, (np.ndarray, pd.Series)) and
is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = to_timedelta(values, errors='coerce')
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif inferred_type == 'floating':
# all nan, so ok, use the other dtype (e.g. timedelta or datetime)
if isnull(values).all():
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
else:
raise TypeError(
'incompatible type [{0}] for a datetime/timedelta '
'operation'.format(np.array(values).dtype))
elif self._is_offset(values):
return values
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
return values
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import _possibly_cast_to_timedelta
coerce = "compat" if pd._np_version_under1p7 else True
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ("datetime64", "datetime", "date", "time"):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if other is not None and other.dtype == "timedelta64[ns]" and all(isnull(v) for v in values):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = tslib.iNaT
# a datetlike
elif not (isinstance(values, (pa.Array, pd.Series)) and com.is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif inferred_type in ("timedelta", "timedelta64"):
# have a timedelta, convert to to ns here
values = _possibly_cast_to_timedelta(values, coerce=coerce)
elif inferred_type == "integer":
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == "m":
values = values.astype("timedelta64[ns]")
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ("__truediv__", "__div__", "__mul__"):
raise TypeError("incompatible type for a datetime/timedelta " "operation [{0}]".format(name))
elif isinstance(values[0], pd.DateOffset):
# handle DateOffsets
os = pa.array([getattr(v, "delta", None) for v in values])
mask = isnull(os)
if mask.any():
raise TypeError(
"cannot use a non-absolute DateOffset in "
"datetime/timedelta operations [{0}]".format(", ".join([com.pprint_thing(v) for v in values[mask]]))
)
values = _possibly_cast_to_timedelta(os, coerce=coerce)
elif inferred_type == "floating":
# all nan, so ok, use the other dtype (e.g. timedelta or datetime)
if isnull(values).all():
values = np.empty(values.shape, dtype=other.dtype)
values[:] = tslib.iNaT
else:
raise TypeError(
"incompatible type [{0}] for a datetime/timedelta " "operation".format(pa.array(values).dtype)
)
else:
raise TypeError(
"incompatible type [{0}] for a datetime/timedelta" " operation".format(pa.array(values).dtype)
)
return values
def test_string_na_nat_conversion(self):
# 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 isnull(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)
tm.assertIsInstance(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'
self.assertRaises(ValueError,
lambda: to_datetime(malformed, errors='raise'))
result = to_datetime(malformed, errors='ignore')
tm.assert_numpy_array_equal(result, malformed)
self.assertRaises(ValueError, to_datetime, malformed, errors='raise')
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'), np.nan,
to_datetime('1/3/2000'), np.nan,
to_datetime('1/5/2000')], index=idx, name='foo')
result = to_datetime(series)
dresult = to_datetime(dseries)
expected = Series(np.empty(5, dtype='M8[ns]'), index=idx)
for i in range(5):
x = series[i]
if isnull(x):
expected[i] = tslib.iNaT
else:
expected[i] = to_datetime(x)
assert_series_equal(result, expected, check_names=False)
self.assertEqual(result.name, 'foo')
assert_series_equal(dresult, expected, check_names=False)
self.assertEqual(dresult.name, 'foo')
def test_coerce_of_invalid_datetimes(self):
arr = np.array(['01-01-2013', 'not_a_date', '1'], dtype=object)
# Without coercing, the presence of any invalid dates prevents
# any values from being converted
self.assert_numpy_array_equal(tslib.array_to_datetime(arr,errors='ignore'), arr)
# With coercing, the invalid dates becomes iNaT
self.assert_numpy_array_equal(
tslib.array_to_datetime(arr, errors='coerce'),
np.array(
[
'2013-01-01T00:00:00.000000000-0000',
tslib.iNaT,
tslib.iNaT
],
dtype='M8[ns]'
)
)
def _possibly_cast_to_datetime(value, dtype, coerce = False):
""" try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """
if isinstance(dtype, basestring):
dtype = np.dtype(dtype)
if dtype is not None and is_datetime64_dtype(dtype):
if np.isscalar(value):
if value == tslib.iNaT or isnull(value):
value = tslib.iNaT
else:
value = np.array(value)
# have a scalar array-like (e.g. NaT)
if value.ndim == 0:
value = tslib.iNaT
# we have an array of datetime & nulls
elif np.prod(value.shape):
try:
value = tslib.array_to_datetime(value, coerce = coerce)
except:
pass
elif dtype is None:
# we might have a array (or single object) that is datetime like, and no dtype is passed
# don't change the value unless we find a datetime set
v = value
if not (is_list_like(v) or hasattr(v,'len')):
v = [ v ]
if len(v):
inferred_type = lib.infer_dtype(v)
if inferred_type == 'datetime':
try:
value = tslib.array_to_datetime(np.array(v))
except:
pass
return value
def _convert_f(arg):
arg = com._ensure_object(arg)
try:
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_f(arg):
arg = com._ensure_object(arg)
try:
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError:
pass
return arg
arg = com._ensure_object(arg)
try:
if format is not None:
result = None
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg)
except:
raise ValueError("cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg, format, coerce=coerce)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
else:
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst,
coerce=coerce, unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError as e:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
return arg
arg = com._ensure_object(arg)
try:
if format is not None:
result = None
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg)
except:
raise ValueError(
"cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
result = tslib.array_strptime(arg, format)
else:
result = tslib.array_to_datetime(arg,
raise_=errors == 'raise',
utc=utc,
dayfirst=dayfirst,
coerce=coerce,
unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _try_datetime(v):
# safe coerce to datetime64
try:
v = tslib.array_to_datetime(v, errors='raise')
except ValueError:
# we might have a sequence of the same-datetimes with tz's
# if so coerce to a DatetimeIndex; if they are not the same,
# then these stay as object dtype
try:
from pandas import to_datetime
return to_datetime(v)
except:
pass
except:
pass
return v.reshape(shape)
def _convert_f(arg):
arg = com._ensure_object(arg)
try:
if format is not None:
result = tslib.array_strptime(arg, format)
else:
result = tslib.array_to_datetime(
arg, raise_=errors == "raise", utc=utc, dayfirst=dayfirst, coerce=coerce, unit=unit
)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz="utc" if utc else None)
return result
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _try_datetime(v):
# safe coerce to datetime64
try:
v = tslib.array_to_datetime(v, errors='raise')
except ValueError:
# we might have a sequence of the same-datetimes with tz's
# if so coerce to a DatetimeIndex; if they are not the same,
# then these stay as object dtype
try:
from pandas import to_datetime
return to_datetime(v)
except:
pass
except:
pass
return v.reshape(shape)
def _convert_to_array(self, values, name=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import _possibly_cast_to_timedelta
coerce = 'compat' if pd._np_version_under1p7 else True
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
# a datetlike
if not (isinstance(values, (pa.Array, pd.Series))
and com.is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = _possibly_cast_to_timedelta(values, coerce=coerce)
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif isinstance(values[0], pd.DateOffset):
# handle DateOffsets
os = pa.array([getattr(v, 'delta', None) for v in values])
mask = isnull(os)
if mask.any():
raise TypeError(
"cannot use a non-absolute DateOffset in "
"datetime/timedelta operations [{0}]".format(','.join(
[com.pprint_thing(v) for v in values[mask]])))
values = _possibly_cast_to_timedelta(os, coerce=coerce)
else:
raise TypeError(
"incompatible type [{0}] for a datetime/timedelta operation".
format(pa.array(values).dtype))
return values
def _convert_to_array(self, values, name=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import _possibly_cast_to_timedelta
coerce = 'compat' if pd._np_version_under1p7 else True
if not is_list_like(values):
values = np.array([values])
inferred_type = lib.infer_dtype(values)
if inferred_type in ('datetime64', 'datetime', 'date', 'time'):
# a datetlike
if not (isinstance(values, (pa.Array, pd.Series)) and
com.is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = _possibly_cast_to_timedelta(values, coerce=coerce)
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif isinstance(values[0], pd.DateOffset):
# handle DateOffsets
os = pa.array([getattr(v, 'delta', None) for v in values])
mask = isnull(os)
if mask.any():
raise TypeError("cannot use a non-absolute DateOffset in "
"datetime/timedelta operations [{0}]".format(
', '.join([com.pprint_thing(v)
for v in values[mask]])))
values = _possibly_cast_to_timedelta(os, coerce=coerce)
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(pa.array(values).dtype))
return values
def _possibly_cast_to_datetime(value, dtype):
""" try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """
if dtype == 'M8[ns]':
if np.isscalar(value):
if value == tslib.iNaT or isnull(value):
value = tslib.iNaT
else:
value = np.array(value)
# have a scalar array-like (e.g. NaT)
if value.ndim == 0:
value = tslib.iNaT
# we have an array of datetime & nulls
elif np.prod(value.shape):
try:
value = tslib.array_to_datetime(value)
except:
pass
return value
def _possibly_cast_to_datetime(value, dtype):
""" try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """
if dtype == 'M8[ns]':
if np.isscalar(value):
if value == tslib.iNaT or isnull(value):
value = tslib.iNaT
else:
value = np.array(value)
# have a scalar array-like (e.g. NaT)
if value.ndim == 0:
value = tslib.iNaT
# we have an array of datetime & nulls
elif np.prod(value.shape):
try:
value = tslib.array_to_datetime(value)
except:
pass
return value
def make_block(values, items, ref_items):
dtype = values.dtype
vtype = dtype.type
klass = None
if issubclass(vtype, np.floating):
klass = FloatBlock
elif issubclass(vtype, np.complexfloating):
klass = ComplexBlock
elif issubclass(vtype, np.datetime64):
klass = DatetimeBlock
elif issubclass(vtype, np.integer):
if vtype != np.int64:
values = values.astype('i8')
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
# try to infer a datetimeblock
if klass is None and np.prod(values.shape):
flat = values.flatten()
inferred_type = lib.infer_dtype(flat)
if inferred_type == 'datetime':
# we have an object array that has been inferred as datetime, so
# convert it
try:
values = tslib.array_to_datetime(flat).reshape(values.shape)
klass = DatetimeBlock
except: # it already object, so leave it
pass
if klass is None:
klass = ObjectBlock
return klass(values, items, ref_items, ndim=values.ndim)
def _convert_listlike(arg, box):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError as e:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
return arg
arg = com._ensure_object(arg)
try:
if format is not None:
result = tslib.array_strptime(arg, format)
else:
result = tslib.array_to_datetime(arg,
raise_=errors == 'raise',
utc=utc,
dayfirst=dayfirst,
coerce=coerce,
unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box, format):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError:
pass
return arg
elif format is None and com.is_integer_dtype(arg) and unit=='ns':
result = arg.astype('datetime64[ns]')
if box:
return DatetimeIndex(result, tz='utc' if utc else None)
return result
arg = com._ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = (
('%Y-%m-%dT%H:%M:%S.%f'.startswith(format) or
'%Y-%m-%d %H:%M:%S.%f'.startswith(format)) and
format != '%Y'
)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, coerce=coerce)
except:
raise ValueError("cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(
arg, format, exact=exact, coerce=coerce
)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(arg, raise_=errors=='raise',
utc=utc, dayfirst=dayfirst,
yearfirst=yearfirst, freq=freq,
coerce=coerce, unit=unit,
require_iso8601=require_iso8601)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box, format, name=None, tz=tz):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz=tz, name=name)
except ValueError:
pass
return arg
elif is_datetime64tz_dtype(arg):
if not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz=tz, name=name)
if utc:
arg = arg.tz_convert(None).tz_localize('UTC')
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, 'values', arg)
result = tslib.array_with_unit_to_datetime(arg, unit,
errors=errors)
if box:
if errors == 'ignore':
from pandas import Index
return Index(result)
return DatetimeIndex(result, tz=tz, name=name)
return result
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = _ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, errors=errors)
except:
raise ValueError("cannot convert the input to "
"'%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg, format, exact=exact,
errors=errors)
except tslib.OutOfBoundsDatetime:
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
arg,
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
require_iso8601=require_iso8601
)
if is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz=tz, name=name)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError:
pass
return arg
arg = com._ensure_object(arg)
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = ('%Y-%m-%dT%H:%M:%S.%f'.startswith(format)
or
'%Y-%m-%d %H:%M:%S.%f'.startswith(format))
if format_is_iso8601:
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, coerce=coerce)
except:
raise ValueError(
"cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg,
format,
exact=exact,
coerce=coerce)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
except ValueError:
# Only raise this error if the user provided the
# datetime format, and not when it was inferred
if not infer_datetime_format:
raise
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(arg,
raise_=errors == 'raise',
utc=utc,
dayfirst=dayfirst,
coerce=coerce,
unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
return tslib.array_to_datetime(lib.try_parse_year_month_day(
carg / 10000, carg / 100 % 100, carg % 100),
coerce=coerce)
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import to_timedelta
ovalues = values
supplied_dtype = None
if not is_list_like(values):
values = np.array([values])
# if this is a Series that contains relevant dtype info, then use this
# instead of the inferred type; this avoids coercing Series([NaT],
# dtype='datetime64[ns]') to Series([NaT], dtype='timedelta64[ns]')
elif isinstance(values, pd.Series) and (
is_timedelta64_dtype(values) or is_datetime64_dtype(values)):
supplied_dtype = values.dtype
inferred_type = supplied_dtype or lib.infer_dtype(values)
if (inferred_type in ('datetime64', 'datetime', 'date', 'time')
or com.is_datetimetz(inferred_type)):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (supplied_dtype is None
and other is not None
and (other.dtype in ('timedelta64[ns]', 'datetime64[ns]'))
and isnull(values).all()):
values = np.empty(values.shape, dtype='timedelta64[ns]')
values[:] = iNaT
# a datelike
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
# datetime with tz
elif isinstance(ovalues, datetime.datetime) and hasattr(ovalues,'tz'):
values = pd.DatetimeIndex(values)
# datetime array with tz
elif com.is_datetimetz(values):
if isinstance(values, pd.Series):
values = values._values
elif not (isinstance(values, (np.ndarray, pd.Series)) and
is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = to_timedelta(values, errors='coerce')
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__', '__rmul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif inferred_type == 'floating':
if isnull(values).all() and name in ('__add__', '__radd__',
'__sub__', '__rsub__'):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
return values
elif self._is_offset(values):
return values
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
return values
def _convert_to_array(self, values, name=None, other=None):
"""converts values to ndarray"""
from pandas.tseries.timedeltas import to_timedelta
ovalues = values
supplied_dtype = None
if not is_list_like(values):
values = np.array([values])
# if this is a Series that contains relevant dtype info, then use this
# instead of the inferred type; this avoids coercing Series([NaT],
# dtype='datetime64[ns]') to Series([NaT], dtype='timedelta64[ns]')
elif (isinstance(values, pd.Series) and
(is_timedelta64_dtype(values) or is_datetime64_dtype(values))):
supplied_dtype = values.dtype
inferred_type = supplied_dtype or lib.infer_dtype(values)
if (inferred_type in ('datetime64', 'datetime', 'date', 'time') or
com.is_datetimetz(inferred_type)):
# if we have a other of timedelta, but use pd.NaT here we
# we are in the wrong path
if (supplied_dtype is None and other is not None and
(other.dtype in ('timedelta64[ns]', 'datetime64[ns]')) and
isnull(values).all()):
values = np.empty(values.shape, dtype='timedelta64[ns]')
values[:] = iNaT
# a datelike
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
# datetime with tz
elif (isinstance(ovalues, datetime.datetime) and
hasattr(ovalues, 'tz')):
values = pd.DatetimeIndex(values)
# datetime array with tz
elif com.is_datetimetz(values):
if isinstance(values, ABCSeries):
values = values._values
elif not (isinstance(values, (np.ndarray, ABCSeries)) and
is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
values = to_timedelta(values, errors='coerce')
elif inferred_type == 'integer':
# py3 compat where dtype is 'm' but is an integer
if values.dtype.kind == 'm':
values = values.astype('timedelta64[ns]')
elif isinstance(values, pd.PeriodIndex):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__', '__rmul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
elif inferred_type == 'floating':
if (isnull(values).all() and
name in ('__add__', '__radd__', '__sub__', '__rsub__')):
values = np.empty(values.shape, dtype=other.dtype)
values[:] = iNaT
return values
elif self._is_offset(values):
return values
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
return values
def _convert_listlike(arg, box, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype="O")
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz="utc" if utc else None)
except ValueError:
pass
return arg
arg = com._ensure_object(arg)
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = "%Y-%m-%dT%H:%M:%S.%f".startswith(format) or "%Y-%m-%d %H:%M:%S.%f".startswith(
format
)
if format_is_iso8601:
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == "%Y%m%d":
try:
result = _attempt_YYYYMMDD(arg)
except:
raise ValueError("cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg, format, coerce=coerce)
except (tslib.OutOfBoundsDatetime):
if errors == "raise":
raise
result = arg
except ValueError:
# Only raise this error if the user provided the
# datetime format, and not when it was inferred
if not infer_datetime_format:
raise
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
arg, raise_=errors == "raise", utc=utc, dayfirst=dayfirst, coerce=coerce, unit=unit
)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz="utc" if utc else None)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def _convert_listlike(arg, box, format, name=None):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg,
tz='utc' if utc else None,
name=name)
except ValueError:
pass
return arg
elif com.is_datetime64tz_dtype(arg):
if not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz='utc' if utc else None)
if utc:
arg = arg.tz_convert(None).tz_localize('UTC')
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, 'values', arg)
result = tslib.array_with_unit_to_datetime(arg,
unit,
errors=errors)
if box:
if errors == 'ignore':
from pandas import Index
return Index(result)
return DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = com._ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, errors=errors)
except:
raise ValueError("cannot convert the input to "
"'%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg,
format,
exact=exact,
errors=errors)
except tslib.OutOfBoundsDatetime:
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
arg,
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
freq=freq,
require_iso8601=require_iso8601)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
format=None, coerce=False, unit='ns'):
"""
Convert argument to datetime
Parameters
----------
arg : string, datetime, array of strings (with possible NAs)
errors : {'ignore', 'raise'}, default 'ignore'
Errors are ignored by default (values left untouched)
dayfirst : boolean, default False
If True parses dates with the day first, eg 20/01/2005
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug).
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well)
box : boolean, default True
If True returns a DatetimeIndex, if False returns ndarray of values
format : string, default None
strftime to parse time, eg "%d/%m/%Y"
coerce : force errors to NaT (False by default)
unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
(e.g. a unix timestamp), which is an integer/float number
Returns
-------
ret : datetime if parsing succeeded
"""
from pandas import Timestamp
from pandas.core.series import Series
from pandas.tseries.index import DatetimeIndex
def _convert_listlike(arg, box):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
if com.is_datetime64_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz='utc' if utc else None)
except ValueError, e:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
return arg
arg = com._ensure_object(arg)
try:
if format is not None:
result = tslib.array_strptime(arg, format)
else:
result = tslib.array_to_datetime(arg, raise_=errors == 'raise',
utc=utc, dayfirst=dayfirst,
coerce=coerce, unit=unit)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz='utc' if utc else None)
return result
except ValueError, e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, None, tz=tz)
except (ValueError, TypeError):
raise e
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
return tslib.array_to_datetime(lib.try_parse_year_month_day(carg/10000,carg/100 % 100, carg % 100), coerce=coerce)
def _convert_listlike(arg, box, format, name=None):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if com.is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg,
tz='utc' if utc else None,
name=name)
except ValueError:
pass
return arg
elif format is None and com.is_integer_dtype(arg) and unit == 'ns':
result = arg.astype('datetime64[ns]')
if box:
return DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
arg = com._ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = (('%Y-%m-%dT%H:%M:%S.%f'.startswith(format)
or '%Y-%m-%d %H:%M:%S.%f'.startswith(format))
and format != '%Y')
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, errors=errors)
except:
raise ValueError(
"cannot convert the input to '%Y%m%d' date format")
# fallback
if result is None:
try:
result = tslib.array_strptime(arg,
format,
exact=exact,
errors=errors)
except (tslib.OutOfBoundsDatetime):
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
arg,
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
freq=freq,
unit=unit,
require_iso8601=require_iso8601)
if com.is_datetime64_dtype(result) and box:
result = DatetimeIndex(result,
tz='utc' if utc else None,
name=name)
return result
except ValueError as e:
try:
values, tz = tslib.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
