def _convert_listlike(arg, box, unit):
if isinstance(arg, (list,tuple)) or ((hasattr(arg,'__iter__') and not hasattr(arg,'dtype'))):
arg = np.array(list(arg), dtype='O')
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
# these are shortcutable
value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg), unit=unit, coerce=coerce)
except:
# try to process strings fast; may need to fallback
try:
value = np.array([ _get_string_converter(r, unit=unit)() for r in arg ],dtype='m8[ns]')
except:
value = np.array([ _coerce_scalar_to_timedelta_type(r, unit=unit, coerce=coerce) for r in arg ])
value = value.astype('timedelta64[ns]', copy=False)
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value,unit='ns')
return value
def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
"""Convert a list of objects to a timedelta index object."""
if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
arg = np.array(list(arg), dtype='O')
# these are shortcut-able
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
value = arg.astype('timedelta64[{0}]'.format(
unit)).astype('timedelta64[ns]', copy=False)
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg),
unit=unit, errors=errors)
value = value.astype('timedelta64[ns]', copy=False)
except ValueError:
if errors == 'ignore':
return arg
else:
# This else-block accounts for the cases when errors='raise'
# and errors='coerce'. If errors == 'raise', these errors
# should be raised. If errors == 'coerce', we shouldn't
# expect any errors to be raised, since all parsing errors
# cause coercion to pd.NaT. However, if an error / bug is
# introduced that causes an Exception to be raised, we would
# like to surface it.
raise
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit='ns', name=name)
return value
def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
"""Convert a list of objects to a timedelta index object."""
if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
arg = np.array(list(arg), dtype='O')
# these are shortcut-able
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
value = arg.astype('timedelta64[{0}]'.format(unit)).astype(
'timedelta64[ns]', copy=False)
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg),
unit=unit,
errors=errors)
value = value.astype('timedelta64[ns]', copy=False)
except ValueError:
if errors == 'ignore':
return arg
else:
# This else-block accounts for the cases when errors='raise'
# and errors='coerce'. If errors == 'raise', these errors
# should be raised. If errors == 'coerce', we shouldn't
# expect any errors to be raised, since all parsing errors
# cause coercion to pd.NaT. However, if an error / bug is
# introduced that causes an Exception to be raised, we would
# like to surface it.
raise
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit='ns', name=name)
return value
def _convert_listlike(arg, box, unit):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
unit = _validate_timedelta_unit(unit)
# these are shortcutable
value = arg.astype(
'timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg),
unit=unit)
except:
value = np.array([
_coerce_scalar_to_timedelta_type(r, unit=unit) for r in arg
])
if box:
from pandas import Series
value = Series(value, dtype='m8[ns]')
return value
def _possibly_cast_to_timedelta(value, coerce=True):
""" try to cast to timedelta64, if already a timedeltalike, then make
sure that we are [ns] (as numpy 1.6.2 is very buggy in this regards,
don't force the conversion unless coerce is True
if coerce='compat' force a compatibilty coercerion (to timedeltas) if needeed
"""
# coercion compatability
if coerce == 'compat' and _np_version_under1p7:
def convert(td, dtype):
# we have an array with a non-object dtype
if hasattr(td, 'item'):
td = td.astype(np.int64).item()
if td == tslib.iNaT:
return td
if dtype == 'm8[us]':
td *= 1000
return td
if td == tslib.compat_NaT:
return tslib.iNaT
# convert td value to a nanosecond value
d = td.days
s = td.seconds
us = td.microseconds
if dtype == 'object' or dtype == 'm8[ns]':
td = 1000 * us + (s + d * 24 * 3600) * 10**9
else:
raise ValueError(
"invalid conversion of dtype in np < 1.7 [%s]" % dtype)
return td
# < 1.7 coercion
if not is_list_like(value):
value = np.array([value])
dtype = value.dtype
return np.array([convert(v, dtype) for v in value], dtype='m8[ns]')
# deal with numpy not being able to handle certain timedelta operations
if isinstance(value, (ABCSeries, np.ndarray)) and value.dtype.kind == 'm':
if value.dtype != 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
# we don't have a timedelta, but we want to try to convert to one (but
# don't force it)
if coerce:
new_value = tslib.array_to_timedelta64(
_values_from_object(value).astype(object), coerce=False)
if new_value.dtype == 'i8':
value = np.array(new_value, dtype='timedelta64[ns]')
return value
def _convert_listlike(arg, box, unit):
if isinstance(arg, (list, tuple)) or ((hasattr(arg, '__iter__')
and not hasattr(arg, 'dtype'))):
arg = np.array(list(arg), dtype='O')
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
# these are shortcutable
value = arg.astype(
'timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg),
unit=unit)
except:
# try to process strings fast; may need to fallback
try:
value = np.array(
[_get_string_converter(r, unit=unit)() for r in arg],
dtype='m8[ns]')
except:
value = np.array([
_coerce_scalar_to_timedelta_type(r, unit=unit)
for r in arg
])
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit='ns')
return value
def _possibly_cast_to_timedelta(value, coerce=True, dtype=None):
""" try to cast to timedelta64, if already a timedeltalike, then make
sure that we are [ns] (as numpy 1.6.2 is very buggy in this regards,
don't force the conversion unless coerce is True
if dtype is passed then this is the target dtype
"""
# deal with numpy not being able to handle certain timedelta operations
if isinstance(value, (ABCSeries, np.ndarray)):
# i8 conversions
if value.dtype == 'int64' and np.dtype(dtype) == 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
elif value.dtype.kind == 'm':
if value.dtype != 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
# we don't have a timedelta, but we want to try to convert to one (but
# don't force it)
if coerce:
new_value = tslib.array_to_timedelta64(
_values_from_object(value).astype(object), coerce=False)
if new_value.dtype == 'i8':
value = np.array(new_value, dtype='timedelta64[ns]')
return value
def _possibly_cast_to_timedelta(value, coerce=True, dtype=None):
""" try to cast to timedelta64, if already a timedeltalike, then make
sure that we are [ns] (as numpy 1.6.2 is very buggy in this regards,
don't force the conversion unless coerce is True
if dtype is passed then this is the target dtype
"""
# deal with numpy not being able to handle certain timedelta operations
if isinstance(value, (ABCSeries, np.ndarray)):
# i8 conversions
if value.dtype == 'int64' and np.dtype(dtype) == 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
elif value.dtype.kind == 'm':
if value.dtype != 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
# we don't have a timedelta, but we want to try to convert to one (but
# don't force it)
if coerce:
new_value = tslib.array_to_timedelta64(
_values_from_object(value).astype(object), coerce=False)
if new_value.dtype == 'i8':
value = np.array(new_value, dtype='timedelta64[ns]')
return value
def _possibly_cast_to_timedelta(value, coerce=True):
""" try to cast to timedelta64, if already a timedeltalike, then make
sure that we are [ns] (as numpy 1.6.2 is very buggy in this regards,
don't force the conversion unless coerce is True
if coerce='compat' force a compatibilty coercerion (to timedeltas) if needeed
"""
# coercion compatability
if coerce == 'compat' and _np_version_under1p7:
def convert(td, dtype):
# we have an array with a non-object dtype
if hasattr(td,'item'):
td = td.astype(np.int64).item()
if td == tslib.iNaT:
return td
if dtype == 'm8[us]':
td *= 1000
return td
if td == tslib.compat_NaT:
return tslib.iNaT
# convert td value to a nanosecond value
d = td.days
s = td.seconds
us = td.microseconds
if dtype == 'object' or dtype == 'm8[ns]':
td = 1000*us + (s + d * 24 * 3600) * 10 ** 9
else:
raise ValueError("invalid conversion of dtype in np < 1.7 [%s]" % dtype)
return td
# < 1.7 coercion
if not is_list_like(value):
value = np.array([ value ])
dtype = value.dtype
return np.array([ convert(v,dtype) for v in value ], dtype='m8[ns]')
# deal with numpy not being able to handle certain timedelta operations
if isinstance(value, (ABCSeries, np.ndarray)) and value.dtype.kind == 'm':
if value.dtype != 'timedelta64[ns]':
value = value.astype('timedelta64[ns]')
return value
# we don't have a timedelta, but we want to try to convert to one (but
# don't force it)
if coerce:
new_value = tslib.array_to_timedelta64(
_values_from_object(value).astype(object), coerce=False)
if new_value.dtype == 'i8':
value = np.array(new_value, dtype='timedelta64[ns]')
return value
def _simple_new(cls, values, name=None, freq=None, **kwargs):
values = np.array(values, copy=False)
if values.dtype == np.object_:
values = tslib.array_to_timedelta64(values)
if values.dtype != _TD_DTYPE:
values = _ensure_int64(values).view(_TD_DTYPE)
result = object.__new__(cls)
result._data = values
result.name = name
result.freq = freq
result._reset_identity()
return result
def _simple_new(cls, values, name=None, freq=None, **kwargs):
values = np.array(values, copy=False)
if values.dtype == np.object_:
values = tslib.array_to_timedelta64(values)
if values.dtype != _TD_DTYPE:
values = _ensure_int64(values).view(_TD_DTYPE)
result = object.__new__(cls)
result._data = values
result.name = name
result.freq = freq
result._reset_identity()
return result
def _convert_listlike(arg, box, unit, name=None):
if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
arg = np.array(list(arg), dtype='O')
# these are shortcutable
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]', copy=False)
else:
value = tslib.array_to_timedelta64(_ensure_object(arg), unit=unit, errors=errors)
value = value.astype('timedelta64[ns]', copy=False)
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value,unit='ns', name=name)
return value
def _convert_listlike(arg, box, unit, name=None):
if isinstance(arg, (list,tuple)) or ((hasattr(arg,'__iter__') and not hasattr(arg,'dtype'))):
arg = np.array(list(arg), dtype='O')
# these are shortcutable
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]', copy=False)
else:
value = tslib.array_to_timedelta64(_ensure_object(arg), unit=unit, errors=errors)
value = value.astype('timedelta64[ns]', copy=False)
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value,unit='ns', name=name)
return value
def _convert_listlike(arg, box, unit):
if isinstance(arg, (list, tuple)) or ((hasattr(arg, "__iter__") and not hasattr(arg, "dtype"))):
arg = np.array(list(arg), dtype="O")
# these are shortcutable
if is_timedelta64_dtype(arg):
value = arg.astype("timedelta64[ns]")
elif is_integer_dtype(arg):
value = arg.astype("timedelta64[{0}]".format(unit)).astype("timedelta64[ns]", copy=False)
else:
value = tslib.array_to_timedelta64(_ensure_object(arg), unit=unit, errors=errors)
value = value.astype("timedelta64[ns]", copy=False)
if box:
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit="ns")
return value
def _convert_listlike(arg, box, unit):
if isinstance(arg, (list,tuple)):
arg = np.array(arg, dtype='O')
if is_timedelta64_dtype(arg):
value = arg.astype('timedelta64[ns]')
elif is_integer_dtype(arg):
# these are shortcutable
value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
else:
try:
value = tslib.array_to_timedelta64(_ensure_object(arg),unit=unit)
except:
value = np.array([ _coerce_scalar_to_timedelta_type(r, unit=unit) for r in arg ])
if box:
from pandas import Series
value = Series(value,dtype='m8[ns]')
return value
