def _possibly_infer_to_datetimelike(value, convert_dates=False):
"""
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/timedelta set
this is pretty strict in that a datetime/timedelta is REQUIRED
in addition to possible nulls/string likes
ONLY strings are NOT datetimelike
Parameters
----------
value : np.array / Series / Index / list-like
convert_dates : boolean, default False
if True try really hard to convert dates (such as datetime.date), other
leave inferred dtype 'date' alone
"""
if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)):
return value
elif isinstance(value, ABCSeries):
if isinstance(value._values, ABCDatetimeIndex):
return value._values
v = value
if not is_list_like(v):
v = [v]
v = np.array(v, copy=False)
shape = v.shape
if not v.ndim == 1:
v = v.ravel()
if len(v):
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 _try_timedelta(v):
# safe coerce to timedelta64
# will try first with a string & object conversion
from pandas import to_timedelta
try:
return to_timedelta(v)._values.reshape(shape)
except:
return v
# do a quick inference for perf
sample = v[:min(3, len(v))]
inferred_type = lib.infer_dtype(sample)
if (inferred_type in ['datetime', 'datetime64'] or
(convert_dates and inferred_type in ['date'])):
value = _try_datetime(v)
elif inferred_type in ['timedelta', 'timedelta64']:
value = _try_timedelta(v)
# It's possible to have nulls intermixed within the datetime or
# timedelta. These will in general have an inferred_type of 'mixed',
# so have to try both datetime and timedelta.
# try timedelta first to avoid spurious datetime conversions
# e.g. '00:00:01' is a timedelta but technically is also a datetime
elif inferred_type in ['mixed']:
if lib.is_possible_datetimelike_array(_ensure_object(v)):
value = _try_timedelta(v)
if lib.infer_dtype(value) in ['mixed']:
value = _try_datetime(v)
return value
def maybe_infer_to_datetimelike(value, convert_dates=False):
"""
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/timedelta set
this is pretty strict in that a datetime/timedelta is REQUIRED
in addition to possible nulls/string likes
ONLY strings are NOT datetimelike
Parameters
----------
value : np.array / Series / Index / list-like
convert_dates : boolean, default False
if True try really hard to convert dates (such as datetime.date), other
leave inferred dtype 'date' alone
"""
if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)):
return value
elif isinstance(value, ABCSeries):
if isinstance(value._values, ABCDatetimeIndex):
return value._values
v = value
if not is_list_like(v):
v = [v]
v = np.array(v, copy=False)
shape = v.shape
if not v.ndim == 1:
v = v.ravel()
if len(v):
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 _try_timedelta(v):
# safe coerce to timedelta64
# will try first with a string & object conversion
from pandas import to_timedelta
try:
return to_timedelta(v)._values.reshape(shape)
except:
return v
# do a quick inference for perf
sample = v[:min(3, len(v))]
inferred_type = lib.infer_dtype(sample)
if (inferred_type in ['datetime', 'datetime64'] or
(convert_dates and inferred_type in ['date'])):
value = _try_datetime(v)
elif inferred_type in ['timedelta', 'timedelta64']:
value = _try_timedelta(v)
# It's possible to have nulls intermixed within the datetime or
# timedelta. These will in general have an inferred_type of 'mixed',
# so have to try both datetime and timedelta.
# try timedelta first to avoid spurious datetime conversions
# e.g. '00:00:01' is a timedelta but technically is also a datetime
elif inferred_type in ['mixed']:
if lib.is_possible_datetimelike_array(_ensure_object(v)):
value = _try_timedelta(v)
if lib.infer_dtype(value) in ['mixed']:
value = _try_datetime(v)
return value