def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True, convert_timedeltas=True, copy=True):
""" if we have an object dtype, try to coerce dates and/or numbers """
# if we have passed in a list or scalar
if isinstance(values, (list, tuple)):
values = np.array(values, dtype=np.object_)
if not hasattr(values, "dtype"):
values = np.array([values], dtype=np.object_)
# convert dates
if convert_dates and values.dtype == np.object_:
# we take an aggressive stance and convert to datetime64[ns]
if convert_dates == "coerce":
new_values = _possibly_cast_to_datetime(values, "M8[ns]", errors="coerce")
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
else:
values = lib.maybe_convert_objects(values, convert_datetime=convert_dates)
# convert timedeltas
if convert_timedeltas and values.dtype == np.object_:
if convert_timedeltas == "coerce":
from pandas.tseries.timedeltas import to_timedelta
new_values = to_timedelta(values, coerce=True)
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
else:
values = lib.maybe_convert_objects(values, convert_timedelta=convert_timedeltas)
# convert to numeric
if values.dtype == np.object_:
if convert_numeric:
try:
new_values = lib.maybe_convert_numeric(values, set(), coerce_numeric=True)
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
except:
pass
else:
# soft-conversion
values = lib.maybe_convert_objects(values)
values = values.copy() if copy else values
return values
def _setitem_with_indexer(self, indexer, value):
from pandas.core.frame import DataFrame, Series
# also has the side effect of consolidating in-place
# mmm, spaghetti
if self.obj._is_mixed_type:
if not isinstance(indexer, tuple):
indexer = self._tuplify(indexer)
if isinstance(value, Series):
value = self._align_series(indexer, value)
het_axis = self.obj._het_axis
het_idx = indexer[het_axis]
if isinstance(het_idx, (int, long)):
het_idx = [het_idx]
plane_indexer = indexer[:het_axis] + indexer[het_axis + 1:]
item_labels = self.obj._get_axis(het_axis)
if isinstance(value, (np.ndarray, DataFrame)) and value.ndim > 1:
raise ValueError('Setting mixed-type DataFrames with '
'array/DataFrame pieces not yet supported')
try:
for item in item_labels[het_idx]:
data = self.obj[item]
values = data.values
if np.prod(values.shape):
value = com._possibly_cast_to_datetime(
value, getattr(data, 'dtype', None))
values[plane_indexer] = value
except ValueError:
for item, v in zip(item_labels[het_idx], value):
data = self.obj[item]
values = data.values
if np.prod(values.shape):
values[plane_indexer] = v
else:
if isinstance(indexer, tuple):
indexer = _maybe_convert_ix(*indexer)
if isinstance(value, Series):
value = self._align_series(indexer, value)
if isinstance(value, DataFrame):
value = self._align_frame(indexer, value)
# 2096
values = self.obj.values
if np.prod(values.shape):
values[indexer] = value
def _possibly_convert_objects(values,
convert_dates=True,
convert_numeric=True,
convert_timedeltas=True,
copy=True):
""" if we have an object dtype, try to coerce dates and/or numbers """
# if we have passed in a list or scalar
if isinstance(values, (list, tuple)):
values = np.array(values, dtype=np.object_)
if not hasattr(values, 'dtype'):
values = np.array([values], dtype=np.object_)
# convert dates
if convert_dates and values.dtype == np.object_:
# we take an aggressive stance and convert to datetime64[ns]
if convert_dates == 'coerce':
new_values = _possibly_cast_to_datetime(values,
'M8[ns]',
errors='coerce')
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
else:
values = lib.maybe_convert_objects(values,
convert_datetime=convert_dates)
# convert timedeltas
if convert_timedeltas and values.dtype == np.object_:
if convert_timedeltas == 'coerce':
from pandas.tseries.timedeltas import to_timedelta
new_values = to_timedelta(values, coerce=True)
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
else:
values = lib.maybe_convert_objects(
values, convert_timedelta=convert_timedeltas)
# convert to numeric
if values.dtype == np.object_:
if convert_numeric:
try:
new_values = lib.maybe_convert_numeric(values,
set(),
coerce_numeric=True)
# if we are all nans then leave me alone
if not isnull(new_values).all():
values = new_values
except:
pass
else:
# soft-conversion
values = lib.maybe_convert_objects(values)
values = values.copy() if copy else values
return values
