def _getitem_multilevel(self, key):
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, Series, np.ndarray, Index)):
new_columns = self.columns[loc]
result_columns = maybe_droplevels(new_columns, key)
if self._is_mixed_type:
result = self.reindex(columns=new_columns)
result.columns = result_columns
else:
new_values = self.values[:, loc]
result = self._constructor(new_values,
index=self.index,
columns=result_columns)
result = result.__finalize__(self)
if len(result.columns) == 1:
top = result.columns[0]
if isinstance(top, tuple):
top = top[0]
if top == '':
result = result['']
if isinstance(result, Series):
result = self._constructor_sliced(result,
index=self.index,
name=key)
result._set_is_copy(self)
return result
else:
return self._get_item_cache(key)
def _getitem_multilevel(self, key):
info = self._info_axis
loc = info.get_loc(key)
if isinstance(loc, (slice, np.ndarray)):
new_index = info[loc]
result_index = maybe_droplevels(new_index, key)
slices = [loc] + [slice(None) for x in range(
self._AXIS_LEN - 1)]
new_values = self.values[slices]
d = self._construct_axes_dict(self._AXIS_ORDERS[1:])
d[self._info_axis_name] = result_index
result = self._constructor(new_values, **d)
return result
else:
return self._get_item_cache(key)
def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not lib.isscalar(key):
raise InvalidIndexError
from pandas.core.indexing import maybe_droplevels
from pandas.core.series import Series
k = com._values_from_object(key)
loc = self.get_loc(k)
new_values = com._values_from_object(series)[loc]
if lib.isscalar(new_values) or new_values is None:
return new_values
new_index = self[loc]
new_index = maybe_droplevels(new_index, k)
return Series(new_values, index=new_index, name=series.name)
def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not np.isscalar(key):
raise InvalidIndexError
from pandas.core.indexing import maybe_droplevels
from pandas.core.series import Series
k = com._values_from_object(key)
loc = self.get_loc(k)
new_values = com._values_from_object(series)[loc]
if np.isscalar(new_values) or new_values is None:
return new_values
new_index = self[loc]
new_index = maybe_droplevels(new_index, k)
return Series(new_values, index=new_index, name=series.name)
