CLN: remove dead code

Author: TomAugspurger

pandas/core/reshape/reshape.py

@@ -12,12 +12,12 @@
 from pandas.core.dtypes.cast import maybe_promote
 from pandas.core.dtypes.common import (
  ensure_platform_int, is_bool_dtype, is_extension_array_dtype, is_list_like,
- is_object_dtype, is_sparse, needs_i8_conversion)
+ is_object_dtype, needs_i8_conversion)
 from pandas.core.dtypes.missing import notna
 
 from pandas import compat
 import pandas.core.algorithms as algos
-from pandas.core.arrays import Categorical, SparseArray
+from pandas.core.arrays import SparseArray
 from pandas.core.arrays.categorical import _factorize_from_iterable
 from pandas.core.frame import DataFrame
 from pandas.core.index import Index, MultiIndex
@@ -82,28 +82,15 @@ class _Unstacker(object):
  def __init__(self, values, index, level=-1, value_columns=None,
  fill_value=None, constructor=None):
 
- self.is_categorical = None
- self.is_sparse = is_sparse(values)
  if values.ndim == 1:
- if isinstance(values, Categorical):
- self.is_categorical = values
- values = np.array(values)
- elif self.is_sparse:
- # XXX: Makes SparseArray *dense*, but it's supposedly
- # a single column at a time, so it's "doable"
- values = values.values
  values = values[:, np.newaxis]
  self.values = values
  self.value_columns = value_columns
  self.fill_value = fill_value
 
  if constructor is None:
- if self.is_sparse:
- self.constructor = SparseDataFrame
- else:
- self.constructor = DataFrame
- else:
- self.constructor = constructor
+ constructor = DataFrame
+ self.constructor = constructor
 
  if value_columns is None and values.shape[1] != 1: # pragma: no cover
  raise ValueError('must pass column labels for multi-column data')
@@ -174,14 +161,6 @@ def get_result(self):
  columns = self.get_new_columns()
  index = self.get_new_index()
 
- # may need to coerce categoricals here
- if self.is_categorical is not None:
- categories = self.is_categorical.categories
- ordered = self.is_categorical.ordered
- values = [Categorical(values[:, i], categories=categories,
- ordered=ordered)
- for i in range(values.shape[-1])]
-
  return self.constructor(values, index=index, columns=columns)
 
  def get_new_values(self):