REF: Factor -> Categorical.

Author: jseabold

pandas/core/frame.py

@@ -1659,8 +1659,8 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, copy=True):
  -------
  converted : DataFrame
  """
- return self._constructor(self._data.convert(convert_dates=convert_dates, 
- convert_numeric=convert_numeric, 
+ return self._constructor(self._data.convert(convert_dates=convert_dates,
+ convert_numeric=convert_numeric,
  copy=copy))
 
  #----------------------------------------------------------------------
@@ -3330,7 +3330,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False,
  a reference to the filled object, which is self if inplace=True
  limit : int, default None
  Maximum size gap to forward or backward fill
- downcast : dict, default is None, a dict of item->dtype of what to 
+ downcast : dict, default is None, a dict of item->dtype of what to
  downcast if possible
 
  See also
@@ -3380,7 +3380,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False,
  result[k].fillna(v, inplace=True)
  return result
  else:
- new_data = self._data.fillna(value, inplace=inplace, 
+ new_data = self._data.fillna(value, inplace=inplace,
  downcast=downcast)
 
  if inplace:
@@ -3791,8 +3791,8 @@ def combine(self, other, func, fill_value=None, overwrite=True):
  result[col] = arr
 
  # convert_objects just in case
- return self._constructor(result, 
- index=new_index, 
+ return self._constructor(result,
+ index=new_index,
  columns=new_columns).convert_objects(
  convert_dates=True,
  copy=False)
@@ -3825,7 +3825,7 @@ def combiner(x, y, needs_i8_conversion=False):
  y_values = y_values.view('i8')
  else:
  mask = isnull(x_values)
- 
+
  return expressions.where(mask, y_values, x_values, raise_on_error=True)
 
  return self.combine(other, combiner, overwrite=False)
@@ -5406,11 +5406,11 @@ def group_agg(values, bounds, f):
 
 def factor_agg(factor, vec, func):
  """
- Aggregate array based on Factor
+ Aggregate array based on Categorical
 
  Parameters
  ----------
- factor : Factor
+ factor : Categorical
  length n
  vec : sequence
  length n
@@ -5419,7 +5419,11 @@ def factor_agg(factor, vec, func):
 
  Returns
  -------
- ndarray corresponding to Factor levels
+ ndarray corresponding to factor levels
+
+ See Also
+ --------
+ pandas.Categorical
  """
  indexer = np.argsort(factor.labels)
  unique_labels = np.arange(len(factor.levels))

pandas/core/panel.py

@@ -10,7 +10,7 @@
  _try_sort, _default_index,
  _infer_dtype_from_scalar,
  notnull)
-from pandas.core.categorical import Factor
+from pandas.core.categorical import Categorical
 from pandas.core.index import (Index, MultiIndex, _ensure_index,
  _get_combined_index)
 from pandas.core.indexing import _maybe_droplevels, _is_list_like
@@ -82,8 +82,8 @@ def panel_index(time, panels, names=['time', 'panel']):
  (1962, 'C')], dtype=object)
  """
  time, panels = _ensure_like_indices(time, panels)
- time_factor = Factor.from_array(time)
- panel_factor = Factor.from_array(panels)
+ time_factor = Categorical.from_array(time)
+ panel_factor = Categorical.from_array(panels)
 
  labels = [time_factor.labels, panel_factor.labels]
  levels = [time_factor.levels, panel_factor.levels]

pandas/core/reshape.py

@@ -749,9 +749,9 @@ def make_axis_dummies(frame, axis='minor', transform=None):
  axis : {'major', 'minor'}, default 'minor'
  transform : function, default None
  Function to apply to axis labels first. For example, to
- get "day of week" dummies in a time series regression 
+ get "day of week" dummies in a time series regression
  you might call::
- 
+
  make_axis_dummies(panel, axis='major',
  transform=lambda d: d.weekday())
  Returns
@@ -810,6 +810,6 @@ def block2d_to_blocknd(values, items, shape, labels, ref_items=None):
 
 
 def factor_indexer(shape, labels):
- """ given a tuple of shape and a list of Factor lables, return the expanded label indexer """
+ """ given a tuple of shape and a list of Categorical labels, return the expanded label indexer """
  mult = np.array(shape)[::-1].cumprod()[::-1]
  return com._ensure_platform_int(np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T)

pandas/tools/merge.py

@@ -5,7 +5,7 @@
 import itertools
 import numpy as np
 
-from pandas.core.categorical import Factor
+from pandas.core.categorical import Categorical
 from pandas.core.frame import DataFrame, _merge_doc
 from pandas.core.generic import NDFrame
 from pandas.core.groupby import get_group_index
@@ -1190,7 +1190,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
  names = [None] * len(zipped)
 
  if levels is None:
- levels = [Factor.from_array(zp).levels for zp in zipped]
+ levels = [Categorical.from_array(zp).levels for zp in zipped]
  else:
  levels = [_ensure_index(x) for x in levels]
  else:
@@ -1228,7 +1228,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
  levels.extend(concat_index.levels)
  label_list.extend(concat_index.labels)
  else:
- factor = Factor.from_array(concat_index)
+ factor = Categorical.from_array(concat_index)
  levels.append(factor.levels)
  label_list.append(factor.labels)