more cleanup

Author: TomAugspurger

pandas/core/arrays/base.py

@@ -680,7 +680,7 @@ def __repr__(self):
  length=len(self),
  dtype=self.dtype)
 
- def _formatter(self, formatter):
+ def _formatter(self, formatter=None):
  # type: (ExtensionArrayFormatter) -> Callable[Any]
  """Formatting function for scalar values.
 
@@ -693,7 +693,7 @@ def _formatter(self, formatter):
  Whether the formatter is to be used by pandas inside a Series
  or DataFrame repr.
  """
- return formatter.formatter or str
+ return getattr(formatter, 'formatter', None) or str
 
  def _formatting_values(self):
  # type: () -> np.ndarray

pandas/core/arrays/categorical.py

@@ -499,6 +499,10 @@ def _constructor(self):
  def _from_sequence(cls, scalars, dtype=None, copy=False):
  return Categorical(scalars, dtype=dtype)
 
+ def _formatter(self, formatter):
+ # backwards compat with old printing.
+ return None
+
  def copy(self):
  """ Copy constructor. """
  return self._constructor(values=self._codes.copy(),

pandas/io/formats/format.py

@@ -18,10 +18,10 @@
 from pandas.core.dtypes.common import (
  is_categorical_dtype, is_datetime64_dtype, is_datetimetz,
  is_extension_array_dtype, is_float, is_float_dtype, is_integer,
- is_integer_dtype, is_list_like, is_numeric_dtype, is_period_arraylike,
- is_scalar, is_timedelta64_dtype)
+ is_integer_dtype, is_list_like, is_numeric_dtype, is_scalar,
+ is_timedelta64_dtype)
 from pandas.core.dtypes.generic import (
- ABCIndex, ABCMultiIndex, ABCSeries, ABCSparseArray)
+ ABCIndexClass, ABCMultiIndex, ABCSeries, ABCSparseArray)
 from pandas.core.dtypes.missing import isna, notna
 
 from pandas import compat
@@ -30,7 +30,6 @@
 from pandas.core.config import get_option, set_option
 from pandas.core.index import Index, ensure_index
 from pandas.core.indexes.datetimes import DatetimeIndex
-from pandas.core.indexes.period import PeriodIndex
 
 from pandas.io.common import _expand_user, _stringify_path
 from pandas.io.formats.printing import adjoin, justify, pprint_thing
@@ -850,9 +849,7 @@ def _get_column_name_list(self):
 def format_array(values, formatter, float_format=None, na_rep='NaN',
  digits=None, space=None, justify='right', decimal='.'):
 
- if is_period_arraylike(values):
- fmt_klass = PeriodArrayFormatter
- elif is_datetime64_dtype(values.dtype):
+ if is_datetime64_dtype(values.dtype):
  fmt_klass = Datetime64Formatter
  elif is_timedelta64_dtype(values.dtype):
  fmt_klass = Timedelta64Formatter
@@ -1128,33 +1125,18 @@ def _format_strings(self):
 class ExtensionArrayFormatter(GenericArrayFormatter):
  def _format_strings(self):
  values = self.values
- if isinstance(values, (ABCIndex, ABCSeries)):
+ if isinstance(values, (ABCIndexClass, ABCSeries)):
  values = values._values
 
- formatter = self.values._formatter(self)
- fmt_values = format_array(np.asarray(self.values),
+ formatter = values._formatter(self)
+ fmt_values = format_array(np.asarray(values),
  formatter,
  float_format=self.float_format,
  na_rep=self.na_rep, digits=self.digits,
  space=self.space, justify=self.justify)
  return fmt_values
 
 
-class PeriodArrayFormatter(IntArrayFormatter):
-
- def _format_strings(self):
- from pandas.core.indexes.period import IncompatibleFrequency
- try:
- values = PeriodIndex(self.values).to_native_types()
- except IncompatibleFrequency:
- # periods may contains different freq
- values = Index(self.values, dtype='object').to_native_types()
-
- formatter = self.formatter or (lambda x: '{x}'.format(x=x))
- fmt_values = [formatter(x) for x in values]
- return fmt_values
-
-
 def format_percentiles(percentiles):
  """
  Outputs rounded and formatted percentiles.