wip

Author: TomAugspurger

pandas/core/arrays/base.py

@@ -680,8 +680,8 @@ def __repr__(self):
  length=len(self),
  dtype=self.dtype)
 
- def _formatter(self, boxed=False):
- # type: (bool) -> Callable[Any]
+ def _formatter(self, formatter):
+ # type: (ExtensionArrayFormatter) -> Callable[Any]
  """Formatting function for scalar values.
 
  This is used in the default '__repr__'. The formatting function
@@ -693,7 +693,7 @@ def _formatter(self, boxed=False):
  Whether the formatter is to be used by pandas inside a Series
  or DataFrame repr.
  """
- return str
+ return formatter.formatter or str
 
  def _formatting_values(self):
  # type: () -> np.ndarray

pandas/core/internals/blocks.py

@@ -1963,9 +1963,7 @@ def formatting_values(self):
  warnings.warn(msg, FutureWarning, stacklevel=10)
  return self.values._formatting_values()
 
- # the future implementation (and current, if not overrode)
- formatter = self.values._formatter(boxed=True)
- return np.array([formatter(x) for x in self.values], dtype=object)
+ return self.values
 
  def concat_same_type(self, to_concat, placement=None):
  """

pandas/io/formats/format.py

@@ -16,11 +16,12 @@
 from pandas.compat import StringIO, lzip, map, u, zip
 
 from pandas.core.dtypes.common import (
- is_categorical_dtype, is_datetime64_dtype, is_datetimetz, is_float,
- is_float_dtype, is_integer, is_integer_dtype, is_interval_dtype,
- is_list_like, is_numeric_dtype, is_period_arraylike, is_scalar,
- is_timedelta64_dtype)
-from pandas.core.dtypes.generic import ABCMultiIndex, ABCSparseArray
+ 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)
+from pandas.core.dtypes.generic import (
+ ABCIndex, ABCMultiIndex, ABCSeries, ABCSparseArray)
 from pandas.core.dtypes.missing import isna, notna
 
 from pandas import compat
@@ -849,22 +850,20 @@ 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_categorical_dtype(values):
- fmt_klass = CategoricalArrayFormatter
- elif is_interval_dtype(values):
- fmt_klass = IntervalArrayFormatter
+ if is_period_arraylike(values):
+ fmt_klass = PeriodArrayFormatter
+ elif is_datetime64_dtype(values.dtype):
+ fmt_klass = Datetime64Formatter
+ elif is_timedelta64_dtype(values.dtype):
+ fmt_klass = Timedelta64Formatter
+ elif is_extension_array_dtype(values.dtype):
+ fmt_klass = ExtensionArrayFormatter
  elif is_float_dtype(values.dtype):
  fmt_klass = FloatArrayFormatter
- elif is_period_arraylike(values):
- fmt_klass = PeriodArrayFormatter
  elif is_integer_dtype(values.dtype):
  fmt_klass = IntArrayFormatter
  elif is_datetimetz(values):
  fmt_klass = Datetime64TZFormatter
- elif is_datetime64_dtype(values.dtype):
- fmt_klass = Datetime64Formatter
- elif is_timedelta64_dtype(values.dtype):
- fmt_klass = Timedelta64Formatter
  else:
  fmt_klass = GenericArrayFormatter
 
@@ -1126,14 +1125,18 @@ def _format_strings(self):
  return fmt_values.tolist()
 
 
-class IntervalArrayFormatter(GenericArrayFormatter):
-
- def __init__(self, values, *args, **kwargs):
- GenericArrayFormatter.__init__(self, values, *args, **kwargs)
-
+class ExtensionArrayFormatter(GenericArrayFormatter):
  def _format_strings(self):
- formatter = self.formatter or str
- fmt_values = np.array([formatter(x) for x in self.values])
+ values = self.values
+ if isinstance(values, (ABCIndex, ABCSeries)):
+ values = values._values
+
+ formatter = self.values._formatter(self)
+ fmt_values = format_array(np.asarray(self.values),
+ formatter,
+ float_format=self.float_format,
+ na_rep=self.na_rep, digits=self.digits,
+ space=self.space, justify=self.justify)
  return fmt_values
 
 
@@ -1152,19 +1155,6 @@ def _format_strings(self):
  return fmt_values
 
 
-class CategoricalArrayFormatter(GenericArrayFormatter):
-
- def __init__(self, values, *args, **kwargs):
- GenericArrayFormatter.__init__(self, values, *args, **kwargs)
-
- def _format_strings(self):
- fmt_values = format_array(self.values.get_values(), self.formatter,
- float_format=self.float_format,
- na_rep=self.na_rep, digits=self.digits,
- space=self.space, justify=self.justify)
- return fmt_values
-
-
 def format_percentiles(percentiles):
  """
  Outputs rounded and formatted percentiles.

pandas/tests/arrays/test_period.py

@@ -215,14 +215,16 @@ def test_repr_large():
  arr = period_array(['2000', '2001'] * 500, freq='D')
  result = str(arr)
  expected = (
- '<PeriodArray>\n'
- '[2000-01-01, 2001-01-01, 2000-01-01, 2001-01-01, 2000-01-01, '
- '2001-01-01,\n' # continuation
- ' 2000-01-01, 2001-01-01, 2000-01-01, 2001-01-01,\n'
- ' ...\n'
- ' 2000-01-01, 2001-01-01, 2000-01-01, 2001-01-01, 2000-01-01, '
- '2001-01-01,\n' # continuation
- ' 2000-01-01, 2001-01-01, 2000-01-01, 2001-01-01]\n'
- 'Length: 1000, dtype: period[D]'
+ "<PeriodArray>\n"
+ "['2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
+ "'2000-01-01',\n"
+ " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
+ "'2001-01-01',\n"
+ " ...\n"
+ " '2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
+ "'2000-01-01',\n"
+ " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
+ "'2001-01-01']\n"
+ "Length: 1000, dtype: period[D]"
  )
  assert result == expected