ENH: Add PeriodBlock

Author: sinhrks

pandas/core/algorithms.py

@@ -14,8 +14,8 @@
  is_categorical_dtype,
  is_extension_type,
  is_datetimetz,
+ is_period,
  is_period_dtype,
- is_period_arraylike,
  is_float_dtype,
  needs_i8_conversion,
  is_categorical,
@@ -367,7 +367,8 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
  raise TypeError("bins argument only works with numeric data.")
  values = cat.codes
 
- if is_extension_type(values) and not is_datetimetz(values):
+ if (is_extension_type(values) and
+ not (is_datetimetz(values) or is_period(values))):
  # handle Categorical and sparse,
  # datetime tz can be handeled in ndarray path
  result = Series(values).values.value_counts(dropna=dropna)
@@ -399,25 +400,14 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
 
 def _value_counts_arraylike(values, dropna=True):
  is_datetimetz_type = is_datetimetz(values)
- is_period_type = (is_period_dtype(values) or
- is_period_arraylike(values))
-
+ is_period_type = is_period_dtype(values)
  orig = values
 
  from pandas.core.series import Series
- values = Series(values).values
+ values = Series(values)._values
  dtype = values.dtype
 
- if needs_i8_conversion(dtype) or is_period_type:
-
- from pandas.tseries.index import DatetimeIndex
- from pandas.tseries.period import PeriodIndex
-
- if is_period_type:
- # values may be an object
- values = PeriodIndex(values)
- freq = values.freq
-
+ if needs_i8_conversion(dtype):
  values = values.view(np.int64)
  keys, counts = htable.value_count_int64(values, dropna)
 
@@ -426,13 +416,14 @@ def _value_counts_arraylike(values, dropna=True):
  keys, counts = keys[msk], counts[msk]
 
  # convert the keys back to the dtype we came in
- keys = keys.astype(dtype)
-
- # dtype handling
  if is_datetimetz_type:
+ from pandas.tseries.index import DatetimeIndex
  keys = DatetimeIndex._simple_new(keys, tz=orig.dtype.tz)
- if is_period_type:
- keys = PeriodIndex._simple_new(keys, freq=freq)
+ elif is_period_type:
+ from pandas.tseries.period import PeriodIndex
+ keys = PeriodIndex._simple_new(keys, freq=orig.dtype.freq)
+ else:
+ keys = keys.astype(dtype)
 
  elif is_integer_dtype(dtype):
  values = _ensure_int64(values)
@@ -476,9 +467,6 @@ def duplicated(values, keep='first'):
  # no need to revert to original type
  if needs_i8_conversion(dtype):
  values = values.view(np.int64)
- elif is_period_arraylike(values):
- from pandas.tseries.period import PeriodIndex
- values = PeriodIndex(values).asi8
  elif is_categorical_dtype(dtype):
  values = values.values.codes
  elif isinstance(values, (ABCSeries, ABCIndex)):
@@ -1015,8 +1003,9 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None,
  if is_categorical(arr):
  return arr.take_nd(indexer, fill_value=fill_value,
  allow_fill=allow_fill)
- elif is_datetimetz(arr):
- return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
+ elif is_extension_type(arr):
+ return arr.take(indexer, fill_value=fill_value,
+ allow_fill=allow_fill)
 
  if indexer is None:
  indexer = np.arange(arr.shape[axis], dtype=np.int64)

pandas/core/frame.py

@@ -39,6 +39,7 @@
  is_datetimetz,
  is_datetime64_dtype,
  is_datetime64tz_dtype,
+ is_period_dtype,
  is_bool_dtype,
  is_integer_dtype,
  is_float_dtype,
@@ -262,8 +263,10 @@ def __init__(self, data=None, index=None, columns=None, dtype=None,
  if isinstance(data, BlockManager):
  mgr = self._init_mgr(data, axes=dict(index=index, columns=columns),
  dtype=dtype, copy=copy)
+
  elif isinstance(data, dict):
  mgr = self._init_dict(data, index, columns, dtype=dtype)
+
  elif isinstance(data, ma.MaskedArray):
  import numpy.ma.mrecords as mrecords
  # masked recarray
@@ -2975,7 +2978,7 @@ def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
 
  def _maybe_casted_values(index, labels=None):
  if isinstance(index, PeriodIndex):
- values = index.asobject.values
+ values = index
  elif isinstance(index, DatetimeIndex) and index.tz is not None:
  values = index
  else:
@@ -3740,6 +3743,11 @@ def combine(self, other, func, fill_value=None, overwrite=True):
  # see if we need to be represented as i8 (datetimelike)
  # try to keep us at this dtype
  needs_i8_conversion_i = needs_i8_conversion(new_dtype)
+
+ if is_period_dtype(new_dtype):
+ # temp for PeriodDtype
+ needs_i8_conversion_i = False
+
  if needs_i8_conversion_i:
  arr = func(series, otherSeries, True)
  else:
@@ -3790,6 +3798,7 @@ def combine_first(self, other):
  """
 
  def combiner(x, y, needs_i8_conversion=False):
+ # ToDo:
  x_values = x.values if hasattr(x, 'values') else x
  y_values = y.values if hasattr(y, 'values') else y
  if needs_i8_conversion: