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: