CLN/API: Refactor of _get_numeric_data/_get_bool_data to core/generic.py, allowing Series/Panel functionaility

Author: jreback

doc/source/release.rst

@@ -193,6 +193,7 @@ See :ref:`Internal Refactoring<whatsnew_0130.refactoring>`
 - Refactor ``rename`` methods to core/generic.py; fixes ``Series.rename`` for (:issue:`4605`), and adds ``rename``
   with the same signature for ``Panel``
 - Series (for index) / Panel (for items) now as attribute access to its elements  (:issue:`1903`)
+- Refactor of ``_get_numeric_data/_get_bool_data`` to core/generic.py, allowing Series/Panel functionaility
 
 **Experimental Features**
 

doc/source/v0.13.0.txt

@@ -268,6 +268,7 @@ and behaviors. Series formerly subclassed directly from ``ndarray``. (:issue:`40
 - ``Series.copy`` no longer accepts the ``order`` parameter and is now consistent with ``NDFrame`` copy
 - Refactor ``rename`` methods to core/generic.py; fixes ``Series.rename`` for (:issue`4605`), and adds ``rename``
   with the same signature for ``Panel``
+- Refactor of ``_get_numeric_data/_get_bool_data`` to core/generic.py, allowing Series/Panel functionaility
 - ``Series`` (for index) / ``Panel`` (for items) now allow attribute access to its elements  (:issue:`1903`)
 
   .. ipython:: python

pandas/core/frame.py

@@ -4388,12 +4388,6 @@ def _get_agg_axis(self, axis_num):
         else:
             raise Exception('Must have 0<= axis <= 1')
 
-    def _get_numeric_data(self):
-        return self._constructor(self._data.get_numeric_data(), index=self.index, copy=False)
-
-    def _get_bool_data(self):
-        return self._constructor(self._data.get_bool_data(), index=self.index, copy=False)
-
     def quantile(self, q=0.5, axis=0, numeric_only=True):
         """
         Return values at the given quantile over requested axis, a la

pandas/core/generic.py

@@ -1319,6 +1319,12 @@ def _protect_consolidate(self, f):
             self._clear_item_cache()
         return result
 
+    def _get_numeric_data(self):
+        return self._constructor(self._data.get_numeric_data())
+
+    def _get_bool_data(self):
+        return self._constructor(self._data.get_bool_data())
+
     #----------------------------------------------------------------------
     # Internal Interface Methods
 

pandas/core/internals.py

@@ -1574,9 +1574,11 @@ def __init__(self, blocks, axes, do_integrity_check=True, fastpath=True):
         if not self.items.is_unique:
             self._set_ref_locs(do_refs=True)
 
-    @classmethod
-    def make_empty(cls):
-        return cls([], [[], []])
+    def make_empty(self, axes=None):
+        """ return an empty BlockManager with the items axis of len 0 """
+        if axes is None:
+            axes = [_ensure_index([]) ] + [ _ensure_index(a) for a in self.axes[1:] ]
+        return self.__class__(np.array([]), axes)
 
     def __nonzero__(self):
         return True
@@ -2074,7 +2076,7 @@ def get_data(self, copy=False, columns=None, **kwargs):
         blocks = self.get_block_map(
             typ='list', copy=copy, columns=columns, **kwargs)
         if len(blocks) == 0:
-            return self.__class__.make_empty()
+            return self.make_empty()
 
         return self.combine(blocks)
 

pandas/tests/test_frame.py

@@ -10667,8 +10667,8 @@ def test_tslib_tz_convert_trans_pos_plus_1__bug(self):
 
         test_vector = pd.Series([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
                                  3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
-                                 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
-                                 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
+                                 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
+                                 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                                  4, 4, 4, 4, 4, 4, 4, 4, 5], dtype=int)
 
         hours = idx.hour

pandas/tests/test_generic.py

@@ -36,16 +36,43 @@ def setUp(self):
         import warnings
         warnings.filterwarnings(action='ignore', category=FutureWarning)
 
+    @property
+    def _ndim(self):
+        return self._typ._AXIS_LEN
+
     def _axes(self):
         """ return the axes for my object typ """
         return self._typ._AXIS_ORDERS
 
-    def _construct(self, shape=None, **kwargs):
-        """ construct an object for the given shape """
+    def _construct(self, shape, value=None, **kwargs):
+        """ construct an object for the given shape
+            if value is specified use that if its a scalar
+            if value is an array, repeat it as needed """
 
         if isinstance(shape,int):
-            shape = tuple([shape] * self._typ._AXIS_LEN)
-        return self._typ(np.random.randn(*shape),**kwargs)
+            shape = tuple([shape] * self._ndim)
+        if value is not None:
+            if np.isscalar(value):
+                if value == 'empty':
+                    arr = None
+
+                    # remove the info axis
+                    kwargs.pop(self._typ._info_axis_name,None)
+                else:
+                    arr = np.empty(shape)
+                    arr.fill(value)
+            else:
+                fshape = np.prod(shape)
+                arr = value.ravel()
+                new_shape = fshape/arr.shape[0]
+                if fshape % arr.shape[0] != 0:
+                    raise Exception("invalid value passed in _construct")
+
+                arr = np.repeat(arr,new_shape).reshape(shape)
+        else:
+            arr = np.random.randn(*shape)
+        return self._typ(arr,**kwargs)
+
 
     def _compare(self, result, expected):
         self._comparator(result,expected)
@@ -68,6 +95,31 @@ def test_rename(self):
 
         # multiple axes at once
 
+    def test_get_numeric_data(self):
+
+        n = 4
+        kwargs = { }
+        for i in range(self._ndim):
+            kwargs[self._typ._AXIS_NAMES[i]] = list(range(n))
+
+        # get the numeric data
+        o = self._construct(n,**kwargs)
+        result = o._get_numeric_data()
+        self._compare(result, o)
+
+        # non-inclusion
+        result = o._get_bool_data()
+        expected = self._construct(n,value='empty',**kwargs)
+        self._compare(result,expected)
+
+        # get the bool data
+        arr = np.array([True,True,False,True])
+        o = self._construct(n,value=arr,**kwargs)
+        result = o._get_numeric_data()
+        self._compare(result, o)
+
+        # _get_numeric_data is includes _get_bool_data, so can't test for non-inclusion
+
 class TestSeries(unittest.TestCase, Generic):
     _typ = Series
     _comparator = lambda self, x, y: assert_series_equal(x,y)