CLN: Address some TODO comments

pandas/core/common.py

@@ -68,17 +68,6 @@ def consensus_name_attr(objs):
     return name
 
 
-# TODO: only used once in frame.py; belongs elsewhere?
-def get_info_slice(obj, indexer):
-    """Slice the info axis of `obj` with `indexer`."""
-    if not hasattr(obj, '_info_axis_number'):
-        msg = 'object of type {typ!r} has no info axis'
-        raise TypeError(msg.format(typ=type(obj).__name__))
-    slices = [slice(None)] * obj.ndim
-    slices[obj._info_axis_number] = indexer
-    return tuple(slices)
-
-
 def maybe_box(indexer, values, obj, key):
 
     # if we have multiples coming back, box em
@@ -432,21 +421,6 @@ def random_state(state=None):
                          "RandomState, or None")
 
 
-# TODO: only used once in indexes.api; belongs elsewhere?
-def get_distinct_objs(objs):
-    """
-    Return a list with distinct elements of "objs" (different ids).
-    Preserves order.
-    """
-    ids = set()
-    res = []
-    for obj in objs:
-        if not id(obj) in ids:
-            ids.add(id(obj))
-            res.append(obj)
-    return res
-
-
 def _pipe(obj, func, *args, **kwargs):
     """
     Apply a function ``func`` to object ``obj`` either by passing obj as the

pandas/core/frame.py

@@ -3205,7 +3205,7 @@ def is_dtype_instance_mapper(idx, dtype):
                 exclude_these.iloc[idx] = not any(map(f, exclude))
 
         dtype_indexer = include_these & exclude_these
-        return self.loc[com.get_info_slice(self, dtype_indexer)]
+        return self.loc[_get_info_slice(self, dtype_indexer)]
 
     def _box_item_values(self, key, values):
         items = self.columns[self.columns.get_loc(key)]
@@ -8040,3 +8040,13 @@ def _from_nested_dict(data):
 
 def _put_str(s, space):
     return u'{s}'.format(s=s)[:space].ljust(space)
+
+
+def _get_info_slice(obj, indexer):
+    """Slice the info axis of `obj` with `indexer`."""
+    if not hasattr(obj, '_info_axis_number'):
+        msg = 'object of type {typ!r} has no info axis'
+        raise TypeError(msg.format(typ=type(obj).__name__))
+    slices = [slice(None)] * obj.ndim
+    slices[obj._info_axis_number] = indexer
+    return tuple(slices)

pandas/core/generic.py

@@ -4069,16 +4069,6 @@ def _reindex_with_indexers(self, reindexers, fill_value=None, copy=False,
 
         return self._constructor(new_data).__finalize__(self)
 
-    # TODO: unused; remove?
-    def _reindex_axis(self, new_index, fill_method, axis, copy):
-        new_data = self._data.reindex_axis(new_index, axis=axis,
-                                           method=fill_method, copy=copy)
-
-        if new_data is self._data and not copy:
-            return self
-        else:
-            return self._constructor(new_data).__finalize__(self)
-
     def filter(self, items=None, like=None, regex=None, axis=None):
         """
         Subset rows or columns of dataframe according to labels in

pandas/core/indexes/api.py

@@ -53,9 +53,23 @@ def _get_objs_combined_axis(objs, intersect=False, axis=0, sort=True):
         return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)
 
 
+def _get_distinct_objs(objs):
+    """
+    Return a list with distinct elements of "objs" (different ids).
+    Preserves order.
+    """
+    ids = set()
+    res = []
+    for obj in objs:
+        if not id(obj) in ids:
+            ids.add(id(obj))
+            res.append(obj)
+    return res
+
+
 def _get_combined_index(indexes, intersect=False, sort=False):
     # TODO: handle index names!
-    indexes = com.get_distinct_objs(indexes)
+    indexes = _get_distinct_objs(indexes)
     if len(indexes) == 0:
         index = Index([])
     elif len(indexes) == 1:

pandas/core/indexes/timedeltas.py

@@ -511,60 +511,20 @@ def _maybe_cast_slice_bound(self, label, side, kind):
 
         return label
 
-    def _get_string_slice(self, key, use_lhs=True, use_rhs=True):
-        freq = getattr(self, 'freqstr',
-                       getattr(self, 'inferred_freq', None))
+    def _get_string_slice(self, key):
         if is_integer(key) or is_float(key) or key is NaT:
             self._invalid_indexer('slice', key)
-        loc = self._partial_td_slice(key, freq, use_lhs=use_lhs,
-                                     use_rhs=use_rhs)
+        loc = self._partial_td_slice(key)
         return loc
 
-    def _partial_td_slice(self, key, freq, use_lhs=True, use_rhs=True):
+    def _partial_td_slice(self, key):
 
         # given a key, try to figure out a location for a partial slice
         if not isinstance(key, compat.string_types):
             return key
 
         raise NotImplementedError
 
-        # TODO(wesm): dead code
-        # parsed = _coerce_scalar_to_timedelta_type(key, box=True)
-
-        # is_monotonic = self.is_monotonic
-
-        # # figure out the resolution of the passed td
-        # # and round to it
-
-        # # t1 = parsed.round(reso)
-
-        # t2 = t1 + to_offset(parsed.resolution) - Timedelta(1, 'ns')
-
-        # stamps = self.asi8
-
-        # if is_monotonic:
-
-        #     # we are out of range
-        #     if (len(stamps) and ((use_lhs and t1.value < stamps[0] and
-        #                           t2.value < stamps[0]) or
-        #                          ((use_rhs and t1.value > stamps[-1] and
-        #                            t2.value > stamps[-1])))):
-        #         raise KeyError
-
-        #     # a monotonic (sorted) series can be sliced
-        #     left = (stamps.searchsorted(t1.value, side='left')
-        #             if use_lhs else None)
-        #     right = (stamps.searchsorted(t2.value, side='right')
-        #              if use_rhs else None)
-
-        #     return slice(left, right)
-
-        # lhs_mask = (stamps >= t1.value) if use_lhs else True
-        # rhs_mask = (stamps <= t2.value) if use_rhs else True
-
-        # # try to find a the dates
-        # return (lhs_mask & rhs_mask).nonzero()[0]
-
     @Substitution(klass='TimedeltaIndex')
     @Appender(_shared_docs['searchsorted'])
     def searchsorted(self, value, side='left', sorter=None):

pandas/core/internals/blocks.py

@@ -2812,9 +2812,7 @@ def _astype(self, dtype, mgr=None, **kwargs):
     def _can_hold_element(self, element):
         tipo = maybe_infer_dtype_type(element)
         if tipo is not None:
-            # TODO: this still uses asarray, instead of dtype.type
-            element = np.array(element)
-            return element.dtype == _NS_DTYPE or element.dtype == np.int64
+            return tipo == _NS_DTYPE or tipo == np.int64
         return (is_integer(element) or isinstance(element, datetime) or
                 isna(element))
 

pandas/tests/frame/test_mutate_columns.py

@@ -233,7 +233,7 @@ def test_pop(self):
         self.frame['foo'] = 'bar'
         self.frame.pop('foo')
         assert 'foo' not in self.frame
-        # TODO assert self.frame.columns.name == 'baz'
+        assert self.frame.columns.name == 'baz'
 
         # gh-10912: inplace ops cause caching issue
         a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[