Avoid accessing private loc methods (#27383)

Author: jbrockmendel

pandas/core/frame.py

@@ -2970,7 +2970,7 @@ def __getitem__(self, key):
         else:
             if is_iterator(key):
                 key = list(key)
-            indexer = self.loc._convert_to_indexer(key, axis=1, raise_missing=True)
+            indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
 
         # take() does not accept boolean indexers
         if getattr(indexer, "dtype", None) == bool:
@@ -3456,7 +3456,7 @@ def __setitem__(self, key, value):
 
     def _setitem_slice(self, key, value):
         self._check_setitem_copy()
-        self.loc._setitem_with_indexer(key, value)
+        self.loc[key] = value
 
     def _setitem_array(self, key, value):
         # also raises Exception if object array with NA values
@@ -3476,7 +3476,9 @@ def _setitem_array(self, key, value):
                 for k1, k2 in zip(key, value.columns):
                     self[k1] = value[k2]
             else:
-                indexer = self.loc._convert_to_indexer(key, axis=1)
+                indexer = self.loc._get_listlike_indexer(
+                    key, axis=1, raise_missing=False
+                )[1]
                 self._check_setitem_copy()
                 self.loc._setitem_with_indexer((slice(None), indexer), value)
 

pandas/core/indexing.py

@@ -223,7 +223,7 @@ def _validate_key(self, key, axis: int):
         """
         raise AbstractMethodError(self)
 
-    def _has_valid_tuple(self, key):
+    def _has_valid_tuple(self, key: Tuple):
         """ check the key for valid keys across my indexer """
         for i, k in enumerate(key):
             if i >= self.obj.ndim:
@@ -236,7 +236,7 @@ def _has_valid_tuple(self, key):
                     "[{types}] types".format(types=self._valid_types)
                 )
 
-    def _is_nested_tuple_indexer(self, tup):
+    def _is_nested_tuple_indexer(self, tup: Tuple):
         if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
             return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
         return False
@@ -260,7 +260,7 @@ def _convert_tuple(self, key, is_setter: bool = False):
                 keyidx.append(idx)
         return tuple(keyidx)
 
-    def _convert_range(self, key, is_setter: bool = False):
+    def _convert_range(self, key: range, is_setter: bool = False):
         """ convert a range argument """
         return list(key)
 
@@ -270,7 +270,7 @@ def _convert_scalar_indexer(self, key, axis: int):
         # a scalar
         return ax._convert_scalar_indexer(key, kind=self.name)
 
-    def _convert_slice_indexer(self, key, axis: int):
+    def _convert_slice_indexer(self, key: slice, axis: int):
         # if we are accessing via lowered dim, use the last dim
         ax = self.obj._get_axis(min(axis, self.ndim - 1))
         return ax._convert_slice_indexer(key, kind=self.name)
@@ -484,7 +484,7 @@ def setter(item, v):
             if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
 
                 # we have an equal len Frame
-                if isinstance(value, ABCDataFrame) and value.ndim > 1:
+                if isinstance(value, ABCDataFrame):
                     sub_indexer = list(indexer)
                     multiindex_indexer = isinstance(labels, MultiIndex)
 
@@ -638,27 +638,23 @@ def _setitem_with_indexer_missing(self, indexer, value):
             self.obj._maybe_update_cacher(clear=True)
             return self.obj
 
-    def _align_series(self, indexer, ser, multiindex_indexer=False):
+    def _align_series(self, indexer, ser: ABCSeries, multiindex_indexer: bool = False):
         """
         Parameters
         ----------
         indexer : tuple, slice, scalar
             The indexer used to get the locations that will be set to
             `ser`
-
         ser : pd.Series
             The values to assign to the locations specified by `indexer`
-
         multiindex_indexer : boolean, optional
             Defaults to False. Should be set to True if `indexer` was from
             a `pd.MultiIndex`, to avoid unnecessary broadcasting.
 
-
         Returns
         -------
         `np.array` of `ser` broadcast to the appropriate shape for assignment
         to the locations selected by `indexer`
-
         """
         if isinstance(indexer, (slice, np.ndarray, list, Index)):
             indexer = tuple([indexer])
@@ -734,7 +730,7 @@ def ravel(i):
 
         raise ValueError("Incompatible indexer with Series")
 
-    def _align_frame(self, indexer, df):
+    def _align_frame(self, indexer, df: ABCDataFrame):
         is_frame = self.obj.ndim == 2
 
         if isinstance(indexer, tuple):
@@ -786,7 +782,7 @@ def _align_frame(self, indexer, df):
 
         raise ValueError("Incompatible indexer with DataFrame")
 
-    def _getitem_tuple(self, tup):
+    def _getitem_tuple(self, tup: Tuple):
         try:
             return self._getitem_lowerdim(tup)
         except IndexingError:
@@ -809,7 +805,7 @@ def _getitem_tuple(self, tup):
 
         return retval
 
-    def _multi_take_opportunity(self, tup):
+    def _multi_take_opportunity(self, tup: Tuple):
         """
         Check whether there is the possibility to use ``_multi_take``.
         Currently the limit is that all axes being indexed must be indexed with
@@ -833,7 +829,7 @@ def _multi_take_opportunity(self, tup):
 
         return True
 
-    def _multi_take(self, tup):
+    def _multi_take(self, tup: Tuple):
         """
         Create the indexers for the passed tuple of keys, and execute the take
         operation. This allows the take operation to be executed all at once -
@@ -859,7 +855,7 @@ def _multi_take(self, tup):
     def _convert_for_reindex(self, key, axis: int):
         return key
 
-    def _handle_lowerdim_multi_index_axis0(self, tup):
+    def _handle_lowerdim_multi_index_axis0(self, tup: Tuple):
         # we have an axis0 multi-index, handle or raise
         axis = self.axis or 0
         try:
@@ -884,7 +880,7 @@ def _handle_lowerdim_multi_index_axis0(self, tup):
 
         return None
 
-    def _getitem_lowerdim(self, tup):
+    def _getitem_lowerdim(self, tup: Tuple):
 
         # we can directly get the axis result since the axis is specified
         if self.axis is not None:
@@ -948,7 +944,7 @@ def _getitem_lowerdim(self, tup):
 
         raise IndexingError("not applicable")
 
-    def _getitem_nested_tuple(self, tup):
+    def _getitem_nested_tuple(self, tup: Tuple):
         # we have a nested tuple so have at least 1 multi-index level
         # we should be able to match up the dimensionality here
 
@@ -1422,7 +1418,7 @@ def _getbool_axis(self, key, axis: int):
         # caller is responsible for ensuring non-None axis
         labels = self.obj._get_axis(axis)
         key = check_bool_indexer(labels, key)
-        inds, = key.nonzero()
+        inds = key.nonzero()[0]
         try:
             return self.obj.take(inds, axis=axis)
         except Exception as detail:
@@ -2037,7 +2033,7 @@ def _getitem_scalar(self, key):
         values = self.obj._get_value(*key, takeable=True)
         return values
 
-    def _validate_integer(self, key, axis):
+    def _validate_integer(self, key: int, axis: int):
         """
         Check that 'key' is a valid position in the desired axis.
 
@@ -2062,7 +2058,7 @@ def _validate_integer(self, key, axis):
         if key >= len_axis or key < -len_axis:
             raise IndexError("single positional indexer is out-of-bounds")
 
-    def _getitem_tuple(self, tup):
+    def _getitem_tuple(self, tup: Tuple):
 
         self._has_valid_tuple(tup)
         try:
@@ -2160,7 +2156,7 @@ class _ScalarAccessIndexer(_NDFrameIndexer):
     """ access scalars quickly """
 
     def _convert_key(self, key, is_setter: bool = False):
-        return list(key)
+        raise AbstractMethodError(self)
 
     def __getitem__(self, key):
         if not isinstance(key, tuple):

pandas/io/pytables.py

@@ -3992,7 +3992,7 @@ def process_filter(field, filt):
                                 filt = filt.union(Index(self.levels))
 
                             takers = op(axis_values, filt)
-                            return obj.loc._getitem_axis(takers, axis=axis_number)
+                            return obj.loc(axis=axis_number)[takers]
 
                         # this might be the name of a file IN an axis
                         elif field in axis_values:
@@ -4005,7 +4005,7 @@ def process_filter(field, filt):
                             if isinstance(obj, DataFrame):
                                 axis_number = 1 - axis_number
                             takers = op(values, filt)
-                            return obj.loc._getitem_axis(takers, axis=axis_number)
+                            return obj.loc(axis=axis_number)[takers]
 
                     raise ValueError(
                         "cannot find the field [{field}] for "