CLN: Collapse private ._take implementation into the public take method

pandas/core/frame.py

@@ -2937,7 +2937,7 @@ def _ixs(self, i, axis=0):
                 return self.loc[:, lab_slice]
             else:
                 if isinstance(label, Index):
-                    return self._take(i, axis=1)
+                    return self.take(i, axis=1)
 
                 index_len = len(self.index)
 
@@ -2999,7 +2999,7 @@ def __getitem__(self, key):
         if getattr(indexer, "dtype", None) == bool:
             indexer = np.where(indexer)[0]
 
-        data = self._take(indexer, axis=1)
+        data = self.take(indexer, axis=1)
 
         if is_single_key:
             # What does looking for a single key in a non-unique index return?
@@ -3032,7 +3032,7 @@ def _getitem_bool_array(self, key):
         # be reindexed to match DataFrame rows
         key = check_bool_indexer(self.index, key)
         indexer = key.nonzero()[0]
-        return self._take(indexer, axis=0)
+        return self.take(indexer, axis=0)
 
     def _getitem_multilevel(self, key):
         loc = self.columns.get_loc(key)

pandas/core/generic.py

@@ -3294,7 +3294,7 @@ def _iget_item_cache(self, item):
         if ax.is_unique:
             lower = self._get_item_cache(ax[item])
         else:
-            lower = self._take(item, axis=self._info_axis_number)
+            lower = self.take(item, axis=self._info_axis_number)
         return lower
 
     def _box_item_values(self, key, values):
@@ -3522,52 +3522,6 @@ def __delitem__(self, key):
         except KeyError:
             pass
 
-    def _take(self, indices, axis=0, is_copy=True):
-        """
-        Return the elements in the given *positional* indices along an axis.
-
-        This means that we are not indexing according to actual values in
-        the index attribute of the object. We are indexing according to the
-        actual position of the element in the object.
-
-        This is the internal version of ``.take()`` and will contain a wider
-        selection of parameters useful for internal use but not as suitable
-        for public usage.
-
-        Parameters
-        ----------
-        indices : array-like
-            An array of ints indicating which positions to take.
-        axis : int, default 0
-            The axis on which to select elements. "0" means that we are
-            selecting rows, "1" means that we are selecting columns, etc.
-        is_copy : bool, default True
-            Whether to return a copy of the original object or not.
-
-        Returns
-        -------
-        taken : same type as caller
-            An array-like containing the elements taken from the object.
-
-        See Also
-        --------
-        numpy.ndarray.take
-        numpy.take
-        """
-        self._consolidate_inplace()
-
-        new_data = self._data.take(
-            indices, axis=self._get_block_manager_axis(axis), verify=True
-        )
-        result = self._constructor(new_data).__finalize__(self)
-
-        # Maybe set copy if we didn't actually change the index.
-        if is_copy:
-            if not result._get_axis(axis).equals(self._get_axis(axis)):
-                result._set_is_copy(self)
-
-        return result
-
     def take(self, indices, axis=0, is_copy=True, **kwargs):
         """
         Return the elements in the given *positional* indices along an axis.
@@ -3644,7 +3598,20 @@ class  max_speed
         3    lion  mammal       80.5
         """
         nv.validate_take(tuple(), kwargs)
-        return self._take(indices, axis=axis, is_copy=is_copy)
+
+        self._consolidate_inplace()
+
+        new_data = self._data.take(
+            indices, axis=self._get_block_manager_axis(axis), verify=True
+        )
+        result = self._constructor(new_data).__finalize__(self)
+
+        # Maybe set copy if we didn't actually change the index.
+        if is_copy:
+            if not result._get_axis(axis).equals(self._get_axis(axis)):
+                result._set_is_copy(self)
+
+        return result
 
     def xs(self, key, axis=0, level=None, drop_level=True):
         """
@@ -3773,9 +3740,9 @@ class   animal   locomotion
             if isinstance(loc, np.ndarray):
                 if loc.dtype == np.bool_:
                     inds, = loc.nonzero()
-                    return self._take(inds, axis=axis)
+                    return self.take(inds, axis=axis)
                 else:
-                    return self._take(loc, axis=axis)
+                    return self.take(loc, axis=axis)
 
             if not is_scalar(loc):
                 new_index = self.index[loc]
@@ -8091,7 +8058,7 @@ def at_time(self, time, asof=False, axis=None):
         except AttributeError:
             raise TypeError("Index must be DatetimeIndex")
 
-        return self._take(indexer, axis=axis)
+        return self.take(indexer, axis=axis)
 
     def between_time(
         self, start_time, end_time, include_start=True, include_end=True, axis=None
@@ -8168,7 +8135,7 @@ def between_time(
         except AttributeError:
             raise TypeError("Index must be DatetimeIndex")
 
-        return self._take(indexer, axis=axis)
+        return self.take(indexer, axis=axis)
 
     def resample(
         self,

pandas/core/groupby/groupby.py

@@ -679,7 +679,7 @@ def get_group(self, name, obj=None):
         if not len(inds):
             raise KeyError(name)
 
-        return obj._take(inds, axis=self.axis)
+        return obj.take(inds, axis=self.axis)
 
     def __iter__(self):
         """

pandas/core/groupby/grouper.py

@@ -194,7 +194,7 @@ def _set_grouper(self, obj, sort=False):
             # use stable sort to support first, last, nth
             indexer = self.indexer = ax.argsort(kind="mergesort")
             ax = ax.take(indexer)
-            obj = obj._take(indexer, axis=self.axis, is_copy=False)
+            obj = obj.take(indexer, axis=self.axis, is_copy=False)
 
         self.obj = obj
         self.grouper = ax

pandas/core/groupby/ops.py

@@ -675,7 +675,7 @@ def _aggregate_series_fast(self, obj, func):
         # avoids object / Series creation overhead
         dummy = obj._get_values(slice(None, 0))
         indexer = get_group_index_sorter(group_index, ngroups)
-        obj = obj._take(indexer)
+        obj = obj.take(indexer)
         group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
         grouper = reduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
         result, counts = grouper.get_result()
@@ -915,7 +915,7 @@ def __iter__(self):
             yield i, self._chop(sdata, slice(start, end))
 
     def _get_sorted_data(self):
-        return self.data._take(self.sort_idx, axis=self.axis)
+        return self.data.take(self.sort_idx, axis=self.axis)
 
     def _chop(self, sdata, slice_obj):
         return sdata.iloc[slice_obj]

pandas/core/indexing.py

@@ -1137,7 +1137,7 @@ def _getitem_iterable(self, key, axis: int):
             # A boolean indexer
             key = check_bool_indexer(labels, key)
             inds, = key.nonzero()
-            return self.obj._take(inds, axis=axis)
+            return self.obj.take(inds, axis=axis)
         else:
             # A collection of keys
             keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
@@ -1448,7 +1448,7 @@ def _getbool_axis(self, key, axis: int):
         key = check_bool_indexer(labels, key)
         inds, = key.nonzero()
         try:
-            return self.obj._take(inds, axis=axis)
+            return self.obj.take(inds, axis=axis)
         except Exception as detail:
             raise self._exception(detail)
 
@@ -1469,7 +1469,7 @@ def _get_slice_axis(self, slice_obj: slice, axis: int):
         else:
             # DatetimeIndex overrides Index.slice_indexer and may
             #  return a DatetimeIndex instead of a slice object.
-            return self.obj._take(indexer, axis=axis)
+            return self.obj.take(indexer, axis=axis)
 
 
 class _LocIndexer(_LocationIndexer):
@@ -2138,7 +2138,7 @@ def _get_list_axis(self, key, axis: int):
         Series object
         """
         try:
-            return self.obj._take(key, axis=axis)
+            return self.obj.take(key, axis=axis)
         except IndexError:
             # re-raise with different error message
             raise IndexError("positional indexers are out-of-bounds")

pandas/core/series.py

@@ -4371,8 +4371,9 @@ def memory_usage(self, index=True, deep=False):
             v += self.index.memory_usage(deep=deep)
         return v
 
-    @Appender(generic.NDFrame._take.__doc__)
-    def _take(self, indices, axis=0, is_copy=False):
+    @Appender(generic.NDFrame.take.__doc__)
+    def take(self, indices, axis=0, is_copy=False, **kwargs):
+        nv.validate_take(tuple(), kwargs)
 
         indices = ensure_platform_int(indices)
         new_index = self.index.take(indices)