CLN: .values -> ._values (#34083)

Author: jbrockmendel

pandas/core/arrays/interval.py

@@ -27,7 +27,6 @@
 from pandas.core.dtypes.dtypes import IntervalDtype
 from pandas.core.dtypes.generic import (
     ABCDatetimeIndex,
-    ABCExtensionArray,
     ABCIndexClass,
     ABCIntervalIndex,
     ABCPeriodIndex,
@@ -767,7 +766,7 @@ def size(self) -> int:
         # Avoid materializing self.values
         return self.left.size
 
-    def shift(self, periods: int = 1, fill_value: object = None) -> ABCExtensionArray:
+    def shift(self, periods: int = 1, fill_value: object = None) -> "IntervalArray":
         if not len(self) or periods == 0:
             return self.copy()
 

pandas/core/dtypes/cast.py

@@ -103,9 +103,9 @@ def is_nested_object(obj) -> bool:
     This may not be necessarily be performant.
 
     """
-    if isinstance(obj, ABCSeries) and is_object_dtype(obj):
+    if isinstance(obj, ABCSeries) and is_object_dtype(obj.dtype):
 
-        if any(isinstance(v, ABCSeries) for v in obj.values):
+        if any(isinstance(v, ABCSeries) for v in obj._values):
             return True
 
     return False

pandas/core/frame.py

@@ -1160,7 +1160,7 @@ def dot(self, other):
             left = self.reindex(columns=common, copy=False)
             right = other.reindex(index=common, copy=False)
             lvals = left.values
-            rvals = right.values
+            rvals = right._values
         else:
             left = self
             lvals = self.values
@@ -1892,7 +1892,7 @@ def to_records(
         if index:
             if isinstance(self.index, MultiIndex):
                 # array of tuples to numpy cols. copy copy copy
-                ix_vals = list(map(np.array, zip(*self.index.values)))
+                ix_vals = list(map(np.array, zip(*self.index._values)))
             else:
                 ix_vals = [self.index.values]
 
@@ -3010,7 +3010,7 @@ def _setitem_frame(self, key, value):
                 raise ValueError("Array conditional must be same shape as self")
             key = self._constructor(key, **self._construct_axes_dict())
 
-        if key.values.size and not is_bool_dtype(key.values):
+        if key.size and not is_bool_dtype(key.values):
             raise TypeError(
                 "Must pass DataFrame or 2-d ndarray with boolean values only"
             )
@@ -7452,7 +7452,7 @@ def applymap(self, func) -> "DataFrame":
         def infer(x):
             if x.empty:
                 return lib.map_infer(x, func)
-            return lib.map_infer(x.astype(object).values, func)
+            return lib.map_infer(x.astype(object)._values, func)
 
         return self.apply(infer)
 

pandas/core/generic.py

@@ -4983,7 +4983,7 @@ def sample(
                 else:
                     raise ValueError("Invalid weights: weights sum to zero")
 
-            weights = weights.values
+            weights = weights._values
 
         # If no frac or n, default to n=1.
         if n is None and frac is None:

pandas/core/groupby/groupby.py

@@ -1970,7 +1970,7 @@ def nth(self, n: Union[int, List[int]], dropna: Optional[str] = None) -> DataFra
 
         grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
         sizes, result = grb.size(), grb.nth(n)
-        mask = (sizes < max_len).values
+        mask = (sizes < max_len)._values
 
         # set the results which don't meet the criteria
         if len(result) and mask.any():

pandas/core/indexes/base.py

@@ -639,13 +639,15 @@ def astype(self, dtype, copy=True):
         elif is_categorical_dtype(dtype):
             from pandas.core.indexes.category import CategoricalIndex
 
-            return CategoricalIndex(self.values, name=self.name, dtype=dtype, copy=copy)
+            return CategoricalIndex(
+                self._values, name=self.name, dtype=dtype, copy=copy
+            )
 
         elif is_extension_array_dtype(dtype):
             return Index(np.asarray(self), name=self.name, dtype=dtype, copy=copy)
 
         try:
-            casted = self.values.astype(dtype, copy=copy)
+            casted = self._values.astype(dtype, copy=copy)
         except (TypeError, ValueError) as err:
             raise TypeError(
                 f"Cannot cast {type(self).__name__} to dtype {dtype}"
@@ -906,7 +908,7 @@ def format(self, name: bool = False, formatter=None, **kwargs):
         return self._format_with_header(header, **kwargs)
 
     def _format_with_header(self, header, na_rep="NaN", **kwargs):
-        values = self.values
+        values = self._values
 
         from pandas.io.formats.format import format_array
 

pandas/core/indexes/datetimes.py

@@ -440,7 +440,7 @@ def to_series(self, keep_tz=lib.no_default, index=None, name=None):
             # preserve the tz & copy
             values = self.copy(deep=True)
         else:
-            values = self.values.copy()
+            values = self._values.view("M8[ns]").copy()
 
         return Series(values, index=index, name=name)
 

pandas/core/indexes/multi.py

@@ -1464,7 +1464,7 @@ def is_monotonic_increasing(self) -> bool:
 
         # reversed() because lexsort() wants the most significant key last.
         values = [
-            self._get_level_values(i).values for i in reversed(range(len(self.levels)))
+            self._get_level_values(i)._values for i in reversed(range(len(self.levels)))
         ]
         try:
             sort_order = np.lexsort(values)
@@ -2455,7 +2455,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None):
                     "tolerance not implemented yet for MultiIndex"
                 )
             indexer = self._engine.get_indexer(
-                values=self.values, target=target, method=method, limit=limit
+                values=self._values, target=target, method=method, limit=limit
             )
         elif method == "nearest":
             raise NotImplementedError(

pandas/core/internals/construction.py

@@ -347,7 +347,7 @@ def _homogenize(data, index, dtype: Optional[DtypeObj]):
                     val = com.dict_compat(val)
                 else:
                     val = dict(val)
-                val = lib.fast_multiget(val, oindex.values, default=np.nan)
+                val = lib.fast_multiget(val, oindex._values, default=np.nan)
             val = sanitize_array(
                 val, index, dtype=dtype, copy=False, raise_cast_failure=False
             )

pandas/core/internals/managers.py

@@ -475,7 +475,7 @@ def get_axe(block, qs, axes):
                     b.mgr_locs = sb.mgr_locs
 
             else:
-                new_axes[axis] = Index(np.concatenate([ax.values for ax in axes]))
+                new_axes[axis] = Index(np.concatenate([ax._values for ax in axes]))
 
             if transposed:
                 new_axes = new_axes[::-1]

pandas/plotting/_matplotlib/core.py

@@ -247,7 +247,7 @@ def _iter_data(self, data=None, keep_index=False, fillna=None):
                 yield col, values.values
 
     @property
-    def nseries(self):
+    def nseries(self) -> int:
         if self.data.ndim == 1:
             return 1
         else: