.values->._values

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -899,7 +899,7 @@ def value_counts(self, dropna=False):
         index = Index(
             cls(result.index.view("i8"), dtype=self.dtype), name=result.index.name
         )
-        return Series(result.values, index=index, name=result.name)
+        return Series(result._values, index=index, name=result.name)
 
     def map(self, mapper):
         # TODO(GH-23179): Add ExtensionArray.map

pandas/core/arrays/interval.py

@@ -153,7 +153,7 @@ class IntervalArray(IntervalMixin, ExtensionArray):
     def __new__(cls, data, closed=None, dtype=None, copy=False, verify_integrity=True):
 
         if isinstance(data, ABCSeries) and is_interval_dtype(data):
-            data = data.values
+            data = data._values
 
         if isinstance(data, (cls, ABCIntervalIndex)):
             left = data.left

pandas/core/arrays/masked.py

@@ -244,11 +244,11 @@ def value_counts(self, dropna: bool = True) -> "Series":
         # TODO(extension)
         # if we have allow Index to hold an ExtensionArray
         # this is easier
-        index = value_counts.index.values.astype(object)
+        index = value_counts.index._values.astype(object)
 
         # if we want nans, count the mask
         if dropna:
-            counts = value_counts.values
+            counts = value_counts._values
         else:
             counts = np.empty(len(value_counts) + 1, dtype="int64")
             counts[:-1] = value_counts

pandas/core/ops/array_ops.py

@@ -50,7 +50,7 @@ def comp_method_OBJECT_ARRAY(op, x, y):
             y = y.astype(np.object_)
 
         if isinstance(y, (ABCSeries, ABCIndex)):
-            y = y.values
+            y = y._values
 
         if x.shape != y.shape:
             raise ValueError("Shapes must match", x.shape, y.shape)

pandas/core/reshape/melt.py

@@ -110,7 +110,7 @@ def melt(
 
     mcolumns = id_vars + var_name + [value_name]
 
-    mdata[value_name] = frame.values.ravel("F")
+    mdata[value_name] = frame._values.ravel("F")
     for i, col in enumerate(var_name):
         # asanyarray will keep the columns as an Index
         mdata[col] = np.asanyarray(frame.columns._get_level_values(i)).repeat(N)

pandas/core/reshape/reshape.py

@@ -541,9 +541,9 @@ def factorize(index):
         )
 
     if frame._is_homogeneous_type:
-        # For homogeneous EAs, frame.values will coerce to object. So
+        # For homogeneous EAs, frame._values will coerce to object. So
         # we concatenate instead.
-        dtypes = list(frame.dtypes.values)
+        dtypes = list(frame.dtypes._values)
         dtype = dtypes[0]
 
         if is_extension_array_dtype(dtype):
@@ -554,11 +554,11 @@ def factorize(index):
             new_values = _reorder_for_extension_array_stack(new_values, N, K)
         else:
             # homogeneous, non-EA
-            new_values = frame.values.ravel()
+            new_values = frame._values.ravel()
 
     else:
         # non-homogeneous
-        new_values = frame.values.ravel()
+        new_values = frame._values.ravel()
 
     if dropna:
         mask = notna(new_values)

pandas/core/window/common.py

@@ -296,7 +296,7 @@ def zsqrt(x):
         mask = x < 0
 
     if isinstance(x, ABCDataFrame):
-        if mask.values.any():
+        if mask._values.any():
             result[mask] = 0
     else:
         if mask.any():