CLN: Avoid unnecessary values_from_object

pandas/_libs/lib.pyx

@@ -100,11 +100,9 @@ def values_from_object(obj: object):
     """
     func: object
 
-    if getattr(obj, '_typ', '') == 'dataframe':
-        return obj.values
-
     func = getattr(obj, '_internal_get_values', None)
     if func is not None:
+        # Includes DataFrame, for which we get frame.values
         obj = func()
 
     return obj

pandas/core/common.py

@@ -122,7 +122,7 @@ def is_bool_indexer(key: Any) -> bool:
         is_array_like(key) and is_extension_array_dtype(key.dtype)
     ):
         if key.dtype == np.object_:
-            key = np.asarray(values_from_object(key))
+            key = np.asarray(key)
 
             if not lib.is_bool_array(key):
                 na_msg = "Cannot mask with non-boolean array containing NA / NaN values"

pandas/core/dtypes/missing.py

@@ -581,7 +581,7 @@ def remove_na_arraylike(arr):
     if is_extension_array_dtype(arr):
         return arr[notna(arr)]
     else:
-        return arr[notna(lib.values_from_object(arr))]
+        return arr[notna(np.asarray(arr))]
 
 
 def is_valid_nat_for_dtype(obj, dtype: DtypeObj) -> bool:

pandas/core/reshape/merge.py

@@ -1923,9 +1923,6 @@ def _factorize_keys(lk, rk, sort=True):
 
 
 def _sort_labels(uniques: np.ndarray, left, right):
-    if not isinstance(uniques, np.ndarray):
-        # tuplesafe
-        uniques = Index(uniques).values
 
     llength = len(left)
     labels = np.concatenate([left, right])

pandas/core/strings.py

@@ -36,7 +36,6 @@
 
 from pandas.core.algorithms import take_1d
 from pandas.core.base import NoNewAttributesMixin
-import pandas.core.common as com
 from pandas.core.construction import extract_array
 
 if TYPE_CHECKING:
@@ -782,7 +781,7 @@ def rep(x, r):
                 return str.__mul__(x, r)
 
         repeats = np.asarray(repeats, dtype=object)
-        result = libops.vec_binop(com.values_from_object(arr), repeats, rep)
+        result = libops.vec_binop(np.asarray(arr), repeats, rep)
         return result