deprivatize

Author: jschendel

pandas/core/arrays/categorical.py

@@ -294,7 +294,7 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
 
         if fastpath:
             self._codes = coerce_indexer_dtype(values, categories)
-            self._dtype = self._dtype._update_dtype(dtype)
+            self._dtype = self._dtype.update_dtype(dtype)
             return
 
         # null_mask indicates missing values we want to exclude from inference.
@@ -358,7 +358,7 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
             full_codes[~null_mask] = codes
             codes = full_codes
 
-        self._dtype = self._dtype._update_dtype(dtype)
+        self._dtype = self._dtype.update_dtype(dtype)
         self._codes = coerce_indexer_dtype(codes, dtype.categories)
 
     @property
@@ -438,7 +438,7 @@ def astype(self, dtype, copy=True):
         """
         if is_categorical_dtype(dtype):
             # GH 10696/18593
-            dtype = self.dtype._update_dtype(dtype)
+            dtype = self.dtype.update_dtype(dtype)
             self = self.copy() if copy else self
             if dtype == self.dtype:
                 return self
@@ -560,7 +560,7 @@ def from_codes(cls, codes, categories, ordered=False):
             raise ValueError(
                 "codes need to be convertible to an arrays of integers")
 
-        categories = CategoricalDtype._validate_categories(categories)
+        categories = CategoricalDtype.validate_categories(categories)
 
         if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
             raise ValueError("codes need to be between -1 and "
@@ -1165,7 +1165,7 @@ def __setstate__(self, state):
 
         # Provide compatibility with pre-0.15.0 Categoricals.
         if '_categories' not in state and '_levels' in state:
-            state['_categories'] = self.dtype._validate_categories(state.pop(
+            state['_categories'] = self.dtype.validate_categories(state.pop(
                 '_levels'))
         if '_codes' not in state and 'labels' in state:
             state['_codes'] = coerce_indexer_dtype(

pandas/core/dtypes/dtypes.py

@@ -181,11 +181,11 @@ def _from_categorical_dtype(cls, dtype, categories=None, ordered=None):
     def _finalize(self, categories, ordered, fastpath=False):
 
         if ordered is not None:
-            self._validate_ordered(ordered)
+            self.validate_ordered(ordered)
 
         if categories is not None:
-            categories = self._validate_categories(categories,
-                                                   fastpath=fastpath)
+            categories = self.validate_categories(categories,
+                                                  fastpath=fastpath)
 
         self._categories = categories
         self._ordered = ordered
@@ -286,7 +286,7 @@ def construct_from_string(cls, string):
         raise TypeError("cannot construct a CategoricalDtype")
 
     @staticmethod
-    def _validate_ordered(ordered):
+    def validate_ordered(ordered):
         """
         Validates that we have a valid ordered parameter. If
         it is not a boolean, a TypeError will be raised.
@@ -306,7 +306,7 @@ def _validate_ordered(ordered):
             raise TypeError("'ordered' must either be 'True' or 'False'")
 
     @staticmethod
-    def _validate_categories(categories, fastpath=False):
+    def validate_categories(categories, fastpath=False):
         """
         Validates that we have good categories
 
@@ -338,7 +338,7 @@ def _validate_categories(categories, fastpath=False):
 
         return categories
 
-    def _update_dtype(self, dtype):
+    def update_dtype(self, dtype):
         """
         Returns a CategoricalDtype with categories and ordered taken from dtype
         if specified, otherwise falling back to self if unspecified

pandas/core/indexes/category.py

@@ -344,7 +344,7 @@ def astype(self, dtype, copy=True):
             return IntervalIndex(np.array(self))
         elif is_categorical_dtype(dtype):
             # GH 18630
-            dtype = self.dtype._update_dtype(dtype)
+            dtype = self.dtype.update_dtype(dtype)
             if dtype == self.dtype:
                 return self.copy() if copy else self
 

pandas/tests/dtypes/test_dtypes.py

@@ -739,15 +739,15 @@ def test_update_dtype(self, ordered, new_categories, new_ordered):
         if expected_ordered is None:
             expected_ordered = dtype.ordered
 
-        result = dtype._update_dtype(new_dtype)
+        result = dtype.update_dtype(new_dtype)
         tm.assert_index_equal(result.categories, expected_categories)
         assert result.ordered is expected_ordered
 
     def test_update_dtype_string(self, ordered):
         dtype = CategoricalDtype(list('abc'), ordered)
         expected_categories = dtype.categories
         expected_ordered = dtype.ordered
-        result = dtype._update_dtype('category')
+        result = dtype.update_dtype('category')
         tm.assert_index_equal(result.categories, expected_categories)
         assert result.ordered is expected_ordered
 
@@ -757,7 +757,7 @@ def test_update_dtype_errors(self, bad_dtype):
         dtype = CategoricalDtype(list('abc'), False)
         msg = 'a CategoricalDtype must be passed to perform an update, '
         with tm.assert_raises_regex(ValueError, msg):
-            dtype._update_dtype(bad_dtype)
+            dtype.update_dtype(bad_dtype)
 
 
 class DummyArray(ExtensionArray):