API: no_default

doc/source/reference/extensions.rst

@@ -69,6 +69,6 @@ behaves correctly.
   api.indexers.check_bool_array_indexer
 
 
-The sentinel ``pandas.api.extensions._no_default`` is used as the default
+The sentinel ``pandas.api.extensions.no_default`` is used as the default
 value in some methods. Use an ``is`` comparison to check if the user
 provides a non-default value.

pandas/_libs/lib.pyx

@@ -2232,14 +2232,14 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=0,
     return objects
 
 
-# Note: _no_default is exported to the public API in pandas.api.extensions
-_no_default = object()  #: Sentinel indicating the default value.
+# Note: no_default is exported to the public API in pandas.api.extensions
+no_default = object()  #: Sentinel indicating the default value.
 
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=1,
-                   object na_value=_no_default, object dtype=object):
+                   object na_value=no_default, object dtype=object):
     """
     Substitute for np.vectorize with pandas-friendly dtype inference.
 
@@ -2270,7 +2270,7 @@ def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=1,
     result = np.empty(n, dtype=dtype)
     for i in range(n):
         if mask[i]:
-            if na_value is _no_default:
+            if na_value is no_default:
                 val = arr[i]
             else:
                 val = na_value

pandas/api/extensions/__init__.py

@@ -1,5 +1,5 @@
 """Public API for extending pandas objects."""
-from pandas._libs.lib import _no_default  # noqa: F401
+from pandas._libs.lib import no_default  # noqa: F401
 
 from pandas.core.dtypes.dtypes import (  # noqa: F401
     ExtensionDtype,

pandas/core/arrays/base.py

@@ -351,7 +351,7 @@ def __iter__(self):
         for i in range(len(self)):
             yield self[i]
 
-    def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default):
+    def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default):
         """
         Convert to a NumPy ndarray.
 
@@ -378,9 +378,9 @@ def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default):
         numpy.ndarray
         """
         result = np.asarray(self, dtype=dtype)
-        if copy or na_value is not lib._no_default:
+        if copy or na_value is not lib.no_default:
             result = result.copy()
-        if na_value is not lib._no_default:
+        if na_value is not lib.no_default:
             result[self.isna()] = na_value
         return result
 

pandas/core/arrays/boolean.py

@@ -317,7 +317,7 @@ def __getitem__(self, item):
         return type(self)(self._data[item], self._mask[item])
 
     def to_numpy(
-        self, dtype=None, copy=False, na_value: "Scalar" = lib._no_default,
+        self, dtype=None, copy=False, na_value: "Scalar" = lib.no_default,
     ):
         """
         Convert to a NumPy Array.
@@ -377,7 +377,7 @@ def to_numpy(
         >>> a.to_numpy(dtype="bool", na_value=False)
         array([ True, False, False])
         """
-        if na_value is lib._no_default:
+        if na_value is lib.no_default:
             na_value = libmissing.NA
         if dtype is None:
             dtype = object

pandas/core/arrays/integer.py

@@ -376,16 +376,16 @@ def __getitem__(self, item):
 
         return type(self)(self._data[item], self._mask[item])
 
-    def _coerce_to_ndarray(self, dtype=None, na_value=lib._no_default):
+    def _coerce_to_ndarray(self, dtype=None, na_value=lib.no_default):
         """
         coerce to an ndarary of object dtype
         """
         if dtype is None:
             dtype = object
 
-        if na_value is lib._no_default and is_float_dtype(dtype):
+        if na_value is lib.no_default and is_float_dtype(dtype):
             na_value = np.nan
-        elif na_value is lib._no_default:
+        elif na_value is lib.no_default:
             na_value = libmissing.NA
 
         if is_integer_dtype(dtype):

pandas/core/arrays/numpy_.py

@@ -421,13 +421,13 @@ def skew(self, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
 
     # ------------------------------------------------------------------------
     # Additional Methods
-    def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default):
+    def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default):
         result = np.asarray(self._ndarray, dtype=dtype)
 
-        if (copy or na_value is not lib._no_default) and result is self._ndarray:
+        if (copy or na_value is not lib.no_default) and result is self._ndarray:
             result = result.copy()
 
-        if na_value is not lib._no_default:
+        if na_value is not lib.no_default:
             result[self.isna()] = na_value
 
         return result

pandas/core/base.py

@@ -768,7 +768,7 @@ def array(self) -> ExtensionArray:
 
         return result
 
-    def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default, **kwargs):
+    def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default, **kwargs):
         """
         A NumPy ndarray representing the values in this Series or Index.
 
@@ -874,9 +874,9 @@ def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default, **kwargs):
 
         result = np.asarray(self._values, dtype=dtype)
         # TODO(GH-24345): Avoid potential double copy
-        if copy or na_value is not lib._no_default:
+        if copy or na_value is not lib.no_default:
             result = result.copy()
-            if na_value is not lib._no_default:
+            if na_value is not lib.no_default:
                 result[self.isna()] = na_value
         return result
 

pandas/core/computation/eval.py

@@ -8,7 +8,7 @@
 from typing import Optional
 import warnings
 
-from pandas._libs.lib import _no_default
+from pandas._libs.lib import no_default
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.computation.engines import _engines
@@ -171,7 +171,7 @@ def eval(
     expr,
     parser="pandas",
     engine: Optional[str] = None,
-    truediv=_no_default,
+    truediv=no_default,
     local_dict=None,
     global_dict=None,
     resolvers=(),
@@ -288,7 +288,7 @@ def eval(
 
     inplace = validate_bool_kwarg(inplace, "inplace")
 
-    if truediv is not _no_default:
+    if truediv is not no_default:
         warnings.warn(
             "The `truediv` parameter in pd.eval is deprecated and will be "
             "removed in a future version.",

pandas/core/generic.py

@@ -106,10 +106,6 @@
             Name or list of names to sort by""",
 )
 
-# sentinel value to use as kwarg in place of None when None has special meaning
-# and needs to be distinguished from a user explicitly passing None.
-sentinel = object()
-
 
 def _single_replace(self, to_replace, method, inplace, limit):
     """
@@ -1086,7 +1082,7 @@ def rename(self, *args, **kwargs):
             return result.__finalize__(self)
 
     @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
-    def rename_axis(self, mapper=sentinel, **kwargs):
+    def rename_axis(self, mapper=lib.no_default, **kwargs):
         """
         Set the name of the axis for the index or columns.
 
@@ -1211,7 +1207,7 @@ class  name
                monkey         2         2
         """
         axes, kwargs = self._construct_axes_from_arguments(
-            (), kwargs, sentinel=sentinel
+            (), kwargs, sentinel=lib.no_default
         )
         copy = kwargs.pop("copy", True)
         inplace = kwargs.pop("inplace", False)
@@ -1227,7 +1223,7 @@ class  name
 
         inplace = validate_bool_kwarg(inplace, "inplace")
 
-        if mapper is not sentinel:
+        if mapper is not lib.no_default:
             # Use v0.23 behavior if a scalar or list
             non_mapper = is_scalar(mapper) or (
                 is_list_like(mapper) and not is_dict_like(mapper)
@@ -1243,7 +1239,7 @@ class  name
 
             for axis in range(self._AXIS_LEN):
                 v = axes.get(self._AXIS_NAMES[axis])
-                if v is sentinel:
+                if v is lib.no_default:
                     continue
                 non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v))
                 if non_mapper:

pandas/core/indexes/datetimes.py

@@ -421,7 +421,7 @@ def _get_time_micros(self):
             values = self._data._local_timestamps()
         return fields.get_time_micros(values)
 
-    def to_series(self, keep_tz=lib._no_default, index=None, name=None):
+    def to_series(self, keep_tz=lib.no_default, index=None, name=None):
         """
         Create a Series with both index and values equal to the index keys
         useful with map for returning an indexer based on an index.
@@ -466,7 +466,7 @@ def to_series(self, keep_tz=lib._no_default, index=None, name=None):
         if name is None:
             name = self.name
 
-        if keep_tz is not lib._no_default:
+        if keep_tz is not lib.no_default:
             if keep_tz:
                 warnings.warn(
                     "The 'keep_tz' keyword in DatetimeIndex.to_series "

pandas/core/indexes/multi.py

@@ -61,8 +61,6 @@
     dict(klass="MultiIndex", target_klass="MultiIndex or list of tuples")
 )
 
-_no_default_names = object()
-
 
 class MultiIndexUIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.UInt64Engine):
     """
@@ -373,7 +371,7 @@ def _verify_integrity(
         return new_codes
 
     @classmethod
-    def from_arrays(cls, arrays, sortorder=None, names=_no_default_names):
+    def from_arrays(cls, arrays, sortorder=None, names=lib.no_default):
         """
         Convert arrays to MultiIndex.
 
@@ -427,7 +425,7 @@ def from_arrays(cls, arrays, sortorder=None, names=_no_default_names):
                 raise ValueError("all arrays must be same length")
 
         codes, levels = factorize_from_iterables(arrays)
-        if names is _no_default_names:
+        if names is lib.no_default:
             names = [getattr(arr, "name", None) for arr in arrays]
 
         return MultiIndex(
@@ -497,7 +495,7 @@ def from_tuples(cls, tuples, sortorder=None, names=None):
         return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
 
     @classmethod
-    def from_product(cls, iterables, sortorder=None, names=_no_default_names):
+    def from_product(cls, iterables, sortorder=None, names=lib.no_default):
         """
         Make a MultiIndex from the cartesian product of multiple iterables.
 
@@ -548,7 +546,7 @@ def from_product(cls, iterables, sortorder=None, names=_no_default_names):
             iterables = list(iterables)
 
         codes, levels = factorize_from_iterables(iterables)
-        if names is _no_default_names:
+        if names is lib.no_default:
             names = [getattr(it, "name", None) for it in iterables]
 
         codes = cartesian_product(codes)

pandas/io/formats/html.py

@@ -7,6 +7,8 @@
 
 from pandas._config import get_option
 
+from pandas._libs import lib
+
 from pandas.core.dtypes.generic import ABCMultiIndex
 
 from pandas import option_context
@@ -245,7 +247,7 @@ def _write_col_header(self, indent: int) -> None:
 
             if self.fmt.sparsify:
                 # GH3547
-                sentinel = object()
+                sentinel = lib.no_default
             else:
                 sentinel = False
             levels = self.columns.format(sparsify=sentinel, adjoin=False, names=False)
@@ -451,7 +453,7 @@ def _write_hierarchical_rows(
 
         if self.fmt.sparsify:
             # GH3547
-            sentinel = object()
+            sentinel = lib.no_default
             levels = frame.index.format(sparsify=sentinel, adjoin=False, names=False)
 
             level_lengths = get_level_lengths(levels, sentinel)

pandas/io/formats/style.py

@@ -15,6 +15,7 @@
 
 from pandas._config import get_option
 
+from pandas._libs import lib
 from pandas.compat._optional import import_optional_dependency
 from pandas.util._decorators import Appender
 
@@ -1475,8 +1476,7 @@ def _get_level_lengths(index, hidden_elements=None):
 
     Result is a dictionary of (level, initial_position): span
     """
-    sentinel = object()
-    levels = index.format(sparsify=sentinel, adjoin=False, names=False)
+    levels = index.format(sparsify=lib.no_default, adjoin=False, names=False)
 
     if hidden_elements is None:
         hidden_elements = []
@@ -1492,10 +1492,10 @@ def _get_level_lengths(index, hidden_elements=None):
         for j, row in enumerate(lvl):
             if not get_option("display.multi_sparse"):
                 lengths[(i, j)] = 1
-            elif (row != sentinel) and (j not in hidden_elements):
+            elif (row is not lib.no_default) and (j not in hidden_elements):
                 last_label = j
                 lengths[(i, last_label)] = 1
-            elif row != sentinel:
+            elif row is not lib.no_default:
                 # even if its hidden, keep track of it in case
                 # length >1 and later elements are visible
                 last_label = j

pandas/tests/extension/decimal/array.py

@@ -5,12 +5,10 @@
 
 import numpy as np
 
-from pandas._libs import lib
-
 from pandas.core.dtypes.base import ExtensionDtype
 
 import pandas as pd
-from pandas.api.extensions import register_extension_dtype
+from pandas.api.extensions import no_default, register_extension_dtype
 from pandas.core.arrays import ExtensionArray, ExtensionScalarOpsMixin
 
 
@@ -86,7 +84,7 @@ def _from_factorized(cls, values, original):
 
     _HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)
 
-    def to_numpy(self, dtype=None, copy=False, na_value=lib._no_default, decimals=None):
+    def to_numpy(self, dtype=None, copy=False, na_value=no_default, decimals=None):
         result = np.asarray(self, dtype=dtype)
         if decimals is not None:
             result = np.asarray([round(x, decimals) for x in result])