Revert "API: decimal nan is na"

This reverts commit 967c674.

Author: TomAugspurger

doc/source/whatsnew/v0.24.0.txt

@@ -1229,7 +1229,6 @@ Missing
 - Bug in :func:`Series.hasnans` that could be incorrectly cached and return incorrect answers if null elements are introduced after an initial call (:issue:`19700`)
 - :func:`Series.isin` now treats all NaN-floats as equal also for `np.object`-dtype. This behavior is consistent with the behavior for float64 (:issue:`22119`)
 - :func:`unique` no longer mangles NaN-floats and the ``NaT``-object for `np.object`-dtype, i.e. ``NaT`` is no longer coerced to a NaN-value and is treated as a different entity. (:issue:`22295`)
-- :meth:`isna` now considers ``decimal.Decimal('NaN')`` a missing value (:issue:`23284`).
 
 
 MultiIndex

pandas/_libs/missing.pyx

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 
 import cython
-import decimal
 from cython import Py_ssize_t
 
 import numpy as np
@@ -34,8 +33,6 @@ cdef inline bint _check_all_nulls(object val):
  res = get_datetime64_value(val) == NPY_NAT
  elif util.is_timedelta64_object(val):
  res = get_timedelta64_value(val) == NPY_NAT
- elif isinstance(val, decimal.Decimal):
- return val.is_nan()
  else:
  res = 0
  return res
@@ -74,8 +71,6 @@ cpdef bint checknull(object val):
  return get_timedelta64_value(val) == NPY_NAT
  elif util.is_array(val):
  return False
- elif isinstance(val, decimal.Decimal):
- return val.is_nan()
  else:
  return val is None or util.is_nan(val)
 

pandas/tests/dtypes/test_missing.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 
-import decimal
 import pytest
 from warnings import catch_warnings, simplefilter
 import numpy as np
@@ -249,43 +248,6 @@ def test_period(self):
  tm.assert_series_equal(isna(s), exp)
  tm.assert_series_equal(notna(s), ~exp)
 
- def test_decimal(self):
- # scalars
- a = decimal.Decimal(1.0)
- assert pd.isna(a) is False
- assert pd.notna(a) is True
-
- b = decimal.Decimal('NaN')
- assert pd.isna(b) is True
- assert pd.notna(b) is False
-
- # array
- arr = np.array([a, b])
- expected = np.array([False, True])
- result = pd.isna(arr)
- tm.assert_numpy_array_equal(result, expected)
-
- result = pd.notna(arr)
- tm.assert_numpy_array_equal(result, ~expected)
-
- # series
- ser = pd.Series(arr)
- expected = pd.Series(expected)
- result = pd.isna(ser)
- tm.assert_series_equal(result, expected)
-
- result = pd.notna(ser)
- tm.assert_series_equal(result, ~expected)
-
- # index
- idx = pd.Index(arr)
- expected = np.array([False, True])
- result = pd.isna(idx)
- tm.assert_numpy_array_equal(result, expected)
-
- result = pd.notna(idx)
- tm.assert_numpy_array_equal(result, ~expected)
-
 
 def test_array_equivalent():
  assert array_equivalent(np.array([np.nan, np.nan]),