API: decimal nan is na

Author: TomAugspurger

doc/source/whatsnew/v0.24.0.txt

@@ -1227,6 +1227,7 @@ 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

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 
 import cython
+import decimal
 from cython import Py_ssize_t
 
 import numpy as np
@@ -33,6 +34,8 @@ 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
@@ -71,6 +74,8 @@ 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

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 
+import decimal
 import pytest
 from warnings import catch_warnings, simplefilter
 import numpy as np
@@ -248,6 +249,43 @@ 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]),