ENH: Add the decimal.Decimal type to infer_dtypes (#15690)

doc/source/whatsnew/v0.21.0.txt

@@ -34,6 +34,7 @@ Other Enhancements
 - ``Series.to_dict()`` and ``DataFrame.to_dict()`` now support an ``into`` keyword which allows you to specify the ``collections.Mapping`` subclass that you would like returned.  The default is ``dict``, which is backwards compatible. (:issue:`16122`)
 - ``RangeIndex.append`` now returns a ``RangeIndex`` object when possible (:issue:`16212`)
 - :func:`to_pickle` has gained a protocol parameter (:issue:`16252`). By default, this parameter is set to `HIGHEST_PROTOCOL <https://docs.python.org/3/library/pickle.html#data-stream-format>`__
+- :func:`api.types.infer_dtype` now infers decimals. (:issue: `15690`)
 
 .. _whatsnew_0210.api_breaking:
 

pandas/_libs/src/inference.pyx

@@ -243,6 +243,7 @@ def infer_dtype(object value):
     - integer
     - mixed-integer
     - mixed-integer-float
+    - decimal
     - complex
     - categorical
     - boolean
@@ -286,6 +287,9 @@ def infer_dtype(object value):
     >>> infer_dtype(['a', 1])
     'mixed-integer'
 
+    >>> infer_dtype([Decimal(1), Decimal(2.0)])
+    'decimal'
+
     >>> infer_dtype([True, False])
     'boolean'
 
@@ -308,7 +312,6 @@ def infer_dtype(object value):
     'categorical'
 
     """
-
     cdef:
         Py_ssize_t i, n
         object val
@@ -407,6 +410,9 @@ def infer_dtype(object value):
         if is_time_array(values):
             return 'time'
 
+    elif is_decimal(val):
+        return 'decimal'
+
     elif util.is_float_object(val):
         if is_float_array(values):
             return 'floating'

pandas/tests/dtypes/test_inference.py

@@ -9,6 +9,7 @@
 import collections
 import re
 from datetime import datetime, date, timedelta, time
+from decimal import Decimal
 import numpy as np
 import pytz
 import pytest
@@ -462,6 +463,16 @@ def test_floats(self):
         result = lib.infer_dtype(arr)
         assert result == 'floating'
 
+    def test_decimals(self):
+        # GH15690
+        arr = np.array([Decimal(1), Decimal(2), Decimal(3)])
+        result = lib.infer_dtype(arr)
+        assert result == 'decimal'
+
+        arr = np.array([1.0, 2.0, Decimal(3)])
+        result = lib.infer_dtype(arr)
+        assert result == 'mixed'
+
     def test_string(self):
         pass