Add Test And Refactor is_decimal

Author: Roger Thomas

pandas/lib.pyx

@@ -313,6 +313,7 @@ def isscalar(object val):
     - instances of datetime.datetime
     - instances of datetime.timedelta
     - Period
+    - instances of decimal.Decimal
 
     """
 
@@ -325,7 +326,8 @@ def isscalar(object val):
             or PyDate_Check(val)
             or PyDelta_Check(val)
             or PyTime_Check(val)
-            or util.is_period_object(val))
+            or util.is_period_object(val)
+            or is_decimal(val))
 
 
 def item_from_zerodim(object val):

pandas/src/inference.pyx

@@ -1,5 +1,5 @@
-from decimal import Decimal
 import sys
+from decimal import Decimal
 cimport util
 from tslib import NaT, get_timezone
 from datetime import datetime, timedelta
@@ -29,6 +29,10 @@ def is_bool(object obj):
 def is_complex(object obj):
     return util.is_complex_object(obj)
 
+
+def is_decimal(object obj):
+    return isinstance(obj, Decimal)
+
 cpdef bint is_period(object val):
     """ Return a boolean if this is a Period object """
     return util.is_period_object(val)
@@ -674,7 +678,7 @@ def maybe_convert_numeric(object[:] values, set na_values,
         elif util.is_complex_object(val):
             complexes[i] = val
             seen_complex = True
-        elif isinstance(val, Decimal):
+        elif is_decimal(val):
             floats[i] = complexes[i] = val
             seen_float = True
         else:

pandas/tools/tests/test_util.py

@@ -229,6 +229,26 @@ def test_numeric(self):
         df_copy[['a', 'b']] = df_copy[['a', 'b']].apply(to_numeric)
         tm.assert_frame_equal(df_copy, expected)
 
+    def test_numeric_lists_and_arrays(self):
+        # Test to_numeric with embedded lists and arrays
+        df = pd.DataFrame(dict(
+            a=[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1]
+        ))
+        df['a'] = df['a'].apply(to_numeric)
+        expected = pd.DataFrame(dict(
+            a=[[3.14, 1.0], 1.6, 0.1],
+        ))
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame(dict(
+            a=[np.array([decimal.Decimal(3.14), 1.0]), 0.1]
+        ))
+        df['a'] = df['a'].apply(to_numeric)
+        expected = pd.DataFrame(dict(
+            a=[[3.14, 1.0], 0.1],
+        ))
+        tm.assert_frame_equal(df, expected)
+
     def test_all_nan(self):
         s = pd.Series(['a', 'b', 'c'])
         res = to_numeric(s, errors='coerce')

pandas/tools/util.py

@@ -1,4 +1,3 @@
-from decimal import Decimal
 import numpy as np
 import pandas.lib as lib
 
@@ -7,6 +6,7 @@
                                  is_datetime_or_timedelta_dtype,
                                  is_list_like,
                                  _ensure_object)
+
 from pandas.types.cast import _possibly_downcast_to_dtype
 
 import pandas as pd
@@ -174,9 +174,9 @@ def to_numeric(arg, errors='raise', downcast=None):
             values = arg.values
     elif isinstance(arg, (list, tuple)):
         values = np.array(arg, dtype='O')
-    elif isinstance(arg, Decimal):
-        return float(arg)
-    elif np.isscalar(arg):
+    elif lib.isscalar(arg):
+        if lib.is_decimal(arg):
+            return float(arg)
         if is_number(arg):
             return arg
         is_scalar = True