diff --git a/tests/test_fetcher.py b/tests/test_fetcher.py
index 4a7596cb..0425aee3 100644
--- a/tests/test_fetcher.py
+++ b/tests/test_fetcher.py
@@ -595,3 +595,36 @@ def before_trading_start(context, data):
         np.testing.assert_array_almost_equal(values[55:64], [2.50233] * 9)
         np.testing.assert_array_almost_equal(values[64:75], [2.550829] * 11)
         np.testing.assert_array_almost_equal(values[75:], [2.64484] * 35)
+
+    def test_fetcher_bad_data(self):
+        self.responses.add(
+            self.responses.GET,
+            'https://fake.urls.com/fetcher_nflx_data.csv',
+            body=NFLX_DATA,
+            content_type='text/csv',
+        )
+
+        sim_params = factory.create_simulation_parameters(
+            start=pd.Timestamp("2013-06-12", tz='UTC'),
+            end=pd.Timestamp("2013-06-14", tz='UTC'),
+            data_frequency="minute"
+        )
+
+        results = self.run_algo("""
+from zipline.api import fetch_csv, symbol
+import numpy as np
+
+def initialize(context):
+    fetch_csv('https://fake.urls.com/fetcher_nflx_data.csv',
+               date_column = 'Settlement Date',
+               date_format = '%m/%d/%y')
+    context.nflx = symbol('NFLX')
+    context.aapl = symbol('AAPL')
+
+def handle_data(context, data):
+    assert np.isnan(data.current(context.nflx, 'invalid_column'))
+    assert np.isnan(data.current(context.aapl, 'invalid_column'))
+    assert np.isnan(data.current(context.aapl, 'dtc'))
+""", sim_params=sim_params, data_frequency="minute")
+
+        self.assertEqual(3, len(results))
diff --git a/zipline/data/data_portal.py b/zipline/data/data_portal.py
index d224460d..457008f3 100644
--- a/zipline/data/data_portal.py
+++ b/zipline/data/data_portal.py
@@ -646,8 +646,8 @@ class DataPortal(object):
         # If we have an extra source with a column called "price", only look
         # at it if it's on something like palladium and not AAPL (since our
         # own price data always wins when dealing with assets).
-        return field in map and not (field in BASE_FIELDS and
-                                     isinstance(asset, Asset))
+
+        return not (field in BASE_FIELDS and isinstance(asset, Asset))
 
     def get_spot_value(self, asset, field, dt, data_frequency):
         """
@@ -680,14 +680,7 @@ class DataPortal(object):
             try:
                 return \
                     self._augmented_sources_map[field][asset].loc[day, field]
-            except:
-                log.error(
-                    "Could not find value for asset={0}, day={1},"
-                    "column={2}".format(
-                        str(asset),
-                        str(day),
-                        str(field)))
-
+            except KeyError:
                 return np.NaN
 
         if field not in BASE_FIELDS: