diff --git a/docs/tutorial.rst b/docs/tutorial.rst
index edeb556..ae7187a 100644
--- a/docs/tutorial.rst
+++ b/docs/tutorial.rst
@@ -65,10 +65,10 @@ Now let's compute a few derived columns in order to make our charting easier. Th
 .. code-block:: python
 
     emissions = emissions.compute([
-        (agate.Formula(agate.Number(), lambda r: r[' Date'].day), 'day'),
-        (agate.Formula(agate.Number(), lambda r: r[' SO2 (tons)'] or 0), 'so2'),
-        (agate.Formula(agate.Number(), lambda r: r[' NOx (tons)'] or 0), 'nox'),
-        (agate.Formula(agate.Number(), lambda r: r[' CO2 (short tons)'] or 0), 'co2')
+        ('day', agate.Formula(agate.Number(), lambda r: r[' Date'].day)),
+        ('so2', agate.Formula(agate.Number(), lambda r: r[' SO2 (tons)'] or 0)),
+        ('nox', agate.Formula(agate.Number(), lambda r: r[' NOx (tons)'] or 0)),
+        ('co2', agate.Formula(agate.Number(), lambda r: r[' CO2 (short tons)'] or 0))
     ])
 
 Of course, for analysis purposes you should always be extremely cautious in assuming that blank fields are equivalent to zero. For the purposes of this tutorial, we will assume this is a valid transformation.
diff --git a/example.py b/example.py
index 30cb643..97c5944 100755
--- a/example.py
+++ b/example.py
@@ -21,10 +21,10 @@
 emissions = agate.Table.from_csv('examples/epa-emissions-20150910.csv', tester)
 
 emissions = emissions.compute([
-    (agate.Formula(agate.Number(), lambda r: r[' Date'].day), 'day'),
-    (agate.Formula(agate.Number(), lambda r: r[' SO2 (tons)'] or 0), 'so2'),
-    (agate.Formula(agate.Number(), lambda r: r[' NOx (tons)'] or 0), 'noX'),
-    (agate.Formula(agate.Number(), lambda r: r[' CO2 (short tons)'] or 0), 'co2')
+    ('day', agate.Formula(agate.Number(), lambda r: r[' Date'].day)),
+    ('so2', agate.Formula(agate.Number(), lambda r: r[' SO2 (tons)'] or 0)),
+    ('noX', agate.Formula(agate.Number(), lambda r: r[' NOx (tons)'] or 0)),
+    ('co2', agate.Formula(agate.Number(), lambda r: r[' CO2 (short tons)'] or 0))
 ])
 
 states = emissions.group_by('State')