diff --git a/src/aneris/methods.py b/src/aneris/methods.py
index a555758..a62a8ec 100644
--- a/src/aneris/methods.py
+++ b/src/aneris/methods.py
@@ -441,7 +441,7 @@ def default_method_choice(
             # or under the absolute threshold
             if (abs(row.dH) > 0.5) & (row.dH_abs < row.dH_abs_thresh):
                 # If dH is large, but dH_abs is small, this suggests
-                # The data being harmonised is a small ~near zero component 
+                # The data being harmonised is a small ~near zero component
                 # (under 20% of the parent variable)
                 if row.dH < 0:
                     # If dH is negative (model data > historical data)
@@ -452,10 +452,10 @@ def default_method_choice(
                 else:
                     # If dH is positive (model data < historical data)
                     # Then we use the offset method, as the ratio method can substantially
-                    # Increase the values and lead to strange pathways, e.g. 
+                    # Increase the values and lead to strange pathways, e.g.
                     # When the variable is small in historical data but growing rapidly (e.g. synfuels)
                     return offset_method
-            
+
             # If dH_abs is large, this suggests that the component being harmonised is not ~near zero
             # In this context we can just rely on the ratio method throughout
             elif abs(row.dH) < 0.5:
diff --git a/tests/test_harmonize.py b/tests/test_harmonize.py
index 5dd1c0f..35b79ed 100644
--- a/tests/test_harmonize.py
+++ b/tests/test_harmonize.py
@@ -271,16 +271,19 @@ def test_harmonize_linear_interpolation():
 @pytest.mark.ipopt
 def test_harmonize_budget():
     df = _df.copy()
+    df.columns = df.columns.astype(int)
     hist = _hist.copy()
+    hist.columns = hist.columns.astype(int)
+
     methods = _methods.copy()
 
     h = harmonize.Harmonizer(df, hist)
     methods["method"] = "budget"
-    res = h.harmonize(year="2015", overrides=methods["method"])
+    res = h.harmonize(year=2015, overrides=methods["method"])
 
     # base year
-    obs = res["2015"]
-    exp = _hist["2015"]
+    obs = res[2015]
+    exp = hist[2015]
     npt.assert_array_almost_equal(obs, exp)
 
     # carbon budget conserved
@@ -295,5 +298,5 @@ def _carbon_budget(emissions):
 
     npt.assert_array_almost_equal(
         _carbon_budget(res),
-        _carbon_budget(df) - _carbon_budget(hist.loc[:, "2010":"2015"]),
+        _carbon_budget(df) - _carbon_budget(hist.loc[:, 2010:2015]),
     )