diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index 8812644f49b32f622bc235f402006b1818b28131..8fbb27e61861121e1932970d6fce5e4476126155 100644
--- a/saqc/lib/ts_operators.py
+++ b/saqc/lib/ts_operators.py
@@ -331,7 +331,7 @@ def interpolateNANs(data, method, order=2, gap_limit=2, extrapolate=None):
     # helper variable for checking numerical value of gap limit, if its a numeric value (to avoid comparison to str)
     gap_check = np.nan if isinstance(gap_limit, str) else gap_limit
     data = pd.Series(data, copy=True)
-    limit_area = "inside" if not extrapolate else "outside"
+    limit_area = None if extrapolate else "inside"
     if gap_check is None:
         # if there is actually no limit set to the gaps to-be interpolated, generate a dummy mask for the gaps
         gap_mask = pd.Series(True, index=data.index, name=data.name)
diff --git a/tests/lib/test_ts_operators.py b/tests/lib/test_ts_operators.py
index 875e78a80bb67e0e7773bdae349ad8762471282e..9c97b40fa10d0a2ab045adbc3451bf544d2d7d43 100644
--- a/tests/lib/test_ts_operators.py
+++ b/tests/lib/test_ts_operators.py
@@ -47,10 +47,7 @@ F = False
 )
 def test_exceedConsecutiveNanLimit(arr, maxc, expected):
     result = tsops._exceedConsecutiveNanLimit(arr, maxc)
-    try:
-        assert result is expected
-    except AssertionError:
-        print("stop")
+    assert result is expected
 
 
 def dtSeries(data, freq="1d"):
@@ -242,7 +239,7 @@ def test_rateOfChange(data, expected):
             4,
             "both",
             [np.nan, 0, np.nan, np.nan, np.nan, 4, np.nan],
-            [np.nan, 0, 1, 2, 3, 4, np.nan],
+            [0, 0, 1, 2, 3, 4, 4],
         ),
         (
             None,
@@ -256,7 +253,4 @@ def test_interpolatNANs(limit, extrapolate, data, expected):
     got = tsops.interpolateNANs(
         pd.Series(data), gap_limit=limit, method="linear", extrapolate=extrapolate
     )
-    try:
-        assert got.equals(pd.Series(expected, dtype=float))
-    except AssertionError:
-        print("stop")
+    assert got.equals(pd.Series(expected, dtype=float))