BUG: Better handling of invalid na_option argument for groupby.rank(pandas-dev#19499)

Author: peterpanmj

pandas/core/groupby/groupby.py

@@ -1705,6 +1705,9 @@ def rank(self, method='average', ascending=True, na_option='keep',
         -----
         DataFrame with ranking of values within each group
         """
+        if na_option not in {'keep', 'top', 'bottom'}:
+            msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+            raise ValueError(msg)
         return self._cython_transform('rank', numeric_only=False,
                                       ties_method=method, ascending=ascending,
                                       na_option=na_option, pct=pct, axis=axis)

pandas/tests/groupby/test_rank.py

@@ -252,14 +252,24 @@ def test_rank_object_raises(ties_method, ascending, na_option,
     with tm.assert_raises_regex(TypeError, "not callable"):
         df.groupby('key').rank(method=ties_method,
                                ascending=ascending,
-                               na_option='bad', pct=pct)
+                               na_option=na_option, pct=pct)
 
-    with tm.assert_raises_regex(TypeError, "not callable"):
-        df.groupby('key').rank(method=ties_method,
-                               ascending=ascending,
-                               na_option=True, pct=pct)
 
-    with tm.assert_raises_regex(TypeError, "not callable"):
+@pytest.mark.parametrize("na_option", [True, "bad", 1])
+@pytest.mark.parametrize("ties_method", [
+    'average', 'min', 'max', 'first', 'dense'])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("pct", [True, False])
+@pytest.mark.parametrize("vals", [
+    ['bar', 'bar', 'foo', 'bar', 'baz'],
+    ['bar', np.nan, 'foo', np.nan, 'baz'],
+    [1, np.nan, 2, np.nan, 3]
+])
+def test_rank_naoption_raises(ties_method, ascending, na_option, pct, vals):
+    df = DataFrame({'key': ['foo'] * 5, 'val': vals})
+    msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+
+    with tm.assert_raises_regex(TypeError, msg):
         df.groupby('key').rank(method=ties_method,
                                ascending=ascending,
-                               na_option=na_option, pct=pct)
+                               na_option='bad', pct=pct)