BUG: Better handling of invalid na_option argument for groupby.rank(#22124) (#22125)

Author: peterpanmj

doc/source/whatsnew/v0.24.0.txt

@@ -659,6 +659,7 @@ Reshaping
 - Bug in :meth:`Series.where` and :meth:`DataFrame.where` with ``datetime64[ns, tz]`` dtype (:issue:`21546`)
 - Bug in :meth:`Series.mask` and :meth:`DataFrame.mask` with ``list`` conditionals (:issue:`21891`)
 - Bug in :meth:`DataFrame.replace` raises RecursionError when converting OutOfBounds ``datetime64[ns, tz]`` (:issue:`20380`)
+- :func:`pandas.core.groupby.GroupBy.rank` now raises a ``ValueError`` when an invalid value is passed for argument ``na_option`` (:issue:`22124`)
 -
 
 Build Changes

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

@@ -172,35 +172,35 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
         [3., 3., np.nan, 1., 3., 2., np.nan, np.nan]),
     ('dense', False, 'keep', True,
         [3. / 3., 3. / 3., np.nan, 1. / 3., 3. / 3., 2. / 3., np.nan, np.nan]),
-    ('average', True, 'no_na', False, [2., 2., 7., 5., 2., 4., 7., 7.]),
-    ('average', True, 'no_na', True,
+    ('average', True, 'bottom', False, [2., 2., 7., 5., 2., 4., 7., 7.]),
+    ('average', True, 'bottom', True,
         [0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875]),
-    ('average', False, 'no_na', False, [4., 4., 7., 1., 4., 2., 7., 7.]),
-    ('average', False, 'no_na', True,
+    ('average', False, 'bottom', False, [4., 4., 7., 1., 4., 2., 7., 7.]),
+    ('average', False, 'bottom', True,
         [0.5, 0.5, 0.875, 0.125, 0.5, 0.25, 0.875, 0.875]),
-    ('min', True, 'no_na', False, [1., 1., 6., 5., 1., 4., 6., 6.]),
-    ('min', True, 'no_na', True,
+    ('min', True, 'bottom', False, [1., 1., 6., 5., 1., 4., 6., 6.]),
+    ('min', True, 'bottom', True,
         [0.125, 0.125, 0.75, 0.625, 0.125, 0.5, 0.75, 0.75]),
-    ('min', False, 'no_na', False, [3., 3., 6., 1., 3., 2., 6., 6.]),
-    ('min', False, 'no_na', True,
+    ('min', False, 'bottom', False, [3., 3., 6., 1., 3., 2., 6., 6.]),
+    ('min', False, 'bottom', True,
         [0.375, 0.375, 0.75, 0.125, 0.375, 0.25, 0.75, 0.75]),
-    ('max', True, 'no_na', False, [3., 3., 8., 5., 3., 4., 8., 8.]),
-    ('max', True, 'no_na', True,
+    ('max', True, 'bottom', False, [3., 3., 8., 5., 3., 4., 8., 8.]),
+    ('max', True, 'bottom', True,
         [0.375, 0.375, 1., 0.625, 0.375, 0.5, 1., 1.]),
-    ('max', False, 'no_na', False, [5., 5., 8., 1., 5., 2., 8., 8.]),
-    ('max', False, 'no_na', True,
+    ('max', False, 'bottom', False, [5., 5., 8., 1., 5., 2., 8., 8.]),
+    ('max', False, 'bottom', True,
         [0.625, 0.625, 1., 0.125, 0.625, 0.25, 1., 1.]),
-    ('first', True, 'no_na', False, [1., 2., 6., 5., 3., 4., 7., 8.]),
-    ('first', True, 'no_na', True,
+    ('first', True, 'bottom', False, [1., 2., 6., 5., 3., 4., 7., 8.]),
+    ('first', True, 'bottom', True,
         [0.125, 0.25, 0.75, 0.625, 0.375, 0.5, 0.875, 1.]),
-    ('first', False, 'no_na', False, [3., 4., 6., 1., 5., 2., 7., 8.]),
-    ('first', False, 'no_na', True,
+    ('first', False, 'bottom', False, [3., 4., 6., 1., 5., 2., 7., 8.]),
+    ('first', False, 'bottom', True,
         [0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.]),
-    ('dense', True, 'no_na', False, [1., 1., 4., 3., 1., 2., 4., 4.]),
-    ('dense', True, 'no_na', True,
+    ('dense', True, 'bottom', False, [1., 1., 4., 3., 1., 2., 4., 4.]),
+    ('dense', True, 'bottom', True,
      [0.25, 0.25, 1., 0.75, 0.25, 0.5, 1., 1.]),
-    ('dense', False, 'no_na', False, [3., 3., 4., 1., 3., 2., 4., 4.]),
-    ('dense', False, 'no_na', True,
+    ('dense', False, 'bottom', False, [3., 3., 4., 1., 3., 2., 4., 4.]),
+    ('dense', False, 'bottom', True,
      [0.75, 0.75, 1., 0.25, 0.75, 0.5, 1., 1.])
 ])
 def test_rank_args_missing(grps, vals, ties_method, ascending,
@@ -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(ValueError, msg):
         df.groupby('key').rank(method=ties_method,
                                ascending=ascending,
                                na_option=na_option, pct=pct)