BUG: Fix problems in group rank when both nans and infinity are present pandas-dev#20561 (pandas-dev#20681)

Author: peterpanmj

doc/source/whatsnew/v0.23.0.txt

@@ -221,6 +221,12 @@ Current Behavior:
 
     s.rank(na_option='top')
 
+These bugs were squashed:
+
+- Bug in :meth:`DataFrame.rank` and :meth:`Series.rank` when ``method='dense'`` and ``pct=True`` in which percentile ranks were not being used with the number of distinct observations (:issue:`15630`)
+- Bug in :meth:`Series.rank` and :meth:`DataFrame.rank` when ``ascending='False'`` failed to return correct ranks for infinity if ``NaN`` were present (:issue:`19538`)
+- Bug in :func:`DataFrameGroupBy.rank` where ranks were incorrect when both infinity and ``NaN`` were present (:issue:`20561`)
+
 .. _whatsnew_0230.enhancements.round-trippable_json:
 
 JSON read/write round-trippable with ``orient='table'``
@@ -1082,14 +1088,12 @@ Offsets
 
 Numeric
 ^^^^^^^
-- Bug in :meth:`DataFrame.rank` and :meth:`Series.rank` when ``method='dense'`` and ``pct=True`` in which percentile ranks were not being used with the number of distinct observations (:issue:`15630`)
 - Bug in :class:`Series` constructor with an int or float list where specifying ``dtype=str``, ``dtype='str'`` or ``dtype='U'`` failed to convert the data elements to strings (:issue:`16605`)
 - Bug in :class:`Index` multiplication and division methods where operating with a ``Series`` would return an ``Index`` object instead of a ``Series`` object (:issue:`19042`)
 - Bug in the :class:`DataFrame` constructor in which data containing very large positive or very large negative numbers was causing ``OverflowError`` (:issue:`18584`)
 - Bug in :class:`Index` constructor with ``dtype='uint64'`` where int-like floats were not coerced to :class:`UInt64Index` (:issue:`18400`)
 - Bug in :class:`DataFrame` flex arithmetic (e.g. ``df.add(other, fill_value=foo)``) with a ``fill_value`` other than ``None`` failed to raise ``NotImplementedError`` in corner cases where either the frame or ``other`` has length zero (:issue:`19522`)
 - Multiplication and division of numeric-dtyped :class:`Index` objects with timedelta-like scalars returns ``TimedeltaIndex`` instead of raising ``TypeError`` (:issue:`19333`)
-- Bug in :meth:`Series.rank` and :meth:`DataFrame.rank` when ``ascending='False'`` failed to return correct ranks for infinity if ``NaN`` were present (:issue:`19538`)
 - Bug where ``NaN`` was returned instead of 0 by :func:`Series.pct_change` and :func:`DataFrame.pct_change` when ``fill_method`` is not ``None`` (:issue:`19873`)
 
 

pandas/_libs/groupby_helper.pxi.in

@@ -417,25 +417,33 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                         ndarray[int64_t] labels,
                         bint is_datetimelike, object ties_method,
                         bint ascending, bint pct, object na_option):
-    """Provides the rank of values within each group
+    """
+	Provides the rank of values within each group.
 
     Parameters
     ----------
     out : array of float64_t values which this method will write its results to
     values : array of {{c_type}} values to be ranked
     labels : array containing unique label for each group, with its ordering
         matching up to the corresponding record in `values`
-    is_datetimelike : bool
+    is_datetimelike : bool, default False
         unused in this method but provided for call compatibility with other
         Cython transformations
-    ties_method :  {'keep', 'top', 'bottom'}
+    ties_method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+        * average: average rank of group
+        * min: lowest rank in group
+        * max: highest rank in group
+        * first: ranks assigned in order they appear in the array
+        * dense: like 'min', but rank always increases by 1 between groups
+    ascending : boolean, default True
+        False for ranks by high (1) to low (N)
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+    pct : boolean, default False
+        Compute percentage rank of data within each group
+    na_option : {'keep', 'top', 'bottom'}, default 'keep'
         * keep: leave NA values where they are
         * top: smallest rank if ascending
         * bottom: smallest rank if descending
-    ascending : boolean
-        False for ranks by high (1) to low (N)
-    pct : boolean
-        Compute percentage rank of data within each group
 
     Notes
     -----
@@ -508,7 +516,8 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
 
             # if keep_na, check for missing values and assign back
             # to the result where appropriate
-            if keep_na and masked_vals[_as[i]] == nan_fill_val:
+
+            if keep_na and mask[_as[i]]:
                 grp_na_count += 1
                 out[_as[i], 0] = nan
             else:
@@ -548,9 +557,9 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
             # reset the dups and sum_ranks, knowing that a new value is coming
             # up. the conditional also needs to handle nan equality and the
             # end of iteration
-            if (i == N - 1 or (
-                    (masked_vals[_as[i]] != masked_vals[_as[i+1]]) and not
-                    (mask[_as[i]] and mask[_as[i+1]]))):
+            if (i == N - 1 or
+                    (masked_vals[_as[i]] != masked_vals[_as[i+1]]) or
+                    (mask[_as[i]] ^ mask[_as[i+1]])):
                 dups = sum_ranks = 0
                 val_start = i
                 grp_vals_seen += 1

pandas/tests/groupby/test_groupby.py

@@ -1965,6 +1965,55 @@ def test_rank_args(self, grps, vals, ties_method, ascending, pct, exp):
         exp_df = DataFrame(exp * len(grps), columns=['val'])
         assert_frame_equal(result, exp_df)
 
+    @pytest.mark.parametrize("grps", [
+        ['qux'], ['qux', 'quux']])
+    @pytest.mark.parametrize("vals", [
+        [-np.inf, -np.inf, np.nan, 1., np.nan, np.inf, np.inf],
+    ])
+    @pytest.mark.parametrize("ties_method,ascending,na_option,exp", [
+        ('average', True, 'keep', [1.5, 1.5, np.nan, 3, np.nan, 4.5, 4.5]),
+        ('average', True, 'top', [3.5, 3.5, 1.5, 5., 1.5, 6.5, 6.5]),
+        ('average', True, 'bottom', [1.5, 1.5, 6.5, 3., 6.5, 4.5, 4.5]),
+        ('average', False, 'keep', [4.5, 4.5, np.nan, 3, np.nan, 1.5, 1.5]),
+        ('average', False, 'top', [6.5, 6.5, 1.5, 5., 1.5, 3.5, 3.5]),
+        ('average', False, 'bottom', [4.5, 4.5, 6.5, 3., 6.5, 1.5, 1.5]),
+        ('min', True, 'keep', [1., 1., np.nan, 3., np.nan, 4., 4.]),
+        ('min', True, 'top', [3., 3., 1., 5., 1., 6., 6.]),
+        ('min', True, 'bottom', [1., 1., 6., 3., 6., 4., 4.]),
+        ('min', False, 'keep', [4., 4., np.nan, 3., np.nan, 1., 1.]),
+        ('min', False, 'top', [6., 6., 1., 5., 1., 3., 3.]),
+        ('min', False, 'bottom', [4., 4., 6., 3., 6., 1., 1.]),
+        ('max', True, 'keep', [2., 2., np.nan, 3., np.nan, 5., 5.]),
+        ('max', True, 'top', [4., 4., 2., 5., 2., 7., 7.]),
+        ('max', True, 'bottom', [2., 2., 7., 3., 7., 5., 5.]),
+        ('max', False, 'keep', [5., 5., np.nan, 3., np.nan, 2., 2.]),
+        ('max', False, 'top', [7., 7., 2., 5., 2., 4., 4.]),
+        ('max', False, 'bottom', [5., 5., 7., 3., 7., 2., 2.]),
+        ('first', True, 'keep', [1., 2., np.nan, 3., np.nan, 4., 5.]),
+        ('first', True, 'top', [3., 4., 1., 5., 2., 6., 7.]),
+        ('first', True, 'bottom', [1., 2., 6., 3., 7., 4., 5.]),
+        ('first', False, 'keep', [4., 5., np.nan, 3., np.nan, 1., 2.]),
+        ('first', False, 'top', [6., 7., 1., 5., 2., 3., 4.]),
+        ('first', False, 'bottom', [4., 5., 6., 3., 7., 1., 2.]),
+        ('dense', True, 'keep', [1., 1., np.nan, 2., np.nan, 3., 3.]),
+        ('dense', True, 'top', [2., 2., 1., 3., 1., 4., 4.]),
+        ('dense', True, 'bottom', [1., 1., 4., 2., 4., 3., 3.]),
+        ('dense', False, 'keep', [3., 3., np.nan, 2., np.nan, 1., 1.]),
+        ('dense', False, 'top', [4., 4., 1., 3., 1., 2., 2.]),
+        ('dense', False, 'bottom', [3., 3., 4., 2., 4., 1., 1.])
+    ])
+    def test_infs_n_nans(self, grps, vals, ties_method, ascending, na_option,
+                         exp):
+        # GH 20561
+        key = np.repeat(grps, len(vals))
+        vals = vals * len(grps)
+        df = DataFrame({'key': key, 'val': vals})
+        result = df.groupby('key').rank(method=ties_method,
+                                        ascending=ascending,
+                                        na_option=na_option)
+        exp_df = DataFrame(exp * len(grps), columns=['val'])
+        assert_frame_equal(result, exp_df)
+
     @pytest.mark.parametrize("grps", [
         ['qux'], ['qux', 'quux']])
     @pytest.mark.parametrize("vals", [