BUG: make dense ranks results scale to 100 percent (#21203)

(cherry picked from commit b237b11)

doc/source/whatsnew/v0.23.1.txt

@@ -49,6 +49,7 @@ Groupby/Resample/Rolling
 
 - Bug in :func:`DataFrame.agg` where applying multiple aggregation functions to a :class:`DataFrame` with duplicated column names would cause a stack overflow (:issue:`21063`)
 - Bug in :func:`pandas.core.groupby.GroupBy.ffill` and :func:`pandas.core.groupby.GroupBy.bfill` where the fill within a grouping would not always be applied as intended due to the implementations' use of a non-stable sort (:issue:`21207`)
+- Bug in :func:`pandas.core.groupby.GroupBy.rank` where results did not scale to 100% when specifying ``method='dense'`` and ``pct=True``
 
 Strings
 ^^^^^^^

pandas/_libs/groupby_helper.pxi.in

@@ -418,7 +418,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                         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
     ----------
@@ -451,8 +451,8 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
     """
     cdef:
         TiebreakEnumType tiebreak
-        Py_ssize_t i, j, N, K, val_start=0, grp_start=0, dups=0, sum_ranks=0
-        Py_ssize_t grp_vals_seen=1, grp_na_count=0
+        Py_ssize_t i, j, N, K, grp_start=0, dups=0, sum_ranks=0
+        Py_ssize_t grp_vals_seen=1, grp_na_count=0, grp_tie_count=0
         ndarray[int64_t] _as
         ndarray[float64_t, ndim=2] grp_sizes
         ndarray[{{c_type}}] masked_vals
@@ -563,6 +563,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                 dups = sum_ranks = 0
                 val_start = i
                 grp_vals_seen += 1
+                grp_tie_count +=1
 
             # Similar to the previous conditional, check now if we are moving
             # to a new group. If so, keep track of the index where the new
@@ -571,11 +572,16 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
             # (used by pct calculations later). also be sure to reset any of
             # the items helping to calculate dups
             if i == N - 1 or labels[_as[i]] != labels[_as[i+1]]:
-                for j in range(grp_start, i + 1):
-                    grp_sizes[_as[j], 0] = i - grp_start + 1 - grp_na_count
+                if tiebreak != TIEBREAK_DENSE:
+                    for j in range(grp_start, i + 1):
+                        grp_sizes[_as[j], 0] = i - grp_start + 1 - grp_na_count
+                else:
+                    for j in range(grp_start, i + 1):
+                        grp_sizes[_as[j], 0] = (grp_tie_count -
+                                                (grp_na_count > 0))
                 dups = sum_ranks = 0
                 grp_na_count = 0
-                val_start = i + 1
+                grp_tie_count = 0
                 grp_start = i + 1
                 grp_vals_seen = 1
 

pandas/tests/groupby/test_rank.py

@@ -59,9 +59,9 @@ def test_rank_apply():
     ('first', False, False, [3., 4., 1., 5., 2.]),
     ('first', False, True, [.6, .8, .2, 1., .4]),
     ('dense', True, False, [1., 1., 3., 1., 2.]),
-    ('dense', True, True, [0.2, 0.2, 0.6, 0.2, 0.4]),
+    ('dense', True, True, [1. / 3., 1. / 3., 3. / 3., 1. / 3., 2. / 3.]),
     ('dense', False, False, [3., 3., 1., 3., 2.]),
-    ('dense', False, True, [.6, .6, .2, .6, .4]),
+    ('dense', False, True, [3. / 3., 3. / 3., 1. / 3., 3. / 3., 2. / 3.]),
 ])
 def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
     key = np.repeat(grps, len(vals))
@@ -126,7 +126,7 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
 @pytest.mark.parametrize("grps", [
     ['qux'], ['qux', 'quux']])
 @pytest.mark.parametrize("vals", [
-    [2, 2, np.nan, 8, 2, 6, np.nan, np.nan],  # floats
+    [2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
     [pd.Timestamp('2018-01-02'), pd.Timestamp('2018-01-02'), np.nan,
      pd.Timestamp('2018-01-08'), pd.Timestamp('2018-01-02'),
      pd.Timestamp('2018-01-06'), np.nan, np.nan]
@@ -167,11 +167,11 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
     ('dense', True, 'keep', False,
         [1., 1., np.nan, 3., 1., 2., np.nan, np.nan]),
     ('dense', True, 'keep', True,
-        [0.2, 0.2, np.nan, 0.6, 0.2, 0.4, np.nan, np.nan]),
+        [1. / 3., 1. / 3., np.nan, 3. / 3., 1. / 3., 2. / 3., np.nan, np.nan]),
     ('dense', False, 'keep', False,
         [3., 3., np.nan, 1., 3., 2., np.nan, np.nan]),
     ('dense', False, 'keep', True,
-        [.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan]),
+        [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,
         [0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875]),
@@ -198,10 +198,10 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
         [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,
-        [0.125, 0.125, 0.5, 0.375, 0.125, 0.25, 0.5, 0.5]),
+     [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,
-        [0.375, 0.375, 0.5, 0.125, 0.375, 0.25, 0.5, 0.5])
+     [0.75, 0.75, 1., 0.25, 0.75, 0.5, 1., 1.])
 ])
 def test_rank_args_missing(grps, vals, ties_method, ascending,
                            na_option, pct, exp):