BUG: Dense ranking with percent now uses 100% basis

- `DataFrame.rank()` and `Series.rank()` when `method='dense'` and
  `pct=True` now scales to 100%.

See #15630

Author: rouzazari

doc/source/whatsnew/v0.20.0.txt

@@ -855,3 +855,4 @@ Bug Fixes
 - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`)
 - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`)
 - Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`)
+- Bug in ``DataFrame.rank()`` and ``Series.rank()`` when ``method='dense'`` and ``pct=True`` (:issue:`15630`)

pandas/_libs/algos_rank_helper.pxi.in

@@ -198,7 +198,10 @@ def rank_1d_{{dtype}}(object in_arr, ties_method='average', ascending=True,
                 sum_ranks = dups = 0
     {{endif}}
     if pct:
-        return ranks / count
+        if tiebreak == TIEBREAK_DENSE:
+            return ranks / total_tie_count
+        else:
+            return ranks / count
     else:
         return ranks
 
@@ -370,7 +373,10 @@ def rank_2d_{{dtype}}(object in_arr, axis=0, ties_method='average',
                         ranks[i, argsorted[i, z]] = total_tie_count
                 sum_ranks = dups = 0
         if pct:
-            ranks[i, :] /= count
+            if tiebreak == TIEBREAK_DENSE:
+                ranks[i, :] /= total_tie_count
+            else:
+                ranks[i, :] /= count
     if axis == 0:
         return ranks.T
     else:

pandas/tests/test_stats.py

@@ -108,6 +108,31 @@ def test_rank_dense_method(self):
                 expected = Series(exp).astype(result.dtype)
                 assert_series_equal(result, expected)
 
+        # GH15630, pct should be on 100% basis even when method='dense'
+        in_out = [([1], [1.]),
+                  ([2], [1.]),
+                  ([0], [1.]),
+                  ([2, 2], [1., 1.1]),
+                  ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]),
+                  ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],),
+                  ([1, 1, 5, 5, 3], [1. / 3, 1. / 3, 3. / 3, 3. / 3, 2. / 3]),
+                  ([-5, -4, -3, -2, -1],
+                   [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]
+
+        for ser, exp in in_out:
+            for dtype in dtypes:
+                s = Series(ser).astype(dtype)
+                result = s.rank(method='dense', pct=True)
+                expected = Series(exp).astype(result.dtype)
+                assert_series_equal(result, expected)
+
+        df = DataFrame([['2012', 'B', 3], ['2012', 'A', 2], ['2012', 'A', 1]])
+        result = df.rank(method='dense', pct=True)
+        expected = DataFrame([[1., 1., 1.],
+                              [1., 0.5, 2. / 3],
+                              [1., 0.5, 1. / 3]])
+        assert_frame_equal(result, expected)
+
     def test_rank_descending(self):
         dtypes = ['O', 'f8', 'i8']