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
pandas-dev · jreback · Mar 9, 2018 · Mar 9, 2017 · Mar 10, 2017 · Apr 5, 2017
commit 62997900fa1cce38f05c9ba1e8b4910be2df4419
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
@@ -1731,3 +1731,4 @@ Other
 - Compat for 32-bit platforms for ``.qcut/cut``; bins will now be ``int64`` dtype (:issue:`14866`)
 - Bug in interactions with ``Qt`` when a ``QtApplication`` already exists (:issue:`14372`)
 - Avoid use of ``np.finfo()`` during ``import pandas`` removed to mitigate deadlock on Python GIL misuse (:issue:`14641`)
+- Bug in ``DataFrame.rank()`` and ``Series.rank()`` when ``method='dense'`` and ``pct=True`` (:issue:`15630`)
diff --git a/pandas/_libs/algos_rank_helper.pxi.in b/pandas/_libs/algos_rank_helper.pxi.in
@@ -213,7 +213,10 @@ 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
 
@@ -385,7 +388,10 @@ 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:

diff --git a/pandas/tests/frame/test_rank.py b/pandas/tests/frame/test_rank.py
@@ -217,6 +217,14 @@ def test_rank_methods_frame(self):
                         expected = expected.astype('float64')
                     tm.assert_frame_equal(result, expected)
 
+    def test_rank_dense_(self):
+        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']
 

diff --git a/pandas/tests/series/test_rank.py b/pandas/tests/series/test_rank.py
@@ -331,6 +331,25 @@ def test_rank_dense_method(self):
                 expected = Series(exp).astype(result.dtype)
                 assert_series_equal(result, expected)
 
+    def test_rank_dense_(self):
+        # GH15630, pct should be on 100% basis even when method='dense'
+        in_out = [([1], [1.]),
+                  ([2], [1.]),
+                  ([0], [1.]),
+                  ([2, 2], [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)
+
     def test_rank_descending(self):
         dtypes = ['O', 'f8', 'i8']
 

diff --git a/pandas/tests/test_stats.py b/pandas/tests/test_stats.py
@@ -0,0 +1,210 @@
+# -*- coding: utf-8 -*-
+from pandas import compat
+
+from distutils.version import LooseVersion
+from numpy import nan
+import numpy as np
+
+from pandas import Series, DataFrame
+
+from pandas.compat import product
+from pandas.util.testing import (assert_frame_equal, assert_series_equal)
+import pandas.util.testing as tm
+
+
+class TestRank(tm.TestCase):
+    s = Series([1, 3, 4, 2, nan, 2, 1, 5, nan, 3])
+    df = DataFrame({'A': s, 'B': s})
+
+    results = {
+        'average': np.array([1.5, 5.5, 7.0, 3.5, nan,
+                             3.5, 1.5, 8.0, nan, 5.5]),
+        'min': np.array([1, 5, 7, 3, nan, 3, 1, 8, nan, 5]),
+        'max': np.array([2, 6, 7, 4, nan, 4, 2, 8, nan, 6]),
+        'first': np.array([1, 5, 7, 3, nan, 4, 2, 8, nan, 6]),
+        'dense': np.array([1, 3, 4, 2, nan, 2, 1, 5, nan, 3]),
+    }
+
+    def test_rank_tie_methods(self):
+        s = self.s
+
+        def _check(s, expected, method='average'):
+            result = s.rank(method=method)
+            tm.assert_series_equal(result, Series(expected))
+
+        dtypes = [None, object]
+        disabled = set([(object, 'first')])
+        results = self.results
+
+        for method, dtype in product(results, dtypes):
+            if (dtype, method) in disabled:
+                continue
+            series = s if dtype is None else s.astype(dtype)
+            _check(series, results[method], method=method)
+
+    def test_rank_methods_series(self):
+        tm.skip_if_no_package('scipy', '0.13', 'scipy.stats.rankdata')
+        import scipy
+        from scipy.stats import rankdata
+
+        xs = np.random.randn(9)
+        xs = np.concatenate([xs[i:] for i in range(0, 9, 2)])  # add duplicates
+        np.random.shuffle(xs)
+
+        index = [chr(ord('a') + i) for i in range(len(xs))]
+
+        for vals in [xs, xs + 1e6, xs * 1e-6]:
+            ts = Series(vals, index=index)
+
+            for m in ['average', 'min', 'max', 'first', 'dense']:
+                result = ts.rank(method=m)
+                sprank = rankdata(vals, m if m != 'first' else 'ordinal')
+                expected = Series(sprank, index=index)
+
+                if LooseVersion(scipy.__version__) >= '0.17.0':
+                    expected = expected.astype('float64')
+                tm.assert_series_equal(result, expected)
+
+    def test_rank_methods_frame(self):
+        tm.skip_if_no_package('scipy', '0.13', 'scipy.stats.rankdata')
+        import scipy
+        from scipy.stats import rankdata
+
+        xs = np.random.randint(0, 21, (100, 26))
+        xs = (xs - 10.0) / 10.0
+        cols = [chr(ord('z') - i) for i in range(xs.shape[1])]
+
+        for vals in [xs, xs + 1e6, xs * 1e-6]:
+            df = DataFrame(vals, columns=cols)
+
+            for ax in [0, 1]:
+                for m in ['average', 'min', 'max', 'first', 'dense']:
+                    result = df.rank(axis=ax, method=m)
+                    sprank = np.apply_along_axis(
+                        rankdata, ax, vals,
+                        m if m != 'first' else 'ordinal')
+                    sprank = sprank.astype(np.float64)
+                    expected = DataFrame(sprank, columns=cols)
+
+                    if LooseVersion(scipy.__version__) >= '0.17.0':
+                        expected = expected.astype('float64')
+                    tm.assert_frame_equal(result, expected)
+
+    def test_rank_dense_method(self):
+        dtypes = ['O', 'f8', 'i8']
+        in_out = [([1], [1]),
+                  ([2], [1]),
+                  ([0], [1]),
+                  ([2, 2], [1, 1]),
+                  ([1, 2, 3], [1, 2, 3]),
+                  ([4, 2, 1], [3, 2, 1],),
+                  ([1, 1, 5, 5, 3], [1, 1, 3, 3, 2]),
+                  ([-5, -4, -3, -2, -1], [1, 2, 3, 4, 5])]
+
+        for ser, exp in in_out:
+            for dtype in dtypes:
+                s = Series(ser).astype(dtype)
+                result = s.rank(method='dense')
+                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']
+
+        for dtype, method in product(dtypes, self.results):
+            if 'i' in dtype:
+                s = self.s.dropna()
+                df = self.df.dropna()
+            else:
+                s = self.s.astype(dtype)
+                df = self.df.astype(dtype)
+
+            res = s.rank(ascending=False)
+            expected = (s.max() - s).rank()
+            assert_series_equal(res, expected)
+
+            res = df.rank(ascending=False)
+            expected = (df.max() - df).rank()
+            assert_frame_equal(res, expected)
+
+            if method == 'first' and dtype == 'O':
+                continue
+
+            expected = (s.max() - s).rank(method=method)
+            res2 = s.rank(method=method, ascending=False)
+            assert_series_equal(res2, expected)
+
+            expected = (df.max() - df).rank(method=method)
+
+            if dtype != 'O':
+                res2 = df.rank(method=method, ascending=False,
+                               numeric_only=True)
+                assert_frame_equal(res2, expected)
+
+            res3 = df.rank(method=method, ascending=False,
+                           numeric_only=False)
+            assert_frame_equal(res3, expected)
+
+    def test_rank_2d_tie_methods(self):
+        df = self.df
+
+        def _check2d(df, expected, method='average', axis=0):
+            exp_df = DataFrame({'A': expected, 'B': expected})
+
+            if axis == 1:
+                df = df.T
+                exp_df = exp_df.T
+
+            result = df.rank(method=method, axis=axis)
+            assert_frame_equal(result, exp_df)
+
+        dtypes = [None, object]
+        disabled = set([(object, 'first')])
+        results = self.results
+
+        for method, axis, dtype in product(results, [0, 1], dtypes):
+            if (dtype, method) in disabled:
+                continue
+            frame = df if dtype is None else df.astype(dtype)
+            _check2d(frame, results[method], method=method, axis=axis)
+
+    def test_rank_int(self):
+        s = self.s.dropna().astype('i8')
+
+        for method, res in compat.iteritems(self.results):
+            result = s.rank(method=method)
+            expected = Series(res).dropna()
+            expected.index = result.index
+            assert_series_equal(result, expected)
+
+    def test_rank_object_bug(self):
+        # GH 13445
+
+        # smoke tests
+        Series([np.nan] * 32).astype(object).rank(ascending=True)
+        Series([np.nan] * 32).astype(object).rank(ascending=False)