Issue pandas-dev#15431

BUG: pd.qcut with q=1 and input with identical values

Author: luca-s

doc/source/whatsnew/v0.20.0.txt

@@ -592,6 +592,7 @@ Bug Fixes
 - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a numpy array (:issue:`15434`)
 - Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`)
 - Bug in ``pd.cut()`` single bin on all 0s array raises ``ValueError`` (:issue:`15428`)
+- Bug in ``pd.qcut()`` single quantile and array with identical values raises ``ValueError`` (:issue:`15431`)
 
 - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`)
 - Bug in ``pd.read_msgpack()`` in which ``Series`` categoricals were being improperly processed (:issue:`14901`)

pandas/tests/tools/test_tile.py

@@ -285,6 +285,36 @@ def test_qcut_duplicates_bin(self):
         # invalid
         self.assertRaises(ValueError, qcut, values, 3, duplicates='foo')
 
+    def test_single_quantile(self):
+        # issue 15431
+        expected = Series([0, 0])
+
+        s = Series([9., 9.])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([-9., -9.])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([0., 0.])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([0])
+
+        s = Series([9])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([-9])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([0])
+        result = qcut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
     def test_single_bin(self):
         # issue 14652
         expected = Series([0, 0])
@@ -297,11 +327,29 @@ def test_single_bin(self):
         result = cut(s, 1, labels=False)
         tm.assert_series_equal(result, expected)
 
+        expected = Series([0])
+
+        s = Series([9])
+        result = cut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([-9])
+        result = cut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
         # issue 15428
+        expected = Series([0, 0])
+
         s = Series([0., 0.])
         result = cut(s, 1, labels=False)
         tm.assert_series_equal(result, expected)
 
+        expected = Series([0])
+
+        s = Series([0])
+        result = cut(s, 1, labels=False)
+        tm.assert_series_equal(result, expected)
+
     def test_datetime_cut(self):
         # GH 14714
         # testing for time data to be present as series

pandas/tools/tile.py

@@ -189,6 +189,13 @@ def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
     else:
         quantiles = q
     bins = algos.quantile(x, quantiles)
+
+    # fix special case: q=1 and all identical values
+    if q == 1 and len(bins) == 2 and bins[0] == bins[1]:
+        bins = np.asarray(bins, np.float64)
+        bins[0] -= .001 * abs(bins[0]) if bins[0] != 0 else .001
+        bins[1] += .001 * abs(bins[1]) if bins[1] != 0 else .001
+
     fac, bins = _bins_to_cuts(x, bins, labels=labels,
                               precision=precision, include_lowest=True,
                               dtype=dtype, duplicates=duplicates)