Improved solution: using same approach as pd.cut

Author: luca-s

doc/source/whatsnew/v0.20.0.txt

@@ -593,7 +593,6 @@ Bug Fixes
 - 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`)
 - Bug in ``Series.ffill()`` with mixed dtypes containing tz-aware datetimes. (:issue:`14956`)

pandas/tools/tile.py

@@ -185,14 +185,17 @@ def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
     x, dtype = _coerce_to_type(x)
 
     if is_integer(q):
-        quantiles = np.linspace(0, 1, q + 1)
+        if x.size == 0:
+            raise ValueError('Cannot qcut empty array')
 
-        if q == 1:
+        rng = (nanops.nanmin(x), nanops.nanmax(x))
+        if rng[0] == rng[1] and q == 1:
             duplicates = 'allow'
+
+        quantiles = np.linspace(0, 1, q + 1)
     else:
         quantiles = q
     bins = algos.quantile(x, quantiles)
-
     fac, bins = _bins_to_cuts(x, bins, labels=labels,
                               precision=precision, include_lowest=True,
                               dtype=dtype, duplicates=duplicates)