diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 0873e4b34b0b1..64c5de6cb100a 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -105,6 +105,7 @@ Other enhancements
   of sorting or an incorrect key. See :ref:`here <advanced.unsorted>`
 
 - ``pd.cut`` and ``pd.qcut`` now support datetime64 and timedelta64 dtypes (:issue:`14714`, :issue:`14798`)
+- ``pd.qcut`` has gained the ``duplicates='raise'|'drop'`` option to control whether to raise on duplicated edges (:issue:`7751`)
 - ``Series`` provides a ``to_excel`` method to output Excel files (:issue:`8825`)
 - The ``usecols`` argument in ``pd.read_csv`` now accepts a callable function as a value  (:issue:`14154`)
 - ``pd.DataFrame.plot`` now prints a title above each subplot if ``suplots=True`` and ``title`` is a list of strings (:issue:`14753`)
diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py
index c9a96d80f35ba..8b180957801f9 100644
--- a/pandas/tools/tests/test_tile.py
+++ b/pandas/tools/tests/test_tile.py
@@ -272,6 +272,18 @@ def test_series_retbins(self):
                                     np.array([0, 0, 1, 1], dtype=np.int8))
         tm.assert_numpy_array_equal(bins, np.array([0, 1.5, 3]))
 
+    def test_qcut_duplicates_drop(self):
+        # GH 7751
+        values = [0, 0, 0, 0, 1, 2, 3]
+        cats = qcut(values, 3, duplicates='drop')
+        ex_levels = ['[0, 1]', '(1, 3]']
+        self.assertTrue((cats.categories == ex_levels).all())
+
+    def test_qcut_duplicates_raise(self):
+        # GH 7751
+        values = [0, 0, 0, 0, 1, 2, 3]
+        self.assertRaises(ValueError, qcut, values, 3, duplicates='raise')
+
     def test_single_bin(self):
         # issue 14652
         expected = Series([0, 0])
diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py
index a372e113f1d7e..2875d9c14dc47 100644
--- a/pandas/tools/tile.py
+++ b/pandas/tools/tile.py
@@ -129,7 +129,7 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
                                 series_index, name)
 
 
-def qcut(x, q, labels=None, retbins=False, precision=3):
+def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
     """
     Quantile-based discretization function. Discretize variable into
     equal-sized buckets based on rank or based on sample quantiles. For example
@@ -151,6 +151,10 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
         as a scalar.
     precision : int
         The precision at which to store and display the bins labels
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+
+        .. versionadded:: 0.20.0
 
     Returns
     -------
@@ -187,7 +191,7 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
     bins = algos.quantile(x, quantiles)
     fac, bins = _bins_to_cuts(x, bins, labels=labels,
                               precision=precision, include_lowest=True,
-                              dtype=dtype)
+                              dtype=dtype, duplicates=duplicates)
 
     return _postprocess_for_cut(fac, bins, retbins, x_is_series,
                                 series_index, name)
@@ -195,14 +199,24 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
 
 def _bins_to_cuts(x, bins, right=True, labels=None,
                   precision=3, include_lowest=False,
-                  dtype=None):
+                  dtype=None, duplicates='raise'):
+
+    if duplicates not in ['raise', 'drop']:
+        raise ValueError("invalid value for 'duplicates' parameter, "
+                         "valid options are: raise, drop")
+
+    unique_bins = algos.unique(bins)
+    if len(unique_bins) < len(bins):
+        if duplicates == 'raise':
+            raise ValueError("Bin edges must be unique: {}. You "
+                             "can drop duplicate edges by setting "
+                             "'duplicates' param".format(repr(bins)))
+        else:
+            bins = unique_bins
 
     side = 'left' if right else 'right'
     ids = bins.searchsorted(x, side=side)
 
-    if len(algos.unique(bins)) < len(bins):
-        raise ValueError('Bin edges must be unique: %s' % repr(bins))
-
     if include_lowest:
         ids[x == bins[0]] = 1