Raise exception on non-unique column index in to_hdf for fixed format.

doc/source/io.rst

@@ -2311,7 +2311,8 @@ Fixed Format
 The examples above show storing using ``put``, which write the HDF5 to ``PyTables`` in a fixed array format, called
 the ``fixed`` format. These types of stores are are **not** appendable once written (though you can simply
 remove them and rewrite). Nor are they **queryable**; they must be
-retrieved in their entirety. These offer very fast writing and slightly faster reading than ``table`` stores.
+retrieved in their entirety. They also do not support dataframes with non-unique column names.
+The ``fixed`` format stores offer very fast writing and slightly faster reading than ``table`` stores.
 This format is specified by default when using ``put`` or ``to_hdf`` or by ``format='fixed'`` or ``format='f'``
 
 .. warning::

doc/source/v0.15.0.txt

@@ -187,6 +187,7 @@ Bug Fixes
 - Bug in Series 0-division with a float and integer operand dtypes  (:issue:`7785`)
 - Bug in ``Series.astype("unicode")`` not calling ``unicode`` on the values correctly (:issue:`7758`)
 - Bug in ``DataFrame.as_matrix()`` with mixed ``datetime64[ns]`` and ``timedelta64[ns]`` dtypes (:issue:`7778`)
+- Raise a ``ValueError`` in ``df.to_hdf`` if ``df`` has non-unique columns as the resulting file will be broken (:issue:`7761`)
 
 
 

pandas/io/pytables.py

@@ -2680,6 +2680,9 @@ def write(self, obj, **kwargs):
 
         self.attrs.ndim = data.ndim
         for i, ax in enumerate(data.axes):
+            if i == 0:
+                if not ax.is_unique:
+                    raise ValueError("Columns index has to be unique for fixed format")
             self.write_index('axis%d' % i, ax)
 
         # Supporting mixed-type DataFrame objects...nontrivial

pandas/io/tests/test_pytables.py

@@ -4370,6 +4370,17 @@ def test_categorical(self):
             # FIXME: TypeError: cannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety
             #result = store.select('df', where = ['index>2'])
             #tm.assert_frame_equal(df[df.index>2],result)
+
+    def test_duplicate_column_name(self):
+        df = DataFrame(columns=["a", "a"], data=[[0, 0]])
+
+        with ensure_clean_path(self.path) as path:
+            self.assertRaises(ValueError, df.to_hdf, path, 'df', format='fixed')
+
+            df.to_hdf(path, 'df', format='table')
+            other = read_hdf(path, 'df')
+            tm.assert_frame_equal(df, other)
+
 
 def _test_sort(obj):
     if isinstance(obj, DataFrame):