diff --git a/larray/core.py b/larray/core.py
index 6e240f903..b4393c9c9 100644
--- a/larray/core.py
+++ b/larray/core.py
@@ -1365,7 +1365,7 @@ def opmethod(self, other):
             elif not np.isscalar(other):
                 raise TypeError("unsupported operand type(s) for %s: '%s' "
                                 "and '%s'" % (opname, type(self), type(other)))
-            return LArray(super_method(self.data, other), self.axes)
+            return LArray(super_method(np.asarray(self), other), self.axes)
         opmethod.__name__ = fullname
         return opmethod
 
@@ -1411,7 +1411,7 @@ def _unaryop(opname):
         super_method = getattr(np.ndarray, fullname)
 
         def opmethod(self):
-            return LArray(super_method(self.data), self.axes)
+            return LArray(super_method(np.asarray(self)), self.axes)
         opmethod.__name__ = fullname
         return opmethod
 
@@ -1541,6 +1541,15 @@ def to_clipboard(self, *args, **kwargs):
     def plot(self, *args, **kwargs):
         self.df.plot(*args, **kwargs)
 
+    #XXX: one less indirection as we have all the info at this level?
+    # @property
+    # def shape(self):
+    #     return tuple(len(a) for a in self.axes)
+    #
+    # @property
+    # def ndim(self):
+    #     return len(self.axes)
+
     @property
     def shape(self):
         return self.data.shape
@@ -1565,7 +1574,7 @@ def __len__(self):
         return len(self.data)
 
     def __array__(self, dtype=None):
-        return self.data
+        return np.asarray(self.data)
 
     __array_priority__ = 100
 
@@ -1648,6 +1657,62 @@ def df_aslarray(df, sort_rows=True, sort_columns=True, **kwargs):
     return LArray(data, axes)
 
 
+class DataFrameWrapper(object):
+    def __init__(self, df):
+        self.df = df
+
+    def __getitem__(self, key):
+        return self.df[key]
+
+    def __getattr__(self, key):
+        return getattr(self.df, key)
+
+    @property
+    def dtype(self):
+        # assumes df is homogeneous !
+        return self.df.dtypes[0]
+
+    @property
+    def ndim(self):
+        return self.df.index.nlevels + 1
+
+    @property
+    def shape(self):
+        shape = [len(level) for level in self.df.index.levels]
+        shape.append(len(self.df.columns))
+        return tuple(shape)
+
+    def copy(self):
+        return DataFrameWrapper(self.df.copy())
+
+    # not caught by __getattr__?
+    def __len__(self):
+        return self.shape[0]
+
+    def __array__(self, dtype=None):
+        return self.df.__array__(dtype).reshape(self.shape)
+
+
+#TODO: implement sort_columns
+def df_aslarray2(df, sort_rows=True, sort_columns=True, **kwargs):
+    axes_names = [decode(name, 'utf8') for name in df.index.names]
+    if axes_names == [None]:
+        last_axis = None, None
+    else:
+        last_axis = axes_names[-1].split('\\')
+    axes_names[-1] = last_axis[0]
+    #FIXME: hardcoded "time"
+    axes_names.append(last_axis[1] if len(last_axis) > 1 else 'time')
+
+    axes_labels = df_labels(df, sort=sort_rows)
+    # pandas treats the "time" labels as column names (strings) so we need
+    # to convert them to values
+    axes_labels.append([parse(cell) for cell in df.columns.values])
+
+    axes = [Axis(name, labels) for name, labels in zip(axes_names, axes_labels)]
+    return LArray(DataFrameWrapper(df), axes)
+
+
 def read_csv(filepath, nb_index=0, index_col=[], sep=',', headersep=None,
              na=np.nan, sort_rows=True, sort_columns=True, **kwargs):
     """
@@ -1733,14 +1798,12 @@ def read_eurostat(filepath, **kwargs):
     return read_csv(filepath, sep='\t', headersep=',', **kwargs)
 
 
-def read_hdf(filepath, key, na=np.nan, sort_rows=True, sort_columns=True,
-             **kwargs):
+def read_hdf(filepath, key, sort_rows=True, sort_columns=True, **kwargs):
     """
     read an LArray from a h5 file with the specified name
     """
     df = pd.read_hdf(filepath, key, **kwargs)
-    return df_aslarray(df, sort_rows=sort_rows, sort_columns=sort_columns,
-                       fill_value=na)
+    return df_aslarray2(df, sort_rows=sort_rows, sort_columns=sort_columns)
 
 
 def read_excel(filepath, sheetname=0, nb_index=0, index_col=[],
diff --git a/larray/tests/test_la.py b/larray/tests/test_la.py
index c12a25105..32e8de8ba 100644
--- a/larray/tests/test_la.py
+++ b/larray/tests/test_la.py
@@ -10,7 +10,7 @@
 import larray
 from larray import (LArray, Axis, ValueGroup, union, to_ticks, to_key,
                     srange, larray_equal, read_csv, read_hdf, df_aslarray,
-                    zeros, zeros_like, AxisCollection)
+                    zeros, zeros_like, AxisCollection, DataFrameWrapper)
 from larray.utils import array_equal, array_nan_equal
 
 
@@ -509,11 +509,23 @@ def setUp(self):
 
         self.array = np.arange(116 * 44 * 2 * 15).reshape(116, 44, 2, 15) \
                                                  .astype(float)
-        self.larray = LArray(self.array,
-                             axes=(self.age, self.geo, self.sex, self.lipro))
+        idx = pd.MultiIndex.from_product([self.age.labels, self.geo.labels,
+                                          self.sex.labels])
+        dfarray = self.array.reshape(116 * 44 * 2, 15)
+        df = pd.DataFrame(dfarray, idx, columns=self.lipro.labels)
+        wrapped = DataFrameWrapper(df)
+        self.larray = LArray(wrapped, (self.age, self.geo, self.sex,
+                                       self.lipro))
+        # self.larray = LArray(self.array,
+        #                      axes=(self.age, self.geo, self.sex, self.lipro))
+        # self.larray = read_hdf('c:/tmp/y.h5', 'y', sort_rows=False)
 
         self.small_data = np.arange(30).reshape(2, 15)
-        self.small = LArray(self.small_data, axes=(self.sex, self.lipro))
+        df = pd.DataFrame(self.small_data, self.sex.labels,
+                          columns=self.lipro.labels)
+        self.small = LArray(DataFrameWrapper(df), (self.sex, self.lipro))
+        # self.small = LArray(self.small_data, axes=(self.sex, self.lipro))
+        # self.small = read_hdf('c:/tmp/x.h5', 'x', sort_rows=False)
 
     def test_zeros(self):
         la = zeros((self.geo, self.age))