API: Raise on iloc indexing with a non-integer based boolean mask (GH3631)

RELEASE.rst

@@ -70,6 +70,9 @@ pandas 0.11.1
   - Add ``squeeze`` keyword to ``groupby`` to allow reduction from 
     DataFrame -> Series if groups are unique. Regression from 0.10.1, 
     partial revert on (GH2893_) with (GH3596_)
+  - Raise on ``iloc`` when boolean indexing with a label based indexer mask
+    e.g. a boolean Series, even with integer labels, will raise. Since ``iloc``
+    is purely positional based, the labels on the Series are not alignable (GH3631_)
 
 **Bug Fixes**
 
@@ -182,6 +185,7 @@ pandas 0.11.1
 .. _GH3624: https://github.com/pydata/pandas/issues/3624
 .. _GH3626: https://github.com/pydata/pandas/issues/3626
 .. _GH3601: https://github.com/pydata/pandas/issues/3601
+.. _GH3631: https://github.com/pydata/pandas/issues/3631
 .. _GH1512: https://github.com/pydata/pandas/issues/1512
 
 

doc/source/indexing.rst

@@ -68,7 +68,6 @@ three types of multi-axis indexing.
   - An integer e.g. ``5``
   - A list or array of integers ``[4, 3, 0]``
   - A slice object with ints ``1:7``
-  - A boolean array
 
   See more at :ref:`Selection by Position <indexing.integer>` 
 
@@ -291,7 +290,6 @@ The ``.iloc`` attribute is the primary access method. The following are valid in
 - An integer e.g. ``5``
 - A list or array of integers ``[4, 3, 0]``
 - A slice object with ints ``1:7``
-- A boolean array
 
 .. ipython:: python
 
@@ -329,12 +327,6 @@ Select via integer list
 
    df1.iloc[[1,3,5],[1,3]]
 
-Select via boolean array
-
-.. ipython:: python
-
-   df1.iloc[:,df1.iloc[0]>0]
-
 For slicing rows explicitly (equiv to deprecated ``df.irow(slice(1,3))``).
 
 .. ipython:: python

doc/source/v0.11.1.txt

@@ -40,6 +40,27 @@ API changes
         # no squeezing (the default, and behavior in 0.10.1)
         df2.groupby("val1").apply(func)
 
+  - Raise on ``iloc`` when boolean indexing with a label based indexer mask
+    e.g. a boolean Series, even with integer labels, will raise. Since ``iloc``
+    is purely positional based, the labels on the Series are not alignable (GH3631_)
+
+    This case is rarely used, and there are plently of alternatives. This preserves the
+    ``iloc`` API to be *purely* positional based.
+
+    .. ipython:: python
+
+       df = DataFrame(range(5), list('ABCDE'), columns=['a'])
+       mask = (df.a%2 == 0)
+       mask
+
+       # this is what you should use
+       df.loc[mask]
+
+       # this will work as well
+       df.iloc[mask.values]
+
+    ``df.iloc[mask]`` will raise a ``ValueError``
+
 
 Enhancements
 ~~~~~~~~~~~~
@@ -74,3 +95,4 @@ on GitHub for a complete list.
 .. _GH3435: https://github.com/pydata/pandas/issues/3435
 .. _GH1512: https://github.com/pydata/pandas/issues/1512
 .. _GH2285: https://github.com/pydata/pandas/issues/2285
+.. _GH3631: https://github.com/pydata/pandas/issues/3631

pandas/core/indexing.py

@@ -775,7 +775,14 @@ class _iLocIndexer(_LocationIndexer):
     _exception   = IndexError
 
     def _has_valid_type(self, key, axis):
-        return isinstance(key, slice) or com.is_integer(key) or com._is_bool_indexer(key) or _is_list_like(key)
+        if com._is_bool_indexer(key):
+            if hasattr(key,'index') and isinstance(key.index,Index):
+                if key.index.inferred_type == 'integer':
+                    raise NotImplementedError("iLocation based boolean indexing on an integer type is not available")
+                raise ValueError("iLocation based boolean indexing cannot use an indexable as a mask")
+            return True
+
+        return isinstance(key, slice) or com.is_integer(key) or _is_list_like(key)
 
     def _getitem_tuple(self, tup):
 
@@ -811,9 +818,11 @@ def _get_slice_axis(self, slice_obj, axis=0):
     def _getitem_axis(self, key, axis=0):
 
         if isinstance(key, slice):
+            self._has_valid_type(key,axis)
             return self._get_slice_axis(key, axis=axis)
 
         elif com._is_bool_indexer(key):
+            self._has_valid_type(key,axis)
             return self._getbool_axis(key, axis=axis)
 
         # a single integer or a list of integers

pandas/tests/test_indexing.py

@@ -888,6 +888,60 @@ def test_multi_assign(self):
         df2.ix[mask, cols]= dft.ix[mask, cols].values
         assert_frame_equal(df2,expected)
 
+    def test_iloc_mask(self):
+
+        # GH 3631, iloc with a mask (of a series) should raise
+        df = DataFrame(range(5), list('ABCDE'), columns=['a'])
+        mask = (df.a%2 == 0)
+        self.assertRaises(ValueError, df.iloc.__getitem__, tuple([mask]))
+        mask.index = range(len(mask))
+        self.assertRaises(NotImplementedError, df.iloc.__getitem__, tuple([mask]))
+
+        # ndarray ok
+        result = df.iloc[np.array([True] * len(mask),dtype=bool)]
+        assert_frame_equal(result,df)
+
+        # the possibilities
+        locs = np.arange(4)
+        nums = 2**locs
+        reps = map(bin, nums)
+        df = DataFrame({'locs':locs, 'nums':nums}, reps)
+
+        expected = {
+            (None,'')     : '0b1100',
+            (None,'.loc')  : '0b1100',
+            (None,'.iloc') : '0b1100',
+            ('index','')  : '0b11',
+            ('index','.loc')  : '0b11',
+            ('index','.iloc') : 'iLocation based boolean indexing cannot use an indexable as a mask',
+            ('locs','')      : 'Unalignable boolean Series key provided',
+            ('locs','.loc')   : 'Unalignable boolean Series key provided',
+            ('locs','.iloc')  : 'iLocation based boolean indexing on an integer type is not available',
+            }
+
+        import warnings
+        warnings.filterwarnings(action='ignore', category=UserWarning)
+        result = dict()
+        for idx in [None, 'index', 'locs']:
+            mask = (df.nums>2).values
+            if idx:
+                mask = Series(mask, list(reversed(getattr(df, idx))))
+            for method in ['', '.loc', '.iloc']:
+                try:
+                    if method:
+                        accessor = getattr(df, method[1:])
+                    else:
+                        accessor = df
+                    ans = str(bin(accessor[mask]['nums'].sum()))
+                except Exception, e:
+                    ans = str(e)
+
+                key = tuple([idx,method])
+                r = expected.get(key)
+                if r != ans:
+                    raise AssertionError("[%s] does not match [%s], received [%s]" %
+                                         (key,ans,r))
+        warnings.filterwarnings(action='always', category=UserWarning)
 
 if __name__ == '__main__':
     import nose