BUG: Bug in groupby transform with a datetime-like grouper (GH5712)

doc/source/release.rst

@@ -823,6 +823,7 @@ Bug Fixes
   - Work around regression in numpy 1.7.0 which erroneously raises IndexError from ``ndarray.item`` (:issue:`5666`)
   - Bug in repeated indexing of object with resultant non-unique index (:issue:`5678`)
   - Bug in fillna with Series and a passed series/dict (:issue:`5703`)
+  - Bug in groupby transform with a datetime-like grouper (:issue:`5712`)
 
 pandas 0.12.0
 -------------

pandas/core/common.py

@@ -2009,6 +2009,14 @@ def needs_i8_conversion(arr_or_dtype):
             is_timedelta64_dtype(arr_or_dtype))
 
 
+def is_numeric_dtype(arr_or_dtype):
+    if isinstance(arr_or_dtype, np.dtype):
+        tipo = arr_or_dtype.type
+    else:
+        tipo = arr_or_dtype.dtype.type
+    return (issubclass(tipo, (np.number, np.bool_))
+            and not issubclass(tipo, (np.datetime64, np.timedelta64)))
+
 def is_float_dtype(arr_or_dtype):
     if isinstance(arr_or_dtype, np.dtype):
         tipo = arr_or_dtype.type

pandas/core/groupby.py

@@ -19,9 +19,11 @@
 import pandas.core.algorithms as algos
 import pandas.core.common as com
 from pandas.core.common import(_possibly_downcast_to_dtype, isnull,
-                               notnull, _DATELIKE_DTYPES)
+                               notnull, _DATELIKE_DTYPES, is_numeric_dtype,
+                               is_timedelta64_dtype, is_datetime64_dtype)
 
 import pandas.lib as lib
+from pandas.lib import Timestamp
 import pandas.algos as _algos
 import pandas.hashtable as _hash
 
@@ -257,6 +259,16 @@ def indices(self):
         """ dict {group name -> group indices} """
         return self.grouper.indices
 
+    def _get_index(self, name):
+        """ safe get index """
+        try:
+            return self.indices[name]
+        except:
+            if isinstance(name, Timestamp):
+                name = name.value
+                return self.indices[name]
+            raise
+
     @property
     def name(self):
         if self._selection is None:
@@ -350,7 +362,7 @@ def get_group(self, name, obj=None):
         if obj is None:
             obj = self.obj
 
-        inds = self.indices[name]
+        inds = self._get_index(name)
         return obj.take(inds, axis=self.axis, convert=False)
 
     def __iter__(self):
@@ -676,7 +688,7 @@ def _try_cast(self, result, obj):
     def _cython_agg_general(self, how, numeric_only=True):
         output = {}
         for name, obj in self._iterate_slices():
-            is_numeric = _is_numeric_dtype(obj.dtype)
+            is_numeric = is_numeric_dtype(obj.dtype)
             if numeric_only and not is_numeric:
                 continue
 
@@ -714,7 +726,7 @@ def _python_agg_general(self, func, *args, **kwargs):
 
                 # since we are masking, make sure that we have a float object
                 values = result
-                if _is_numeric_dtype(values.dtype):
+                if is_numeric_dtype(values.dtype):
                     values = com.ensure_float(values)
 
                 output[name] = self._try_cast(values[mask], result)
@@ -1080,7 +1092,7 @@ def aggregate(self, values, how, axis=0):
                 raise NotImplementedError
             out_shape = (self.ngroups,) + values.shape[1:]
 
-        if _is_numeric_dtype(values.dtype):
+        if is_numeric_dtype(values.dtype):
             values = com.ensure_float(values)
             is_numeric = True
         else:
@@ -1474,6 +1486,15 @@ def __init__(self, index, grouper=None, name=None, level=None,
                     self.grouper = None  # Try for sanity
                     raise AssertionError(errmsg)
 
+        # if we have a date/time-like grouper, make sure that we have Timestamps like
+        if getattr(self.grouper,'dtype',None) is not None:
+            if is_datetime64_dtype(self.grouper):
+                from pandas import to_datetime
+                self.grouper = to_datetime(self.grouper)
+            elif is_timedelta64_dtype(self.grouper):
+                from pandas import to_timedelta
+                self.grouper = to_timedelta(self.grouper)
+
     def __repr__(self):
         return 'Grouping(%s)' % self.name
 
@@ -1821,7 +1842,7 @@ def transform(self, func, *args, **kwargs):
             # need to do a safe put here, as the dtype may be different
             # this needs to be an ndarray
             result = Series(result)
-            result.iloc[self.indices[name]] = res
+            result.iloc[self._get_index(name)] = res
             result = result.values
 
         # downcast if we can (and need)
@@ -1860,7 +1881,7 @@ def true_and_notnull(x, *args, **kwargs):
             return b and notnull(b)
 
         try:
-            indices = [self.indices[name] if true_and_notnull(group) else []
+            indices = [self._get_index(name) if true_and_notnull(group) else []
                        for name, group in self]
         except ValueError:
             raise TypeError("the filter must return a boolean result")
@@ -1921,7 +1942,7 @@ def _cython_agg_blocks(self, how, numeric_only=True):
         for block in data.blocks:
             values = block.values
 
-            is_numeric = _is_numeric_dtype(values.dtype)
+            is_numeric = is_numeric_dtype(values.dtype)
 
             if numeric_only and not is_numeric:
                 continue
@@ -2412,7 +2433,7 @@ def filter(self, func, dropna=True, *args, **kwargs):
                 res = path(group)
 
             def add_indices():
-                indices.append(self.indices[name])
+                indices.append(self._get_index(name))
 
             # interpret the result of the filter
             if isinstance(res, (bool, np.bool_)):
@@ -2973,12 +2994,6 @@ def _reorder_by_uniques(uniques, labels):
 }
 
 
-def _is_numeric_dtype(dt):
-    typ = dt.type
-    return (issubclass(typ, (np.number, np.bool_))
-            and not issubclass(typ, (np.datetime64, np.timedelta64)))
-
-
 def _intercept_function(func):
     return _func_table.get(func, func)
 

pandas/tests/test_groupby.py

@@ -627,6 +627,14 @@ def test_transform_broadcast(self):
             for idx in gp.index:
                 assert_fp_equal(res.xs(idx), agged[idx])
 
+    def test_transform_bug(self):
+        # GH 5712
+        # transforming on a datetime column
+        df = DataFrame(dict(A = Timestamp('20130101'), B = np.arange(5)))
+        result = df.groupby('A')['B'].transform(lambda x: x.rank(ascending=False))
+        expected = Series(np.arange(5,0,step=-1),name='B')
+        assert_series_equal(result,expected)
+
     def test_transform_multiple(self):
         grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month])