Merge pull request #7779 from jreback/td

BUG: unwanted conversions of timedelta dtypes when in a mixed datetimelike frame (GH7778)

Author: jreback

doc/source/v0.15.0.txt

@@ -186,7 +186,7 @@ Bug Fixes
 ~~~~~~~~~
 
 - 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`)
 
 
 

pandas/core/frame.py

@@ -3539,6 +3539,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True):
             except Exception:
                 pass
 
+        dtype = object if self._is_mixed_type else None
         if axis == 0:
             series_gen = (self.icol(i) for i in range(len(self.columns)))
             res_index = self.columns
@@ -3547,7 +3548,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True):
             res_index = self.index
             res_columns = self.columns
             values = self.values
-            series_gen = (Series.from_array(arr, index=res_columns, name=name)
+            series_gen = (Series.from_array(arr, index=res_columns, name=name, dtype=dtype)
                           for i, (arr, name) in
                           enumerate(zip(values, res_index)))
         else:  # pragma : no cover

pandas/core/internals.py

@@ -25,7 +25,7 @@
 from pandas.util.decorators import cache_readonly
 
 from pandas.tslib import Timestamp
-from pandas import compat
+from pandas import compat, _np_version_under1p7
 from pandas.compat import range, map, zip, u
 from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type
 
@@ -1290,6 +1290,16 @@ def to_native_types(self, slicer=None, na_rep=None, **kwargs):
         return rvalues.tolist()
 
 
+    def get_values(self, dtype=None):
+        # return object dtypes as datetime.timedeltas
+        if dtype == object:
+            if _np_version_under1p7:
+                return self.values.astype('object')
+            return lib.map_infer(self.values.ravel(),
+                                 lambda x: timedelta(microseconds=x.item()/1000)
+                                 ).reshape(self.values.shape)
+        return self.values
+
 class BoolBlock(NumericBlock):
     __slots__ = ()
     is_bool = True
@@ -2595,7 +2605,7 @@ def as_matrix(self, items=None):
         else:
             mgr = self
 
-        if self._is_single_block:
+        if self._is_single_block or not self.is_mixed_type:
             return mgr.blocks[0].get_values()
         else:
             return mgr._interleave()
@@ -3647,9 +3657,11 @@ def _lcd_dtype(l):
     has_non_numeric = have_dt64 or have_td64 or have_cat
 
     if (have_object or
-        (have_bool and have_numeric) or
+        (have_bool and (have_numeric or have_dt64 or have_td64)) or
         (have_numeric and has_non_numeric) or
-        have_cat):
+        have_cat or
+        have_dt64 or
+        have_td64):
         return np.dtype(object)
     elif have_bool:
         return np.dtype(bool)
@@ -3670,10 +3682,6 @@ def _lcd_dtype(l):
             return np.dtype('int%s' % (lcd.itemsize * 8 * 2))
         return lcd
 
-    elif have_dt64 and not have_float and not have_complex:
-        return np.dtype('M8[ns]')
-    elif have_td64 and not have_float and not have_complex:
-        return np.dtype('m8[ns]')
     elif have_complex:
         return np.dtype('c16')
     else:

pandas/core/series.py

@@ -237,14 +237,14 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
         self._set_axis(0, index, fastpath=True)
 
     @classmethod
-    def from_array(cls, arr, index=None, name=None, copy=False,
+    def from_array(cls, arr, index=None, name=None, dtype=None, copy=False,
                    fastpath=False):
         # return a sparse series here
         if isinstance(arr, ABCSparseArray):
             from pandas.sparse.series import SparseSeries
             cls = SparseSeries
 
-        return cls(arr, index=index, name=name, copy=copy, fastpath=fastpath)
+        return cls(arr, index=index, name=name, dtype=dtype, copy=copy, fastpath=fastpath)
 
     @property
     def _constructor(self):

pandas/tests/test_frame.py

@@ -9635,6 +9635,15 @@ def test_apply(self):
             [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['a', 'a', 'c'])
         self.assertRaises(ValueError, df.apply, lambda x: x, 2)
 
+    def test_apply_mixed_datetimelike(self):
+        tm._skip_if_not_numpy17_friendly()
+
+        # mixed datetimelike
+        # GH 7778
+        df = DataFrame({ 'A' : date_range('20130101',periods=3), 'B' : pd.to_timedelta(np.arange(3),unit='s') })
+        result = df.apply(lambda x: x, axis=1)
+        assert_frame_equal(result, df)
+
     def test_apply_empty(self):
         # empty
         applied = self.empty.apply(np.sqrt)

pandas/tests/test_internals.py

@@ -3,7 +3,7 @@
 import nose
 import numpy as np
 
-from pandas import Index, MultiIndex, DataFrame, Series
+from pandas import Index, MultiIndex, DataFrame, Series, Categorical
 from pandas.compat import OrderedDict, lrange
 from pandas.sparse.array import SparseArray
 from pandas.core.internals import *
@@ -41,9 +41,11 @@ def create_block(typestr, placement, item_shape=None, num_offset=0):
         * complex, c16, c8
         * bool
         * object, string, O
-        * datetime, dt
+        * datetime, dt, M8[ns]
+        * timedelta, td, m8[ns]
         * sparse (SparseArray with fill_value=0.0)
         * sparse_na (SparseArray with fill_value=np.nan)
+        * category, category2
 
     """
     placement = BlockPlacement(placement)
@@ -67,8 +69,14 @@ def create_block(typestr, placement, item_shape=None, num_offset=0):
                             shape)
     elif typestr in ('bool'):
         values = np.ones(shape, dtype=np.bool_)
-    elif typestr in ('datetime', 'dt'):
+    elif typestr in ('datetime', 'dt', 'M8[ns]'):
         values = (mat * 1e9).astype('M8[ns]')
+    elif typestr in ('timedelta', 'td', 'm8[ns]'):
+        values = (mat * 1).astype('m8[ns]')
+    elif typestr in ('category'):
+        values = Categorical([1,1,2,2,3,3,3,3,4,4])
+    elif typestr in ('category2'):
+        values = Categorical(['a','a','a','a','b','b','c','c','c','d'])
     elif typestr in ('sparse', 'sparse_na'):
         # FIXME: doesn't support num_rows != 10
         assert shape[-1] == 10
@@ -556,7 +564,54 @@ def _compare(old_mgr, new_mgr):
         self.assertEqual(new_mgr.get('h').dtype, np.float16)
 
     def test_interleave(self):
-        pass
+
+
+        # self
+        for dtype in ['f8','i8','object','bool','complex','M8[ns]','m8[ns]']:
+            mgr = create_mgr('a: {0}'.format(dtype))
+            self.assertEqual(mgr.as_matrix().dtype,dtype)
+            mgr = create_mgr('a: {0}; b: {0}'.format(dtype))
+            self.assertEqual(mgr.as_matrix().dtype,dtype)
+
+        # will be converted according the actual dtype of the underlying
+        mgr = create_mgr('a: category')
+        self.assertEqual(mgr.as_matrix().dtype,'i8')
+        mgr = create_mgr('a: category; b: category')
+        self.assertEqual(mgr.as_matrix().dtype,'i8'),
+        mgr = create_mgr('a: category; b: category2')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: category2')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: category2; b: category2')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+
+        # combinations
+        mgr = create_mgr('a: f8')
+        self.assertEqual(mgr.as_matrix().dtype,'f8')
+        mgr = create_mgr('a: f8; b: i8')
+        self.assertEqual(mgr.as_matrix().dtype,'f8')
+        mgr = create_mgr('a: f4; b: i8')
+        self.assertEqual(mgr.as_matrix().dtype,'f4')
+        mgr = create_mgr('a: f4; b: i8; d: object')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: bool; b: i8')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: complex')
+        self.assertEqual(mgr.as_matrix().dtype,'complex')
+        mgr = create_mgr('a: f8; b: category')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: M8[ns]; b: category')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: M8[ns]; b: bool')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: M8[ns]; b: i8')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: m8[ns]; b: bool')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: m8[ns]; b: i8')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
+        mgr = create_mgr('a: M8[ns]; b: m8[ns]')
+        self.assertEqual(mgr.as_matrix().dtype,'object')
 
     def test_interleave_non_unique_cols(self):
         df = DataFrame([