Factor the common type discovery to an internal function.

Author: sstanovnik

pandas/core/internals.py

@@ -35,7 +35,8 @@
                                _infer_dtype_from_scalar,
                                _soft_convert_objects,
                                _possibly_convert_objects,
-                               _astype_nansafe)
+                               _astype_nansafe,
+                               _find_common_type)
 from pandas.types.missing import (isnull, array_equivalent,
                                   _is_na_compat,
                                   is_null_datelike_scalar)
@@ -4460,7 +4461,7 @@ def _interleaved_dtype(blocks):
     elif have_int and not have_float and not have_complex:
         # if we are mixing unsigned and signed, then return
         # the next biggest int type (if we can)
-        lcd = np.find_common_type([b.dtype for b in counts[IntBlock]], [])
+        lcd = _find_common_type([b.dtype for b in counts[IntBlock]])
         kinds = set([i.dtype.kind for i in counts[IntBlock]])
         if len(kinds) == 1:
             return lcd
@@ -4477,7 +4478,7 @@ def _interleaved_dtype(blocks):
         return np.dtype('c16')
     else:
         introspection_blks = counts[FloatBlock] + counts[SparseBlock]
-        return np.find_common_type([b.dtype for b in introspection_blks], [])
+        return _find_common_type([b.dtype for b in introspection_blks])
 
 
 def _consolidate(blocks):

pandas/core/ops.py

@@ -30,7 +30,7 @@
                                  is_bool_dtype, is_datetimetz,
                                  is_list_like,
                                  _ensure_object)
-from pandas.types.cast import _maybe_upcast_putmask
+from pandas.types.cast import _maybe_upcast_putmask, _find_common_type
 from pandas.types.generic import ABCSeries, ABCIndex, ABCPeriodIndex
 
 # -----------------------------------------------------------------------------
@@ -616,7 +616,7 @@ def na_op(x, y):
                                           raise_on_error=True, **eval_kwargs)
         except TypeError:
             if isinstance(y, (np.ndarray, ABCSeries, pd.Index)):
-                dtype = np.find_common_type([x.dtype, y.dtype], [])
+                dtype = _find_common_type([x.dtype, y.dtype])
                 result = np.empty(x.size, dtype=dtype)
                 mask = notnull(x) & notnull(y)
                 result[mask] = op(x[mask], _values_from_object(y[mask]))

pandas/tests/frame/test_indexing.py

@@ -2760,10 +2760,10 @@ def test_indexing_multiple(self):
         tm.assert_sp_frame_equal(self.sdf, self.sdf.loc[:])
         tm.assert_sp_frame_equal(self.sdf.iloc[[1, 2]],
                                  pd.SparseDataFrame({
-                                     'string': ['b', 'c'],
-                                     'int': [2, 3],
-                                     'float': [1.2, 1.3],
-                                     'object': [{}, set()]
+                                     'string': self.string_series.iloc[[1, 2]],
+                                     'int': self.int_series.iloc[[1, 2]],
+                                     'float': self.float_series.iloc[[1, 2]],
+                                     'object': self.object_series.iloc[[1, 2]]
                                  }, index=[1, 2])[self.cols])
         tm.assert_sp_frame_equal(self.sdf[['int', 'string']],
                                  pd.SparseDataFrame({

pandas/types/cast.py

@@ -861,3 +861,9 @@ def _possibly_cast_to_datetime(value, dtype, errors='raise'):
             value = _possibly_infer_to_datetimelike(value)
 
     return value
+
+
+def _find_common_type(types):
+    """Find a common data type among the given dtypes."""
+    # TODO: enable using pandas specific types
+    return np.find_common_type(types, [])