Use argument dtype to inform coercion

Master:

```python
>>> import dask.dataframe as dd

>>> s = dd.core.Scalar({('s', 0): 10}, 's', 'i8')
>>> pdf = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7],
...                     'b': [7, 6, 5, 4, 3, 2, 1]})
>>> (pdf + s).dtypes
a    object
b    object
dtype: object

Head:

```
>>> (pdf + s).dtypes
a    int64
b    int64
dtype: object
```

This is more consistent with 0.20.3, while still most of the changes in
pandas-dev#16821

Closes pandas-dev#17767

Author: TomAugspurger

Diff for: pandas/core/dtypes/cast.py

@@ -483,6 +483,39 @@ def infer_dtype_from_array(arr, pandas_dtype=False):
     return arr.dtype, arr
 
 
+def _maybe_infer_dtype_type(element):
+    """Try to infer an object's dtype's type, for use in arithmetic ops
+
+    Uses `element.dtype` if that's available.
+    Objects implementing the iterator protocol are cast to a NumPy array,
+    and from there the array's type is used.
+
+    Parameters
+    ----------
+    element : object
+        Possibly has a `.dtype` attribute, and possibly the iterator
+        protocol.
+
+    Returns
+    -------
+    tipo : type
+
+    Examples
+    --------
+    >>> from collections import namedtuple
+    >>> Foo = namedtuple("dtype")
+    >>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
+    numpy.int64
+    """
+    tipo = None
+    if hasattr(element, 'dtype'):
+        tipo = element.dtype.type
+    elif is_list_like(element):
+        element = np.asarray(element)
+        tipo = element.dtype.type
+    return tipo
+
+
 def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False):
     """ provide explict type promotion and coercion
 
@@ -509,6 +542,7 @@ def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False):
     return values, fill_value
 
 
+
 def maybe_cast_item(obj, item, dtype):
     chunk = obj[item]
 

Diff for: pandas/core/internals.py

@@ -44,7 +44,8 @@
     soft_convert_objects,
     maybe_convert_objects,
     astype_nansafe,
-    find_common_type)
+    find_common_type,
+    _maybe_infer_dtype_type)
 from pandas.core.dtypes.missing import (
     isna, notna, array_equivalent,
     _isna_compat,
@@ -629,9 +630,8 @@ def convert(self, copy=True, **kwargs):
     def _can_hold_element(self, element):
         """ require the same dtype as ourselves """
         dtype = self.values.dtype.type
-        if is_list_like(element):
-            element = np.asarray(element)
-            tipo = element.dtype.type
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
             return issubclass(tipo, dtype)
         return isinstance(element, dtype)
 
@@ -1806,9 +1806,8 @@ class FloatBlock(FloatOrComplexBlock):
     _downcast_dtype = 'int64'
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
-            element = np.asarray(element)
-            tipo = element.dtype.type
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
             return (issubclass(tipo, (np.floating, np.integer)) and
                     not issubclass(tipo, (np.datetime64, np.timedelta64)))
         return (isinstance(element, (float, int, np.floating, np.int_)) and
@@ -1856,9 +1855,9 @@ class ComplexBlock(FloatOrComplexBlock):
     is_complex = True
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
-            element = np.array(element)
-            return issubclass(element.dtype.type,
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
+            return issubclass(tipo,
                               (np.floating, np.integer, np.complexfloating))
         return (isinstance(element,
                            (float, int, complex, np.float_, np.int_)) and
@@ -1874,9 +1873,8 @@ class IntBlock(NumericBlock):
     _can_hold_na = False
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
-            element = np.array(element)
-            tipo = element.dtype.type
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
             return (issubclass(tipo, np.integer) and
                     not issubclass(tipo, (np.datetime64, np.timedelta64)) and
                     self.dtype.itemsize >= element.dtype.itemsize)
@@ -1917,9 +1915,8 @@ def _box_func(self):
         return lambda x: tslib.Timedelta(x, unit='ns')
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
-            element = np.array(element)
-            tipo = element.dtype.type
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
             return issubclass(tipo, np.timedelta64)
         return isinstance(element, (timedelta, np.timedelta64))
 
@@ -2018,9 +2015,9 @@ class BoolBlock(NumericBlock):
     _can_hold_na = False
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
-            element = np.asarray(element)
-            return issubclass(element.dtype.type, np.bool_)
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
+            return issubclass(tipo, np.bool_)
         return isinstance(element, (bool, np.bool_))
 
     def should_store(self, value):
@@ -2450,7 +2447,9 @@ def _astype(self, dtype, mgr=None, **kwargs):
         return super(DatetimeBlock, self)._astype(dtype=dtype, **kwargs)
 
     def _can_hold_element(self, element):
-        if is_list_like(element):
+        tipo = _maybe_infer_dtype_type(element)
+        if tipo:
+            # TODO: this still uses asarray, instead of dtype.type
             element = np.array(element)
             return element.dtype == _NS_DTYPE or element.dtype == np.int64
         return (is_integer(element) or isinstance(element, datetime) or

Diff for: pandas/tests/internals/test_internals.py

@@ -2,6 +2,7 @@
 # pylint: disable=W0102
 
 from datetime import datetime, date
+import operator
 import sys
 import pytest
 import numpy as np
@@ -1213,3 +1214,63 @@ def assert_add_equals(val, inc, result):
 
             with pytest.raises(ValueError):
                 BlockPlacement(slice(2, None, -1)).add(-1)
+
+
+class DummyElement(object):
+    def __init__(self, value, dtype):
+        self.value = value
+        self.dtype = np.dtype(dtype)
+
+    def __array__(self):
+        return np.array(self.value, dtype=self.dtype)
+
+    def __str__(self):
+        return "DummyElement({}, {})".format(self.value, self.dtype)
+
+    def __repr__(self):
+        return str(self)
+
+    def astype(self, dtype, copy=False):
+        self.dtype = dtype
+        return self
+
+    def view(self, dtype):
+        return type(self)(self.value.view(dtype), dtype)
+
+    def any(self, axis=None):
+        return bool(self.value)
+
+
+class TestCanHoldElement(object):
+    @pytest.mark.parametrize('value, dtype', [
+        (1, 'i8'),
+        (1.0, 'f8'),
+        (1j, 'complex128'),
+        (True, 'bool'),
+        # (np.timedelta64(20, 'ns'), '<m8[ns]'),
+        (np.datetime64(20, 'ns'), '<M8[ns]'),
+    ])
+    @pytest.mark.parametrize('op', [
+        operator.add,
+        operator.sub,
+        operator.mul,
+        operator.truediv,
+        operator.mod,
+        operator.pow,
+    ], ids=lambda x: x.__name__)
+    def test_binop_other(self, op, value, dtype):
+        skip = {(operator.add, 'bool'),
+                (operator.sub, 'bool'),
+                (operator.mul, 'bool'),
+                (operator.truediv, 'bool'),
+                (operator.mod, 'i8'),
+                (operator.mod, 'complex128'),
+                (operator.mod, '<M8[ns]'),
+                (operator.pow, 'bool')}
+        if (op, dtype) in skip:
+            pytest.skip("Invalid combination {},{}".format(op, dtype))
+        e = DummyElement(value, dtype)
+        s = pd.DataFrame({"A": [e.value, e.value]}, dtype=e.dtype)
+        result = op(s, e).dtypes
+        expected = op(s, value).dtypes
+        assert_series_equal(result, expected)