BUG: Fix __truediv__ numexpr error

RELEASE.rst

@@ -250,6 +250,8 @@ pandas 0.11.1
     not converting dtypes (GH3911_)
   - Fixed a bug where ``DataFrame.replace`` with a compiled regular expression
     in the ``to_replace`` argument wasn't working (GH3907_)
+  - Fixed ``__truediv__`` in Python 2.7 with ``numexpr`` installed to actually do true division when dividing
+    two integer arrays with at least 10000 cells total (GH3764_)
 
 .. _GH3164: https://github.com/pydata/pandas/issues/3164
 .. _GH2786: https://github.com/pydata/pandas/issues/2786
@@ -351,6 +353,7 @@ pandas 0.11.1
 .. _GH3907: https://github.com/pydata/pandas/issues/3907
 .. _GH3911: https://github.com/pydata/pandas/issues/3911
 .. _GH3912: https://github.com/pydata/pandas/issues/3912
+.. _GH3764: https://github.com/pydata/pandas/issues/3764
 
 pandas 0.11.0
 =============

pandas/core/expressions.py

@@ -51,7 +51,7 @@ def set_numexpr_threads(n = None):
         pass
 
 
-def _evaluate_standard(op, op_str, a, b, raise_on_error=True):
+def _evaluate_standard(op, op_str, a, b, raise_on_error=True, **eval_kwargs):
     """ standard evaluation """
     return op(a,b)
 
@@ -79,7 +79,7 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check):
 
     return False
 
-def _evaluate_numexpr(op, op_str, a, b, raise_on_error = False):
+def _evaluate_numexpr(op, op_str, a, b, raise_on_error = False, **eval_kwargs):
     result = None
 
     if _can_use_numexpr(op, op_str, a, b, 'evaluate'):
@@ -92,7 +92,7 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error = False):
             result = ne.evaluate('a_value %s b_value' % op_str, 
                                  local_dict={ 'a_value' : a_value, 
                                               'b_value' : b_value }, 
-                                 casting='safe')
+                                 casting='safe', **eval_kwargs)
         except (ValueError), detail:
             if 'unknown type object' in str(detail):
                 pass
@@ -142,7 +142,7 @@ def _where_numexpr(cond, a, b, raise_on_error = False):
 # turn myself on
 set_use_numexpr(True)
 
-def evaluate(op, op_str, a, b, raise_on_error=False, use_numexpr=True):
+def evaluate(op, op_str, a, b, raise_on_error=False, use_numexpr=True, **eval_kwargs):
     """ evaluate and return the expression of the op on a and b
 
         Parameters
@@ -158,7 +158,7 @@ def evaluate(op, op_str, a, b, raise_on_error=False, use_numexpr=True):
         """
 
     if use_numexpr:
-        return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error)
+        return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error, **eval_kwargs)
     return _evaluate_standard(op, op_str, a, b, raise_on_error=raise_on_error)
 
 def where(cond, a, b, raise_on_error=False, use_numexpr=True):

pandas/core/frame.py

@@ -190,10 +190,10 @@ class DataConflictError(Exception):
 # Factory helper methods
 
 
-def _arith_method(op, name, str_rep = None, default_axis='columns', fill_zeros=None):
+def _arith_method(op, name, str_rep = None, default_axis='columns', fill_zeros=None, **eval_kwargs):
     def na_op(x, y):
         try:
-            result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True)
+            result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs)
             result = com._fill_zeros(result,y,fill_zeros)
 
         except TypeError:
@@ -853,12 +853,17 @@ def __contains__(self, key):
     __sub__ = _arith_method(operator.sub, '__sub__', '-', default_axis=None)
     __mul__ = _arith_method(operator.mul, '__mul__', '*', default_axis=None)
     __truediv__ = _arith_method(operator.truediv, '__truediv__', '/',
-                                default_axis=None, fill_zeros=np.inf)
+                                default_axis=None, fill_zeros=np.inf, truediv=True)
+    # numexpr produces a different value (python/numpy: 0.000, numexpr: inf)
+    # when dividing by zero, so can't use floordiv speed up (yet)
+    # __floordiv__ = _arith_method(operator.floordiv, '__floordiv__', '//',
     __floordiv__ = _arith_method(operator.floordiv, '__floordiv__',
                                  default_axis=None, fill_zeros=np.inf)
     __pow__ = _arith_method(operator.pow, '__pow__', '**', default_axis=None)
 
-    __mod__ = _arith_method(operator.mod, '__mod__', '*', default_axis=None, fill_zeros=np.nan)
+    # currently causes a floating point exception to occur - so sticking with unaccelerated for now
+    # __mod__ = _arith_method(operator.mod, '__mod__', '%', default_axis=None, fill_zeros=np.nan)
+    __mod__ = _arith_method(operator.mod, '__mod__', default_axis=None, fill_zeros=np.nan)
 
     __radd__ = _arith_method(_radd_compat, '__radd__', default_axis=None)
     __rmul__ = _arith_method(operator.mul, '__rmul__', default_axis=None)
@@ -879,7 +884,7 @@ def __contains__(self, key):
     # Python 2 division methods
     if not py3compat.PY3:
         __div__ = _arith_method(operator.div, '__div__', '/',
-                                default_axis=None, fill_zeros=np.inf)
+                                default_axis=None, fill_zeros=np.inf, truediv=False)
         __rdiv__ = _arith_method(lambda x, y: y / x, '__rdiv__',
                                  default_axis=None, fill_zeros=np.inf)
 

pandas/tests/test_expressions.py

@@ -30,6 +30,7 @@
 _frame2 = DataFrame(np.random.randn(100, 4),   columns = list('ABCD'), dtype='float64')
 _mixed  = DataFrame({ 'A' : _frame['A'].copy(), 'B' : _frame['B'].astype('float32'), 'C' : _frame['C'].astype('int64'), 'D' : _frame['D'].astype('int32') })
 _mixed2 = DataFrame({ 'A' : _frame2['A'].copy(), 'B' : _frame2['B'].astype('float32'), 'C' : _frame2['C'].astype('int64'), 'D' : _frame2['D'].astype('int32') })
+_integer  = DataFrame(np.random.randint(1, 100, size=(10001, 4)), columns = list('ABCD'), dtype='int64')
 
 class TestExpressions(unittest.TestCase):
 
@@ -41,7 +42,56 @@ def setUp(self):
         self.frame2 = _frame2.copy()
         self.mixed  = _mixed.copy()
         self.mixed2 = _mixed2.copy()
-
+        self.integer = _integer.copy()
+        self._MIN_ELEMENTS = expr._MIN_ELEMENTS
+
+    def tearDown(self):
+        expr._MIN_ELEMENTS = self._MIN_ELEMENTS
+
+    #TODO: add test for Panel
+    #TODO: add tests for binary operations
+    @nose.tools.nottest
+    def run_arithmetic_test(self, df, assert_func, check_dtype=False):
+        expr._MIN_ELEMENTS = 0
+        operations = ['add', 'sub', 'mul','mod','truediv','floordiv','pow']
+        if not py3compat.PY3:
+            operations.append('div')
+        for arith in operations:
+            op = getattr(operator, arith)
+            expr.set_use_numexpr(False)
+            expected = op(df, df)
+            expr.set_use_numexpr(True)
+            result = op(df, df)
+            try:
+                if check_dtype:
+                    if arith == 'div':
+                        assert expected.dtype.kind == df.dtype.kind
+                    if arith == 'truediv':
+                        assert expected.dtype.kind == 'f'
+                assert_func(expected, result)
+            except Exception:
+                print("Failed test with operator %r" % op.__name__)
+                raise
+
+    def test_integer_arithmetic(self):
+        self.run_arithmetic_test(self.integer, assert_frame_equal)
+        self.run_arithmetic_test(self.integer.icol(0), assert_series_equal,
+                                 check_dtype=True)
+
+    def test_float_arithemtic(self):
+        self.run_arithmetic_test(self.frame, assert_frame_equal)
+        self.run_arithmetic_test(self.frame.icol(0), assert_series_equal,
+                                check_dtype=True)
+
+    def test_mixed_arithmetic(self):
+        self.run_arithmetic_test(self.mixed, assert_frame_equal)
+        for col in self.mixed.columns:
+            self.run_arithmetic_test(self.mixed[col], assert_series_equal)
+
+    def test_integer_with_zeros(self):
+        self.integer *= np.random.randint(0, 2, size=np.shape(self.integer))
+        self.run_arithmetic_test(self.integer, assert_frame_equal)
+        self.run_arithmetic_test(self.integer.icol(0), assert_series_equal)
 
     def test_invalid(self):