Merge pull request pandas-dev#4924 from cpcloud/eval-datetime-in-python

ENH: evaluate datetime ops in python with eval

Author: cpcloud

doc/source/release.rst

@@ -335,6 +335,9 @@ Experimental Features
 - A :meth:`~pandas.DataFrame.query` method has been added that allows
   you to select elements of a ``DataFrame`` using a natural query syntax nearly
   identical to Python syntax.
+- ``pd.eval`` and friends now evaluate operations involving ``datetime64``
+  objects in Python space because ``numexpr`` cannot handle ``NaT`` values
+  (:issue:`4897`).
 
 .. _release.bug_fixes-0.13.0:
 

pandas/computation/align.py

@@ -111,14 +111,20 @@ def _align_core(terms):
     typ = biggest._constructor
     axes = biggest.axes
     naxes = len(axes)
+    gt_than_one_axis = naxes > 1
 
-    for term in (terms[i] for i in term_index):
-        for axis, items in enumerate(term.value.axes):
-            if isinstance(term.value, pd.Series) and naxes > 1:
-                ax, itm = naxes - 1, term.value.index
+    for value in (terms[i].value for i in term_index):
+        is_series = isinstance(value, pd.Series)
+        is_series_and_gt_one_axis = is_series and gt_than_one_axis
+
+        for axis, items in enumerate(value.axes):
+            if is_series_and_gt_one_axis:
+                ax, itm = naxes - 1, value.index
             else:
                 ax, itm = axis, items
-            axes[ax] = axes[ax].join(itm, how='outer')
+
+            if not axes[ax].is_(itm):
+                axes[ax] = axes[ax].join(itm, how='outer')
 
     for i, ndim in compat.iteritems(ndims):
         for axis, items in zip(range(ndim), axes):
@@ -136,7 +142,7 @@ def _align_core(terms):
                     warnings.warn("Alignment difference on axis {0} is larger"
                                   " than an order of magnitude on term {1!r}, "
                                   "by more than {2:.4g}; performance may suffer"
-                                  "".format(axis, term.name, ordm),
+                                  "".format(axis, terms[i].name, ordm),
                                   category=pd.io.common.PerformanceWarning)
 
                 if transpose:

pandas/computation/expr.py

@@ -493,8 +493,15 @@ def _possibly_evaluate_binop(self, op, op_class, lhs, rhs,
                                  maybe_eval_in_python=('==', '!=')):
         res = op(lhs, rhs)
 
-        # "in"/"not in" ops are always evaluated in python
+        if (res.op in _cmp_ops_syms and
+            lhs.is_datetime or rhs.is_datetime and
+            self.engine != 'pytables'):
+            # all date ops must be done in python bc numexpr doesn't work well
+            # with NaT
+            return self._possibly_eval(res, self.binary_ops)
+
         if res.op in eval_in_python:
+            # "in"/"not in" ops are always evaluated in python
             return self._possibly_eval(res, eval_in_python)
         elif (lhs.return_type == object or rhs.return_type == object and
               self.engine != 'pytables'):

pandas/computation/ops.py

@@ -5,6 +5,7 @@
 import operator as op
 from functools import partial
 from itertools import product, islice, chain
+from datetime import datetime
 
 import numpy as np
 
@@ -161,24 +162,16 @@ def raw(self):
                                           self.type))
 
     @property
-    def kind(self):
+    def is_datetime(self):
         try:
-            return self.type.__name__
+            t = self.type.type
         except AttributeError:
-            return self.type.type.__name__
+            t = self.type
+
+        return issubclass(t, (datetime, np.datetime64))
 
     @property
     def value(self):
-        kind = self.kind.lower()
-        if kind == 'datetime64':
-            try:
-                return self._value.asi8
-            except AttributeError:
-                return self._value.view('i8')
-        elif kind == 'datetime':
-            return pd.Timestamp(self._value)
-        elif kind == 'timestamp':
-            return self._value.asm8.view('i8')
         return self._value
 
     @value.setter
@@ -248,6 +241,15 @@ def return_type(self):
     def isscalar(self):
         return all(operand.isscalar for operand in self.operands)
 
+    @property
+    def is_datetime(self):
+        try:
+            t = self.return_type.type
+        except AttributeError:
+            t = self.return_type
+
+        return issubclass(t, (datetime, np.datetime64))
+
 
 def _in(x, y):
     """Compute the vectorized membership of ``x in y`` if possible, otherwise
@@ -424,24 +426,20 @@ def stringify(value):
 
         lhs, rhs = self.lhs, self.rhs
 
-        if (is_term(lhs) and lhs.kind.startswith('datetime') and is_term(rhs)
-                and rhs.isscalar):
+        if is_term(lhs) and lhs.is_datetime and is_term(rhs) and rhs.isscalar:
             v = rhs.value
             if isinstance(v, (int, float)):
                 v = stringify(v)
-            v = _ensure_decoded(v)
-            v = pd.Timestamp(v)
+            v = pd.Timestamp(_ensure_decoded(v))
             if v.tz is not None:
                 v = v.tz_convert('UTC')
             self.rhs.update(v)
 
-        if (is_term(rhs) and rhs.kind.startswith('datetime') and
-                is_term(lhs) and lhs.isscalar):
+        if is_term(rhs) and rhs.is_datetime and is_term(lhs) and lhs.isscalar:
             v = lhs.value
             if isinstance(v, (int, float)):
                 v = stringify(v)
-            v = _ensure_decoded(v)
-            v = pd.Timestamp(v)
+            v = pd.Timestamp(_ensure_decoded(v))
             if v.tz is not None:
                 v = v.tz_convert('UTC')
             self.lhs.update(v)

pandas/computation/tests/test_eval.py

@@ -1003,7 +1003,7 @@ def check_performance_warning_for_poor_alignment(self, engine, parser):
                 expected = ("Alignment difference on axis {0} is larger"
                             " than an order of magnitude on term {1!r}, "
                             "by more than {2:.4g}; performance may suffer"
-                            "".format(1, 's', np.log10(s.size - df.shape[1])))
+                            "".format(1, 'df', np.log10(s.size - df.shape[1])))
                 assert_equal(msg, expected)
 
     def test_performance_warning_for_poor_alignment(self):

pandas/core/frame.py

@@ -1894,29 +1894,6 @@ def _getitem_frame(self, key):
             raise ValueError('Must pass DataFrame with boolean values only')
         return self.where(key)
 
-    def _get_index_resolvers(self, axis):
-        # index or columns
-        axis_index = getattr(self, axis)
-        d = dict()
-
-        for i, name in enumerate(axis_index.names):
-            if name is not None:
-                key = level = name
-            else:
-                # prefix with 'i' or 'c' depending on the input axis
-                # e.g., you must do ilevel_0 for the 0th level of an unnamed
-                # multiiindex
-                level_string = '{prefix}level_{i}'.format(prefix=axis[0], i=i)
-                key = level_string
-                level = i
-
-            d[key] = Series(axis_index.get_level_values(level).values,
-                            index=axis_index, name=level)
-
-        # put the index/columns itself in the dict
-        d[axis] = axis_index
-        return d
-
     def query(self, expr, **kwargs):
         """Query the columns of a frame with a boolean expression.
 
@@ -2037,8 +2014,7 @@ def eval(self, expr, **kwargs):
         """
         resolvers = kwargs.pop('resolvers', None)
         if resolvers is None:
-            index_resolvers = self._get_index_resolvers('index')
-            index_resolvers.update(self._get_index_resolvers('columns'))
+            index_resolvers = self._get_resolvers()
             resolvers = [self, index_resolvers]
         kwargs['local_dict'] = _ensure_scope(resolvers=resolvers, **kwargs)
         return _eval(expr, **kwargs)

pandas/core/generic.py

@@ -272,6 +272,42 @@ def _get_block_manager_axis(self, axis):
             return m - axis
         return axis
 
+    def _get_axis_resolvers(self, axis):
+        # index or columns
+        axis_index = getattr(self, axis)
+        d = dict()
+        prefix = axis[0]
+
+        for i, name in enumerate(axis_index.names):
+            if name is not None:
+                key = level = name
+            else:
+                # prefix with 'i' or 'c' depending on the input axis
+                # e.g., you must do ilevel_0 for the 0th level of an unnamed
+                # multiiindex
+                key = '{prefix}level_{i}'.format(prefix=prefix, i=i)
+                level = i
+
+            level_values = axis_index.get_level_values(level)
+            s = level_values.to_series()
+            s.index = axis_index
+            d[key] = s
+
+        # put the index/columns itself in the dict
+        if isinstance(axis_index, MultiIndex):
+            dindex = axis_index
+        else:
+            dindex = axis_index.to_series()
+
+        d[axis] = dindex
+        return d
+
+    def _get_resolvers(self):
+        d = {}
+        for axis_name in self._AXIS_ORDERS:
+            d.update(self._get_axis_resolvers(axis_name))
+        return d
+
     @property
     def _info_axis(self):
         return getattr(self, self._info_axis_name)