CLN: remove dead code in pandas.computation

Author: cpcloud

pandas/computation/align.py

@@ -34,40 +34,6 @@ def _zip_axes_from_type(typ, new_axes):
     return axes
 
 
-def _maybe_promote_shape(values, naxes):
-    # test to see if we have an array else leave since must be a number
-    if not isinstance(values, np.ndarray):
-        return values
-
-    ndims = values.ndim
-    if ndims > naxes:
-        raise AssertionError('cannot have more dims than axes, '
-                             '{0} > {1}'.format(ndims, naxes))
-    if ndims == naxes:
-        return values
-
-    ndim, nax = range(ndims), range(naxes)
-
-    axes_slice = [slice(None)] * naxes
-
-    # set difference of numaxes and ndims
-    slices = list(set(nax) - set(ndim))
-
-    if ndims == naxes:
-        if slices:
-            raise AssertionError('slices should be empty if ndims == naxes '
-                                 '{0}'.format(slices))
-    else:
-        if not slices:
-            raise AssertionError('slices should NOT be empty if ndim != naxes '
-                                 '{0}'.format(slices))
-
-    for sl in slices:
-        axes_slice[sl] = np.newaxis
-
-    return values[tuple(axes_slice)]
-
-
 def _any_pandas_objects(terms):
     """Check a sequence of terms for instances of PandasObject."""
     return any(isinstance(term.value, pd.core.generic.PandasObject)
@@ -83,12 +49,7 @@ def wrapper(terms):
 
         term_values = (term.value for term in terms)
 
-        # only scalars or indexes
-        if all(isinstance(term.value, pd.Index) or term.isscalar for term in
-               terms):
-            return _result_type_many(*term_values), None
-
-        # no pandas objects
+        # we don't have any pandas objects
         if not _any_pandas_objects(terms):
             return _result_type_many(*term_values), None
 
@@ -148,44 +109,13 @@ def _align_core(terms):
                     f = partial(ti.reindex_axis, reindexer, axis=axis,
                                 copy=False)
 
-                # need  to fill if we have a bool dtype/array
-                if (isinstance(ti, (np.ndarray, pd.Series))
-                        and ti.dtype == object
-                        and pd.lib.is_bool_array(ti.values)):
-                    r = f(fill_value=True)
-                else:
-                    r = f()
-
-                terms[i].update(r)
+                terms[i].update(f())
 
-        res = _maybe_promote_shape(terms[i].value.T if transpose else
-                                   terms[i].value, naxes)
-        res = res.T if transpose else res
-
-        try:
-            v = res.values
-        except AttributeError:
-            v = res
-        terms[i].update(v)
+        terms[i].update(terms[i].value.values)
 
     return typ, _zip_axes_from_type(typ, axes)
 
 
-def _filter_terms(flat):
-    # numeric literals
-    literals = frozenset(filter(lambda x: isinstance(x, Constant), flat))
-
-    # these are strings which are variable names
-    names = frozenset(flat) - literals
-
-    # literals are not names and names are not literals, so intersection should
-    # be empty
-    if literals & names:
-        raise ValueError('literals cannot be names and names cannot be '
-                         'literals')
-    return names, literals
-
-
 def _align(terms):
     """Align a set of terms"""
     try:
@@ -231,10 +161,7 @@ def _reconstruct_object(typ, obj, axes, dtype):
     except AttributeError:
         pass
 
-    try:
-        res_t = np.result_type(obj.dtype, dtype)
-    except AttributeError:
-        res_t = dtype
+    res_t = np.result_type(obj.dtype, dtype)
 
     if (not isinstance(typ, partial) and
             issubclass(typ, pd.core.generic.PandasObject)):

pandas/computation/common.py

@@ -16,10 +16,7 @@ def _result_type_many(*arrays_and_dtypes):
     try:
         return np.result_type(*arrays_and_dtypes)
     except ValueError:
-        # length 0 or length > NPY_MAXARGS both throw a ValueError, so check
-        # which one we're dealing with
-        if len(arrays_and_dtypes) == 0:
-            raise ValueError('at least one array or dtype is required')
+        # we have > NPY_MAXARGS terms in our expression
         return reduce(np.result_type, arrays_and_dtypes)
 
 

pandas/computation/engines.py

@@ -126,7 +126,6 @@ def _evaluate(self):
             raise UndefinedVariableError(msg)
 
 
-
 class PythonEngine(AbstractEngine):
 
     """Evaluate an expression in Python space.

pandas/computation/eval.py

@@ -69,8 +69,8 @@ def _check_resolvers(resolvers):
         for resolver in resolvers:
             if not hasattr(resolver, '__getitem__'):
                 name = type(resolver).__name__
-                raise AttributeError('Resolver of type {0!r} must implement '
-                                     'the __getitem__ method'.format(name))
+                raise TypeError('Resolver of type %r does not implement '
+                                'the __getitem__ method' % name)
 
 
 def _check_expression(expr):

pandas/computation/expr.py

@@ -308,9 +308,6 @@ def visit(self, node, **kwargs):
         if isinstance(node, string_types):
             clean = self.preparser(node)
             node = ast.fix_missing_locations(ast.parse(clean))
-        elif not isinstance(node, ast.AST):
-            raise TypeError("Cannot visit objects of type {0!r}"
-                            "".format(node.__class__.__name__))
 
         method = 'visit_' + node.__class__.__name__
         visitor = getattr(self, method)
@@ -533,7 +530,7 @@ def visit_Call(self, node, side=None, **kwargs):
 
         args = [self.visit(targ).value for targ in node.args]
         if node.starargs is not None:
-            args = args + self.visit(node.starargs).value
+            args += self.visit(node.starargs).value
 
         keywords = {}
         for key in node.keywords:
@@ -651,10 +648,6 @@ def parse(self):
         """Parse an expression"""
         return self._visitor.visit(self.expr)
 
-    def align(self):
-        """align a set of Terms"""
-        return self.terms.align(self.env)
-
     @property
     def names(self):
         """Get the names in an expression"""

pandas/computation/ops.py

@@ -29,13 +29,12 @@ class UndefinedVariableError(NameError):
 
     """NameError subclass for local variables."""
 
-    def __init__(self, *args):
-        msg = 'name {0!r} is not defined'
-        subbed = args[0].replace(_LOCAL_TAG, '')
-        if subbed != args[0]:
-            subbed = '@' + subbed
+    def __init__(self, name, is_local):
+        if is_local:
             msg = 'local variable {0!r} is not defined'
-        super(UndefinedVariableError, self).__init__(msg.format(subbed))
+        else:
+            msg = 'name {0!r} is not defined'
+        super(UndefinedVariableError, self).__init__(msg.format(name))
 
 
 class Term(StringMixin):
@@ -73,11 +72,6 @@ def _resolve_name(self):
         res = self.env.resolve(self.local_name, is_local=self.is_local)
         self.update(res)
 
-        if res is None:
-            if not isinstance(key, string_types):
-                return key
-            raise UndefinedVariableError(key)
-
         if hasattr(res, 'ndim') and res.ndim > 2:
             raise NotImplementedError("N-dimensional objects, where N > 2,"
                                       " are not supported with eval")
@@ -97,10 +91,7 @@ def update(self, value):
 
         # if it's a variable name (otherwise a constant)
         if isinstance(key, string_types):
-            try:
-                self.env.swapkey(self.local_name, key, new_value=value)
-            except KeyError:
-                raise UndefinedVariableError(key)
+            self.env.swapkey(self.local_name, key, new_value=value)
 
         self.value = value
 
@@ -156,10 +147,7 @@ def name(self, new_name):
 
     @property
     def ndim(self):
-        try:
-            return self._value.ndim
-        except AttributeError:
-            return 0
+        return self._value.ndim
 
 
 class Constant(Term):

pandas/computation/pytables.py

@@ -67,10 +67,6 @@ def __init__(self, value, env, side=None, encoding=None):
     def _resolve_name(self):
         return self._name
 
-    @property
-    def name(self):
-        return self._value
-
 
 class BinOp(ops.BinOp):
 
@@ -233,9 +229,6 @@ def format(self):
 
     def evaluate(self):
 
-        if not isinstance(self.lhs, string_types):
-            return self
-
         if not self.is_valid:
             raise ValueError("query term is not valid [%s]" % self)
 
@@ -307,9 +300,6 @@ def format(self):
 
     def evaluate(self):
 
-        if not isinstance(self.lhs, string_types):
-            return self
-
         if not self.is_valid:
             raise ValueError("query term is not valid [%s]" % self)
 
@@ -390,9 +380,6 @@ def visit_UnaryOp(self, node, **kwargs):
         elif isinstance(node.op, ast.UAdd):
             raise NotImplementedError('Unary addition not supported')
 
-    def visit_USub(self, node, **kwargs):
-        return self.const_type(-self.visit(node.operand).value, self.env)
-
     def visit_Index(self, node, **kwargs):
         return self.visit(node.value).value
 

pandas/computation/scope.py

@@ -186,7 +186,7 @@ def resolve(self, key, is_local):
                 # e.g., df[df > 0]
                 return self.temps[key]
             except KeyError:
-                raise UndefinedVariableError(key)
+                raise UndefinedVariableError(key, is_local)
 
     def swapkey(self, old_key, new_key, new_value=None):
         """Replace a variable name, with a potentially new value.
@@ -209,12 +209,8 @@ def swapkey(self, old_key, new_key, new_value=None):
 
         for mapping in maps:
             if old_key in mapping:
-                if new_value is None:
-                    mapping[new_key] = mapping.pop(old_key)
-                else:
-                    mapping[new_key] = new_value
+                mapping[new_key] = new_value
                 return
-        raise KeyError(old_key)
 
     def _get_vars(self, stack, scopes):
         """Get specifically scoped variables from a list of stack frames.