ENH: _maybe_upcast_putmask now has the keyword, change to provide inline

       putmask changes to an object (series)
     apply in BlockManager now has a keyword, filter to allow acting
       on only those items contained in the filter (if supplied)
CLN: consolidated all replace subs to main replace in DataFrame
       (which calls replace in BlockManager)
TST: now passed test in GH 3064

pandas/core/common.py

@@ -745,17 +745,36 @@ def _maybe_promote(dtype, fill_value=np.nan):
     return dtype, fill_value
 
 
-def _maybe_upcast_putmask(result, mask, other, dtype=None):
+def _maybe_upcast_putmask(result, mask, other, dtype=None, change=None):
     """ a safe version of put mask that (potentially upcasts the result
-        return the result and a changed flag """
-    try:
-        np.putmask(result, mask, other)
-    except:
-        # our type is wrong here, need to upcast
-        if (-mask).any():
-            result, fill_value = _maybe_upcast(result, fill_value=other, dtype=dtype, copy=True)
+        return the result
+        if change is not None, then MUTATE the change (and change the dtype)
+        return a changed flag
+        """
+
+    if mask.any():
+
+        def changeit():
+            # our type is wrong here, need to upcast
+            if (-mask).any():
+                r, fill_value = _maybe_upcast(result, fill_value=other, dtype=dtype, copy=True)
+                np.putmask(r, mask, other)
+
+                # we need to actually change the dtype here
+                if change is not None:
+                    change.dtype = r.dtype
+                    change[:] = r
+
+                return r, True
+
+        new_dtype, fill_value = _maybe_promote(result.dtype,other)
+        if new_dtype != result.dtype:
+            return changeit()
+
+        try:
             np.putmask(result, mask, other)
-            return result, True
+        except:
+            return changeit()
 
     return result, False
 

pandas/core/frame.py

@@ -3467,14 +3467,21 @@ def replace(self, to_replace, value=None, method='pad', axis=0,
             if len(self.columns) == 0:
                 return self
 
+            new_data = self._data
             if isinstance(to_replace, dict):
                 if isinstance(value, dict):  # {'A' : NA} -> {'A' : 0}
-                    return self._replace_both_dict(to_replace, value, inplace)
+                    new_data = self._data
+                    for c, src in to_replace.iteritems():
+                        if c in value and c in self:
+                            new_data = new_data.replace(src, value[c], filter = [ c ], inplace=inplace)
 
                 elif not isinstance(value, (list, np.ndarray)):
-                    return self._replace_src_dict(to_replace, value, inplace)
-
-                raise ValueError('Fill value must be scalar or dict')
+                    new_data = self._data
+                    for k, src in to_replace.iteritems():
+                        if k in self:
+                            new_data = new_data.replace(src, value, filter = [ k ], inplace=inplace)
+                else:
+                    raise ValueError('Fill value must be scalar or dict')
 
             elif isinstance(to_replace, (list, np.ndarray)):
                 # [NA, ''] -> [0, 'missing']
@@ -3491,25 +3498,29 @@ def replace(self, to_replace, value=None, method='pad', axis=0,
                     new_data = self._data.replace(to_replace, value,
                                                   inplace=inplace)
 
-                if inplace:
-                    self._data = new_data
-                    return self
-                else:
-                    return self._constructor(new_data)
             else:
+
+                # dest iterable dict-like
                 if isinstance(value, dict):  # NA -> {'A' : 0, 'B' : -1}
-                    return self._replace_dest_dict(to_replace, value, inplace)
+
+                    new_data = self._data
+                    for k, v in value.iteritems():
+                        if k in self:
+                            new_data = new_data.replace(to_replace, v, filter = [ k ], inplace=inplace)
+
                 elif not isinstance(value, (list, np.ndarray)):  # NA -> 0
                     new_data = self._data.replace(to_replace, value,
                                                   inplace=inplace)
-                    if inplace:
-                        self._data = new_data
-                        return self
-                    else:
-                        return self._constructor(new_data)
+                else:
+                    raise ValueError('Invalid to_replace type: %s' %
+                                     type(to_replace))  # pragma: no cover
+
 
-            raise ValueError('Invalid to_replace type: %s' %
-                             type(to_replace))  # pragma: no cover
+        if inplace:
+            self._data = new_data
+            return self
+        else:
+            return self._constructor(new_data)
 
     def _interpolate(self, to_replace, method, axis, inplace, limit):
         if self._is_mixed_type and axis == 1:
@@ -3543,27 +3554,6 @@ def _interpolate(self, to_replace, method, axis, inplace, limit):
             else:
                 return self._constructor(new_data)
 
-    def _replace_dest_dict(self, to_replace, value, inplace):
-        rs = self if inplace else self.copy()
-        for k, v in value.iteritems():
-            if k in rs:
-                rs[k].replace(to_replace, v, inplace=True)
-        return rs if not inplace else None
-
-    def _replace_src_dict(self, to_replace, value, inplace):
-        rs = self if inplace else self.copy()
-        for k, src in to_replace.iteritems():
-            if k in rs:
-                rs[k].replace(src, value, inplace=True)
-        return rs if not inplace else None
-
-    def _replace_both_dict(self, to_replace, value, inplace):
-        rs = self if inplace else self.copy()
-        for c, src in to_replace.iteritems():
-            if c in value and c in rs:
-                rs[c].replace(src, value[c], inplace=True)
-        return rs if not inplace else None
-
     #----------------------------------------------------------------------
     # Rename
 

pandas/core/internals.py

@@ -880,10 +880,23 @@ def _verify_integrity(self):
                                  'block items')
 
     def apply(self, f, *args, **kwargs):
-        """ iterate over the blocks, collect and create a new block manager """
+        """ iterate over the blocks, collect and create a new block manager
+        
+        Parameters
+        ----------
+        f : the callable or function name to operate on at the block level
+        axes : optional (if not supplied, use self.axes)
+        filter : callable, if supplied, only call the block if the filter is True
+        """
+
         axes = kwargs.pop('axes',None)
+        filter = kwargs.pop('filter',None)
         result_blocks = []
         for blk in self.blocks:
+            if filter is not None:
+                if not blk.items.isin(filter).any():
+                    result_blocks.append(blk)
+                    continue
             if callable(f):
                 applied = f(blk, *args, **kwargs)
             else:

pandas/core/series.py

@@ -732,13 +732,8 @@ def where(self, cond, other=nan, inplace=False):
         if len(other) != len(ser):
             raise ValueError('Length of replacements must equal series length')
 
-        result, changed = com._maybe_upcast_putmask(ser,~cond,other)
-        if changed:
-
-            # need to actually change ser here
-            if inplace:
-                ser.dtype = result.dtype
-                ser[:] = result
+        change = ser if inplace else None
+        result, changed = com._maybe_upcast_putmask(ser,~cond,other,change=change)
 
         return None if inplace else ser
 
@@ -2680,11 +2675,17 @@ def replace(self, to_replace, value=None, method='pad', inplace=False,
         -------
         replaced : Series
         """
-        result = self.copy() if not inplace else self
+
+        if inplace:
+            result = self
+            change = self
+        else:
+            result = self.copy()
+            change = None
 
         def _rep_one(s, to_rep, v):  # replace single value
             mask = com.mask_missing(s.values, to_rep)
-            np.putmask(s.values, mask, v)
+            com._maybe_upcast_putmask(s.values,mask,v,change=change)
 
         def _rep_dict(rs, to_rep):  # replace {[src] -> dest}
 
@@ -2701,7 +2702,7 @@ def _rep_dict(rs, to_rep):  # replace {[src] -> dest}
                     masks[d] = com.mask_missing(rs.values, sset)
 
                 for d, m in masks.iteritems():
-                    np.putmask(rs.values, m, d)
+                    com._maybe_upcast_putmask(rs.values,m,d,change=change)
             else:  # if no risk of clobbering then simple
                 for d, sset in dd.iteritems():
                     _rep_one(rs, sset, d)

pandas/tests/test_frame.py

@@ -5621,6 +5621,16 @@ def test_replace_mixed(self):
         result = df.replace([1,2], ['foo','bar'])
         assert_frame_equal(result,expected)
 
+        # test case from 
+        from pandas.util.testing import makeCustomDataframe as mkdf
+        df = DataFrame({'A' : Series([3,0],dtype='int64'), 'B' : Series([0,3],dtype='int64') })
+        result = df.replace(3, df.mean().to_dict())
+        expected = df.copy().astype('float64')
+        m = df.mean()
+        expected.iloc[0,0] = m[0]
+        expected.iloc[1,1] = m[1]
+        assert_frame_equal(result,expected)
+
     def test_replace_interpolate(self):
         padded = self.tsframe.replace(nan, method='pad')
         assert_frame_equal(padded, self.tsframe.fillna(method='pad'))