Refactor for reindex

pandas/core/frame.py

@@ -41,7 +41,6 @@
     is_datetimetz,
     is_datetime64_any_dtype,
     is_datetime64tz_dtype,
-    is_dict_like,
     is_bool_dtype,
     is_integer_dtype,
     is_float_dtype,
@@ -2769,6 +2768,46 @@ def reindexer(value):
 
         return np.atleast_2d(np.asarray(value))
 
+    def _validate_axis_style_args(self, arg, arg_name, index, columns,
+                                  axis, method_name):
+        if axis is not None:
+            # Using "axis" style, along with a positional arg
+            # Both index and columns should be None then
+            axis = self._get_axis_name(axis)
+            if index is not None or columns is not None:
+                msg = (
+                    "Can't specify both 'axis' and 'index' or 'columns'. "
+                    "Specify either\n"
+                    "\t.{method_name}.rename({arg_name}, axis=axis), or\n"
+                    "\t.{method_name}.rename(index=index, columns=columns)"
+                ).format(arg_name=arg_name, method_name=method_name)
+                raise TypeError(msg)
+            if axis == 'index':
+                index = arg
+            elif axis == 'columns':
+                columns = arg
+
+        elif all(x is not None for x in (arg, index, columns)):
+            msg = (
+                "Cannot specify all of '{arg_name}', 'index', and 'columns'. "
+                "Specify either {arg_name} and 'axis', or 'index' and "
+                "'columns'."
+            ).format(arg_name=arg_name)
+            raise TypeError(msg)
+
+        elif axis is None and (arg is not None and index is not None):
+            # This is the "ambiguous" case, so emit a warning
+            msg = (
+                "Interpreting call to '.{method_name}(a, b)' as "
+                "'.{method_name}(index=a, columns=b)'. "
+                "Use keyword arguments to remove any ambiguity."
+            ).format(method_name=method_name)
+            warnings.warn(msg)
+            index, columns = arg, index
+        elif index is None and columns is None:
+            index = arg
+        return index, columns
+
     @property
     def _series(self):
         result = {}
@@ -2910,34 +2949,9 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
     @Appender(_shared_docs['rename'] % _shared_doc_kwargs)
     def rename(self, mapper=None, index=None, columns=None, axis=None,
                **kwargs):
-        if axis is not None:
-            # Using "axis" style, along with a positional mapper
-            # Both index and columns should be None then
-            axis = self._get_axis_name(axis)
-            if index is not None or columns is not None:
-                raise TypeError("Can't specify both 'axis' and 'index' or "
-                                "'columns' Specify either\n"
-                                "\t.rename(mapper, axis=axis), or\n"
-                                "\t.rename(index=index, columns=columns)")
-            if axis == 'index':
-                index = mapper
-            elif axis == 'columns':
-                columns = mapper
-        elif all(x is not None for x in (mapper, index, columns)):
-            raise TypeError("Cannot specify all of 'mapper', 'index', and "
-                            "'columns'. Specify 'mapper' and 'axis', or "
-                            "'index' and 'columns'.")
-        elif axis is None and (mapper is not None and index is not None):
-            # Determine if they meant df.rename(idx_map, col_map)
-            if callable(index) or is_dict_like(index):
-                warnings.warn("Interpreting call to '.rename(a, b)' as "
-                              "'.rename(index=a, columns=b)'. "
-                              "Use keyword arguments to remove ambiguity.",
-                              UserWarning)
-                index, columns = mapper, index
-        elif index is None and columns is None:
-            index = mapper
-
+        index, columns = self._validate_axis_style_args(mapper, 'mapper',
+                                                        index, columns,
+                                                        axis, 'rename')
         return super(DataFrame, self).rename(index=index, columns=columns,
                                              **kwargs)