Merge pull request rapidsai#129 from vyasr/feat/rename_proxy_xdf_members

Rename proxy members

python/cudf/cudf/pandas/_wrappers/common.py

@@ -19,7 +19,7 @@
 
 
 def array_method(self: _FastSlowProxy, *args, **kwargs):
-    return self._xdf_slow.__array__(*args, **kwargs)
+    return self._fsproxy_slow.__array__(*args, **kwargs)
 
 
 def array_function_method(self, func, types, args, kwargs):
@@ -43,15 +43,15 @@ def arrow_array_method(self: _FastSlowProxy, *args, **kwargs):
     import pyarrow as pa
 
     try:
-        return self._xdf_fast.to_arrow(*args, **kwargs)
+        return self._fsproxy_fast.to_arrow(*args, **kwargs)
     except Exception:
-        return pa.array(self._xdf_slow, *args, **kwargs)
+        return pa.array(self._fsproxy_slow, *args, **kwargs)
 
 
 @property  # type: ignore
 def cuda_array_interface(self: _FastSlowProxy):
-    return self._xdf_fast.__cuda_array_interface__
+    return self._fsproxy_fast.__cuda_array_interface__
 
 
 def custom_iter(self: _FastSlowProxy):
-    return iter(self._xdf_slow)
+    return iter(self._fsproxy_slow)

python/cudf/cudf/pandas/_wrappers/numpy.py

@@ -101,13 +101,13 @@ def wrap_ndarray(cls, arr: cupy.ndarray | numpy.ndarray, constructor):
     else:
         # Note, this super call means that the constructed ndarray
         # class cannot be subclassed (because then super(cls,
-        # cls)._xdf_wrap produces an infinite loop). Really this
+        # cls)._fsproxy_wrap produces an infinite loop). Really this
         # should be super(ndarray, cls), but we don't have access to
         # the ndarray type until after we need to pass this function
         # in. So it works for now since without subclassing,
         # super(ndarray, cls) == super(ndarray, ndarray) == super(cls,
         # cls)
-        return super(cls, cls)._xdf_wrap(arr, constructor)
+        return super(cls, cls)._fsproxy_wrap(arr, constructor)
 
 
 ndarray = make_final_proxy_type(
@@ -128,7 +128,7 @@ def wrap_ndarray(cls, arr: cupy.ndarray | numpy.ndarray, constructor):
         # iter(numpy-array) produces an iterable of scalars)
         "__iter__": custom_iter,
         # Special wrapping to handle scalar values
-        "_xdf_wrap": classmethod(wrap_ndarray),
+        "_fsproxy_wrap": classmethod(wrap_ndarray),
     },
 )