Eagerly populate the class dict for cudf.pandas proxy types

docs/cudf/source/cudf_pandas/faq.md

@@ -151,15 +151,3 @@ for testing or benchmarking purposes. To do so, set the
 ```bash
 CUDF_PANDAS_FALLBACK_MODE=1 python -m cudf.pandas some_script.py
 ```
-
-## Slow tab completion in IPython?
-
-You may experience slow tab completion when inspecting the
-methods/attributes of large dataframes. We expect this issue to be
-resolved in an upcoming release. In the mean time, you may execute the
-following command in IPython before loading `cudf.pandas` to work
-around the issue:
-
-```
-%config IPCompleter.jedi_compute_type_timeout=0
-```

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

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2023-2024, NVIDIA CORPORATION & AFFILIATES.
 # All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
@@ -17,9 +17,9 @@ def array_method(self: _FastSlowProxy, *args, **kwargs):
 
 def array_function_method(self, func, types, args, kwargs):
     try:
-        return _FastSlowAttribute("__array_function__").__get__(self)(
-            func, types, args, kwargs
-        )
+        return _FastSlowAttribute("__array_function__").__get__(
+            self, type(self)
+        )(func, types, args, kwargs)
     except Exception:
         # if something went wrong with __array_function__ we
         # attempt to call the function directly on the slow

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

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: Copyright (c) 2023-2024, NVIDIA CORPORATION & AFFILIATES.
 # All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
@@ -10,6 +10,7 @@
 import numpy.core.multiarray
 
 from ..fast_slow_proxy import (
+    _FastSlowAttribute,
     make_final_proxy_type,
     make_intermediate_proxy_type,
 )
@@ -122,6 +123,7 @@ def wrap_ndarray(cls, arr: cupy.ndarray | numpy.ndarray, constructor):
         "__iter__": custom_iter,
         # Special wrapping to handle scalar values
         "_fsproxy_wrap": classmethod(wrap_ndarray),
+        "base": _FastSlowAttribute("base", private=True),
     },
 )
 

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

@@ -107,14 +107,16 @@ class _AccessorAttr:
     """
 
     def __init__(self, typ):
-        self.__typ = typ
+        self._typ = typ
+
+    def __set_name__(self, owner, name):
+        self._name = name
 
     def __get__(self, obj, cls=None):
         if obj is None:
-            return self.__typ
+            return self._typ
         else:
-            # allow __getattr__ to handle this
-            raise AttributeError()
+            return _FastSlowAttribute(self._name).__get__(obj, type(obj))
 
 
 def Timestamp_Timedelta__new__(cls, *args, **kwargs):
@@ -214,6 +216,7 @@ def _DataFrame__dir__(self):
         "__dir__": _DataFrame__dir__,
         "_constructor": _FastSlowAttribute("_constructor"),
         "_constructor_sliced": _FastSlowAttribute("_constructor_sliced"),
+        "_accessors": set(),
     },
 )
 
@@ -236,6 +239,7 @@ def _DataFrame__dir__(self):
         "cat": _AccessorAttr(_CategoricalAccessor),
         "_constructor": _FastSlowAttribute("_constructor"),
         "_constructor_expanddim": _FastSlowAttribute("_constructor_expanddim"),
+        "_accessors": set(),
     },
 )
 
@@ -273,6 +277,9 @@ def Index__new__(cls, *args, **kwargs):
         "__new__": Index__new__,
         "_constructor": _FastSlowAttribute("_constructor"),
         "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+        "_accessors": set(),
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
     },
 )
 
@@ -337,7 +344,11 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=lambda fast: fast.to_pandas(),
     slow_to_fast=cudf.from_pandas,
     bases=(Index,),
-    additional_attributes={"__init__": _DELETE},
+    additional_attributes={
+        "__init__": _DELETE,
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 DatetimeArray = make_final_proxy_type(
@@ -346,6 +357,10 @@ def Index__new__(cls, *args, **kwargs):
     pd.arrays.DatetimeArray,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
+    additional_attributes={
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 DatetimeTZDtype = make_final_proxy_type(
@@ -364,7 +379,11 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=lambda fast: fast.to_pandas(),
     slow_to_fast=cudf.from_pandas,
     bases=(Index,),
-    additional_attributes={"__init__": _DELETE},
+    additional_attributes={
+        "__init__": _DELETE,
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 NumpyExtensionArray = make_final_proxy_type(
@@ -385,6 +404,10 @@ def Index__new__(cls, *args, **kwargs):
     pd.arrays.TimedeltaArray,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
+    additional_attributes={
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 PeriodIndex = make_final_proxy_type(
@@ -394,7 +417,11 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
     bases=(Index,),
-    additional_attributes={"__init__": _DELETE},
+    additional_attributes={
+        "__init__": _DELETE,
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 PeriodArray = make_final_proxy_type(
@@ -403,6 +430,11 @@ def Index__new__(cls, *args, **kwargs):
     pd.arrays.PeriodArray,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
+    additional_attributes={
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+    },
 )
 
 PeriodDtype = make_final_proxy_type(
@@ -464,6 +496,10 @@ def Index__new__(cls, *args, **kwargs):
     pd.arrays.StringArray,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
+    additional_attributes={
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 StringDtype = make_final_proxy_type(
@@ -472,7 +508,10 @@ def Index__new__(cls, *args, **kwargs):
     pd.StringDtype,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
-    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+    additional_attributes={
+        "__hash__": _FastSlowAttribute("__hash__"),
+        "storage": _FastSlowAttribute("storage"),
+    },
 )
 
 BooleanArray = make_final_proxy_type(
@@ -482,7 +521,9 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
     additional_attributes={
-        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
     },
 )
 
@@ -502,7 +543,9 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
     additional_attributes={
-        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
     },
 )
 
@@ -586,7 +629,11 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=lambda fast: fast.to_pandas(),
     slow_to_fast=cudf.from_pandas,
     bases=(Index,),
-    additional_attributes={"__init__": _DELETE},
+    additional_attributes={
+        "__init__": _DELETE,
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 IntervalArray = make_final_proxy_type(
@@ -595,6 +642,10 @@ def Index__new__(cls, *args, **kwargs):
     pd.arrays.IntervalArray,
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
+    additional_attributes={
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
+    },
 )
 
 IntervalDtype = make_final_proxy_type(
@@ -622,7 +673,9 @@ def Index__new__(cls, *args, **kwargs):
     fast_to_slow=_Unusable(),
     slow_to_fast=_Unusable(),
     additional_attributes={
-        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+        "_data": _FastSlowAttribute("_data", private=True),
+        "_mask": _FastSlowAttribute("_mask", private=True),
     },
 )
 
@@ -798,6 +851,14 @@ def Index__new__(cls, *args, **kwargs):
         pd_Styler,
         fast_to_slow=_Unusable(),
         slow_to_fast=_Unusable(),
+        additional_attributes={
+            "css": _FastSlowAttribute("css"),
+            "ctx": _FastSlowAttribute("ctx"),
+            "index": _FastSlowAttribute("ctx"),
+            "data": _FastSlowAttribute("data"),
+            "_display_funcs": _FastSlowAttribute("_display_funcs"),
+            "table_styles": _FastSlowAttribute("table_styles"),
+        },
     )
 except ImportError:
     # Styler requires Jinja to be installed
@@ -813,7 +874,7 @@ def _get_eval_locals_and_globals(level, local_dict=None, global_dict=None):
     return local_dict, global_dict
 
 
-@register_proxy_func(pd.eval)
+@register_proxy_func(pd.core.computation.eval.eval)
 @nvtx.annotate(
     "CUDF_PANDAS_EVAL",
     color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
@@ -843,6 +904,24 @@ def _eval(
     )
 
 
+_orig_df_eval_method = DataFrame.eval
+
+
+@register_proxy_func(pd.core.accessor.register_dataframe_accessor)
+def _register_dataframe_accessor(name):
+    return pd.core.accessor._register_accessor(name, DataFrame)
+
+
+@register_proxy_func(pd.core.accessor.register_series_accessor)
+def _register_series_accessor(name):
+    return pd.core.accessor._register_accessor(name, Series)
+
+
+@register_proxy_func(pd.core.accessor.register_index_accessor)
+def _register_index_accessor(name):
+    return pd.core.accessor._register_accessor(name, Index)
+
+
 @nvtx.annotate(
     "CUDF_PANDAS_DATAFRAME_EVAL",
     color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
@@ -853,11 +932,14 @@ def _df_eval_method(self, *args, local_dict=None, global_dict=None, **kwargs):
     local_dict, global_dict = _get_eval_locals_and_globals(
         level, local_dict, global_dict
     )
-    return super(type(self), self).__getattr__("eval")(
-        *args, local_dict=local_dict, global_dict=global_dict, **kwargs
+    return _orig_df_eval_method(
+        self, *args, local_dict=local_dict, global_dict=global_dict, **kwargs
     )
 
 
+_orig_query_eval_method = DataFrame.query
+
+
 @nvtx.annotate(
     "CUDF_PANDAS_DATAFRAME_QUERY",
     color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
@@ -870,8 +952,8 @@ def _df_query_method(self, *args, local_dict=None, global_dict=None, **kwargs):
     local_dict, global_dict = _get_eval_locals_and_globals(
         level, local_dict, global_dict
     )
-    return super(type(self), self).__getattr__("query")(
-        *args, local_dict=local_dict, global_dict=global_dict, **kwargs
+    return _orig_query_eval_method(
+        self, *args, local_dict=local_dict, global_dict=global_dict, **kwargs
     )
 
 
@@ -1277,6 +1359,7 @@ def holiday_calendar_factory_wrapper(*args, **kwargs):
     additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
 )
 
+
 MonthBegin = make_final_proxy_type(
     "MonthBegin",
     _Unusable,