VibhuJawa · February 21, 2020 02:50
diff --git a/dgx_2_profile_tcp.html b/dgx_2_profile_tcp.html




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core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\n","result = _execute_task(task, cache)\n","return func(*args2)\n","return func(*args, **kwargs)\n","df = func(*args, **kwargs)\n","return hash_object_dispatch(df, index=False) % int(npartitions)\n","meth = self.dispatch(type(arg))\n","register()\n","import dask_cudf  # noqa: F401\n","","","","","","from .io import read_csv, read_json, read_orc, to_orc\n","","","","","","from .parquet import read_parquet, to_parquet\n","","","","","","","","","","from dask.dataframe.io.parquet.arrow import ArrowEngine\n","","","","","","class ArrowEngine(Engine):\n","","","","","","","","","","","","","","","","","","","","","","","","","","","pass\n","","","","","","Series._bind_comparison_method(name, meth)\n","setattr(cls, name, derived_from(pd.Series)(meth))\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","setattr(cls, name, derived_from(pd.DataFrame)(meth))\n","return method\n","return doc\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","","","","","","","","","","","","","","return safe_hash(frame)\n","frame[col] = _handle_string(frame[col])\n","self._index, how=\"right\", allow_non_unique=True\n","if self.index.equals(index):\n","result = self == other\n","return self._apply_op(\"__eq__\", other)\n","return as_index(op(other))\n","return self._unordered_compare(other, \"eq\")\n","outcol = self._column.unordered_compare(cmpops, other)\n","return _numeric_column_compare(self, rhs, op=cmpop)\n","return _numeric_column_binop(lhs, rhs, op, out_dtype=np.bool_)\n","out = out.fillna(op == \"ne\")\n","return build_column(data, dtype, mask=mask, children=children)\n","return Series(table_to_hash._hash()).values\n","return cupy.asarray(self._column.data_array_view)\n","return result\n","owner=obj)\n","return fn(*args, **kws)\n","return da\n","return devptr\n","return core.array(a, dtype, False, order)\n","'version': 2,\n","return ary[tuple(core_index)]\n","return fn(*args, **kws)\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","base = _preprocess('', options, arch)\n","result = prog.compile(options)\n","ptx = prog.compile(options)\n","self.path = tempfile.mkdtemp()\n","prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n","dir = gettempdir()\n","tempdir = _get_default_tempdir()\n","_os.unlink(filename)\n","return ''.join(letters)\n","(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n","if os.path.isdir(dst):\n","if not os.path.exists(nvcc_path):\n","return df.assign(**kwargs)\n","new = self.copy()\n","data[k] = self._data[k].copy(deep)\n","result = as_index(self._values.copy(deep=deep))\n","c = np.mod(c, npartitions).astype(typ, copy=False)\n","ptx = compile_using_nvrtc(source, options, arch, name + '.cu')\n","ptx = prog.compile(options)\n","self.path = tempfile.mkdtemp()\n","(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n","os.rename(src, real_dst)\n","return meth(arg, *args, **kwargs)\n","return dict(zip(range(k), df.scatter_by_map(c, map_size=k)))\n","return tables\n","return df.set_index(self[index])\n","return out\n","return self._data.to_pandas_index()\n","return result\n","elif is_categorical_dtype(data) or is_categorical_dtype(dtype):\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(type(None))\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return condition(type(None))\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return cls._simple_new(subarr, name)\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\n","return condition(arr_or_dtype.type)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(tipo)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","result = np.asarray(values, dtype=dtype)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.columns = columns\n","object.__setattr__(self, key, col)\n","if columns is None:\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","elif is_categorical_dtype(data) or is_categorical_dtype(dtype):\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(type(None))\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return cls._simple_new(subarr, name)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return False\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","ctype_base = cls.__mro__[-2]\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\n","return condition(arr_or_dtype.type)\n","dtype = _get_dtype(arr_or_dtype)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return result._reset_identity()\n","result = np.asarray(values, dtype=dtype)\n","value = self.func(instance)\n","return tuple(self.keys())\n","return KeysView(self)\n","def __init__(self, data={}, multiindex=False, level_names=None):\n","value = as_column(value)\n","from cudf.core.series import Series\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","self[key]\n","value = self.func(instance)\n","return tuple(self.values())\n","yield self._mapping[key]\n","if is_datetimelike(self._values):\n","or is_timedelta64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","if key != \"_data\" and key in self._data:\n","self[key]\n","self._data.insert(name, value, loc=loc)\n","self._data[name] = value\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","def is_scalar(val):\n","or isinstance(element, numbers.Number))\n","for val in data.values()\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","_ndarray_values = self._ndarray_values\n","return self.values  # type: ignore\n","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return False\n","from cudf.core.series import Series\n","or np.isscalar(val)\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","from collections.abc import Sequence\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","kwargs = _setdefault_name(arbitrary, kwargs)\n","if not hasattr(values, \"name\"):\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\n","names = kwargs.get(\"names\")\n","from copy import deepcopy\n","idx = self._shallow_copy()\n","return self._simple_new(values, **attributes)\n","return {k: getattr(self, k, None) for k in self._attributes}\n","self.name = values[0]\n","df = self.copy(deep=False)\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","subarr = com.asarray_tuplesafe(data, dtype=object)\n","is_interval_dtype(data) or is_interval_dtype(dtype)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","elif is_extension_array_dtype(arr_or_dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(type(None))\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.insert(i, col_name, data[col_name])\n","if len(self) == 0:\n","return len(self.index)\n","name = self._pad_key(name)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","if len(value) != len(first):\n","from cudf.core.index import Index\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","object.__setattr__(self, key, col)\n","level_names=columns.names,\n","self._data = OrderedColumnDict(data)\n","def __setitem__(self, key, value):\n","from cudf.core.index import Index\n","value = self.func(instance)\n","return tuple(self.values())\n","def __len__(self):\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","def _gc_callback(self, phase, info):\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","dtype = _get_dtype(arr_or_dtype)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","ctype_base = cls.__mro__[-2]\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","elif isinstance(dtype, cls):\n","raise TypeError(\"could not construct PeriodDtype\")\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","result = np.asarray(values, dtype=dtype)\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","if key != \"_data\" and key in self._data:\n","self[key]\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","_ndarray_values = self._ndarray_values\n","if is_extension_array_dtype(self):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return map(self._values.item, range(self._values.size))\n","or isinstance(arr, ABCPeriodIndex)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return condition(tipo)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","for val in data.values()\n","from cudf.core.column import numerical, categorical, datetime, string\n","or np.isscalar(val)\n","if isinstance(obj, (Sequence,)) and not isinstance(obj, (str, bytes)):\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","def __getattr__(self, key):\n","self[key]\n","new_index = self._shallow_copy()\n","attributes = self._get_attributes_dict()\n","return {k: getattr(self, k, None) for k in self._attributes}\n","idx = self._shallow_copy()\n","if isinstance(values, ABCDatetimeArray):\n","object.__setattr__(self, key, col)\n","if len(self._data) &gt; 0 and new_length != old_length:\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx.rename(**kwargs, inplace=True)\n","self.name = name\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._clear_cache()\n","value = as_column(value)\n","from cudf.core.index import Index\n","del self[key]\n","self._clear_cache()\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","result = as_index(self._values.copy(deep=deep))\n","children=self.base_children,\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","return pandas_dtype(obj).type is CategoricalDtypeType\n","col = self._data.pop(self.name)\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","return len(self._mapping)\n","del self[key]\n","self._data.__delitem__(key)\n","self.set_by_label(key, value)\n","key = self._pad_key(key)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","def __setitem__(self, key, value):\n","return arbitrary\n","if not hasattr(values, \"name\"):\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","return KeysView(self)\n","return len(self._values)\n","return next(iter(self._data.columns))\n","value = self.func(instance)\n","kwargs = _setdefault_name(arbitrary, kwargs)\n","if not hasattr(values, \"name\"):\n","return self._data.names[0]\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","def as_index(arbitrary, **kwargs):\n","from cudf.core.series import Series\n","self._data = OrderedColumnDict(data)\n","def __setitem__(self, key, value):\n","return self._get_columns_by_label(arg, downcast=True)\n","new_data, name=labels, index=self.index\n","self._index = RangeIndex(len(data)) if index is None else index\n","value = self.func(instance)\n","return tuple(self.values())\n","return ValuesView(self)\n","self.level_names = tuple(level_names)\n","elif pd.api.types.is_list_like(key) and not isinstance(key, tuple):\n","return self.__class__({key: result})\n","return value\n","del self._data[name]\n","self._clear_cache()\n","self._data.insert(name, value, loc=loc)\n","self._clear_cache()\n","from cudf.core.column import as_column\n","from cudf.core.index import Index\n","from cudf.core.index import Index\n","self[key]\n","or isinstance(val, numbers.Number)\n","return len(self.index)\n","out = self._gather(positions)\n","libcudfxx.copying.gather(self, as_column(gather_map))\n","return self.as_numerical_column(dtype, **kwargs)\n","nan_as_null=nan_as_null,\n","if is_categorical_dtype(dtype):\n","from cudf.core.index import CategoricalIndex\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","return self.as_numerical_column(dtype, **kwargs)\n","super().__init__()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","def __getattr__(self, key):\n","except KeyError:\n","if level_names is None:\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._data[key] = value\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","return next(iter(self._data.names))\n","return cupy.asarray(self._column.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","return da\n","return devptr\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetCurrent(byref(hctx))\n","return core.array(a, dtype, False, order)\n","'version': 2,\n","return ary[tuple(core_index)]\n","return fn(*args, **kws)\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","return args[0] if not args2 else methods.concat(args2, uniform=True)\n","dfs, axis=axis, join=join, uniform=uniform, filter_warning=filter_warning\n","return cudf.concat(dfs, axis=axis)\n","return DataFrame._concat(objs, axis=axis, ignore_index=ignore_index)\n","for i, c in enumerate(unique_columns_ordered_ls)\n","for i, c in enumerate(unique_columns_ordered_ls)\n","return cls(data=col, index=index, name=name)\n","return col\n","return self.as_numerical_column(dtype, **kwargs)\n","offset=casted.offset,\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","for o in objs\n","return self._get_columns_by_label(arg, downcast=True)\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.insert(i, col_name, data[col_name])\n","value = Series(value)._align_to_index(self._index, how=\"right\")\n","if self.index.equals(index):\n","result = self == other\n","return self._apply_op(\"__eq__\", other)\n","return as_index(op(other))\n","return self._unordered_compare(other, \"eq\")\n","outcol = self._column.unordered_compare(cmpops, other)\n","return _numeric_column_compare(self, rhs, op=cmpop)\n","return _numeric_column_binop(lhs, rhs, op, out_dtype=np.bool_)\n","_ = libcudf.binops.apply_op(lhs, rhs, out, op)\n","data = Buffer.empty(row_count * dtype.itemsize)\n","data = ColumnBase._concat([o._values for o in objs])\n","col = libcudf.concat._column_concat(objs, col)\n","return func(*args, **kwargs)\n","out = lhs.merge(*args, **kwargs)\n","lhs._data, rhs._data, left_on, right_on, how, method\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","result2 = group_split_dispatch(df, ind.values.view(np.int64), n)\n","return meth(arg, *args, **kwargs)\n","return dict(zip(range(k), df.scatter_by_map(c, map_size=k)))\n","index_names=index_names,\n","return df.set_index(self[index])\n","df = self.copy(deep=False)\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.insert(i, col_name, data[col_name])\n","if len(self) == 0:\n","return len(self.index)\n","self._clear_cache()\n","value = as_column(value)\n","from cudf.core.series import Series\n","self[key]\n","from cudf.core.series import Series\n","object.__setattr__(self, key, col)\n","level_names=columns.names,\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.series import Series\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","is_datetime64_any_dtype(data)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","return cls._simple_new(subarr, name)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","value = self.func(instance)\n","return tuple(self.keys())\n","return KeysView(self)\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","value = self.func(instance)\n","return tuple(self.values())\n","return len(self._mapping)\n","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","_ndarray_values = self._ndarray_values\n","if is_extension_array_dtype(self):\n","for val in data.values()\n","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx.rename(**kwargs, inplace=True)\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","elif data is None or is_scalar(data):\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","elif is_list_like(arr_or_dtype):\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return cls._simple_new(subarr, name)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","def is_timedelta64_dtype(arr_or_dtype):\n","return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.signedinteger))\n","return condition(arr_or_dtype.type)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","value = self.func(instance)\n","return tuple(self.keys())\n","new_index = self._shallow_copy()\n","attributes = self._get_attributes_dict()\n","return {k: getattr(self, k, None) for k in self._attributes}\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx = arbitrary.copy(deep=False)\n","result = as_index(self._values.copy(deep=deep))\n","return GenericIndex(arbitrary, **kwargs)\n","from cudf.core.series import Series\n","self._data = OrderedColumnDict(data)\n","from cudf.core.column import as_column\n","children=self.base_children,\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._data[key] = value\n","if not hasattr(values, \"name\"):\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","return len(self._mapping)\n","return as_index(arbitrary._column, **kwargs)\n","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","if not hasattr(values, \"name\"):\n","return self._data.names[0]\n","value = self.func(instance)\n","return tuple(self.keys())\n","return len(self._mapping)\n","return self._data[self.name]\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","self._init_from_dict_like(data, index=index, columns=columns)\n","keys = [key for key in data.keys() if key in columns]\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","return self._engine_type(lambda: _ndarray_values, len(self))\n","object.__setattr__(self, key, col)\n","dict(zip(columns, self._data.columns)),\n","value = self.func(instance)\n","return tuple(self.values())\n","yield self._mapping[key]\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","elif is_extension_array_dtype(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","def __init__(self, data={}, multiindex=False, level_names=None):\n","from cudf.core.column import as_column\n","from cudf.core.index import Index\n","if len(value) != len(first):\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","self._data.insert(name, value, loc=loc)\n","from cudf.core.column import numerical, categorical, datetime, string\n","self._clear_cache()\n","value = as_column(value)\n","from cudf.core.series import Series\n","self[key]\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","for val in data.values()\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)\n","subarr = com.asarray_tuplesafe(data, dtype=object)\n","result = np.asarray(values, dtype=dtype)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","names = self._validate_names(name=name, names=names, deep=deep)\n","idx = self._shallow_copy()\n","return self._simple_new(values, **attributes)\n","for k, v in kwargs.items():\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return self._get_columns_by_label(arg, downcast=True)\n","new_data, name=labels, index=self.index\n","name, data = data.names[0], data.columns[0]\n","value = self.func(instance)\n","return tuple(self.keys())\n","return tuple(self.values())\n","yield self._mapping[key]\n","elif pd.api.types.is_list_like(key) and not isinstance(key, tuple):\n","return self.__class__({key: result})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","if len(self) == 0:\n","return len(self.index)\n","from cudf.core.series import Series\n","self._data[name] = value\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","or isinstance(val, numbers.Number)\n","def build_column(data, dtype, mask=None, size=None, offset=0, children=()):\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","from cudf.core.column import ColumnBase, CategoricalColumn\n","return self.as_numerical_column(dtype, **kwargs)\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._data[key] = value\n","from cudf.core.column import as_column\n","super().__init__()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","return self._column.max(dtype=dtype)\n","return self._getitem_tuple_arg(arg)\n","return df\n","return column_empty(0, self.dtype, masked=True)\n","res = self._values[index]\n","return column_empty(0, self.dtype, masked=True)\n","data=self._column.astype(dtype, **kwargs)\n","return self.as_numerical_column(dtype, **kwargs)\n","return cupy.asarray(self._column.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetCurrent(byref(hctx))\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(o)\n","return columns + index\n","columns = sum(col.__sizeof__() for col in self._data.columns)\n","return n\n"],"line_number":[0,982,149,119,29,5047,588,504,491,18,983,967,677,728,219,13,983,967,677,728,219,6,983,967,924,1294,1258,1396,677,728,219,192,983,972,682,728,219,122,983,972,924,1294,1258,1396,95,90,924,1294,1258,1396,857,924,1294,1258,1384,95,89,1044,219,983,972,682,728,219,84,983,972,682,728,219,669,2962,677,649,564,4013,677,649,544,544,1044,219,983,972,924,1294,1258,1396,208,906,1280,1252,1402,63,54,556,2581,292,263,205,1022,992,93,517,505,887,3115,172,96,68,225,54,1755,66,134,665,225,112,129,403,137,98,82,46,356,126,294,214,157,547,555,22,261,704,1573,778,659,166,82,46,547,566,505,80,4231,1485,1581,1298,128,314,708,256,605,1918,1200,536,1918,574,514,1616,1906,708,256,605,1928,1200,574,261,639,675,1110,269,261,639,932,1789,212,268,406,1304,1298,126,314,708,269,675,605,1918,639,514,1200,574,261,536,250,85,845,1616,1906,1662,1789,1789,604,250,329,86,674,16,342,1004,382,409,666,329,90,762,1203,857,605,409,666,1647,160,342,1003,356,1003,125,1864,284,240,4232,716,1027,1203,858,574,261,1004,130,355,180,382,386,1367,1038,1068,550,1005,1340,1465,650,617,1386,1489,1579,1298,126,535,321,675,1110,256,1789,1677,1789,1200,574,256,536,605,261,85,708,267,1200,536,605,1918,329,86,720,212,266,1628,630,147,342,1003,354,1005,132,1864,406,1319,31,339,1005,329,90,701,1298,126,536,470,708,605,1921,587,183,452,1070,1516,675,1110,261,1200,574,261,536,1662,250,85,845,639,932,264,883,675,1110,267,261,382,409,409,666,240,4232,716,1027,1789,1203,858,536,1928,605,284,1003,130,177,355,382,408,668,1003,638,617,1465,645,386,1358,386,1367,1044,329,109,45,293,342,1005,801,50,104,329,86,778,348,934,41,108,108,104,329,86,702,801,49,45,291,342,1003,1047,771,31,339,1011,1068,104,329,86,674,793,775,329,1038,1068,228,329,86,720,1016,752,31,339,457,40,135,329,90,682,36,194,212,331,1765,50,1647,165,340,1005,1005,668,132,630,1554,60,708,1288,914,108,41,708,172,382,408,668,33,109,45,292,342,1003,104,172,87,68,225,54,1755,112,122,387,66,134,665,225,112,129,403,95,367,34,97,1891,1891,1173,267,708,165,934,41,1887,457,1298,126,212,266,1643,2581,292,263,205,1022,992,93,517,502,880,189,265,29,218,2515,934,41,613,505,80,4217,1485,1489,1579,212,266,1628,630,165,342,1004,666,1004,406,1319,31,342,1004,1298,126,329,1200,536,675,1110,267,514,1200,574,256,329,86,674,382,409,329,90,702,1203,858,574,240,4232,716,1022,284,1198,858,386,1367,1044,550,1298,126,518,1200,574,267,536,1917,708,267,514,1200,574,256,261,85,577,1060,1906,639,329,86,1003,638,617,386,1367,1043,778,1047,752,31,340,348,934,41,109,45,292,1068,104,329,86,702,1055,1047,771,1068,228,329,86,702,145,1579,212,240,240,4232,717,406,1317,329,90,762,1298,126,536,470,1200,574,256,675,261,85,1200,855,536,16,340,1005,354,382,1647,1003,165,342,1004,666,1198,855,536,605,1921,284,1298,126,536,462,261,708,639,1006,1465,650,602,189,452,1070,1516,1750,457,40,113,329,86,90,762,194,212,31,342,1003,1628,630,1004,160,342,1003,129,890,41,76,708,109,45,292,340,172,382,409,1833,114,317,412,810,412,1485,708,172,87,68,224,112,122,387,49,1754,14,505,20,616,614,149],"name":["","__call__","get","_execute_task","apply","apply_and_enforce","partitioning_index","__call__","dispatch","_register_cudf","_find_and_load","_find_and_load_unlocked","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_find_and_load","_find_and_load_unlocked","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_find_and_load","_find_and_load_unlocked","_find_spec","find_spec","_get_spec","find_spec","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_find_and_load","_find_and_load_unlocked","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_find_and_load","_find_and_load_unlocked","_find_spec","find_spec","_get_spec","find_spec","_path_isfile","_path_is_mode_type","_find_spec","find_spec","_get_spec","find_spec","get_code","_find_spec","find_spec","_get_spec","find_spec","_path_isfile","_path_is_mode_type","_handle_fromlist","_call_with_frames_removed","_find_and_load","_find_and_load_unlocked","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_find_and_load","_find_and_load_unlocked","_load_unlocked","exec_module","_call_with_frames_removed","&lt;module&gt;","_bind_operator_method","wrapper","_derived_from","extra_titles","_bind_operator_method","wrapper","_derived_from","skip_doctest","&lt;listcomp&gt;","_handle_fromlist","_call_with_frames_removed","_find_and_load","_find_and_load_unlocked","_find_spec","find_spec","_get_spec","find_spec","_lock_unlock_module","_find_spec","find_spec","_get_spec","find_spec","hash_object_cudf","safe_hash","__setitem__","_align_to_index","equals","__eq__","_apply_op","__eq__","_unordered_compare","unordered_compare","_numeric_column_compare","_numeric_column_binop","column_empty","hash_columns","values","data_array_view","as_cuda_array","_require_cuda_context","from_cuda_array_interface","get_devptr_for_active_ctx","asarray","__cuda_array_interface__","array_core","_require_cuda_context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