quasiben/profiled-main-optimized.html

## profiled-main-optimized.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(*(_execute_task(a, cache) for a in args))\n","return func(*args, **kwargs)\n","df = func(*args, **kwargs)\n","","data = self._column.astype(dtype)\n","return self.as_categorical_column(dtype, **kwargs)\n","labels = sr.label_encoding(cats=cats, dtype=label_dtype, na_sentinel=1)\n","codes = codes.sort_values(\"order\")[\"code\"].fillna(na_sentinel)\n","self[by].argsort(ascending=ascending, na_position=na_position),\n","return self._sort(ascending=ascending, na_position=na_position)[1]\n","ascending=ascending, na_position=na_position\n","col_inds = self.as_frame()._get_sorted_inds(ascending, na_position)\n","","","self[by].argsort(ascending=ascending, na_position=na_position),\n","return func(*args, **kwds)\n","ascending=ascending, na_position=na_position\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._data = self.astype(dtype)._data\n","return self._mimic_inplace(result, inplace=inplace)\n","out = self._gather(positions, keep_index=keep_index)\n","return self.agg(func)\n","result = super().agg(func)\n","return func(*args, **kwds)\n","result = self._groupby.aggregate(self.obj, normalized_aggs)\n","return func(*args, **kwds)\n","ascending=ascending, na_position=na_position\n","indices = self._values.argsort(ascending=ascending, **kwargs)\n","ascending=ascending, na_position=na_position\n","out = self._gather(positions, keep_index=keep_index)\n","return func(*args, **kwds)\n","return self._data.to_pandas_index()\n","self.names, columns=self.level_names, dtype=\"object\"\n","return cls.from_arrays(columns, sortorder=sortorder, names=names)\n","codes, levels = factorize_from_iterables(arrays)\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","cat = Categorical(values, ordered=False)\n","codes, categories = factorize(values, sort=True)\n","values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value\n","yield k, self._ixs(i, axis=1)\n","result = self._box_col_values(values, i)\n","return klass(values, index=self.index, name=name, fastpath=True)\n","self.name = name\n","object.__setattr__(self, name, value)\n","return self._data.to_pandas_index()\n","self.names, columns=self.level_names, dtype=\"object\"\n","return cls.from_arrays(columns, sortorder=sortorder, names=names)\n","codes, levels = factorize_from_iterables(arrays)\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","cat = Categorical(values, ordered=False)\n","codes, categories = factorize(values, sort=True)\n","values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value\n","object.__getattribute__(self, key)\n","return func(*args, **kwds)\n","return self._data.to_pandas_index()\n","self.names, columns=self.level_names, dtype=\"object\"\n","return cls.from_arrays(columns, sortorder=sortorder, names=names)\n","codes, levels = factorize_from_iterables(arrays)\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))\n","cat = Categorical(values, ordered=False)\n","codes, categories = factorize(values, sort=True)\n","values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value\n","return super().__getattribute__(key)\n","value = self.func(instance)\n","return self._getitem_tuple_arg(arg)\n","return func(*args, **kwds)\n","return self._downcast_to_series(df, arg)\n","return cudf.DataFrame(self._df._get_columns_by_index(arg))\n","data, columns=data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","return func(*args, **kwds)\n","suffixes=suffixes,\n","to_return = mergeop.perform_merge()\n","right_index=self.right_index,\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","suffixes,\n","if not (len_left_on + left_index * lhs._num_indices) == (\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","boolmask = queryutils.query_execute(self, expr, callenv)\n","for col in columns\n","for col in columns\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","return kernel[griddim, blockdim, self.stream, self.sharedmem](*args)\n","self.stream, self.sharedmem)\n","kernel.launch(args, griddim, blockdim, stream, sharedmem)\n","wb()\n","return cufunc\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","None)\n","devary = wrap_arg(val).to_device(retr, stream)\n","stream=stream)\n","return numba.cuda.as_cuda_array(obj), False\n","owner=obj)\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","cc = get_current_device().compute_capability\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","[self.typingctx.resolve_argument_type(a) for a in args])\n","return typeof(numba.cuda.as_cuda_array(val), Purpose.argument)\n","owner=obj)\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","_, tpb = ctx.get_max_potential_block_size(**kwargs)\n","memsize, blocksizelimit)\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","cc = compute_capability or get_current_device().compute_capability\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","cuctx = get_context()\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","data = Buffer.empty(row_count * dtype.itemsize)\n","self, as_column(boolean_mask)\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","return self._binaryop(other, \"eq\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","out = libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)\n","subset=subset, keep=keep, ignore_index=ignore_index\n","return self.to_frame().reset_index(drop=drop)\n","result = self.copy()\n","return func(*args, **kwds)\n","out = DataFrame(data=self._data.copy(deep=deep))\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","self._values.copy(deep=deep).astype(dtype), name=name\n","copy_data[name] = copy_data[name].fillna(value[name],)\n","level_names=self.level_names,\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","result = self.copy()\n","return func(*args, **kwds)\n","out = DataFrame(data=self._data.copy(deep=deep))\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","self._values.copy(deep=deep).astype(dtype), name=name\n","return self._getitem_tuple_arg(arg)\n","return func(*args, **kwds)\n","df = columns_df._apply_boolean_mask(tmp_arg[0])\n","self, as_column(boolean_mask)\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","return self._df._get_columns_by_label(arg)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","","self[by].argsort(ascending=ascending, na_position=na_position),\n","return func(*args, **kwds)\n","ascending=ascending, na_position=na_position\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","out = self._gather(positions, keep_index=keep_index)\n","result = self.copy()\n","return func(*args, **kwds)\n","out = DataFrame(data=self._data.copy(deep=deep))\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","self._values.copy(deep=deep).astype(dtype), name=name\n","","","return self._shift(periods)\n","return self.__class__._from_table(Frame(data, self._index))\n","self._level_names = level_names\n","data_columns = (col.shift(offset, fill_value) for col in self._columns)\n","mask=bools_to_mask(output_mask),\n","return kernel[griddim, blockdim, self.stream, self.sharedmem](*args)\n","return specialization\n","[self.typingctx.resolve_argument_type(a) for a in args])\n","return typeof(numba.cuda.as_cuda_array(val), Purpose.argument)\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.cuCtxGetDevice(byref(hdevice))\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","self.stream, self.sharedmem)\n","kernel.launch(args, griddim, blockdim, stream, sharedmem)\n","wb()\n","None)\n","devary = wrap_arg(val).to_device(retr, stream)\n","stream=stream)\n","return numba.cuda.as_cuda_array(obj), False\n","owner=obj)\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","return da\n","return devptr\n","cuctx = get_context()\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","func=kernel._func.get(),\n","cuctx = get_context()\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","return self.definition._func\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","return (gridsize.value, blocksize.value)\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","return column_empty(row_count, dtype, masked)\n","data, dtype, mask=mask, size=row_count, children=children\n","return self._repeat(repeats)\n","libcudf.filling.repeat(self, count)\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","self._sr._column[key] = value\n","out = input.as_frame()._scatter(key, [value])._as_column()\n","return data\n","return out_col\n","return result\n","owner=obj)\n","return fn(*args, **kws)\n","return da\n","return devptr\n","return fn(*args, **kws)\n","return self._do_setitem(key, value)\n","rhs, _ = auto_device(value, stream=stream)\n","devobj = from_array_like(obj, stream=stream)\n","writeback=ary, stream=stream, gpu_data=gpu_data)\n","gpu_data = devices.get_context().memalloc(self.alloc_size)\n","return self.memory_manager.memalloc(bytesize)\n","return fn(*args, **kws)\n","stream=stream)\n","fn(device_pointer(dst), host_pointer(src, readonly=True), size, *varargs)\n","blockdim = self._compute_thread_per_block(kernel)\n","_, tpb = ctx.get_max_potential_block_size(**kwargs)\n","memsize, blocksizelimit)\n","self.stream, self.sharedmem)\n","kernel.launch(args, griddim, blockdim, stream, sharedmem)\n","wb()\n","None)\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","data, dtype, mask=mask, size=row_count, children=children\n","return result\n","return func(*args, **kwds)\n","return out\n","return result\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","input_col, output_col, output_mask, periods\n","return kernel[griddim, blockdim, self.stream, self.sharedmem](*args)\n","self.stream, self.sharedmem)\n","kernel.launch(args, griddim, blockdim, stream, sharedmem)\n","kernelargs)\n","None)\n","raise NotImplementedError(ty, val)\n","return devary\n","return numba.cuda.as_cuda_array(obj), False\n","owner=obj)\n","return fn(*args, **kws)\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","return da\n","return devptr\n","return cufunc\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","return specialization\n","[self.typingctx.resolve_argument_type(a) for a in args])\n","return typeof(numba.cuda.as_cuda_array(val), Purpose.argument)\n","owner=obj)\n","return fn(*args, **kws)\n","return da\n","return devptr\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","return tpb\n","return self.definition._func\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","memsize, blocksizelimit)\n","cuctx = get_context()\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","return column_empty(row_count, dtype, masked)\n","data = Buffer.empty(row_count * dtype.itemsize)\n","return ret\n","return cupy.ndarray(shape, dtype, order=order)\n","return self._binaryop(other, \"ne\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","out = libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)\n","return func(*args, **kwds)\n","return self._apply_boolean_mask(mask)\n","self, as_column(boolean_mask)\n","{self.name: self._values}\n","return value\n","self._start, self._stop, self._step, dtype=self.dtype\n","data = self._sr._column[arg]\n","return self.element_indexing(arg)\n","return self._bitwise_binop(other, \"or\")\n","ser = self._binaryop(other, op)\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","","return self._apply_boolean_mask(boolmask)\n","return out.set_mask(out_mask).fillna(False)\n","for col in columns\n","return result\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 kernel[griddim, blockdim, self.stream, self.sharedmem](*args)\n","return tpb\n","return cufunc\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","memsize, blocksizelimit)\n","cc = compute_capability or get_current_device().compute_capability\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","with driver.get_active_context() as ac:\n","driver.cuCtxGetDevice(byref(hdevice))\n","self.stream, self.sharedmem)\n","kernel.launch(args, griddim, blockdim, stream, sharedmem)\n","wb()\n","raise NotImplementedError(ty, val)\n","return devary\n","return numba.cuda.as_cuda_array(obj), False\n","owner=obj)\n","return fn(*args, **kws)\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","None)\n","return cufunc\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","[self.typingctx.resolve_argument_type(a) for a in args])\n","[self.typingctx.resolve_argument_type(a) for a in args])\n","return typeof(numba.cuda.as_cuda_array(val), Purpose.argument)\n","owner=obj)\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","return da\n","return devptr\n","return current_context().device\n","return _runtime.get_or_create_context(devnum)\n","return self._get_or_create_context_uncached(devnum)\n","return ctx\n","return self\n","data = Buffer.empty(row_count * dtype.itemsize)\n","return result\n","{self.name: self._values}\n","return value\n","self._start, self._stop, self._step, dtype=self.dtype\n","return self._binaryop(other, \"ne\")\n","return func(*args, **kwds)\n","return result\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","return out\n","return self._binaryop(other, \"sub\")\n","return func(*args, **kwds)\n","return result\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","res = self._column.unique()\n",".drop_duplicates(keep=\"first\", ignore_index=True)\n","return self._bitwise_binop(other, \"and\")\n","ser = self._binaryop(other, op)\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","return self._binaryop(other, \"le\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","out = out.fillna(op == \"ne\")\n","return self._binaryop(other, \"eq\")\n","return func(*args, **kwds)\n","return result\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","return out\n","cudf.to_datetime(df[\"d_date\"], format=\"%Y-%m-%d\")\n","format=format,\n","dtype=_unit_dtype_map[unit], format=format,\n","return self._as_datetime_or_timedelta_column(out_dtype, format)\n","if (self == \"None\").any():\n","return self.binary_operator(\"eq\", other)\n","return _string_column_binop(self, rhs, op=op, out_dtype=\"bool\")\n","return func(*args, **kwds)\n","return self.func(*args)\n","dfs = [func(fs, rg, columns.copy(), index, **kwargs) for rg in part]\n","dfs = [func(fs, rg, columns.copy(), index, **kwargs) for rg in part]\n","**kwargs.get(\"read\", {}),\n","use_pandas_metadata=use_pandas_metadata,\n","return fs.isdir(path_or_data)\n","return self.info(path)[\"type\"] == \"directory\"\n","out = os.stat(path, follow_symlinks=False)\n","if os.path.exists(paths[0]):\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","return group_split_dispatch(df, c, k, ignore_index=ignore_index)\n","return meth(arg, *args, **kwargs)\n","keep_index=not ignore_index,\n","return func(*args, **kwds)\n","tables = self._partition(map_index, map_size, keep_index)\n","result.append(self._empty_like(keep_index))\n","libcudf.copying.table_empty_like(self, keep_index)\n","self._copy_categories(other, include_index=include_index)\n","self._data.keys(), self._data.values(), other._data.values()\n","for key in self._mapping:\n","return self._data.__iter__()\n","self, splits, keep_index=keep_index\n","result = [self.__class__._from_table(tbl) for tbl in result]\n","index = Index._from_table(table._index)\n","values = next(iter(table._data.values()))\n","for key in self._mapping:\n","return self._data.__iter__()\n","def _gc_callback(self, phase, info):\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","object.__getattribute__(self, key)\n","def _gc_callback(self, phase, info):\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","def _gc_callback(self, phase, info):\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","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","null_count=null_count,\n","null_count=null_count,\n","super().__init__(\n","def _gc_callback(self, phase, info):\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","def _gc_callback(self, phase, info):\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","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","self._copy_struct_names(other, include_index=include_index)\n","other._index._data.values(),\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","super().__setitem__(key, value)\n","return safe_hash(frame)\n","return cudf.Series(frame.hash_columns(), index=index)\n","return Series(table_to_hash._hash()).values\n","return self._column.values\n","return cupy.asarray(self.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetCurrent(byref(hctx))\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","self.name = name\n","object.__getattribute__(self, name)\n","return self._column.values\n","return cupy.asarray(self.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","return self._binaryop(other, \"mod\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","","use_pandas_metadata=use_pandas_metadata,\n","return fs.isdir(path_or_data)\n","return self.info(path)[\"type\"] == \"directory\"\n","out = os.stat(path, follow_symlinks=False)\n","null_count=null_count,\n","null_count=null_count,\n","if os.path.exists(paths[0]):\n","result = self.copy()\n","return func(*args, **kwds)\n","out.index = self.index.copy(deep=deep)\n","self._values.copy(deep=deep).astype(dtype), name=name\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","{k: v.copy(deep=True) for k, v in self._data.items()},\n","return func(*args, **kwds)\n","suffixes=suffixes,\n","to_return = mergeop.perform_merge()\n","right_index=self.right_index,\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","return libcudf.copying.copy_column(self)\n","null_count=null_count,\n","super().__init__(\n","null_count=null_count,\n","null_count=null_count,\n","suffixes,\n","if not (len_left_on + left_index * lhs._num_indices) == (\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","return self._apply_boolean_mask(mask)\n","self, as_column(boolean_mask)\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","return self.__class__._from_table(Frame(data, self._index))\n","self._data = OrderedColumnDict(data)\n","data_columns = (col.notnull() for col in self._columns)\n","return self._binaryop(other, \"mul\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","return self._binaryop(other, \"add\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","for col, a, b in dtypes.fillna(\"-\").itertuples()\n","arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))\n","arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))\n","return self._getitem_tuple(key)\n","return self._getitem_lowerdim(tup)\n","section = self._getitem_axis(key, axis=i)\n","return self.obj._ixs(key, axis=axis)\n","values = self._mgr.iget(i)\n","block = self.blocks[self.blknos[i]]\n","self._rebuild_blknos_and_blklocs()\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","- 2 * sizeof(list())\n","return meth(arg, *args, **kwargs)\n","map(sizeof, random.sample(seq, samples))\n","return meth(arg, *args, **kwargs)\n","return getsizeof(o)\n","columns = sum(col.__sizeof__() for col in self._data.columns)\n","columns = sum(col.__sizeof__() for col in self._data.columns)\n","if not isinstance(population, _Sequence):\n","return getsizeof(o)\n","columns = sum(col.__sizeof__() for col in self._data.columns)\n","columns = sum(col.__sizeof__() for col in self._data.columns)\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","**kwargs\n","return cudf.concat(dfs, axis=axis, ignore_index=ignore_index)\n","sort=sort,\n","return func(*args, **kwds)\n","libcudf.concat.concat_tables(tables, ignore_index=ignore_index)\n","if cols[idx].valid_count &gt; 0:\n","return len(self) - self.null_count\n","return func(*args, **kwds)\n","out.index = self.index.copy(deep=deep)\n","self._values.copy(deep=deep).astype(dtype), name=name\n","return _dtype_to_index[arbitrary.dtype.type](arbitrary, **kwargs)\n","out._initialize(data, **kwargs)\n","super(Index, self).__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\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","super().__setitem__(key, value)\n","for i, f in enumerate(objs)\n","+ [f._data[name] if name in f._data else None for name in names]\n","self[key]\n","if all(is_numerical_dtype(col.dtype) for col in cols):\n","if all(is_numerical_dtype(col.dtype) for col in cols):\n","or np.issubdtype(obj, np.floating)\n","return issubclass(arg1, arg2)\n","return _dtype_to_index[arbitrary.dtype.type](arbitrary, **kwargs)\n","out._initialize(data, **kwargs)\n","kwargs = _setdefault_name(values, **kwargs)\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","super().__setitem__(key, value)\n","return arbitrary.astype(dtype)\n","elif is_list_dtype(dtype):\n","or (hasattr(obj, \"dtype\") and is_list_dtype(obj.dtype))\n","return pd.api.types.is_categorical_dtype(obj)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","value = self.func(instance)\n","return tuple(self.values())\n","return len(self._mapping)\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","first = next(iter(self.values()))\n","cols[idx] = cols[idx].astype(dtype)\n","elif pd.api.types.pandas_dtype(dtype).type in {\n","if not issubclass_(arg1, generic):\n","null_count=null_count,\n","null_count=null_count,\n","out = lhs.merge(rhs, *args, **kwargs)\n","return func(*args, **kwds)\n","suffixes=suffixes,\n","to_return = mergeop.perform_merge()\n","compute_result_col_names(self.lhs, self.rhs, self.how)\n","return func(*args, **kwds)\n","return self._get_columns_by_label(mask)\n","new_data, columns=new_data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","suffixes,\n","if not (len_left_on + left_index * lhs._num_indices) == (\n","{self.name: self._values}\n","value = self.func(instance)\n","self._start, self._stop, self._step, dtype=self.dtype\n","o = aca_chunk(x, **kwargs)\n","return getattr(obj, self.method)(*args, **kwargs)\n","skipna=skipna, dtype=dtype, min_count=min_count\n","skipna=skipna, min_count=min_count\n","result_col = self.nans_to_nulls()\n","col = self.fillna(np.nan)\n","result2 = group_split_dispatch(df, ind.values.view(), n, ignore_index=ignore_index)\n","return meth(arg, *args, **kwargs)\n","keep_index=not ignore_index,\n","return func(*args, **kwds)\n","tables = self._partition(map_index, map_size, keep_index)\n","result = partitioned._split(output_offsets, keep_index=keep_index)\n","self, splits, keep_index=keep_index\n","elif dtype.type in (np.object_, np.str_):\n","null_count=null_count,\n","super().__init__(\n","def _gc_callback(self, phase, info):\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","self.ptr = data\n","result = [self.__class__._from_table(tbl) for tbl in result]\n","index = Index._from_table(table._index)\n","if not isinstance(table, RangeIndex):\n","for key in self._mapping:\n","return self._data.__iter__()\n","def _gc_callback(self, phase, info):\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","def values(self):\n","pass\n","def _gc_callback(self, phase, info):\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","def _gc_callback(self, phase, info):\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","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","super().__setitem__(key, value)\n","def _gc_callback(self, phase, info):\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","if dtype is not None:\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","def _gc_callback(self, phase, info):\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","[int(x) * PAGESIZE for x in f.readline().split()[:7]]\n","self._copy_struct_names(other, include_index=include_index)\n","self._data.keys(), self._data.values(), other._data.values()\n","yield self._mapping[key]\n","return ValuesView(self)\n","return KeysView(self)\n","yield from self._mapping\n","self._data.keys(), self._data.values(), other._data.values()\n","yield from self._mapping\n","def __iter__(self):\n","return KeysView(self)\n","def values(self):\n","return safe_hash(frame)\n","return cudf.Series(frame.hash_columns(), index=index)\n","return Series(table_to_hash._hash()).values\n","return self._column.values\n","return cupy.asarray(self.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetCurrent(byref(hctx))\n","return self._binaryop(other, \"mod\")\n","return func(*args, **kwds)\n","outcol = lhs._column.binary_operator(fn, rhs, reflect=reflect)\n","lhs=self, rhs=rhs, op=binop, out_dtype=out_dtype, reflect=reflect\n","return func(*args, **kwds)\n","return self._getitem_tuple_arg(arg)\n","return func(*args, **kwds)\n","columns_df = self._get_column_selection(arg[1])\n","return cudf.DataFrame(self._df._get_columns_by_index(arg))\n","data, columns=data.to_pandas_index(), index=self.index\n","return func(*args, **kwds)\n","self._reindex(columns=columns, deep=True, inplace=True)\n","cols[name] = df._data[name].copy(deep=deep)\n","result = pd.Index(self.names, name=self.name, tupleize_cols=False)\n","elif data is None or is_scalar(data):\n","return result\n","return self._data.to_pandas_index()\n","result = pd.Index(self.names, name=self.name, tupleize_cols=False)\n","return self._column.max(skipna=skipna, dtype=dtype)\n","return self._column.values\n","return cupy.asarray(self.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\n","return func(*args, **kwds)\n","return 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