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= getattr(df, kind)[start:stop]\n","return self._getitem_axis(maybe_callable, axis=axis)\n","return self._get_slice_axis(key, axis=axis)\n","slice_obj.start, slice_obj.stop, slice_obj.step, kind=self.name\n","start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)\n","start_slice = self.get_slice_bound(start, \"left\", kind)\n","slc = self.get_loc(label)\n","return self.searchsorted(label, side=side)\n","return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)\n","slc = self.get_loc(label)\n","if self.is_monotonic_increasing:\n","return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)\n","return self.is_monotonic_increasing\n","return func(*args, size=size, **kwargs)\n","ignore_index=ignore_index,\n","return pd.concat(dfs, axis=axis, join=join, sort=False)\n","return op.get_result()\n","df = cons(data, index=index)\n","mgr = init_dict(data, index, columns, dtype=dtype)\n","return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)\n","return create_block_manager_from_arrays(arrays, arr_names, axes)\n","return homogenized\n","return super().reindex(index=index, **kwargs)\n","return self\n","if ax is not None\n","and type(self) == type(other)\n","com.values_from_object(self), com.values_from_object(other)\n","return np.array_equal(left, right)\n","","return bool(asarray(a1 == a2).all())\n","blocks = form_blocks(arrays, names, axes)\n","return blocks\n","return new_blocks\n","self._rebuild_blknos_and_blklocs()\n","self.new_axes = self._get_new_axes()\n","new_axes[i] = self._get_comb_axis(i)\n","self.objs, axis=data_axis, intersect=self.intersect, sort=self.sort\n","return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)\n","index = _union_indexes(indexes, sort=sort)\n","result = result.union(other)\n","return self._union(other, sort=sort)\n","return super()._union(other, sort)\n","return self._get_reconciled_name_object(other)\n","com.values_from_object(self), com.values_from_object(other)\n","return np.array_equal(left, right)\n","","return bool(asarray(a1 == a2).all())\n","return 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","return DataFrame.from_pandas(obj)\n","result = df.set_index(index)\n","index = index if isinstance(index, Index) else as_index(index)\n","return as_index(column.as_column(arbitrary), **kwargs)\n","nan_as_null=nan_as_null,\n","arbitrary\n","padata = pyarrow_buffer_to_cudf_buffer(buffers[1])\n","return Buffer(arrow_buf)\n","self._init_from_array_like(np.asarray(data))\n","data = column.as_column(data, nan_as_null=nan_as_null, dtype=dtype)\n","data = as_column(rmm.to_device(arbitrary), nan_as_null=nan_as_null)\n","return to\n","return device_array(ary.shape, ary.dtype, ary.strides, stream=stream)\n","shape, strides, dtype, gpu_data=mem\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.cuCtxGetCurrent(byref(hctx))\n","return fn(*args, **kws)\n","_driver.host_to_device(self, ary_core, self.alloc_size, stream=stream)\n","fn(device_pointer(dst), host_pointer(src, readonly=True), size, *varargs)\n","return next(self.gen)\n","self._set_attached_context(oldctx)\n","return self\n","mask = libcudfxx.transform.nans_to_nulls(data)\n","self._init_from_array_like(data)\n","data.__cuda_array_interface__\n","if array_core(self).flags['C_CONTIGUOUS']:\n","return ary[tuple(core_index)]\n","with _runtime.ensure_context():\n","return next(self.gen)\n","with driver.get_active_context():\n","driver.cuCtxGetDevice(byref(hdevice))\n","and columns.equals(df.columns)\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","subclass in cls._abc_negative_cache):\n","df2 = df.copy(deep=False)\n","out.index = index\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","super().__init__({name: values})\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\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 .json import read_json\n","","","","","","","","","","","","","from dask.dataframe.io.parquet.arrow import ArrowEngine\n","","","","","","","","","","","","","pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n","GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True\n","stderr=(subprocess.PIPE if hide_stderr else None),\n","restore_signals, start_new_session)\n","stdout, stderr = self._communicate(input, endtime, timeout)\n","ready = selector.select(timeout)\n","(pid, sts) = self._try_wait(0)\n","return abspath(path)\n","return path, True\n","","","","","","","","pass\n","","","","","","Series._bind_comparison_method(name, meth, **kwargs)\n","","","","","","","","","","","","","","","","","","","return safe_hash(frame)\n","return frame.hash_columns()\n","raise TypeError(msg.format(type(arg)))\n","return self\n","return result._values.all()\n","return self._apply_op(\"__eq__\", other)\n","return as_index(op(other))\n","return self._unordered_compare(other, \"eq\")\n","return result\n","return _numeric_column_compare(self, rhs, op=cmpop)\n","return _numeric_column_binop(lhs, rhs, op, out_dtype=np.bool_)\n","return out\n","data = Buffer.empty(row_count * dtype.itemsize)\n","return Series(table_to_hash._hash()).values\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","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\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","extra_source, backend)\n","base = _preprocess('', options, arch, backend)\n","result = prog.compile(options)\n","return func(*(_execute_task(a, cache) for a in args))\n","return [_execute_task(a, cache) for a in arg]\n","return [_execute_task(a, cache) for a in arg]\n","return func(*(_execute_task(a, cache) for a in args))\n","return self._get_columns_by_label(mask)\n","new_data = self._data.get_by_label(labels)\n","return self.get_by_label_list_like(key)\n","to_flat_dict({k: self._grouped_data[k] for k in key}),\n","return {k: v for k, v in _inner(d)}\n","return {k: v for k, v in _inner(d)}\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)\n","subarr = _try_cast(data, dtype, copy, raise_cast_failure)\n","subarr = maybe_cast_to_integer_array(arr, dtype)\n","if np.array_equal(arr, casted):\n","","return bool(asarray(a1 == a2).all())\n","c = np.mod(c, npartitions).astype(typ, copy=False)\n","return meth(arg, *args, **kwargs)\n","df.scatter_by_map(c, map_size=k, keep_index=not ignore_index),\n","index_names=index_names,\n","return df.set_index(self[index])\n","df.index = index\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","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","return arbitrary\n","children=self.base_children,\n","if is_categorical_dtype(dtype):\n","if obj is cudf.core.dtypes.CategoricalDtype:\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","col = self._data.pop(self.name)\n","del self[key]\n","self._clear_cache()\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","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","self.name = name\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","yield from self._mapping\n","value = self[key]\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 len(self._values)\n","return next(iter(self._data.columns))\n","value = self.func(instance)\n","return tuple(self.values())\n","for key in self._mapping:\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","self._data.insert(name, value, loc=loc)\n","self._data[name] = value\n","super().__setitem__(key, value)\n","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","subclass in cls._abc_negative_cache):\n","return False\n","return len(self.index)\n","from cudf.core.series import Series\n","self[key]\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","object.__setattr__(self, key, col)\n","if not len(columns) == len(self.columns):\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","elif is_timedelta64_dtype(data) or (\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","elif is_timedelta64_dtype(data) or (\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","for k, v in kwargs.items():\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\n","return array(a, dtype, copy=False, order=order)\n","return 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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.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 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","return cls.construct_from_string(dtype) is not None\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 super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","subclass in cls._abc_negative_cache):\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(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","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","if isinstance(arbitrary, ColumnBase):\n","if len(value) != len(first):\n","value = self.func(instance)\n","return tuple(self.values())\n","yield self._mapping[key]\n","return ValuesView(self)\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if is_datetimelike(self._values):\n","or is_datetime64tz_dtype(arr)\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","if key != \"_data\" and key in self._data:\n","self[key]\n","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return condition(tipo)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","keys = [key for key in data.keys() if key in columns]\n","@Appender(_index_shared_docs[\"contains\"] % _index_doc_kwargs)\n","_ndarray_values = self._ndarray_values\n","or isinstance(val, numbers.Number)\n","subclass in cls._abc_negative_cache):\n","or isinstance(element, numbers.Number))\n","for val in data.values()\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","if isinstance(obj, (Sequence,)) and not isinstance(obj, (str, bytes)):\n","if subclass in cls._abc_cache:\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","elif is_period_dtype(data) and not is_object_dtype(dtype):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return False\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data):\n","if isinstance(arr_or_dtype, ABCIndexClass):\n","return pandas_dtype(arr_or_dtype)\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(tipo)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","elif isinstance(values, ABCIndexClass):\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\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 super().is_dtype(dtype)\n","arr_or_dtype, classes_and_not_datetimelike(np.unsignedinteger)\n","return condition(arr_or_dtype.type)\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","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","if subclass in cls._abc_cache:\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","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._set_names(names, level=level)\n","self.name = values[0]\n","if isinstance(values, ABCDatetimeArray):\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\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","kwargs = _setdefault_name(arbitrary, kwargs)\n","kwargs.setdefault(\"name\", values.name)\n","return self._data.names[0]\n","value = self.func(instance)\n","return tuple(self.keys())\n","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","if isinstance(arbitrary, ColumnBase):\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","self.insert(i, col_name, data[col_name])\n","if is_scalar(value):\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","subclass in cls._abc_negative_cache):\n","subclass in cls._abc_negative_cache):\n","return len(self.index)\n","from cudf.core.column import numerical, categorical, datetime, string\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","self[key]\n","object.__setattr__(self, key, col)\n","if not len(columns) == len(self.columns):\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","return cls._simple_new(subarr, name)\n","return result._reset_identity()\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 _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(tipo)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return 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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\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 CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","if _name_includes_bit_suffix(dtype):\n","return PeriodDtype.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","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 DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","if subclass in cls._abc_cache:\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","if issubclass(result.dtype.type, str):\n","return array(a, dtype, copy=False, order=order)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","elif isinstance(dtype, cls):\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.keys())\n","value = self.func(instance)\n","return tuple(self.values())\n","return ValuesView(self)\n","yield self._mapping[key]\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.index import Index\n","super().__setitem__(key, value)\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","object.__setattr__(self, key, col)\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","hash(key)\n","_ndarray_values = self._ndarray_values\n","@property\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","from cudf.core.column import numerical, categorical, datetime, string\n","if is_datetimelike(self._values):\n","or is_datetime64tz_dtype(arr)\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","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","subclass in cls._abc_negative_cache):\n","for val in data.values()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","def __getattr__(self, key):\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","elif is_period_dtype(data) and not is_object_dtype(dtype):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","or isinstance(string, ABCDateOffset)\n","return cls._simple_new(subarr, name)\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return 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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return condition(tipo)\n","elif is_extension_array_dtype(arr_or_dtype):\n","return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.signedinteger))\n","if arr_or_dtype is None:\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.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","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","elif is_list_like(arr_or_dtype):\n","if subclass in cls._abc_cache:\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.timedelta64))\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","except TypeError:\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","subclass in cls._abc_negative_cache):\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","new_index = new_index.set_names(names)\n","idx = self._shallow_copy()\n","values = self.values\n","return result._reset_identity()\n","return {k: getattr(self, k, None) for k in self._attributes}\n","rss = self._proc.memory_info().rss\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","return self._simple_new(values, **attributes)\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","object.__setattr__(self, key, col)\n","@index.setter\n","idx = arbitrary.copy(deep=False)\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\n","if not hasattr(values, \"name\"):\n","return self._get_columns_by_label(arg, downcast=True)\n","new_data = self._data.get_by_label(labels)\n","return self.get_by_label_grouped(key)\n","return self.__class__({key: result})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.index import Index\n","if not isinstance(data, column.ColumnBase):\n","self._data = OrderedColumnDict(data)\n","from cudf.core.column import as_column\n","value = self.func(instance)\n","return tuple(self.keys())\n","return tuple(self.values())\n","yield self._mapping[key]\n","del self._data[name]\n","self._clear_cache()\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","from cudf.core.column import ColumnBase, CategoricalColumn\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","first = next(iter(self.values()))\n","or isinstance(val, numbers.Number)\n","subclass in cls._abc_negative_cache):\n","or type(element) in ScalarType\n","ABCMeta._abc_invalidation_counter and\n","from cudf.core.series import Series\n","return len(self.index)\n","self[key]\n","self._index = RangeIndex(0)\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\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","arbitrary = arbitrary.cast(np_to_pa_dtype(new_dtype))\n","arbitrary\n","padata = Buffer.empty(0)\n","col = self._data.pop(self.name)\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","del self[key]\n","self._clear_cache()\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","return fn(*args, **kws)\n","return da\n","return devptr\n","return core.array(a, dtype, False, order)\n","extra_source, backend)\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","ignore_index=ignore_index,\n","return cudf.concat(dfs, axis=axis, ignore_index=ignore_index)\n","return DataFrame._concat(objs, axis=axis, ignore_index=ignore_index)\n","for i, c in enumerate(unique_columns_ordered_ls)\n","return result\n","return col\n","for i, c in enumerate(unique_columns_ordered_ls)\n","col = libcudf.concat._column_concat(objs, col)\n","if is_categorical_dtype(dtype):\n","obj, (Index, Series, ColumnBase, pd.Index, pd.Series, np.ndarray)\n","not_null_cols = list(filter(lambda o: len(o) != o.null_count, objs))\n","from cudf.core.index import CategoricalIndex\n","or np.issubdtype(o.dtype, np.datetime64)\n","if not issubclass_(arg1, generic):\n","from cudf.core.index import CategoricalIndex\n","for o in objs\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","elif is_unsigned_integer_dtype(data.dtype):\n","arr_or_dtype, classes_and_not_datetimelike(np.unsignedinteger)\n","result = object.__new__(cls)\n","if isinstance(arr_or_dtype, ABCIndexClass):\n","return pandas_dtype(arr_or_dtype)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\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 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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return condition(tipo)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\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","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.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","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","elif is_list_like(arr_or_dtype):\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 DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return self._get_columns_by_label(arg, downcast=True)\n","new_data = self._data.get_by_label(labels)\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.index import Index\n","name, data = data.names[0], data.columns[0]\n","def __get__(self, instance, cls):\n","return tuple(self.values())\n","for key in self._mapping:\n","return tuple(self.keys())\n","yield from self._mapping\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.series import Series\n","return key in self._engine\n","df, ind.values.view(np.int64), n, ignore_index=ignore_index\n","return meth(arg, *args, **kwargs)\n","df.scatter_by_map(c, map_size=k, keep_index=not ignore_index),\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","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","_ndarray_values = self._ndarray_values\n","if is_extension_array_dtype(self):\n","value = column.as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","or np.isscalar(val)\n","or type(element) in ScalarType\n","subclass in cls._abc_negative_cache):\n","if (cls._abc_negative_cache_version ==\n","self._data[name] = value\n","value = as_column(value)\n","from cudf.core.index import Index\n","super().__setitem__(key, value)\n","return len(self.index)\n","object.__setattr__(self, key, col)\n","if not len(columns) == len(self.columns):\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 condition(type(None))\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data):\n","elif isinstance(values, ABCIndexClass):\n","return result._reset_identity()\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 CategoricalDtype.is_dtype(arr_or_dtype)\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 PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","value = self.func(instance)\n","return tuple(self.values())\n","for key in self._mapping:\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","if subclass in cls._abc_cache:\n","subtype = type(instance)\n","elif is_extension_array_dtype(self._values):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","or is_timedelta64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","for val in data.values()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","all(hasattr(obj, attr) for attr in dict_like_attrs)\n","return self._data.to_pandas_index()\n","self.names, name=self.level_names[0], tupleize_cols=False\n","value = self.func(instance)\n","return tuple(self.keys())\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 PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","subarr = com.asarray_tuplesafe(data, dtype=object)\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\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_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","new_index = new_index.set_names(names)\n","idx._set_names(names, level=level)\n","return self._simple_new(values, **attributes)\n","return deepcopy(self.names) if deep else self.names\n","return self._simple_new(values, **attributes)\n","object.__setattr__(self, key, col)\n","self._index = idx\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx.rename(**kwargs, inplace=True)\n","self.name = name\n","col = self._data.pop(self.name)\n","return next(iter(self._data.names))\n","setattr(instance, self.func.__name__, value)\n","return tuple(self.keys())\n","return len(self._mapping)\n","return KeysView(self)\n","self.set_by_label(key, value)\n","self._data[key] = value\n","value = as_column(value)\n","from cudf.core.index import Index\n","result = as_index(self._values.copy(deep=deep))\n","kwargs = _setdefault_name(arbitrary, kwargs)\n","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.series import Series\n","col = self._data.pop(self.name)\n","@property\n","value = self.func(instance)\n","return tuple(self.keys())\n","children=self.base_children,\n","from cudf.core.column.numerical import NumericalColumn\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","if not hasattr(values, \"name\"):\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return len(self._values)\n","return next(iter(self._data.columns))\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","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","self.level_names = tuple((None,) * self.nlevels)\n","value = as_column(value)\n","from cudf.core.index import Index\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","elif len(value) > 0:\n","self._data[name] = value\n","value = as_column(value)\n","from cudf.core.series import Series\n","super().__setitem__(key, value)\n","from cudf.core.series import Series\n","return len(self.index)\n","or isinstance(val, numbers.Number)\n","or isinstance(element, numbers.Number))\n","object.__setattr__(self, key, col)\n","if not len(columns) == len(self.columns):\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","elif is_list_like(arr_or_dtype):\n","if subclass in cls._abc_cache:\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","for k, v in kwargs.items():\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","dtype = getattr(dtype, \"dtype\", dtype)\n","elif is_extension_array_dtype(arr_or_dtype):\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return condition(tipo)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\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.values())\n","yield self._mapping[key]\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","or isinstance(val, pd.Timestamp)\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","dtype = getattr(arr_or_dtype, \"dtype\", arr_or_dtype)\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","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return False\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","def __getattr__(self, 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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.signedinteger))\n","return condition(arr_or_dtype.type)\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 PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","value = self.func(instance)\n","return tuple(self.keys())\n","new_index = new_index.set_names(names)\n","idx._set_names(names, level=level)\n","if not is_list_like(values):\n","if subclass in cls._abc_cache:\n","return self._simple_new(values, **attributes)\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","return {k: getattr(self, k, None) for k in self._attributes}\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","kwargs = _setdefault_name(arbitrary, kwargs)\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\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","self._data = OrderedColumnDict(data)\n","return self.get_by_label_grouped(key)\n","return self.__class__({key: result})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","def as_column(arbitrary, nan_as_null=True, dtype=None, length=None):\n","del self._data[name]\n","self._clear_cache()\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","from cudf.core.column import ColumnBase, CategoricalColumn\n","self._data.insert(name, value, loc=loc)\n","self._data[name] = value\n","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","from cudf.core.index import Index\n","or isinstance(val, numbers.Number)\n","if subclass in cls._abc_cache:\n","or isinstance(element, numbers.Number))\n","def __instancecheck__(cls, instance):\n","self[key]\n","super().__init__()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\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","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\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","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, ptr)\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 self._getitem_tuple_arg(arg)\n","return df\n","return column_empty(0, self.dtype, masked=True)\n","return build_column(data, dtype, mask=mask, children=children)\n","res = self._values[index]\n","return column_empty(0, self.dtype, masked=True)\n","mask = create_null_mask(row_count, state=MaskState.ALL_NULL)\n","data=self._column.astype(dtype, **kwargs)\n","return self.as_numerical_column(dtype, **kwargs)\n","return self._column.max(dtype=dtype)\n","return func(*args, **kwargs)\n","out = lhs.merge(*args, **kwargs)\n","return gdf_result\n","return sizeof(obj)\n","return meth(arg, *args, 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= _main(fd)\n","return self._bootstrap()\n","self.run()\n","self._target(*self._args, **self._kwargs)\n","target(*args, **kwargs)\n","loop.run_sync(run)\n","self.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle._run()\n","self._callback(*self._args)\n","comm, every_cycle=[self.ensure_communicating, self.ensure_computing]\n","msgs = await comm.read()\n","n = await stream.read_into(frame)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","res = _from_frames()\n","frames, deserialize=deserialize, deserializers=deserializers\n","value = _deserialize(head, fs, deserializers=deserializers)\n","deserializers=deserializers,\n","return loads(header, frames)\n","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","","","","","","","","","","","","","","","","","from .core import (\n","","","","","","import pandas as pd\n","","","","","","from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib\n","","","","","","","","","","","","","","","","","","","","from .tslibs import (\n","","","","","","from .conversion import localize_pydatetime, normalize_date\n","","","","","","","","","","","","","","","","","","","","","class DeprecatedTzFormatWarning(Warning):\n","","","","","","class _nullcontext(object):\n","","","","","","","","","","","","","","","","","","","","","","","","","","list.extend(self, fill_iter.pop())\n","tz for tz in all_timezones if resource_exists(tz))\n","return True\n","return open(filename, 'rb')\n","os.stat(path)\n","","","","","","","","","","","","","","from pandas._config import dates # noqa:F401\n","","","","","","","","","","","","","","","","","from .isoparser import isoparser, isoparse\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from pandas.core.arrays.integer import (\n","","","","","","","","","from .array_ import array # noqa: F401\n","","","","","","from pandas.core.dtypes.common import (\n","","","","","","from pandas.core.dtypes.dtypes import (\n","","","","","","","","","","","","","","from .base import ExtensionDtype\n","","","","","","","","","","","","","","","from pandas.compat.numpy import function as nv\n","","","","","","","","from pandas.util._validators import (\n","","","","","","","","","from pandas.core.util.hashing import hash_array, hash_pandas_object # noqa\n","","","","","","def _hash_scalar(val, encoding=\"utf8\", hash_key=None):\n","","","","","","def maybe_cast_to_integer_array(arr, dtype, copy=False):\n","","","","","","","","","","","","","","","","","","from . import missing\n","","","","","","","","def dispatch_fill_zeros(op, left, right, result):\n","","","","","","","","","","","","","","","","","","","","","","","","","","import pandas.core.algorithms as algorithms\n","","","","","","","","from pandas.core.arrays import datetimelike as dtl\n","","","","","","","","from pandas.tseries import frequencies\n","","","","","","","","from pandas.tseries.offsets import (\n","","","","","","","","","","","","","","","","","","","","from pandas.core.indexes.base import Index, ensure_index\n","","","","","","from pandas.core.strings import StringMethods\n","","","","","","def _ensure_frozen(array_like, categories, copy=False):\n","","","","","","","","","","","","from pandas.core.tools.numeric import to_numeric\n","","","","","","","from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs\n","","","","","","import pandas.core.nanops as nanops\n","","","","","","bn = import_optional_dependency(\"bottleneck\", raise_on_missing=False, on_version=\"warn\")\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from pandas.core.groupby.generic import ( # noqa: F401\n","","","","","","from pandas.core.frame import DataFrame\n","","","","","","from pandas.core.generic import NDFrame, _shared_docs\n","","","","","","from pandas.core.index import (\n","","","","","","from pandas.core.indexes.api import ( # noqa:F401\n","","","","","","from pandas.core.indexes.interval import IntervalIndex # noqa:F401\n","","","","","","","","","","","","","","def _time_to_micros(time):\n","","","","","","","","","from .blocks import ( # noqa: F401\n","","","","","","import pandas._libs.internals as libinternals\n","","","","","","","","","","","","","","","","","","","","","","","from pandas.io.common import _expand_user, _stringify_path\n","","","","","","import gzip\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","import pandas.plotting\n","","","","","","from pandas.plotting._core import (\n","","","","","","import pandas.plotting._matplotlib # noqa\n","","","","","","from pandas.plotting._matplotlib.boxplot import (\n","","","","","","from matplotlib.artist import setp\n","","","","","","","","","from . import cbook, rcsetup\n","","","","","","","","from matplotlib.fontconfig_pattern import parse_fontconfig_pattern\n","","","","","","","","","","def generate_fontconfig_pattern(d):\n","","","","","","\"\"\")\n","return self\n","return wrapper\n","return [frame_summary[:2]]\n","return stack\n","return result\n","return self._line\n","return lines[lineno-1]\n","return updatecache(filename, module_globals)\n","return lines\n","return text\n","return _makeTags(tagStr, False)\n","return openTag, closeTag\n","return self\n","return wrapper\n","return [frame_summary[:2]]\n","return stack\n","return result\n","del cache[filename]\n","\"\"\"Parse action to convert tokens to lower case.\"\"\"\n","return self\n","return wrapper\n","return [frame_summary[:2]]\n","return stack\n","return result\n","del cache[filename]\n","self.ParseException = ParseException\n","return self\n","return wrapper\n","return [frame_summary[:2]]\n","return stack\n","return result\n","del cache[filename]\n","def _gc_callback(self, phase, info):\n","","","","","","","","def wrapper(*args, **kwargs):\n","info[\"uncollectable\"],\n","from . import ft2font\n","","","","","","","","","def _gc_callback(self, phase, info):\n","exec(class_definition, namespace)\n","info[\"uncollectable\"],\n","","","","from pandas.plotting._matplotlib.tools import (\n","","","","","","","","","","","","","","import matplotlib.table\n","","","","","","from .text import Text\n","","","","","","class ConnectionPatch(FancyArrowPatch):\n","","","","","","","","","","","","def draw(self, renderer):\n","\"AvailableArrowstyles\": _pprint_styles(_style_list)}\n","return functools.partial(_register_style, style_list, name=name)\n","return textwrap.indent(rst_table, prefix=' ' * 2)\n","for name, cls in sorted(_styles.items())]]\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_bound_method(sig)\n","return sig.replace(parameters=params)\n","return textwrap.indent(rst_table, prefix=' ' * 2)\n","for name, cls in sorted(_styles.items())]]\n","return rendered\n","return formatted\n","docstring.interpd.update(Text=artist.kwdoc(Text))\n","'Properties:\\n' + '\\n'.join(ai.pprint_setters(leadingspace=4)))\n","self.aliasd = self.get_aliases()\n","return aliases\n","and callable(getattr(self.o, name))]\n","return lines\n","return match.group(1)\n","return _compile(pattern, flags).search(string)\n","return p\n","groupindex, indexgroup\n","return p\n","return items[0]\n","return subpattern\n","return code\n","code[skip] = len(code) - skip\n","return self.width\n","return setters\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","self._return_annotation = return_annotation\n","for param in parameters))\n","","","","","","class TextPath(Path):\n","@functools.lru_cache(50)\n","return wrapper\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","self._name = name\n","return finalize(wrapper, new_doc)\n","return '\\n'.join(lines)\n","","","","","from .transforms import (Bbox, IdentityTransform, Transform, TransformedBbox,\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","import matplotlib.dates as dates\n","","","","","","import matplotlib.ticker as ticker\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from pandas.core import window as rwindow\n","","","","","","","","import pandas._libs.window as libwindow\n","","","","","","","","","","","","","","","","","","","","","","downcast=None,\n","func.__doc__ = dedent(self.join.join(docitems))\n","return text\n","return _compile(pattern, flags).sub(repl, string, count)\n","closed=None,\n","func.__doc__ = dedent(self.join.join(docitems))\n","return text\n","ops.add_special_arithmetic_methods(SparseDataFrame)\n","add_methods(cls, new_methods=new_methods)\n","return new_methods\n","return f\n","return opname\n","add_methods(cls, new_methods=new_methods)\n","return new_methods\n","return f\n","return func\n","return text\n","","","","","","","","\"\"\"\n","indents = _leading_whitespace_re.findall(text)\n","exec(_def_str)\n","","","","","","","","","","","","","","","","","","","","from pandas.core.reshape.pivot import crosstab, pivot, pivot_table\n","","","","","","","","","","","","","","from pandas.io.excel import ExcelFile, ExcelWriter, read_excel\n","","","","","","from pandas.io.excel._xlsxwriter import _XlsxWriter\n","","","","","","class ExcelFile:\n","def __exit__(self, exc_type, exc_value, traceback):\n","","","","","","","","","","","","","","","","","","","","","class FixedWidthFieldParser(PythonParser):\n","","","","","","","","","","","","","","","from pandas.compat import pickle_compat as pc\n","","","","","","","","","from pandas.io.msgpack import ExtType, Packer as _Packer, Unpacker as _Unpacker\n","","","","","","dumps = packb\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from pandas.util.testing import (\n","","","","","","","","","","","def convert_rows_list_to_csv_str(rows_list):\n","","","","","","","","","","","from pandas.core.computation.eval import eval\n","","","","","","from pandas.core.computation.engines import _engines\n","","","","","","from pandas.core.computation.ops import UndefinedVariableError, _mathops, _reductions\n","","","","","","def _reconstruct_object(typ, obj, axes, dtype):\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from .core import (\n","","","","","","from .utils import meta_from_array\n","","","","","","","","","","","","","","","","","","","","","","","","","from .ufunc import multiply, sqrt\n","","","","","","@derived_from(np)\n","derived_from(np)(self)\n","return method\n","return doc\n","for n, p in s.parameters.items()\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_bound_method(sig)\n","return sig.replace(parameters=params)\n","return_annotation=return_annotation)\n","self._return_annotation = return_annotation\n","return _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","return \"\\n\".join(lines)\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","return derived_from(source)(wrapped)\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\n","return p\n","groupindex, indexgroup\n","return code\n","raise error(\"internal: unsupported operand type %r\" % (op,))\n","@derived_from(np)\n","","","","","","","","","","","original_klass, method, ua_args=ua_args, extra=extra\n","return doc\n","for n, p in s.parameters.items()\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","self._return_annotation = return_annotation\n","for param in parameters))\n","def trace(a, offset=0, axis1=0, axis2=1, dtype=None):\n","return method\n","return doc\n","not_supported = [m for m in original_args if m not in method_args]\n","from ..blockwise import blockwise as core_blockwise\n","","","","","","","","","","","","","","","","","","","","","","","","","","","_state = RandomState()\n","self._RandomState = RandomState\n","return func(*args, **kwds)\n","","return x >> (numbytes * 8 - k) # trim excess bits\n","def zipf(self, a, size=None, chunks=\"auto\"):\n","return method\n","return doc\n","s = inspect.signature(func)\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","skip_bound_arg=skip_bound_arg)\n","obj in (type, object))\n","return isinstance(object, types.BuiltinFunctionType)\n","from .utils import (\n","","","","","","def drop_by_shallow_copy(df, columns, errors=\"raise\"):\n","","","","","","","","","","","","","","","","","","","","","def append(self, other, interleave_partitions=False):\n","original_klass, method, ua_args=ua_args, extra=extra\n","doc = skip_doctest(doc)\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","for i, line in enumerate(lines)\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\n","p, loc = _cache[type(pattern), pattern, flags]\n","s = inspect.signature(func)\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_from_function(sigcls, obj)\n","positional = tuple(arg_names[:pos_count])\n","self._return_annotation = return_annotation\n","doc = \"\".join(bits)\n","def memory_usage(self, index=True, deep=False):\n","original_klass, method, ua_args=ua_args, extra=extra\n","method_args = get_named_args(method)\n","n\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","obj = unwrap(obj, stop=(lambda f: hasattr(f, \"__signature__\")))\n","while _is_wrapper(func):\n","def _is_wrapper(f):\n","__validate_parameters__=is_duck_function)\n","for param in parameters))\n","params = OrderedDict(((param.name, param)\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","def to_html(self, max_rows=5):\n","original_klass, method, ua_args=ua_args, extra=extra\n","doc = unsupported_arguments(doc, not_supported)\n","for i, line in enumerate(lines)\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\n","return p\n","groupindex, indexgroup\n","return p\n","s = inspect.signature(func)\n","return Signature.from_callable(obj, follow_wrapped=follow_wrapped)\n","follow_wrapper_chains=follow_wrapped)\n","return _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","for i in range(len(lines) - 1)\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","setattr(cls, name, derived_from(original)(meth))\n","original_klass, method, ua_args=ua_args, extra=extra\n","doc = skip_doctest(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","if stripped == \">>>\" or stripped.startswith(\">>> #\"):\n","setattr(cls, name, derived_from(original)(meth))\n","original_klass, method, ua_args=ua_args, extra=extra\n","doc = skip_doctest(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","if stripped == \">>>\" or stripped.startswith(\">>> #\"):\n","setattr(cls, name, derived_from(original)(meth))\n","original_klass, method, ua_args=ua_args, extra=extra\n","doc = skip_doctest(doc)\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","from .csv import read_csv, to_csv, read_table, read_fwf\n","","","","","","from ...bytes import read_bytes, open_file, open_files\n","","","","","","import fsspec\n","","","","","","from .spec import AbstractFileSystem\n","","","","","","import pyarrow as pa\n","","","","","","from pyarrow.serialization import (default_serialization_context,\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","deserializers=deserializers,\n","name = header.get(\"serializer\")\n","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","return loads(header, frames)\n","cudf_obj = cudf_typ.deserialize(header, frames)\n","return cls(dict(zip(column_names, columns)), index=index)\n","return columns\n","return build_column(data=data, dtype=dtype, mask=mask)\n","return buf\n","self._init_from_array_like(np.asarray(data))\n","f\"Cannot construct Buffer from {data.__class__.__name__}\"\n","register()\n","from cudf.comm import serialize\n","","","","","","","","","","","","","","import cudf.core.groupby.groupby\n","","","","","","","","","","","","","","","","","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","def _gc_callback(self, phase, info):\n","for c, frame in zip(header[\"compression\"], frames)\n","for c, frame in zip(header[\"compression\"], frames)\n","if all(len(f) == l for f, l in zip(frames, lengths)):\n","if all(len(f) == l for f, l in zip(frames, lengths)):\n","return len(frame)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self.log.append((dep, \"new-dep\", state))\n","def _gc_callback(self, phase, info):\n","rv.update(d)\n","self.tasks[key] = self._maybe_deserialize_task(key)\n","function, args, kwargs = _deserialize(*self.tasks[key])\n","function = loads_function(function)\n","result = pickle.loads(bytes_object)\n","return pickle.loads(x)\n","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","handler_func(fileobj, events)\n","self._handle_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","connection, address = sock.accept()\n","return sock, addr\n","data = self._ssock.recv(4096)\n","self.rpc, deps, worker, who=self.address\n","return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n","operation=operation,\n","return await coro()\n","max_connections=max_connections,\n","response = await comm.read(deserializers=deserializers)\n","n = await stream.read_into(frame)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","res = _from_frames()\n","frames, deserialize=deserialize, deserializers=deserializers\n","value = _deserialize(head, fs, deserializers=deserializers)\n","return loads(header, frames)\n","return loads(header, frames)\n","cudf_obj = cudf_typ.deserialize(header, frames)\n","columns = column.deserialize_columns(header[\"columns\"], column_frames)\n","colobj = col_typ.deserialize(meta, frames[:col_frame_count])\n","data = Buffer.deserialize(header[\"data\"], [frames[0]])\n","buf = cls(frames[0], **header[\"constructor-kwargs\"])\n","self._init_from_array_like(np.asarray(data))\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","index = col_typ.deserialize(h, frames[: header[\"frame_count\"]])\n","data = Buffer.deserialize(header[\"data\"], [frames[0]])\n","buf = cls(frames[0], **header[\"constructor-kwargs\"])\n","self._init_from_array_like(np.asarray(data))\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","super().__init__()\n","def _gc_callback(self, phase, info):\n","return loads(header, frames)\n","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","def _new_Index(cls, d):\n","for c, frame in zip(header[\"compression\"], frames)\n","info[\"uncollectable\"],\n","def _gc_callback(self, phase, info):\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","state = func(dep, **kwargs)\n","self.put_key_in_memory(dep, value)\n","self.data[key] = value\n","self.device_buffer[key] = value\n","if self.weight(key, value) <= self.n:\n","return sizeof(v)\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","self._init_from_array_like(np.asarray(data))\n","info[\"uncollectable\"],\n","ret = callback()\n","return self.callback()\n","frame, True, self.profile_recent, stop=\"distributed/worker.py\"\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","\"description\": info_frame(frame),\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","return lines\n","buffer = _builtin_open(filename, 'rb')\n","state = process(prev, frame, state, stop=stop)\n","\"description\": info_frame(frame),\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","def _gc_callback(self, phase, info):\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","lines = fp.readlines()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","lines = fp.readlines()\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","stat = os.stat(fullname)\n","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\n","cpu = self.proc.cpu_percent()\n","pt2 = self._proc.cpu_times()\n","return fun(self, *args, **kwargs)\n","values = self._parse_stat_file()\n","return fun(self, *args, **kwargs)\n","ret = self._cache[fun] = fun(self)\n","with open_binary(\"%s/%s/stat\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","rawdict = _psplatform.net_io_counters()\n","lines = f.readlines()\n","return open(fname, \"rt\", **kwargs)\n","return self._proc.num_fds()\n","return fun(self, *args, **kwargs)\n","return len(os.listdir(\"%s/%s/fd\" % (self._procfs_path, self.pid)))\n","ret = self._cache[fun] = 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","return open(fname, \"rb\", **kwargs)\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded) # type: ignore\n","return task\n","return task\n","self.data.add(ref(item, self._remove))\n","return\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return pmem(rss, vms, shared, text, lib, data, dirty)\n","return open(fname, \"rb\", **kwargs)\n","compressed = await comm.write(msg, serializers=serializers)\n","context={\"sender\": self._local_addr, \"recipient\": self._peer_addr},\n","return _to_frames()\n","msg, serializers=serializers, on_error=on_error, context=context\n","for key, value in data.items()\n","if type(value) is Serialize\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","def __reduce__(self):\n","return {k: getattr(self, k, None) for k in self._attributes}\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return pmem(rss, vms, shared, text, lib, data, dirty)\n","return {k: getattr(self, k, None) for k in self._attributes}\n","block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n","block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n","return promote(getitem(key))\n","return self._shallow_copy_with_infer(values=values, **kwargs)\n","return Index(values, **attributes)\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","def __reduce__(self):\n","return isinstance(x, pd.core.generic.NDFrame)\n","header, frames = dumps(x)\n","frames = [f.to_host_array().data for f in frames]\n","frames = [f.to_host_array().data for f in frames]\n","rmm._lib.device_buffer.copy_ptr_to_host(self.ptr, data)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","column_header, column_frames = column.serialize_columns(self._columns)\n","header_columns = [c.serialize() for c in columns]\n","header_columns = [c.serialize() for c in columns]\n","data_header, data_frames = self.data.serialize()\n","header[\"type-serialized\"] = pickle.dumps(type(self))\n","def _gc_callback(self, phase, info):\n","@property\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","header[\"dtype\"] = pickle.dumps(self.dtype)\n","data_header, data_frames = self.data.serialize()\n","header[\"type-serialized\"] = pickle.dumps(type(self))\n","def _gc_callback(self, phase, info):\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","return KeysView(self)\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","return self._data.__iter__()\n","return _GeneratorContextManager(func, args, kwds)\n","self.gen = func(*args, **kwds)\n","next(self.gen)\n","return next(self.gen)\n","from .comm import CommClosedError\n","","","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","register()\n","from cudf.comm import serialize\n","","","","","","","","pass\n","","","","","","","","for obj in x\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","def __reduce__(self):\n","return (DataFrame, (self._data, self.index))\n","return self.__class__, (self.to_host_array(),)\n","rmm._lib.device_buffer.copy_ptr_to_host(self.ptr, data)\n","compression = dask.config.get(\"distributed.comm.compression\")\n","return result\n","return k\n","def nbytes(frame, _bytes_like=(bytes, bytearray)):\n","if max(map(nbytes, frames)) <= n:\n","return len(frame)\n","return Packer(**kwargs).pack(o)\n","def _gc_callback(self, phase, info):\n","return meth(arg, *args, **kwargs)\n","return getsizeof(d) + sum(map(sizeof, d.keys())) + sum(map(sizeof, d.values()))\n","info[\"uncollectable\"],\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","def _gc_callback(self, phase, info):\n","future.set_result(value)\n","return handle\n","return handle\n","info[\"uncollectable\"],\n","def _gc_callback(self, phase, info):\n","def get_event_loop():\n","struct.pack(\"Q\", x) for x in lengths\n","def _gc_callback(self, phase, info):\n","n_frames = await stream.read_bytes(8)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self.ensure_computing()\n","self.tasks[key] = self._maybe_deserialize_task(key)\n","function, args, kwargs = _deserialize(*self.tasks[key])\n","function = loads_function(function)\n","result = pickle.loads(bytes_object)\n","return pickle.loads(x)\n","","","","","","","","","","","","from cudf import core, datasets\n","","","","","","","","","","","","","from cudf.core import buffer, column\n","","","","","","","","from cudf.core.column.categorical import CategoricalColumn # noqa: F401\n","","","","","","import cudf._lib as libcudf\n","","","","","","from . import (\n","","","","","","","","","","","","","","","","","","","","from . import (\n","","","","","","","","","","","","","","","","","","","","","","","","","","def _compare_keys(target, key):\n","","","","","","def to_nested_dict(d):\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf._libxx.strings.replace import (\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf.utils import applyutils, cudautils, ioutils, queryutils, utils\n","","","","","","","","","","","","","","from cudf.core.window import Rolling\n","","","","","","","","","","","","","","","","","","","","","","","from cudf.io.parquet import read_parquet, read_parquet_metadata\n","","","","","","statistics=\"ROWGROUP\",\n","","","","","","","","","","","","","","","def to_orc(df, fname, compression=None, enable_statistics=False):\n","","","","","","","@ioutils.doc_to_feather()\n","","","","","","","","def read_table(source, columns=None):\n","","","","","","","","","","","","","pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n","cwd=root,\n","stderr=(subprocess.PIPE if hide_stderr else None),\n","restore_signals, start_new_session)\n","part = os.read(errpipe_read, 50000)\n","stdout = self.stdout.read()\n","(pid, sts) = self._try_wait(0)\n","(pid, sts) = os.waitpid(self.pid, wait_flags)\n","return abspath(path)\n","return path, True\n","return stat.S_ISLNK(st.st_mode)\n","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","runner = Runner(result, future, yielded)\n","self.run()\n","return False\n","info[\"uncollectable\"],\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","memory = proc.memory_info().rss\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","return open(fname, \"rb\", **kwargs)\n","metrics=await self.get_metrics(),\n","operation=operation,\n","return await coro()\n","result = await send_recv(comm=comm, op=key, **kwargs)\n","await comm.write(msg, serializers=serializers, on_error=\"raise\")\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","n_frames = await stream.read_bytes(8)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","self.release_dep(key)\n","del self.data[dep]\n","del self.device_buffer[key]\n","raise KeyError(key)\n","del self.d[key]\n","raise KeyError(key)\n","self.total_weight -= self.weights.pop(key)\n","del self.d[key]\n","self[key]\n","return self.host_buffer[key]\n","return self.fast[key]\n","return result\n","self._decrease_key(len(self.heap)-1)\n","return value\n","self.popitem()\n","return wrapper[1], wrapper[0]\n","i = low\n","del self.data[key]\n","del self.device_buffer[key]\n","del self.fast[key]\n","del self.d[key]\n","await self.comm_handler(comm)\n","msg = await comm.read()\n","n_frames = await stream.read_bytes(8)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","return unparse_host_port(*stream.socket.getsockname()[:2])\n","ip, port, max_buffer_size=MAX_BUFFER_SIZE, **kwargs\n","af, addr, stream = await connector.start(connect_timeout=timeout)\n","return self.future\n","future, functools.partial(self.on_connect_done, addrs, af, addr)\n","return stream, stream.connect(addr)\n","return future\n","self._closed = False\n","lambda f: self._run_callback(functools.partial(callback, future))\n","ret = callback()\n","self.run()\n","self.running = False\n","return False\n","info[\"uncollectable\"],\n","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded) # type: ignore\n","return task\n","return task\n","info[\"uncollectable\"],\n","return func(*args, **kwargs)\n","out = lhs.merge(*args, **kwargs)\n","method,\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","ignore_index=ignore_index,\n","return cudf.concat(dfs, axis=axis, ignore_index=ignore_index)\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","objs[i] = objs[i].astype(common_dtype)\n","return \"OK\"\n","raise\n","del self.device_buffer[key]\n","raise KeyError(key)\n","self.total_weight -= 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