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jakirkham/dask-cudf_multi_partition_merge.ipynb

Last active February 21, 2020 00:07
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dask-report-dgx1-tcp.html
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<!DOCTYPE html>
<html lang="en">
<head>
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<title>Dask Performance Report</title>
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Bokeh.set_log_level("info");
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<div class="bk-root" id="28d486b0-f130-414f-b5c4-41207af45c08" data-root-id="11327"></div>
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func(*args, size=size, **kwargs)\n","result = 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","dfs, axis=axis, join=join, uniform=uniform, filter_warning=filter_warning\n","return pd.concat(dfs, axis=axis, join=join, sort=False)\n","sort=sort,\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","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","mgr = BlockManager(blocks, axes)\n","self._rebuild_blknos_and_blklocs()\n","int_blocks = _multi_blockify(items_dict[\"IntBlock\"])\n","values, placement = _stack_arrays(list(tup_block), dtype)\n","return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\n","result = _execute_task(task, cache)\n","return func(*args2)\n","return func(*args, **kwargs)\n","df = func(*args, **kwargs)\n","return DataFrame.from_pandas(obj)\n","df[i] = Series(vals, nan_as_null=nan_as_null)\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","mask = libcudf.unaryops.nans_to_nulls(data)\n","self._init_from_array_like(np.asarray(data))\n","f\"Cannot construct Buffer from {data.__class__.__name__}\"\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","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","to.copy_to_device(ary, stream=stream)\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","ary1sq, ary2sq = ary1.squeeze(), ary2.squeeze()\n","gpu_data=self.gpu_data,\n","dtype.itemsize)\n","return cls(dims, itemsize)\n","self.flags = self._compute_layout()\n","return Extent(start, stop)\n","return sum(d.get_offset(i) for i, d in zip(indices, dims))\n","return device_array(ary.shape, ary.dtype, ary.strides, stream=stream)\n","buf = librmm.DeviceBuffer(size=datasize, stream=stream)\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","index = index if isinstance(index, Index) else as_index(index)\n","return as_index(column.as_column(arbitrary), **kwargs)\n","return data\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","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","","","","","","","","pass\n","","","","","","Series._bind_comparison_method(name, meth)\n","","","","","","","","setattr(cls, name, derived_from(pd.DataFrame)(meth))\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","setattr(cls, name, derived_from(pd.Series)(meth))\n","return method\n","","","","","","","","","","","","","","from .parquet import read_parquet, to_parquet\n","","","","","","to_parquet = partial(dd.to_parquet, engine=CudfEngine)\n","","","","","","","return safe_hash(frame)\n","return frame.hash_columns()\n","return Series(numerical.column_hash_values(*cols)).values\n","result = column_empty(len(column0), dtype=np.int32, masked=False)\n","return build_column(data, dtype, mask=mask, children=children)\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","self._index, how=\"right\", allow_non_unique=True\n","if self.index.equals(index):\n","result = self == other\n","return self._apply_op(\"__eq__\", other)\n","return as_index(op(other))\n","return self._unordered_compare(other, \"eq\")\n","outcol = self._column.unordered_compare(cmpops, other)\n","return _numeric_column_compare(self, rhs, op=cmpop)\n","return _numeric_column_binop(lhs, rhs, op, out_dtype=np.bool_)\n","_ = libcudf.binops.apply_op(lhs, rhs, out, op)\n","data = Buffer.empty(row_count * dtype.itemsize)\n","extra_source, backend)\n","base = _preprocess('', options, arch, backend)\n","result = prog.compile(options)\n","return df.assign(**kwargs)\n","new = self.copy()\n","data[k] = self._data[k].copy(deep)\n","result = as_index(self._values.copy(deep=deep))\n","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","np.floor_divide(c, k ** stage, out=c)\n","extra_source, backend)\n","if os.path.exists(path):\n","return meth(arg, *args, **kwargs)\n","return dict(zip(range(k), df.scatter_by_map(c, map_size=k)))\n","index_names=index_names,\n","df = self.copy(deep=False)\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.columns = columns\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","arr_or_dtype, classes_and_not_datetimelike(np.unsignedinteger)\n","return _is_dtype_type(arr_or_dtype, classes(np.floating))\n","elif is_extension_array_dtype(arr_or_dtype):\n","for k, v in kwargs.items():\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","elif isinstance(values, ABCIndexClass):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","if result.ndim == 2:\n","return array(a, dtype, copy=False, order=order)\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","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","value = self.func(instance)\n","return tuple(self.keys())\n","def __get__(self, instance, cls):\n","return tuple(self.values())\n","for key in self._mapping:\n","return self._data.__iter__()\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return ValuesView(self)\n","return len(self._mapping)\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.index import Index\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","if is_datetimelike(self._values):\n","or isinstance(arr, ABCPeriodIndex)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","def __setattr__(self, key, col):\n","raise AttributeError(\"'DataFrame' object has no attribute %r\" % key)\n","raise AttributeError(\"'DataFrame' object has no attribute %r\" % key)\n","self._data.insert(name, value, loc=loc)\n","self._clear_cache()\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","return len(self.index)\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","from cudf.core.series import Series\n","self[key]\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","_ndarray_values = self._ndarray_values\n","if is_extension_array_dtype(self):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","for val in data.values()\n","return map(self._values.item, range(self._values.size))\n","or is_datetime64tz_dtype(arr)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","from cudf.core.column import numerical, categorical, datetime, string\n","def __setattr__(self, key, col):\n","def __getattr__(self, key):\n","from collections.abc import Sequence\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","elif is_timedelta64_dtype(data) or (\n","subarr = com.asarray_tuplesafe(data, dtype=object)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","for k, v in kwargs.items():\n","def is_float_dtype(arr_or_dtype):\n","def _name_get(dtype):\n","return CategoricalDtype.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","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","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 _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return 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","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","object.__setattr__(self, key, col)\n","self._index = idx\n","def as_index(arbitrary, **kwargs):\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\n","new_index = new_index.set_names(names)\n","idx = self._shallow_copy()\n","values = self.values\n","return {k: getattr(self, k, None) for k in self._attributes}\n","self.name = values[0]\n","values = self.values\n","from copy import deepcopy\n","df = self.copy(deep=False)\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.columns = columns\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_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","is_datetime64_any_dtype(data)\n","if arr_or_dtype is None:\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\n","return array(a, dtype, copy=False, order=order)\n","name = _kind_name(dtype)\n","return issubclass(dtype.type, np.bool_)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\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(type(None))\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return value\n","return tuple(self.keys())\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","value = self.func(instance)\n","return tuple(self.values())\n","for key in self._mapping:\n","return len(self._mapping)\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","first = next(iter(self.values()))\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","or np.isscalar(val)\n","from cudf.core.column import numerical, categorical, datetime, string\n","self[key]\n","return len(self.index)\n","elif is_extension_array_dtype(self._values):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","or isinstance(arr, ABCPeriodIndex)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\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","if is_extension_array_dtype(self):\n","for val in data.values()\n","from cudf.core.column import numerical, categorical, datetime, string\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","self[key]\n","from collections.abc import Sequence\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_categorical_dtype(data) or is_categorical_dtype(dtype):\n","return CategoricalDtype.is_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","result._data = values\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","dtype = _get_dtype(arr_or_dtype)\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","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","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","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return False\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","dtype = getattr(dtype, \"dtype\", dtype)\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","value = self.func(instance)\n","return tuple(self.keys())\n","return KeysView(self)\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","new_index = new_index.set_names(names)\n","idx._set_names(names, level=level)\n","self.name = values[0]\n","return self._simple_new(values, **attributes)\n","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","values = self.values\n","from copy import deepcopy\n","return as_index(arbitrary._column, **kwargs)\n","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.index import Index\n","if \"name\" not in kwargs:\n","return self._data.names[0]\n","value = self.func(instance)\n","return tuple(self.keys())\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx = arbitrary.copy(deep=False)\n","result.name = self.name\n","col = self._data.pop(self.name)\n","del self[key]\n","self._clear_cache()\n","self.set_by_label(key, value)\n","def set_by_label(self, 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","children=self.base_children,\n","dtype = pd.api.types.pandas_dtype(dtype)\n","from cudf.core.column import ColumnBase, CategoricalColumn\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","return GenericIndex(arbitrary, **kwargs)\n","super().__init__({name: values})\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","self.name = name\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._clear_cache()\n","value = as_column(value)\n","del self[key]\n","self._clear_cache()\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","return ValuesView(self)\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.values())\n","yield self._mapping[key]\n","return tuple(self.keys())\n","yield from self._mapping\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","return arbitrary\n","return self.get_by_label_grouped(key)\n","return self.__class__({key: result})\n","return value\n","self.level_names = tuple(level_names)\n","super().__setitem__(key, value)\n","return arbitrary\n","def _drop_column(self, name):\n","self._clear_cache()\n","self._data.insert(name, value, loc=loc)\n","self._data[name] = value\n","from cudf.core.column import as_column\n","from cudf.core.index import Index\n","return len(self.index)\n","from cudf.core.index import Index\n","or np.isscalar(val)\n","or isinstance(element, numbers.Number))\n","self[key]\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","from cudf.core.column.datetime import DatetimeColumn\n","from cudf.core.index import CategoricalIndex\n","data, size=size, dtype=dtype, mask=mask, offset=offset\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","del self[key]\n","self._clear_cache()\n","self._clear_cache()\n","self._data[key] = value\n","value = as_column(value)\n","super().__init__()\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","def __getattr__(self, key):\n","def __contains__(self, key):\n","return self.as_numerical_column(dtype, **kwargs)\n","casted = libcudf.typecast.cast(self, dtype)\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","super().__init__(\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 args[0] if not args2 else methods.concat(args2, uniform=True)\n","dfs, axis=axis, join=join, uniform=uniform, filter_warning=filter_warning\n","return cudf.concat(dfs, axis=axis)\n","return DataFrame._concat(objs, axis=axis, ignore_index=ignore_index)\n","for i, c in enumerate(unique_columns_ordered_ls)\n","for i, c in enumerate(unique_columns_ordered_ls)\n","return cls(data=col, index=index, name=name)\n","return col\n","return self.as_numerical_column(dtype, **kwargs)\n","return pandas_dtype(obj).type is CategoricalDtypeType\n","offset=casted.offset,\n","from cudf.core.column.numerical import NumericalColumn\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","if not issubclass_(arg1, generic):\n","or np.issubdtype(o.dtype, np.datetime64)\n","return issubclass(arg1, arg2)\n","not_null_cols = list(filter(lambda o: len(o) != o.null_count, objs))\n","from cudf.core.index import CategoricalIndex\n","self._init_from_dict_like(data, index=index, columns=columns)\n","self.insert(i, col_name, data[col_name])\n","value = Series(value)._align_to_index(self._index, how=\"right\")\n","if self.index.equals(index):\n","result = self == other\n","return self._apply_op(\"__eq__\", other)\n","return as_index(op(other))\n","return self._unordered_compare(other, \"eq\")\n","outcol = self._column.unordered_compare(cmpops, other)\n","return _numeric_column_compare(self, rhs, op=cmpop)\n","return _numeric_column_binop(lhs, rhs, op, out_dtype=np.bool_)\n","out = out.fillna(op == \"ne\")\n","data = Buffer.empty(row_count * dtype.itemsize)\n","data = ColumnBase._concat([o._values for o in objs])\n","col = libcudf.concat._column_concat(objs, col)\n","return func(*args, **kwargs)\n","out = lhs.merge(*args, **kwargs)\n","result2 = group_split_dispatch(df, ind.values.view(np.int64), n)\n","return meth(arg, *args, **kwargs)\n","return dict(zip(range(k), df.scatter_by_map(c, map_size=k)))\n","index_names=index_names,\n","return df.set_index(self[index])\n","df = self.copy(deep=False)\n","out = DataFrame(data=data, columns=self.columns.copy(deep=deep))\n","self._init_from_dict_like(data, index=index, columns=columns)\n","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","self._data.insert(name, value, loc=loc)\n","self._clear_cache()\n","super().__setitem__(key, value)\n","first = next(iter(self.values()))\n","from cudf.core.column import numerical, categorical, datetime, string\n","from cudf.core.index import Index\n","return len(self.index)\n","or np.isscalar(val)\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","inferred = lib.infer_dtype(subarr, skipna=False)\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","if isinstance(values, (ABCSeries, ABCIndexClass)):\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","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 isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","value = self.func(instance)\n","return tuple(self.keys())\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","self._data = OrderedColumnDict(data)\n","super().__setitem__(key, value)\n","def as_column(arbitrary, nan_as_null=True, dtype=None, length=None):\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","raise AttributeError(\"'DataFrame' object has no attribute %r\" % key)\n","raise AttributeError(\"'DataFrame' object has no attribute %r\" % key)\n","if is_datetimelike(self._values):\n","or is_timedelta64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","tipo = pandas_dtype(arr_or_dtype).type\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","from cudf.core.index import Index\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\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_categorical_dtype(data) or is_categorical_dtype(dtype):\n","return CategoricalDtype.is_dtype(arr_or_dtype)\n","dtype = getattr(dtype, \"dtype\", dtype)\n","return result._reset_identity()\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(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 _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","return IntervalDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return cls.construct_from_string(dtype) is not None\n","new_index = new_index.set_names(names)\n","idx = self._shallow_copy()\n","values = self.values\n","return self._simple_new(values, **attributes)\n","object.__setattr__(self, key, col)\n","idx = as_index(value)\n","idx = arbitrary.copy(deep=False)\n","return RangeIndex(start=self._start, stop=self._stop, name=self.name)\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","from cudf.core.column import as_column\n","self._data[value] = col\n","self.set_by_label(key, value)\n","self._data[key] = value\n","value = as_column(value)\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","self.base_data,\n","if is_categorical_dtype(dtype):\n","if obj is CategoricalDtypeType:\n","if not hasattr(values, \"name\"):\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","def __len__(self):\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","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.series import Series\n","return len(self._values)\n","return next(iter(self._data.columns))\n","value = self.func(instance)\n","return tuple(self.values())\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 GenericIndex(arbitrary, **kwargs)\n","from cudf.core.series import Series\n","self._data = OrderedColumnDict(data)\n","from cudf.core.column import as_column\n","from cudf.core.column import numerical, categorical, datetime, string\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","value = as_column(value)\n","from cudf.core.series import Series\n","or isinstance(val, numbers.Number)\n","from cudf.core.index import Index\n","return len(self.index)\n","self[key]\n","object.__setattr__(self, key, col)\n","level_names=columns.names,\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.column import numerical, categorical, datetime, string\n","if len(value) != len(first):\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","elif is_extension_array_dtype(arr_or_dtype):\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","if not (isinstance(values, (list, tuple)) or hasattr(values, \"__array__\")):\n","return array(a, dtype, copy=False, order=order)\n","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","return CategoricalDtype.is_dtype(arr_or_dtype)\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","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\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","self[key]\n","keys = [key for key in data.keys() if key in columns]\n","return key in self._engine\n","or isinstance(val, numbers.Number)\n","if is_datetimelike(self._values):\n","is_datetime64_dtype(arr)\n","return _is_dtype_type(arr_or_dtype, classes(np.datetime64))\n","if getattr(self, \"_allow_setattr_to_setitem\", False):\n","if key != \"_data\" and key in self._data:\n","raise AttributeError(\"'DataFrame' object has no attribute %r\" % key)\n","self.level_names = tuple((None,) * self.nlevels)\n","new_index = new_index.set_names(names)\n","idx = self._shallow_copy()\n","return self._simple_new(values, **attributes)\n","return {k: getattr(self, k, None) for k in self._attributes}\n","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","return self._simple_new(values, **attributes)\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 DatetimeTZDtype.is_dtype(arr_or_dtype)\n","return cls.construct_from_string(dtype) is not None\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","return is_datetime64_dtype(arr_or_dtype) or is_datetime64tz_dtype(arr_or_dtype)\n","return DatetimeTZDtype.is_dtype(arr_or_dtype)\n","if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):\n","for k, v in kwargs.items():\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","return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))\n","result = np.asarray(values, dtype=dtype)\n","return array(a, dtype, copy=False, order=order)\n","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 = 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","from cudf.core.column import as_column\n","from cudf.core.series import Series\n","super().__init__({name: data})\n","self._data = OrderedColumnDict(data)\n","value = as_column(value)\n","from cudf.core.index import Index\n","value = self.func(instance)\n","return tuple(self.values())\n","del self._data[name]\n","if is_categorical_dtype(dtype):\n","return pandas_dtype(obj).type is CategoricalDtypeType\n","def __init__(self, data, dtype, mask=None, size=None, offset=0):\n","self._data.insert(name, value, loc=loc)\n","self._data[name] = value\n","value = as_column(value)\n","from cudf.core.series import Series\n","if len(value) != len(first):\n","or np.isscalar(val)\n","self[key]\n","from cudf.core.column import numerical, categorical, datetime, string\n","return len(self.index)\n","return self.as_numerical_column(dtype, **kwargs)\n","super().__init__()\n","object.__setattr__(self, key, col)\n","if key != \"_data\" and key in self._data:\n","self._data[value] = col\n","self._clear_cache()\n","self._data[key] = value\n","value = as_column(value)\n","from cudf.core.series import Series\n","return self._getitem_tuple_arg(arg)\n","return df\n","return column_empty(0, self.dtype, masked=True)\n","res = self._values[index]\n","return column_empty(0, self.dtype, masked=True)\n","return self._column.max(dtype=dtype)\n","data=self._column.astype(dtype, **kwargs)\n","return self.as_numerical_column(dtype, **kwargs)\n","return cupy.asarray(self._column.data_array_view)\n","result = cuda.as_cuda_array(self.data)\n","owner=obj)\n","return fn(*args, **kws)\n","devptr = driver.get_devptr_for_active_ctx(desc['data'][0])\n","driver.cuPointerGetAttribute(byref(devptr), attr, 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**self._kwargs)\n","loop.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","ret = callback()\n","return self.callback()\n","memory = proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\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","ret = self._cache[fun] = fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","rawdict = _psplatform.net_io_counters()\n","with open_text(\"%s/net/dev\" % get_procfs_path()) as f:\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","if self.proc.cpu_percent() &lt; 50:\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","return fun(self)\n","data = f.read()\n","return open(fname, \"rb\", **kwargs)\n","await self.comm_handler(comm)\n","await comm.write(result, serializers=serializers)\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","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","future.set_result(value)\n","return handle\n","return handle\n","self._source_traceback = None\n","return self._debug\n","connection, address = sock.accept()\n","fd, addr = self._accept()\n","lambda f: self._run_callback(functools.partial(callback, future))\n","ret = callback()\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(payload, binary=True, locked=locked)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","yield conn.write_message(self.content_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","yield conn.write_message(header, locked=locked)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","yield self._schedule(work)\n","yielded = next(result)\n","yield self.send_message(work)\n","yielded = next(result)\n","yield message.send(self)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(self.header_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","yield self.waker.wait(self.next_deadline)\n","return timeout_fut\n","return result\n","info[\"uncollectable\"],\n","self._loop.add_future(invoke(), on_done)\n","result = self._func()\n","return self.with_document_locked(callback, *args, **kwargs)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","result = yield yield_for_all_futures(func(self, *args, **kwargs))\n","return func(*args, **kwargs)\n","return doc._with_self_as_curdoc(invoke)\n","return f()\n","return f(*args, **kwargs)\n","doc.add_periodic_callback(lambda: update(ref), interval)\n","comp.update()\n","return input_function(*args, **kwargs)\n","update(self.source, result)\n","return input_function(*args, **kwargs)\n","source.data.update(d)\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","self._trigger(obj, old, value, hint=hint, setter=setter)\n","obj.trigger(self.name, old, value, hint, setter)\n","super(Model, self).trigger(attr, old, new, hint=hint, setter=setter)\n","self._document._notify_change(self, attr, old, new, hint, setter, invoke)\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","return f()\n","cb(event)\n","self._callbacks[receiver] = lambda event: event.dispatch(receiver)\n","super(ModelChangedEvent, self).dispatch(receiver)\n","receiver._document_patched(self)\n","self._pending_writes.append(connection.send_patch_document(event))\n","return self._socket.send_message(msg)\n","yielded = next(result)\n","yield message.send(self)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(self.header_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","update(self.source, d)\n","return input_function(*args, **kwargs)\n","source.data.update(d)\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","self._trigger(obj, old, value, hint=hint, setter=setter)\n","obj.trigger(self.name, old, value, hint, setter)\n","super(Model, self).trigger(attr, old, new, hint=hint, setter=setter)\n","self._document._notify_change(self, attr, old, new, hint, setter, invoke)\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","return f()\n","cb(event)\n","self._callbacks[receiver] = lambda event: event.dispatch(receiver)\n","super(ModelChangedEvent, self).dispatch(receiver)\n","receiver._document_patched(self)\n","self._pending_writes.append(connection.send_patch_document(event))\n","return self._socket.send_message(msg)\n","yielded = next(result)\n","yield message.send(self)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(self.header_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","self.source.stream(rectangles, self.n_rectangles)\n","self._stream(new_data, rollover)\n","self.data._stream(self.document, self, new_data, rollover, setter)\n","hint=ColumnsStreamedEvent(doc, source, new_data, rollover, setter))\n","descriptor._notify_mutated(owner, old, hint=hint)\n","self._real_set(obj, old, value, hint=hint)\n","self._trigger(obj, old, value, hint=hint, setter=setter)\n","obj.trigger(self.name, old, value, hint, setter)\n","super(Model, self).trigger(attr, old, new, hint=hint, setter=setter)\n","invoke()\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","set_curdoc(old_doc)\n","cb(event)\n","self._callbacks[receiver] = lambda event: event.dispatch(receiver)\n","receiver._document_model_changed(self)\n","receiver._document_patched(self)\n","self._pending_writes.append(connection.send_patch_document(event))\n","return self._socket.send_message(msg)\n","future = None # type: ignore\n","yield message.send(self)\n","future = None # type: ignore\n","self.run()\n","self.running = False\n","yield conn.write_message(self.content_json, locked=False)\n","future = None # type: ignore\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","return asyncio.ensure_future(wrapper())\n","return self.stream.write(frame)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","del data\n","return all(self.matches(new[k], old[k]) for k in new)\n","return all(self.matches(new[k], old[k]) for k in new)\n","return np.array_equal(new, old)\n","","return bool(asarray(a1 == a2).all())\n","","return concatenate((arr, values), axis=axis)\n","","self.fig.title.text = \"Bandwidth: \" + format_bytes(self.scheduler.bandwidth)\n","super(HasProps, self).__setattr__(name, value)\n","self._internal_set(obj, value, setter=setter)\n","self._real_set(obj, old, value, hint=hint, setter=setter)\n","self._trigger(obj, old, value, hint=hint, setter=setter)\n","obj.trigger(self.name, old, value, hint, setter)\n","super(Model, self).trigger(attr, old, new, hint=hint, setter=setter)\n","self._document._notify_change(self, attr, old, new, hint, setter, invoke)\n","self._trigger_on_change(event)\n","self._with_self_as_curdoc(invoke_callbacks)\n","return f()\n","cb(event)\n","self._callbacks[receiver] = lambda event: event.dispatch(receiver)\n","super(ModelChangedEvent, self).dispatch(receiver)\n","receiver._document_patched(self)\n","self._pending_writes.append(connection.send_patch_document(event))\n","return self._socket.send_message(msg)\n","yielded = next(result)\n","yield message.send(self)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(self.header_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","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","return x, ser, bytestrings\n","_extract_serialize(v, ser, path + (k,))\n","_extract_serialize(v, ser, path + (k,))\n","for k, v in x.items():\n","for k, v in enumerate(x):\n","return list(map(container_copy, c))\n","return valmap(container_copy, c)\n","return valmap(container_copy, c)\n","return valmap(container_copy, c)\n","typ = type(c)\n","if type(value) is Serialize\n","for obj in x\n","for obj in x\n","for obj in x\n","for obj in x\n","or type(x) is dict\n","dumps = dask_serialize.dispatch(type(x))\n","raise TypeError(\"No dispatch for {0}\".format(cls))\n","for obj in x\n","header, frames = dumps(x, context=context) if wants_context else dumps(x)\n","type_name = typename(type(x))\n","return typ.__module__ + \".\" + typ.__name__\n","raise TypeError(\"No dispatch for {0}\".format(cls))\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","return self.__class__, (self.to_host_array(),)\n","return data\n","return\n","dumps = dask_serialize.dispatch(type(x))\n","raise TypeError(\"No dispatch for {0}\".format(cls))\n","type_name = typename(type(x))\n","return typ.__module__ + \".\" + typ.__name__\n","register()\n","from cudf.comm import serialize\n","","","","","","","","","header, frames = x.serialize()\n","return header, frames\n","return headers, frames\n","header_columns = [c.serialize() for c in columns]\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","if _always_use_pickle_for(x) or b\"__main__\" not in result:\n","def __reduce__(self):\n","if max(map(nbytes, frames)) &lt;= n:\n","return len(frame)\n","return Packer(**kwargs).pack(o)\n","payload = msgpack.dumps(msg, use_bin_type=True)\n","return Packer(**kwargs).pack(o)\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","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","return memoryview(b)[pos : pos + size]\n","def _gc_callback(self, phase, info):\n","future.set_result(value)\n","handle = self._call_soon(callback, args, context)\n","handle = events.Handle(callback, args, self, context)\n","if self._loop.get_debug():\n","await self.handle_stream(comm=comm, extra={\"client\": client})\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","msg = loads_msgpack(small_header, small_payload)\n","return msgpack.loads(payload, use_list=False, **msgpack_opts)\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","holder[keys[-1]] = val\n","ts = self.new_task(k, tasks.get(k), \"released\")\n","prefix_key = key_split(key)\n","for word in words[1:]:\n","def _gc_callback(self, phase, info):\n","self.nbytes = None\n","self.states[ts.state] += 1\n","@property\n","new = self.transition(key, finish)\n","recommendations = func(key, *args, **kwargs)\n","ws = self.decide_worker(ts)\n","self.workers.values(), key=operator.attrgetter(\"occupancy\")\n","return SortedValuesView(self)\n","self._mapping = mapping\n","if p &gt; nc and pending &gt; 0.4 and pending &gt; 1.9 * avg:\n","def transition(self, key, start, finish, *args, **kwargs):\n","ts.waiters = {dts for dts in ts.dependents if dts.state == \"waiting\"}\n","ts.waiters = {dts for dts in ts.dependents if dts.state == \"waiting\"}\n","def _gc_callback(self, phase, info):\n","return self._state\n","self._state = value\n","@property\n","def transition(self, key, start, finish, *args, **kwargs):\n","self.socket.send(\"update_graph\", {\"client\": client})\n","self.write_message(data)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","dep not in self.tasks and dep not in tasks for dep in deps\n","logger.debug(\"Client %s releases keys: %s\", client, keys)\n","if self.isEnabledFor(DEBUG):\n","return self._cache[level]\n","new = self.transition(key, finish)\n","recommendations = func(key, *args, **kwargs)\n","self._propagate_forgotten(ts, recommendations)\n","for ws in ts.who_has:\n","@state.setter\n","self.report({\"op\": \"cancelled-key\", \"key\": key}, ts=ts, client=client)\n","if not any(tg.states.get(s) for s in ALL_TASK_STATES):\n","result = await result\n","await self.ws_connection.accept_connection(self)\n","await self._accept_connection(handler)\n","await self._receive_frame_loop()\n","await self._receive_frame()\n","handled_future = self._handle_message(opcode, data)\n","return self._run_callback(self.handler.on_message, decoded)\n","result = callback(*args, **kwargs)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield self._schedule(work)\n","yielded = next(result)\n","yield self.send_message(work)\n","yielded = next(result)\n","yield message.send(self)\n","runner = Runner(result, future, yielded)\n","self.run()\n","yielded = self.gen.send(value)\n","yield conn.write_message(self.header_json, locked=False)\n","yielded = next(result)\n","yield super(WSHandler, self).write_message(message, binary)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","data = await self.stream.read_bytes(n)\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","await self.handle_worker(comm=comm, worker=address)\n","await self.handle_stream(comm=comm, extra={\"worker\": worker})\n","handler(**merge(extra, msg))\n","r = self.stimulus_task_finished(key=key, worker=worker, **msg)\n","recommendations = self.transition(key, \"memory\", worker=worker, **kwargs)\n","plugin.transition(key, start, finish2, *args, **kwargs)\n","self.socket.send(\"transition\", data)\n","self.write_message(data)\n","return self.ws_connection.write_message(message, binary=binary)\n","fut = self._write_frame(True, opcode, message, flags=flags)\n","return self.stream.write(frame)\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","return memoryview(b)[pos : pos + size]\n","def _gc_callback(self, phase, info):\n","new = self.transition(key, finish)\n","plugin.transition(key, start, finish2, *args, **kwargs)\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","msgs = await self.scheduler_comm.comm.read()\n","n_frames = await stream.read_bytes(8)\n","return future\n","self._add_io_state(ioloop.IOLoop.READ)\n","return self._find_read_pos()\n","del buf\n","del 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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._context.run(self._callback, *self._args)\n","ret = callback()\n","return self.callback()\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","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","rawdict = _psplatform.net_io_counters()\n","lines = f.readlines()\n","return open(fname, \"rt\", **kwargs)\n","ret = self._cache[fun] = fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\n","frame, True, self.profile_recent, stop=\"distributed/worker.py\"\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","buffer = _builtin_open(filename, 'rb')\n","first = read_or_stop()\n","return readline()\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","lines = fp.readlines()\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","with tokenize.open(fullname) as fp:\n","buffer = _builtin_open(filename, 'rb')\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","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","stat = os.stat(fullname)\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","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","stat = os.stat(fullname)\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","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","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","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","return dispatch(args[0].__class__)(*args, **kw)\n","return _wrap_awaitable(yielded) # type: ignore\n","return task\n","return task\n","info[\"uncollectable\"],\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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","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","return True\n","","","","","","","from pandas._config import dates # noqa:F401\n","","","","","","","","","","","","","","","","","","","","class DeprecatedTzFormatWarning(Warning):\n","","","","","","class _nullcontext(object):\n","","","","","","","","","","","","","from ._parser import parse, parser, parserinfo, ParserError\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","","","","","","","","","","","","","","","from . import missing\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False, verify=True):\n","","","","","","def diff(arr, n, axis=0):\n","","","","","","","from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs\n","","","","","","import pandas.core.nanops as nanops\n","","","","","","","","","","","","","def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):\n","return None\n","return _bootstrap._gcd_import(name[level:], package, level)\n","","","","","","","","","","","","","","","","","","from pandas.core.arrays._ranges import generate_regular_range\n","","","","","","","","","","","","","","","","","","","def _ensure_datetimelike_to_i8(other, to_utc=False):\n","","","","","","","","def _maybe_add_count(base, count):\n","","","","","","FY5253Quarter,\n","","","","","","","","","","","","","","from pandas.core.indexes.base import Index, ensure_index\n","","","","","","import pandas.core.dtypes.concat as _concat\n","","","","","","","","","","","","","","","","","","","","","","","from pandas.core.tools.numeric import to_numeric\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","","","","","","","","","","def _all_indexes_same(indexes):\n","","","","","","start=None, end=None, periods=None, freq=None, name=None, closed=\"right\"\n","","","","","","","","","","","","","","def _time_to_micros(time):\n","","","","","","","","","","","","from pandas.io.common import _expand_user, _stringify_path\n","","","","","","from http.client import HTTPException # noqa\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from .blocks import ( # noqa: F401\n","","","","","","","","","","","","","","","","from pandas.core.indexes.accessors import CombinedDatetimelikeProperties\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.colors import is_color_like\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","","","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 _check_in_list(values, **kwargs):\n","","","","","","","","","","","","","","","","","","","return rc_params_from_file(matplotlib_fname(), fail_on_error)\n","if os.path.exists(fname) and not os.path.isdir(fname):\n","os.stat(path)\n","yield os.path.join(get_configdir(), 'matplotlibrc')\n","ret = func()\n","return _get_config_or_cache_dir(_get_xdg_config_dir())\n","configdir.mkdir(parents=True, exist_ok=True)\n","self._accessor.mkdir(self, mode)\n","","","","","","from pandas.plotting._matplotlib.style import _get_standard_colors\n","","","","","","import matplotlib.cm as cm\n","","","","","","class ScalarMappable(object):\n","","","","","","","return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)\n","return LinearSegmentedColormap(name, cdict, N, gamma)\n","return rgba\n","return c\n","import matplotlib.table\n","","","","","","from .text import Text\n","","","","","","docstring.interpd.update(Text=artist.kwdoc(Text))\n","leadingspace=2))\n","return lines\n","return setters\n","return ds.startswith('Alias for ')\n","return cleandoc(doc)\n","return '\\n'.join(lines)\n","return match.group(1)\n","return _compile(pattern, flags).search(string)\n","return p\n","groupindex, tuple(indexgroup)\n","return p\n","return items[0]\n","return subpattern\n","return this\n","self.next = char\n","return isinstance(obj, (str, bytes))\n","self.aliasd = self.get_aliases()\n","return aliases\n","return ds.startswith('Alias for ')\n","","","","","","from matplotlib.mathtext import MathTextParser\n","","","","","","","","","","","","","","","","","","class ConnectionPatch(FancyArrowPatch):\n","","","","","","","","","","","","","","","","","","","","","","from .transforms import (Bbox, IdentityTransform, Transform, TransformedBbox,\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","import matplotlib.dates as dates\n","","","","","","","info[\"uncollectable\"],\n","","","units.registry[datetime.datetime] = DateConverter()\n","","","","","","","info[\"uncollectable\"],\n","","","","rrulestr = _rrulestr()\n","info[\"uncollectable\"],\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","method=\"pad\",\n","func.__doc__ = dedent(self.join.join(docitems))\n","return text\n","return _compile(pattern, flags).sub(repl, string, count)\n","@wraps(func)\n","return wrapper\n","cls, arith_method, comp_method, bool_method, special=True\n","rxor=bool_method(cls, rxor, special),\n","@Appender(doc)\n","self.addendum = addendum\n","empty_value=True,\n","def logical_func(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):\n","func.__doc__ = func.__doc__ and func.__doc__ % self.params\n","from pandas.core.sparse.series import SparseSeries\n","","","","","","ops.add_special_arithmetic_methods(SparseSeries)\n","","","","","","","","","","","","","","","","exec(_def_str)\n","","func.__doc__ = dedent(self.join.join(docitems))\n","text = _whitespace_only_re.sub('', text)\n","","","","","","","","","","","","","","","","","","","","","","","from pandas.core.reshape.pivot import crosstab, pivot, pivot_table\n","","","","","","from pandas.core.reshape.util import cartesian_product\n","","","","","","","","","","","","","","","","","from pandas.io.excel import ExcelFile, ExcelWriter, read_excel\n","","","","","","from pandas.io.excel._base import ExcelFile, ExcelWriter, read_excel\n","","","","","","class ExcelFile:\n","","","","","","class FixedWidthFieldParser(PythonParser):\n","","","","","","","","","","","","","","","","","","","","","","","def __exit__(self, exc_type, exc_value, traceback):\n","","","","","","","","","","","","","","from pandas.io.msgpack import ExtType, Packer as _Packer, Unpacker as _Unpacker\n","","","","","","dumps = packb\n","","","","","","","","","","","","","","from pandas.core.computation.pytables import Expr, maybe_expression\n","","","","","","def maybe_expression(s):\n","","","","","","","","","from pandas.io.json._json import dumps, loads, read_json, to_json\n","","","","","","from ._table_schema import build_table_schema, parse_table_schema\n","","","","","","","from pandas.compat import pickle_compat as pc\n","","","","","","","","","","","","","","","","","","","","","from pandas.core.computation.eval import eval\n","","","","","","from pandas.core.computation.engines import _engines\n","","","","","","_engines = {\"numexpr\": NumExprEngine, \"python\": PythonEngine}\n","","","","","","class FuncNode:\n","","","","","","class Scope(StringMixin):\n","","","","","","","","","","def _reconstruct_object(typ, obj, axes, dtype):\n","","","","","","","","'utils', 'zoneinfo']\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from .core import (\n","","","","","","from .optimization import optimize, fuse_slice\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 _signature_from_function(sigcls, obj)\n","__validate_parameters__=is_duck_function)\n","self._name = name\n","return doc\n","return _compile(pattern, flags).match(string)\n","return _cache[type(pattern), pattern, flags]\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 \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","original_klass, method, ua_args=ua_args, extra=extra\n","return doc\n","not_supported = [m for m in original_args if m not in method_args]\n","","","","","","","","","","","","","","","","","","","","class RandomState(object):\n","def zipf(self, a, size=None, chunks=\"auto\"):\n","return method\n","return doc\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","import scipy.fftpack\n","","","","","","from .pseudo_diffs import *\n","","","","","","def ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False):\n","","","","","","","","","","","","","","","","from .utils import (\n","","","","","","def drop_by_shallow_copy(df, columns, errors=\"raise\"):\n","","","","","","\"register_series_accessor\",\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","def corr(self, method=\"pearson\", min_periods=None, split_every=False):\n","original_klass, method, ua_args=ua_args, extra=extra\n","method_args = get_named_args(method)\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","return func\n","return \"\\n\".join([_skip_doctest(line) for line in doc.split(\"\\n\")])\n","exclude=None,\n","original_klass, method, ua_args=ua_args, extra=extra\n","return doc\n","return \"\\n\".join(lines)\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","for i, line in enumerate(lines)\n","if re.match(r\"^\\s*\" + arg + \" ?:\", line)\n","return _compile(pattern, flags).match(string)\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","self._name = name\n","_META_DESCRIPTION, initial_indent=indent, subsequent_indent=indent, width=78\n","return w.wrap(text)\n","return self._wrap_chunks(chunks)\n","return lines\n","@derived_from(pd.Series)\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","for i in range(len(lines) - 1)\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","if lines[i + 1].strip() and all(c == \"-\" for c in lines[i + 1].strip())\n","setattr(cls, name, derived_from(pd.DataFrame)(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","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","setattr(cls, name, derived_from(pd.Series)(meth))\n","original_klass, method, ua_args=ua_args, extra=extra\n","method_args = get_named_args(method)\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","self._return_annotation = return_annotation\n","for param in parameters))\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.filesystem import FileSystem, LocalFileSystem\n","","","","","","","","","","","return\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","children=children,\n","return loads(header, frames)\n","return pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","return loads(header, frames)\n","def pickle_loads(header, frames):\n","loads = dask_deserialize.dispatch(typ)\n","register()\n","from cudf.comm import serialize\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","assert sum(lengths) == sum(map(nbytes, frames))\n","return len(frame)\n","def _gc_callback(self, phase, info):\n","holder = reduce(operator.getitem, keys[:-1], coll)\n","for c, frame in zip(header[\"compression\"], frames)\n","for c, frame in zip(header[\"compression\"], frames)\n","return x\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","def add_task(\n","","rss = self._proc.memory_info().rss\n","return fun(self)\n","return self._proc.memory_info()\n","return fun(self, *args, **kwargs)\n","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\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","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","children=children,\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","return {k: getattr(self, k, None) for k in self._attributes}\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","header[\"index\"], index_frames = self._index.serialize()\n","header[\"index_column\"], frames = self._values.serialize()\n","return next(iter(self._data.columns))\n","def _gc_callback(self, phase, info):\n","data_header, data_frames = self.data.serialize()\n","header[\"desc\"] = self.__cuda_array_interface__.copy()\n","\"version\": 0,\n","def _gc_callback(self, phase, info):\n","return self._values.dtype\n","@property\n","return next(iter(self._data.names))\n","value = self.func(instance)\n","return tuple(self.keys())\n","def __len__(self):\n","def __len__(self):\n","def _gc_callback(self, phase, info):\n","yield from self._mapping\n","return self._data.__iter__()\n","def _gc_callback(self, phase, info):\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 = {}\n","\"data\": (self.ptr, False),\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","value = self.func(instance)\n","return tuple(self.keys())\n","yield from self._mapping\n","return self._data.__iter__()\n","return KeysView(self)\n","def __init__(self, mapping):\n","return next(self.gen)\n","from .comm import CommClosedError\n","","next(self.gen)\n","raise\n","register()\n","from cudf.comm import serialize\n","","","","","","","","","","","","","import cudf.core.groupby.groupby\n","","","","","","","result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n","return typ.__module__ + \".\" + typ.__name__\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 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","return Packer(**kwargs).pack(o)\n","def nbytes(frame, _bytes_like=(bytes, bytearray)):\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","return memoryview(b)[pos : pos + size]\n","if size &gt; self._large_buf_threshold:\n","elif not self._state &amp; state:\n","lengths = [nbytes(frame) for frame in frames]\n","if isinstance(frame, _bytes_like):\n","struct.pack(\"Q\", x) for x in lengths\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","handler_func(fileobj, events)\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","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","connection, address = sock.accept()\n","fd, addr = self._accept()\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","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","return cls(dict(zip(column_names, columns)), index=index)\n","return idx_typ(index, name=name)\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","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","size = functools.reduce(operator.mul, array_interface[\"shape\"])\n","def _gc_callback(self, phase, info):\n","def _gc_callback(self, phase, info):\n","return array(a, dtype, copy=False, order=order)\n","def _gc_callback(self, phase, info):\n","register()\n","from cudf.comm import serialize\n","","","","","","","","import cudf.core.groupby.groupby\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","def _gc_callback(self, phase, info):\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","def _gc_callback(self, phase, info):\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","weight = self.weight(key, value)\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","def _gc_callback(self, phase, info):\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","with open_binary(\"%s/%s/statm\" % (self._procfs_path, self.pid)) as f:\n","return open(fname, \"rb\", **kwargs)\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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf.utils.docutils import docfmt_partial\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf.core.dtypes import CategoricalDtype\n","","","","","","","","from cudf.core._sort import get_sorted_inds\n","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf.core.series import Series\n","","","","","","from cudf.core.window import Rolling\n","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","from cudf.io.parquet import read_parquet, read_parquet_metadata\n","","","","","","","","","","","","","","","statistics=\"ROWGROUP\",\n","","","","","","","","","","","self._init_from_array_like(data)\n","dbuf = DeviceBuffer(ptr=ptr, size=size)\n","data=data, dtype=dtype, mask=mask, size=size, offset=offset\n","data, size=size, dtype=dtype, mask=mask, offset=offset\n","children=children,\n","future.add_done_callback(lambda _: runner)\n","info[\"uncollectable\"],\n","raise gen.Return(result)\n","self._schedule_next()\n","self._timeout = self.io_loop.add_timeout(self._next_timeout, self._run)\n","return self.call_at(deadline, callback, *args, **kwargs)\n","functools.partial(callback, *args, **kwargs),\n","return timer\n","return timer\n","self._scheduled = False\n","info[\"uncollectable\"],\n","self.run()\n","return False\n","future, lambda f: self.add_callback(callback, future)\n","future.add_done_callback(callback)\n","return self._state in [CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED]\n","info[\"uncollectable\"],\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","return typ([pack_data(x, d, key_types=key_types) for x in o])\n","def _gc_callback(self, phase, info):\n","data = self._ssock.recv(4096)\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.popitem()\n","return wrapper[1], wrapper[0]\n","i = low\n","h[j][2] = j\n","del self.data[key]\n","del self.device_buffer[key]\n","del self.fast[key]\n","del self.d[key]\n","self[key]\n","elif key in self.host_buffer:\n","return key in self.fast or key in self.slow\n","return key in self.d\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","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","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","self._closed = False\n","self.socket.connect(address)\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","future, lambda f: self.add_callback(callback, future)\n","info[\"uncollectable\"],\n","yield self.waker.wait(self.next_deadline)\n","return timeout_fut\n","return result\n","return self.call_at(deadline, callback, *args, **kwargs)\n","functools.partial(callback, *args, **kwargs),\n","info[\"uncollectable\"],\n","return sum(lengths)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","del data\n","return _to_frames()\n","msg, serializers=serializers, on_error=on_error, context=context\n","] + out_frames\n","if type(value) is Serialize\n","return header, frames\n","return header, frames\n","return header, frames\n","frames = [f.to_host_array().data for f in frames]\n","return data\n","n = await stream.read_into(frame)\n","return future\n","return\n","return self._find_read_pos()\n","return bytes_read\n","del buf\n","return future\n","self._add_io_state(ioloop.IOLoop.READ)\n","return self._find_read_pos()\n","del buf\n","del buf\n","return res\n","frames, deserialize=deserialize, deserializers=deserializers\n","return msg\n","return loads(header, frames)\n","return loads(header, frames)\n","return cudf_obj\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 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&lt;h1&gt; Dask Performance Report &lt;/h1&gt;\n\n &lt;i&gt; Select different tabs on the top for additional information &lt;/i&gt;\n\n &lt;h2&gt; Duration: 61.68 s &lt;/h2&gt;\n\n &lt;h2&gt; Scheduler Information &lt;/h2&gt;\n &lt;ul&gt;\n &lt;li&gt; Address: tcp://127.0.0.1:33751 &lt;/li&gt;\n &lt;li&gt; Workers: 8 &lt;/li&gt;\n &lt;li&gt; Threads: 8 &lt;/li&gt;\n &lt;li&gt; Memory: 1.08 TB &lt;/li&gt;\n &lt;/ul&gt;\n\n &lt;h2&gt; Calling Code &lt;/h2&gt;\n &lt;pre&gt;\nrows_1, parts_1 = 140_176_770, 245\nrows_2, parts_2 = 21_004_393, 171\n\nwith performance_report(filename=\"dask-report.html\"):\n df_1 = create_random_data(n_rows=rows_1, n_parts=parts_1, col_prefix='a')\n df_2 = create_random_data(n_rows=rows_2, n_parts=parts_2, col_prefix='b')\n\n merged_df = df_1.merge(df_2,\n left_on=['a_0', 'a_1', 'a_2'],\n right_on=['b_0', 'b_1', 'b_2'])\n get_ipython().run_line_magic('time', 'len(merged_df)')\n\n &lt;/pre&gt;\n 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