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November 5, 2023 14:35
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| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>Dask Performance Report</title> | |
| <link rel="shortcut icon" href="https://docs.dask.org/en/latest/_static/images/favicon.ico" /> | |
| <style> | |
| html, body { | |
| box-sizing: border-box; | |
| display: flow-root; | |
| height: 100%; | |
| margin: 0; | |
| padding: 0; | |
| } | |
| </style> | |
| <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js"></script> | |
| <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js"></script> | |
| <script type="text/javascript"> | |
| Bokeh.set_log_level("info"); | |
| </script> | |
| </head> | |
| <body> | |
| <div id="dd980aaa-7002-4d26-b606-6f714af7ac03" data-root-id="p2460" style="display: contents;"></div> | |
| <script type="application/json" id="p2471"> | |
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</li>\n <li> Workers: 16 </li>\n <li> Threads: 64 </li>\n <li> Memory: 114.56 GiB </li>\n <li> Dask Version: 2023.10.0+11.gf8a0f1501 </li>\n <li> Dask.Distributed Version: 2023.10.0+15.gb4eee3ff7.dirty </li>\n </ul>\n\n <h2> Calling Code </h2>\n <pre>\n@pytest.fixture\ndef client(\n request,\n cluster,\n testrun_uid,\n cluster_kwargs,\n benchmark_time,\n restart,\n scale,\n local,\n query,\n):\n with cluster.get_client() as client:\n if restart:\n client.restart()\n client.run(lambda: None)\n\n local = \"local\" if local else \"cloud\"\n if request.config.getoption(\"--performance-report\"):\n if not os.path.exists(\"performance-reports\"):\n os.mkdir(\"performance-reports\")\n with performance_report(\n filename=os.path.join(\n \"performance-reports\", f\"{local}-{scale}-{query}.html\"\n )\n ):\n with benchmark_time:\n yield client\n else:\n with benchmark_time:\n yield client\n\n </pre>\n "}}}},{"type":"object","name":"TabPanel","id":"p2455","attributes":{"title":"Task 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dask.core.get(graph, name)\n","result = _execute_task(task, cache)\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*(_execute_task(a, cache) for a in args))\n","return [_execute_task(a, cache) for a in arg]\n","return [_execute_task(a, cache) for a in arg]\n","return func(*(_execute_task(a, cache) for a in args))\n","return read_parquet_part(\n","dfs = [\n","func(\n","arrow_table = cls._read_table(\n","arrow_table = _read_table_from_path(\n","with _open_input_files(\n","return [\n","fsspec_parquet.open_parquet_file(\n","data = _get_parquet_byte_ranges(\n","file_sizes = fs.sizes(paths)\n","return sync(self.loop, func, *args, **kwargs)\n","if event.wait(1):\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","for path, start, stop, data in zip(*ranges, fs.cat_ranges(*ranges)):\n","return sync(self.loop, func, *args, **kwargs)\n","if event.wait(1):\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","f = self._open(\n","return S3File(\n","super().__init__(\n","self.size = self.details[\"size\"]\n","self._details = self.fs.info(self.path)\n","return sync(self.loop, func, *args, **kwargs)\n","if event.wait(1):\n","signaled = self._cond.wait(timeout)\n","loop.call_soon_threadsafe(callback)\n","self._write_to_self()\n","res = arrow_table.to_pandas(categories=categories, **_kwargs)\n","blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\n","columns = _pandas_api.pd.MultiIndex.from_tuples(\n","return meth(self_or_cls, *args, **kwargs)\n","return cls.from_arrays(arrays, sortorder=sortorder, names=names)\n","codes, levels = factorize_from_iterables(arrays)\n","codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))\n","codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))\n","cat = Categorical(values, ordered=False)\n","codes, categories = factorize(values, sort=True)\n","codes, uniques = factorize_array(\n","order2 = sorter.argsort()\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","subarr = maybe_infer_to_datetimelike(data)\n","if not index.is_unique:\n","return self._engine.is_unique\n","offsets = np.concatenate([lev_bits[1:], [0]]).astype(\"uint64\")\n","data = self._take_with_is_copy(indexer, axis=1)\n","return result\n","return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(\n","return self.reindex_indexer(\n","return new_mgr\n","return blocks\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_values = algos.take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_data = take(\n","indexer = self.get_indexer_for(keyarr)\n","return self.get_indexer(target)\n","data = self._take_with_is_copy(indexer, axis=1)\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return arr.take(\n","new_data = take(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","indexer = self.get_indexer_for(keyarr)\n","return self.get_indexer(target)\n","return func(\n","out = pd.concat(dfs3, join=join, sort=False)\n","return op.get_result()\n","for ax, new_labels in enumerate(self.new_axes):\n","return [\n","self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)\n","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","return self._concat(to_concat, name)\n","np.concatenate([x._values for x in rng_indexes])\n","np.concatenate([x._values for x in rng_indexes])\n","return self._data\n","values = np.concatenate(vals, axis=1) # type: ignore[arg-type]\n","concat_values = concat_compat(to_concat, axis=1)\n","return obj._concat_same_type(\n","new_obj = super()._concat_same_type(to_concat, axis)\n","return obj._concat_same_type(to_concat_eas)\n","[(df.dtypes == \"category\").to_frame().T for df in dfs3],\n","return self.transpose()\n","result = self._constructor(\n","return method(self, other)\n","return self._cmp_method(other, operator.eq)\n","res_values = ops.comparison_op(lvalues, rvalues, op)\n","res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues)\n","result = self._logical_func(\n","return self._reduce(\n","out = out.astype(out_dtype)\n","new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n","return self.apply(\n","applied = getattr(b, f)(**kwargs)\n","new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n","new_values = astype_array(values, dtype, copy=copy)\n","return method(self, other)\n","return self._cmp_method(other, operator.eq)\n","res_values = ops.comparison_op(lvalues, rvalues, op)\n","res_values = op(lvalues, rvalues)\n","return method(self, other)\n","return self._cmp_method(other, operator.eq)\n","result = pc_func(self._pa_array, self._box_pa(other))\n","return self._cmp_method(other, operator.lt)\n","res_values = ops.comparison_op(lvalues, rvalues, op)\n","res_values = op(lvalues, rvalues)\n","return method(self, other)\n","return self._cmp_method(other, operator.lt)\n","mask = self._isnan | o_mask\n","return self._cmp_method(other, operator.gt)\n","res_values = ops.comparison_op(lvalues, rvalues, op)\n","res_values = op(lvalues, rvalues)\n","return method(self, other)\n","return self._cmp_method(other, operator.gt)\n","result = op(self._ndarray.view(\"i8\"), other_vals.view(\"i8\"))\n","return self._arith_method(other, operator.mul)\n","return base.IndexOpsMixin._arith_method(self, other, op)\n","result = ops.arithmetic_op(lvalues, rvalues, op)\n","res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]\n","result = func(left, right)\n","return _evaluate(op, op_str, a, b) # type: ignore[misc]\n","out = self._constructor(result, index=self.index, dtype=dtype, copy=False)\n","name = ibase.maybe_extract_name(name, data, type(self))\n","return self._arith_method(other, roperator.rsub)\n","return base.IndexOpsMixin._arith_method(self, other, op)\n","result = ops.arithmetic_op(lvalues, rvalues, op)\n","res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]\n","result = func(left, right)\n","return _evaluate(op, op_str, a, b) # type: ignore[misc]\n","return op(a, b)\n","return self._getitem_bool_array(key)\n","indexer = key.nonzero()[0]\n","return check_array_indexer(index, result)\n","return indexer\n","return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = [\n","blk.take_nd(\n","new_values = algos.take_nd(\n","return arr.take(\n","new_data = take(\n","result = take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","func(arr, indexer, out, fill_value)\n","_take_nd_object(\n","arr.take(indexer, axis=axis, out=out)\n","min_idx = indices.min()\n","func(arr, indexer, out, fill_value)\n","mask = indexer == -1\n","mask = (indices >= n) | (indices < 0)\n","taken = algos.take(\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_data = take(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","res = self._ixs(loc, axis=1)\n","col_mgr = self._mgr.iget(i)\n","values = block.iget(self.blklocs[i])\n","return self.values[i] # type: ignore[index]\n","result._freq = self._get_getitem_freq(key)\n","keyarr = self.reindex(keyarr)[0]\n","indexer = self.get_indexer(\n","return self._get_indexer(target, method, limit, tolerance)\n","return func(*args, **kwargs)\n","return _apply_chunk(df, *by, **kwargs)\n","return func(g[columns], **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","result = self._agg_general(\n","result = self._cython_agg_general(\n","new_mgr = data.grouped_reduce(array_func)\n","res = func(arr)\n","result = self.grouper._cython_operation(\n","cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)\n","return bool((self.group_info[0] < 0).any())\n","comp_ids, obs_group_ids = self._get_compressed_codes()\n","group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)\n","return [ping.codes for ping in self.groupings]\n","return [ping.codes for ping in self.groupings]\n","return self._codes_and_uniques[0]\n","codes, uniques = algorithms.factorize( # type: ignore[assignment]\n","codes, uniques = factorize_array(\n","return super().factorize(use_na_sentinel=use_na_sentinel)\n","codes, uniques = factorize_array(\n","uniques, codes = table.factorize(\n","ordered = values.take(sorter)\n","return nargsort(\n","indexer = non_nan_idx[non_nans.argsort(kind=kind)]\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_data = take(\n","comp_ids, obs_group_ids = table.get_labels_groupby(group_index)\n","uniques = uniques.take(sorter)\n","","return _wrapreduction(a, np.logical_and, 'all', axis, None, out,\n","return len(self.result_index)\n","codes = self.reconstructed_codes\n","return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)\n","out = _decons_group_index(obs_ids, shape)\n","labels = (x - y) % (factor * shape[i]) // factor\n","result._set_codes(codes, copy=copy, validate=False)\n","new_codes = FrozenList(\n","_coerce_indexer_frozen(level_codes, lev, copy=copy).view()\n","array_like = coerce_indexer_dtype(array_like, categories)\n","return self._cython_op_ndim_compat(\n","res = self._call_cython_op(\n","func(\n","return getattr(__obj, self.method)(*args, **kwargs)\n","return df.reset_index(\n","new_obj = self.copy(deep=None)\n","data = self._mgr.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","result = take_nd(\n","return arr.take(\n","new_data = take(\n","validate_indices(indices, arr.shape[axis])\n","max_idx = indices.max()\n","value, refs = self._sanitize_column(value)\n","return sanitize_array(value, self.index, copy=True, allow_2d=True), None\n","subarr = data.copy()\n","return cls.reduction_chunk(df, **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()\n","return self.compute(\"nlargest\")\n","cur_frame = frame = frame.reset_index(drop=True)\n","new_obj = self.copy(deep=None)\n","data = self._mgr.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","self.blocks = _consolidate(self.blocks)\n","merged_blocks, _ = _merge_blocks(\n","new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]\n","","return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)\n","return selectn.SelectNSeries(self, n=n, keep=keep).nlargest()\n","return self.compute(\"nlargest\")\n","dropped = self.obj.dropna()\n","result = remove_na_arraylike(self)\n","return arr[notna(np.asarray(arr))]\n","return self._get_rows_with_mask(key)\n","new_mgr = self._mgr.get_rows_with_mask(indexer)\n","new_idx = self.index[indexer]\n","return super().__getitem__(key)\n","result = getitem(key)\n","df[name] = val\n","self._set_item(key, value)\n","self._set_item_mgr(key, value, refs)\n","return _reindex_for_setitem(value, self.index)\n","self._mgr.insert(len(self._info_axis), key, value, refs)\n","self._insert_update_blklocs_and_blknos(loc)\n","self._blknos = np.append(self._blknos, len(self.blocks))\n","","return concatenate((arr, values), axis=axis)\n","","return shuffle_transfer(\n","return get_worker_plugin().add_partition(\n","shuffle_run = self.get_or_create_shuffle(spec)\n","return sync(\n","wait(10)\n","return e.wait(timeout)\n","signaled = self._cond.wait(timeout)\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._write_to_self()\n","wait(10)\n","return e.wait(timeout)\n","signaled = self._cond.wait(timeout)\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._write_to_self()\n","return hash_object_dispatch(df, index=False) % int(npartitions)\n","return meth(arg, *args, **kwargs)\n","return pd.util.hash_pandas_object(\n","h = combine_hash_arrays(hashes, num_items)\n","first = next(arrays)\n","hash_array(series._values, encoding, hash_key, categorize)\n","return _hash_ndarray(vals, encoding, hash_key, categorize)\n","return self._mgr.internal_values()\n","return self._block.values\n","","multiarray.copyto(res, z, casting='unsafe')\n","return method(self, other)\n","return self._arith_method(other, operator.mod)\n","return base.IndexOpsMixin._arith_method(self, other, op)\n","result = ops.arithmetic_op(lvalues, rvalues, op)\n","res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]\n","result = func(left, right)\n","return _evaluate_standard(op, op_str, a, b)\n","data = self.copy(deep=None)\n","data = self._mgr.copy(deep=deep)\n","res._consolidate_inplace()\n","self.blocks = _consolidate(self.blocks)\n","merged_blocks, _ = _merge_blocks(\n","new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]\n","","return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)\n","","return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)\n","applied = getattr(b, f)(**kwargs)\n","values = values.copy()\n","self._set_item(key, value)\n","value, refs = self._sanitize_column(value)\n","return _reindex_for_setitem(value, self.index)\n","self._mgr.insert(len(self._info_axis), key, value, refs)\n","self._insert_update_blklocs_and_blknos(loc)\n","self._blklocs = np.append(self._blklocs, 0)\n","","return concatenate((arr, values), axis=axis)\n","selected_df = df[column_names]\n","return data\n","return result\n","return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(\n","return self.reindex_indexer(\n","return new_mgr\n","return blocks\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_values = algos.take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","left = ext.get_output_partition(\n","shuffle_run = self.get_shuffle_run(shuffle_id, run_id)\n","return sync(\n","wait(10)\n","return e.wait(timeout)\n","signaled = self._cond.wait(timeout)\n","gotit = waiter.acquire(True, timeout)\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._write_to_self()\n","wait(10)\n","return e.wait(timeout)\n","signaled = self._cond.wait(timeout)\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._write_to_self()\n","return super().drop(\n","obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n","new_mgr = self._mgr.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return arr.take(\n","new_data = take(\n","out = lhs.merge(rhs, *args, **kwargs)\n","return merge(\n","return op.get_result(copy=copy)\n","join_index, left_indexer, right_indexer = self._get_join_info()\n","(left_indexer, right_indexer) = self._get_join_indexers()\n","return get_join_indexers(\n","zipped = zip(*mapped)\n","_factorize_keys(left_keys[n], right_keys[n], sort=sort, how=how)\n","llab = rizer.factorize(lk) # type: ignore[arg-type]\n","llab = rizer.factorize(lk) # type: ignore[arg-type]\n","lmgr = left._mgr.reindex_indexer(\n","new_blocks = [\n","blk.take_nd(\n","new_values = algos.take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","func(arr, indexer, out, fill_value)\n","needs_masking = bool(mask.any())\n","new_data = take(\n","validate_indices(indices, arr.shape[axis])\n","max_idx = indices.max()\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","func(arr, indexer, out, fill_value)\n","_take_nd_object(\n","arr.take(indexer, axis=axis, out=out)\n","return op.get_result()\n","new_data = concatenate_managers(\n","mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)\n","mgr = mgr.copy()\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","values = values.copy()\n","self.blocks = _consolidate(self.blocks)\n","merged_blocks, _ = _merge_blocks(\n","new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]\n","","return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)\n","","return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)\n","return self._getitem_slice(key)\n","return self._slice(slobj)\n","new_mgr = self._mgr.get_slice(slobj, axis=axis)\n","self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels\n","self._maybe_coerce_merge_keys()\n","rk_is_object = is_object_dtype(rk.dtype)\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","if _any(self.left_on) and _any(self.right_on):\n","if self._is_label_reference(key, axis=axis):\n","and any(key in self.axes[ax] for ax in other_axes)\n","and any(key in self.axes[ax] for ax in other_axes)\n","return key in self._engine\n","return self._engine_type(target_values) # type: ignore[arg-type]\n","return self[key]\n","return self._get_item_cache(key)\n","res = self._ixs(loc, axis=1)\n","label = self.columns[i]\n","obj = self._constructor_sliced_from_mgr(values, axes=values.axes)\n","ser = self._sliced_from_mgr(mgr, axes=axes)\n","return Series._from_mgr(mgr, axes)\n","NDFrame.__init__(obj, mgr)\n","casted_key = self._maybe_cast_indexer(key)\n","mgr = self._mgr._get_data_subset(predicate).copy(deep=None)\n","return self._combine(blocks, copy=False)\n","axes = [self.items[:0]] + self.axes[1:]\n","def __getitem__(self, key):\n","result = type(self)._simple_new(res, name=self._name, refs=self._references)\n","converted_dtypes.append(infer_dtype_from_object(dtype))\n","dtype = pandas_dtype(dtype)\n","result = registry.find(dtype)\n","return dtype_type.construct_from_string(dtype)\n","return cls(ordered=None)\n","self._finalize(categories, ordered, fastpath=False)\n","res._consolidate_inplace()\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return max(1200, p)\n","return {\n","return {\n","return meth(arg, *args, **kwargs)\n","p += object_size(i)\n","p = 1200 + sizeof(s.index) + s.memory_usage(index=False, deep=False)\n","return meth(arg, *args, **kwargs)\n","p = sum(sizeof(lev) for lev in i.levels)\n","p = sum(sizeof(lev) for lev in i.levels)\n","return meth(arg, *args, **kwargs)\n","p = 400 + i.memory_usage(deep=False)\n","result += self._engine.sizeof(deep=deep)\n","return self._engine_type(target_values) # type: ignore[arg-type]\n","data = self._take_with_is_copy(indexer, axis=1)\n","result = self.take(indices=indices, axis=axis)\n","new_data = self._mgr.take(\n","return self.reindex_indexer(\n","new_blocks = self._slice_take_blocks_ax0(\n","nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n","new_values = algos.take_nd(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","new_data = take(\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","tb_bytes = protocol.pickle.dumps(tb)\n","and getattr(inspect.getmodule(x), \"__name__\", None)\n","os.path.realpath(f)] = module.__name__\n","","_filename = getsourcefile(object) or getfile(object)\n","if os.path.exists(filename):\n","","return extract_tb(tb, limit=limit).format()\n","return StackSummary._extract_from_extended_frame_gen(\n","return result\n","return _groupby_aggregate(_concat(inputs), **kwargs)\n","else methods.concat(args2, uniform=True, ignore_index=ignore_index)\n","return func(\n","out = pd.concat(dfs2, join=join, **kwargs)\n","return op.get_result()\n","new_index = self.new_axes[0]\n","return [\n","self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)\n","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","level_codes = [\n","return self._wrap_setop_result(other, result)\n","return self._outer_indexer(other)[0]\n","result = algos.safe_sort(result) # type: ignore[assignment]\n","return self._get_indexer(target, method, limit, tolerance)\n","return self.is_unique\n","return np.concatenate(to_concat_arrs, axis=axis)\n","return super()._union(other, sort)._with_freq(\"infer\")\n","and self.is_monotonic_increasing\n","freq = to_offset(self.inferred_freq)\n","return frequencies.infer_freq(self)\n","return inferer.get_freq()\n","return self._infer_daily_rule()\n","quarterly_rule = self._get_quarterly_rule()\n","if len(self.mdiffs) > 1:\n","recode_for_categories(\n","new_categories.get_indexer_for(old_categories), new_categories\n","return self.get_indexer(target)\n","return self._get_indexer(target, method, limit, tolerance)\n","return self.is_unique\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","return np.concatenate(to_concat_arrs, axis=axis)\n","return aggfunc(grouped, **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","result = self._agg_general(\n","result = self._cython_agg_general(\n","new_mgr = data.grouped_reduce(array_func)\n","res = func(arr)\n","result = self.grouper._cython_operation(\n","cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)\n","return bool((self.group_info[0] < 0).any())\n","comp_ids, obs_group_ids = self._get_compressed_codes()\n","group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)\n","return [ping.codes for ping in self.groupings]\n","return [ping.codes for ping in self.groupings]\n","return self._codes_and_uniques[0]\n","codes, uniques = algorithms.factorize( # type: ignore[assignment]\n","return values.factorize(sort=sort, use_na_sentinel=use_na_sentinel)\n","codes, uniques = algorithms.factorize(\n","codes, uniques = factorize_array(\n","return super().factorize(use_na_sentinel=use_na_sentinel)\n","codes, uniques = factorize_array(\n","uniques, codes = table.factorize(\n","obs_group_ids = group_index[unique_mask]\n","return len(self.result_index)\n","codes = self.reconstructed_codes\n","return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)\n","out = _decons_group_index(obs_ids, shape)\n","labels = (x - y) % (factor * shape[i]) // factor\n","result._set_codes(codes, copy=copy, validate=False)\n","new_codes = FrozenList(\n","_coerce_indexer_frozen(level_codes, lev, copy=copy).view()\n","array_like = coerce_indexer_dtype(array_like, categories)\n","return self._cython_op_ndim_compat(\n","res = self._call_cython_op(\n","result = maybe_fill(np.empty(out_shape, dtype=out_dtype))\n","return SeriesGroupBy(\n","grouper, exclusions, obj = get_grouper(\n","Grouping(\n","index_level = index.get_level_values(ilevel)\n","values = self._get_level_values(level)\n","filled = algos.take_nd(lev._values, level_codes, fill_value=lev._na_value)\n","return arr.take(\n","new_data = take(\n","indices = ensure_platform_int(indices)\n","return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n","func(arr, indexer, out, fill_value)\n","_take_nd_object(\n","indexer = ensure_platform_int(indexer)\n","mask = indexer == -1\n","return get_worker_plugin().barrier(id, run_ids)\n","result = sync(self.worker.loop, self._barrier, shuffle_id, run_ids)\n","loop.add_callback(f)\n","call_soon(self._run_callback, functools.partial(callback, *args, **kwargs))\n","self._write_to_self()\n","return e.wait(timeout)\n","signaled = self._cond.wait(timeout)\n","next(self.gen)\n","self.digest_metric(label, delta, unit)\n","for cb in 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importlib._bootstrap_external>","/opt/coiled/env/lib/python3.11/site-packages/dask_expr/_groupby.py","/opt/coiled/env/lib/python3.11/site-packages/dask/dataframe/groupby.py","/opt/coiled/env/lib/python3.11/site-packages/dask/utils.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/groupby.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/groupby.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/groupby.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/base.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/groupby.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/sorting.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/multi.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/multi.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/multi.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/multi.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/groupby/ops.py","/opt/coiled/env/lib/python3.11/site-packages/dask_expr/_expr.py","/opt/coiled/env/lib/python3.11/site-packages/dask/core.py","/opt/coiled/env/lib/python3.11/site-packages/dask/core.py","/opt/coiled/env/lib/python3.11/site-packages/dask/utils.py","/opt/coiled/env/lib/python3.11/site-packages/dask/utils.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/series.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/frame.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/generic.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/algorithms.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/array_algos/take.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/arrays/_mixins.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/algorithms.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexers/utils.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/frame.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/frame.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/construction.py","/opt/coiled/env/lib/python3.11/site-packages/dask_expr/_reductions.py","/opt/coiled/env/lib/python3.11/site-packages/dask/utils.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/frame.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/methods/selectn.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/methods/selectn.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/frame.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/generic.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","<__array_function__ internals>","/opt/coiled/env/lib/python3.11/site-packages/numpy/core/shape_base.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/series.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/methods/selectn.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/methods/selectn.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/series.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/dtypes/missing.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/series.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/series.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/internals/managers.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/range.py","/opt/coiled/env/lib/python3.11/site-packages/pandas/core/indexes/base.py","/opt/coiled/env/lib/python3.11/asyncio/base_events.py","/opt/coiled/env/lib/python3.11/asyncio/base_events.py","/opt/coiled/env/lib/python3.11/asyncio/base_events.py","/opt/coiled/env/lib/python3.11/asyncio/events.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/worker.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/worker.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/core.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/comm/tcp.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/comm/utils.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/comm/utils.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/protocol/core.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/protocol/core.py","/opt/coiled/env/lib/python3.11/site-packages/distributed/protocol/pickle.py","<frozen 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importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiobotocore/client.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiobotocore/args.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiobotocore/endpoint.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiobotocore/httpsession.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiohttp/__init__.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/aiohttp/client.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/charset_normalizer/__init__.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/charset_normalizer/api.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/charset_normalizer/cd.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/charset_normalizer/md.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>","/opt/coiled/env/lib/python3.11/site-packages/yarl/__init__.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/yarl/_url.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/idna/__init__.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap>","/opt/coiled/env/lib/python3.11/site-packages/idna/core.py","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>","<frozen importlib._bootstrap_external>"]],["line",["","","exitcode = _main(fd, parent_sentinel)\n","return self._bootstrap(parent_sentinel)\n","self.run()\n","self._target(*self._args, **self._kwargs)\n","target(*args, **kwargs)\n","asyncio_run(run(), loop_factory=get_loop_factory())\n","return runner.run(main)\n","return self._loop.run_until_complete(task)\n","self.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","return await method(self, *args, **kwargs) # type: ignore\n","await self.handle_stream(comm)\n","msgs = await comm.read()\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","return pickle.loads(sub_header[\"pickled-obj\"], buffers=sub_frames)\n","return pickle.loads(x, buffers=buffers)\n","","","","","","","","","from dask_expr import _version, datasets\n","","","","","","","","from dask_expr._collection import new_collection\n","","","","","","from dask.dataframe import methods\n","","","","","","from dask.dataframe import backends, dispatch, rolling\n","","","","","","","","from dask.dataframe.core import DataFrame, Index, Scalar, Series, _Frame\n","","","","","","","","","from dask.base import compute\n","","","","","","","","def where(condition, x=None, y=None):\n","","","","","","def triu_indices_from(arr, k=0):\n","return method\n","return doc\n","return \"\\n\".join(lines)\n","subset = [\n","return _compile(pattern, flags).match(string)\n","from .core import S3FileSystem, S3File\n","","","","","","import aiobotocore.session\n","","","","","","from .client import AioBaseClient, AioClientCreator\n","","","","","","from .args import AioClientArgsCreator\n","","","","","","from .endpoint import AioEndpointCreator\n","","","","","","from aiobotocore.httpsession import AIOHTTPSession\n","","","","","","import aiohttp # lgtm [py/import-and-import-from]\n","","","","","","from .client import (\n","","","","","","from .client_ws import ClientWebSocketResponse as ClientWebSocketResponse\n","","","","","","class ClientConnectorCertificateError(*cert_errors_bases): # type: ignore[misc]\n","def __init__(self, message: Union[RawResponseMessage, str, None] = None) -> None:\n","return cached(*args, **kwds)\n","return self._getitem(self, parameters)\n","return _UnionGenericAlias(self, parameters)\n","if info[\"uncollectable\"] > 0:\n","class Trace:\n","return builder.build_class()\n","return self\n","return _make_method(\"__hash__\", script, unique_filename, globs)\n","return locs[name]\n","eval(bytecode, globs, locs)\n","self._cls_dict[\"__setstate__\"],\n","return _Attributes((AttrsClass(attrs), base_attrs, base_attr_map))\n","return globs[attr_class_name]\n","eval(bytecode, globs, locs)\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 self.read(nbytes, buffer)\n","raise\n","self.handle_stimulus(event)\n","return method(self, *args, **kwargs)\n","super().handle_stimulus(*stims)\n","instructions = self.state.handle_stimulus(*stims)\n","recs, instr = self._handle_event(stim)\n","return method.__get__(obj, cls)(*args, **kwargs)\n","@_handle_event.register\n","handler_func(fileobj, events)\n","self.io_loop.update_handler(self.fileno(), self._state)\n","super()._handle_read()\n","self._read_from_buffer(pos)\n","return self._find_read_pos()\n","return bytes_read\n","del buf\n","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","self.socket.do_handshake()\n","self._sslobj.do_handshake()\n","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","return self._sslobj.write(data)\n","self.socket.do_handshake()\n","self._sslobj.do_handshake()\n","callback(connection, address)\n","stream = SSLIOStream(\n","return context.wrap_socket(\n","return self.sslsocket_class._create(\n","return self\n","await val\n","return await func(*args, **kwargs)\n","memory = worker.monitor.get_process_memory()\n","return 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\", buffering=FILE_READ_BUFFER_SIZE)\n","state = profile.process(\n","new_state = process(prev, frame, state, stop=stop, depth=depth - 1)\n","new_state = process(prev, frame, state, stop=stop, depth=depth - 1)\n","new_state = process(prev, frame, state, stop=stop, depth=depth - 1)\n","new_state = process(prev, frame, state, stop=stop, depth=depth - 1)\n","new_state = process(prev, frame, state, stop=stop, depth=depth - 1)\n","if any(frame.f_code.co_filename.endswith(o) for o in omit):\n","def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n","return {\n","return lines[lineno - 1]\n","return updatecache(filename, module_globals)\n","return lines\n","await self.process(part_id, shards, size)\n","await self._process(id, shards)\n","await self.send(address, shards)\n","return await retry(\n","return await coro()\n","return await self.rpc(address).shuffle_receive(\n","return 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","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","del data\n","return self._sslobj.write(data)\n","frames_nbytes = await stream.read_bytes(fmt_size)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","return await self._connect(addr=addr, timeout=timeout)\n","comm = await connect(\n","comm = await wait_for(\n","return await fut\n","stream = await self.client.connect(\n","af, addr, stream = await connector.start(connect_timeout=timeout)\n","return self.future\n","future_add_done_callback(\n","return stream, stream.connect(addr)\n","self._closed = False\n","return msg\n","return future\n","return\n","return self._find_read_pos()\n","return bytes_read\n","del buf\n","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","return frames_nbytes_total\n","return future\n","super()._handle_write()\n","while self._write_futures:\n","del data\n","return self._sslobj.write(data)\n","with open(\n","msg = await comm.read()\n","n = await stream.read_into(chunk)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","return self.read(nbytes, buffer)\n","return self._sslobj.read(len, buffer)\n","return await func(self, *args, **kwargs)\n","compressed = await comm.write(msg, serializers=serializers)\n","frames = await to_frames(\n","return _to_frames()\n","return list(protocol.dumps(msg, **kwargs))\n","frames[0] = msgpack.dumps(msg, default=_encode_default, use_bin_type=True)\n","return Packer(**kwargs).pack(o)\n","return {\"__Serialized__\": offset}\n","return [sub_header] + sub_frames\n","with self._recreate_cm():\n","return False\n","self.digest_metric(label, delta, unit)\n","for cb in cbs.values():\n","loop.call_soon_threadsafe(self.digest_metric, name, value)\n","return handle\n","csock.send(b'\\0')\n","compression[i], frames[i] = maybe_compress(\n","with self._recreate_cm():\n","next(self.gen)\n","self.digest_metric(label, delta, unit)\n","for cb in cbs.values():\n","loop.call_soon_threadsafe(self.digest_metric, name, value)\n","return handle\n","csock.send(b'\\0')\n","shuffle_run = await self._get_shuffle_run(shuffle_id, run_id)\n","return await self.shuffle_runs.get_with_run_id(\n","shuffle_run = await self._refresh(\n","shuffle_run = result.spec.create_run_on_worker(\n","return DataFrameShuffleRun(\n","self.worker_for = pd.Series(worker_for, name=\"_workers\").astype(\"category\")\n","new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n","return self.apply(\n","applied = getattr(b, f)(**kwargs)\n","new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n","new_values = astype_array(values, dtype, copy=copy)\n","values = _astype_nansafe(values, dtype, copy=copy)\n","return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)\n","return cls(scalars, dtype=dtype, copy=copy)\n","codes, categories = factorize(values, sort=True)\n","codes, uniques = factorize_array(\n","uniques, codes = table.factorize(\n","return frames_nbytes_total\n","return future\n","super()._handle_write()\n","while self._write_futures:\n","del data\n","return self._sslobj.write(data)\n","data = self._ssock.recv(4096)\n","await shuffle_run.close()\n","await self._disk_buffer.close()\n","shutil.rmtree(self.directory)\n","_rmtree_safe_fd(fd, path, onerror)\n","os.unlink(entry.name, dir_fd=topfd)\n","self.handle_stimulus(stim)\n","return method(self, *args, **kwargs)\n","super().handle_stimulus(*stims)\n","for inst in instructions:\n","return instructions\n","return instructions\n","while recs:\n","return recs, instructions\n","return self._transition_to_memory(\n","return recommendations, instructions\n","with self._capture_metrics(), self._handle_errors(key):\n","self.fast[key] = value\n","self.evict_until_below_target()\n","with self.lock:\n","self.total_weight += weight\n","return safe_sizeof(value)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return max(1200, p)\n","return meth(arg, *args, **kwargs)\n","return p\n","return sample_nbytes\n","return await func(*args, **kwargs)\n","return await method(self, *args, **kwargs) # type: ignore\n","result = await run_in_executor_with_context(\n","return await loop.run_in_executor(\n","executor.submit(func, *args), loop=self)\n","self._adjust_thread_count()\n","t.start()\n","self._started.wait()\n","signaled = self._cond.wait(timeout)\n","waiter.acquire()\n","stream.write(b\"\")\n","self._handle_write()\n","super()._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data) # type: ignore\n","return self._sslobj.write(data)\n","return self._bootstrap(parent_sentinel)\n","self.run()\n","self._target(*self._args, **self._kwargs)\n","target(*args, **kwargs)\n","asyncio_run(run(), loop_factory=get_loop_factory())\n","return runner.run(main)\n","return self._loop.run_until_complete(task)\n","self.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","return await method(self, *args, **kwargs) # type: ignore\n","await self.handle_stream(comm)\n","msgs = await comm.read()\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","return pickle.loads(sub_header[\"pickled-obj\"], buffers=sub_frames)\n","return pickle.loads(x, buffers=buffers)\n","","","","","","","","","from .core import S3FileSystem, S3File\n","","","","","","import aiobotocore.session\n","","","","","","from .client import AioBaseClient, AioClientCreator\n","","","","","","from .args import AioClientArgsCreator\n","","","","","","from .endpoint import AioEndpointCreator\n","","","","","","from aiobotocore.httpsession import AIOHTTPSession\n","","","","","","from aiobotocore._endpoint_helpers import _IOBaseWrapper, _text\n","","","","","","pass\n","","","","","","def request(\n","","","","","","logging.getLogger(\"charset_normalizer\").addHandler(logging.NullHandler())\n","","","","","","def is_binary(\n","","","","","","def coherence_ratio(\n","","","","","","","return _groupby_aggregate(_concat(inputs), **kwargs)\n","return aggfunc(grouped, **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","result = self._agg_general(\n","result = self._cython_agg_general(\n","new_mgr = data.grouped_reduce(array_func)\n","res = func(arr)\n","result = self.grouper._cython_operation(\n","ngroups = self.ngroups\n","return len(self.result_index)\n","codes = self.reconstructed_codes\n","return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)\n","out = _decons_group_index(obs_ids, shape)\n","result._set_codes(codes, copy=copy, validate=False)\n","new_codes = FrozenList(\n","_coerce_indexer_frozen(level_codes, lev, copy=copy).view()\n","array_like = coerce_indexer_dtype(array_like, categories)\n","return self._cython_op_ndim_compat(\n","res = self._call_cython_op(\n","return dask.core.get(graph, name)\n","result = _execute_task(task, cache)\n","return func(*(_execute_task(a, cache) for a in args))\n","return func(*args, **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","return df.reset_index(\n","new_obj = self.copy(deep=None)\n","data = self._mgr.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","result = take_nd(\n","return arr.take(\n","new_data = take(\n","validate_indices(indices, arr.shape[axis])\n","max_idx = indices.max()\n","value, refs = self._sanitize_column(value)\n","return sanitize_array(value, self.index, copy=True, allow_2d=True), None\n","subarr = data.copy()\n","return cls.reduction_chunk(df, **kwargs)\n","return getattr(__obj, self.method)(*args, **kwargs)\n","return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()\n","return self.compute(\"nlargest\")\n","cur_frame = frame = frame.reset_index(drop=True)\n","new_obj = self.copy(deep=None)\n","data = self._mgr.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","self.blocks = _consolidate(self.blocks)\n","merged_blocks, _ = _merge_blocks(\n","new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]\n","","return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)\n","return selectn.SelectNSeries(self, n=n, keep=keep).nlargest()\n","return self.compute(\"nlargest\")\n","dropped = self.obj.dropna()\n","result = remove_na_arraylike(self)\n","return arr[notna(np.asarray(arr))]\n","return self._get_rows_with_mask(key)\n","new_mgr = self._mgr.get_rows_with_mask(indexer)\n","new_idx = self.index[indexer]\n","return super().__getitem__(key)\n","result = getitem(key)\n","self.run_forever()\n","self._run_once()\n","handle._run()\n","self._context.run(self._callback, *self._args)\n","return await method(self, *args, **kwargs) # type: ignore\n","await self.handle_stream(comm)\n","msgs = await comm.read()\n","msg = await from_frames(\n","res = _from_frames()\n","return protocol.loads(\n","return msgpack.loads(\n","return pickle.loads(sub_header[\"pickled-obj\"], buffers=sub_frames)\n","return pickle.loads(x, buffers=buffers)\n","","","","","","","","","from .core import S3FileSystem, 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std3_rules: bool = False) -> 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