jakirkham/dask-report.html

## dask-report.html


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      <title>Dask Performance Report</title>


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= make[dt](len(index), state, **kws)\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","mgr = BlockManager(blocks, axes)\n","return blocks\n","return new_blocks\n","self._rebuild_blknos_and_blklocs()\n","**kwargs,\n","values, _tz = generate_regular_range(start, end, periods, freq)\n","vals, n = _percentile(data, qs, interpolation=interpolation)\n","return np.percentile(a, q, interpolation=interpolation), n\n","","a, q, axis, out, overwrite_input, interpolation, keepdims)\n","interpolation=interpolation)\n","r = func(a, **kwargs)\n","diff = np.ediff1d(qs, 0.0, 0.0)\n","","return getattr(obj, self.method)(*args, **kwargs)\n","f, name, axis=axis, skipna=skipna, numeric_only=numeric_only\n","return op(delegate, skipna=skipna, **kwds)\n","return result\n","return _maybe_null_out(result, axis, mask, values.shape)\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","partitions = divisions.searchsorted(s, side=\"right\") - 1\n","return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)\n","result = arr.searchsorted(value, side=side, sorter=sorter)\n","result = PandasArray._from_sequence(data, dtype=dtype, copy=copy)\n","return method(self, other)\n","return _construct_result(self, res_values, index=self.index, name=res_name)\n","return getattr(obj, self.method)(*args, **kwargs)\n","labels, kind=kind, ascending=ascending, na_position=na_position\n","non_nans = items[~mask]\n","return _isna(obj)\n","return _isna_ndarraylike(np.asarray(obj))\n","","return _wrapfunc(a, 'nonzero')\n","indexer = maybe_convert_indices(indexer, n)\n","mask = (indices &gt;= n) | (indices &lt; 0)\n","for blk in self.blocks\n","for blk in self.blocks\n","values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n","data = self._data.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","df2 = df.drop(\"_partitions\", axis=1).set_index(index_name, drop=drop)\n","errors=errors,\n","obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n","result = self.reindex(**{axis_name: new_axis})\n","return func(*args, **kwargs)\n","return self._ensure_type(super().reindex(**kwargs))\n",").__finalize__(self)\n","self._protect_consolidate(f)\n","return result\n","self._data = self._data.consolidate()\n","return bm\n","self._rebuild_blknos_and_blklocs()\n","return new_blocks\n","return make_block(new_values, placement=new_mgr_locs)\n","","return _nx.concatenate(arrs, 0)\n","columns, method, copy, level, fill_value, limit, tolerance\n","allow_dups=False,\n","copy=copy,\n","new_blocks = self._slice_take_blocks_ax0(indexer, fill_tuple=(fill_value,))\n","fill_tuple=None,\n","values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n","frame = self.copy()\n","data = self._data.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","self._data.delete(key)\n","for blkno, _ in _fast_count_smallints(self._blknos[affected_start:]):\n","return np.c_[nz, counts[nz]]\n","res = self.concatenate(tuple(objs), axis=axis)\n","return df.assign(**kwargs)\n","data = self.copy()\n","data = self._data.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\n","self._rebuild_blknos_and_blklocs()\n","indexer = convert_to_index_sliceable(self, key)\n","return idx._get_string_slice(key)\n","_, parsed, reso = parsing.parse_time_string(key, freq)\n","","","","","","","return getattr(self._data, name)\n","return frequencies.infer_freq(self)\n","return inferer.get_freq()\n","self.index._is_monotonic_increasing or self.index._is_monotonic_decreasing\n","elif not self.is_unique_asi8:\n","return len(self.deltas_asi8) == 1\n","return self.__func__(*args, **kwds)\n","return root[k]\n","l = _timelex.split(timestr)         # Splits the timestr into tokens\n","return list(cls(s))\n","value = self._sanitize_column(key, value)\n","self._data.set(key, value)\n","self.insert(len(self.items), item, value)\n","for blkno, count in _fast_count_smallints(self._blknos[loc:]):\n","return np.c_[nz, counts[nz]]\n","defaxes = list(range(ndmin))\n","","return concatenate((arr, values), axis=axis)\n","args2 = [_execute_task(a, cache) for a in args]\n","args2 = [_execute_task(a, cache) for a in args]\n","args2 = [_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 [_execute_task(a, cache) for a in arg]\n","return [_execute_task(a, cache) for a in arg]\n","np.floor_divide(c, k ** stage, out=c)\n","return meth(arg, *args, **kwargs)\n","indexer, locations = pd._libs.algos.groupsort_indexer(c, k)\n","self._consolidate_inplace()\n","self._protect_consolidate(f)\n","result = f()\n","self._data = self._data.consolidate()\n","bm._consolidate_inplace()\n","self.blocks = tuple(_consolidate(self.blocks))\n","list(group_blocks), dtype=dtype, _can_consolidate=_can_consolidate\n","new_values = np.vstack([b.values for b in blocks])\n","","return _nx.concatenate(arrs, 0)\n","indexer = maybe_convert_indices(indexer, n)\n","mask = (indices &gt;= n) | (indices &lt; 0)\n","self, indices, axis, allow_fill, fill_value, **kwargs\n","na_value=self._na_value,\n","taken = values.take(indices)\n","self.asi8, indices, allow_fill=allow_fill, fill_value=fill_value\n","for blk in self.blocks\n","for blk in self.blocks\n","values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n","func(arr, indexer, out, fill_value)\n","self._rebuild_blknos_and_blklocs()\n","parts = [df2.iloc[a:b] for a, b in zip(locations[:-1], locations[1:])]\n","return self._getitem_axis(maybe_callable, axis=axis)\n","return self._get_slice_axis(key, axis=axis)\n","return self._slice(indexer, axis=axis, kind=\"iloc\")\n","return self.obj._slice(obj, axis=axis, kind=kind)\n","result = self._constructor(self._data.get_slice(slobj, axis=axis))\n","bm = type(self)(new_blocks, new_axes, do_integrity_check=False)\n","self._rebuild_blknos_and_blklocs()\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(d) + sum(map(sizeof, d.keys())) + sum(map(sizeof, d.values()))\n","return meth(arg, *args, **kwargs)\n","return int(p) + 1000\n","yield from self.items()\n","yield k, self._ixs(i, axis=1)\n","return res\n","return self.iget(loc)\n","self.axes[1],\n","return self._box_col_values(values, items)\n","return klass(values, index=self.index, name=items, fastpath=True)\n","self._set_axis(0, index, fastpath=True)\n","is_all_dates = labels.is_all_dates\n","object.__setattr__(self, \"_is_copy\", None)\n","return meth(arg, *args, **kwargs)\n","p = int(i.memory_usage())\n","result += self._engine.sizeof(deep=deep)\n","return self._engine_type(lambda: _ndarray_values, len(self))\n","return len(self._data)\n","return v\n","return v\n","return self._data._block.array_values()\n","return PandasArray(self.values)\n","self._dtype = PandasDtype(values.dtype)\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","out = pd.concat(dfs3, join=join, sort=False)\n","sort=sort,\n","self.new_axes = self._get_new_axes()\n","for i in range(ndim)\n","for i in range(ndim)\n","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","return self._concat(to_concat, name)\n","return self._concat_same_dtype(to_concat, name=name)\n","new_data = type(self._values)._concat_same_type(to_concat).asi8\n","values = np.concatenate([x.asi8 for x in to_concat])\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy\n","elif is_uniform_join_units(join_units):\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","return False\n","return _isna(obj)\n","return _isna_ndarraylike(obj)\n","[blk.values for blk in to_concat], axis=self.ndim - 1\n","concat_values = concat_compat(to_concat, axis=concat_axis)\n","return np.concatenate(to_concat, axis=axis)\n","elif is_bool_dtype(dtype):\n","elif is_extension_array_dtype(arr_or_dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","for ju in join_units\n","values = algos.take_nd(values, indexer, axis=ax, fill_value=fill_value)\n","if is_object_dtype(dtype):\n","return _is_dtype_type(arr_or_dtype, classes(np.object_))\n","self._consolidate_check()\n","ftypes = [blk.ftype for blk in self.blocks]\n","ftypes = [blk.ftype for blk in self.blocks]\n","return f\"{dtype}:{self._ftype}\"\n","yield k, self._ixs(i, axis=1)\n","return res\n","return self.iget(loc)\n","self.axes[1],\n","return make_block(values, placement=placement, ndim=ndim, klass=type(self))\n","return klass(values, ndim=ndim, placement=placement)\n","f\"Wrong number of items passed {len(self.values)}, \"\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","return False\n","return _isna(obj)\n","return 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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","Worker._instances.add(self)\n","io_loop=self.io_loop,\n","self.__stopped = False\n","return log\n","self._started.wait()\n","return signaled\n","pass\n","self.start()\n","self.asyncio_loop.run_forever()\n","self._run_once()\n","handle = None  # Needed to break cycles when an exception occurs.\n","self = None  # Needed to break cycles when an exception occurs.\n","self.scheduler_delay,\n","future = None  # type: ignore\n","yield future\n","return f\n","t.start()\n","self._started.wait()\n","return signaled\n","pass\n","state = func(key, **kwargs)\n","self.put_key_in_memory(key, value, transition=False)\n","self.data[key] = value\n","if self.weight(key, value) &lt;= self.n:\n","return sizeof(v)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","return getsizeof(d) + sum(map(sizeof, d.keys())) + sum(map(sizeof, d.values()))\n","return meth(arg, *args, **kwargs)\n","if col.dtype == object:\n","def npy_ctypes_check(cls):\n","ret = callback()\n","return self.callback()\n","frame, True, self.profile_recent, stop=\"distributed/worker.py\"\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","state = process(prev, frame, state, stop=stop)\n","\"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","return text\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","\"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","\"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","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","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","lines = fp.readlines()\n","return text\n","return default, [first]\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","lines = fp.readlines()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","lines = fp.readlines()\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","lines = fp.readlines()\n","encoding, lines = detect_encoding(buffer.readline)\n","first = read_or_stop()\n","return readline()\n","line = linecache.getline(co.co_filename, frame.f_lineno, frame.f_globals).lstrip()\n","lines = getlines(filename, module_globals)\n","return updatecache(filename, module_globals)\n","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","stat = os.stat(fullname)\n","buffer = _builtin_open(filename, 'rb')\n","ioc = psutil.net_io_counters()\n","return _common.snetio(*[sum(x) for x in zip(*rawdict.values())])\n","return _wn.run(input_dict, name)\n","return new_dict\n","stream.write(b)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","del data\n","handler_func(fileobj, events)\n","callback(connection, address)\n","app_log.error(\"Error in connection callback\", exc_info=True)\n","super(IOStream, self).__init__(*args, **kwargs)\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","data = self._ssock.recv(4096)\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","comm, every_cycle=[self.ensure_communicating, self.ensure_computing]\n","msgs = 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","res = _from_frames()\n","frames, deserialize=deserialize, deserializers=deserializers\n","return msg\n","return msgpack.loads(payload, use_list=False, **msgpack_opts)\n","info[\"uncollectable\"],\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","] + out_frames\n","if type(value) is Serialize\n","return header, frames\n","return {\"serializer\": \"pickle\"}, [pickle.dumps(x)]\n","return result\n","def __reduce__(self):\n","def __reduce__(self):\n","block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n","block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n","return promote(getitem(key))\n","values = getattr(values, \"_values\", values)\n","meta = {k: getattr(self, k, None) for k in self._metadata}\n","dumps = dask_serialize.dispatch(type(x))\n","raise TypeError(\"No dispatch for {0}\".format(cls))\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.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","return future\n","self._add_io_state(ioloop.IOLoop.READ)\n","return self._find_read_pos()\n","return bytes_read\n","del 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 pickle.loads(b\"\".join(frames))\n","return pickle.loads(x)\n","self._post_setstate()\n","self._rebuild_blknos_and_blklocs()\n","new_blklocs[rl.indexer] = np.arange(len(rl))\n","return cls.__new__(cls, **d)\n","new_data, dtype=new_dtype, copy=False, name=name, **kwargs\n","elif is_period_dtype(data) or is_period_dtype(dtype):\n","return PeriodDtype.is_dtype(arr_or_dtype)\n","return super().is_dtype(dtype)\n","return False\n","return getattr(inst, attr, \"_typ\") in comp\n","return result._reset_identity()\n","self._id = _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 False\n","return getattr(inst, attr, \"_typ\") in comp\n","inferred = lib.infer_dtype(subarr, skipna=False)\n","return getattr(inst, attr, \"_typ\") in comp\n","def _new_DatetimeIndex(cls, d):\n","return await loop.run_in_executor(_offload_executor, lambda: fn(*args, **kwargs))\n","return futures.wrap_future(executor.submit(func, *args), loop=self)\n","return f\n","_threads_queues[t] = self._work_queue\n","self._started.wait()\n","return signaled\n","pass\n","self._try_inline_read()\n","pos = self._read_to_buffer_loop()\n","if self._read_to_buffer() == 0:\n","bytes_read = self.read_from_fd(buf)\n","return self.socket.recv_into(buf, len(buf))\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data)  # type: ignore\n","stream.write(b)\n","self._handle_write()\n","num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n","return self.socket.send(data)  # type: ignore\n","state = func(dep, **kwargs)\n","self.put_key_in_memory(dep, value)\n","self.data[key] = value\n","if self.weight(key, value) &lt;= self.n:\n","return sizeof(v)\n","return sizeof(obj)\n","return meth(arg, *args, **kwargs)\n","for name, col in df.iteritems():\n","yield from self.items()\n","yield k, self._get_item_cache(k)\n","values = self._data.get(item)\n","return self.iget(loc)\n","block = self.blocks[self._blknos[i]]\n","self.release_dep(key)\n","del self.data[dep]\n","del self.fast[key]\n","self.total_weight -= self.weights.pop(key)\n","self.popitem()\n","del self.data[key]\n","del self.fast[key]\n","del self.d[key]\n","ip, port, max_buffer_size=MAX_BUFFER_SIZE, **kwargs\n","addrinfo = await self.resolver.resolve(host, port, af)\n","return async_future\n","return f\n","t.start()\n","self._started.wait()\n","return signaled\n","pass\n","metrics=await self.get_metrics(),\n","operation=operation,\n","return await coro()\n","result = await send_recv(comm=comm, op=key, **kwargs)\n","response = await comm.read(deserializers=deserializers)\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","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","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 getattr(obj, self.method)(*args, **kwargs)\n","labels, kind=kind, ascending=ascending, na_position=na_position\n","indexer = non_nan_idx[non_nans.argsort(kind=kind)]\n","return _isna(obj)\n","return _isna_ndarraylike(np.asarray(obj))\n","","return _wrapfunc(a, 'nonzero')\n","new_labels = self.axes[axis].take(indexer)\n","for blk in self.blocks\n","for blk in self.blocks\n","values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n","df2 = df.drop(\"_partitions\", axis=1).set_index(index_name, drop=drop)\n","errors=errors,\n","obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n","result = self.reindex(**{axis_name: new_axis})\n","return func(*args, **kwargs)\n","return self._ensure_type(super().reindex(**kwargs))\n","self._consolidate_inplace()\n","self._protect_consolidate(f)\n","result = f()\n","self._data = self._data.consolidate()\n","bm._consolidate_inplace()\n","self.blocks = tuple(_consolidate(self.blocks))\n","list(group_blocks), dtype=dtype, _can_consolidate=_can_consolidate\n","new_values = np.vstack([b.values for b in blocks])\n","","return _nx.concatenate(arrs, 0)\n","return frame\n","allow_dups=False,\n","return self._constructor(new_data).__finalize__(self)\n","return type(self)(new_blocks, new_axes)\n","return blocks\n","return self.make_block_same_class(new_values, new_mgr_locs)\n","frame = self.copy()\n","data = self._data.copy(deep=deep)\n","res = self.apply(\"copy\", deep=deep)\n","applied = getattr(b, f)(**kwargs)\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","out = pd.concat(dfs3, join=join, sort=False)\n","sort=sort,\n","self.new_axes = self._get_new_axes()\n","for i in range(ndim)\n","for i in range(ndim)\n","concat_axis = _concat_indexes(indexes)\n","return indexes[0].append(indexes[1:])\n","return self._concat(to_concat, name)\n","return self._concat_same_dtype(to_concat, name=name)\n","is_diff_evenly_spaced = len(unique_deltas(new_data)) == 1\n","values = np.concatenate([x.asi8 for x in to_concat])\n","mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy\n","concatenate_join_units(join_units, concat_axis, copy=copy),\n","concat_values = concat_compat(to_concat, axis=concat_axis)\n","return np.concatenate(to_concat, axis=axis)\n","elif is_bool_dtype(dtype):\n","elif is_extension_array_dtype(arr_or_dtype):\n","return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n","[blk.values for blk in to_concat], axis=self.ndim - 1\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","all(not ju.is_na or ju.block.is_extension for ju in join_units)\n","values_flat = values.ravel(order=\"K\")\n","return _isna(obj)\n","return _isna_ndarraylike(obj)\n","return sum(lengths)\n","return future\n","future_set_result_unless_cancelled(future, None)\n","del 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</html>

## shuffle_script.py
import sys

import dask.dataframe as dd
from dask.distributed import Client, performance_report, progress, wait


def make_data():
    df = dd.demo.make_timeseries(
        "2000",
        "2001",
        {"x": float, "y": float, "id": int},
        freq="1s",
        partition_freq="7d",
        seed=2,
    )
    df = df.persist()
    wait(df)
    return df


def run_set_index(df):
    df2 = df.set_index("id")
    df2 = df2.persist()
    wait(df2)
    return df2


def main(*argv):
    c = Client()

    df = make_data()
    with performance_report(filename="dask-report.html"):
        df2 = run_set_index(df)

    return 0


if __name__ == "__main__":
    sys.exit(main(*sys.argv))
	import sys

	import dask.dataframe as dd
	from dask.distributed import Client, performance_report, progress, wait


	def make_data():
	df = dd.demo.make_timeseries(
	"2000",
	"2001",
	{"x": float, "y": float, "id": int},
	freq="1s",
	partition_freq="7d",
	seed=2,
	)
	df = df.persist()
	wait(df)
	return df


	def run_set_index(df):
	df2 = df.set_index("id")
	df2 = df2.persist()
	wait(df2)
	return df2


	def main(*argv):
	c = Client()

	df = make_data()
	with performance_report(filename="dask-report.html"):
	df2 = run_set_index(df)

	return 0


	if __name__ == "__main__":
	sys.exit(main(*sys.argv))