D:\数据可视化\代码\stage9\demo1.py:35: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead. print(data.groupby('Name')['Chinese', 'Math'].sum())

时间: 2024-02-23 20:00:02 浏览: 131
PPT

数据库管理系统概述英文版课件:9 Introduction to Indexing and Hash Index.ppt

这个警告是因为在使用 groupby 方法时,传递给 agg 函数的参数是字典形式,而字典在 Python 中是无序的,因此不能保证每次的顺序都相同。在 Python 3.7 之前,字典的顺序是不确定的,因此在这些版本的 Pandas 中,会出现上述警告。 为了避免这个警告,可以将需要聚合的列名放在一个列表中,然后传递给 groupby 方法,如下所示: ```python print(data.groupby('Name')[['Chinese', 'Math']].sum()) ``` 这样就可以避免警告了。
阅读全文

相关推荐

Traceback (most recent call last): File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-1430dfa068df>", line 1, in <module> runfile('D:\\Users\\Admin\\PycharmProjects\\pythonProject2\\线性分析预测.py', wdir='D:\\Users\\Admin\\PycharmProjects\\pythonProject2') File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "D:\Users\Admin\PycharmProjects\pythonProject2\线性分析预测.py", line 9, in <module> y = data.iloc[:, 4].values File "D:\anaconda\lib\site-packages\pandas\core\indexing.py", line 1067, in __getitem__ return self._getitem_tuple(key) File "D:\anaconda\lib\site-packages\pandas\core\indexing.py", line 1563, in _getitem_tuple tup = self._validate_tuple_indexer(tup) File "D:\anaconda\lib\site-packages\pandas\core\indexing.py", line 873, in _validate_tuple_indexer self._validate_key(k, i) File "D:\anaconda\lib\site-packages\pandas\core\indexing.py", line 1466, in _validate_key self._validate_integer(key, axis) File "D:\anaconda\lib\site-packages\pandas\core\indexing.py", line 1557, in _validate_integer raise IndexError("single positional indexer is out-of-bounds") IndexError: single positional indexer is out-of-bounds

D:\anaconda\envs\mytest\python.exe D:/PyCharm/learning/1/BPmain.py (100, 35) (50, 35) (100, 1) (50, 1) Int64Index([5], dtype='int64') Traceback (most recent call last): File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexes\base.py", line 3081, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 1625, in pandas._libs.hashtable.Int64HashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 1632, in pandas._libs.hashtable.Int64HashTable.get_item KeyError: 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\PyCharm\learning\1\BPmain.py", line 30, in <module> if test_output.loc[i, 0] == y_pred[i, 0]: File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 889, in __getitem__ return self._getitem_tuple(key) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 1060, in _getitem_tuple return self._getitem_lowerdim(tup) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 831, in _getitem_lowerdim return getattr(section, self.name)[new_key] File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 895, in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 1124, in _getitem_axis return self._get_label(key, axis=axis) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexing.py", line 1073, in _get_label return self.obj.xs(label, axis=axis) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\generic.py", line 3739, in xs loc = index.get_loc(key) File "D:\anaconda\envs\mytest\lib\site-packages\pandas\core\indexes\base.py", line 3083, in get_loc raise KeyError(key) from err KeyError: 0 进程已结束,退出代码1

/var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['cluster_label'] = db.labels_ /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:8: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['hour'] = device_df['timestamp'].map(lambda x: time.localtime(x).tm_hour) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_night'] = device_df['hour'].map(lambda x: 1 if x >= 22 or x < 6 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:10: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_daytime'] = device_df['hour'].map(lambda x: 1 if x >= 10 or x < 17 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:11: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_weekend'] = device_df['timestamp'].map(lambda x: 1 if datetime.datetime.utcfromtimestamp(x).weekday() >= 5 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index. night_cnt = device_cluster_df[device_df['is_night'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:19: UserWarning: Boolean Series key will be reindexed to match DataFrame index. daytime_cnt = device_cluster_df[device_df['is_daytime'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:20: UserWarning: Boolean Series key will be reindexed to match DataFrame index. weekend_cnt = device_cluster_df[device_df['is_weekend'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_35021/1920266051.py:21: UserWarning: Boolean Series key will be reindexed to match DataFrame index. weekday_cnt = device_cluster_df[device_df['is_weekend'] == 0]['event_day'].drop_duplicates().count()jupyter notebook出现这段报错的原因

/var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['cluster_label'] = db.labels_ /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:8: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['hour'] = device_df['timestamp'].map(lambda x: time.localtime(x).tm_hour) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_night'] = device_df['hour'].map(lambda x: 1 if x >= 22 or x < 6 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:10: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_daytime'] = device_df['hour'].map(lambda x: 1 if x >= 10 or x < 17 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:11: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy device_df['is_weekend'] = device_df['timestamp'].map(lambda x: 1 if datetime.datetime.utcfromtimestamp(x).weekday() >= 5 else 0) /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:18: UserWarning: Boolean Series key will be reindexed to match DataFrame index. night_cnt = device_cluster_df[device_df['is_night'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:19: UserWarning: Boolean Series key will be reindexed to match DataFrame index. daytime_cnt = device_cluster_df[device_df['is_daytime'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:20: UserWarning: Boolean Series key will be reindexed to match DataFrame index. weekend_cnt = device_cluster_df[device_df['is_weekend'] == 1]['event_day'].drop_duplicates().count() /var/folders/gk/ryl0f4y10m9ccnhw_1vlpjzh0000gn/T/ipykernel_41405/1920266051.py:21: UserWarning: Boolean Series key will be reindexed to match DataFrame index. weekday_cnt = device_cluster_df[device_df['is_weekend'] == 0]['event_day'].drop_duplicates().count() ​解释一下这段信息为什么出现

Traceback (most recent call last): File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 3802, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 165, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 5745, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 5753, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'Column1' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "e:\Mydata\py\提取指定范围excel数据.py", line 7, in <module> data = df.loc[0:9, 'Column1':'Column3'] # 例如提取第1行到第10行,第1列到第3列的数据 File "D:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 1067, in __getitem__ return self._getitem_tuple(key) File "D:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 1256, in _getitem_tuple return self._getitem_tuple_same_dim(tup) File "D:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 924, in _getitem_tuple_same_dim retval = getattr(retval, self.name)._getitem_axis(key, axis=i) File "D:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 1290, in _getitem_axis return self._get_slice_axis(key, axis=axis) File "D:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 1324, in _get_slice_axis indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step) File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 6559, in slice_indexer start_slice, end_slice = self.slice_locs(start, end, step=step) File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 6767, in slice_locs start_slice = self.get_slice_bound(start, "left") File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 6686, in get_slice_bound raise err File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 6680, in get_slice_bound slc = self.get_loc(label) File "D:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 3804, in get_loc raise KeyError(key) from err KeyError: 'Column1'

分析错误信息D:\Anaconda3 2023.03-1\envs\pytorch\lib\site-packages\torch\functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3484.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Model Summary: 283 layers, 7063542 parameters, 7063542 gradients, 16.5 GFLOPS Transferred 354/362 items from F:\Desktop\yolov5-5.0\weights\yolov5s.pt Scaled weight_decay = 0.0005 Optimizer groups: 62 .bias, 62 conv.weight, 59 other Traceback (most recent call last): File "F:\Desktop\yolov5-5.0\train.py", line 543, in <module> train(hyp, opt, device, tb_writer) File "F:\Desktop\yolov5-5.0\train.py", line 189, in train dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, File "F:\Desktop\yolov5-5.0\utils\datasets.py", line 63, in create_dataloader dataset = LoadImagesAndLabels(path, imgsz, batch_size, File "F:\Desktop\yolov5-5.0\utils\datasets.py", line 385, in __init__ cache, exists = torch.load(cache_path), True # load File "D:\Anaconda3 2023.03-1\envs\pytorch\lib\site-packages\torch\serialization.py", line 815, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "D:\Anaconda3 2023.03-1\envs\pytorch\lib\site-packages\torch\serialization.py", line 1033, in _legacy_load magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: STACK_GLOBAL requires str Process finished with exit code 1

最新推荐

recommend-type

python入门-30.寻找列表中只出现一次的数字-寻找单身狗.py

python入门-30.寻找列表中只出现一次的数字——寻找单身狗.py
recommend-type

布尔教育linux优化笔记

linux优化笔记,配套视频:https://www.bilibili.com/list/474327672?sid=4496133&spm_id_from=333.999.0.0&desc=1
recommend-type

知识付费系统-直播+讲师入驻+课程售卖+商城系统-v2.1.9版本搭建以及资源分享下载

知识付费系统-直播+讲师入驻+课程售卖+商城系统-v2.1.9版本搭建以及资源分享下载,CRMEB知识付费分销与直播营销系统是由西安众邦科技自主开发的一款在线教育平台,该系统不仅拥有独立的知识产权,还采用了先进的ThinkPhp5.0框架和Vue前端技术栈,集成了在线直播教学及课程分销等多种功能,旨在为用户提供全方位的学习体验,默认解压密码youyacaocom
recommend-type

美妆神域-JAVA-基于springBoot美妆神域设计与实现

美妆神域-JAVA-基于springBoot美妆神域设计与实现
recommend-type

原生js制作Google粘土logo动画涂鸦代码.zip

原生js制作Google粘土logo动画涂鸦代码.zip
recommend-type

火炬连体网络在MNIST的2D嵌入实现示例

资源摘要信息:"Siamese网络是一种特殊的神经网络,主要用于度量学习任务中,例如人脸验证、签名识别或任何需要判断两个输入是否相似的场景。本资源中的实现例子是在MNIST数据集上训练的,MNIST是一个包含了手写数字的大型数据集,广泛用于训练各种图像处理系统。在这个例子中,Siamese网络被用来将手写数字图像嵌入到2D空间中,同时保留它们之间的相似性信息。通过这个过程,数字图像能够被映射到一个欧几里得空间,其中相似的图像在空间上彼此接近,不相似的图像则相对远离。 具体到技术层面,Siamese网络由两个相同的子网络构成,这两个子网络共享权重并且并行处理两个不同的输入。在本例中,这两个子网络可能被设计为卷积神经网络(CNN),因为CNN在图像识别任务中表现出色。网络的输入是成对的手写数字图像,输出是一个相似性分数或者距离度量,表明这两个图像是否属于同一类别。 为了训练Siamese网络,需要定义一个损失函数来指导网络学习如何区分相似与不相似的输入对。常见的损失函数包括对比损失(Contrastive Loss)和三元组损失(Triplet Loss)。对比损失函数关注于同一类别的图像对(正样本对)以及不同类别的图像对(负样本对),鼓励网络减小正样本对的距离同时增加负样本对的距离。 在Lua语言环境中,Siamese网络的实现可以通过Lua的深度学习库,如Torch/LuaTorch,来构建。Torch/LuaTorch是一个强大的科学计算框架,它支持GPU加速,广泛应用于机器学习和深度学习领域。通过这个框架,开发者可以使用Lua语言定义模型结构、配置训练过程、执行前向和反向传播算法等。 资源的文件名称列表中的“siamese_network-master”暗示了一个主分支,它可能包含模型定义、训练脚本、测试脚本等。这个主分支中的代码结构可能包括以下部分: 1. 数据加载器(data_loader): 负责加载MNIST数据集并将图像对输入到网络中。 2. 模型定义(model.lua): 定义Siamese网络的结构,包括两个并行的子网络以及最后的相似性度量层。 3. 训练脚本(train.lua): 包含模型训练的过程,如前向传播、损失计算、反向传播和参数更新。 4. 测试脚本(test.lua): 用于评估训练好的模型在验证集或者测试集上的性能。 5. 配置文件(config.lua): 包含了网络结构和训练过程的超参数设置,如学习率、批量大小等。 Siamese网络在实际应用中可以广泛用于各种需要比较两个输入相似性的场合,例如医学图像分析、安全验证系统等。通过本资源中的示例,开发者可以深入理解Siamese网络的工作原理,并在自己的项目中实现类似的网络结构来解决实际问题。"
recommend-type

管理建模和仿真的文件

管理Boualem Benatallah引用此版本:布阿利姆·贝纳塔拉。管理建模和仿真。约瑟夫-傅立叶大学-格勒诺布尔第一大学,1996年。法语。NNT:电话:00345357HAL ID:电话:00345357https://theses.hal.science/tel-003453572008年12月9日提交HAL是一个多学科的开放存取档案馆,用于存放和传播科学研究论文,无论它们是否被公开。论文可以来自法国或国外的教学和研究机构,也可以来自公共或私人研究中心。L’archive ouverte pluridisciplinaire
recommend-type

L2正则化的终极指南:从入门到精通,揭秘机器学习中的性能优化技巧

![L2正则化的终极指南:从入门到精通,揭秘机器学习中的性能优化技巧](https://img-blog.csdnimg.cn/20191008175634343.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTYxMTA0NQ==,size_16,color_FFFFFF,t_70) # 1. L2正则化基础概念 在机器学习和统计建模中,L2正则化是一个广泛应用的技巧,用于改进模型的泛化能力。正则化是解决过拟
recommend-type

如何构建一个符合GB/T19716和ISO/IEC13335标准的信息安全事件管理框架,并确保业务连续性规划的有效性?

构建一个符合GB/T19716和ISO/IEC13335标准的信息安全事件管理框架,需要遵循一系列步骤来确保信息系统的安全性和业务连续性规划的有效性。首先,组织需要明确信息安全事件的定义,理解信息安全事态和信息安全事件的区别,并建立事件分类和分级机制。 参考资源链接:[信息安全事件管理:策略与响应指南](https://wenku.csdn.net/doc/5f6b2umknn?spm=1055.2569.3001.10343) 依照GB/T19716标准,组织应制定信息安全事件管理策略,明确组织内各个层级的角色与职责。此外,需要设置信息安全事件响应组(ISIRT),并为其配备必要的资源、
recommend-type

Angular插件增强Application Insights JavaScript SDK功能

资源摘要信息:"Microsoft Application Insights JavaScript SDK-Angular插件" 知识点详细说明: 1. 插件用途与功能: Microsoft Application Insights JavaScript SDK-Angular插件主要用途在于增强Application Insights的Javascript SDK在Angular应用程序中的功能性。通过使用该插件,开发者可以轻松地在Angular项目中实现对特定事件的监控和数据收集,其中包括: - 跟踪路由器更改:插件能够检测和报告Angular路由的变化事件,有助于开发者理解用户如何与应用程序的导航功能互动。 - 跟踪未捕获的异常:该插件可以捕获并记录所有在Angular应用中未被捕获的异常,从而帮助开发团队快速定位和解决生产环境中的问题。 2. 兼容性问题: 在使用Angular插件时,必须注意其与es3不兼容的限制。es3(ECMAScript 3)是一种较旧的JavaScript标准,已广泛被es5及更新的标准所替代。因此,当开发Angular应用时,需要确保项目使用的是兼容现代JavaScript标准的构建配置。 3. 安装与入门: 要开始使用Application Insights Angular插件,开发者需要遵循几个简单的步骤: - 首先,通过npm(Node.js的包管理器)安装Application Insights Angular插件包。具体命令为:npm install @microsoft/applicationinsights-angularplugin-js。 - 接下来,开发者需要在Angular应用的适当组件或服务中设置Application Insights实例。这一过程涉及到了导入相关的类和方法,并根据Application Insights的官方文档进行配置。 4. 基本用法示例: 文档中提到的“基本用法”部分给出的示例代码展示了如何在Angular应用中设置Application Insights实例。示例中首先通过import语句引入了Angular框架的Component装饰器以及Application Insights的类。然后,通过Component装饰器定义了一个Angular组件,这个组件是应用的一个基本单元,负责处理视图和用户交互。在组件类中,开发者可以设置Application Insights的实例,并将插件添加到实例中,从而启用特定的功能。 5. TypeScript标签的含义: TypeScript是JavaScript的一个超集,它添加了类型系统和一些其他特性,以帮助开发更大型的JavaScript应用。使用TypeScript可以提高代码的可读性和可维护性,并且可以利用TypeScript提供的强类型特性来在编译阶段就发现潜在的错误。文档中提到的标签"TypeScript"强调了该插件及其示例代码是用TypeScript编写的,因此在实际应用中也需要以TypeScript来开发和维护。 6. 压缩包子文件的文件名称列表: 在实际的项目部署中,可能会用到压缩包子文件(通常是一些JavaScript库的压缩和打包后的文件)。在本例中,"applicationinsights-angularplugin-js-main"很可能是该插件主要的入口文件或者压缩包文件的名称。在开发过程中,开发者需要确保引用了正确的文件,以便将插件的功能正确地集成到项目中。 总结而言,Application Insights Angular插件是为了加强在Angular应用中使用Application Insights Javascript SDK的能力,帮助开发者更好地监控和分析应用的运行情况。通过使用该插件,可以跟踪路由器更改和未捕获异常等关键信息。安装与配置过程简单明了,但是需要注意兼容性问题以及正确引用文件,以确保插件能够顺利工作。