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Microsoft Fabric 许可证。 有关如何启用免费 Fabric 试用版许可证的详细信息,请参阅 [Fabric 入门](https://learn.microsoft.com/fabric/get-started/fabric-trial)。 执行此操作需要 Microsoft 学校或工作帐户 。 如果没有,可以[注册 Microsoft Office 365 E3 或更高版本的试用版](https://www.microsoft.com/microsoft-365/business/compare-more-office-365-for-business-plans)。 + +## 创建工作区 + +在 Fabric 中处理数据之前,在已启用的 Fabric 试用版中创建工作区。 + +1. 登录到 [Microsoft Fabric](https://app.fabric.microsoft.com) (`https://app.fabric.microsoft.com`),然后选择 Power BI。 +2. 在左侧菜单栏中,选择“工作区”(图标类似于 🗇)。 +3. 新建一个工作区并为其指定名称,并选择包含 Fabric 容量(试用版、高级版或 Fabric)的许可模式 。 +4. 打开新工作区时,它应为空,如下所示: + + ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) + +## 创建笔记本 + +若要训练模型,可以创建笔记本。 笔记本提供了一个交互式环境,你可在其中编写和运行作为试验的(多种语言的)代码。 + +1. 在 Power BI 门户的左下角,选择“**PowerBI**”图标并切换到“**数据科学**”体验。 + +1. 在“数据科学”主页中,创建新的笔记本 。 + + 几秒钟后,一个包含单个单元格的新笔记本将会打开。 笔记本由一个或多个单元格组成,这些单元格可以包含代码或 markdown(格式化文本) 。 + +1. 选择第一个单元格(当前是代码单元格),然后在其右上角的动态工具栏中,使用 M↓ 按钮将单元格转换为 markdown 单元格。 + + 当单元格更改为 markdown 单元格时,它包含的文本将会呈现。 + +1. 使用 🖉(“编辑”)按钮将单元格切换到编辑模式,然后删除内容并输入以下文本: + + ```text + # Perform data exploration for data science + + Use the code in this notebook to perform data exploration for data science. + ``` + +## 将数据加载到数据帧中 + +现在可以运行代码来获取数据了。 将使用 Azure 开放数据集中的[**糖尿病数据集**](https://learn.microsoft.com/azure/open-datasets/dataset-diabetes?tabs=azureml-opendatasets?azure-portal=true)。 加载数据后,将数据转换为 Pandas 数据帧,这是处理行和列中数据的常见结构。 + +1. 在笔记本中,使用最新单元格下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中。 输入以下代码以将数据集加载到数据帧中。 + + ```python + # Azure storage access info for open dataset diabetes + blob_account_name = "azureopendatastorage" + blob_container_name = "mlsamples" + blob_relative_path = "diabetes" + blob_sas_token = r"" # Blank since container is Anonymous access + + # Set Spark config to access blob storage + wasbs_path = f"wasbs://%s@%s.blob.core.windows.net/%s" % (blob_container_name, blob_account_name, blob_relative_path) + spark.conf.set("fs.azure.sas.%s.%s.blob.core.windows.net" % (blob_container_name, blob_account_name), blob_sas_token) + print("Remote blob path: " + wasbs_path) + + # Spark read parquet, note that it won't load any data yet by now + df = spark.read.parquet(wasbs_path) + ``` + +1. 使用单元格左侧的“▷ 运行单元格”按钮运行单元格。 或者,可以按键盘上的 `SHIFT` + `ENTER` 来运行单元格。 + + > 注意:由于这是你第一次在此会话中运行 Spark 代码,因此必须启动 Spark 池。 这意味着会话中的第一次运行可能需要一分钟左右才能完成。 后续运行速度会更快。 + +1. 使用单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + display(df) + ``` + +1. 单元格命令完成后,查看单元格下方的输出,该输出应如下所示: + + |年龄|SEX|BMI|BP|S1|S2|S3|S4|S5|S6|Y| + |---|---|---|--|--|--|--|--|--|--|--| + |59|2|32.1|101.0|157|93.2|38.0|4.0|4.8598|87|151| + |48|1|21.6|87.0|183|103.2|70.0|3.0|3.8918|69|75| + |72|2|30.5|93.0|156|93.6|41.0|4.0|4.6728|85|141| + |24|1|25.3|84.0|198|131.4|40.0|5.0|4.8903|89|206| + |50|1|23.0|101.0|192|125.4|52.0|4.0|4.2905|80|135| + | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | + + 输出显示糖尿病数据集的行和列。 + +1. 数据作为 Spark 数据帧加载。 Scikit-learn 将期望输入数据集是 Pandas 数据帧。 运行以下代码将数据集转换为 Pandas 数据帧: + + ```python + df = df.toPandas() + df.head() + ``` + +## 检查数据的形状 + +既然已经加载了数据,就可以检查数据集的结构,例如行和列的数量、数据类型和缺少的值。 + +1. 使用单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + # Display the number of rows and columns in the dataset + print("Number of rows:", df.shape[0]) + print("Number of columns:", df.shape[1]) + + # Display the data types of each column + print("\nData types of columns:") + print(df.dtypes) + ``` + + 数据集包含 **442 行**和 **11 列**。 这表示数据集中有 442 个样本和 11 个特征或变量。 `SEX` 变量可能包含分类或字符串数据。 + +## 检查是否缺少数据 + +1. 使用单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + missing_values = df.isnull().sum() + print("\nMissing values per column:") + print(missing_values) + ``` + + 代码检查是否缺少值。 请注意,数据集中没有缺少的数据。 + +## 为数值变量生成描述性统计信息 + +现在,让我们生成描述性统计数据来理解数值变量的分布。 + +1. 使用单元格输出下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中,然后输入以下代码。 + + ```python + desc_stats = df.describe() + print(desc_stats) + ``` + + 平均年龄约为 48.5 年,标准偏差为 13.1 年。 年龄最小的是 19 岁,年龄最大的是 79 岁。 平均 `BMI` 约为 26.4,根据 [WHO 标准](https://www.who.int/health-topics/obesity#tab=tab_1),属于**超重**类别。 最小值 `BMI` 为 18,最大值为 42.2。 + +## 绘制数据分布 + +验证 `BMI` 特征,并绘制其分布图,以更好地理解其特征。 + +1. 将另一个代码单元格添加到笔记本。 然后,在此单元格中输入以下代码并执行它。 + + ```python + import matplotlib.pyplot as plt + import seaborn as sns + import numpy as np + + # Calculate the mean, median of the BMI variable + mean = df['BMI'].mean() + median = df['BMI'].median() + + # Histogram of the BMI variable + plt.figure(figsize=(8, 6)) + plt.hist(df['BMI'], bins=20, color='skyblue', edgecolor='black') + plt.title('BMI Distribution') + plt.xlabel('BMI') + plt.ylabel('Frequency') + + # Add lines for the mean and median + plt.axvline(mean, color='red', linestyle='dashed', linewidth=2, label='Mean') + plt.axvline(median, color='green', linestyle='dashed', linewidth=2, label='Median') + + # Add a legend + plt.legend() + plt.show() + ``` + + 从此图中可以观察到数据集中 `BMI` 的范围和分布。 例如,大多数 `BMI` 都在 23.2 和 29.2 之间,数据是右偏的。 + +## 执行多变量分析 + +让我们生成诸如散点图和方框图之类的可视化效果,以揭示数据中的模式和关系。 + +1. 使用单元格输出下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中,然后输入以下代码。 + + ```python + import matplotlib.pyplot as plt + import seaborn as sns + + # Scatter plot of Quantity vs. Price + plt.figure(figsize=(8, 6)) + sns.scatterplot(x='BMI', y='Y', data=df) + plt.title('BMI vs. Target variable') + plt.xlabel('BMI') + plt.ylabel('Target') + plt.show() + ``` + + 可以看到,随着 `BMI` 的增加,目标变量也会增加,这表明这两个变量之间存在正线性关系。 + +1. 将另一个代码单元格添加到笔记本。 然后,在此单元格中输入以下代码并执行它。 + + ```python + import seaborn as sns + import matplotlib.pyplot as plt + + fig, ax = plt.subplots(figsize=(7, 5)) + + # Replace numeric values with labels + df['SEX'] = df['SEX'].replace({1: 'Male', 2: 'Female'}) + + sns.boxplot(x='SEX', y='BP', data=df, ax=ax) + ax.set_title('Blood pressure across Gender') + plt.tight_layout() + plt.show() + ``` + + 这些观察结果表明,男性和女性患者的血压状况存在差异。 平均而言,女性患者的血压高于男性患者。 + +1. 聚合数据可以使可视化效果和分析更易于管理。 将另一个代码单元格添加到笔记本。 然后,在此单元格中输入以下代码并执行它。 + + ```python + import matplotlib.pyplot as plt + import seaborn as sns + + # Calculate average BP and BMI by SEX + avg_values = df.groupby('SEX')[['BP', 'BMI']].mean() + + # Bar chart of the average BP and BMI by SEX + ax = avg_values.plot(kind='bar', figsize=(15, 6), edgecolor='black') + + # Add title and labels + plt.title('Avg. Blood Pressure and BMI by Gender') + plt.xlabel('Gender') + plt.ylabel('Average') + + # Display actual numbers on the bar chart + for p in ax.patches: + ax.annotate(format(p.get_height(), '.2f'), + (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', + xytext = (0, 10), + textcoords = 'offset points') + + plt.show() + ``` + + 此图显示女性患者的平均血压高于男性患者。 此外,研究表明,女性的平均体重指数 (BMI) 略高于男性。 + +1. 将另一个代码单元格添加到笔记本。 然后,在此单元格中输入以下代码并执行它。 + + ```python + import matplotlib.pyplot as plt + import seaborn as sns + + plt.figure(figsize=(10, 6)) + sns.lineplot(x='AGE', y='BMI', data=df, ci=None) + plt.title('BMI over Age') + plt.xlabel('Age') + plt.ylabel('BMI') + plt.show() + ``` + + 19 至 30 岁年龄组的平均 BMI 值最低,而 65 至 79 岁年龄组平均 BMI 值最高。 此外,观察大多数年龄组的平均 BMI 都在超重范围内。 + +## 相关性分析 + +计算不同特征之间的相关性,以理解它们的关系和依赖项。 + +1. 使用单元格输出下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中,然后输入以下代码。 + + ```python + df.corr(numeric_only=True) + ``` + +1. 热度地图是有用的工具,可以快速可视化变量对之间关系的强度和方向。 它可以突出强的正相关性或负相关性,以及识别缺乏任何相关性的配对。 若要创建热度地图,请在笔记本中添加另一个代码单元,然后输入以下代码。 + + ```python + plt.figure(figsize=(15, 7)) + sns.heatmap(df.corr(numeric_only=True), annot=True, vmin=-1, vmax=1, cmap="Blues") + ``` + + `S1` 和 `S2` 变量具有 **0.89** 的高正关联性,表明它们在同一方向上移动。 当 `S1` 增加时,`S2` 也趋于增加,反之亦然。 此外,`S3` 与 `S4` 具有 **-0.73** 的强负关联性。 这表示,随着 `S3` 增加,`S4` 往往会减少。 + +## 保存笔记本并结束 Spark 会话 + +现在已经完成了对数据的探索,可以用有意义的名称保存笔记本并结束 Spark 会话。 + +1. 在笔记本菜单栏中,使用 ⚙️“设置”图标查看笔记本设置。 +2. 将笔记本的**名称**设置为“**为数据科学探索数据**”,然后关闭设置窗格。 +3. 在笔记本菜单上,选择“停止会话”以结束 Spark 会话。 + +## 清理资源 + +在本练习中,已创建并使用笔记本进行数据探索。 还执行了代码来计算摘要统计信息,并创建可视化效果以更好地理解数据中的模式和关系。 + +如果已完成模型和试验探索,可以删除为本练习创建的工作区。 + +1. 在左侧栏中,选择工作区的图标以查看其包含的所有项。 +2. 在工具栏上的“...”菜单中,选择“工作区设置” 。 +3. 在“其他”部分中,选择“删除此工作区” 。 diff --git a/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md b/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md new file mode 100644 index 0000000..a6cb61a --- /dev/null +++ b/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md @@ -0,0 +1,308 @@ +--- +lab: + title: 使用 Microsoft Fabric 中的数据整理器对数据进行预处理 + module: Preprocess data with Data Wrangler in Microsoft Fabric +--- + +# 使用 Microsoft Fabric 中的数据整理器对数据进行预处理 + +在本实验室中,你将了解如何使用 Microsoft Fabric 中的数据整理器对数据进行预处理,以及如何使用常见数据科学操作库生成代码。 + +完成本实验室大约需要 30 分钟。 + +> 注意:需要 Microsoft Fabric 许可证才能完成本练习。 有关如何启用免费 Fabric 试用版许可证的详细信息,请参阅 [Fabric 入门](https://learn.microsoft.com/fabric/get-started/fabric-trial)。 执行此操作需要 Microsoft 学校或工作帐户 。 如果没有,可以[注册 Microsoft Office 365 E3 或更高版本的试用版](https://www.microsoft.com/microsoft-365/business/compare-more-office-365-for-business-plans)。 + +## 创建工作区 + +在 Fabric 中处理数据之前,在已启用的 Fabric 试用版中创建工作区。 + +1. 登录到 [Microsoft Fabric](https://app.fabric.microsoft.com) (`https://app.fabric.microsoft.com`),然后选择 Power BI。 +2. 在左侧菜单栏中,选择“工作区”(图标类似于 🗇)。 +3. 新建一个工作区并为其指定名称,并选择包含 Fabric 容量(试用版、高级版或 Fabric)的许可模式 。 +4. 打开新工作区时,它应为空,如下所示: + + ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) + +## 创建笔记本 + +若要训练模型,可以创建笔记本。 笔记本提供了一个交互式环境,你可在其中编写和运行作为试验的(多种语言的)代码。 + +1. 在 Power BI 门户的左下角,选择“PowerBI”图标并切换到“数据科学”体验 。 + +1. 在“数据科学”主页中,创建新的笔记本 。 + + 几秒钟后,一个包含单个单元格的新笔记本将会打开。 笔记本由一个或多个单元格组成,这些单元格可以包含代码或 markdown(格式化文本) 。 + +1. 选择第一个单元格(当前是代码单元格),然后在其右上角的动态工具栏中,使用 M↓ 按钮将单元格转换为 markdown 单元格。 + + 当单元格更改为 markdown 单元格时,它包含的文本将会呈现。 + +1. 使用 🖉(“编辑”)按钮将单元格切换到编辑模式,然后删除内容并输入以下文本: + + ```text + # Perform data exploration for data science + + Use the code in this notebook to perform data exploration for data science. + ``` + +## 将数据加载到数据帧中 + +现在可以运行代码来获取数据了。 将使用 Azure 开放数据集中的 [OJ Sales 数据集](https://learn.microsoft.com/en-us/azure/open-datasets/dataset-oj-sales-simulated?tabs=azureml-opendatasets?azure-portal=true)。 加载数据后,将数据转换为 Pandas 数据帧,这是数据整理器支持的结构。 + +1. 在笔记本中,使用最新单元格下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中。 输入以下代码以将数据集加载到数据帧中。 + + ```python + # Azure storage access info for open dataset diabetes + blob_account_name = "azureopendatastorage" + blob_container_name = "ojsales-simulatedcontainer" + blob_relative_path = "oj_sales_data" + blob_sas_token = r"" # Blank since container is Anonymous access + + # Set Spark config to access blob storage + wasbs_path = f"wasbs://%s@%s.blob.core.windows.net/%s" % (blob_container_name, blob_account_name, blob_relative_path) + spark.conf.set("fs.azure.sas.%s.%s.blob.core.windows.net" % (blob_container_name, blob_account_name), blob_sas_token) + print("Remote blob path: " + wasbs_path) + + # Spark reads csv + df = spark.read.csv(wasbs_path, header=True) + ``` + +1. 使用单元格左侧的“▷ 运行单元格”按钮运行单元格。 或者,可以按键盘上的 `SHIFT` + `ENTER` 来运行单元格。 + + > 注意:由于这是你第一次在此会话中运行 Spark 代码,因此必须启动 Spark 池。 这意味着会话中的第一次运行可能需要一分钟左右才能完成。 后续运行速度会更快。 + +1. 使用单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + import pandas as pd + + df = df.toPandas() + df = df.sample(n=500, random_state=1) + + df['WeekStarting'] = pd.to_datetime(df['WeekStarting']) + df['Quantity'] = df['Quantity'].astype('int') + df['Advert'] = df['Advert'].astype('int') + df['Price'] = df['Price'].astype('float') + df['Revenue'] = df['Revenue'].astype('float') + + df = df.reset_index(drop=True) + df.head(4) + ``` + +1. 单元格命令完成后,查看单元格下方的输出,该输出应如下所示: + + ``` + WeekStarting Store Brand Quantity Advert Price Revenue + 0 1991-10-17 947 minute.maid 13306 1 2.42 32200.52 + 1 1992-03-26 1293 dominicks 18596 1 1.94 36076.24 + 2 1991-08-15 2278 dominicks 17457 1 2.14 37357.98 + 3 1992-09-03 2175 tropicana 9652 1 2.07 19979.64 + ``` + + 输出显示 OJ Sales 数据集的前四行。 + +## 查看摘要统计信息 + +由于已加载数据,下一步是使用数据整理器对其进行预处理。 预处理是所有机器学习工作流中的关键步骤。 它涉及清理数据,并将数据转换为可馈送到机器学习模型中的格式。 + +1. 在笔记本功能区中选择“数据”,然后选择“启动数据整理器”下拉列表 。 + +1. 选择 `df` 数据集。 当数据整理器启动时,它会在“摘要”面板中生成所显示的数据帧的描述性概述。 + +1. 选择“收入”特征,并观察此特征的数据分布。 + +1. 查看“摘要”侧面板详细信息,并观察统计信息值。 + + ![“数据整理器”页的屏幕截图,其中显示了摘要面板详细信息。](./Images/data-wrangler-summary.png) + + 可以从中得出哪些见解? 平均收入约为 33,459.54 美元,标准偏差为 8,032.23 美元 。 这表明收入值分散在平均值附近约 8,032.23 美元的范围内。 + +## 设置文本数据格式 + +现在,让我们对 **Brand** 特征应用一些转换。 + +1. 在“数据整理器”仪表板上,选择网格上的 `Brand` 特征。 + +1. 导航到“操作”面板,展开“查找和替换”,然后选择“查找和替换”。 + +1. 在“查找和替换”面板上,更改以下属性: + + - 旧值:"." + - 新值:" "(空格字符) + + 你可以在显示网格中看到自动预览的操作结果。 + +1. 选择**应用**。 + +1. 返回到“操作”面板,展开“格式” 。 + +1. 选择“将文本转换为大写”。 切换“将所有单词设为大写”切换开关,然后选择“应用” 。 + +1. 选择“将代码添加到笔记本”。 此外,还可以将转换后的数据集另存为 .csv 文件。 + + >注意:代码会自动复制到笔记本单元格,并可供使用。 + +1. 将第 10 行和第 11 行替换为代码 `df = clean_data(df)`,因为数据整理器中生成的代码不会覆盖原始数据帧。 最终块应如下所示: + + ```python + def clean_data(df): + # Replace all instances of "." with " " in column: 'Brand' + df['Brand'] = df['Brand'].str.replace(".", " ", case=False, regex=False) + # Convert text to capital case in column: 'Brand' + df['Brand'] = df['Brand'].str.title() + return df + + df = clean_data(df) + ``` + +1. 运行代码单元格,并检查 `Brand` 变量。 + + ```python + df['Brand'].unique() + ``` + + 结果显示 Minute Maid、Dominicks 和 Tropicana 。 + +你已了解如何使用数据整理器以图形方式操作文本数据并轻松生成代码。 + +## 应用独热编码转换 + +现在,让我们生成代码,在预处理步骤的过程中将独热编码转换应用于我们的数据。 为了使方案更加实用,我们首先生成一些示例数据。 这样,我们就能够模拟现实世界的情况,并为自身提供一个可行的特征。 + +1. 在 `df` 数据帧的顶部菜单中启动数据整理程序。 + +1. 选择网格上的 `Brand` 特征。 + +1. 在“操作”面板上,展开“公式”,然后选择“独热编码” 。 + +1. 在“独热编码”面板中,选择“应用”。 + + 导航到数据整理器显示网格的末尾。 请注意,它添加了三个新特征(`Brand_Dominicks`、`Brand_Minute Maid` 和 `Brand_Tropicana`),并移除了 `Brand` 特征。 + +1. 关闭数据整理器而不生成代码。 + +## 排序和筛选操作 + +假设我们需要查看特定商店的收入数据,然后对产品价格进行排序。 在以下步骤中,我们使用数据整理器筛选和分析 `df` 数据帧。 + +1. 针对 `df` 数据帧启动据整理器。 + +1. 在“操作”面板上,展开“排序和筛选”。 + +1. 选择“筛选”。 + +1. 在“筛选器”面板上,添加以下条件: + + - 目标列:Store + - 运算:等于 + - 值:1227 + +1. 选择“应用”,并注意数据整理器显示网格中的更改。 + +1. 选择“收入”特征,然后查看“摘要”侧面板详细信息 。 + + 可以从中得出哪些见解? 偏度为 -0.751,表示稍微向左倾斜(负倾斜)。 这意味着,分布的左尾部略长于右尾部。 换句话说,有许多时段的收入明显低于平均值。 + +1. 返回到“操作”面板,展开“排序和筛选”。 + +1. 选择“对值进行排序”。 + +1. 在“对值进行排序”面板上,选择以下属性: + + - 列名:Price + - 排序顺序:降序 + +1. 选择**应用**。 + + 商店 1227 的最高产品价格为 2.68 美元 。 只有几条记录时,可以更轻松地确定最高的产品价格,但请考虑处理数千个结果时的复杂性。 + +## 浏览和移除步骤 + +假设你犯了一个错误,需要移除你在上一步中创建的排序。 请按以下步骤来移除它: + +1. 导航到“清理步骤”面板。 + +1. 选择“对值进行排序”步骤。 + +1. 选择删除图标以将其移除。 + + ![“数据整理器”页面的屏幕截图,其中显示了“查找和替换”面板。](./Images/data-wrangler-delete.png) + + > 重要说明:网格视图和摘要仅限于当前步骤。 + + 请注意,更改将还原到上一步,即“筛选”步骤。 + +1. 关闭数据整理器而不生成代码。 + +## 聚合数据 + +假设我们需要了解每个品牌产生的平均收入。 在以下步骤中,我们使用数据整理器对 `df` 数据帧执行分组依据操作。 + +1. 针对 `df` 数据帧启动据整理器。 + +1. 返回到“操作”面板,选择“分组依据和聚合”。 + +1. 在“分组依据列:”属性上,选择 `Brand` 特征。 + +1. 选择“添加聚合”。 + +1. 在“要聚合的列”属性上,选择 `Revenue` 特征。 + +1. 为“聚合类型”属性选择“平均值” 。 + +1. 选择**应用**。 + +1. 选择“将代码添加到笔记本”。 + +1. 将 `Brand` 变量转换中的代码与 `clean_data(df)` 函数中的聚合步骤生成的代码组合在一起。 最终块应如下所示: + + ```python + def clean_data(df): + # Replace all instances of "." with " " in column: 'Brand' + df['Brand'] = df['Brand'].str.replace(".", " ", case=False, regex=False) + # Convert text to capital case in column: 'Brand' + df['Brand'] = df['Brand'].str.title() + + # Performed 1 aggregation grouped on column: 'Brand' + df = df.groupby(['Brand']).agg(Revenue_mean=('Revenue', 'mean')).reset_index() + + return df + + df = clean_data(df) + ``` + +1. 运行单元格代码。 + +1. 检查数据帧中的数据。 + + ```python + print(df) + ``` + + 结果: + ``` + Brand Revenue_mean + 0 Dominicks 33206.330958 + 1 Minute Maid 33532.999632 + 2 Tropicana 33637.863412 + ``` + +你为某些预处理操作生成了代码,并将其作为函数保存回笔记本,然后可以根据需要重复使用或修改该函数。 + +## 保存笔记本并结束 Spark 会话 + +对用于建模的数据完成预处理后,可以使用有意义的名称保存笔记本并结束 Spark 会话。 + +1. 在笔记本菜单栏中,使用 ⚙️“设置”图标查看笔记本设置。 +2. 将笔记本的“名称”设置为“使用数据整理器预处理数据”,然后关闭设置窗格 。 +3. 在笔记本菜单上,选择“停止会话”以结束 Spark 会话。 + +## 清理资源 + +在本练习中,你创建了一个笔记本,并使用了数据整理器来探索和预处理用于机器学习模型的数据。 + +如果你已完成对预处理步骤的探索,则可以删除为此练习创建的工作区。 + +1. 在左侧栏中,选择工作区的图标以查看其包含的所有项。 +2. 在工具栏上的“...”菜单中,选择“工作区设置” 。 +3. 在“其他”部分中,选择“删除此工作区” 。 diff --git a/Instructions/Labs/08b-data-science-preprocess.md b/Instructions/Labs/08b-data-science-preprocess.md new file mode 100644 index 0000000..c40c082 --- /dev/null +++ b/Instructions/Labs/08b-data-science-preprocess.md @@ -0,0 +1,243 @@ +--- +lab: + title: 使用 Microsoft Fabric 中的数据整理器对数据进行预处理 + module: Preprocess data with Data Wrangler in Microsoft Fabric +--- + +# 使用笔记本在 Microsoft Fabric 中训练模型 + +在本实验室中,你将了解如何使用 Microsoft Fabric 中的数据整理器对数据进行预处理,以及如何使用常见数据科学操作库生成代码。 + +完成本实验室大约需要 30 分钟。 + +> 注意:需要 Microsoft Fabric 许可证才能完成本练习。 有关如何启用免费 Fabric 试用版许可证的详细信息,请参阅 [Fabric 入门](https://learn.microsoft.com/fabric/get-started/fabric-trial)。 执行此操作需要 Microsoft 学校或工作帐户 。 如果没有,可以[注册 Microsoft Office 365 E3 或更高版本的试用版](https://www.microsoft.com/microsoft-365/business/compare-more-office-365-for-business-plans)。 + +## 创建工作区 + +在 Fabric 中处理数据之前,在已启用的 Fabric 试用版中创建工作区。 + +1. 登录到 [Microsoft Fabric](https://app.fabric.microsoft.com) (`https://app.fabric.microsoft.com`),然后选择 Power BI。 +2. 在左侧菜单栏中,选择“工作区”(图标类似于 🗇)。 +3. 新建一个工作区并为其指定名称,并选择包含 Fabric 容量(试用版、高级版或 Fabric)的许可模式 。 +4. 打开新工作区时,它应为空,如下所示: + + ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) + +## 创建湖屋并上传文件 + +有了工作区后,可在门户中切换到数据科学体验,并为要分析的数据文件创建一个数据湖屋。 + +1. 在 Power BI 门户左下角,选择 Power BI 图标并切换到“数据工程”体验 。 +1. 在“数据工程”主页中,使用所选名称创建一个新的湖屋 。 + + 大约一分钟后,将完成创建一个不包含表或文件的新湖屋 。 需要将一些数据引入数据湖屋进行分析。 可通过多种方法执行此操作,但在本练习中,只需将文本文件的文件夹下载并解压缩到本地计算机或实验室 VM(如果适用),然后将其上传到湖屋。 + +1. 待办事项:从 [https://raw.githubusercontent.com/MicrosoftLearning/dp-data/main/XXXXX.csv](https://raw.githubusercontent.com/MicrosoftLearning/dp-data/main/XXXXX.csv) 下载此练习的 `dominicks_OJ.csv` CSV 文件并将其保存。 + + +1. 返回到包含湖屋的 Web 浏览器标签页,在“湖屋”窗格的“文件”节点的“...”菜单中,依次选择“上传”和“上传文件”,然后将 dominicks_OJ.csv 文件从本地计算机(或实验室 VM,如适用)上传到湖屋 。 +6. 上传文件后,展开“文件”并验证 CSV 文件是否已上传。 + +## 创建笔记本 + +若要训练模型,可以创建笔记本。 笔记本提供了一个交互式环境,你可在其中编写和运行作为试验的(多种语言的)代码。 + +1. 在 Power BI 门户左下角,选择“数据工程”图标并切换到“数据科学”体验 。 + +1. 在“数据科学”主页中,创建新的笔记本 。 + + 几秒钟后,一个包含单个单元格的新笔记本将会打开。 笔记本由一个或多个单元格组成,这些单元格可以包含代码或 markdown(格式化文本) 。 + +1. 选择第一个单元格(当前是代码单元格),然后在其右上角的动态工具栏中,使用 M↓ 按钮将单元格转换为 markdown 单元格。 + + 当单元格更改为 markdown 单元格时,它包含的文本将会呈现。 + +1. 使用 🖉(“编辑”)按钮将单元格切换到编辑模式,然后删除内容并输入以下文本: + + ```text + # Train a machine learning model and track with MLflow + + Use the code in this notebook to train and track models. + ``` + +## 将数据加载到数据帧中 + +现在,你已准备好运行代码来准备数据和训练模型。 若要处理数据,需要使用数据帧。 Spark 中的数据帧类似于 Python 中的 Pandas 数据帧,提供一种用于处理行和列中的数据的通用结构。 + +1. 在“添加湖屋”窗格中,选择“添加”以添加湖屋。 +1. 选择“现有湖屋”,然后选择“添加” 。 +1. 选择在上一部分创建的湖屋。 +1. 展开“文件”文件夹,以便在笔记本编辑器旁边列出 CSV 文件。 +1. 在“churn.csv”的“...”菜单中,选择“加载数据” > “Pandas” 。 应在笔记本中添加包含以下代码的新代码单元格: + + ```python + import pandas as pd + df = pd.read_csv("/lakehouse/default/" + "Files/dominicks_OJ.csv") + display(df.head(5)) + ``` + + > 提示:可以隐藏左侧包含文件的窗格,方法是使用其 << 图标 。 这样做有助于专注于笔记本。 + +1. 使用单元格左侧的“▷ 运行单元格”按钮运行单元格。 + + > 注意:由于这是你第一次在此会话中运行 Spark 代码,因此必须启动 Spark 池。 这意味着会话中的第一次运行可能需要一分钟左右才能完成。 后续运行速度会更快。 + +## 查看摘要统计信息 + +当数据整理器启动时,它会在“摘要”面板中生成所显示的数据帧的描述性概述。 + +1. 选择顶部菜单中的“数据”,然后选择“数据整理器”下拉列表以浏览 `df` 数据集。 + + ![“启动数据整理器”选项的屏幕截图。](./Images/launch-data-wrangler.png) + +1. 选择“大型 HH”列,观察你能够多么轻松地确定此特征的数据分布。 + + ![“数据整理器”页的屏幕截图,其中显示了特定列的数据分布。](./Images/data-wrangler-distribution.png) + + 请注意,此特征遵循正态分布。 + +1. 检查“摘要”侧面板,并观察百分位范围。 + + ![“数据整理器”页的屏幕截图,其中显示了摘要面板详细信息。](./Images/data-wrangler-summary.png) + + 可以看到,大多数数据位于 **0.098** 和 **0.132** 之间,并且 50% 的数据值在该范围内。 + +## 设置文本数据格式 + +现在,让我们对 **Brand** 特征应用一些转换。 + +1. 在“数据整理器”页上,选择 `Brand` 特征。 + +1. 导航到“操作”面板,展开“查找和替换”,然后选择“查找和替换”。 + +1. 在“查找和替换”面板上,更改以下属性: + + - 旧值:"." + - 新值:" "(空格字符) + + ![“数据整理器”页面的屏幕截图,其中显示了“查找和替换”面板。](./Images/data-wrangler-find.png) + + 你可以在显示网格中看到自动预览的操作结果。 + +1. 选择**应用**。 + +1. 返回到“操作”面板,展开“格式” 。 + +1. 选择“将文本转换为大写”。 + +1. 在“将文本转换为大写”面板上,选择“应用”。 + +1. 选择“将代码添加到笔记本”。 此外,还可以将转换后的数据集另存为 .csv 文件。 + + 请注意,代码会自动复制到笔记本单元格,并可供使用。 + +1. 运行代码。 + +> 重要说明:生成的代码不会覆盖原始数据帧。 + +你已了解如何使用“数据整理器”操作轻松生成代码并操作文本数据。 + +## 应用独热编码器转换 + +现在,让我们生成代码,以应用独热编码器转换作为预处理步骤。 + +1. 选择顶部菜单中的“数据”,然后选择“数据整理器”下拉列表以浏览 `df` 数据集。 + +1. 返回到“操作”面板,展开“公式” 。 + +1. 选择“独热编码”。 + +1. 在“独热编码”面板中,选择“应用”。 + + 导航到数据整理器显示网格的末尾。 请注意,它添加了三个新特征,并移除了 `Brand` 特征。 + +1. 选择“将代码添加到笔记本”。 + +1. 运行代码。 + +## 排序和筛选操作 + +1. 选择顶部菜单中的“数据”,然后选择“数据整理器”下拉列表以浏览 `df` 数据集。 + +1. 在“操作”面板上,展开“排序和筛选”。 + +1. 选择“筛选”。 + +1. 在“筛选器”面板上,添加以下条件: + + - 目标列:Store + - 运算:等于 + - 值:2 + +1. 选择**应用**。 + + 请注意数据整理器显示网格中的更改。 + +1. 返回到“操作”面板,展开“排序和筛选”。 + +1. 选择“对值进行排序” + +1. 在“价格”面板上,添加以下条件: + + - 列名:Price + - 排序顺序:降序 + +1. 选择**应用**。 + + 请注意数据整理器显示网格中的更改。 + +## 聚合数据 + +1. 返回到“操作”面板,选择“分组依据和聚合”。 + +1. 在“分组依据列:”属性上,选择 `Store` 特征。 + +1. 选择“添加聚合”。 + +1. 在“要聚合的列”属性上,选择 `Quantity` 特征。 + +1. 为“聚合类型”属性选择“计数”。 + +1. 选择**应用**。 + + 请注意数据整理器显示网格中的更改。 + +## 浏览和移除步骤 + +假设你犯了一个错误,需要移除你在上一步中创建的聚合。 请按以下步骤来移除它: + +1. 展开“清理步骤”面板。 + +1. 选择“分组依据和聚合”步骤。 + +1. 选择删除图标以将其移除。 + + ![“数据整理器”页面的屏幕截图,其中显示了“查找和替换”面板。](./Images/data-wrangler-delete.png) + + > 重要说明:网格视图和摘要仅限于当前步骤。 + + 请注意,更改将还原到上一步,即“对值进行排序”步骤。 + +1. 选择“将代码添加到笔记本”。 + +1. 运行代码。 + +你为某些预处理操作生成了代码,并将其作为函数保存回笔记本,然后可以根据需要重复使用或修改该函数。 + +## 保存笔记本并结束 Spark 会话 + +对用于建模的数据完成预处理后,可以使用有意义的名称保存笔记本并结束 Spark 会话。 + +1. 在笔记本菜单栏中,使用 ⚙️“设置”图标查看笔记本设置。 +2. 将笔记本的“名称”设置为“使用数据整理器预处理数据”,然后关闭设置窗格 。 +3. 在笔记本菜单上,选择“停止会话”以结束 Spark 会话。 + +## 清理资源 + +在本练习中,你创建了一个笔记本,并使用了数据整理器来探索和预处理用于机器学习模型的数据。 + +如果你已完成对预处理步骤的探索,则可以删除为此练习创建的工作区。 + +1. 在左侧栏中,选择工作区的图标以查看其包含的所有项。 +2. 在工具栏上的“...”菜单中,选择“工作区设置” 。 +3. 在“其他”部分中,选择“删除此工作区” 。 diff --git a/Instructions/Labs/09-real-time-analytics-eventstream.md b/Instructions/Labs/09-real-time-analytics-eventstream.md index 752fa56..59f4ddf 100644 --- a/Instructions/Labs/09-real-time-analytics-eventstream.md +++ b/Instructions/Labs/09-real-time-analytics-eventstream.md @@ -3,9 +3,9 @@ lab: title: Microsoft Fabric 中的 Real-Time Analytics 入门 module: Get started with Real-Time Analytics in Microsoft Fabric --- -# 实时分析 (RTA) 中的 EventStream 入门 +# Real-Time Analytics (RTA) 中的 EventStream 入门 -事件流是 Microsoft Fabric 中的一项功能,可捕获、转换实时事件并将其路由到各种目标,并且无需编写代码。 可向事件流添加事件数据源、路由目标以及事件处理程序(如果需要转换)。 Microsoft Fabric 的 EventStore 是一个监视选项,用于维护群集中的事件,并提供一种方法来了解群集或工作负载在给定时间点的状态。 可以向 EventStore 服务查询可用于群集中的每个实体和实体类型的事件。 这意味着可在群集、节点、应用程序、服务、分区和分区副本等不同级别查询事件。 EventStore 服务还能够将群集中的事件相关联。 通过查看在同一时间从可能已相互影响的不同实体写入的事件,EventStore 服务能够将这些事件进行关联来帮助查明群集中发生各项活动的原因。 另一种监视和诊断 Microsoft Fabric 群集的方法是使用 EventFlow 聚合和收集事件。 +事件流是 Microsoft Fabric 中的一项功能,可捕获、转换实时事件并将其路由到各种目标,并且无需编写代码。 可向事件流添加事件数据源、路由目标以及事件处理程序(如果需要转换)。 Microsoft Fabric 的 EventStore 是一个监视选项,用于维护群集中的事件,并提供一种方法来了解群集或工作负载在给定时间点的状态。 可以向 EventStore 服务查询可用于群集中的每个实体和实体类型的事件。 这意味着可在群集、节点、应用程序、服务、分区和分区副本等不同级别查询事件。 EventStore 服务还能够将群集中的事件相关联。 通过查看在同一时间从可能已相互影响的不同实体写入的事件,EventStore 服务可以将这些事件进行关联来帮助查明群集中发生各项活动的原因。 另一种监视和诊断 Microsoft Fabric 群集的方法是使用 EventFlow 聚合和收集事件。 \ No newline at end of file +--> diff --git a/Instructions/Labs/11-data-activator.md b/Instructions/Labs/11-data-activator.md index cdc9897..978f888 100644 --- a/Instructions/Labs/11-data-activator.md +++ b/Instructions/Labs/11-data-activator.md @@ -23,69 +23,69 @@ Microsoft Fabric 中的 Data Activator 根据数据发生的情况采取操作 ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) -在本实验中,你将使用 Fabric 中的 Data Activator 创建一个 Reflex。 Data Activator 可以方便地提供一个示例数据集,供你用来探索 Data Activator 的功能。 可以使用此示例数据创建一个 Reflex,用于分析某些实时数据并创建一个触发器,以便在满足条件时发送电子邮件。 +在本实验中,你将使用 Fabric 中的 Data Activator 创建一个 reflex。 Data Activator 可以方便地提供一个示例数据集,供你用来探索 Data Activator 的功能。 你将使用此示例数据创建一个 reflex,用于分析某些实时数据并创建一个触发器,以便在满足条件时发送电子邮件。 -> 注意:Data Activator 示例进程会在后台生成一些随机数据。 创建的条件和筛选器越复杂,没有任何事件满足触发条件和筛选器的可能性就越大。 如果图形中未出现任何数据,请等待几分钟再刷新页面。 也就是说,你无需等待数据显示在图形中即可继续实验。 +> 注意:Data Activator 示例进程会在后台生成一些随机数据。 条件和筛选器越复杂,触发它们所需的时间就越长。 如果图形中未出现任何数据,请等待几分钟再刷新页面。 也就是说,你无需等待数据显示在图形中即可继续实验。 ## 方案 -在此场景中,你是一家销售和交付各种产品的公司的数据分析师。 你负责分析所有发往 Redmond 市的产品的发货和销售数据。 你想要创建一个 Reflex 用于监视要交付的包裹。 你要交付的一个产品类别是医疗处方药,此类产品在运输过程中需要在特定的温度下冷藏。 你想要创建一个 Reflex,以便在处方药的包裹温度高于或低于特定阈值时向发货部门发送电子邮件。 理想温度应介于 33 度至 41 度之间。 由于 Reflex 事件已包含类似的触发器,因此你专门为发往 Redmond 市的包裹创建一个触发器。 现在就开始吧! +在此场景中,你是一家销售和交付各种产品的公司的数据分析师。 你负责分析所有发往 Redmond 市的产品的发货和销售数据。 你想要创建一个 reflex 用于监视要交付的包裹。 你要交付的一个产品类别是医疗处方药,此类产品在运输过程中需要在特定的温度下冷藏。 你想要创建一个 reflex,以便在处方药的包裹温度高于或低于特定阈值时向发货部门发送电子邮件。 理想温度应介于 33 度至 41 度之间。 由于 reflex 事件已包含类似的触发器,因此你专门为发往 Redmond 市的包裹创建一个触发器。 现在就开始吧! -## 创建 Reflex +## 创建 reflex 1. 在 Microsoft Fabric 体验门户中选择 Data Activator 体验,方法是首先选择屏幕左下角的当前 Fabric 体验图标,然后从菜单中选择“Data Activator” 。 例如,在以下屏幕截图中,当前 Fabric 体验是“Power BI”。 ![选择 Data Activator 体验的屏幕截图。](./Images/data-activator-select-experience.png) -1. 你现在应该位于 Data Activator 主页屏幕中。 右下角的 Fabric Experience 图标也变成了 Data Activator 图标。 让我们通过选择“Reflex (预览版)”按钮创建新的 Reflex。 +1. 你现在应该位于 Data Activator 主页屏幕中。 右下角的 Fabric Experience 图标也变成了 Data Activator 图标。 让我们通过选择“reflex (预览版)”按钮创建新的 reflex。 ![Data Activator 主页屏幕的屏幕截图。](./Images/data-activator-home-screen.png) -1. 在现实生产环境中,你会使用自己的数据。 但在本实验中,你将使用 Data Activator 提供的示例数据。 选择“使用示例数据”按钮完成 Reflex 的创建。 +1. 在现实生产环境中,你会使用自己的数据。 但在本实验中,你将使用 Data Activator 提供的示例数据。 选择“使用示例数据”按钮完成 reflex 的创建。 ![Data Activator“获取数据”屏幕的屏幕截图。](./Images/data-activator-get-started.png) -1. 默认情况下,Data Activator 会创建名为“Reflex YYYY-MM-DD hh:mm:ss”的 Reflex。 由于工作区中可能有多个 Reflex,因此应将默认 Reflex 的名称更改为更具描述性的名称。 在本示例中,请选择左上角当前 Reflex 名称旁边的下拉列表,然后将名称更改为“Contoso Shipping Reflex”。 +1. 默认情况下,Data Activator 会创建名为“Reflex YYYY-MM-DD hh:mm:ss”的 Reflex。 由于工作区中可能有多个 reflex,因此应将默认 Reflex 的名称更改为更具描述性的名称。 在本示例中,请选择左上角当前 Reflex 名称旁边的下拉列表,然后将名称更改为“Contoso Shipping Reflex”。 - ![Data Activator Reflex 主页屏幕的屏幕截图。](./Images/data-activator-reflex-home-screen.png) + ![Data Activator reflex 主页屏幕的屏幕截图。](./Images/data-activator-reflex-home-screen.png) -现已创建 Reflex,接下来可以开始为其添加触发器和操作。 +现已创建 reflex,接下来可以开始为其添加触发器和操作。 -## 熟悉 Reflex 主页屏幕 +## 熟悉 reflex 主页屏幕 -Reflex 的主页屏幕分为两个部分:“设计”模式和“数据”模式 。 可以通过选择屏幕左下角的相应选项卡来选择模式。 在“设计”模式选项卡中,可为对象定义触发器、属性和事件。 在“数据”模式选项卡中,可以添加数据源和查看 Reflex 处理的数据。 让我们看看“设计”模式选项卡,当你创建 Reflex 时,该选项卡默认应会打开。 +reflex 的主页屏幕分为两个部分:“设计”模式和“数据”模式 。 可以通过选择屏幕左下角的相应选项卡来选择模式。 在“设计”模式选项卡中,可为对象定义触发器、属性和事件。 在“数据”模式选项卡中,可以添加数据源和查看 reflex 处理的数据。 让我们看看“设计”模式选项卡,当你创建 reflex 时,该选项卡默认应会打开。 ### 设计模式 如果你当前未进入“设计”模式,请选择屏幕左下角的“设计”选项卡。 -![Data Activator Reflex 设计模式的屏幕截图。](./Images/data-activator-design-tab.png) +![Data Activator reflex 设计模式的屏幕截图。](./Images/data-activator-design-tab.png) 若要熟悉“设计”模式,请选择屏幕的不同部分:触发器、属性和事件。 以下各部分将更详细地介绍每个屏幕部分。 ### 数据模式 -1. 如果你当前未进入“数据”模式,请选择屏幕左下角的“数据”选项卡。 在现实示例中,你可以在此处通过 EventStreams 和 Power BI 视觉对象添加自己的数据源。 对于本实验,你将使用 Data Activator 提供的示例数据。 对于 Data Activator 提供的示例数据,已经为其设置了三个用于监视包裹交付状态的 EventStreams。 +如果你当前未进入“数据”模式,请选择屏幕左下角的“数据”选项卡。 在现实示例中,你可以在此处通过 EventStreams 和 Power BI 视觉对象添加自己的数据源。 对于本实验,你将使用 Data Activator 提供的示例数据。 对于此示例,已经为其设置了三个用于监视包裹交付状态的 EventStreams。 -![Data Activator Reflex 数据模式的屏幕截图。](./Images/data-activator-data-tab.png) +![Data Activator reflex 数据模式的屏幕截图。](./Images/data-activator-data-tab.png) -1. 选择每个不同的事件以查看该事件处理的数据。 +选择每个不同的事件,并观察流中使用的数据。 -![Data Activator Reflex 数据模式事件的屏幕截图。](./Images/data-activator-get-data-tab-event-2.png) +![Data Activator reflex 数据模式事件的屏幕截图。](./Images/data-activator-get-data-tab-event-2.png) -现在可以为 Reflex 添加触发器,但首先让我们创建一个新对象。 +现在可以为 reflex 添加触发器,但首先让我们创建一个新对象。 ## 创建对象 -在现实方案中,可能不需要为此 Reflex 创建新对象,因为 Data Activator 示例已包含一个名为 Package 的对象。 但对于本实验,我们将创建一个新对象来演示创建方法。 让我们创建一个名为 Redmond Packages 的新对象。 +在现实方案中,可能不需要为此 reflex 创建新对象,因为 Data Activator 示例已包含一个名为 Package 的对象。 但对于本实验,我们将创建一个新对象来演示创建方法。 让我们创建一个名为 Redmond Packages 的新对象。 1. 如果你当前未进入“数据”模式,请选择屏幕左下角的“数据”选项卡。 -1. 选择“包裹运输中”事件。 请密切关注 PackageId、Temperature、ColdChainType、City 和 SpecialCare 列中的值 。 可以使用这些列来创建触发器。 +1. 选择“包裹运输中”事件。 请密切关注 PackageId、Temperature、ColdChainType、City 和 SpecialCare 列中的值 。 你将使用这些列来创建触发器。 1. 如果“分配数据”对话框尚未在右侧打开,请选择屏幕右侧的“分配数据”按钮。 - ![Data Activator Reflex 数据模式 - 分配数据按钮的屏幕截图。](./Images/data-activator-data-tab-assign-data-button.png) + ![Data Activator reflex 数据模式 - 分配数据按钮的屏幕截图。](./Images/data-activator-data-tab-assign-data-button.png) 1. 在“分配数据”对话框中,选择“分配到新对象”选项卡并输入以下值: @@ -93,45 +93,47 @@ Reflex 的主页屏幕分为两个部分:“设计”模式和“数据”模 - 分配键列:PackageId - 分配属性:City、ColdChainType、SpecialCare、Temperature - ![Data Activator Reflex 数据模式 - 分配数据对话框的屏幕截图。](./Images/data-activator-data-tab-assign-data.png) + ![Data Activator reflex 数据模式 - 分配数据对话框的屏幕截图。](./Images/data-activator-data-tab-assign-data.png) 1. 选择“保存”,然后选择“保存并进入设计模式” 。 1. 现在你应会返回“设计”模式。 现在已添加了一个名为 Redmond Packages 的新对象。 选择此新对象,展开其“事件”,然后选择“包裹运输中”事件。 - ![Data Activator Reflex 设计模式和新对象的屏幕截图。](./Images/data-activator-design-tab-new-object.png) + ![Data Activator reflex 设计模式和新对象的屏幕截图。](./Images/data-activator-design-tab-new-object.png) 现在让我们创建触发器。 ## 创建触发器 -让我们回顾一下你希望触发器执行的操作:你希望创建一个 Reflex,以便在处方药包裹的温度高于或低于特定阈值时向发货部门发送电子邮件。理想温度应该介于 33 度到 41 度之间。由于 Reflex 事件已包含类似的触发器,因此你专门为发往 Redmond 市的包裹创建一个触发器。 +让我们回顾一下你希望触发器执行的操作:你希望创建一个 reflex,以便在处方药包裹的温度高于或低于特定阈值时向发货部门发送电子邮件。理想温度应该介于 33 度到 41 度之间。由于 reflex 事件已包含类似的触发器,因此你将专门为发往 Redmond 市的包裹创建一个触发器。 -1. 在顶部菜单中选择“新建触发器”按钮。 已创建一个默认名称为“无标题”的新触发器,现在请将名称更改为“药物温度超出范围”以更好地定义触发器。 +1. 在“Redmond Packages”对象的“包裹运输中”事件中,选择顶部菜单上的“新建触发器”按钮。 已创建一个默认名称为“无标题”的新触发器,现在请将名称更改为“药物温度超出范围”以更好地定义触发器。 - ![Data Activator Reflex 设计 - 创建新触发器的屏幕截图。](./Images/data-activator-trigger-new.png) + ![Data Activator reflex 设计 - 创建新触发器的屏幕截图。](./Images/data-activator-trigger-new.png) -1. 现在让我们选择用于触发 Reflex 的属性或事件列。 由于在创建对象时创建了多个属性,因此请选择“现有属性”按钮并选择“温度”属性。 选择此属性后,应会返回一个包含示例历史温度值的图形。 +1. 现在让我们选择用于触发 reflex 的属性或事件列。 由于在创建对象时创建了多个属性,因此请选择“现有属性”按钮并选择“温度”属性。 - ![Data Activator Reflex 设计 - 选择属性的屏幕截图。](./Images/data-activator-trigger-select-property.png) + ![Data Activator reflex 设计 - 选择属性的屏幕截图。](./Images/data-activator-trigger-select-property.png) + + 选择此属性后,应会返回一个包含示例历史温度值的图形。 ![Data Activator 历史值属性图形的屏幕截图。](./Images/data-activator-trigger-property-sample-graph.png) -1. 现在需要确定你想从此属性触发哪种类型的条件。 在本例中,你希望在温度高于 41 度或低于 33 度时触发 Reflex。 由于我们要查找数字范围,因此请选择“数字”按钮并选择“退出范围”条件 。 +1. 现在需要确定你想从此属性触发哪种类型的条件。 在本例中,你希望在温度高于 41 度或低于 33 度时触发 reflex。 由于我们要查找数字范围,因此请选择“数字”按钮并选择“退出范围”条件 。 - ![Data Activator Reflex 设计 - 选择条件类型的屏幕截图。](./Images/data-activator-trigger-select-condition-type.png) + ![Data Activator reflex 设计 - 选择条件类型的屏幕截图。](./Images/data-activator-trigger-select-condition-type.png) 1. 现在需要输入条件的值。 输入 33 和 44 作为范围值。 由于选择了“退出数字范围”条件,因此当温度低于 33 度或高于 44 度时,触发器应会触发 。 - ![Data Activator Reflex 设计 - 输入条件值的屏幕截图。](./Images/data-activator-trigger-select-condition-define.png) + ![Data Activator reflex 设计 - 输入条件值的屏幕截图。](./Images/data-activator-trigger-select-condition-define.png) -1. 到目前为止,你定义了触发触发器所依据的属性和条件,但这种定义尚不包括全部所需的参数。 你仍然需要确保触发器仅针对城市“Redmond”和特殊护理类型“医疗”触发。 让我们继续为这些条件添加几个筛选器。 选择“添加筛选器”按钮,然后选择“City”属性。 输入 Redmond 作为值。 然后,再次选择“添加筛选器”按钮并选择“SpecialCare”属性。 输入 Medicine 作为值。 +1. 到目前为止,你定义了触发触发器所依据的属性和条件,但尚未包括你需要的所有参数。 你仍然需要确保触发器仅针对城市“Redmond”和特殊护理类型“医疗”触发。 让我们继续为这些条件添加几个筛选器。 选择“添加筛选器”按钮,将属性设置为“City”,将关系设置为“等于”,然后输入“Redmond”作为值。 然后,添加包含“SpecialCare”属性的一个新筛选器,将关系设置为“等于”,然后输入“Medicine”作为值。 - ![Data Activator Reflex 设计 - 添加筛选器的屏幕截图。](./Images/data-activator-trigger-select-condition-add-filter.png) + ![Data Activator reflex 设计 - 添加筛选器的屏幕截图。](./Images/data-activator-trigger-select-condition-add-filter.png) -1. 让我们再添加一个筛选器,以确保药物得到冷藏。 选择“添加筛选器”按钮,然后选择“ColdChainType”属性。 输入 Refrigerated 作为值。 +1. 让我们再添加一个筛选器,以确保药物得到冷藏。 选择“添加筛选器”按钮,设置“ColdChainType”属性,将关系设置为“等于”,然后输入“Refrigerated”作为值。 - ![Data Activator Reflex 设计 - 添加筛选器的屏幕截图。](./Images/data-activator-trigger-select-condition-add-filter-additional.png) + ![Data Activator reflex 设计 - 添加筛选器的屏幕截图。](./Images/data-activator-trigger-select-condition-add-filter-additional.png) 1. 即将完成! 只需定义触发器触发时要执行的操作。 在本例中,你希望向发货部门发送电子邮件。 选择“电子邮件”按钮。 @@ -144,13 +146,13 @@ Reflex 的主页屏幕分为两个部分:“设计”模式和“数据”模 - 标题:温度太高或太低 - 附加信息:从复选框列表中选择“Temperature”属性。 - ![Data Activator Reflex 设计 - 启动触发器的屏幕截图。](./Images/data-activator-trigger-start.png) + ![Data Activator 定义操作的屏幕截图。](./Images/data-activator-trigger-define-action.png) 1. 选择“保存”,然后从顶部菜单中选择“启动” 。 现已在 Data Activator 中创建并启动了一个触发器。 -## 更新触发器 +## 更新和停止触发器 此触发器的唯一问题是,虽然触发器发送了包含温度信息的电子邮件,但触发器并未发送包裹的 PackageId。 让我们继续更新触发器以包含 PackageId。 @@ -172,17 +174,13 @@ Reflex 的主页屏幕分为两个部分:“设计”模式和“数据”模 ![Data Activator - 更新触发器的屏幕截图。](./Images/data-activator-trigger-updated.png) -触发器现已更新。 - -## 停止触发器 - -若要停止触发器,请从顶部菜单中选择“停止”按钮。 +1. 从顶部菜单中选择“停止”按钮以停止触发器。 ## 清理资源 -在本练习中,你在 Data Activator 中创建了一个带有触发器的 Reflex。 现在,你应该熟悉了 Data Activator 界面,并知道如何创建 Reflex 及其对象、触发器和属性。 +在本练习中,你在 Data Activator 中创建了一个带有触发器的 reflex。 现在,你应该熟悉了 Data Activator 界面,并知道如何创建 reflex 及其对象、触发器和属性。 -如果你已完成 Data Activator Reflex 的探索,可以删除为本练习创建的工作区。 +如果你已完成 Data Activator reflex 的探索,可以删除为本练习创建的工作区。 1. 在左侧栏中,选择工作区的图标以查看其包含的所有项。 2. 在工具栏上的“...”菜单中,选择“工作区设置” 。 diff --git a/Instructions/Labs/Images/data-activator-data-tab-assign-data-button.png b/Instructions/Labs/Images/data-activator-data-tab-assign-data-button.png new file mode 100644 index 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