试用 BigQuery DataFrames

在本快速入门中，您可以使用 BigQuery 笔记本中的 BigQuery DataFrames API 执行以下分析和机器学习 (ML) 任务：

• 创建基于 bigquery-public-data.ml_datasets.penguins 公共数据集的 DataFrame。
• 计算企鹅的平均身体质量。
• 创建一个线性回归模型。
• 基于企鹅数据的一部分创建 DataFrame，以将其用作训练数据。
• 清理训练数据。
• 设置模型参数。
• 拟合模型。
• 对模型进行评分。

准备工作

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

1. Verify that billing is enabled for your Google Cloud project.

2. 验证已启用 BigQuery API。

   启用 API

   如果您创建了一个新项目，则系统会自动启用 BigQuery API。

所需权限

如需创建和运行笔记本，您需要以下 Identity and Access Management (IAM) 角色：

• BigQuery User (roles/bigquery.user)
• Notebook Runtime User (roles/aiplatform.notebookRuntimeUser)
• Code Creator (roles/dataform.codeCreator)

创建笔记本

按照通过 BigQuery 编辑器创建笔记本中的说明创建新的笔记本。

试用 BigQuery DataFrames

请按照以下步骤试用 BigQuery DataFrames：

1. 在笔记本中创建新的代码单元。

2. 在代码单元中添加以下代码：

   import bigframes.pandas as bpd
   
   # Set BigQuery DataFrames options
   # Note: The project option is not required in all environments.
   # On BigQuery Studio, the project ID is automatically detected.
   bpd.options.bigquery.project = your_gcp_project_id
   
   # Use "partial" ordering mode to generate more efficient queries, but the
   # order of the rows in DataFrames may not be deterministic if you have not
   # explictly sorted it. Some operations that depend on the order, such as
   # head() will not function until you explictly order the DataFrame. Set the
   # ordering mode to "strict" (default) for more pandas compatibility.
   bpd.options.bigquery.ordering_mode = "partial"
   
   # Create a DataFrame from a BigQuery table
   query_or_table = "bigquery-public-data.ml_datasets.penguins"
   df = bpd.read_gbq(query_or_table)
   
   # Efficiently preview the results using the .peek() method.
   df.peek()
   

3. 修改 bpd.options.bigquery.project = your_gcp_project_id 行以指定您的 Google Cloud 项目 ID。例如 bpd.options.bigquery.project = "myProjectID"。

4. 运行该代码单元。

   该代码会返回一个包含企鹅数据的 DataFrame 对象。

5. 在笔记本中创建一个新的代码单元，并添加以下代码：

   # Use the DataFrame just as you would a pandas DataFrame, but calculations
   # happen in the BigQuery query engine instead of the local system.
   average_body_mass = df["body_mass_g"].mean()
   print(f"average_body_mass: {average_body_mass}")
   

6. 运行该代码单元。

   该代码会计算企鹅的平均体重，并将其输出到Google Cloud 控制台。

7. 在笔记本中创建一个新的代码单元，并添加以下代码：

   # Create the Linear Regression model
   from bigframes.ml.linear_model import LinearRegression
   
   # Filter down to the data we want to analyze
   adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"]
   
   # Drop the columns we don't care about
   adelie_data = adelie_data.drop(columns=["species"])
   
   # Drop rows with nulls to get our training data
   training_data = adelie_data.dropna()
   
   # Pick feature columns and label column
   X = training_data[
       [
           "island",
           "culmen_length_mm",
           "culmen_depth_mm",
           "flipper_length_mm",
           "sex",
       ]
   ]
   y = training_data[["body_mass_g"]]
   
   model = LinearRegression(fit_intercept=False)
   model.fit(X, y)
   model.score(X, y)
   

8. 运行该代码单元。

   该代码会返回模型的评估指标。

清理

为了避免产生费用，最简单的方法是删除您为本教程创建的项目。

要删除项目，请执行以下操作：

注意：删除项目会产生以下影响：

• 项目中的所有内容都会被删除。如果您将现有项目用于本文档中的任务，则删除该项目后，还将删除您已在该项目中完成的任何其他工作。
• 自定义项目 ID 丢失。创建此项目时，您可能创建了要在将来使用的自定义项目 ID。要保留使用该项目 ID 的网址（例如 appspot.com 网址），请删除项目内的所选资源，而不是删除整个项目。

如果您打算探索多个架构、教程或快速入门，则重复使用项目可以帮助您避免超出项目配额限制。

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

后续步骤

• 继续了解如何使用 BigQuery DataFrames。
• 了解如何使用 BigQuery DataFrames 直观呈现图表。
• 了解如何使用 BigQuery DataFrames 笔记本。