Power BI:如何在 Power Query 编辑器中将 Python 与多个表一起使用?

Power BI: How to use Python with multiple tables in the Power Query Editor?(Power BI:如何在 Power Query 编辑器中将 Python 与多个表一起使用?)
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如何使用 Python 脚本创建一个新表,该脚本使用两个现有表作为输入?例如,通过使用




这里有两个可以存储为 CSV 文件并使用 Home > 加载的表格.获取数据 >文本/CSV

表 1





这是针对 R 脚本描述的相同挑战



1. 使用 Get Data 将表格作为 CSV 文件加载到 Power BI Desktop 中.







7. 这将在 Queries 下插入一个名为 Table3 的空表,这正是我们想要的:

8.进入Transform标签并点击Run Python Script:

9. 这将打开 Run Python Script 编辑器.您可以从这里开始编写脚本,但这会使接下来的步骤变得不必要地复杂.所以什么都不做,只点击OK:

10. 在公式栏中,您将看到公式 = Python.Execute("# 'dataset' 保存此脚本的输入数据#(lf)",[dataset=#"更改类型"]).请注意,您在 Applied Steps 下有一个名为 Run Python Script 的新步骤:

11. 上面的截图中有几个有趣的细节,但首先我们要分解函数 = Python.Execute("# 'dataset' 的参数此脚本的输入数据#(lf)",[dataset=#"Changed Type"]).

"# 'dataset'" 部分保存此脚本的输入数据#(lf)" 只是插入您可以在 Python 脚本编辑器中看到的注释. 所以它并不重要,但你也不能把它留空.我喜欢使用更短的东西,比如 "# Python:".

[dataset=#"Changed Type"]部分是一个指针,指向处于Changed TypeTable3>.因此,如果您在插入 Python 脚本之前所做的最后一件事不是更改数据类型,那么这部分看起来会有所不同.然后使用 dataset 作为 pandas 数据框,可以在您的 python 脚本中使用该表.考虑到这一点,我们可以对公式进行一些非常有用的更改:

12. 将公式栏更改为 = Python.Execute("# Python:",[df1=Table1, df2=Table2]) 并点击 输入.这将使 Table1Table2 可用于您的 Python 脚本作为两个分别名为 df1df2 的 pandas 数据帧.

13.点击Applied StepsRun Python script旁边的齿轮(还是一朵花?)图标:

14. 插入以下代码段:


将 pandas 导入为 pddf3 = pd.merge(df1, df2, how = 'left', on = ['Date'])df3['Value3'] = df1['Value1']*df2['Value2']

这将在 Date 列 上连接 df1df2,并插入一个名为 Value3 的新计算列.不太花哨,但通过此设置,您可以任何在 Power BI 世界中使用您的数据并借助 Python 的强大功能.


您将看到 df3 在蓝色方块中的输入数据框 df1df2 下列出.如果您已在 Python 脚本中指定任何其他数据框作为计算步骤,它们也会在此处列出.要将其变成 Power BI 可访问的表格,只需单击绿色箭头所示的 Table.

16. 就是这样:

请注意,Date 列 的数据类型默认设置为Date,但您可以将其更改为Text,如前所述.

点击首页>关闭并应用 退出 Power Query 编辑器 并返回到 Power BI Desktop 中所有开始的位置.

How can you create a new table with a Python script that uses two existing tables as input? For example by performing a left join using pandas merge?

Some details:

Using Home > Edit queries you can utilize Python under Transform > Run Python Script. This opens a Run Python Script dialog box where your're told that '#dataset' holds the input data for this script. And you'll find the same phrase if you just click OK and look at the formula bar:

= Python.Execute("# 'dataset' holds the input data for this script#(lf)",[dataset=#"Changed Type"])

This also adds a new step under Applied Steps called Run Python script where you can edit the Python script by clicking the gear symbol on the right:

How can you change that setup to reference more than one table?

Sample data

Here are two tables that can be stored as CSV files and loaded using Home > Get Data > Text/CSV






This is the same challenge that has been described for R scripts here. That setup should work for Python too. However, I've found that that approach has one drawback: It stores the new joined or calculated table as an edited version of one of the previous tables. The following suggestion will demonstrate how you can produce a completely new calculated table without altering the input tables (except changing the data type of the Date columns from Date to Text because of this.)

Short answer:

In the Power Query editor, follow these steps:

  1. Change the data type of the Date columns in both columns to Text.

  2. Click Enter Data. Only click OK.

  3. Activate the new Table3 and use Transform > Run Python Script. Only click OK.

  4. Activate the formula bar and replace what's in it with = Python.Execute("# Python:",[df1=Table1, df2=Table2]). Click Enter.

  5. If you're prompted to do so, click Edit Permission and Run in the next step.

  6. Under Applied Steps, in the new step named Run Python Script, click the gear icon to open the Run Python Script editor.

  7. Insert the snippet below and click OK.


import pandas as pd
df3 = pd.merge(df1, df2, how = 'left', on = ['Date'])
df3['Value3'] = df1['Value1']*df2['Value2']

Next to df3, click Table, and that's it:

The details:

The list above will have to be followed very carefully to get things working. So here are all of the dirty little details:

1. Load the tables as CSV files in Power BI Desktop using Get Data.

2. Click Edit Queries.

3. In Table1, Click the symbol next to the Date column, select Text and click Replace Current

4. Do the same for Table2

5. On the Home tab, click Enter Data

6. In the appearing box, do nothing else than clicking OK.

7. This will insert an empty table named Table3 under Queries, and that's exactly what we want:

8. Go to the Transform tab and click Run Python Script:

9. This opens the Run Python Script editor. And you can start writing you scripts right here, but that will make things unnecessarily complicated in the next steps. So do nothing but click OK:

10. In the formula bar you will se the formula = Python.Execute("# 'dataset' holds the input data for this script#(lf)",[dataset=#"Changed Type"]). And notice that you've got a new step under Applied Steps named Run Python Script:

11. There are several interesting details in the screenshot above, but first we're going to break down the arguments of the function = Python.Execute("# 'dataset' holds the input data for this script#(lf)",[dataset=#"Changed Type"]).

The part "# 'dataset'" holds the input data for this script#(lf)" simply inserts the comment that you can see in the Python Script Editor. So it's not important, but you can't just leave it blank either. I like to use something shorter like "# Python:".

The part [dataset=#"Changed Type"] is a pointer to the empty Table3 in the state that it is under Changed Type. So if the last thing that you do before inserting a Python Script is something else than changing data types, this part will look different. The table is then made available in your python script using dataset as a pandas data frame. With this in mind, we can make som very useful changes to the formula:

12. Change the formula bar to = Python.Execute("# Python:",[df1=Table1, df2=Table2]) and hit Enter. This will make Table1 and Table2 available for your Python scripts as two pandas dataframes named df1 and df2, respectively.

13. Click the gear (or is it a flower?) icon next to Run Python script under Applied Steps:

14. Insert the following snippet:


import pandas as pd
df3 = pd.merge(df1, df2, how = 'left', on = ['Date'])
df3['Value3'] = df1['Value1']*df2['Value2']

This will join df1 and df2 on the Date column, and insert a new calculated column named Value3. Not too fancy, but with this setup you can do anything you want with your data in the world of Power BI and with the power of Python.

15. Click OK and you'll se this:

You'll see df3 listed under the input dataframes df1 and df2 in the blue square. If you've assigned any other dataframes as a step in your calculations in the Python script, they will be listed here too. In order to turn it into an accessible table for Power BI, just click Table as indicated by the green arrow.

16. And that's it:

Note that the data type of the Date column is set to Date by default, but you can change that to Text as explained earlier.

Click Home > Close&Apply to exit the Power Query Editor and go back to where it all started in Power BI Desktop.

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