Python script
Power Query M language code is an essential tool for connecting and transforming data in Power BI. It provides a vast range of functionalities that allow users to clean, transform, and analyze data sources easily. One of the most significant advantages of Power Query M language code is its ability to integrate with Python scripts. This feature enables users to connect to Python data sources, import and transform data, and visualize it in Power BI dashboards.
In this article, we will explore the Power Query M language code for connecting to Python script data sources from inside Power BI. We will look at the steps involved in setting up the Python environment in Power BI, importing Python modules, and executing Python scripts using Power Query M language.
Setting up the Python Environment in Power BI
Before we can start using Python scripts in Power BI, we need to set up the Python environment. The following steps outline the setup process.
Step 1: Install Python
First, we need to install Python on our computer. Go to the Python website and download the latest version of Python suitable for your operating system. Follow the installation wizard to complete the installation process.
Step 2: Install Python Libraries
Next, we need to install the required Python libraries that we will use in our scripts. The easiest way to install Python libraries is by using the pip package manager. Open a command prompt and enter the following command.
pip install pandas numpy
This command installs the pandas and numpy libraries, which are commonly used in data analysis and manipulation.
Step 3: Configure Python Environment in Power BI
Open Power BI and go to the File menu. Click on Options and settings and select Options. In the Options dialog box, select Python scripting and click on the “Python scripting settings” button. In the Python scripting settings dialog box, select the “Python home directory” option and enter the path to the Python installation directory. Click on OK to save the changes.
Importing Python Modules in Power Query M Language
Once we have set up the Python environment in Power BI, we can start importing Python modules in Power Query M language. The following steps show how to import Python modules.
Step 1: Open Query Editor
Open Power BI and go to the Home tab. Click on the “Edit Queries” button to open the Query Editor.
Step 2: Create a Blank Query
Click on the “New Source” button and select “Blank Query” from the drop-down menu.
Step 3: Open Advanced Editor
Click on the “Advanced Editor” button to open the Power Query M language editor.
Step 4: Import Python Module
To import a Python module, we use the following code.
let
Source = Python.Execute(“#(lf)import pandas as pd #(lf)import numpy as np #(lf)”),
LoadTable = Source{0}[Column1]
in
LoadTable
This code imports the pandas and numpy modules using the `Python.Execute` function. The `LoadTable` variable is used to load the results of the Python script.
Executing Python scripts in Power Query M Language
With the Python modules imported, we can start executing Python scripts in Power Query M language. The following steps show how to execute Python scripts.
Step 1: Create a Python script
Create a Python script that performs the required data transformation. For example, the following script reads a CSV file and returns a Pandas DataFrame.
import pandas as pd
def load_data(file_path):
data = pd.read_csv(file_path)
return data
Step 2: Import Python script
To import a Python script, we use the following code.
let
Source = Python.Execute(“#(lf)import pandas as pd #(lf)import numpy as np #(lf)exec(open(‘script.py’).read()) #(lf)data = load_data(‘data.csv’) #(lf)”),
LoadTable = Source{0}[Column1]
in
LoadTable
This code imports the pandas and numpy modules, executes the `script.py` script, and loads the data from the CSV file using the `load_data` function.
Conclusion
Power Query M language code provides a powerful tool for connecting to Python script data sources from inside Power BI. With the ability to import Python modules and execute Python scripts, users can seamlessly integrate Python data sources into their Power BI dashboards. By following the steps outlined in this article, users can leverage the full potential of Power Query M language code to streamline their data analysis workflow.
