How to Integrate Python Scripts in Power BI

Power BI is a powerful business intelligence tool that helps visualize and analyze data for better decision-making. By integrating Python scripts in Power BI, users can enhance their dashboards with advanced data manipulation and custom visualizations. Python makes it easier to clean, transform, and analyze datasets directly within Power BI. In this blog, we’ll explore how to create and visualize sample data in Power BI using Python.

Table of Contents:

• Why do we need to integrate Python in Power BI?
• Integrating Python Scripts in Power BI
• Importing a Dataset into Power BI
• Using Python Scripts for Visualization in Power BI
  1. Bar Chart of Sales by Category
  2. Pie Chart for Sales Category
  3. Funnel chart for Units sold by Category
• Precautions to Take When Using Python Scripts in Power BI
• Best Practices for Integrating Python Scripts
• Conclusion

Why do we need to Integrate Python Scripts in Power BI?

Python script integration brings more advanced control to Power BI. Let’s now explore the need to integrate Python scripts in Power BI:

• Using Python scripts in Power BI allows for improved analysis of your data, which enables deeper data analysis.
• Python and its libraries help clean and prepare data in Power BI in a faster and more flexible way.
• Python within Power BI helps users to create custom visuals to study their data.
• Python in Power BI supports automation, making repetitive data tasks quicker and easier to handle.

Integrating Python Scripts in Power BI

Let’s now explore the steps involved in integrating Python scripts in Power BI:

Step 1: Install Python

To work with Python in Power BI, you need to download and install Python on your system.

Step 2: Install the Python libraries.

Install libraries like pandas, numpy, or matplotlib for data manipulation.

Step 3: Enable Python in Power BI

After installing Python, you need to configure Power BI to enable Python scripts.

• Open Power BI Desktop > Go to File > Options and Settings > Options.
• Under Global, select Python scripting.
• In the Python home directory, set up the location where Python is installed.

Importing a Dataset into Power BI

We will use a simple sales dataset, which you can import into Power BI:

Note: You can reference and use the Sales dataset linked below for this analysis

Explanation: The date is used to define dates from January 2025 to October 2025, Category is used to contain a set of categories. Sales_Amount is the number between 100 and 1000 representing the total sales amount for each entry. Units_Sold are the integers between 1 and 50 representing the number of units sold for each sale.

Using Python Scripts for Visualization in Power BI

Once you have created a dataset, you can fetch insights from the graph.

1. Bar Chart of Sales by Category

A bar chart helps compare total sales across different product categories, making it easy to identify the highest and lowest performers. It visually highlights category-wise contribution to overall sales.

Output:

This is how the bar chart looks after creating it in Power BI

2. Pie Chart for Sales Category

 A pie chart shows how each product category contributes to total sales, helping you quickly understand the proportion or share of each category in the overall revenue.

Output:

This is how a pie chart looks after creating it in Power BI.

3. Funnel chart for Units sold by Category

A funnel chart visually displays units sold across categories in decreasing order, making it easy to spot drop-offs and compare performance at each level.

Output:

This is how a Funnel chart looks after creating it in Power BI.

Precautions to Take When Using Python Scripts in Power BI

Now, after creating the graphs, let us look at some precautions that you need to take when using Python in Power BI

1. Performance: Make sure that the size of the datasets is smaller, given that the use of Python scripts can slow down your report if you are dealing with large datasets.
2. Data Security: Be careful in the usage of Power BI when handling sensitive or confidential data, and when sharing your Power BI file with others. 
3. Version Compatibility: Be sure to check that the Python libraries you are using are compatible with the version of Python and Power BI. 
4. Debugging Errors: Debugging errors is also difficult with Power BI, so it is best to test the Python scripts in the local Python compiler instead.

Best Practices for Integrating Python Scripts

Let us look at some best practices for integrating Python scripts in Power BI

1. Keep Python scripts simple: Use clean and minimal code to avoid performance issues and make debugging easier.
2. Add comments to your code: Commenting helps explain logic and makes scripts easier to maintain or modify.
3. Select the correct table before running scripts: Ensure you’re applying the Python script to the right dataset inside Power BI’s Query Editor.
4. Verify your Python output beforehand: Test scripts in a local IDE to confirm they work before running them in Power BI.
5. Use clear table names: When working with multiple datasets, name your tables properly to avoid confusion.

Conclusion

Python makes working with datasets easy, allowing you to create interactive reports and visualizations. These reports help you analyze data more effectively for better decision-making. Python enhances report building in Power BI, which offers better readability and customization options compared to R scripts. You can create graphs tailored to your needs and focus on the most useful information, making data evaluation more efficient and user-friendly.

To learn more about Power BI and its functions, check out this Power BI Course, and also explore Power BI Interview Questions prepared by industry experts.

Check out other blogs related to business intelligence by Intellipaat:

How to Integrate Python Scripts in Power BI – FAQs