Power BI is a tool used for visualizing and analyzing data, which makes the process of decision-making better. Integrating Python with Power BI helps in efficiently constructing a dashboard. Python allows you to reshape, create, and manipulate data based on conditions. Using Python with Power BI improves data analysis that leads to better decision-making. In this blog, let us discuss in detail how to create data samples in Power BI using Python.
Table of Contents:
Why do we need to integrate Python with Power BI?
- Python with Power BI allows for improved analysis of your data, which provides you with the ability to study the data.
- Python and its libraries provide an efficient and effective tool for cleaning data.
- Python within Power BI helps users to create custom visuals to study their data.
- Python with Machine Learning for the predictive analysis of models.
Setting Up Python 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 set up your 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.
Generating a Reproducible Sample Using Python
We will generate a dataset representing sales data, which includes product categories, sales amounts, and dates, which will be used to create a report on sales, with the help of which we measure performance across categories.
Output:
Explanation: The date is used to define dates from January 2023 to October 2023, 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.
Visualization with the dataset
Once you have created a dataset, then you can fetch insights from the graph.
1. Bar Chart of Sales by Category
We will use a bar chart to display each category with its total sales and the highest sales.
2. Line Chart of Sales Over Time
We will use a line chart to show sales changes over time.
3. Pie Chart for Sales Category
A pie chart is used to show the parts of the total sales for each category.
This is how a pie chart looks after creating it in Power BI.
4. Funnel chart for Units sold by Category
A Funnel chart is used to show units sold from each category.
This is how a Funnel chart looks after creating it in Power BI.
Precautions to take when using Python in Power BI
- 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.
- Data Security: Be careful in the usage of Power BI, given the important/sensitive data, when sharing your Power BI file with others.
- Version Compatibility: Be sure to check that the Python libraries you are using are compatible with the version of Python and Power BI.
- 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
- Python scripting in Power BI should remain simple and uncomplicated, as large, complicated Python scripts will be much more difficult to debug.
- Commenting on your scripts will provide you with a better understanding of what the code is doing.
- From your query editor, make sure you select the correct table before you implement the Python script.
- You should always verify if your Python code is producing a result by testing it using test cases. When you are working with multiple tables, always give your tables proper names.
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.
Integrating Python with Power BI – FAQs
Q1. Can I modify the elements of an array while using the for-each loop?
No, you cannot modify elements of a primitive array(e.g., int[], boolean[]), etc.) using a for-each loop because it works with a copy of each element (pass-by-value). However, for an array of objects, you can modify their properties since the references still point to the same objects in memory.
Q2. Is it possible to print a multidimensional array using Arrays.toString()?
No, Arrays.toString() only works with one-dimensional arrays. We have to use Arrays.deepToString() for multidimensional arrays.
Q3. Can I use the Stream API with arrays of primitive types?
Yes, Arrays.stream() supports arrays of primitive types like int[].
Q4. How to print an array in Java without a loop?
You can print an array in Java without using a loop by using built-in methods from the Arrays class Arrays.toString().
Q5. How to get the length of an array?
You can get the length of an array in Java using the array.length property.