How to Use Plotly in Power BI with Python and R

Why Use Plotly in Power BI?

Plotly is an open-source visualization library for Python and R that enables interactive charts in Power BI, including zoom, hover tooltips, filtering, and advanced data exploration. Instead of relying only on built-in reports, analysts can create dynamic dashboards with zooming, panning, hover tooltips, and detailed data exploration.

Plotly works with both Python and R scripts in Power BI, making it suitable for different analytics workflows. Python is commonly chosen for data preparation with pandas and interactive visualizations using Plotly Express or Graph Objects, while R users often combine Plotly with ggplot2 to add interactivity to existing charts.

Common Plotly visualizations in Power BI include interactive line charts, scatter plots, bar charts, bubble charts, heatmaps, 3D visualizations, and geographic maps. These visuals are especially useful for exploring large datasets, identifying trends, and presenting business insights in a more engaging way than static charts.

To create Plotly visualizations in Power BI, enable Python or R scripting, prepare your dataset, and use Plotly libraries inside a script visual. Once the script is executed, the interactive chart is rendered directly in your report. This approach allows you to build custom visualizations that go beyond the capabilities of standard Power BI charts.

Python libraries for Power BI

• Plotly – interactive charts and dashboards.
• Pandas – data preparation and transformation.
• Matplotlib & Seaborn – static and statistical visualizations.
• Scikit-learn – machine learning.
• Statsmodels & Prophet – statistics and forecasting.

In Power BI, Python helps build interactive charts, perform complex analytics, and make forecasts directly in dashboards.

R libraries for Power BI

• R ggplot2, R lattice — flexible visualization for various analysis scenarios.
• R plotly, R highcharter — interactive charts with extensive customization options.
• R dplyr, R tidyr, data.table R — preparation, cleaning, and transformation of large data sets.
• R forecast, prophet — forecasting time series, trends, and seasonality.
• R caret, randomForest R — machine learning algorithms for modeling and classification.

Using R in Power BI allows you to create complex statistical models, forecasts, and interactive visualizations, greatly expanding the capabilities of standard tools.

Python and R libraries in Power BI from Aristeem

Aristeem has integrated ready-made Python and R libraries for Power BI into its cloud service, so users no longer need to spend time installing or configuring anything — everything works right out of the box.

What does this give you?

• Visualization: matplotlib, seaborn, plotly, bokeh, ggplot2, highcharter — classic and interactive charts right in your reports.
• Data handling: pandas, numpy, dplyr, tidyr, data.table — fast processing, cleaning, and transformation of large arrays.
• Statistics and forecasts: scipy, statsmodels, forecast, prophet — analysis and forecasting of time series.
• Machine learning: scikit-learn, caret, randomForest — classification, regression, and clustering models in dashboards.

Advantages of Aristeem

• No installation required: libraries are integrated into the Power BI cloud.
• Works even on low-end hardware thanks to server-side computing.
• A complete environment for deep analytics and visualization right out of the box.

Thus, Aristeem transforms Power BI into a ready-to-use platform for deep analytics with Python and R — quickly, conveniently, and without technical barriers.

Technical support

If you have questions about cloud services and programs, you will find the FAQ section of our website useful, or you can quickly contact our specialists via Telegram.

Conclusion

With Python and R integration available out of the box, Power BI in Aristeem becomes a universal solution for advanced analytics, enabling businesses to combine visualization, statistics, machine learning, and forecasting in one platform.