Python and R in Power BI: new opportunities for analytics

Power BI is a powerful business analytics tool that can be significantly expanded through integration with Python and R. This opens up access to libraries for visualization, statistics, machine learning, and forecasting directly in reports.

Python libraries for Power BI

• matplotlib, seaborn — classic and advanced charts for basic and advanced visualization.
• plotly, bokeh — interactive charts and dashboards with data exploration capabilities.
• pandas, numpy — efficient data processing and fast mathematical calculations.
• scipy, statsmodels — tools for statistics, modeling, and hypothesis testing.
• scikit-learn — machine learning: classification, regression, clustering.
• prophet — time series forecasting, convenient for business forecasts.

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

R libraries for Power BI

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