Hi @HamidBee ,
Based on my knowledge and experience, they apply to different scenarios. And if you can use the built-in visuals, use the built-in ones. After that, if they don't meet your needs, consider using other methods.
They can realize most requirements, and some other requirements can also be fulfilled by some indirect means. This not only improves the performance of the report, but also saves the extra cost of custom visuals.
They can achieve more needs in data display. If you are already proficient in these languages, you can try to use them to render images. But they have some limitations:
Python visuals in Power BI Desktop have a few limitations:
- Data size limitations. Data used by the Python visual for plotting is limited to 150,000 rows. If more than 150,000 rows are selected, only the top 150,000 rows are used and a message is displayed on the image. Additionally, the input data has a limit of 250 MB.
- If the input dataset of a Python Visual has a column that contains a string value longer than 32766 characters, that value is truncated.
- Resolution. All Python visuals are displayed at 72 DPI.
- Calculation time limitation. If a Python visual calculation exceeds five minutes the execution times out which results in an error.
- Relationships. As with other Power BI Desktop visuals, if data fields from different tables with no defined relationship between them are selected, an error occurs.
- Python visuals are refreshed upon data updates, filtering, and highlighting. However, the image itself isn't interactive and can't be the source of cross-filtering.
- Python visuals respond to highlighting other visuals, but you can't click on elements in the Python visual to cross filter other elements.
- Only plots that are plotted to the Python default display device are displayed correctly on the canvas. Avoid explicitly using a different Python display device.
- Python visuals do not support renaming input columns. Columns will be referred to by their original name during script execution.
Reference: Create Power BI visuals by using Python
R visuals in Power BI Desktop have the following limitations:
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Data sizes: Data used by an R visual for plotting is limited to 150,000 rows. If more than 150,000 rows are selected, only the top 150,000 rows are used and a message is displayed on the image.
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Output size : R visual has an output size limit of 2MB.
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Resolution: All R visuals are displayed at 72 DPI.
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Plotting device: Only plotting to the default device is supported.
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Calculation times: If an R visual calculation exceeds five minutes, it causes a time-out error.
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Relationships: As with other Power BI Desktop visuals, if data fields from different tables with no defined relationship between them are selected, an error occurs.
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Refreshes: R visuals are refreshed upon data updates, filtering, and highlighting. However, the image itself isn't interactive and can't be the source of cross-filtering.
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Highlights: R visuals respond if you highlight other visuals, but you can't select elements in the R visual to cross filter other elements.
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Display devices: Only plots that are plotted to the R default display device are displayed correctly on the canvas. Avoid explicitly using a different R display device.
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Column renaming: R visuals do not support renaming input columns. Columns will be referred to by their original name during script execution.
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RRO installations: In this release, the 32-bit version of Power BI Desktop doesn't automatically identify RRO installations; you must manually provide the path to the R installation directory in Options and settings > Options > R Scripting.
Reference: Create Power BI visuals using R
- Microsoft Charticulator visual
I use this less often. But as can be seen from the official website, it is an amazing way to generate totally new/original/amazing custom visuals for Power BI without writing a single line of code. And another advantage is that Charticulator lets you export chart designs into reusable templates including Microsoft Power BI custom visuals.
Here is a blog: Announcing the new Charticulator visual (Public Preview) | Microsoft Power BI Blog | Microsoft Power....
In addition, this blog describes visualizations built in many different ways: Advanced Power BI Custom Visuals: Options & Tradeoffs – Olivier Travers. Just for a reference.
Table of Contents
1. Make the Most of Existing Visuals
2. Review the Available Third-Party Visuals
3. Use Visual Tricks with Animated Gifs, SVGs, Or CSS
4. No-Code Diagrams with Visio
5. No-Code PowerPoint Builder with PureViz Infographic
6. No-Code Customizable Visuals with Charticulator & SandDance
7. Low-Code Visuals with Deneb
8. Mid-Code Visuals with R or Python
9. “Pro Code” Visuals with TypeScript and the Visual SDK
10. Other Options That Reinvent Power BI for Serious Enterprise Reporting
Best Regards,
Icey
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