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Stacked Column Chart Data Limit Issue

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I am encountering an issue with a stacked column chart in Power BI. Even though my data source only contains 200 rows, I'm still seeing the "i" icon (indicating a data limit issue). My x-axis shows phases, and my y-axis represents indexes (used as the legend to create the stack). I need the columns to be stacked as they currently are, but I'm unsure why this error is occurring with such a small dataset and how to resolve it.

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Pow3Range
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Possible Causes:

  1. Too Many Unique Categories in the Legend (Indexes)

    • Even though your dataset is small, if the "Index" column used as the legend has too many unique values, Power BI might struggle to render the stacked chart efficiently.

  2. Aggregation Issue

    • If your measure used on the Y-axis is not aggregated properly, Power BI might be trying to plot too many individual data points instead of aggregating them into stacks.

  3. Default Data Point Limit on Stacked Charts

    • Power BI has a default rendering limit of around 3500 data points for visualizations. If your chart is exceeding this due to high cardinality in the legend (Index), it could trigger this warning.


      Possible Solutions:

      1. Reduce the Number of Unique Legend Entries

      • If your Index field has a high number of unique values, try grouping them or creating a summarized version.

      • You can use binning or categorization to reduce the number of distinct values.

      2. Change Aggregation Type

      • Ensure that the measure on your Y-axis is properly aggregated (e.g., SUM, AVERAGE, COUNT).

      • You can check this by clicking on the Y-axis measure in the Values section and selecting an appropriate aggregation.

      3. Filter Out Unnecessary Data

      • If possible, use a filter to limit the number of unique values displayed in the legend.

      4. Use a Different Visual

      • If reducing the number of unique legend entries is not possible, consider using a different visual such as a 100% stacked column chart or a small multiples visualization.

        If this solution helps, please accept it and give a kudos, it would be greatly appreciated.

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