Skip to main content
cancel
Showing results for 
Search instead for 
Did you mean: 

The Power BI Data Visualization World Championships is back! It's time to submit your entry. Live now!

Reply
Julia1234
Helper I
Helper I

"Too Many Values" in stacked bar chart

I have a table with combinations of Customer and Product (6K records), and created horisontal stacket bar chart Y- Customer , X- Avount, Legend - Product. This chart shows no bars for some customers unless filtered, and also has "too many values" ! sign.

 

Adding [Top N] or ranking filter does not help much with filtering. Are there any better filtering approaches or visuals that could work with "too many values" 🙂

Julia1234_0-1759868599155.png

 


Customer | Year  | Product | Amount
Smith       | 2024 | Pen        | 1

Smith       | 2025 |  Pencil    | 2

.....

1 ACCEPTED SOLUTION

Thank you @v-saisrao-msft and @marcelsmaglhaes for your help.
Unfortunately, I cannot filter seemingly blank X Values, as they are in fact non blank, it is just power bi data processing limitation.
Also, additional grouping and binning as not applicabled as the values already grouped to proper granularity.

I ended up creating deneb stacked bar chart visual, which worked as required and accepts more datapoints than regular bae chart.
 

View solution in original post

7 REPLIES 7
v-saisrao-msft
Community Support
Community Support

Hi @Julia1234,

Thank you @marcelsmaglhaes , for your insights. Please see the links below, as they should help address your issue.

Solved: HELP - Stacked Bar Chart with Complicated Legend (... - Microsoft Fabric Community

Solved: Stacked bar chart not showing all data - Microsoft Fabric Community

 

Thank you.

 

marcelsmaglhaes
Super User
Super User

@Julia1234 

You can choose filter the visual to show no blank value in the measure on axis X. 
Let me know if this solve your question.

marcelsmaglhaes_0-1759886688279.png

 


Regards,
Marcel Magalhães
Microsoft Power BI Official Partner
MCT | Certified PL-300 Power BI

If I've helped, don't forget to mark my post as a solution!



Hi @marcelsmaglhaes 

The sum(amount) for seemingly blank Cust9, Cust33, Cust34, Cust35 is >0, it just not beling rendered by the visual.

If I set a filter to show Cust9, Cust33, Cust34, Cust35, the bars will be rendered, as this filter limits the data and mitigates "too many values".

Power Bi is analytical tool but it does not allow to visualize "large" datasets. Is there are workaround?

Hi @Julia1234,

Have you had a chance to review the solution we shared earlier? If the issue persists, feel free to reply so we can help further.

 

Thank you.

Thank you @v-saisrao-msft and @marcelsmaglhaes for your help.
Unfortunately, I cannot filter seemingly blank X Values, as they are in fact non blank, it is just power bi data processing limitation.
Also, additional grouping and binning as not applicabled as the values already grouped to proper granularity.

I ended up creating deneb stacked bar chart visual, which worked as required and accepts more datapoints than regular bae chart.
 

Hi! I am facing the same issue regarding a limited view in my stacked bar chart. Can you elaborate on the steps you took to create a deneb stacked bar chart?

Hi @oliviadummer 
In Power BI -> "Get more visuals" -> Deneb
Drag Deneb visual to the page,drag required fields to Values pane.
Right-click on the visual -> Edit.
Now you would need to fill the specification with json code using your fields, ChatGPT can help with this code. I used Vega-lite template.

Julia1234_1-1760549585197.png

 


for interactivity and cross filtering , enable these in Settings

Julia1234_0-1760549515612.png

 

For example for Y axis:  product_name
X-axis: amount
Legend (stacked by): customer_name

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": { "name": "dataset" },
  "mark": "bar",
  "params": [
    { "name": "customerFilter", "select": { "type": "point", "fields": ["customer_name"], "on": "click" } },
    { "name": "customerHover", "select": { "type": "point", "fields": ["customer_name"], "on": "mouseover" } }
  ],
  "encoding": {
    "y": {
      "field": "product_name",
      "type": "nominal",
      "sort": { "op": "sum", "field": "amount", "order": "descending" },
      "axis": { "title": null, "labelFontSize": 11, "labelColor": "#605E5C", "labelFont": "Segoe UI" }
    },
    "x": {
      "field": "amount",
      "type": "quantitative",
      "stack": "zero",
      "axis": {
        "title": null,
        "labelFontSize": 11,
        "labelColor": "#605E5C",
        "labelFont": "Segoe UI",
        "orient": "top",
        "tickCount": 5,
        "grid": true,
        "gridDash": [2,2],
        "gridColor": "#CCCCCC",
        "format": "~s"
      }
    },
    "color": {
      "field": "customer_name",
      "type": "nominal",
      "sort": { "op": "sum", "field": "amount", "order": "descending" },
      "legend": {
        "title": "Customer",
        "orient": "right",
        "labelLimit": 100,
        "labelFontSize": 10,
        "labelFont": "Segoe UI",
        "labelColor": "#605E5C",
        "titleFontSize": 10,
        "titleFont": "Segoe UI",
        "titleColor": "#605E5C",
        "symbolSize": 60
      },
      "scale": { "range": [ /* your colors */ 
"#EEA5AD", "#0154B0", "#02BF3B",
"#A9DFBF", "#F39C12", "#02BF3B", "#8E44AD", "#F5CBA7", "#D5F5E3", "#F9E79F", "#EAECEE", "#D2B4DE", "#9A7D0A",
    "#85929E", "#EAECEE", "#4A235A", "#EC7063", "#943126", "#3498DB", "#5B2C6F", "#F2D7D5", "#F8F9F9", "#F8C471",
    "#6C3483", "#AF7AC5", "#7B241C", "#FBFCFC", "#0E6251", "#B9770E", "#AAB7B8", "#F5B7B1", "#EBDEF0", "#E5E8E8",
    "#99A3A4", "#FDF2F2", "#7D3C98", "#616A6B", "#F5B041", "#515A5A", "#154360", "#922B21", "#5DADE2", "#7D6608",
    "#F5EEF8", "#FAD7A0", "#EAF2F8", "#C39BD3", "#A3E4D7", "#EEA5AD", "#424949", "#6E2C00", "#B7950B", "#F2F3F4",
    "#707B7C", "#FDFEFE", "#78281F", "#5499C7", "#F5B041", "#641E16", "#E6B0AA", "#229954", "#0E4C92", "#2C3E50",
    "#7F8C8D", "#D68910", "#CCD1D1", "#F9E79F", "#D7BDE2", "#B03A2E", "#D4E6F1", "#273746", "#F4F6F7", "#F4D03F",
    "#F9EBEA", "#1C2833", "#BFC9CA", "#F5B7B1", "#2471A3", "#17202A", "#A9CCE3", "#48C9B0", "#D1F2EB", "#85C1E9",
    "#F5B041", "#D6EAF8", "#FDEDEC", "#2980B9", "#FADBD8", "#9C640C", "#CD6155", "#AED6F1", "#7FB3D5", "#5D6D7E",
    "#2874A6", "#76D7C4", "#F1C40F", "#C0392B", "#EBF5FB", "#34495E", "#EAFAF1", "#1E8449", "#7DCEA0", "#52BE80",
    "#F8F9F9", "#148F77", "#117864", "#145A32", "#CB4335", "#AAB7B8", "#D4AC0D", "#A93226", "#9B59B6", "#E74C3C",
    "#28B463", "#0154B0", "#212F3C", "#2E86C1", "#D5DBDB", "#F39C12", "#2C3E50", "#196F3D", "#FDEBD0", "#F1948A",
    "#F5EEF8", "#E6B0AA", "#FDEBD0", "#D5DBDB", "#7F8C8D", "#424949", "#F1948A", "#CCD1D1", "#FAD7A0", "#FDF2F2",
    "#F9E79F", "#F4F6F7", "#FDEBD0", "#FAD7A0", "#F8C471", "#F5B041", "#F39C12", "#D68910", "#B9770E", "#9C640C",
    "#7E5109", "#6E2C00", "#F5EEF8", "#EBDEF0", "#D7BDE2", "#C39BD3", "#AF7AC5", "#9B59B6", "#8E44AD", "#7D3C98",
    "#6C3483", "#5B2C6F", "#4A235A", "#FDEDEC", "#FADBD8", "#F5B7B1", "#F1948A", "#EC7063", "#E74C3C", "#CB4335",
    "#B03A2E", "#943126", "#78281F", "#641E16", "#FDF2F2", "#F9EBEA", "#F2D7D5", "#E6B0AA", "#D98880", "#CD6155",
    "#C0392B", "#A93226", "#922B21", "#7B241C", "#641E16", "#F8F9F9", "#F4F6F7", "#EAECEE", "#D5DBDB", "#BFC9CA",
    "#AAB7B8", "#99A3A4", "#7F8C8D", "#707B7C", "#616A6B", "#515A5A", "#424949", "#2C3E50", "#1C2833", "#17202A",
    "#01B538", "#E29DA4", "#004EA3", "#94666B", "#DD99A0", "#00346D", "#017724", "#C1858C", "#AA767C", "#01982F",
    "#004C9F", "#003671", "#BB8288", "#A57278", "#01942D", "#9F6E74", "#018A2A", "#C68990", "#0052AB", "#D7959C",
    "#01BA39", "#01A733", "#00448E", "#017C26", "#004897", "#003E82", "#003A7A", "#003269", "#00428A", "#004A9B",
    "#017223", "#8E6367", "#018F2C", "#01AB35", "#01B036", "#D29198", "#004086", "#B67E84", "#0050A7", "#01A232",
    "#019D30", "#003876", "#003C7E", "#004693", "#018529", "#B07A80", "#996A6F", "#E8A1A8", "#018027", "#CC8D94",

    "#D44353", "#E24556", "#DA5C6A", "#E6606E", "#E07581", "#EA7A86", "#E58E98", "#EE959E", "#EBA7AF", "#F2AFB6",
    "#F0C0C5", "#F6CACE", "#F6D9DC", "#FAE4E7", "#FCF2F3", "#FEFFFF", "#FEFFFF", "#011934", "#002145", "#022852",
    "#002F64", "#033770", "#003E83", "#04458D", "#004CA1", "#0554AB", "#005BC0", "#0662C9", "#0069DE", "#0771E6",
    "#0078FD", "#1580F7", "#1D88FF", "#3290F8", "#024416", "#005519", "#03611F", "#007423", "#047F29", "#00932C",
    "#059C33", "#00B135", "#06BA3C", "#00D03E", "#07D746", "#00EE48", "#08F550", "#0EFE57", "#25F764", "#2DFF6C"


] }
    },
 
    "order": { "field": "amount", "type": "quantitative", "sort": "descending" },
 
    "tooltip": [
      { "field": "product_name", "type": "nominal", "title": "product" },
      { "field": "customer_name", "type": "nominal", "title": "Customer (right-click to drill/export)" },
      { "field": "amount", "type": "quantitative", "title": "Amount, kt", "format": ",.0f" }
    ],
 
    "opacity": {
      "condition": [
        { "test": "datum['__selected__'] === 'on'", "value": 1 },
        { "param": "customerFilter", "value": 1 },
        { "param": "customerHover", "value": 0.8 }
      ],
      "value": 0.2
    },
 
    "stroke": {
      "condition": { "test": "datum['__selected__'] === 'on'", "value": "#333" },
      "value": null
    },
 
    "strokeWidth": {
      "condition": { "test": "datum['__selected__'] === 'on'", "value": 0.6 },
      "value": 0
    }
  },
 
  "config": {
    "axis": { "labelFontSize": 11, "labelFont": "Segoe UI", "labelColor": "#605E5C", "titleFontSize": 11, "titleFont": "Segoe UI", "titleColor": "#605E5C" },
    "legend": { "labelFontSize": 10, "labelFont": "Segoe UI", "labelColor": "#605E5C", "titleFontSize": 10, "titleFont": "Segoe UI", "titleColor": "#605E5C" },
    "view": { "stroke": "transparent", "continuousHeight": 300, "continuousWidth": 600, "padding": { "right": 120 } }
  },
  "height": { "step": 20 },
  "autosize": { "type": "pad", "contains": "padding" }
}

 

Helpful resources

Announcements
FabCon Atlanta 2026 carousel

FabCon Atlanta 2026

Join us at FabCon Atlanta, March 16-20, for the ultimate Fabric, Power BI, AI and SQL community-led event. Save $200 with code FABCOMM.