PowerBI.tips

Center the X-Axis on a Line Chart

July 9, 2018 By Seth Bauer
Center the X-Axis on a Line Chart

Sometimes when your working on a line chart you want the x-axis to stay centered on a chart. This tutorial will walk you through how to create an X-Axis that will always center it’s self on the graph.

Video Support Material:

The measures discussed within this tutorial are:

Variance All = SUM(Sales[Variance])

The column name Variance is found in the data table called sales.  This is just a numerical column.

After summing up all the variances we can calculate the min and max lines.

Const Max Line = [Variance All] * 1.2

Const Min Line = -1 * [Const Max Line]

Finally to calculate the variance to date you can use this filtered measure, which will only produce historical values.

Variance To Date = CALCULATE([Variance All], FILTER(‘Sales’,‘Sales’[Date] <= EOMONTH(TODAY(),0)))

Thanks for watching our short tutorial.  If you like this video please be sure to follow me (Seth Bauer) on Twitter, LinkedIn and be sure to subscribe to the PowerBI.Tips YouTube channel.

Previous

Power BI and Sudoku – Yes Please

More Posts

Oct 6, 2026

Where Writeback Belongs – Ep.569

An anonymous mailbag asks whether Power BI should let people act on a number without leaving the report. Tommy Puglia and Mike Carlo put writeback in Fabric apps, keep the report for consumption, and treat translytical task flows as the exception.

Oct 1, 2026

FabCon Barcelona - Feature Draft – Ep.568

Tommy Puglia and Mike Carlo draft a team from the FabCon Barcelona feature list, starting with Fabric app backends and the ontology authoring agent. The picks that follow are pay-as-you-go capacity, the Fabric notebook toolkit, deployment hooks, and text search in semantic models.

Sep 29, 2026

The Right Model for the Task – Ep.567

Tommy Puglia and Mike Carlo use Kurt Buhler and Eugene Meidinger's Tabular Editor piece to decide which model and effort level belongs on a Fabric task. The same hour covers FabCon Barcelona's Fabric app backend, semantic views, and why monthly active agents may matter more than monthly active users.