Power BI Chart for Multiple X axis variants

Try this ...

Create a calendar table with a Day offset, Month offset, Year offset and Quarter offset column.

This example is based on 14/06/2025 

QTD ave = 
CALCULATE(
    [Ave price],
    ALL('Calendar'),
    'Calendar'[Quarter offset] = 0)

You can learn about calendar tables and offsets here.

If you spend time and build a good one, then you will use it again and again on all your reports.

So it is really important to learn about Calendars and Offsets.

https://www.youtube.com/watch?app=desktop&v=BtYn1hfdSAM&t=0s

https://www.youtube.com/watch?v=XjVLaVLluYE

Your months will then have these values (based on 14/06/2025)

Build a 1:M relationship from the calendar table to your fact table

Create measures (not calculated columns)

Ave price = 
DIVIDE( SUM(Sales[Value]) , SUM(Sales[Qty]) )

MTD ave = 
CALCULATE(
    [Ave price],
    ALL('Calendar'),
    'Calendar'[Month offset] = 0)

QTD ave = 
CALCULATE(
    [Ave price],
    ALL('Calendar'),
    'Calendar'[Quarter offset] = 0)

YTD ave = 
CALCULATE(
    [Ave price],
    ALL('Calendar'),
    'Calendar'[Year offset] = 0)

The CALCULATE and ALL command remove the natural dates filter and then apply the desired offset filter.

Now draw a Clustered column chart

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Then click [accept solution] if it works.  Thank you ! 😀😀😀

PBIX Sales Comparison

Hi,

I’ve linked a dummy data file and a PBIX file that demonstrate my approach. The explanation below is based on these files.

To create a Power BI bar chart that compares four different time frames—YTD (Year-to-Date), QTD (Quarter-to-Date), CM (Current Month), and Yearly Average Pricing—on a single X-axis with distinct category labels, here's how you can approach it:

1. Prepare your data model:
   • Include a date table (see Building a date table for best practices).
   • Include a sales table (I used dummy data based on your structure).
   • Ensure a many-to-one active relationship between the date columns of both tables.
2. Create separate measures for each time frame:

   YTD Pricing = CALCULATE(AVERAGE(Sales[VAL_SLS]), DATESYTD('Date'[Date]))

   QTD Pricing = CALCULATE(AVERAGE(Sales[VAL_SLS]), DATESQTD('Date'[Date]))

   CM Pricing = CALCULATE(AVERAGE(Sales[SLS_VOL]), DATESBETWEEN('Date'[Date], STARTOFMONTH('Date'[Date]), ENDOFMONTH('Date'[Date])))

   Year Avg Pricing = CALCULATE(AVERAGE(Sales[SLS_VOL]), FILTER(ALL('Date'), YEAR('Date'[Date]) = YEAR(MAX('Date'[Date]))))

3. Create a supporting table for categories:

   TimeFrameTable = DATATABLE(
     "TimeFrame", STRING,
     {
       {"YTD"},
       {"QTD"},
       {"CM"},
       {"Year Avg"}
     }
   )

4. Create a clustered column chart:
   • X-axis: TimeFrame column
   • Y-axis: Pricing by TimeFrame measure
   • Small multiples: Supplier column

Best regards,
hnam_2006

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