Intermediate Power BI (session 2)

This is part of our intermediate-level Power BI desktop course. The course structure is as follows:

• session 1: a simple build-a-dashboard session as a warmup, reminder about core Power BI techniques, and sharing some suggestions for good workflow practices
• session 2 (this session): DAX language foundations, including FILTER and CALCULATE
• session 3: core PowerQuery functions to manage sources, join data sets, pivot, and tidy values
• session 4: PowerQuery M language foundations, including a basic overview of syntax, a core function toolkit, advanced mode, and reproducibility
• session 5: a second, more ambitious, build-a-dashboard session to put all that PowerQuery and DAX knowledge into practice

Session materials

• .xlsx session data

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Forthcoming session(s)

Booking link	Date
Intermediate Power BI (session 2)	13:00-15:00 Wed 12th November 2025

Session outline

In this session, we’ll concentrate on DAX. Specifically, we’ll:

• look in more detail at date tables as a warmup
• look at the foundations of DAX, especially concentrating on the differences between measures and tables
• discuss iterator functions
• think about the filter context, and the ways you can use (and modify) it with FILTER() and CALCULATE()
• introduce variables as a way of simplifying some DAX expressions
• and finish with a brief introduction to calculation groups as a means of reducing the number of DAX measures you need to write

Data

• this session uses a bit of synthetic data extending the Dundee traffic dataset from the previous session
• please use this link to import into Power BI. You should be able to do that directly from the Excel connector.

DAX reminders

• functions vs formulas
• arguments and return values
• references
• columns vs measures

A warmup: date tables

We briefly discussed and implemented a date table in the previous session. Date tables are good practice, because they help you deal with patchy data (especially when you have missing dates), and prevent repetition by consolidating all your date info into one place.

Task

• create a dates table now using the MIN and MAX values from traffic[date]
  • note that date is a reserved keyword, so probably better avoided as a table name
• now add some columns:
  • a day of the week, using WEEKDAY
  • a day name, using FORMAT
  • a quarter, using QUARTER
• finally, check the model to ensure that your date table is linked to your traffic table

Column functions

• Many DAX functions can return either columns or measures
• let’s try a practical example:

Task

• in the traffic data, select New column then add the DAX formula temp_col = 66
• now repeat with New measure then temp_measure = 66
• now add temp_measure to a card on a new report page

Column vs measure

• your temp_xxx functions will be identical: the value of temp_measure will be the same as each row of the temp_col in your data
• the values are the same
• the filter context is also the same

DAX defaults

Task

• now tweak your temp_col formula to use the UPPER function on the [Detail] column: temp = UPPER(traffic[Detail])
• inspect the result

• an important result: DAX column functions default to row-wise filter context
• if you try the same formula in temp_measure you’ll cause an error: Power BI won’t know which row you want to change the case of
• this indicates a different filter context at play in this DAX formula when used as either a column or as a measure

FILTER()

Task

• now update temp_col = COUNTROWS(filter(traffic, traffic[number] = 3))

• note that this function now isn’t working row-by-row, but across the entire table
• FILTER() has changed the default row-by-row context

Filter context

Task

• to illustrate, please build a simple dashboard with:
  • a card containing the minimum value of traffic[temp_col]
  • a slicer containing traffic[time]

• note that the value of temp seems to be incorrect - it’s the same no matter how the slicer is set

Iterator functions

Task

• add two new columns:
  • col_sum = SUM(traffic[number])
  • col_sumx = SUMX(traffic, traffic[number])
• add two new measures
  • meas_sum = SUM(traffic[number])
  • meas_sumx = SUMX(traffic, traffic[number])
• finally, add all 4 to cards, summarising by min when necessary

What’s the difference?

• in the measure, SUM() takes the report filter context, and sums for that time of day
• in the column, SUM() calculates the total for an entire column
• in both cases, SUMX() gives the same result, but it calculates it in a different way…

SUMX()

Task

• to illustrate, change both SUMX calculations to SUMX(filter(traffic, traffic[number] < 15), traffic[number])

• all four values will now disagree:
  • col_sum is showing the total for the entire column
  • meas_sum is showing the total for the filtered column (so traffic[time] = morning)
  • col_sumx is showing the total for the filtered column (values of traffic[number] > 15 only)
  • meas_sumx is showing the total for the doubly-filtered column (values of traffic[number] > 15 where traffic[time] = morning)

SUMX() is an iterator

• SUMX() calculates row-by-row
• in our initial case, it was calculating row-by-row, and then aggregating all the rows
• when we added a filter, SUMX() included rows meeting the filter condition

ALL()

• strictly, SUM(x[y]) is a sweetened form of SUMX(ALL(x), x[y])
• ALL() removes any filter context to include every row of a table

ALL() and CALCULATE()

• much of the power of DAX lies in that ability to alter the filter context. Let’s tweak meas_sum to show that:

Task

• meas_sum = CALCULATE(SUM(traffic[number]), ALL())
• compare and contrast col_sum and meas_sum

• CALCULATE() is a generic function that changes the filter context
• ALL(), as we’ve already seen, removes any filtering from the current filter context

ALLEXCEPT()

• ALLEXCEPT() allows you to remove part of the filter context

Task

• add an additional slicer to your report slicing on traffic[Type]
• add a new measure with meas_sum_time = CALCULATE(SUM(traffic[number]), ALLEXCEPT(traffic, traffic[time])) and add to a card

• that should give you a measure that reponds to time, but not to Type
• this could be extended - e.g. to meas_sum_type_time = CALCULATE(SUM(traffic[number]), ALLEXCEPT (traffic, traffic[Type], traffic[time]))

Variables

• DAX formulas get horrid quickly. In this section, we’ll demonstrate the use of variables
• add a new measure:

temp_var = 
  VAR my_var = 66
  RETURN my_var

Variable considerations

• useful to avoid repeating yourself
• also useful for avoiding clutter (e.g. component measures)
• local scope - you can’t refer to variables in other expressions
• potentially improve performance

Calculation groups

• calculation groups are a way of applying functions to many measures at once

Task

• in the model view, select Calculation group
• select Yes when warned about implicit measures
• that will add a new section to your model
• select Calculation item in the Model sidebar
• add the formula round_10 = ROUND(SELECTEDMEASURE(), -1) to the formula bar
• back on the report page, add a slicer and populate with your Calculation group