Automatical or Bulk- Rename Columns in Power BI and Power Query

Edit 7th Feb 2017: Friendly reader Roknic pointed out in the comments below that there’s actually an existing function for it in M:¬†Table.TransformColumnNames ūüôā

So the first of my example below would actually look like this:

Table.TransformColumnNames(Source, each Text.Replace(_, " ", "_"))

But still keeping my original post here, as the transformations in them might help for other use cases:

If you want to rename all of your table’s columns with a common rule, like “replace all spaces by underscore” or just “delete all spaces”, check out this easy method:

The above formula will replace all spaces (” “) by underscores (“_”).

How does it work:

The 2nd argument in the Table.RenameColumns-formula is a list of lists, just like in Table.TransformColumnType from this article. So we apply the same technique here: List.Transform transforms a single element from a list into a list-item, whose 2nd argument will be calculated with a Text.Replace-function.


Rename Columns Variations

Only replace FirstN or LastN elements from the column names:

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Conditions in FirstN, LastN and other xN-functions in M, PowerBI and Power Query

Today I discovered that we can use conditions in many of the N-selecting functions where one/I would normally expect just a number-expression for the N:

Table.RemoveFirstN( table as table, optional countOrCondition as any)

So apart from being able to select a certain number of rows to be removed, we can pass a condition (as function). This condition will iteratively be checked for every row in the table (from top or bottom) and as long as every (next) step returns true, the resulting range will be removed. So as soon as one row breaks the condition, the process will stop.

I find that totally awesome, as we can now remove all top-rows who have an empty field in Column3 like this for example:

Table.RemoveFirstN(<MyTable>, each each (_[Column3] = null or _[Column3] = “”))

Yes, this will remove the first sequence of consecutive nulls in the table. So all other rows with nulls in the table coming later after a non-null value has “broken in”, will remain.

This is the list of function, where you can use this M-agic:

Enjoy & stay queryious ūüôā

Comfort Functions for Easy Profit & Loss statements in Power BI and Excel – Part2

Here comes some long awaited comfort functions for part 2 of my easy P&L series. In the first section I’ve presented the general principle on how to work with a structure using an accounts-group-table.¬†Today I will present¬†2¬†alternatives to define the reports without¬†specifying single accounts.¬†So if a new accounts are¬†added to the chart of accounts, you don’t have to adjust your report definitions: Just make sure that to fill in all the fields in your account-group-table and you’re ready to go ūüôā

No need to specify single accounts

So you only need to adjust your report definitions¬†if you add new group items. If that’s still too much,¬†take the 2nd solution, which¬†will even eliminate that requirement:

  1. Individual Account Layout: Just define each subtotal and determine for which subtotals single accounts shall be shown

No more specification of individual accounts

How to use it:

How to use Individual Report Layout

2. Ultrashort Account Layout: Further simplification of just defining the groups (hierarchy) that shall be shown (with option to filter on one of them)

No need to define individual group items

How to use it:

How to use Ultrashort Layout

So these 2 different layouts will both produce the same reports incl. all accounts – just like in the first example. So you can choose which layout-style suits you best – actually, you can use all 3 in parallel. You just have to make sure to grab your pivot-rows from the correct tables and in Excel to grab the matching measures, as they all have their own bridge-tables (which need to be used in the measures):


How it works

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Easy Profit & Loss (and other account) statements in PowerBI and Excel – Part2

Welcome to part 2 of my series of easy Profit & Loss and other account statements in Power BI and Excel. In the first part I introduced the general principle of creating asymmetric shaped reports who use just one measure per column (you should have read this article in order to understand this post here).

How the technique works

This technique capitalises the aggregation power of the Vertipaq engine and creates a bridge-table between your DimAccount-table and the ReportsAccountsLayout-table. In there for every line of your report, all accounts that belong to the (sub-)totals¬†are matched (“AccountsAllocation”). This table can get very long, but the engine can handle this easily:

Different use case: Account-groups-tables

In the first example we’ve worked with a chart of accounts, which had a parent-chield-hierarchy defining all the subtotals of the report. In this example we’re working with a different setup, using the good old DimAccountsGroups-table. Just one row per account and the columns are coming in pairs, containing the group-criteria and the sort-order for the report:

Individual Report-Layout

We also need a second table (ReportsAccountsLayout) that holds the definitions of the report-layouts like this:

Read more

Incremental Load in Power BI using DAX UNION

This article describes the latest workaround for incremental load in PBI (thx to Taylor Clark for stressing this out!). It’s not very dynamic, as it doesn’t automatically load the difference to the existing data. Instead you have one query that contains your old data (which will be kept) and another query that grabs all data that comes after the last item from your old data. But at least it’s a technique that works without a hack:

Incremental Load Process


1 Create “Old Data”: “DontRefresh”

So it’s up to you to split up your long table or web-load activities and load your “old” stuff into one query (“DontRefresh”),¬†perform your transformations and¬†then load into the data model once. Then go back to the query-editor and disable the option “Include in Report Refresh”.

2 Create “New Data”: “Refresh”

Then take the cut-off-filter-criterium and use it to define the load of the new data that will subsequently be refreshed (“Refresh”). Transform your transformations from the first query into a function to make sure both tables have the same structure and load it to the data model (leaving the default load options to refresh).

3 Create table using UNION in DAX

In the data model, you create a new table that appends both tables to each other (and hide both input-tables from client view):

Create table in DAX using UNION

Another reason why this is not ideal is that fact that you cannot perform data transformations in the query-editor that iterate over the whole table. So I really hope that incremental load will once be a native functionality in PBI. Please vote for it here, as Microsoft prioritizes many of its activities on customer feedback: I vote for incremental load

Just picked up a useful tip from Mimoune Djouallah, to use a syntax like this:

union (summarize(table_current, field1,field2),summarize(table_history,field1,field2))

Which highlights the difference between the Append-command from M and the Union from DAX: The Union function requires the columns to have the same order in your table.

4 Why not use Append in the query-editor instead?

Another drawback of the current implementation is a somewhat unintuitive behaviour¬†of queries which have been set to “Don’t include in Report Refresh”: As a standalone-query, they will behave as expected and not refresh. But once you reference them by a separate query or within an append-operation, they will refresh their results. So beware of this potential trap!:

Unexpected Behaviour Warning


Link to file:

csv-sample data:

So don’t forget to vote¬†and stay queryious ūüėČ

Dynamic & bulk type transformation in Power Query, Power BI and M

This is just a quick code-share-of-the-day of different scenarios for dynamic type transformation of multiple columns at once.

The syntax to transform the format of 2 columns (“Column1” and “Column2”)¬†in a table (“Source”) looks like this:

Now if you would like to do one of the following:

1. Force all columns of the table to be transformed to one type (text)

The second argument of Table.TransformColumnTypes is a list of lists, whose elements contain 2 arguments: The name of the column to transform and the type to be applied.

For this dynamic approach we start with a list of the table’s column names (Table.ColumnNames) and transfer it magically to a list of list, using List.Transform¬†with an expression with curly brackets again like this: each {_, type text}: This operation iterates through every element of the list (List.Transform) and performs the actions that follow the “each” on every element of the list (which is represented by the underscore: _)

2. Transform all newly added columns of a table to one specific type

Imagine you have a table with different column types where users can add new columns with random names. You want these columns automatically to be converted to text:

Same procedure as the first, just that you need to identify the newly added column names. Therefore you use List.Intersect with the two tables to compare in list-format (curly brackets) as shown in line 3 above.

3. Transform all columns whose name are in a list to one specific type

Let’s close with the easiest case, which you’d probably be able to find out by yourself: Say your query returns a dynamic list somewhere with column names who then shall all be converted to a specific type:

You can directly reference the List as the first argument of the List.Transform-command.

You can download the file here: ChgTypeOfColumns.xlsx

Enjoy & stay queryious ūüôā