Monthly Archives of October 2015

Tip for Parameter Tables in Power Query and Power BI

Parameter tables in Excel are normally set up like this:

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In order to retrieve the currency for example, you could write this:

= Parameters[Value]{2}

This returns the content of the “Value”-column of the query named “Parameter” that sits in the 3rd row (! Power Query starts to count at zero): EUR. Which is not bad, but also not ideal, because you have to count the number of rows. Actually, over time this method could cause problems: So if you decide to add a parameter in the second row for example, all parameters referenced by numbers after that would need to be adjusted.

So this way is easier and more robust: After you’ve passed your parameter table to Power Query, transpose it and promote the headers.

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This will always return the “Currency”-Parameter, no matter where it’s located within the Parameter table and it is also easier to write.

= BetterParameters[Currency]{0}

Your column (sitting in [ ]) is the name of the parameter and the line is always the first (Power Query language: {0}).

BetterParameters1.xlsx

 

Enjoy & stay queryious 🙂

Happy Spreadsheet Day! (Or how to escape Excel-hell)

When reading horror-stories about Excel-hell describing how dangerous it is to use Excel in corporate environments, I cannot help but to think of this hilarious video describing the fatal consequences of acting without common sense: Just don’t do stupid things with it.

Although Excel comes nearly for free (in relation to what value it delivers) this doesn’t mean that you don’t need to invest in applying proper techniques (like in any other profession). There’s training and best practices for every different need.

But the best thing about Excel seems largely unknown still: Since the invention of Power Query it has never been easier to be save around Excel than before: A magic tool that can solve many of the problems that cause Excel hell: Repetitive tasks: The little adjustments and extensions that pile up when you use your workbook again and again and are often performed without realizing the (meanwhile complex) context of all the standard-Excel-elements involved: Power Query will prevent this mess. It will help you organize and automize your repetitive tasks in Excel.

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How to auto-parametrize your Power Query queries

This is about a technique that I’m going to use in my upcoming articles on ICT reconciliation. But as it is useful in other areas as well, it’s getting its own post here:

Say we want to pass the year and month as well as the type of accounts as filters/parameters into our reconciliation query. If our file is stored here:

C:\Users\Imke\Desktop\2015\08\\ICTRec201508_Transactions.xlsx,

we have everything we need. And when the next month comes, we even don’t have to adjust our query, as it will automatically take 09 as the months parameter, providing we store it in the correct folder.

Using: CELL(“filename”) will extract this information into the cell in Excel. Check this cell and pass it to Power Query (as table).

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Now we just have to extract the relevant parts, using an ultracool ninja-trick I just picked up in the TechNet Forum:

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How to compare everything with everything using Power BI or Power Query

 

A question in Mr. Excel-Forum about how to design your data model if you want to compare values from 10 different (fact-) tables with each other got me curious: As this simply felt so wrong… and where there are 10 tables today, there will be probably be 12 tables next …?

Time for Power Query to play out its strengths on dynamic approaches then: Key is to create one consolidated table from all the different input-tables with an additional column that contains the name of each source table.

If your data sits in Excel tables, you can find the description on how this works in the new Power Query book from Ken Puls and Miguel Escobar, starting on page 47 (key is to keep the name-column!).

In my example I’m using web-data that already has the format of a consolidated table, comparing inflations rates of different countries with each other. So the data-structure looks like this:

1DataStructure

You then start a new query that refers to your source data and merge it with the source again on the year-column. The year-column shall stay the same, as we don’t want to compare different years against each other, but only different countries (Sources).

This will actually create a crossjoin of all countries against the country in the respective row – but within the same year. Last step is to create an additional column that calculates the difference between the values. This tells you how the inflation rate of the country to compare against is in relation to the source-country:

2Table

& this is the code:

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