A generic SWITCH-function for the query editor in Power BI and Power Query

Although you can easily replicate the DAX SWITCH-function via list-, table- or record functions in M, I thought it would be convenient for many newbies to have a comfortable M-SWITCH-function that uses (almost) the same syntax than its DAX-equivalent:

SWITCH (
[Month],
    1“January”,
    2“February”,
    3“March”,
    4“April”,
    5“May”,
    6“June”,
    7“July”,
    8“August”,
    9“September”,
    10“October”,
    11“November”,
    12“December”,
    “Unknown month number”
)
DAX Formatter by SQLBI

The DAX-SWITCH-function will retrieve the content of its first argument (expression) ([Month]) and check it against he first parameters of the following pairs (value). If there is a match, the second parameter of the pairs (result, here: month name) is returned and if there is no match, “Unknown month number” will be returned.

How it works

The syntax for the M-function looks like so:

M.Switch(Expression as any, Values as list, Results as list, optional Else as text)

So we have 4 parameters: The Expression just like in DAX, but then the Values and Results come as separate lists. The last optional argument is just similar to DAX again.

This allows for a very convenient entry of function parameters:

1. You can quickly enter numerical ranges:

M.Switch(Month, {1..12}, {"January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"}, "Unknown month number")

2. You can super-easily refer to switch-values in tables:

It has just one minor flaw: When you refer to a parameter or another query in the Expression-field, you will be default get an error first. But removing the 2 quotes will quickly fix it:

This is because I’ve set the format of this field to “any”, as the condition can actually be of any type. But the function-dialogue has no way to handle different types currently and will transform all entries to text by default in that case.

It uses a technique that I’ve used in this article already: There you can see that the results can also be functions for example.

Function code

Most of the code is documentation (row 7 onwards) or handles the missing values: Row 5+6 will return the value from the optional 4th argument (Else) if used, otherwise the default-value: “Value not found” will be returned. The main function logic (in row 4) is the positional index indicator: {List.PositionOf(Values, Expression)} that is applied to the list of Results. List.OfPositions will return the position (number) of where the Expression has been found in the list of Values. That x-th value will then be picked from the list.

Enjoy & stay queryious 😉

Web Scraping 1: Combine multiple tables from one page in Power BI and Power Query

This is a step-by-step description of how to combine multiple tables from one webpage into one table with categories as output. You can also apply this technique to combine tables from other sources as well (like from folder method for example or multiple different webpages (see in an upcoming article)).

Sometimes the page you want to scrape has multiple tables like here:

0 – Combine multiple tables into one: Input

And you want to combine them into 1 with a Category-column like so:

1 – Combine multiple tables into one: Result

Overview

I will present 2 methods here:

  1. Append-method: This is the obvious one and is fast for just a few tables.
  2. Add-Column-method: A bit more complicated but will be faster for a large number of tables and is also suitable for a dynamic number of tables.

You will also find 2 options at the end of this article:

  1. Use custom functions for multi-step table transformations
  2. Use dynamic filters to select the desired tables

 

Append method

Read more

List.SelectPositions in Power BI and Power Query

With this new custom function “List.SelectPositions” you can easily select items from a list by just passing a list of their positions within it as the parameter.

What it does

Say you have a list with numbers {1..5} and want to select the 1st, 4th and 5th element from it. Then you can pass these positions to the function as another list: {0, 3, 4}.

ListSelectPositions({1..5}, {0, 3, 4}) will return: {1,4,5}

You see that I’ve decided to follow the zero-based counting principle here, that you find throughout M in the query editor. If you don’t like that, you can use the optional 3rd parameter to let it start to count from 1 instead:

ListSelectPositions({1..5}, {1, 4, 5}, 1) will return {1, 4, 5}

But if you have entered positions that don’t exist, the function will return an error in their positions by default:

ListSelectPositions({1..5}, {1, 4, 5}) will return {2, 5, Error}

because there is no 6th element (you’ve omitted the 3rd parameter that allows you to start counting with 1).

But you can change this behaviour as well through the last optional 4th parameter: Setting it to 0 will fill the missing positions with null like this:

ListSelectPositions({1..5}, {1, 4, 5}, null, 0) will return {2, 5, null}

and setting it to 1 will eliminate it and shorten the list like this:

ListSelectPositions({1..5}, {1, 4, 5}, null, 1) will return {2, 5}

These additional error-handling-options of the 4th parameters are useful for dealing with badly formatted data and if you want to learn more about it, just let me know in the comments so that I can prioritize it.

Function code

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Table.Group: Exploring the 5th element in Power BI and Power Query

In this post I’ll show you the magic stuff you can do with the 5th parameter (the optional comparer function) of the Table.Group M-function in Power BI and Power Query:

Table.Group parameters

  1. table as table,
  2. key as any,
  3. aggregatedColumns as list,
  4. optional groupKind as nullable number,
  5. optional comparer as nullable function

If you’re not familiar with the 4th parameter (groupKind) already, I strongly recommend to read Chris Webb’s article, as we will build on its knowledge here.

Another aspect worth mentioning for the modus in GroupKind.local is the performance aspect: It runs MUCH faster for large datasets than the default-setting. So if you are sure that your data will always be sorted accordingly, you can speed up your grouping-operations considerably. That means: Your data has to be sorted correctly by default. At least for my tests, you would loose the performance-gain once you’d sort your table by an explicit step before.

You can find an overview of comparer functions here.

Case insensitive grouping

Imagine there was a twist in Chris’ dataset and it would look like so:

Table.Group – Modified Source Data

We would probably not be happy with these results then:

Table.Group Problem with Case Sensitivity

Because M is by default case sensitive, we get more groups than we want. Let’s try Comparer.OrdinalIgnoreCase to the rescue then:

Pretty neat, isn’t it 😉  (You can use that comparer in other functions as well, see here for text- and list operations)

Something like this was what I’ve showed Huang Caiguang the other day, who asked me what the 5th parameter of this function was about (or so I understood). He then sent me a link to one of his articles, which demanded a good 2 hours for me to digest and understand: We can also use custom functions to create all different sorts of grouping behaviours here. These are my 2 favourites:

Read more

Date.DatesBetween to retrieve dates between 2 dates in Power BI and Power Query

Today I’m sharing a handy function with you that allows you to retrieve all or just a couple of dates between 2 given dates: Date.DatesBetween.

Usage

This function takes 3 parameters:

  1. From- or Start-date
  2. To- or End-date
  3. A selection of ONE of these intervals: Year, Quarter, Month, Week or Day

All dates will be created at the end of the chosen interval: So if you want to analyse events with a duration for example, where you want to transform your data to show one day per (monthly) event, this function generates month-end-dates for every month within the timespan. Please not that if the To-/End-date is within a month, the last element of the list will NOT be that day, but the day of the end of that month.

The default-value for the 3rd parameter is “Day”, so if you omit the specification, the function will return a list of all days in between.

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How Power Query can return clickable hyperlinks with friendly names to Excel

When you use Power Query as an Excel-automation-tool rather than just to feed the data model, you might want to return clickable hyperlinks that carry friendly names. This doesn’t work out of the box, but with a little tweak it will be fine:

The trick

Return a text-string that contains the Excel (!)-formula for hyperlinks, preceded by an apostrophe  ‘ . After the data has been loaded to the sheet, check the column and replace ‘= by = to activate your Excel-formula:

Activate the HYPERLINK formula by replacing ‘= with =

You can then format the column to “Hyperlink”:

Read more

How to open a complex JSON record in Power BI and Power Query

Today I’ll show you a very useful technique how to deal with a JSON record that contains a wild mixture of different elements like this:

If you click on one of the expandable elements, their content will be shown, but you’ll loose all the “surrounding” information (metadata) that is visible now. This is often an issue, regardless if you want to create multiple tables from it to build a star-schema or just need a handful of fields or a denormalized table. But with a little help from M, you’re good to go:

Table.FromRecords( { MyJsonRecord } )

Will returns this:

With this move, every expansion of one of the expandable elements will keep the existing data in place:

Create one big flat table

Simply expand one element after each other to create a denormalized table

Create star schema

For multiple tables, keep this query and reference it to create you (sub-)tables. Always keep the Id-column as the key (!) to combine all the tables in your data model later. (Provided you use this in a function for multiple entities/series)

Best is to play with it, so just past this code into the advanced editor:

 

If your JSON-record has a different structure with “just” header and data in different fields, this technique will be more suitable for you: http://www.thebiccountant.com/2016/04/23/universal-json-opener-for-quandl/

Enjoy & stay queryious 🙂

How to import from Excel with cell coordinates in Power Query and Power BI

There might be occasions where you want to import data from Excel into Power Query or Power BI using cell coordinates like a range from E3 until G9 for example (“A1 cell reference style”). The function I provide below also caters for the potential pitfalls of this task that Maxim Zelensky has described in his article.

Background

If your worksheet has one leading empty row and column, the import will ignore them and automatically return the range starting from B2. So to fetch the range E3:G9 you have to delete the first row and the first 3 columns. But as Maxim has found out, remaining formats on empty cells will lead to an import of empty rows and columns. So the number of rows and columns to delete will vary and is hard/impossible to predict.

Method

The range that PowerQuery or PowerBI will import is stored in the Excel-file already in the sheet-data and the xml looks like this (“Sample3” from Maxims data):

The imported range is E1 till J12, as the first rows contain formatting instructions, and will therefore be imported as well. In the 3rd row E3 shows up with the first value, which is surrounded by “<v>”.

This is how it looks like in the Xml.Table in the query editor:

Task is to calculate the number of rows and columns delete, considering the individual offset that is caused by the formatted empty cells.

Code

So I’ve cooked together these ingredients in a pretty massive code that you can download here: fnImportFromExcelCellCoordinates.txt

How to use the function

Read more

New M-function: Table.TransformColumnTypesToFirstRowsTypes for PowerBI and PowerQuery

The following function automatically transforms all columns to the types that have been detected in the cells of its first row. Provided they come as: Number, date or text (but you can add additional type conversions if you need them.).

It also has some rough edges: If the first value is empty, the column will be converted to text. Also, it contains the (improved) logic from this article:  So if a date is written in a way that it could also be a number, then it will be converted as a number. To minimize the room for errors here, I’ve converted the values to text first, but this is still something to watch out for. But in very many cases it will just do what you have long been looking for:

Use cases:

  • You don’t want to use the automatic but static/hard coded type-conversion in the 2nd step (because you know you’re table is going to have more columns in the future and you want to cater for proper type-conversion of them as well)
  • You’ve lost your column types due to some other command (like Table.ReplaceValues)

M-Code

Code to download: TableColumnTypesToFirstRowsTypes.txt

 

Enjoy & stay queryious 😉

How to expand a column that cannot be expanded in Power BI and Power Query in Excel

Especially when working with JSON-data, you might end up with a column that has elements of mixed types in it. The expand column – arrows will be missing, but some elements still need to be expanded, like here:

But there is an easy way to fix it:

Transform to expandable column

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type list) then _ else {_} }} )

It transforms the “Column1” from table “Source” by checking, if the content of the each row ( _ ) is of type list and if yes, keep that value ( _ ) and if not, transform it to a list (by framing it into curly brackets {_} )

Syntax for tables

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type table) then _ else #table({“Column1”}, {{_}} ) }} )

Syntax for records

Table.TransformColumns(Source, {{“Column1”, each if Value.Is(_, type record) then _ else [a=_] }} )

File for Subscribers to download: HTExpandColumnThatCannotBeExpanded.zip

This technique should be applied to columns where the expandable elements all have the same structure. If that’s not the case, you should use this technique instead.

Enjoy & stay queryious 😉