Next Level Power BI – M Language Basics

This is a series of posts and exercises from a recent full-day workshop called Next Level Power BI. Check back for additional lessons and freely post your questions and feedback in the comments section after this post.

Power Query is a modern data acquisition and transformation technology and it is the “Get Data” component of Power BI and many other Microsoft tools. The query script language of Power Query is known as “M” or the Microsoft Data Mashup language. The menu options and selections in the Power Query Editor interface build this “M” script for you but to use Power Query at an expert level, you must understand fundamental concepts of the language. The post will step you through some essential mechanics of the language to build the necessary fundamental skills.

What you learn here can be applied to the Power Query implementation of other Microsoft products, which currently include:

  • Power BI Desktop Query Editor
  • Power BI Data Flows
  • Excel “Get & Transform” on the Data ribbon
  • SQL Server Analysis Services (SSAS) Tabular projects in SSDT/Visual Studio, or Azure Analysis Services.
  • Azure Data Factory: Power Query task

By the time you read this, there are likely to be more implementations of Power Query.

Basic Syntax

A quick review of the characters that are used to define or encapsulate groups of objects will help to better understand and recognize what you are seeing in the following code examples.

Parentheses, square brackets and Set braces all have different meanings. For references, here they are in the same order:

( ) – Parentheses are used to pass parameters to a function and to control mathematical order of operations.
[ ] – Square brackets encapsulate a set of records.
{ } – Set braces encapsulate a list of values.

Most functions in M are part of an object class (such as Date, Table or Text) and the syntax is something like this:

  • ObjectClass.Function( ),
  • ObjectClass. Function( Parameter ),
  • ObjectClass. Function( Parameter1, Parameter2 )

Examples:

  • Date.AddYear ( <date value> )
  • Table.FromList ( <list object> )
  • Text.End ( <text string>, 2 ) – returns last two characters from right of string

A few older functions that were part of the initial release of Power Query are not prefixed with the object class name. For example the #date( ) function has three parameters: Year number, Month number and Day number.

Any word may be used as a variable. Common examples include step names like Source. Variables are often multiple words containing no spaces, such as ServerName. You are strongly encouraged to rename steps with short but meaningful phrases to make them self-documenting and easy to understand.

Are you with me so far?

The Mighty #shared Function

This little function is literally the key to the entire kingdom and will return every Power Query object defined in your file along with every available M object class and function in the entire language along with complete documentation and code examples. This is the best kept secret in Power Query.

Add a new blank query and type the following into the formula bar, and watch while magic things happen:

=#shared()

The results will be returned as a list of objects and functions. Convert that to a table and you can view the definition of several hundred objects and functions. The M laguange contains nearly 900 functions and more are frequently added to new product versions.

Click the cell next to the link for any function to peek at the definition or click the link to explore related details.

Getting Started

The following examples are hand-written queries entered into the Advanced Editor in Power Query. None of these queries have an actual data source. Values are generated within the code itself but all of these examples can be applied to real queries based on any data source.

Each of the following queries adds a little more to the previous query to explore progressively more advanced objects and code techniques in the M language.

If you create a new blank query in Power Query, it will generate code like this:

let
Source = “”
in
Source

This is the fundamental pattern to get you started.

Values

Let’s modify the script to give the Source variable a text value of “This is a value” (creative, I know).

Use the Advanced Editor to view the generated M script.

Here’s just the script outside of the Advanced Editor:

//——————————————————-
//#1: Object Types: Value
//——————————————————-
let
Source = “This is a value”
in
Source

Use the Done button to close the Advanced Editor to see the result.

Unless they are explicitly data-typed, variable object and data types are automatically assigned, which you will see in the following examples.

The Source variable represents a value type object that is implicitly typed as text. You can see the resulting value in the query designer:

Records

In M, records are encapsulated in square brackets. This script generates two single column records.

//——————————————————-
//#2: Object Types: Record
//——————————————————-
let
Source = [Record1 = “Value 1”, Record2 = “Value 2”]
in
Source

The result is two single column records:

Lists

The List object in M is denoted using Set braces, sometimes called “squiggly brackets”. The following script creates a list of four text-type values:

//——————————————————-
//#3: Object Types: List of Values
//——————————————————-
let
Source =
{ “Value 1”, “Value 2”, “Value 3”, “Value 4” }
in
Source

The list is shown below. Note the column header in the preview grid reminds us that this is a List object. Also note that the designer shows the List Tools ribbon with options to convert the List to a Table, because that’s what we normally do in Power Query to be able to consume List data.

Now, we can start to combine these concepts and techniques. The following query script creates two records, each containing two columns with corresponding values:

//——————————————————-
//#4: Object Types: List of Records
//——————————————————-
let
Source =
{
[Column1 = “Value 1”, Column2 = “Value 2”],
[Column1 = “Value 3”, Column2 = “Value 4”]
}
in
Source

The designer shows two records as links. This is because these are not consumable objects. To use them, you can either convert them to a table or expand one record by clicking the link to view the values.

To peek inside a record without adding a new query step, you can click in a cell but not on the link and the value will be displayed in the preview area at the bottom of the screen.

The first time you see a link presented in this way, you are likely to click on it to see what happens. No damage done, but this will generate a step that produces a single record and you will lose sight of the entire list. To go back, just delete the generated step in the Applied Steps list.

Tables

If you use the Convert to Table button on the toolbar, that will add the steps you see in this query.

The Table.FromList function is a good example of several similar functions that serve a similar purpose. As you look through all the functions within the different object classes, you will notice several different “From” and “To” functions that convert an object from one type to another type. These are both easy to use and they are easy to identify and to understand their purpose. This one should be obvious: As the FromList function in the Table class, it converts a List type object to a Table. Another example, the ExpandRecordColumn in the Table class expands a column containing delimited values into separate columns.

//——————————————————-
//#5: Object Types: Table
//——————————————————-
let
Source =
Table.FromList(
{
[Column1 = “Value 1”, Column2 = “Value 2”],
[Column1 = “Value 3”, Column2 = “Value 4”]
},
Splitter.SplitByNothing(), null, null, ExtraValues.Error
),
#”Expanded Column1″ = Table.ExpandRecordColumn(Source, “Column1”, {“Column1”, “Column2”}, {“Column1”, “Column2”})
in
#”Expanded Column1″

The result of these steps produces the results you see here. We began with a list of two records, each having comma-separated values. After the conversion steps, we now have a Table object with two rows and two columns with values and headers.

Transforming Each Row

When you use the Transform ribbon or menu options to perform most any transformation on a column, you are in essence saying “I want to perform this action on the specified column in each row of the table.” The key word in this statement is “each” and this is an important differentiation in the following M code that was generated when extracting the last character of the column named Column2.

An example of this generated query is shown in Query #6 below.

//——————————————————-
//#6: Object Types: Transform each row
//——————————————————-
let
Source =
Table.FromList(
{
[Column1 = “Value 1”, Column2 = “Value 2”],
[Column1 = “Value 3”, Column2 = “Value 4”]
},
Splitter.SplitByNothing(), null, null, ExtraValues.Error
),
#”Expanded Column1″ = Table.ExpandRecordColumn(Source, “Column1”, {“Column1”, “Column2”}, {“Column1”, “Column2”}),
#”Extracted Last Characters” = Table.AddColumn(#”Expanded Column1″, “Right Char”, each Text.End([Column2], 1), type text)
in
#”Extracted Last Characters”

Let’s isolate just that step and examine the M code in the last line of the query:

#”Extracted Last Characters” = Table.AddColumn(#”Expanded Column1″, “Right Char”, each Text.End([Column2], 1), type text)

The ‘#”Extracted Last Characters”‘ variable is being assigned the outcome of The AddColumn function. We know that the return object type is a table because of the object class Table always returns a Table type object. The AddColumn function takes three parameters:

  • A table type object (usually the name of the previous step variable)
  • Name of the new column as text
  • An expression that returns a value for the new column

The last parameter argument in this case is the output of the Text.End function. You might guess that this is going to be a text type value. The End function, similar to the RIGHT function in Visual Basic, returns a specified number of characters from the end of a text string. But, since Column2 exists in every record in the table returned by the ‘ #”Expanded Column1″ ‘ variable, how do we specify that we want to do this for each record, one-at-a-time? …go got it. We use “each“.

Note to the Right Char column in the following results. It is the right-most character from Column2:

Transforming Columns In-place

Rather than creating a new column containing a value extracted from a previously created column, you can also modify an existing column. This is done using the TransformColumns function of the Table class. Again, we’re looking at the last step of the following query that was generated by using the Extract menu on the Transform ribbon or menu:

//——————————————————-
//#7: Object Types: Transform each row col in place
//——————————————————-
let
Source =
Table.FromList(
{
[Column1 = “Value 1”, Column2 = “Value 2”],
[Column1 = “Value 3”, Column2 = “Value 4”]
},
Splitter.SplitByNothing(), null, null, ExtraValues.Error
),
#”Expanded Column1″ = Table.ExpandRecordColumn(Source, “Column1”, {“Column1”, “Column2”}, {“Column1”, “Column2”}),
#”Extracted Last Characters” = Table.TransformColumns(#”Expanded Column1″, {{“Column2”, each Text.End(_, 1), type text}})
in
#”Extracted Last Characters”

Here is the last step isolated:

#”Extracted Last Characters” = Table.TransformColumns(#”Expanded Column1″, {{“Column2”, each Text.End(_, 1), type text}})

Instead of the column name being passed to the Text.End function, we see an underscore. This is just a shorthand reference that means “use the column currently being transformed”.

Creating a Table from Records

Now back to completely hand-written code… This one’s easy:
By default, new columns are data-typed as “Any” which is essentially an unclassified data type and this is not typically a best practice. Columns should be strongly typed so that grouping, aggregation and DAX functions work correctly in the data model.

In the following query, we apply the TransformColumnTypes function from the Table class. The function requires a table and at least one column name and data type reference. You can pass any number of columns to this function, each encapsulated with set braces, and with another set of braces around the whole thing.

Here, you can see the results: one table with headers, two columns and three records. This all looks good.

Creating a Table from a Set of Records

Now, we’re going to start putting things together by using object type variables. I think you’ll agree that the code is much easier to read.

And we see similar results as before but with a much cleaner approach and readable code.

Extracting a Record from a Table

As a final assignment, I need to get only one record so I essentially need to unwind the objects that were used to assemble this table and get only one record from it. I want lemons so I need to figure out what the ordinal record number is within the table’s record collection.

Since there are three records, there is automatically an ordinal (index) number assigned to each. Sometimes you just have to guess if you don’t already know, if the first record starts at zero or one. Turns out that all these collections are zero-based so if you want to identify the second record (for Lemons) in the table, it’s going to be ID = 1.

Here’s the syntax:

let
Rec1 = [Product ID = 123, Product Name = “Oranges”],
Rec2 = [Product ID = 124, Product Name = “Lemons”],
Rec3 = [Product ID = 125, Product Name = “Apples”],

MySet = { Rec1, Rec2, Rec3 },

MyTable = Table.FromRecords( MySet ),
MyLemons = MyTable{1}

in
MyLemons

SO what’s all the fuss… what does this actually do?

I’m glad you asked. Let’s take a look.

Isn’t she a beauty?

It’s the best looking lemon I’ve seen all day.

Now you’ve seen the mechanics of how M language functions can be used to perform very simple value manipulations and transformations. More importantly, you have learned about the relationship between important objects like Values, Records, Lists of Values and Lists of Records, oh – and let’s not forget – Tables. That’s the one that actually does stuff in a data model,

I hope this has helped you to broaden your horizons with Power Query and the M language. Please comment with you feedback.

6 thoughts on “Next Level Power BI – M Language Basics

  1. Thank you for taking the time to put all this together. If you have more posts like this planned, I’d be grateful. Also for other new folks – the humble comma seems quite the powerful wrecking ball in M so be careful where your commas do and don’t appear. I’ve long wondered where folks like you and Chris Webb learned all this M-fu.

    Like

  2. Pingback: Next Level Power BI – M Language Basics — Paul Turley’s SQL Server BI Blog | MS Excel | Power Pivot | DAX | SSIS |SQL

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