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What Language is Power BI Advanced Editor?

Cody Schneider••8 min read

If you've ever clicked on the "Advanced Editor" button in Power BI's Power Query, you've likely felt a mix of curiosity and panic. You're suddenly looking at a page of code, and you need to know: what language is this? This article will clear up the confusion, explain exactly what this language is, and show you why learning even a little bit of it can significantly upgrade your Power BI skills.

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It’s M, Not DAX: Clearing Up a Common Power BI Misconception

The first and most important thing to know is that the language in the Power BI Advanced Editor is not DAX. This is the single biggest point of confusion for users new to Power BI.

Both DAX and the language we're discussing are crucial to Power BI, but they serve completely different purposes at different stages of your reporting workflow.

  • DAX (Data Analysis Expressions): This is the language you use after your data is already imported and modeled. You write DAX to create calculated columns and measures that perform analysis on your data. Think of it as the language for asking questions of your data model, like "What were our total sales for the last quarter?" or "How does this year's revenue compare to last year's?" You typically write DAX in the report view or data view of Power BI Desktop.
  • M (Power Query M Formula Language): This is the language used in the Power Query Editor (and its Advanced Editor window). M is for data transformation and preparation. It's the language that defines the step-by-step process of connecting to data, cleaning it up, reshaping it, and getting it ready before it's loaded into your data model where DAX can analyze it.

A simple analogy helps clarify the difference: Imagine you're cooking a meal. M is the messy kitchen prep work. It’s you grabbing ingredients (data sources), washing the vegetables (removing errors), chopping the onions (splitting columns), and mixing the marinade (merging queries). All of this happens behind the scenes before everything goes into the oven. The Advanced Editor is just a window that shows you the exact recipe for this prep work. DAX is combining the prepped ingredients to create the final dish. You take the cleaned, chopped, and prepared ingredients and use DAX formulas to calculate things like "total calories" (Total Sales), "cooking time per pound" (Average Sale Amount), or "spiciness level" (Year-Over-Year Growth). It's the final analysis you present on the plate.

In short, when you're in the Power Query Editor, you're working with M. Once you click "Close & Apply," you move into the world of DAX.

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What Exactly is M? A Quick Introduction

"M" is the informal name for the Power Query M formula language. It was developed by Microsoft specifically as the engine for behind-the-scenes data mashup and transformation tasks in both Power BI and Excel's Power Query.

When you use Power Query's user-friendly interface to click buttons like "Remove Columns," "Split Column by Delimiter," or "Change Type," you are actually generating lines of M code in the background. The Advanced Editor simply gives you a complete, top-to-bottom view of all the code generated by your clicks, allowing you to view, edit, and write your own transformation steps manually.

Key Characteristics of M Code

  • It's a Functional Language: Don't let the term "functional" scare you. It simply means that M is focused on executing a series of steps (functions) in a specific order. Each step takes the output from the previous step, applies a transformation, and passes the result along to the next step.
  • It's Case-Sensitive: This is a big one. Unlike DAX, which is not case-sensitive, M treats myTable and MyTable as two completely different things. Misspelling or mismatched casing is a frequent source of errors for beginners.
  • It's Step-Based: M queries are built sequentially. You start with a source, apply step two, then step three, and so on. This makes it relatively easy to follow the logic and debug problems, as you can go back step-by-step until you find where things went wrong.
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Demystifying the M Script: The "let" and "in" Statements

When you first open the Advanced Editor, you might feel intimidated, but almost every M script follows the same fundamental structure: the let and in block.

let
    // ... all your transformation steps are defined here ...
in
    // ... the final result you want to output is specified here ...
  • The let statement essentially says, "Here are a list of things I'm going to do." Each transformation step inside the let is assigned a variable. Typically, each new step refers to the variable from the previous step as input. This creates that step-by-step chain of events.
  • The in statement simply declares which one of the variables defined in the let block should be the final output of the query. This is almost always the name of the very last step you created.

A Practical Example Explained

Let's say you connect to a CSV file from the web, promote the first row to be headers, and change a column's data type. Even without learning to code, the Power Query UI generates something that looks like this:

let
    // Step 1: Connects to the web-based CSV file
    Source = Csv.Document(Web.Contents("https://samplecsvs.s3.amazonaws.com/SalesJan2009.csv"), [Delimiter=",", Columns=8, Encoding=1252, QuoteStyle=QuoteStyle.None]),
    
    // Step 2: The "Promote Headers" button takes the results of Step 1 ("Source"), and promotes the first row
    PromotedHeaders = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),

    // Step 3: The "Change Type" button applies transformations to the output of Step 2 and converts column types
    ChangedType = Table.TransformColumnTypes(PromotedHeaders, {{"Price", type number}, {"Latitude", type number}})
in
    // This specifies that the final output of the query should be the result of the last step
    ChangedType

Let's break that down:

  • Source: This is the first variable. It calls a function called Csv.Document, which, combined with Web.Contents, fetches that CSV file and defines this as the initial table.
  • Promoted Headers: Notice the # and quotes. They are used automatically when assigning a variable name containing spaces or special characters. This step references the previous step, Source, and uses the Table.PromoteHeaders() function to transform.
  • Changed Type: This variable in turn references the PromotedHeaders variable and uses the function Table.TransformColumnTypes to convert the "Price" and "Latitude" columns to numeric types.
  • in ChangedType: Tells M that the result of the query is what is stored in the ChangedType variable.

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Why You Should Bother Learning M?

Power Query's graphical interface is fantastic, and you can accomplish quite a bit with it without ever looking at a single line of code. But there comes a point where some advanced tasks require you to manually edit M. That's where knowing M can be a game-changer.

  • Precision Automation & Troubleshooting: When queries break, being able to dive into the Advanced Editor and pinpoint the error details yourself allows you to solve complex problems.
  • Advanced Transformations: Being able to write custom functions, dynamically change query steps, or perform recursive transformations upon user input are key tasks that require M skills.
  • Efficient Function Use: M has powerful functions like List.Accumulate to create custom lists efficiently. Understanding how and when to leverage these can significantly save time.
  • Documentation in Your Code: Keeping your M code well-documented helps ensure that you and others understand the actual steps taken, avoiding the "black box" effect. This makes editing or debugging queries much easier, especially if you return to them months later.

Getting Started with M: Practical Tips to Learn M

Tackling a new language can be daunting, but you don't have to start from a blank page and panic. Instead, take a more gradual approach that isn't overwhelming:

  • Observe and Learn: Start by using Power Query Editor UI to create simple queries. Pay attention to the M format generated as you make changes, then open the Advanced Editor to see how those actions translate into code.
  • Experiment with Modifications: Use the knowledge generated to make small changes to your queries in the Editor. Change column names or types, and modify text values to see how it builds familiarity with the syntax.
  • Practice by Example: Enable the "Formula Bar" in Power Query Editor, so you can better understand M code while writing and editing scripts in the Advanced Editor for a small project.
  • Use Online Resources: Find credible M resources and communities online to learn from examples. Sites like Microsoft Documentation or DataChant provide tutorials and blog posts by experts.

In summary, if you have to work with the Power Query Advanced Editor, it's helpful to understand the basics of its language for a smoother data transformation experience. Enhancing your M skills can significantly ease the process of working with Power BI tools. That's why we created a platform designed to help you in your learning journey, supporting the mastering of M's transformative capabilities. As you dive deeper, you'll start seeing how learning M can unlock vast new capabilities within your existing data workflows. Try using these practical steps to elevate your Power BI capability further.

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