Introduction to Power Query and Its Importance

power query article

Power Query, an integral component of the Microsoft Power BI suite, stands as a pivotal tool for data transformation and preparation. It simplifies the process of gathering and shaping data from various sources, making it an indispensable asset for analysts and data professionals.

In this era of data-driven decision-making, the ability to efficiently manipulate and transform data is crucial. Power Query, with its user-friendly interface and powerful capabilities, enables users to clean, reshape, and consolidate data without needing advanced programming skills.

This greatly streamlines the data preparation process and ensures consistency and reliability in data handling. By leveraging Power Query, businesses and individuals can make more informed decisions, backed by accurately processed data.

In the next section, we will discuss the benefits of using functions in Power Query.


The Benefits of Standardizing Transformations in Data Processing

Standardizing transformations in data processing is a key practice for ensuring accuracy and efficiency, especially when tools like Power Query are involved. This standardization, often implemented through functions, ensures that data from various sources is processed uniformly, maintaining consistency and integrity across datasets.

For instance, consider the common challenge of aggregating data from different departments within a company, where each department might have its own format for recording information. By standardizing the transformation process, you can harmonize these diverse data formats into a single, coherent structure, making it easier to perform comprehensive analysis and reporting.

This standardization not only streamlines the data transformation process but also significantly reduces the likelihood of errors and inconsistencies, which are common in manual or ad-hoc data handling methods. Moreover, a standardized approach promotes clarity and simplicity in data workflows.

When a transformation is defined in a reusable, well-documented function, it becomes easier for team members to understand, modify, and leverage these processes, fostering collaboration and efficiency.

In essence, standardizing data transformations, particularly in an environment like Power Query, is a strategic move towards more reliable, scalable, and collaborative data management practices.

Moving on, we’ll discuss the nature of the M language, a powerful tool used in Power Query for data transformation, focusing on its unique features such as memory constraints and immutable data.

Understanding the M Language and Its Application in Power Query

The M language, fundamental to Power Query, is a potent and functional language crafted specifically for data manipulation and transformation. A key feature of Power Query is its user-friendly graphical interface that lets users create complex data transformations through simple point-and-click actions.

Behind each of these actions, Power Query generates M code, which reflects the transformation steps. This feature demystifies the process of coding in M, making it accessible even to those with little to no programming background.

In Power Query, the data you work with is essentially part of a query. This query starts with a source – your raw data – and is transformed through a sequence of steps, each represented by M code. The beauty of this approach lies in its clarity and precision. Each step in the transformation process is dependent on the outcome of the previous one, forming a chain of transformations.

What’s crucial to understand here is that these steps do not alter the original data. Instead, they create a new version of the data with each step. This immutability is a core principle of the M language, ensuring the integrity and traceability of your data.

For instance, if you’re using Power Query to clean a sales dataset, you might start by removing unnecessary columns, then filtering out sales below a certain threshold, and finally sorting the data by date. In the Power Query interface, these actions are intuitive clicks and selections.

Behind the scenes, each action generates M code that precisely defines the transformation. The original dataset remains untouched, while the transformations create a step-by-step evolution of the data, culminating in the final, cleaned dataset.

This approach not only makes data transformations transparent but also allows for easy modification and debugging. You can visually track each transformation step in the Power Query editor, understand the M code generated for each step, and modify it as needed.

Next, we’ll explore the various data types and operations in the M language, providing insights into how different types of data are handled and the kinds of operations that can be performed.

Data Types and Operations in the M Language

Understanding the various data types in the M language and their corresponding operations is key to harnessing its full potential for data transformation. Here’s a breakdown of the primary data types, their characteristics, examples, memory usage, and common operations:

Text (String)
CharacteristicsStores textual data.
Example“Hello, World!”
Common OperationsConcatenation (&): “Hello, ” & “World!” → “Hello, World!”

Equality (=): “Text1” = “Text2”
Text FunctionsText.Length(“Hello”) → 5
SubtypesInteger, Decimal, Currency, etc.
Example42, 3.14, £10.00
Common OperationsArithmetic (+, -, *, /): 10 * 2 → 20

Comparison (>, <, =): 5 > 3
Numeric FunctionsNumber.Round(3.14) → 3
Logical (Boolean)
CharacteristicsRepresents true or false value
Exampletrue, false
Common OperationsLogical operations (and, or, not): true and false → false
Conditional checksif x > 10 then true else false
Date and Time
SubtypesDate, Time, DateTime, Duration
Example #date(2024, 1, 6), #time(12, 0, 0), #datetime(2024, 1, 6, 12, 0, 0)
Common OperationsDate arithmetic: #date(2024, 1, 6) + #duration(1,0,0,0) → #date(2024, 1, 7)
Date FunctionsDate.Day(#date(2024, 1, 6)) → 6
CharacteristicsA collection of fields, each with a name and value.
Example[Name=”John”, Age=30, City=”New York”]
Common OperationsField access: Record.FieldName
Record FunctionsRecord.FieldCount(record)
CharacteristicsA collection of records, each record represents a row.
ExampleA table with columns for Name, Age, and City.
Common OperationsTable manipulation (adding/removing columns, filtering rows)
Table FunctionsTable.Sort(table, {“Name”, Order.Ascending})
CharacteristicsAn ordered collection of values (can be of mixed types).
Example{1, “two”, 3.0}
Common OperationsList manipulation (adding/removing items)
List FunctionsList.Sum({1, 2, 3}) → 6
CharacteristicsRepresents binary data.
ExampleFile content, image data.
Common OperationsBinary processing (read/write)
Binary FunctionsBinary.Length(binaryData)
CharacteristicsRepresents a missing or undefined value.
Common OperationsNull checks: if x = null then …

Each data type in M language is designed to handle specific kinds of data and comes with a set of operations and functions tailored for it. Understanding these data types and their operations is crucial for effective data transformation and analysis in Power Query.

Data Types and Operations in the M Language (Extended Focus on Type Conversion)

When working with type conversions in the M language, especially within Power Query, understanding both function-based and implicit conversions is crucial. Here are some detailed examples to illustrate these concepts:

Function-Based Type Conversions for Tables:
Converting a column’s data type:If you have a column of text representing numbers and want to convert it to a numeric type, you can use Table.TransformColumnTypes.

Example: Table.TransformColumnTypes(Source, {{“Column1”, type number}})
Extracting a column as a list and performing operations: You can extract a column from a table and work with it as a list.

Example: Source[Column1] would return the contents of “Column1” as a list.
Implicit Type Conversions:
Concatenating strings with other data types: In M language, you can concatenate a string with another data type, and the non-string data type will be implicitly converted to a string.

Example: “Order Number: ” & 123 → “Order Number: 123”

Other Type Conversion Scenarios:

Using arithmetic operations to implicitly convert types:When you perform arithmetic operations, types can convert implicitly to accommodate the operation.

Example: 1 + 1.0 results in 2.0, implicitly converting the integer 1 to a float.
Converting dates and times: You can convert a date to text or vice versa, which is often required for formatting or parsing purposes.

Example: Text.From(#date(2024, 1, 6)) → “2024-01-06”

These examples highlight the flexibility of the M language in handling different data types, allowing for seamless integration and manipulation of data. Understanding these conversion mechanisms is key to effective data processing in Power Query.

Conditionals in the M Language

Conditionals in the M language are essential for controlling the flow of data transformations based on specific criteria or conditions. They allow for decision-making processes within your data queries, enabling more dynamic and responsive data manipulation. Here’s an overview of how conditionals work in M language:

Basic Conditional Structure:
  • The primary conditional structure in M is the if…then…else statement.
  • Syntax: if [condition] then [expression if true] else [expression if false]
  • Example: if x > 10 then “Greater than 10” else “10 or less”
  • This structure evaluates the condition (x > 10), and if it’s true, it returns the first expression; otherwise, it returns the second expression.
Nested Conditionals:
  • For more complex decision-making, conditionals can be nested within each other.
  • Example: if x > 10 then “Greater” else if x = 10 then “Equal” else “Lesser”
  • In this example, if x is greater than 10, it returns “Greater”. If x is exactly 10, it returns “Equal”, and for all other values, it returns “Lesser”.
Using Conditionals in Table Transformations:
  • Conditionals are particularly useful in transforming table data.
  • Example: Adding a conditional column in Power Query that categorizes data based on a condition.
    • Table.AddColumn(Source, “Category”, each if [Sales] > 1000 then “High” else “Low”)
    • This adds a new column named “Category”, where each row is categorized as “High” if its Sales value is greater than 1000, otherwise “Low”.
Conditional Operations on Lists:
  • You can also apply conditionals to lists, for instance, filtering a list based on a condition.
  • Example: List.Select({1, 2, 3, 4, 5}, each _ > 3) → {4, 5}
  • This filters the list to include only elements greater than 3.

Understanding and effectively using conditionals allows for more nuanced data queries, enabling the data to be shaped and refined in response to dynamic criteria. This flexibility is particularly valuable in scenarios where data needs to be categorized, filtered, or transformed based on specific conditions:

Introduction to Functions in the M Language

Functions in the M language are powerful tools for encapsulating repetitive or complex data transformation logic. They allow for more modular, readable, and maintainable code in Power Query. They can take input (arguments), process it, and returns a result. Functions can range from simple operations, like adding two numbers, to complex data transformations.

Creating a Basic Function:
  • Functions are defined using the let…in syntax. Example:
(number1 as number, number2 as number) as number =>
    result = number1 + number2

Example usage
addNumbers(5, 3) // This will return 8
  • This example defines a function addNumbers that takes two numbers as input and returns their sum.
Using Functions for Data Transformations:
  • Functions are particularly useful in data transformations, allowing you to apply the same logic to multiple data sets or columns.
  • Example: Creating a function to standardize text formatting.
    standardizeText = (inputText as text) as text =>
    standardizeText(" example text ") // Returns "EXAMPLE TEXT"
  • This function takes a text input, trims any leading/trailing spaces, and converts it to uppercase.
Benefits of Using Functions:
  • Reusability: Once defined, functions can be reused across different queries and transformations.
  • Clarity: Functions encapsulate specific tasks, making your code more organized and easier to understand.
  • Efficiency: By abstracting repetitive tasks into functions, you can make your overall workflow more efficient and less prone to errors.

Understanding and utilizing functions is a significant step in mastering data transformation in Power Query. It enables a more structured approach to handling complex data tasks, making your data processing workflows both robust and flexible.

Writing Basic Functions in M Language

Writing basic functions in the M language is a foundational skill for effective data manipulation in Power Query. Basic functions allow you to encapsulate common data transformation tasks into reusable blocks of code. Here’s how to create and use basic functions:

Structure of a Basic Function:
  • Basic functions are defined using the `let…in` syntax, with parameters and a return value.
  • Syntax Example:
    myFunction = (parameter1 as type, parameter2 as type) as return_type =>
        result = // transformation logic using parameters
    myFunction(value1, value2)
Example of a Simple Function:
  • Let’s create a function that calculates the average of two numbers.
(n as number) as number =>
    result =  if n <= 1 then 1 else n * factorial(n - 1)
//factorial(5)  Returns 120
  • This function takes two numbers, calculates their average, and returns the result.
Applying Functions to Data:
  • Functions can be applied to tables, records, and lists within Power Query.
  • For example, to apply the ‘average’ function to a table column:
Table.AddColumn(Source, "AverageColumn", each average([Column1], [Column2]))
  • This adds a new column to the table, where each row contains the average of values from ‘Column1’ and ‘Column2’.
Debugging and Testing Functions:
  • Test your functions with different inputs to ensure they handle various scenarios, including edge cases.
  • Use the Power Query Editor to step through the function and inspect intermediate results for debugging.

Creating and using basic functions in M language empowers you to streamline your data transformation processes. By abstracting common tasks into functions, you make your code more efficient, readable, and maintainable.

Recursive Functions as an Alternative to Loops in M Language

Recursive functions in the M language offer a powerful alternative to traditional looping constructs, particularly useful in scenarios where iterative processes are required for data transformation. A recursive function is a function that calls itself until it reaches a base condition. Here’s how recursive functions work and can be applied in M language:

Understanding Recursive Functions:
  • A recursive function typically has two main components: a base case and a recursive case.
  • The base case defines the condition under which the function stops calling itself, preventing infinite loops.
  • The recursive case is where the function calls itself with modified parameters.
Example of a Recursive Function:
  • Consider a function that calculates the factorial of a number (n! = n * (n-1) * (n-2) * … * 1).
(n as number) as number =>
    result =  if n <= 1 then 1 else n * factorial(n - 1)
//factorial(5)  Returns 120
  • In this example, the base case is when n <= 1, and the recursive case involves calling factorial with n – 1.
Using Recursive Functions for Data Transformations:
  • Recursive functions can be particularly useful for processing hierarchical or nested data structures, like nested lists or hierarchical tables.
  • Example: A recursive function to flatten a nested list.
 (lst as list) as list => 
    result = List.Accumulate(lst, {}, (state, current) => 
    state & (if Type.Is(Value.Type(current), type list) then flattenList(current) else {current})) 
//flattenList({1, {2, 3}, 4, {5, {6, 7}}})   Returns {1, 2, 3, 4, 5, 6, 7}
Advantages of Recursive Functions:
  • Recursive functions are valuable for tasks that have a naturally recursive structure, like tree traversals or repetitive processing of elements.
  • They can make the code more readable and concise compared to traditional loops.

Recursive functions in M language are a testament to its functional programming capabilities, providing an elegant solution for complex data transformations. Understanding how to write and use recursive functions expands the range of tasks you can perform efficiently in Power Query.

Implementing Iterative Loops with Recursive Functions in M Language

While the M language doesn’t have traditional loop structures like for or while loops found in other programming languages, you can achieve similar iterative functionality using recursive functions. This is particularly useful for tasks that require iteration with a counter. Here’s how to implement an iterative loop using a recursive function in M language:

Concept of Recursive Loop with a Counter:
  • The idea is to use a recursive function that keeps track of iterations through a counter parameter.
  • The function calls itself with an updated counter until a certain condition is met.
Example: Iterative Loop to Sum Numbers Up to N:
  • Let’s create a recursive function that sums all numbers from 1 up to a given number n.
 (n as number, counter as number, accumulator as number) as number =>
    result = if counter > n then accumulator
    else sumUpToN(n, counter + 1, accumulator + counter)
//sumUpToN(5, 1, 0)  Returns 15 (1+2+3+4+5)
  • In this example, the function sumUpToN takes three parameters: n (the target number), counter (current iteration), and accumulator (running sum).
  • The base case is when counter > n, and the recursive case updates counter and accumulator.
Using Recursive Functions for Iterative Tasks:
  • Recursive functions can effectively handle tasks typically addressed by loops in other languages.
  • They are particularly useful in Power Query for sequential data processing or cumulative calculations.
Advantages and Considerations:
  • Recursive functions can make the code more readable and align with the functional programming style of M language.
  • However, it’s important to carefully manage the base case to avoid infinite recursion.

This approach to simulating iterative loops using recursive functions demonstrates the versatility of the M language in handling various data processing patterns. By understanding and applying these techniques, you can effectively tackle a wide range of iterative tasks in Power Query.

Typed Functions in M Language: Parameters, Nullable, and Optional Types

In M language, functions can be more precisely defined using typed parameters. This includes specifying types for parameters and defining whether they are nullable or optional. Typed functions enhance the clarity and robustness of your code in Power Query. Here’s an overview:

Defining Typed Functions:
  • When you create a function in M, you can specify the expected type for each parameter.
  • This helps in ensuring that the function is used correctly and makes the code more readable and self-documenting.
Example of a Typed Function:
  • A function to multiply two numbers, specifying that both parameters should be of the number type:
(num1 as number, num2 as number) as number =>
    result = num1 * num2
//multiply(5, 10)  Returns 50
Nullable and Optional Parameters:
  • Parameters in M can also be marked as nullable or optional.
  • Nullable means the parameter can accept null values.
  • Optional parameters are not required for the function to execute.
  • This adds flexibility to your functions, allowing them to handle a wider range of input scenarios.
Example with Nullable and Optional Parameters:
  • A function that adds two numbers, where the second number is optional and defaults to 0 if not provided:
(num1 as number, optional num2 as nullable number) as number =>
    result = num1 + if num2 = null then 0 else num2
// addNumbers(10, 5)  Returns 15
// and addNumbers(10)  Returns 10, treating num2 as 0

Typed functions with well-defined parameters play a crucial role in ensuring that your data transformations in Power Query are accurate, predictable, and error-free. By leveraging typed, nullable, and optional parameters, you can create functions that are both powerful and adaptable to various data scenarios.

Creating Fail-Safe Functions in M Language: Exception Handling and Graceful Failure

In M language, crafting fail-safe functions involves implementing strategies for exception handling and graceful failure. This ensures that your functions can cope with unexpected inputs or scenarios without causing the entire data transformation process to break down. Here’s how you can create robust, fail-safe functions in Power Query:

Understanding Exception Handling:

  • Exception handling in M language is about anticipating and managing potential errors or unusual conditions that may arise during function execution.
  • The goal is to prevent errors from propagating and causing larger issues in your data processing workflow.

Using try…otherwise for Error Handling:

  • M language provides the try…otherwise construct to handle potential errors in a controlled manner.
  • The try block contains the code that might produce an error, and the otherwise block defines what to do if an error occurs.

Example of a Fail-Safe Function:

  • A function to divide two numbers, which handles division by zero (a common error scenario):
(numerator as number, denominator as number) as any =>
    result = try (numerator / denominator) otherwise null
//safeDivide(10, 2)  Returns 5
// and safeDivide(10, 0)  Returns null instead of causing an error

Graceful Failure:

  • Graceful failure means that when a function encounters an error, it fails in a way that is manageable and does not disrupt the overall process.
  • In the example above, returning null in case of an error (division by zero) is an instance of graceful failure.

Logging Errors:

  • In more complex scenarios, you might want to log errors or provide specific feedback.
  • This can be achieved by returning a record or a custom message indicating the error, instead of just nullifying the result.

Creating fail-safe functions in M language is a crucial aspect of developing reliable and robust Power Query solutions. By effectively managing and responding to errors, you can ensure that your data transformation processes are more resilient and user-friendly.


In conclusion, mastering the M language and its application in Power Query is a journey that opens up vast opportunities in data transformation and analysis. Through this blog, we’ve explored the fundamental aspects of the M language, starting from its unique characteristics and the importance of standardizing transformations, to the intricacies of various data types, the power of conditionals, and the versatility of functions.

Recap of Key Points:
  • We began by understanding the importance of Power Query and the role of the M language in data transformations.
  • We delved into data types, highlighting their characteristics, operations, and conversion techniques, emphasizing the list nature of table columns.
  • The exploration of conditionals illustrated their critical role in dynamic data manipulation.
  • The focus on functions, from basic to recursive, and typed to fail-safe functions, showcased the power and flexibility of M language in encapsulating data transformation logic.
Empowerment Through Knowledge:
  • Equipped with this knowledge, you can tackle a wide range of data transformation challenges, making your workflows more efficient, reliable, and scalable.
  • The skills in crafting functions, handling various data types, and applying conditionals enable you to transform raw data into insightful, actionable information.
Encouragement for Practice and Exploration:
  • As with any language, proficiency in M comes with practice. I encourage you to apply these concepts in real-world scenarios, experiment with different functions, and explore the nuances of the M language.
  • Remember, the journey in data transformation is ongoing, and there’s always more to learn and discover.
Resources for Further Learning:
  • To continue advancing your skills, consider exploring advanced topics in M language, engaging with community forums, and experimenting with real-life data sets.
Final Thoughts:
  • Your journey in mastering data transformation with Power Query and the M language is not just about technical skill development; it’s about empowering yourself to make data-driven decisions and bring clarity to complex information.

Thank you for joining me on this explorative journey into the world of Power Query and the M language. I hope this blog serves as a valuable resource in your data transformation endeavors and sparks further exploration and mastery in this exciting field.

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Victor is an Analytics Consultant for Iteration Insights. With a Computer Science background and a certificate in Business Intelligence and Data Analytics, Victor finds elegant and efficient solutions that scale to serve his client's needs. Be it on the Front-End or Back-End of analytics development.

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