Power Query: Working with Dynamic Column Headings

Power Query is a robust data transformation and manipulation tool within Microsoft Excel and Power BI, helping users automate and streamline data processes. However, working with dynamic column headings—where column names might change from one dataset to the next—can present a challenge. Fortunately, Power Query offers flexible solutions to handle such scenarios, making your queries adaptable to changing data structures.

In this article, we’ll explore how to manage and work with dynamic column headings in Power Query and ensure that your data transformation process remains smooth and effective.

Why Are Dynamic Column Headings a Challenge?

Dynamic column headings are common when you’re working with multiple datasets that share similar structures but have different column names. For example, monthly sales reports might have slightly different headers for each period (e.g., “January Sales,” “Feb Sales,” etc.). This can cause problems if you create queries that rely on static column names. When column headers change, your Power Query steps can break, leading to errors in your data processing.

To handle this, you need flexible solutions that can detect and adjust to changes in column names without manual intervention.

Approaches to Handle Dynamic Column Headings

1. Rename Columns Dynamically

One method is to dynamically rename columns using logic that identifies column headers based on a pattern or position rather than a specific static name. For instance, if the column name contains a particular keyword (e.g., “Sales”), you can rename it automatically without worrying about changes in month or period.

Example:

If you’re working with sales data where the column name changes from “January Sales” to “Feb Sales,” you can implement a transformation that looks for any column containing the word “Sales” and renames it to a generic title like “Total Sales.”

Here’s how you can do it in Power Query:

1. Load Data: Start by loading your dataset into Power Query.
2. Add Custom Step: After loading the data, go to the “Advanced Editor” and modify the M-code to rename columns based on logic.

For example:

let

Source = Excel.Workbook(File.Contents(“yourfile.xlsx”), null, true),

Data = Source{[Name=”Sheet1″]}[Data],

RenameColumns = Table.RenameColumns(Data, List.Transform(Table.ColumnNames(Data), each if Text.Contains(_, “Sales”) then {“_” & _, “Total Sales”} else _))

in

RenameColumns

This code identifies any column with “Sales” in its name and renames it to “Total Sales.” The underscore is a placeholder for any other text in the header, keeping the logic flexible.

2. Use Position-Based Column Indexing

When your datasets have consistent structures but fluctuating column names, you can bypass the column header issue by referencing columns by their position rather than name. This is especially useful when you know that the data you want will always be in the same position.

Here’s how to do it:

1. Import Data: Load the dataset into Power Query.
2. Index Columns by Position: Instead of referencing columns by name (which might change), you reference them by position. For example, to select the second column regardless of its name, you can use this code:

let

Source = Excel.Workbook(File.Contents(“yourfile.xlsx”), null, true),

Data = Source{[Name=”Sheet1″]}[Data],

ColumnByPosition = Table.SelectColumns(Data, {Table.ColumnNames(Data){1}})

in

ColumnByPosition

This selects the second column (position index 1) regardless of its name.

3. Unpivot Data for Flexible Column Headers

Another approach is to unpivot your data, transforming columns into rows. This way, you can treat the column headers as values that are easier to manipulate dynamically.

Example:

Let’s say you have monthly sales data with dynamic column names like “January Sales,” “Feb Sales,” etc. You can unpivot the data so that these headers become a row under a “Month” or “Category” column, allowing for more dynamic handling.

Steps:

1. Load Data: Load the dataset into Power Query.
2. Select Columns to Unpivot: Select all the columns you want to unpivot (these would be your dynamically named columns).
3. Unpivot Columns: Go to the “Transform” tab and click “Unpivot Columns.”
4. Rename the New Columns: After unpivoting, rename the “Attribute” column to something more meaningful, like “Month” or “Category.”

Now, all the dynamic headers are neatly listed in a single column, making your dataset easier to manage.

4. Programmatically Handle Column Changes with Lists

Power Query allows you to store column names in lists, which can then be used to apply transformations dynamically. This is useful if you need to consistently apply certain actions (e.g., replacing values) to columns whose names change.

Example:

If you need to replace certain values in columns whose names might change (e.g., replacing nulls with zeroes), you can create a list of column names, even if those names change.

let

Source = Excel.Workbook(File.Contents(“yourfile.xlsx”), null, true),

Data = Source{[Name=”Sheet1″]}[Data],

ColumnList = Table.ColumnNames(Data),

ReplaceNulls = Table.TransformColumns(Data, List.Transform(ColumnList, each {_, each if _ = null then 0 else _}))

in

ReplaceNulls

This code dynamically applies a transformation to all columns, replacing null values with zeroes, even if the column names vary.

5. Use Conditional Logic to Handle Specific Headers

Sometimes, datasets contain a mix of consistent and changing column names. You can use conditional logic within Power Query to check for specific column headers and apply transformations only if they exist.

Example:

Suppose you’re working with data that sometimes includes a “Discount” column but not always. You can check for the presence of this column and perform actions accordingly.

let

Source = Excel.Workbook(File.Contents(“yourfile.xlsx”), null, true),

Data = Source{[Name=”Sheet1″]}[Data],

ConditionalColumn = if Table.HasColumns(Data, “Discount”) then Table.TransformColumns(Data, {{“Discount”, each _ * 0.9}}) else Data

in

ConditionalColumn

Here, the code checks if the “Discount” column exists and applies a 10% reduction if it’s present. If not, it leaves the data unchanged.

To sum up, working with dynamic column headings in Power Query can seem daunting at first, but with the right techniques, it becomes a manageable and efficient process. Whether you’re renaming columns dynamically, indexing by position, unpivoting data, or using lists and conditional logic, these approaches help you build queries that adapt seamlessly to changing data structures.

By mastering these strategies, you can make your Power Query workflows more flexible and resilient, saving time and ensuring consistency in your data transformations—no matter how often the column names change!