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How to use Power Query to Combine Multiple Files that have different headings

90.9K views
•
February 5, 2022
by
Access Analytic
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How to use Power Query to Combine Multiple Files that have different headings

TL;DR

With a simple tweak to the code, you can consolidate multiple files in Power Query and bring in all columns, even if the files have different headings.

Transcript

thanks to one line of code from a gilreve blog it is so easy to make it super safe when you're consolidating multiple files if one of those files has different headings to the other files it'll bring them in i'll show you how straightforward it is let's go this is the scenario i want to consolidate these three files a b and c here they are a b and ... Read More

Key Insights

  • 🔒 Consolidating multiple files in Power Query is made super safe by making a small tweak to the code.
  • 📂 When using "Get Data from File from Folder" in Power Query, make sure to check for any missing columns that may not be consolidated.
  • 💡 Expanding columns in Power Query only expands the columns from the first file, which can be problematic if subsequent files have different columns.
  • 🔄 A simple solution to expand all columns in Power Query involves using the "Drill Down" option and writing a line of code to get a list of column names.
  • 📝 The list of column names can be used to replace the column headings in the expanding step, ensuring all columns are included in the consolidation.
  • 💡 It is recommended to save the code for future use, as it is challenging to remember the specific syntax.
  • 👏 The provided code and technique were shared by a blog post from Gil Revive, who also has a book on transforming data in Power Query.
  • 💼 Sharing this information and channel with others can help them streamline their data consolidation processes in Power Query.

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Questions & Answers

Q: Why does the standard method of consolidating files in Power Query not bring in all columns if the files have different headings?

The standard method of consolidating files in Power Query only expands columns from the sample file, which is typically the first file in the folder. As a result, any columns that are unique to other files are not included in the consolidation.

Q: What is the solution to ensure that all columns are brought in during consolidation?

The solution involves using a modification to the code that uses the "list.transform" function to gather the column names from each table and then the "list.union" function to create a list of unique column names. This ensures that all columns are included when expanding the data.

Q: How can you modify the code to bring in all columns during consolidation?

To modify the code, you need to wrap the existing code in a "list.transform" function, using "each table.column name" to gather the column names from each table. Then, use "list.union" to create a list of unique column names. This modified code will bring in all columns during consolidation.

Q: What is the benefit of using the modified code to consolidate files in Power Query?

The benefit of using the modified code is that it allows you to bring in all columns from multiple files, even if the files have different headings. This ensures that no data is left out during the consolidation process.

Q: Are there any limitations or considerations when using the modified code?

One limitation is that the modified code may be difficult to remember, so it is recommended to save it for future use. Additionally, if you need to format the columns after consolidation, you may need to reference them or use other techniques, which may require additional steps.

Summary & Key Takeaways

  • The standard method of consolidating multiple files in Power Query may not bring in all columns if the files have different headings.

  • By using a solution from a blog post by Gil Reiv, you can modify the code to ensure that all columns from all files are brought in during consolidation.

  • The modification involves using the "list.transform" function to gather the column names from each table and then using "list.union" to create a list of unique column names before expanding the data.


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