Text to Columns Tool Explained

Welcome back to Alteryx Snack, your go-to source for bite-sized lessons on how to master Alteryx! Today, we’re diving into a powerful but simple tool: the Text to Columns tool. When you need to split up text-based data into distinct fields—such as addresses, product codes, or full names—this tool will make the task fast and easy.

And for today’s snack, we’ve paired this tool with a fresh and satisfying treat: apple slices with peanut butter. Much like this delicious snack that divides the crisp apple and creamy peanut butter, the Text to Columns tool neatly splits your text data into easy-to-handle portions.

What Does the Text to Columns Tool Do?

The Text to Columns tool in Alteryx splits a single text field into multiple columns based on a delimiter (such as a comma, space, or any character that separates values). This makes it ideal for cases where you have combined information in one field, like full names you want to split into first and last names or product IDs that you want to separate into distinct components.

How Does It Work?

1. Drag and Drop: Add the Text to Columns tool from the Parse category onto your canvas, and connect your data source.

2. Configure the Tool:

   • Select the Field: In the configuration pane, choose the text field you want to split.

   • Delimiter: Specify the character or delimiter that separates your data. Common delimiters include spaces, commas, slashes, or any other custom character.

   • Number of Columns: Choose how many columns you want to split into, or allow the tool to split the data into as many columns as necessary based on the number of delimiters in your text.

3. Advanced Options:

   • Extra Delimiters: You can choose how Alteryx should handle rows with more delimiters than expected. Options include creating extra columns or discarding the additional data.

   • Ignore Null Fields: This option allows you to skip over empty or null fields when splitting. For example, if there’s a blank space between delimiters in your data, the tool will ignore it instead of creating an empty column.

   • Limit Output Columns: You can restrict the maximum number of columns, which is helpful when you know the exact structure of your data or want to avoid excessive splitting that could create too many null fields.

4. Output: The tool produces new columns for each value split from the original text field.

Best Practices for Using the Text to Columns Tool

• Consistent Delimiters: Ensure your data has consistent delimiters throughout; irregular delimiters will result in an unpredictable number of columns and rows with null values.

• Extra Delimiters: Always check for extra delimiters in your data, as they can cause unexpected results. Use the advanced options to control how these are handled.

• Inspect the Results: After splitting, use a Browse Tool or a quick visual inspection to ensure the data has split correctly and there are no null or misaligned columns.

Split to Rows: An Alternative Approach

In addition to Text to Columns, Alteryx offers the Split to Rows functionality, which operates similarly but instead of splitting a field into columns, it separates the values into multiple rows. This is especially useful when you have multiple values in one cell that should be treated as separate records, such as a list of tags or keywords. Using this option, each split value gets its own row, simplifying downstream analysis.

For example, if you have a list of products in a single field (Product A, Product B, Product C), using Split to Rows will turn that single row into three separate rows, with each product in its own row, while keeping the rest of the data intact.

Alteryx Text to Columns vs. Excel’s “Text to Columns”

If you’re familiar with Excel’s Text to Columns function (available under the Data tab), you know it serves a similar purpose. However, there are some important differences between Excel and Alteryx.

Comparison Matrix: Alteryx vs. Excel

Excel’s Approach:

In Excel, Text to Columns works by selecting a cell range, choosing the delimiter, and manually running a wizard that splits the text into columns. While effective for small datasets, Excel’s solution requires manual steps and cannot be easily automated.

Alteryx Advantage:

Alteryx’s Text to Columns tool excels in automation, allowing users to set up reusable workflows that can process large datasets with dynamic splitting. Alteryx also offers greater control over how to handle inconsistencies like nulls or extra delimiters, making it a better choice for complex or repetitive tasks.

Advanced Splitting and Dynamic Data

One of the most powerful aspects of Alteryx's Text to Columns tool is its ability to handle dynamic data. If your dataset varies in structure (for example, some rows have two delimiters while others have four), the tool can adapt by creating as many columns as needed. You can also choose whether to discard extra data or create new columns dynamically as the delimiter count increases, providing flexibility that Excel doesn’t offer.

This is particularly useful in cases where data input formats aren’t strictly controlled, like customer input forms or log files, allowing the tool to still make sense of the input and output clean, usable data.

Example Use Case

Imagine you're dealing with a product database where each product ID includes a country code, category code, and item code, all separated by hyphens (e.g., US-123-4567). You need to split these IDs into three distinct fields for analysis.

Using the Text to Columns tool in Alteryx:

• You can set the hyphen (-) as the delimiter.

• Split the data into three columns (Country, Category, and Item Code).

• Any extra data or variations in format (e.g., US-123) can be handled dynamically or removed, depending on your advanced settings.

This streamlined approach allows you to handle variations in your dataset, unlike Excel, where every change requires manual adjustment.

Error Handling and Output Options

In Alteryx’s Text to Columns tool, error handling is built into the workflow. If there are discrepancies, such as mismatched delimiter counts across rows, the tool will handle these by outputting null values or extra columns based on your configuration. Reviewing the output with a Browse Tool can help catch any misalignments early.

Selecting Which Columns to Keep

Sometimes, after splitting text data into columns, you might not need all the resulting fields. For instance, if you're splitting an address field into street, city, state, and ZIP code, but you're only interested in the ZIP codes, you can easily manage this by using the Select Tool after the Text to Columns process. This tool will let you discard any unneeded columns, ensuring that your output remains concise and relevant to your analysis.

Snack Break: Apple Slices with Peanut Butter

Today’s snack, apple slices with peanut butter, is a refreshing and nutritious option. Much like how the Text to Columns tool breaks down your data into simple, easy-to-understand columns, this snack offers a balance of flavors in convenient, bite-sized portions.

Conclusion

The Text to Columns tool in Alteryx is an efficient, flexible, and powerful tool for breaking down complex or combined text data. Whether you’re parsing product IDs, addresses, or multi-level codes, this tool saves time and ensures your data is in the right format for further analysis. With more advanced options, such as ignoring null values and dynamically handling delimiters, it’s a step up from Excel’s Text to Columns function, especially when working with larger datasets or creating reusable workflows.

So the next time you need to split data, reach for the Text to Columns tool in Alteryx—and maybe enjoy some apple slices with peanut butter while you’re at it!

Happy snacking and analyzing!