Editing columns

Using the Edit Columns Step in Parabola

The Edit Columns step is one of the most commonly used steps in Parabola because it helps keep your data clean and organized at the beginning of your flow. Almost every workflow starts with an Edit Columns step to ensure data is structured properly before applying further transformations.

Adding the Edit Columns Step

• If this is the first step in your flow, you can drop it directly into the drop zone so data flows into it automatically.
• You can also place it anywhere on the canvas, then connect it to other steps by dragging the arrows.

What the Edit Columns Step Does

This step performs three key functions:

1. Selecting which columns to keep or remove – Remove unnecessary fields to declutter your dataset.
2. Reordering columns – Arrange fields in a logical order for easier analysis.
3. Renaming columns – Standardize column names for clarity.

Example: Cleaning Up Inventory Data

Let’s say we have an inventory dataset, and we only want to keep Product Title, SKU, and In Stock.

• We keep only these columns.
• We rename “In Stock” to “Available” for clarity.
• Once we update the results, the dataset instantly reflects these changes.

If you ever need to compare before and after, you can click Input 1 to view the original dataset and switch back to the results view.

Pro Tip: Standardize Column Names

Clear naming conventions make downstream steps much easier to work with, especially when combining data from multiple sources.
For example:

• If both Shopify and Amazon datasets have an “Order ID” column, rename them as:
  • Shopify Order ID
  • Amazon Order ID

This avoids confusion later in your flow and ensures smooth data integration.

Next Step: Filtering Your Rows

Now that our columns are clean, we can shift focus to rows using the Filter Rows step. Let’s dive in! 🚀

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