Using Multiple Views to Compare Transformations

You're five steps into a pipeline. Filter by region, group by product, sum the revenue, sort descending. It's looking good. Then a thought hits: "What if I grouped by customer instead of product?" You don't want to tear apart the pipeline you've built. You want to try the other path without losing the first one.

This is exactly what multi-view tabs solve in ExploreMyData.

What is a view?

A view is an independent analysis branch on the same file. Each view has its own pipeline, filters, sort order, and search state. They all read from the same source data, but everything after that is separate.

Think of it like browser tabs for your analysis. Tab 1 has your product-level summary. Tab 2 has your customer-level summary. Same CSV, two completely different pipelines running simultaneously. Switch between them instantly.

Creating a new view

To create a new view, look at the file tab at the top. Each loaded file has a tab, and each tab can spawn additional views. Click the option to create a new view from the file tab, and you get a fresh workspace -- no pipeline steps, no filters, just the raw data.

The new view starts clean. It doesn't inherit the pipeline from your first view. That's intentional. You want a blank slate for a different approach.

1. Click on the file tab for the data you want to explore.
2. Select the option to create a new view.
3. A new view tab appears. You're now looking at the raw data again.
4. Build your alternative pipeline from scratch.

Example: Comparing two aggregation strategies

Let's say you have a sales dataset with columns for region, product, customer, and revenue.

View 1 -- Product summary:

1. Filter: region = 'West'
2. Group By: product, aggregate SUM(revenue)
3. Sort: revenue descending

View 2 -- Customer summary:

1. Filter: region = 'West'
2. Group By: customer, aggregate SUM(revenue)
3. Sort: revenue descending

Now you can flip between tabs to compare. Which products drive the most revenue? Which customers? The data comes from the same source and the same region filter, so the totals should reconcile. Two perspectives, zero pipeline teardown.

Same filter (region = West), two different GROUP BY dimensions. Same grand total ($99,500) confirms both views read the same underlying data.

When to use multiple views

A/B pipeline comparison. You want to test whether filtering before grouping gives different results than grouping the full dataset and filtering after. Build each approach in its own view. Compare the output. This is especially useful when you're not sure if NULLs or edge cases affect your aggregation.

Different audiences, same data. Your manager wants a high-level summary grouped by quarter. Your analyst colleague wants the row-level detail with calculated columns. Build both as separate views. When someone asks a question, switch to the relevant view instead of reconstructing it.

Raw vs. cleaned data. Keep your first view as the raw import -- no transformations, no filters. Use it as a reference to check original values when something looks off in your cleaned view. "Wait, was that NULL in the original data or did my Fill Missing step create it?" One click to check.

Iterative exploration. You're not sure what analysis to run. Start a view, poke around, realize it's a dead end. Instead of undoing everything, leave that view as-is and start a new one. If the dead end turns out to be useful later, it's still there.

Each view is fully independent

This is worth emphasizing. Changing the pipeline in View 2 does not affect View 1 at all. They share the source file, and nothing else. Each view maintains its own:

• Pipeline steps (all operations and transformations)
• Filter state
• Sort order
• Search/find state
• Column visibility and ordering

Deleting a step in one view doesn't touch the other. Adding a column in one view doesn't add it to the other. They're parallel universes that happen to start from the same data.

Practical tips

Don't overdo it. Three or four views on one file is useful. Ten views gets confusing. If you find yourself with that many, you probably need to export intermediate results and work with separate files.

Name your mental models. Keep track of what each view is for. "View 1 is my cleaned data. View 2 is the quarterly summary. View 3 is the outlier investigation." Having a clear purpose for each view keeps your exploration organized.

Use one view as ground truth. Designate your first view as the "don't touch" reference. Raw data, no pipeline. This gives you a quick sanity check when results in other views look unexpected.

Try multi-view analysis on your data →