Reformatting and Structuring Data

Even when your data values are correct, they might be in the wrong format or structure for your analysis. Dates in different formats, currencies with and without symbols, or data organized in columns when it should be in rows—AI can handle all of these transformations.

Fixing Date Formats

Mixed date formats are one of the most common problems, especially when data comes from multiple sources or countries:

Handling Ambiguous Dates

Some dates are genuinely ambiguous. "03/04/2024" could be March 4th or April 3rd depending on the regional format used:

Standardizing Currency and Numbers

Financial data often mixes formats:

Converting Units

When data combines different measurement units:

Splitting Columns

Sometimes a single column contains multiple pieces of information that should be separated:

Merging Columns

The opposite problem—data spread across too many columns that should be combined:

Reshaping Data: Wide to Long

Data in "wide" format (months as separate columns) often needs to be "long" format (one row per month) for analysis:

Reshaping Data: Long to Wide

Sometimes you need the opposite transformation:

Changing Data Types

Sometimes numbers are stored as text or text is stored as numbers:

Key Takeaway

Reformatting is about making your data consistent and analysis-ready. AI can handle complex transformations like reshaping data, splitting columns, and standardizing formats—tasks that would normally require formulas or programming knowledge.