FREE WEB TOOL

CSV column type profiler

Infer integer, number, boolean, date, or string types for each CSV column and show blank rates.

Use tool

How to use

Use this before sending a CSV to another system when you want to quickly find columns that look numeric, date-like, boolean, text-heavy, or unusually sparse. It is a triage tool, not a schema generator.

Paste the CSV. The first row is treated as the header.
Run the profiler to see an inferred type, blank count, and blank percentage for each column.
Compare the inference with the business meaning of the column before changing any data type.

When is this useful?

A 100-column CSV is hard to review by eye. Profiling gives you a shortlist: date-looking fields, numeric-looking fields, and columns with lots of blanks. That lets you spend attention where conversion risk is highest.

The important thing to know

The inferred type is based on the values, not on a schema stored in the CSV. A column full of 00123-style values may look like integers while actually being product identifiers. Treat the result as a review signal, not permission to convert the column.

How the current inference works

Blank cells are excluded from type inference and reported separately.
The current order is integer → number → boolean → date → string, so a column containing only 0 and 1 is inferred as integer.
Date inference uses browser date parsing as one signal, so “date” still needs a business-meaning check.

Example

If order_id is inferred as integer and created_at as date, pause before converting anything. A long or zero-padded order ID should probably stay text even though it looks numeric.

How to read the result

If an inferred type surprises you, inspect a few raw values from that column. The label matters less than why the values matched it. Blank rate is reported separately, so you can use type + sparsity together to prioritize review before import.

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