# Data Profiler

Find out what is actually in a dataset before you build anything on top of it.

## Profile a dataset in one pass

## Drop a CSV to get started

Or choose a file from your computer. Everything is processed in this tab, so nothing is uploaded and no account is needed.

Choose filePaste data

Try it with sample data

## Look at the data before you trust it

Every dataset arrives with a story about what it contains, and the story is usually optimistic. A column that should be a number is forty percent empty. An identifier that should be unique repeats. A category that should have six values has nineteen, because three of them are spelling variants. Profiling is the ten-second version of finding all of that out.

### Missing values change every calculation

An average over a column that is a third empty is an average of two thirds of your data. Knowing the fill rate per column tells you which numbers you can quote and which need a caveat, and it is the first thing worth checking after a merge or an export.

### Distinct counts reveal the shape

A column with two distinct values is a flag. One with as many distinct values as rows is an identifier. One with a handful is a category worth grouping by. The distinct count usually tells you more about how to use a column than its name does.

### Distribution catches the outliers

Mean and median sitting far apart is the signature of a skewed column or a stray value with too many zeros. The distribution strip makes that visible without plotting anything, which is often enough to decide whether to investigate.

## Questions

### Is my data uploaded?

### How are column types decided?

### What counts as missing?

### What is the standard deviation for?

### Can I keep the profile?

## We already know your next customer

Describe your ideal customer. ManyPI finds validated leads, reaches out, and turns emails into sales.