Explore Data

Profile and explore datasets: null rates, distributions, duplicates, data quality issues.

data-profiling eda data-quality exploration

When to use

Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze next.

Examples

Profile a new dataset

Get a quick overview of a dataset's shape and quality

Profile this CSV file for me. What's the shape, null rates per column, and any obvious data quality issues?

Find data quality issues

Identify problems before running analysis

Check this orders table for data quality issues: nulls, duplicates, outliers, and inconsistent formats.

Understand distributions

Explore how key metrics are distributed

Show me the distribution of order values and customer ages. Flag any suspicious outliers or skew.