Data Cleaning Checklist Generator
Upload a CSV and get an automated report of 10 data quality checks — nulls, duplicates, outliers, type mismatches and more.
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What does this tool check?
The tool runs 10 automated checks on your CSV and produces a prioritised list of data quality issues, from critical errors down to informational notes.
The 10 checks explained
Duplicate rows: Identical rows that will skew aggregations and counts.
Missing values: Null or empty cells per column, shown as a percentage.
Mixed data types: Columns where some rows are numbers and others are text — usually data entry errors.
Inconsistent casing: Columns with "London", "london", and "LONDON" treated as different values.
Leading / trailing whitespace: Invisible spaces that cause joins and lookups to fail silently.
Outliers: Numeric values more than 3 standard deviations from the mean.
Constant columns: Columns with only one unique value — usually safe to drop before modelling.
High cardinality: Columns with nearly all unique values — likely ID or free-text fields.
Date columns stored as text: String columns that look like dates but are not typed as dates.
Very long strings: String values over 500 characters — possible merged cells or encoding errors.