Automatically identify column data types
What types of data are actually in each column? This tool scans every value and reports the detected type, integer, float, boolean, date, email, URL, phone number, or plain text, along with confidence scores and sample values. It catches mixed-type columns that could cause import failures.
Initializing in your browser…
Find and report empty cells and missing values
Generate realistic fake data for testing and development. Create names, usernames, emails, addresses, phone numbers, and more. Export to JSON or CSV format
Validate CSV data against custom rules
Before importing into a typed database you need to know which columns are integers, dates, or messy mixed text.
Input
users.csv (10 columns)
Inferred types
id → integer signup → date (ISO) zip → string (leading zeros!) age → integer (3% blank) score → mixed: numbers + "N/A"
Each column is sampled to infer a type and flag the traps, `zip` must stay a string or it loses leading zeros, and a "mixed" column warns of values like "N/A" that will break a numeric import. This is the schema reconnaissance that prevents a failed or silently corrupt load.
What types of data are actually in each column? This tool scans every value and reports the detected type, integer, float, boolean, date, email, URL, phone number, or plain text, along with confidence scores and sample values. It catches mixed-type columns that could cause import failures.
Use detected types to define column types (INT, VARCHAR, DATE, etc.) when creating a new database table.
Identify mixed-type columns that need cleaning before they cause type-casting errors in your application.
Generate a type map that your ETL scripts can use to cast values correctly during ingestion.
Export the type schema as a data dictionary to share with teammates or attach to a dataset.
CSV files are inherently untyped: every value is just a string. But downstream systems, databases, analytics tools, programming languages, need to know whether a column holds integers, decimals, dates, or text. Manual inspection works for small files, but a 50-column, 100K-row dataset demands automation.
The detector examines every cell in each column, tests it against type-specific patterns (ISO dates, RFC 5322 emails, E.164 phone numbers, numeric formats across locales), and assigns a type with a confidence percentage. Columns where more than one type appears get flagged as mixed-type with a breakdown showing, for example, "85% integer, 10% float, 5% text."
Integer, float, boolean, date, datetime, email, URL, phone number, and general text. It also flags columns that contain a mix of types.
The detector recognizes ISO 8601, US (MM/DD/YYYY), European (DD/MM/YYYY), and several other common date formats.
It reflects the percentage of non-empty values in the column that match the detected type. A score of 100% means every value fits; lower scores indicate mixed or ambiguous data.
Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.