// analyze a column and infer its data type automatically
Analyze any column of data and automatically detect its type: integer, float, boolean, date, email, URL, JSON, UUID, and more. Free browser-based tool.
Ready to analyze
Paste a column of values and click Detect TypeCopy a column of values from a spreadsheet, CSV, or database query result — one value per line.
The tool analyzes all values and scores each possible data type by how many values match.
See the best-fit type, confidence scores for all detected types, numeric stats, and sample values.
The Data Type Detector analyzes a column of raw values and infers the most likely data type using pattern matching and confidence scoring. It supports 15+ types and surfaces useful statistics like null rate, uniqueness, and numeric distributions — all client-side with no data sent to any server.
For each candidate type, the tool tests every non-null value against a regex or validator. The confidence score is the percentage of values that match the type's pattern. A score of 95% means 95% of values fit that type.
Empty lines, and values like null, nil, none, NA, N/A, and NaN (case-insensitive) are all treated as null/missing. The null count is shown separately in the stats row.
Yes. If the first line looks like a plain text label (letters, underscores, spaces — no numbers or special chars), it's detected as a column header and excluded from type analysis.
No. All analysis happens in your browser using JavaScript on the client side. Your data never leaves your machine.
Integer matches whole numbers only (e.g. 42, -7). Float matches decimal numbers including scientific notation (e.g. 3.14, -2.5e8). If all values are whole numbers, Integer wins over Float.
Yes. If a column has mixed types (e.g. integers and floats, or strings and numbers), the tool ranks all matching types by confidence. A column that's 60% integers and 40% floats will show both with their respective scores.
Common ISO and locale formats: YYYY-MM-DD, DD/MM/YYYY, MM-DD-YYYY, and YYYY/MM/DD. DateTime detection also handles timestamps with time components like 2024-01-15 14:30:00.
The tool is designed for data profiling use cases — thousands of rows work well. For very large datasets (100k+ rows), consider sampling your column first to keep browser performance smooth.
A data type detector is a tool that automatically infers the most appropriate data type for a column of raw values. Instead of manually inspecting a dataset and guessing whether a field is an integer, a date, or an email address, a type detector runs each value through a series of pattern matchers and reports a confidence score for each candidate type.
This kind of automatic type inference is a core step in data engineering workflows — it speeds up schema design, helps catch data quality issues, and eliminates the guesswork when working with unfamiliar datasets or third-party APIs.
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Raw data rarely comes with reliable type information attached. CSVs are plain text. JSON values are often serialized as strings. Database exports may lose metadata. When you receive a column labeled "created_at" with values like 2024-01-15, you still need to verify those are actually valid dates — not just strings that happen to look like dates.
Automatic type inference answers that question statistically. By testing every value against a set of type validators and computing what percentage of the column matches each type, the tool gives you an actionable confidence score: "This column is 98% likely to be ISO dates."
This tool detects the following types out of the box:
The confidence score for each type is the percentage of non-null values that match the type's pattern. Null values (empty rows, "NULL", "N/A", "NaN") are counted separately and excluded from type scoring.
A confidence score of 100% means every non-null value in the column matches that type perfectly. A score of 80% means 80% of values match — which often indicates data quality issues: some rows have bad values, mixed formats, or the column is genuinely multi-typed.
The tool ranks all matching types from highest to lowest confidence. Often you'll see a clear winner (e.g. Email at 97%) and several partial matches at low confidence (Text at 100%, since all strings are technically text). Use the top-ranked type with the most specific pattern as your authoritative inference.
When the top-detected type is Integer or Float, the tool also computes descriptive statistics: minimum, maximum, mean, median, standard deviation, and sum. These quick stats help you assess the range and distribution of the column without needing to open a spreadsheet or write a Python script.
Database schema design: When building a table from a CSV import, knowing that a column is UUID (not just VARCHAR) or that a column is genuinely Integer (not FLOAT) helps you write a tighter, more efficient schema from the start.
ETL pipeline validation: Before loading data into a warehouse, type detection can flag columns where values don't match the expected type — catching upstream data quality issues early.
API documentation: When reverse-engineering an undocumented API, pasting field values into a type detector quickly tells you whether a field is a Unix timestamp, an ISO date string, or a ULID — saving time on manual inspection.
Data cleaning and preprocessing: Type inference combined with null rate and uniqueness metrics helps you understand which columns need cleaning before analysis: high null rate, low uniqueness, and a "Text" type with no better match are all red flags.
No automatic type detector is perfect. A column of values like 1, 0, 1 could be Boolean or Integer — both are valid interpretations. Context matters, and the tool surfaces both possibilities with their respective confidence scores so you can make the final call.
Phone number detection is particularly tricky because phone formats vary enormously by country. The tool uses a liberal pattern that catches most common formats, but may produce false positives on some numeric strings. Always visually verify the sample values shown alongside the type inference.