Data Quality
CSV Data Profiler
Analyze CSV files for column statistics, null types, and data patterns.
Headers are required. Empty lines are skipped automatically.
Rows
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Columns
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Run the profiler to validate the CSV structure and inspect each column.
Pattern notation
Each character is classified: digits → N, letters → L, whitespace → ·, and punctuation/symbols stay as-is. Example: 123-AB becomes NNN-LL.
Coverage
Coverage shows what percentage of populated rows match the dominant pattern for a column. It is calculated as (rows matching pattern ÷ populated rows) × 100.
Profile results will appear here after validating the CSV format and running the analysis.
About CSV Data Profiler
The CSV Data Profiler scans your datasets to provide a comprehensive data quality report. It calculates a unique count, identifies null values, and detects formatting patterns for every column, essentially serving as an instant data profiler in your browser.
Understand your data before importing it into a database or analytics tool. By spotting missing values (null count) and cardinality issues early, you avoid costly cleanup steps later. It helps data engineers and analysts perform rapid exploratory data analysis (EDA) without writing code.
Drag and drop your CSV file onto the page or paste raw CSV text. The tool applies pattern analysis to generate a column-by-column breakdown, showing the data type (string, number, boolean), min/max values, and non-null percentages.
Under the Hood
The profiler relies on `PapaParse` for high-performance CSV streaming. It processes the file in chunks (streaming mode) to handle datasets larger than available memory. For each column, it aggregates statistics in a single pass: Min/Max/Ave for numbers, string lengths, and cardinality estimates using a Set for small count approximations. Type inference uses regex heuristics to detect Dates, Booleans, and Floats.