Compute CSV column statistics

Provide CSV text and the exact numeric column name, and this capability returns its minimum, maximum, arithmetic mean, median, and population standard deviation. It validates every selected cell before calculating, so missing columns, blank cells, labels, infinities, and other non-numeric values produce a clear error instead of a misleading partial result. Quoted fields, escaped quotes, configurable delimiters, and common line endings are handled deterministically.

Provide the CSV text and select one column

Start with CSV text whose first record is a header and whose remaining records contain the observations. Set column to the exact header you want to analyze. Header whitespace is ignored, but spelling and letter case otherwise matter, which prevents a request for one field from silently selecting another. Commas are used by default. If your file uses a semicolon, tab, pipe, or another one-character separator, pass it through the delimiter field. Standard quoted CSV values are supported, including delimiters and line breaks inside quotes as well as doubled quote characters. The parser treats every record after the header as data, so remove unrelated footer notes before submitting a file. A file must contain at least one data record. The response identifies the selected column and reports the number of observations alongside all five statistics, making it easy to confirm that the intended values were included before using the result in a report, validation rule, or automated import decision.

Understand the five returned statistics

Minimum and maximum describe the observed range. Mean is the arithmetic average: all selected values are summed and divided by their count. Median is the middle value after numeric sorting; for an even number of observations, it is the average of the two central values. Standard deviation is calculated as the population standard deviation, so squared distances from the mean are divided by the full observation count before taking the square root. This definition is appropriate when the CSV column is the complete population you intend to describe. If the rows are instead a sample used to estimate a larger population, apply a sample correction in your downstream analysis. Results use JavaScript numbers and are normalized to fifteen significant digits to avoid distracting floating-point artifacts while retaining useful precision. The capability does not infer units, remove outliers, weight observations, or reinterpret formatted currency. It summarizes exactly the finite numeric values supplied in the chosen column.

Use strict validation to protect downstream work

Reliable statistics depend on a consistently numeric input column, so validation is intentionally strict. The request fails when the named header does not exist or when any selected data cell is empty, missing, infinite, or not a finite number. It does not quietly skip bad rows, because silently calculating over a smaller dataset can produce a plausible but incorrect answer. Values such as currency symbols, thousands separators, percentages, and textual null markers should be normalized before this capability is called. The error identifies the column and, for invalid cell values, the one-based data-row position, which helps you find and repair the source record. This behavior makes the capability useful as a quality gate before ingestion: a successful result confirms both that the expected column exists and that every row can participate in the calculation. Computation is deterministic, requires no network access, and retains no file. Browser execution is convenient for an immediate check, while automated API requests cost $0.002 each.