CSV Statistics Summary

Compute column-level statistics - mean, median, mode, std dev, min/max, percentiles - from any CSV.

Tested tool guide Tested browser tools Checked August 16, 2026

What CSV Statistics Summary does, with a checked example

CSV Statistics Summary profiles the numeric content of a comma-separated table one column at a time. It separates records into fields, groups values by column, and reports mean, median, mode, standard deviation, minimum, maximum, and percentiles. The summary is descriptive, not relational: it does not show whether two columns move together. The most common surprise is that standard deviation and percentiles have multiple valid calculation conventions, so results can differ from a spreadsheet even when the underlying values match.

Worked example

A concrete input and expected output from the current implementation.

Input

score
5
5

Expected output

score: mean 5; median 5; mode 5; standard deviation 0; minimum 5; maximum 5; every reported percentile 5.

Both observations equal 5, so their average, middle value, most frequent value, endpoints, and every percentile are 5. Both deviations from the mean are zero, making the standard deviation zero under either the sample or population convention.

How the result is produced

CSV column grouping

The input is interpreted as comma-separated records. Delimiters define columns, while CSV quoting keeps a comma or line break inside a field. Statistics are produced per column, so values from different fields are not pooled into one overall average. A header row, when present, provides the label used to identify each column.

Descriptive calculations

Within a numeric column, the tool reports the middle sorted value, or the average of the two middle values for an even count, as the median; the most frequent value as the mode; a standard-deviation measure; endpoints; and percentile cut points. These figures describe the observed column independently. They do not measure relationships between columns or determine whether an extreme observation is erroneous.

Good uses

Profile measurement columns in a CSV export before beginning a deeper analysis.
Compare mean and median to spot a column whose values may be strongly skewed.
Check ranges and percentile cut points after cleaning or converting a tabular dataset.

Limits and checks

If a column mixes numbers with blanks, labels, or symbols, confirm which cells contributed to its numeric summary before trusting the results.
Standard deviation can use either a sample or population divisor. Do not compare it with another program until you know the conventions match.
Percentile programs can use different ranking or interpolation rules. Differences are especially visible in short columns and do not necessarily indicate arithmetic errors.

Common questions

Why do the results differ from my spreadsheet?

First compare the exact values included in each calculation. Blank cells, text-looking numbers, symbols, and malformed rows can change the usable observations. If those match, check whether the spreadsheet uses sample or population standard deviation and which percentile interpolation rule it applies. Either convention can produce a legitimate but different result.

Does the tool upload my CSV?

No. CSV Statistics Summary runs entirely in the browser, so the CSV contents are processed locally rather than uploaded. This is useful for inspecting files you are permitted to handle on that device. The resulting summary can still expose sensitive ranges or values if you copy, save, or share it elsewhere.

References and verification

The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.