Get quick statistics from a CSV column

The job

Before pandas, before the spreadsheet: is this column actually numeric? How many blanks? Is there an outlier? A fast per-column summary answers the hygiene questions without importing anything.

Paste or drop

Drop a .csv file or paste the text. The parser handles quoted fields, commas, and CRLF; it detects a header row and reports count, blanks, non-numeric entries, min, max, mean, median, and sample SD per column.

• Numbers with thousand separators like "34,500" count as numeric when the field is quoted.
• Mixed columns are reported honestly: numeric rows get stats, the rest are counted as non-numeric.

Worked example

A 3-column export shows the price column has 2 empty cells and 1 non-numeric "n/a" out of 500 rows — clean enough to proceed after fixing the flag.

Where it stops

Descriptive stats only — no charting, filtering, or type coercion. Input is capped at 8 MB of text and 200,000 cells; the comma is the only delimiter — semicolon and tab files are not split. The file is parsed in memory and never uploaded.