CSV Value Counts

frequency table · percent · cumulative share · case-insensitive option

Pick a column and see how often each value occurs, sorted by frequency, with each value's share and a running cumulative percentage. It's the fastest way to find out what's actually in a column before you trust it — the typos, the stray casing, the one country code that accounts for 80% of your rows.

How to count values in a CSV column

1. Paste the CSV or drop a file, then choose the column.
2. Leave trim spaces on so "UK " and "UK" count together; add ignore case to fold uk in as well.
3. Raise min count to hide the long tail when you only care about the common values.
4. Read the table on screen, or download the frequency table as CSV for a report.

Reading the cumulative column

The cumulative percentage answers "how many distinct values do I need to cover most of the data?" If the top three values reach 95%, the rest is noise you can bucket as other. It's also how you spot a column that's effectively a unique key — when every value appears exactly once, the cumulative share climbs in equal tiny steps and the distinct count matches the row count.

What this finds that a glance won't

• Near-duplicates from data entry — Germany, germany, Germany sitting in the list as separate rows until you enable trimming and case folding.
• Placeholder values — N/A, unknown, -, or 0 quietly standing in for missing data.
• Unexpected cardinality — a "status" column with 40 distinct values usually means free text where an enum was intended.
• Skew — one value covering nearly every row, which invalidates averages computed over the column.

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