Converter Tools

CSV Column Stats

Profile any CSV in your browser. Per-column type, missing-value counts, distinct counts, min, max, mean, median, top values, and a JSON or Markdown summary.

Source CSV

Paste, drop, or open your CSV file

The whole file is parsed and analyzed in your browser. Nothing is uploaded.

Delimiter

First row is header

Extra missing-value tokens (comma separated, case-insensitive)

Blank cells are always treated as missing. Add any other strings your team uses for missing values so they are counted correctly.

CSV input

Table overview

12 data rows x 9 columns

Delimiter: comma. Header: yes. Input: 934 B.

Rows

Columns

Missing cells

3 of 108

id
column 1
Integer
12 non-missing
0 missing (0%)
12 distinct
Min
1
Max
12
Mean
6.5
Median
6.5
Std dev
3.6056
Sum
78
Distribution (integer)
12 equal-width buckets between min and max.
Top values
- 1
  1(8.3%)
- 10
  1(8.3%)
- 11
  1(8.3%)
- 12
  1(8.3%)
- 2
  1(8.3%)
name
column 2
Text
12 non-missing
0 missing (0%)
12 distinct
Min length
11
Max length
17
Mean length
13
Top values
- Ada Lovelace
  1(8.3%)
- Alan Turing
  1(8.3%)
- Barbara Liskov
  1(8.3%)
- Brian Kernighan
  1(8.3%)
- Dennis Ritchie
  1(8.3%)
email
column 3
Text
12 non-missing
0 missing (0%)
12 distinct
Min length
15
Max length
28
Mean length
19
Top values
- ada@analytics.example
  1(8.3%)
- alan@cipher.example
  1(8.3%)
- barbara@substitution.example
  1(8.3%)
- brian@k.example
  1(8.3%)
- dennis@unix.example
  1(8.3%)
plan
column 4
Text
12 non-missing
0 missing (0%)
3 distinct
Min length
3
Max length
4
Mean length
3.4
Top values
- Pro
  7(58%)
- Team
  4(33%)
- Solo
  1(8.3%)
signup_date
column 5
Date
12 non-missing
0 missing (0%)
12 distinct
Earliest
2024-08-12T00:00:00
Latest
2025-02-09T00:00:00
Range
181 days
Top values
- 2024-08-12
  1(8.3%)
- 2024-08-15
  1(8.3%)
- 2024-09-02
  1(8.3%)
- 2024-09-21
  1(8.3%)
- 2024-10-04
  1(8.3%)
country
column 6
Text
12 non-missing
0 missing (0%)
4 distinct
Min length
2
Max length
2
Mean length
2.0
Top values
- US
  7(58%)
- UK
  3(25%)
- CA
  1(8.3%)
- FI
  1(8.3%)
monthly_spend
column 7
Decimal
11 non-missing
1 missing (8.3%)
3 distinct
Min
19
Max
99
Mean
64.4545
Median
49
Std dev
28.7623
Sum
709
Distribution (decimal)
12 equal-width buckets between min and max.
Top values
- 49.00
  6(55%)
- 99.00
  4(36%)
- 19.00
  1(9.1%)
active
column 8
Boolean
11 non-missing
1 missing (8.3%)
2 distinct
Min length
4
Max length
5
Mean length
4.2
Top values
- true
  9(82%)
- false
  2(18%)
referrer
column 9
Text
11 non-missing
1 missing (8.3%)
4 distinct
Min length
4
Max length
7
Mean length
5.7
Top values
- direct
  5(45%)
- blog
  3(27%)
- partner
  2(18%)
- podcast
  1(9.1%)

How to use

Paste your CSV into the input, drop a file onto the page, or click Open CSV file to load one from disk.
Confirm the delimiter (auto-detected from the first lines) and whether the first row is a header.
Adjust the extra missing-value tokens if your export uses something other than blank, null, n/a, na, none, or dash for missing values.
Read each column's stat card: type badge, non-missing and missing counts, distinct count, the right summary block (numeric, date, or text length), and the top five values.
Click Copy JSON or Copy Markdown to grab a ready-to-paste summary, or use Download to save the profile to disk.

About this tool

CSV Column Stats is a per-column profiler for any CSV (or TSV, semicolon, or pipe-separated) document. Paste, drop, or open a file and the tool parses every row with a strict RFC 4180 parser, classifies each cell as integer, decimal, boolean, date, text, or empty, and then computes the statistics that match each column's type so the results read the way pandas DataFrame.describe(), SQL stat queries, and BI quick-look summaries do, without sending the file to a server. Numeric columns (integer or decimal) get count, missing, distinct, minimum, maximum, sum, mean, median, sample standard deviation, and a twelve-bucket sparkline of the value distribution. Date columns (ISO 8601, US, and European forms are all recognized) report the earliest and latest timestamps plus the range in days. Text columns get minimum, maximum, and mean string length. Every column also lists its top five most common values with their counts and share of non-missing rows, which makes data-quality issues obvious at a glance: a country column where the top value is the empty string, a status column where two near-duplicate spellings split the distribution, an ID column whose distinct count does not match the row count. Missing values are configurable: blank cells are always counted as missing, and a comma-separated list of extra missing tokens (default null, n/a, na, none, dash) covers the strings most exports use for nulls. The output panel shows a high-level overview (row count, column count, total missing cells), one statistics card per column, and two ready-to-copy summary formats: a Markdown table that drops cleanly into a Notion page, GitHub README, or pull-request description, and a JSON object that flows into a notebook, data-quality tracking spreadsheet, or downstream pipeline. The delimiter is auto-detected from the first eight lines (and easy to override) so TSV, semicolon-delimited European exports, and pipe-delimited logs all profile without configuration. Everything runs locally in your browser: the CSV, the column names, the values, and the computed statistics never leave your device, which makes the tool safe for customer exports, employee data, internal analytics dumps, and any file you would rather not upload.

Free to use. Works in your browser. No signup, no login.

Related tools

CSV Column Stats

id

name

email

plan

signup_date

country

monthly_spend

active

referrer

How to use

About this tool

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