Converter Tools

CSV Viewer

Paste or open a CSV file and view it as a sortable, filterable, paginated table. Column type inference, blank counts, and instant CSV or JSON export.

CSV input568 chars

Delimiter

First row

Whitespace

Rows

Columns

Visible rows

Page

1 of 1

Filter rows

Rows per page

Row	Integer	Text	Text	Date	Boolean	Number
1	1	Jane Smith	jane@example.com	2024-02-14	true	87.5
2	2	Carlos Diaz	carlos@example.com	2024-04-02	true	92
3	3	Mei Ling	mei.ling@example.org	2025-01-22	false	74.2
4	4	Erika "Sparks" Volkov	erika@example.net	2024-11-30	true	99
5	5	Akin Owolabi	akin@example.com	2025-07-04	false	61
6	6	Priya Raman	priya@example.com	2024-06-18	true	--
7	7	Tom Watanabe	tom@example.com	2025-03-09	true	55.8
8	8	Nora Hansen	nora@example.com	2025-08-12	true	80
9	9	Liam O'Brien	liam@example.com	2024-12-25	false	68.3
10	10	Sasha Petrov	sasha@example.com	2025-02-14	true	72

Column overview

Column	Type	Unique	Blanks
id	Integer	10	0
name	Text	10	0
email	Text	10	0
signed_up	Date	10	0
active	Boolean	2	0
score	Number	9	1

CSV (cleaned)

id,name,email,signed_up,active,score
1,Jane Smith,jane@example.com,2024-02-14,true,87.5
2,Carlos Diaz,carlos@example.com,2024-04-02,true,92
3,Mei Ling,mei.ling@example.org,2025-01-22,false,74.2
4,"Erika ""Sparks"" Volkov",erika@example.net,2024-11-30,true,99
5,Akin Owolabi,akin@example.com,2025-07-04,false,61
6,Priya Raman,priya@example.com,2024-06-18,true,
7,Tom Watanabe,tom@example.com,2025-03-09,true,55.8
8,Nora Hansen,nora@example.com,2025-08-12,true,80
9,Liam O'Brien,liam@example.com,2024-12-25,false,68.3
10,Sasha Petrov,sasha@example.com,2025-02-14,true,72

JSON (typed)

[
  {
    "id": 1,
    "name": "Jane Smith",
    "email": "jane@example.com",
    "signed_up": "2024-02-14",
    "active": true,
    "score": 87.5
  },
  {
    "id": 2,
    "name": "Carlos Diaz",
    "email": "carlos@example.com",
    "signed_up": "2024-04-02",
    "active": true,
    "score": 92
  },
  {
    "id": 3,
    "name": "Mei Ling",
    "email": "mei.ling@example.org",
    "signed_up": "2025-01-22",
    "active": false,
    "score": 74.2
  },
  {
    "id": 4,
    "name": "Erika \"Sparks\" Volkov",
    "email": "erika@example.net",
    "signed_up": "2024-11-30",
    "active": true,
    "score": 99
  },
  {
    "id": 5,
    "name": "Akin Owolabi",
    "email": "akin@example.com",
    "signed_up": "2025-07-04",
    "active": false,
    "score": 61
  },
  {
    "id": 6,
    "name": "Priya Raman",
    "email": "priya@example.com",
    "signed_up": "2024-06-18",
    "active": true,
    "score": null
  },
  {
    "id": 7,
    "name": "Tom Watanabe",
    "email": "tom@example.com",
    "signed_up": "2025-03-09",
    "active": true,
    "score": 55.8
  },
  {
    "id": 8,
    "name": "Nora Hansen",
    "email": "nora@example.com",
    "signed_up": "2025-08-12",
    "active": true,
    "score": 80
  },
  {
    "id": 9,
    "name": "Liam O'Brien",
    "email": "liam@example.com",
    "signed_up": "2024-12-25",
    "active": false,
    "score": 68.3
  },
  {
    "id": 10,
    "name": "Sasha Petrov",
    "email": "sasha@example.com",
    "signed_up": "2025-02-14",
    "active": true,
    "score": 72
  }
]

How to use

Paste a CSV into the input, or click Open CSV file to load a .csv or .tsv from your device. Click Load sample to try the viewer with example data.
Pick the right delimiter (comma, tab, semicolon, or pipe) and choose whether the first row is a header.
Use the filter box to narrow rows, click any column header to sort, and step through pages with the pagination controls.
Read the column overview to see each column's inferred type, unique value count, and blank count.
Copy the cleaned CSV or the typed JSON version when you are ready to use the data elsewhere.

About this tool

CSV Viewer parses any CSV (or TSV) you paste or open and renders it as an interactive table directly in your browser. The parser is RFC 4180 compliant: it handles quoted fields, embedded newlines, doubled-quote escapes ("" inside a quoted field), and the three standard line endings (LF, CR, CRLF), and reports the line and column of the first malformed row so a broken file is easy to fix. Pick the delimiter (comma, tab, semicolon, or pipe) and toggle whether the first row is a header. The viewer infers a type for every column (integer, number, boolean, date, or text) by sampling all non-empty values, so dates sort chronologically, numbers sort numerically, and text falls back to a locale-aware natural sort. Click any column header to sort ascending, click again for descending, click again to clear. The filter box runs a case-insensitive substring search across every cell so you can narrow large files to the rows you care about. Pagination keeps very large CSVs responsive (25 to 500 rows per page), and a column overview lists each column's inferred type, unique value count, and number of blank cells so problems jump out before you load the file into another tool. When you are done exploring, copy the cleaned CSV (re-emitted with the chosen delimiter and proper quoting) or copy a typed JSON version where numbers and booleans are real JSON values, ready to paste into a script, a fixture, or another converter. Useful for quickly scanning exports from databases, analytics dashboards, billing reports, mailing lists, survey results, log dumps, and any CSV that comes back to you and needs a sanity check before you trust it. Everything runs locally on your device, so the file you paste here is never uploaded.

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

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