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NeatUtility

CSV & TSV Viewer & Validator

CSV & Tables100% Client-Side
Ready
CSV
0 lines0 chars
0 B / 4.77 MB
About this tool, examples & FAQ

Preview, inspect, and validate CSV and TSV tabular files in your browser. Automatically identifies delimiters, detects malformed or ragged rows, flags duplicate column headers, and parses records into structured JSON without sending your data over the internet.

Key facts

Verified capabilities and limits of this utility
Delimiter DetectionAutomatic detection of commas, tabs, semicolons, and pipes
Integrity ChecksRagged rows, column counts, unclosed quotes, and duplicate headers
Type InferenceOptional conversion of numeric strings and booleans to native types
Maximum Input5 MB
Execution Deadline3 s

How to use

  1. Paste your raw CSV, TSV, or delimited table data into the editor.
  2. Leave Delimiter on 'Auto-detect' or pick your specific delimiter.
  3. Toggle 'First Row is Header' depending on whether your dataset includes column names.
  4. Click Inspect & Validate (or press Ctrl/Cmd + Enter).
  5. Review the tabular metadata statistics (row count, column count, delimiter) and structured JSON preview.

Example

Inspect and Validate Sample CSV

Input

id,city,population
1,Tokyo,37400000
2,Delhi,29300000

Example output

[
  {
    "id": 1,
    "city": "Tokyo",
    "population": 37400000
  },
  {
    "id": 2,
    "city": "Delhi",
    "population": 29300000
  }
]

Edge cases

Ragged rows and mismatched field counts

If some rows contain more or fewer fields than expected, the validator flags those specific line numbers with clear warning messages while still displaying the rest of the dataset.

Duplicate column header names

CSV files with identical header titles can cause data loss in downstream analytics tools and database loaders. The validator highlights duplicate names and their column indices.

Unclosed quotes in multi-line cells

RFC 4180 permits newlines inside quotes, but unmatched quotation marks swallow subsequent rows. The inspector diagnoses quote syntax errors and pinpoints the error position.

How it works

  1. Analyzes the first several lines using heuristic sampling to auto-detect delimiter and line break characters.
  2. Parses all rows via PapaParse in a Web Worker to verify cell boundary uniformity.
  3. Calculates total rows, data rows, distinct column names, and byte metrics.
  4. Outputs structured JSON records alongside diagnostic status details.

Related workflows

Frequently asked questions

What is a ragged row in CSV?

A ragged row is a record that has a different number of fields than the header or previous rows. This often happens when an unescaped comma is present inside text or when optional trailing values were omitted.

Is it safe to inspect confidential customer or financial CSVs?

Yes. The entire analysis runs locally within your browser tab via a Web Worker. No spreadsheet content or metadata is ever transmitted across the network.

Can this validator handle large CSV files?

The tool handles multi-megabyte CSV files smoothly using streaming parsing and memory limits up to 5 MB per run without freezing the user interface.

References