CSV cleaner and profiler
Paste a messy CSV export and instantly clean it, profile every column, and download a normalized result. It runs entirely in your browser with no upload step.
The default example includes whitespace, blank rows, duplicates, missing cells, and one malformed row so the cleanup is visible immediately.
Malformed rows
Rows flagged by the parser for inconsistent field counts.
| Row | Code | Message |
|---|
Column profile
Compact stats for the cleaned output after every selected transform.
| Header | Inferred type | Non-empty | Missing | Unique |
|---|
What is CSV cleaning?
CSV cleaning turns a raw export into a file that is easier to import, inspect, and transform. Spreadsheet exports and admin downloads often include extra spaces, blank lines, repeated rows, and headers that look fine to a human but break downstream scripts.
A browser-based cleaner fixes those issues quickly and pairs the cleaned output with a profile so you can see whether the dataset is actually ready for the next step.
When the profile is useful
- Checking unknown CSV exports before writing import code
- Finding sparse columns and missing values before a database load
- Spotting duplicate rows in spreadsheet or vendor exports
- Normalizing human-readable headers into consistent field names
- Reviewing whether a column looks numeric, boolean, date-like, or mixed
How header normalization works
When enabled, headers are trimmed, lowercased, converted to underscores, stripped of punctuation, and made unique. This gives you output that is easier to map into SQL columns, JSON keys, and typed application code.
Why clean CSV in the browser
Many CSVs contain internal, customer, or operational data. Keeping the cleanup step client-side is faster and more private, while still giving you copy, download, and parser diagnostics in a single workflow.
From raw CSV to usable analysis
Once the file is clean, you can convert it to JSON, test downstream transformations, or load it into analytics workflows. Basedash helps on that next step by turning real data into charts, metrics, and dashboards with plain-English prompts.
FAQ
What does this CSV cleaner do?
It cleans pasted CSV data entirely in the browser, with options to trim whitespace, remove empty rows, deduplicate identical rows, and normalize headers into consistent machine-friendly names.
What does the profiler show?
The summary reports row count, column count, missing cell count, duplicate row count, and malformed row count. The per-column profile shows each header, inferred type, non-empty count, missing count, and unique count.
How are headers normalized?
Normalized headers are lowercased, stripped of punctuation, converted to underscores, and made unique. For example, "Full Name" becomes "full_name".
What counts as a malformed row?
Malformed rows are rows flagged by the CSV parser for issues like too many or too few fields compared with the header row. Those rows still appear in the output so you can inspect them.
Is my CSV uploaded or stored anywhere?
No. Parsing, cleanup, profiling, copy, and download all happen entirely in your browser using JavaScript and Papa Parse.
When should I use this before other tools?
Use the cleaner first when a CSV export has stray spaces, blank lines, repeated rows, or inconsistent headers. After cleanup, convert the result to JSON or generate fresh sample data for testing.
Move from cleanup to analysis faster
Connect your real data and describe the chart or metric you want. Basedash handles the query, visualization, and dashboard.