Clean CSV text by trimming cells, removing identical rows and sanitizing formula-like values. Preview the result before downloading; comparison uses the whole row.
Enable Remove duplicate rows to keep one copy of each identical parsed data row while preserving the header. Whitespace trimming happens before duplicate comparison when enabled, so spaces around a cell can affect which rows become equal.
Exact-row duplication and repeated customer identifiers are different tasks.
The leading and trailing spaces are removed before row comparison, so the two data rows become equal.
The IDs differ, so the complete rows are different. The shared name alone does not make them duplicates in this tool.
The repeated complete row is removed. The row with ID 2 is different even though its email matches.
Check the delimiter and preview before downloading. Formula sanitization prefixes cells starting with =, +, - or @ with an apostrophe. The parser does not support quoted cells spanning multiple lines; keep a copy of your source CSV. Use simple single-line CSV records. The cleaner does not support multiline quoted fields or key-column deduplication.
The CSV Cleaner prepares small delimited text tables by trimming cell whitespace, removing duplicate rows, and optionally prefixing formula-like values with an apostrophe. Select the delimiter that matches the source, inspect the table preview, and download the cleaned data. It is useful for straightforward contact lists, inventory examples, and exports that need consistent spacing. The first nonempty row is treated as the header and retained during deduplication.
The parser is a local JavaScript routine, not PapaParse or a streaming Web Worker. It splits the input into nonempty lines, then tracks a basic quoted-field state while scanning each line. Quoted delimiters in a single line can be handled, but multiline fields and standard doubled-quote escaping are not fully supported. Do not use this view as a complete CSV conformance validator. Review complex spreadsheet exports with a standards-compliant parser before relying on the result.
Cleaning applies rules in sequence: trim cells, remove duplicate body rows, then prefix cells beginning with =, +, -, or @ when formula protection is enabled. Duplicate keys join cell values with a literal separator, so unusual data containing that separator can collide. Formula-prefix protection is a precaution for some spreadsheet import behaviors, not a guarantee for every application or future transformation. Your text remains in the browser during preview and export.
Choose a local text file or replace the sample input. Select the source delimiter and confirm that the first row contains the intended column names. If records contain embedded line breaks, use a complete CSV parser instead of assuming the preview has reconstructed them.
Enable trimming when outer whitespace is accidental, or disable it when spaces are meaningful. Deduplication compares entire rows after trimming rather than a selected business key. Decide whether negative values or other formula-like strings should receive an apostrophe for the receiving spreadsheet.
Inspect rows with quotation marks, empty cells, repeated values, and numeric-looking identifiers. A change in row count can indicate duplicate removal, but it can also reveal unsupported parsing. Keep the original source to distinguish intended cleanup from accidental data loss.
Export the result and open a copy in the intended receiving application. Confirm column alignment, leading zeros, quote handling, and formula-prefix behavior. The downloaded text may be interpreted differently by a spreadsheet according to its locale and import settings.
Trim leading and trailing cell spaces while retaining interior text. This can make repeated rows comparable, but should be disabled when spacing is part of an identifier, fixed-width value, or carefully formatted text.
Keep the first occurrence of each constructed body-row key and retain the header. This does not merge partially matching contacts, choose the newest record, or deduplicate on selected columns.
The serializer quotes cells containing the chosen delimiter, quotes, or line breaks and doubles output quotes. Correct serialization cannot restore data already misread by the limited input parser.
Two rows with the same name may refer to different people or transactions. Decide which columns identify a duplicate before removing records, and distinguish missing values from intentional blanks. Compare row counts and a few known records after sorting or cleaning so useful data is not silently lost.
Clean and standardize messy CSV datasets before loading into Python Pandas DataFrames, Jupyter Notebooks, or SQL databases.
Deduplicate and sanitize contact lists and email subscriber exports before importing into HubSpot, Salesforce, or Mailchimp.
Clean bank transaction exports, reconcile invoice lists, and strip trailing whitespace from financial ledger sheets.
Format product catalog spreadsheets, SKU lists, and shipping manifest tables for e-commerce store imports.
| Input or option | Reference | What to check |
|---|---|---|
| Delimiter | Character separating fields | Match the source format |
| Whitespace | Trimming changes cell values | Preserve meaningful spaces |
| Duplicates | Repeated records may be removed | Choose the matching columns |
Not reliably. Input is split into lines before field parsing, so an embedded newline can become a separate record. Use a complete CSV parser for that data and compare record counts before applying any cleaning rules.
This version compares entire rows. Two records with the same email but different IDs or other cell values remain separate. It does not choose the newest record, merge customer data or deduplicate by a selected key. Decide the key and retention rule in a spreadsheet or database when your task needs that behavior.
No universal guarantee is possible. This rule adds an apostrophe to specific leading characters, but applications differ in how they interpret imported cells. Inspect the output and use explicit text-column import settings where appropriate.
The trim rule removes spaces at cell boundaries rather than all spaces inside the value. Even boundary spaces can matter for fixed-width identifiers or deliberate formatting, so leave the rule off when the source requires exact preservation.
No. The input, parsed rows, and output remain in memory, and parsing runs on the main thread. There is no streaming parser or virtualized table. Use smaller files on phones and a dedicated data workflow for large exports.
An internet connection is required to open tools and refresh a temporary session. Processing stays on your device; the handshake sends a random challenge, not files or text inputs. Libraries, fonts or models may download. Local processing cannot remove risks from an untrusted device or extension.
Paste a simple CSV or load a supported file, choose its delimiter and enable duplicate-row removal. Review the cleaned table before downloading. The first row is treated as the header and retained. Duplicate comparison uses the complete data row after optional whitespace trimming, rather than a selected column.
