CSV Viewer

What this tool does

CSV Viewer turns raw comma-, semicolon-, tab- or pipe-separated data into a readable table you can sort, search and page through. A column summary detects each column’s type and reports blanks, unique values, minimum, maximum and sum, and the current filtered and sorted view can be saved as CSV or JSON.

Opening a CSV file in a text editor shows a wall of commas, and opening it in a spreadsheet can quietly damage it: long numeric IDs turn into scientific notation, leading zeros vanish from postcodes and product codes, and some date-like strings are rewritten. When you only need to look at the data, check a column or find a few rows, a viewer that keeps every value exactly as written is safer and faster.

This viewer parses the file with Papa Parse, which follows RFC 4180 rules for quoted fields, so commas and line breaks inside double quotes stay in their cell. The delimiter is detected automatically or can be forced. Values are never converted: sorting uses numeric comparison only when every non-blank value in the column is a number, and natural text ordering otherwise. Parsing, searching and profiling all happen in your browser; the file is not uploaded anywhere.

How to use CSV Viewer

1. Paste CSV text into CSV data, click Open file, or drag a .csv, .tsv or .txt file onto the editor.
2. Check the Delimiter and First row is a header options; Detect automatically handles commas, semicolons, tabs and pipes.
3. Click a column heading to sort ascending, again for descending and a third time to restore the original order.
4. Type in Search rows to filter, and use Search in to limit the match to one column.
5. Switch to Column summary to see each column’s detected type, blank count, unique values, minimum, maximum and sum.
6. Use Download CSV or Download JSON to save exactly the rows that match your current search and sort.

Tips for better results

• European exports from Excel often use a semicolon because the comma is the decimal separator; if every row lands in one column, pick Semicolon explicitly.
• A high blank count in the Column summary is a quick way to spot an optional field before you design a database column as NOT NULL.
• If Unique equals the row count, that column is a candidate key you can use when joining or deduplicating.
• Set Rows per page to 25 on a phone so the table stays responsive; the export always includes every matching row, not just the visible page.

Common use cases

• Checking a bank, payment or analytics export before importing it into accounting software.
• Finding a handful of orders or customers in a large export without loading a spreadsheet application.
• Profiling an unfamiliar dataset to decide on column types for a database table.
• Extracting a filtered subset, such as all refunded orders, as a smaller CSV or JSON file.

Common mistakes to avoid

• Leaving First row is a header switched on for headerless files, which turns your first record into column names.
• Assuming a column is numeric because most values look like numbers. One stray entry such as “N/A” makes the summary report Text, which is a useful warning before an import.
• Ignoring the warning about rows with a different number of fields; it usually means an unquoted comma inside a value has shifted the remaining cells.