CSV Viewer & Converter

How to Use the CSV Viewer & Converter

1. Paste your CSV data into the text area, or click the file upload button to load a .csv, .tsv, or .txt file from your device.
2. Select the delimiter that matches your data: comma, semicolon, tab, or pipe.
3. Check "First row is header" if your first row contains column names rather than data.
4. Click "View as Table" to render your CSV as a scrollable HTML table with row and column counts.
5. Click "Convert to JSON" to transform your CSV into a JSON array. If headers are enabled, each row becomes an object with header keys. Copy the output with one click.
6. Click "Convert to TSV" to convert your data to tab-separated format, useful for pasting into spreadsheets.

What is CSV?

CSV stands for Comma-Separated Values. It is one of the oldest and most widely used data interchange formats, predating JSON and XML by decades. A CSV file stores tabular data as plain text, with each line representing a row and fields within each row separated by a delimiter character, typically a comma.

The format is governed by RFC 4180, which defines standard rules for handling special cases. Fields that contain the delimiter character, newlines, or double quotes must be enclosed in double quotes. A double quote within a quoted field is escaped by preceding it with another double quote. For example, the value She said "hello" would be encoded as "She said ""hello""" in a CSV file.

CSV files are everywhere in data work. Spreadsheet applications like Microsoft Excel and Google Sheets export data as CSV. Databases support CSV import and export. APIs often offer CSV as a download format alongside JSON. Data scientists use CSV files extensively with tools like Python pandas, R, and SQL. The simplicity of the format — it is just text — makes it universally compatible across programming languages, operating systems, and applications.

CSV vs Other Data Formats

Different data formats serve different purposes. Here is how CSV compares to the most common alternatives:

Working with CSV Files

CSV files are deceptively simple. While the basic format is straightforward, real-world CSV data often comes with quirks that can cause issues if you are not prepared.

Opening CSV in Excel

When you double-click a .csv file, Excel opens it automatically. However, Excel may misinterpret certain data: leading zeros in numbers like ZIP codes (00501) get stripped, long numbers get converted to scientific notation, and dates may be reformatted. To avoid this, use Excel's "Import Data" wizard (Data > From Text/CSV) which lets you specify column data types before import.

Encoding Issues (UTF-8 BOM)

Many CSV files use UTF-8 encoding, but some applications (particularly Excel on Windows) expect a Byte Order Mark (BOM) at the beginning of the file to correctly display special characters like accented letters, CJK characters, or currency symbols. If you see garbled characters when opening a CSV, the file likely needs to be saved with UTF-8 BOM encoding. Most modern text editors offer this option in their "Save As" dialog.

Handling Large Files

For CSV files larger than a few hundred megabytes, browser-based tools and even spreadsheet applications may struggle. In these cases, command-line tools like csvkit (Python), xsv (Rust), or database imports (PostgreSQL's COPY command, SQLite's .import) are more efficient. For analysis, Python's pandas.read_csv() with chunked reading handles files that exceed available memory.

Data Cleaning Tips

Before working with CSV data, check for common issues: inconsistent delimiters (some rows use commas, others use semicolons), trailing whitespace in fields, empty rows at the end of the file, inconsistent quoting, and mixed line endings (Windows CRLF vs Unix LF). This tool's proper CSV parser handles quoted fields and mixed content correctly, but upstream data quality matters for reliable results.

Frequently Asked Questions