CSV Tools

What you can do with these CSV tools

CSV (comma-separated values) is the most widely used format for moving tabular data between systems. It comes out of every database export, every spreadsheet download, and every analytics platform report. The problem is that the tool you need the data in almost never accepts raw CSV — you need JSON for an API, SQL for a database import, or Excel for a finance team. These tools handle all of those conversions without requiring you to write a script.

Beyond format conversion, the set includes tools for working with CSV data directly: a viewer that renders your CSV as a table so you can check it before processing, an editor that lets you modify cell values, and manipulation tools that filter rows, sort by column, merge two files, add or delete columns, and extract specific columns into a new CSV.

Format conversions covered

CSV to JSON converts your data to a JSON array of objects, with each header becoming a key. This is the standard input format for REST APIs, Node.js scripts, and Python data pipelines.

CSV to SQL generates ready-to-run INSERT statements for any relational database. Set the table name before converting and the output is a script you can paste directly into your database client.

CSV to Excel downloads an .xlsx file that opens natively in Microsoft Excel or Google Sheets. This is useful when stakeholders need an editable spreadsheet rather than a raw text file.

CSV to XML and CSV to YAML are useful for configuration systems and data serialization pipelines that expect those formats. The XML converter wraps each row in a record element with child elements for each column. The YAML converter outputs a sequence of mappings.

CSV to HTML generates a styled HTML table you can drop directly into a web page or email. CSV to TSV swaps the delimiter to tabs, which is required by some database import tools and the TSV format used by bioinformatics pipelines.

Data manipulation tools

The filter tool lets you select a column and set a condition (equals, contains, greater than, less than, starts with) to keep only the rows that match. This is useful for extracting a subset of records before importing — for example, filtering a customer export to only rows where the country column equals "US".

The sorter reorders all rows by any column you choose, in ascending or descending order. The merger stacks two CSV files vertically, combining rows from both files into one output. It handles files with the same headers, which is the common case when you have monthly export files you want to combine into a single dataset.

The column tools let you reshape the schema: add a new empty column with a fixed default value, delete columns you do not need, or extract only the columns you want into a clean output file.

Frequently asked questions