- Highlight columns in comma (.csv), tab (.tsv), semicolon and pipe - separated files in different colors
- Transform and filter tables using built-in SQL-like query language
- Provide info about column on hover
- Automatic consistency check for csv files (CSVLint)
- Multi-cursor column edit
- Align columns with spaces and Shrink (trim spaces from fields)
- Works in browser (vscode.dev) with limited functionality
Rainbow CSV looks better and is much more usable in general with dark mode.
If your csv, semicolon-separated or tab-separated file doesn't have .csv or .tsv extension, you can manually enable highlighting by clicking on the current language label mark in the right bottom corner and then choosing "CSV", "TSV", "CSV (semicolon)" or "CSV (pipe)" depending on the file content, see this screenshot.
Another way to do this: select one separator character with mouse cursor -> right click -> "Set as Rainbow separator".
||Ignored inside double-quoted fields
||Ignored inside double-quoted fields
||Consecutive whitespaces are merged
||any char or string
Content-based separator autodetection
Rainbow CSV runs a table autodetection algorithm for all "Plain Text" and "*.csv" files. In most cases, this is a very cheap operation because autodetection usually stops after checking only 1 or 2 topmost lines.
Autodetection can be disabled in the extension settings.
By default only comma, tab, semicolon and pipe are tried during autodetection, but you can adjust the list of candidate separators in extension settings.
Customizing file extension - separator association
If you often work with csv files with one specific extension (e.g. ".dat") and you don't want to rely on the autodetection algorithm, you can associate that extension with one of the supported separators.
For example to associate ".dat" extension with pipe-separated files and ".csv" with semicolon-separated files add the following lines to your VS Code json config:
"*.dat": "csv (pipe)",
"*.csv": "csv (semicolon)"
Important: language identifiers in the config must be specified in lower case! E.g. use
csv (semicolon), not
List of supported language ids:
"csv", "tsv", "csv (semicolon)", "csv (pipe)", "csv (whitespace)", "dynamic csv".
Working with arbitrary separators
Rainbow CSV allows using an arbitrary character or string as a separator.
You can add the separator to the list of autodetected separators in the VSCode settings or if you just want to use it once you can either:
Dynamic CSV filetype (bottom right corner) and then enter the separator text in the popup dialog.
- Select the separator text with the cursor and run
Rainbow CSV: Set separator ... command.
Dynamic CSV filetype also supports multiline CSV fields escaped in double quotes as described in RFC-4180.
CSVLint consistency check
The linter checks the following:
- Consistency of double quotes usage in CSV rows.
- Consistency of number of fields per CSV row.
To recheck a csv file click on "CSVLint" button.
Working with large files
To enable Rainbow CSV for very big files (more than 300K lines or 20MB) disable "Editor:Large File Optimizations" option in VS Code settings.
You can preview huge files by clicking "Preview... " option in VS Code File Explorer context menu.
All Rainbow CSV features would be disabled by VSCode if the file is bigger than 50MB.
Some CSV files can contain comment lines e.g. metadata before the header line.
To allow CSVLint, content-based autodetection algorithms, and Align, Shrink, ColumnEdit commands to work properly with such files you need to adjust your settings.
You can align columns in CSV files by clicking "Align" status-line button or using Align command
To shrink the table, i.e. remove leading and trailing whitespaces, click "Shrink" status-line button or use Shrink command
You can customize Rainbow CSV in the extension settings section of VSCode settings.
There you can find the list of available options and their description.
Align columns with whitespaces or shrink them (remove leading/trailing whitespaces)
Set the currently selected text (single character or multiline string) as a separator and re-highlight the file.
ColumnEditBefore, ColumnEditAfter, ColumnEditSelect
Activate multi-cursor column editing for the column under the cursor. Works only for files with less than 10000 lines. For larger files you can use an RBQL query.
WARNING: This is a dangerous mode. It is possible to accidentally corrupt table structure by incorrectly using "Backspace" or entering separator or double quote characters. Use RBQL if you are not sure.
To remove cursor/selection from the header line use "Alt+Click" on it.
Input a comma-separated string with column names to adjust column names displayed in hover tooltips. The actual header line and file content won't be affected.
"Virtual" header is persistent and will be associated with the parent file across VSCode sessions.
Uses the current line to adjust column names displayed in hover tooltips. The actual header line and file content won't be affected.
This is a "Virtual" header and will be persistent and will be associated with the parent file across VSCode sessions.
Enter RBQL - SQL-like language query editing mode.
Set a custom name for the current file so you can use it instead of the file path in RBQL JOIN queries
You can customize Rainbow CSV colors to increase contrast. Instructions
SQL-like "RBQL" query language
Rainbow CSV has a built-in RBQL query language interpreter that allows you to run SQL-like queries using a1, a2, a3, ... column names.
SELECT a1, a2 * 10 WHERE a1 == "Buy" && a4.indexOf('oil') != -1 ORDER BY parseInt(a2), a4 LIMIT 100
To enter query-editing mode, execute RBQL VSCode command.
RBQL is a very simple and powerful tool that would allow you to quickly and easily perform the most common data-manipulation tasks and convert your csv tables to bash scripts, single-line json, single-line xml files, etc.
It is very easy to start using RBQL even if you don't know SQL. For example to cut out the third and first columns use
SELECT a3, a1
You can use RBQL command for all possible types of files (e.g. .js, .xml, .html), but for non-table files, only two variables: NR and a1 would be available.
Screenshot of RBQL Console:
Comparison of Rainbow CSV technology with traditional graphical column alignment
- Familiar editing environment of your favorite text editor
- Zero-cost abstraction: Syntax highlighting is essentially free, while graphical column alignment can be computationally expensive
- High information density: Rainbow CSV shows more data per screen because it doesn't insert column-aligning whitespaces.
- Color -> column association allows locating the column of interest more quickly when looking back and forth between the data and other objects on the screen (with column alignment one has to locate the header or count the columns to find the right one)
- Ability to visually associate two same-colored columns from two different windows. This is not possible with graphical column alignment
- Rainbow CSV may be less effective for CSV files with many (> 10) columns and for files with multiline fields, although textual alignment can significantly improve the situation.
- Rainbow CSV may be less usable with light mode because font colors become less distinguishable when compared to a dark mode (this phenomenon is also described here).
These extensions can work well together with Rainbow CSV and provide additional functionality e.g. export to Excel format:
Rainbow CSV and similar plugins in other editors:
- Rainbow CSV extension in Vim
- rainbow-csv package in Atom
- rainbow_csv plugin in Sublime Text
- rainbow_csv plugin in gedit - doesn't support quoted commas in csv
- rainbow_csv_4_nedit in NEdit
- CSV highlighting in Nano
- Rainbow CSV in IntelliJ IDEA
- Library and CLI App for Python RBQL