- 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)
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
||~ ^ : " = . -
Content-based separator autodetection
Rainbow CSV runs 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 at the extension settings page.
The autodetection algorithm skips files that have less than N=10 non-comment lines; value of N can be adjusted in the settings.
By default only comma, tab, semicolon and pipe are tried during autodetection, but you can adjust the list of candidate separators: add the following line to your VSCode config and edit it by removing or including any of the supported separators:
"rainbow_csv.autodetect_separators": ["\t", ",", ";", "|"],
If the autodetection algorithm makes an error and highlights a non-csv file, you can press "Rainbow OFF" button inside the status line.
Customizing file extension - separator association
If you often work with spreadsheet 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)", "csv (tilde)", "csv (caret)", "csv (colon)", "csv (double quote)", "csv (equals)", "csv (dot)", "csv (hyphen)"
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 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 work properly with such files you need to adjust your settings.
You can align columns in CSV files by clicking "Align" statusline button or use Align command
To shrink the table, i.e. remove leading and trailing whitespaces, click "Shrink" statusline button or use Shrink command
You can customize Rainbow CSV at the extension settings section of VSCode settings.
There you can find the list of available options and their description.
Enter RBQL - SQL-like language query editing mode.
Align columns with whitespaces or shrink them (remove leading/trailing whitespaces)
ColumnEditBefore, ColumnEditAfter, ColumnEditSelect
Activate multi-cursor column editing for 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. 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. 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.
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 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 which would allow you to quickly and easily perform most common data-manipulation tasks and convert your csv tables to bash scripts, single-lines 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 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 to locate 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.
- Rainbow CSV can't correctly handle newlines inside double-quoted CSV fields (well, theorethically it can, but only under specific conditions)
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