Awesome
Rainbow CSV
Installation
Use your favorite package manager.
Vundle: Plugin 'mechatroner/rainbow_csv'
VimPlug: Plug 'mechatroner/rainbow_csv'
dein: call dein#add('mechatroner/rainbow_csv')
No additional steps required - Rainbow CSV will work out of the box.
Overview
Main features:
- Highlight CSV columns in different rainbow colors.
- Provide info about column under the cursor
- Provide
SELECT
andUPDATE
queries in RBQL: SQL-like transprogramming query language. - Consistency check for csv files (CSVLint)
- Align and Shrink CSV fields (add/remove trailing spaces in fields)
- Cell-level cursor navigation
There are 4 ways to enable csv columns highlighting:
- CSV autodetection based on file content and/or extension
- Manual CSV delimiter selection with
:RainbowDelim
command with cursor over the delimiter - Manual CSV delimiter selection with
:RainbowMultiDelim
for multi-character delimiters selecting them in "VISUAL" mode - Explicitly activate one of the built-in filetypes, e.g.
:set ft=csv
The core functionality of the plugin is written in pure vimscript, no additional libraries required.
Plugin description
Built-in and autogenerated filetypes
Rainbow CSV has 7 built-in CSV filetypes and infinite number of autogenerated filetypes.
Each Rainbow CSV filetype is mapped to a separator and "policy" which describes additional properties e.g. if separator can be escaped inside double quoted field.
If you run :RainbowDelim
or :RainbowMultiDelim
to select a separator that doesn't map to one of the built-in filetypes, Rainbow CSV will dynamically generate the filetype syntax file and put it into the "syntax" folder.
List of built-in filetypes:
Filetype | Separator | Extension | Properties |
---|---|---|---|
csv | , (comma) | .csv | Ignored inside double-quoted fields |
tsv | \t (TAB) | .tsv .tab | |
csv_semicolon | ; (semicolon) | Ignored inside double-quoted fields | |
csv_whitespace | whitespace | Consecutive whitespaces are merged | |
csv_pipe | | (pipe) | ||
rfc_csv | , (comma) | Same as "csv" but allows multiline fields | |
rfc_semicolon | ; (semicolon) | Same as "csv_semicolon" but allows multiline fields |
Associating file extensions with CSV dialects
In most cases the built-in autodetection algorithm should correctly detect correct CSV dialect for all CSV tables that you open in Vim, but if you have disabled the autodetection algorithm or don't want to rely on it for some reason, you can manually associate file extensions with available csv dialects.
Example: to associate ".dat" extension with "csv_pipe" dialect and ".csv" extension with "csv_semicolon" add the folowing lines to your .vimrc:
autocmd BufNewFile,BufRead *.csv set filetype=csv_semicolon
autocmd BufNewFile,BufRead *.dat set filetype=csv_pipe
Working with multiline CSV fields
In rare cases some CSV files can contain double-quoted fields spanning multiple lines.
To work with such files you can set filetype to either "rfc_csv" or "rfc_semicolon" e.g. :set ft=rfc_csv
.
Syntax highlighting for rfc_csv and rfc_semicolon dialects can sometimes go out of sync with the file content, use :syntax sync fromstart
command in that case.
rfc_csv and rfc_semicolon are fully supported by RBQL which among other things allows you to easily convert them to line-by-line CSV by replacing newlines in fields with sequences of 4 spaces or something like that.
rfc_csv and rfc_semicolon take their name from RFC 4180 memo with which they are fully compatible.
Built-in RBQL query language
Rainbow CSV comes with built-in lightweight RBQL SQL-like query engine.
To run an RBQL query either use :Select
command e.g. :Select a1, a2
or run :RainbowQuery
command to enter query editing mode.
Demonstration of rainbow_csv highlighting and RBQL queries
In this demo python expressions were used, but JavaScript is also available.
Rainbow highlighting for non-table files
You can use rainbow highlighting and RBQL even for non-csv/tsv files.
E.g. you can highlight records in log files, one-line xmls and other delimited records.
You can even highlight function arguments in your programming language using comma or comma+whitespaces as a delimiter for :RainbowDelim
or :RainbowMultiDelim
commands.
And you can always turn off the rainbow highlighting using :NoRainbowDelim
command.
Commands
:RainbowDelim
Mark current file as a table and highlight it's columns in rainbow colors. Character under the cursor will be used as a delimiter. The delimiter will be saved in a config file for future vim sessions.
You can also use this command for non-csv files, e.g. to highlight function arguments
in source code in different colors. To return back to original syntax highlighting run :NoRainbowDelim
:RainbowMultiDelim
Same as :RainbowDelim
, but works with multicharacter separators.
Visually select the multicharacter separator (e.g. ~#~
) and run :RainbowMultiDelim
command.
:NoRainbowDelim
Disable rainbow columns highlighting for the current file.
:RainbowCellGoUp
Move cursor one cell up.
Consider mapping this to Ctrl+[Up Arrow], see the "Key Mappings" section.
:RainbowCellGoDown
Move cursor one cell down.
Consider mapping this to Ctrl+[Down Arrow], see the "Key Mappings" section.
:RainbowCellGoLeft
Move cursor one cell left.
Consider mapping this to Ctrl+[Left Arrow], see the "Key Mappings" section.
:RainbowCellGoRight
Move cursor one cell right.
Consider mapping this to Ctrl+[Right Arrow], see the "Key Mappings" section.
:RainbowComment
Mark the character under the cursor as the comment prefix, e.g. #
. By default Rainbow CSV doesn't highlight comments in CSV files.
You can also use :RainbowCommentMulti
to mark a visual selection as a multicharacter comment prefix
:NoRainbowComment
Disable all comments for the current CSV file.
This command is especially useful when you have set g:rainbow_comment_prefix
variable and want to exclude just one particular file.
:CSVLint
The linter checks the following:
- consistency of double quotes usage in CSV rows
- consistency of number of fields per CSV row
:RainbowAlign
Align CSV columns with whitespaces.
Don't run this command if you treat leading and trailing whitespaces in fields as part of the data.
You can edit aligned CSV file in Vim column-edit mode (Ctrl+v).
:RainbowShrink
Remove leading and trailing whitespaces from all fields. Opposite to RainbowAlign
:Select ...
Allows to enter RBQL select query as vim command.
E.g. :Select a1, a2 order by a1
:Update ...
Allows to enter RBQL update query as vim command.
E.g. :Update a1 = a1 + " " + a2
:RainbowQuery
Enter RBQL Query editing mode.
When in the query editing mode, execute :RainbowQuery
again to run the query.
Consider mapping :RainbowQuery
to <F5>
key i.e. nnoremap <F5> :RainbowQuery<CR>
:RainbowName <name>
Assign any name to the current table. You can use this name in join operation instead of the table path. E.g.
JOIN customers ON a1 == b1
intead of:
JOIN /path/to/my/customers/table ON a1 == b1
:RainbowCopyBack
This command only applicable for RBQL output files.
Replace the content of the original file that was used to run the RBQL query with the query result set data.
Key Mappings
Plugin does not create any new key mappings, but you can define your own in your .vimrc file.
All highlighted files have a special buffer variable b:rbcsv
set to 1, so you can use this to define conditional csv-only key mappings.
For example, to conditionally map Ctrl+Arrow keys to cell navigation commands you can use this snippet:
nnoremap <expr> <C-Left> get(b:, 'rbcsv', 0) == 1 ? ':RainbowCellGoLeft<CR>' : '<C-Left>'
nnoremap <expr> <C-Right> get(b:, 'rbcsv', 0) == 1 ? ':RainbowCellGoRight<CR>' : '<C-Right>'
nnoremap <expr> <C-Up> get(b:, 'rbcsv', 0) == 1 ? ':RainbowCellGoUp<CR>' : '<C-Up>'
nnoremap <expr> <C-Down> get(b:, 'rbcsv', 0) == 1 ? ':RainbowCellGoDown<CR>' : '<C-Down>'
You can also map arrow keys unconditionally, but this will have no effect outside highlighted CSV files, e.g.
nnoremap <C-Right> :RainbowCellGoRight<CR>
Configuration
g:disable_rainbow_hover
Set to 1
to stop showing info about the column under the cursor in Vim command line
Example:
let g:disable_rainbow_hover = 1
g:rcsv_delimiters
Default: ["\t", ",", ";", "|"]
List of separators to try for content-based autodetection
You can add or remove values from the list. Example:
let g:rcsv_delimiters = ["\t", ",", "^", "~#~"]
g:disable_rainbow_csv_autodetect
Set to 1
to disable CSV autodetection mechanism
Example:
let g:disable_rainbow_csv_autodetect = 1
Manual delimiter selection would still be possible. You can also manually associate specific file extensions with 'csv' or 'tsv' filetypes
g:rainbow_comment_prefix
Default: ''
A string to use as a comment prefix for all CSV files you open in Vim.
This setting is helpful if you are dealing with lots of CSV files which consistently use the same comment prefix e.g. '#'
or '>>'
If you want to enable comments on file-by-file basis, use the :RainbowComment
or :RainbowCommentMulti
commands instead.
To cancel the effect of g:rainbow_comment_prefix
just for the current file use :NoRainbowComment
command.
g:rcsv_max_columns
Default: 30
Autodetection will fail if buffer has more than g:rcsv_max_columns columns.
You can increase or decrease this limit.
g:rcsv_colorpairs
List of color name pairs to customize rainbow highlighting.
Each entry in the list is a pair of two colors: the first color is for terminal mode, the second one is for GUI mode.
Example:
let g:rcsv_colorpairs = [['red', 'red'], ['blue', 'blue'], ['green', 'green'], ['magenta', 'magenta'], ['NONE', 'NONE'], ['darkred', 'darkred'], ['darkblue', 'darkblue'], ['darkgreen', 'darkgreen'], ['darkmagenta', 'darkmagenta'], ['darkcyan', 'darkcyan']]
g:multiline_search_range
Default: 10
This settings is only relevant for rfc_csv and rfc_semicolon dialects.
If some multiline records contain more lines that this value, hover info will not work correctly. It is not recommended to significantly increase this value because it will have negative impact on hover info performance
g:rbql_backend_language
Default: 'python'
Supported values: 'python'
, 'js'
Scripting language to use in RBQL expressions.
g:rbql_encoding
Default: utf-8
Supported values: 'utf-8'
, 'latin-1'
CSV files encoding for RBQL.
g:rbql_output_format
Default: 'input'
Supported values: 'tsv'
, 'csv'
, 'input'
Format of RBQL result set tables.
- input: same format as the input table
- tsv: doesn't allow quoted tabs inside fields.
- csv: is Excel-compatible and allows quoted commas.
Essentially format is a pair: delimiter + quoting policy.
This setting for example can be used to convert files between tsv and csv format:
- To convert csv to tsv: 1. open csv file. 2.
:let g:rbql_output_format='tsv'
3.:Select *
- To convert tsv to csv: 1. open tsv file. 2.
:let g:rbql_output_format='csv'
3.:Select *
g:rbql_use_system_python
Set to 1
to use system python interpreter for RBQL queries instead of the python interpreter built into your vim/neovim editor.
g:rbql_with_headers
If most of the CSV files that you work with have headers, you can set this value to 1. In this case RBQL will treat first records in files as headers by default.
Example: :let g:rbql_with_headers = 1
You can also adjust (or override) this setting by adding WITH (header)
or WITH (noheader)
to the end of your RBQL queries.
g:rbql_trim_spaces
Default: 1
Trim/Strip all trailing and leading spaces in all fields when running RBQL.
Set to 0
to preserve leading and trailing spaces in output, this might also slightly improve performance.
RBQL (Rainbow Query Language) Description
RBQL is an eval-based SQL-like query engine for (not only) CSV file processing. It provides SQL-like language that supports SELECT queries with Python or JavaScript expressions.
RBQL is best suited for data transformation, data cleaning, and analytical queries.
RBQL is distributed with CLI apps, text editor plugins, Python and JS libraries.
Main Features
- Use Python or JavaScript expressions inside
SELECT
,UPDATE
,WHERE
andORDER BY
statements - Supports multiple input formats
- Result set of any query immediately becomes a first-class table on its own
- No need to provide FROM statement in the query when the input table is defined by the current context.
- Supports all main SQL keywords
- Supports aggregate functions and GROUP BY queries
- Supports user-defined functions (UDF)
- Provides some new useful query modes which traditional SQL engines do not have
- Lightweight, dependency-free, works out of the box
Limitations:
- RBQL doesn't support nested queries, but they can be emulated with consecutive queries
- Number of tables in all JOIN queries is always 2 (input table and join table), use consecutive queries to join 3 or more tables
Supported SQL Keywords (Keywords are case insensitive)
- SELECT
- UPDATE
- WHERE
- ORDER BY ... [ DESC | ASC ]
- [ LEFT | INNER ] JOIN
- DISTINCT
- GROUP BY
- TOP N
- LIMIT N
- AS
All keywords have the same meaning as in SQL queries. You can check them online
RBQL variables
RBQL for CSV files provides the following variables which you can use in your queries:
- a1, a2,..., a{N}
Variable type: string
Description: value of i-th field in the current record in input table - b1, b2,..., b{N}
Variable type: string
Description: value of i-th field in the current record in join table B - NR
Variable type: integer
Description: Record number (1-based) - NF
Variable type: integer
Description: Number of fields in the current record - a.name, b.Person_age, ... a.{Good_alphanumeric_column_name}
Variable type: string
Description: Value of the field referenced by it's "name". You can use this notation if the field in the header has a "good" alphanumeric name - a["object id"], a['9.12341234'], b["%$ !! 10 20"] ... a["Arbitrary column name!"]
Variable type: string
Description: Value of the field referenced by it's "name". You can use this notation to reference fields by arbitrary values in the header
UPDATE statement
UPDATE query produces a new table where original values are replaced according to the UPDATE expression, so it can also be considered a special type of SELECT query.
Aggregate functions and queries
RBQL supports the following aggregate functions, which can also be used with GROUP BY keyword:
COUNT, ARRAY_AGG, MIN, MAX, ANY_VALUE, SUM, AVG, VARIANCE, MEDIAN
Limitation: aggregate functions inside Python (or JS) expressions are not supported. Although you can use expressions inside aggregate functions.
E.g. MAX(float(a1) / 1000)
- valid; MAX(a1) / 1000
- invalid.
There is a workaround for the limitation above for ARRAY_AGG function which supports an optional parameter - a callback function that can do something with the aggregated array. Example:
SELECT a2, ARRAY_AGG(a1, lambda v: sorted(v)[:5]) GROUP BY a2
- Python; SELECT a2, ARRAY_AGG(a1, v => v.sort().slice(0, 5)) GROUP BY a2
- JS
JOIN statements
Join table B can be referenced either by its file path or by its name - an arbitrary string which the user should provide before executing the JOIN query.
RBQL supports STRICT LEFT JOIN which is like LEFT JOIN, but generates an error if any key in the left table "A" doesn't have exactly one matching key in the right table "B".
Table B path can be either relative to the working dir, relative to the main table or absolute.
Limitation: JOIN statements can't contain Python/JS expressions and must have the following form: <JOIN_KEYWORD> (/path/to/table.tsv | table_name ) ON a... == b... [AND a... == b... [AND ... ]]
SELECT EXCEPT statement
SELECT EXCEPT can be used to select everything except specific columns. E.g. to select everything but columns 2 and 4, run: SELECT * EXCEPT a2, a4
Traditional SQL engines do not support this query mode.
UNNEST() operator
UNNEST(list) takes a list/array as an argument and repeats the output record multiple times - one time for each value from the list argument.
Example: SELECT a1, UNNEST(a2.split(';'))
LIKE() function
RBQL does not support LIKE operator, instead it provides "like()" function which can be used like this:
SELECT * where like(a1, 'foo%bar')
WITH (header) and WITH (noheader) statements
You can set whether the input (and join) CSV file has a header or not using the environment configuration parameters which could be --with_headers
CLI flag or GUI checkbox or something else.
But it is also possible to override this selection directly in the query by adding either WITH (header)
or WITH (noheader)
statement at the end of the query.
Example: select top 5 NR, * with (header)
User Defined Functions (UDF)
RBQL supports User Defined Functions
You can define custom functions and/or import libraries in two special files:
~/.rbql_init_source.py
- for Python~/.rbql_init_source.js
- for JavaScript
Examples of RBQL queries
With Python expressions
SELECT TOP 100 a1, int(a2) * 10, len(a4) WHERE a1 == "Buy" ORDER BY int(a2) DESC
SELECT a.id, a.weight / 1000 AS weight_kg
SELECT * ORDER BY random.random()
- random sortSELECT len(a.vehicle_price) / 10, a2 WHERE int(a.vehicle_price) < 500 and a['Vehicle type'] in ["car", "plane", "boat"] limit 20
- referencing columns by names from header and using Python's "in" to emulate SQL's "in"UPDATE SET a3 = 'NPC' WHERE a3.find('Non-playable character') != -1
SELECT NR, *
- enumerate records, NR is 1-basedSELECT * WHERE re.match(".*ab.*", a1) is not None
- select entries where first column has "ab" patternSELECT a1, b1, b2 INNER JOIN ./countries.txt ON a2 == b1 ORDER BY a1, a3
- example of join querySELECT MAX(a1), MIN(a1) WHERE a.Name != 'John' GROUP BY a2, a3
- example of aggregate querySELECT *a1.split(':')
- Using Python3 unpack operator to split one column into many. Do not try this with other SQL engines!
With JavaScript expressions
SELECT TOP 100 a1, a2 * 10, a4.length WHERE a1 == "Buy" ORDER BY parseInt(a2) DESC
SELECT a.id, a.weight / 1000 AS weight_kg
SELECT * ORDER BY Math.random()
- random sortSELECT TOP 20 a.vehicle_price.length / 10, a2 WHERE parseInt(a.vehicle_price) < 500 && ["car", "plane", "boat"].indexOf(a['Vehicle type']) > -1 limit 20
- referencing columns by names from headerUPDATE SET a3 = 'NPC' WHERE a3.indexOf('Non-playable character') != -1
SELECT NR, *
- enumerate records, NR is 1-basedSELECT a1, b1, b2 INNER JOIN ./countries.txt ON a2 == b1 ORDER BY a1, a3
- example of join querySELECT MAX(a1), MIN(a1) WHERE a.Name != 'John' GROUP BY a2, a3
- example of aggregate querySELECT ...a1.split(':')
- Using JS "destructuring assignment" syntax to split one column into many. Do not try this with other SQL engines!
General info
Comparison of Rainbow CSV technology with traditional graphical column alignment
Advantages
- WYSIWYG
- 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.
- Works with non-table and semi-tabular files (text files that contain both table(s) and non-table data like text)
- Ability to visually associate two same-colored columns from two different windows. This is not possible with graphical column alignment
Disadvantages
- Rainbow CSV technology may be less effective for CSV files with many (> 10) columns
References
Rainbow CSV and similar plugins in other editors:
- Rainbow CSV for Neovim - fork of Vim plugin, has some improvements and optimizations.
- Rainbow CSV extension in Visual Studio Code
- rainbow_csv plugin in Sublime Text
- Rainbow CSV in IntelliJ IDEA
- CSVLint for Notepad++
- rainbow_csv_4_nedit in NEdit
- CSV highlighting in Nano
- rainbow-csv package in Atom
- rainbow_csv plugin in gedit - doesn't support quoted commas in csv
RBQL
- RBQL website
- Library and CLI App for JavaScript
- Library and CLI App for Python
- Demo Google Colab IPython notebook
Related vim plugins:
Rainbow CSV name and original implementation was significantly influenced by rainbow_parentheses Vim plugin.
There also exists an old vim syntax file csv_color which, despite it's name, can highlight only *.tsv files.
And, of course, there is csv.vim