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README.md

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 and UPDATE 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)

There are 4 ways to enable csv columns highlighting:

  1. CSV autodetection based on file content and/or extension
  2. Manual CSV delimiter selection with :RainbowDelim command with cursor over the delimiter
  3. Manual CSV delimiter selection with :RainbowMultiDelim for multi-character delimiters selecting them in "VISUAL" mode
  4. Explicitly activate one of the built-in filetypes, e.g. :set ft=csv

To run an RBQL query either press F5 or enter the query in vim command line e.g. :Select a1, a2
As soon as you finish entering ":select" (or ":update") and press whitespace, the plugin will show column names in the status line.

The core functionality of the plugin is written in pure vimscript, no additional libraries required.

Demonstration of rainbow_csv highlighting and RBQL queries

demo_screencast

In this demo python expressions were used, but JavaScript is also available.

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 user uses :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

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.

Here is an example of how to extract some fields from a bunch of uniform single-line xmls:

demo_xml_screencast

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".
Syntax highlighting for rfc_csv and rfc_semicolon dialects can go out of sync with the file content under specific conditions, 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.

Key Mappings

Key mappings are only assigned for files with active rainbow highlighting

Key Action
F5 Start query editing for the current csv file
F5 Execute currently edited query
F7 Copy query result set to the parent buffer

You can disable these mappings by setting let g:disable_rainbow_key_mappings = 1

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.

: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

: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

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: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: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:disable_rainbow_key_mappings

Disable default key mappings introduced by the extension

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 *

Optional "Header" file feature

Rainbow csv allows you to create a special "header" file for any of your spreadsheet table files. It must have the same name as the table file but with ".header" suffix (e.g. for "table.tsv" table the header file is "table.tsv.header"). The only purpose of header file is to provide csv column names.

RBQL (Rainbow Query Language) Description

RBQL is a technology for (not only) CSV files processing. It provides SQL-like language that supports SELECT queries with Python or JavaScript expressions.
RBQL is distributed with CLI apps, text editor plugins, Python and JS libraries and can work in web browsers.
RBQL core module is very generic and can process all kind of objects and record formats, but most popular RBQL implementation works with CSV files.

Official Site

Main Features

  • Use Python or JavaScript expressions inside SELECT, UPDATE, WHERE and ORDER BY statements
  • Result set of any query immediately becomes a first-class table on it's own
  • Supports input tables with inconsistent number of fields per record
  • Output records appear in the same order as in input unless ORDER BY is provided
  • Each record has a unique NR (record number) identifier
  • Supports all main SQL keywords
  • Supports aggregate functions and GROUP BY queries
  • Provides some new useful query modes which traditional SQL engines do not have
  • Supports both TOP and LIMIT keywords
  • Supports user-defined functions (UDF)
  • Works out of the box, no external dependencies

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

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 first (header) CSV line 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 first (header) CSV line, even when there is no header at all

Notes:

  • You can mix all variable types in a single query, example: select a1, b2 JOIN /path/to/b.csv ON a['Item Id'] == b.Identifier WHERE NR > 1 and int(a.Weight) * 100 > int(b["weight of the item"])
  • Referencing fields by header names does not automatically skip the header line (you can use where NR > 1 trick to skip it)
  • If you want to use RBQL as a library for your own app you can define your own custom variables and do not have to support the above mentioned CSV-related variables.

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. This prevents accidental data loss from poorly written queries.
UPDATE SET is synonym to UPDATE, because in RBQL there is no need to specify the source table.

Aggregate functions and queries

RBQL supports the following aggregate functions, which can also be used with GROUP BY keyword:
COUNT, ARRAY_AGG, MIN, MAX, 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 it's file path or by it's name - an arbitary string which 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 left table "A" doesn't have exactly one matching key in the right table "B".
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')

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 * order by random.random() where NR > 1 - skip header record and random sort
  • select len(a.vehicle_price) / 10, a2 where NR > 1 and a['Vehicle type'] in ["car", "plane", "boat"] limit 20 - referencing columns by names from header record, skipping the 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-based
  • select * where re.match(".*ab.*", a1) is not None - select entries where first column has "ab" pattern
  • select a1, b1, b2 inner join ./countries.txt on a2 == b1 order by a1, a3 - example of join query
  • select MAX(a1), MIN(a1) where a.Name != 'John' group by a2, a3 - example of aggregate query
  • select *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 * order by Math.random() where NR > 1 - skip header record and random sort
  • select top 20 a.vehicle_price.length / 10, a2 where NR > 1 and ["car", "plane", "boat"].indexOf(a['Vehicle type']) > -1 limit 20 - referencing columns by names from header record and skipping the header
  • update set a3 = 'NPC' where a3.indexOf('Non-playable character') != -1
  • select NR, * - enumerate records, NR is 1-based
  • select a1, b1, b2 inner join ./countries.txt on a2 == b1 order by a1, a3 - example of join query
  • select MAX(a1), MIN(a1) where a.Name != 'John' group by a2, a3 - example of aggregate query
  • select ...a1.split(':') - Using JS "destructuring assignment" syntax to split one column into many. Do not try this with other SQL engines!

FAQ

How do I skip header record in CSV files?

You can use the following trick: add ... where NR > 1 ... to your query

And if you are doing math operation you can modify your query like this, example:
select int(a3) * 1000, a2 -> select int(a3) * 1000 if NR > 1 else a3, a2

How does RBQL work?

RBQL parses SQL-like user query, generates new Python or JavaScript code and executes it.

Explanation of simplified Python version of RBQL algorithm by example.

  1. User enters the following query, which is stored as a string Q:
    SELECT a3, int(a4) + 100, len(a2) WHERE a1 != 'SELL'
  1. RBQL replaces all a{i} substrings in the query string Q with a[{i - 1}] substrings. The result is the following string:
    Q = "SELECT a[2], int(a[3]) + 100, len(a[1]) WHERE a[0] != 'SELL'"
  1. RBQL searches for "SELECT" and "WHERE" keywords in the query string Q, throws the keywords away, and puts everything after these keywords into two variables S - select part and W - where part, so we will get:
    S = "a[2], int(a[3]) + 100, len(a[1])"
    W = "a[0] != 'SELL'"
  1. RBQL has static template script which looks like this:
    for line in sys.stdin:
        a = line.rstrip('\n').split(',')
        if %%%W_Expression%%%:
            out_fields = [%%%S_Expression%%%]
            print ','.join([str(v) for v in out_fields])
  1. RBQL replaces %%%W_Expression%%% with W and %%%S_Expression%%% with S so we get the following script:
    for line in sys.stdin:
        a = line.rstrip('\n').split(',')
        if a[0] != 'SELL':
            out_fields = [a[2], int(a[3]) + 100, len(a[1])]
            print ','.join([str(v) for v in out_fields])
  1. RBQL runs the patched script against user's data file (real RBQL implementation calls "exec" in Python or "eval" in JS):
    ./tmp_script.py < data.tsv > result.tsv

Result set of the original query (SELECT a3, int(a4) + 100, len(a2) WHERE a1 != 'SELL') is in the "result.tsv" file.
Adding support of TOP/LIMIT keywords is trivial and to support "ORDER BY" we can introduce an intermediate array.

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
  • Current Rainbow CSV implementations do not support newlines inside double-quoted csv fields. Adding multiline fields support is technically possible under certain conditions but would impair other Rainbow CSV features and advantages.

References

Rainbow CSV and similar plugins in other editors:

RBQL

  • RBQL website RBQL
  • Library and CLI App for JavaScript RBQL
  • Library and CLI App for Python RBQL

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

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