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CSV Lint

A ruby gem to support validating CSV files to check their syntax and contents. You can either use this gem within your own Ruby code, or as a standolone command line application

Summary of features

  • Validation that checks the structural formatting of a CSV file
  • Validation of a delimiter-separated values (dsv) file accesible via URL, File, or an IO-style object (e.g. StringIO)
  • Validation against CSV dialects
  • Validation against multiple schema standards; JSON Table Schema and CSV on the Web


ruby version 2.1.4


The codebase includes both rspec and cucumber tests, which can be run together using:

$ rake

or separately:

$ rake spec
$ rake features

When the cucumber tests are first run, a script will create tests based on the latest version of the CSV on the Web test suite, including creating a local cache of the test files. This requires an internet connection and some patience. Following that download, the tests will run locally; there's also a batch script:

$ bin/run-csvw-tests

which will run the tests from the command line.

If you need to refresh the CSV on the Web tests:

$ rm bin/run-csvw-tests
$ rm features/csvw_validation_tests.feature
$ rm -r features/fixtures/csvw

and then run the cucumber tests again or:

$ ruby features/support/load_tests.rb


Add this line to your application's Gemfile:

gem 'csvlint'

And then execute:

$ bundle

Or install it yourself as:

$ gem install csvlint


You can either use this gem within your own Ruby code, or as a standolone command line application

On the command line

After installing the gem, you can validate a CSV on the command line like so:

csvlint myfile.csv

You will then see the validation result, together with any warnings or errors e.g.

myfile.csv is INVALID
1. blank_rows. Row: 3
1. title_row.
2. inconsistent_values. Column: 14

You can also optionally pass a schema file like so:

csvlint myfile.csv --schema=schema.json

In your own Ruby code

Currently the gem supports retrieving a CSV accessible from a URL, File, or an IO-style object (e.g. StringIO)

require 'csvlint'

validator = "" )
validator ="/path/to/my/data.csv" ))
validator = my_data_in_a_string ) )

When validating from a URL the range of errors and warnings is wider as the library will also check HTTP headers for best practices

#invoke the validation

#check validation status

#access array of errors, each is an Csvlint::ErrorMessage object

#access array of warnings

#access array of information messages

#get some information about the CSV file that was validated

#retrieve HTTP headers from request

Controlling CSV Parsing

The validator supports configuration of the CSV Dialect used in a data file. This is specified by passing a dialect hash to the constructor:

dialect = {
	"header" => true,
	"delimiter" => ","
validator = "", dialect )

The options should be a Hash that conforms to the CSV Dialect JSON structure.

While these options configure the parser to correctly process the file, the validator will still raise errors or warnings for CSV structure that it considers to be invalid, e.g. a missing header or different delimiters.

Note that the parser will also check for a header parameter on the Content-Type header returned when fetching a remote CSV file. As specified in RFC 4180 the values for this can be present and absent, e.g:

Content-Type: text/csv; header=present

Error Reporting

The validator provides feedback on a validation result using instances of Csvlint::ErrorMessage. Errors are divided into errors, warnings and information messages. A validation attempt is successful if there are no errors.

Messages provide context including:

  • category has a symbol that indicates the category or error/warning: :structure (well-formedness issues), :schema (schema validation), :context (publishing metadata, e.g. content type)
  • type has a symbol that indicates the type of error or warning being reported
  • row holds the line number of the problem
  • column holds the column number of the issue
  • content holds the contents of the row that generated the error or warning


The following types of error can be reported:

  • :wrong_content_type -- content type is not text/csv
  • :ragged_rows -- row has a different number of columns (than the first row in the file)
  • :blank_rows -- completely empty row, e.g. blank line or a line where all column values are empty
  • :invalid_encoding -- encoding error when parsing row, e.g. because of invalid characters
  • :not_found -- HTTP 404 error when retrieving the data
  • :stray_quote -- missing or stray quote
  • :unclosed_quote -- unclosed quoted field
  • :whitespace -- a quoted column has leading or trailing whitespace
  • :line_breaks -- line breaks were inconsistent or incorrectly specified


The following types of warning can be reported:

  • :no_encoding -- the Content-Type header returned in the HTTP request does not have a charset parameter
  • :encoding -- the character set is not UTF-8
  • :no_content_type -- file is being served without a Content-Type header
  • :excel -- no Content-Type header and the file extension is .xls
  • :check_options -- CSV file appears to contain only a single column
  • :inconsistent_values -- inconsistent values in the same column. Reported if <90% of values seem to have same data type (either numeric or alphanumeric including punctuation)
  • :empty_column_name -- a column in the CSV header has an empty name
  • :duplicate_column_name -- a column in the CSV header has a duplicate name
  • :title_row -- if there appears to be a title field in the first row of the CSV

Information Messages

There are also information messages available:

  • :nonrfc_line_breaks -- uses non-CRLF line breaks, so doesn't conform to RFC4180.
  • :assumed_header -- the validator has assumed that a header is present

Schema Validation

The library supports validating data against a schema. A schema configuration can be provided as a Hash or parsed from JSON. The structure currently follows JSON Table Schema with some extensions and rudinmentary CSV on the Web Metadata.

An example JSON Table Schema schema file is:

	"fields": [
			"name": "id",
			"constraints": {
				"required": true,
				"type": ""
			"name": "price",
			"constraints": {
				"required": true,
				"minLength": 1 
			"name": "postcode",
			"constraints": {
				"required": true,
				"pattern": "[A-Z]{1,2}[0-9][0-9A-Z]? ?[0-9][A-Z]{2}"

An equivalent CSV on the Web Metadata file is:

	"@context": "",
	"url": "",
	"tableSchema": {
		"columns": [
				"name": "id",
				"required": true,
				"datatype": { "base": "integer" }
				"name": "price",
				"required": true,
				"datatype": { "base": "string", "minLength": 1 }
				"name": "postcode",
				"required": true

Parsing and validating with a schema (of either kind):

schema = Csvlint::Schema.load_from_json(uri)
validator = "", nil, schema )

CSV on the Web Validation Support

This gem passes all the validation tests in the official CSV on the Web test suite (though there might still be errors or parts of the CSV on the Web standard that aren't tested by that test suite).

JSON Table Schema Support

Supported constraints:

  • required -- there must be a value for this field in every row
  • unique -- the values in every row should be unique
  • minLength -- minimum number of characters in the value
  • maxLength -- maximum number of characters in the value
  • pattern -- values must match the provided regular expression
  • type -- specifies an XML Schema data type. Values of the column must be a valid value for that type
  • minimum -- specify a minimum range for values, the value will be parsed as specified by type
  • maximum -- specify a maximum range for values, the value will be parsed as specified by type
  • datePattern -- specify a strftime compatible date pattern to be used when parsing date values and min/max constraints

Supported data types (this is still a work in progress):

  • String -- (effectively a no-op)
  • Integer -- or
  • Float --
  • Double --
  • URI --
  • Boolean --
  • Non Positive Integer --
  • Positive Integer --
  • Non Negative Integer --
  • Negative Integer --
  • Date --
  • Date Time --
  • Year --
  • Year Month --
  • Time --

Use of an unknown data type will result in the column failing to validate.

Schema validation provides some additional types of error and warning messages:

  • :missing_value (error) -- a column marked as required in the schema has no value
  • :min_length (error) -- a column with a minLength constraint has a value that is too short
  • :max_length (error) -- a column with a maxLength constraint has a value that is too long
  • :pattern (error) -- a column with a pattern constraint has a value that doesn't match the regular expression
  • :malformed_header (warning) -- the header in the CSV doesn't match the schema
  • :missing_column (warning) -- a row in the CSV file has a missing column, that is specified in the schema. This is a warning only, as it may be legitimate
  • :extra_column (warning) -- a row in the CSV file has extra column.
  • :unique (error) -- a column with a unique constraint contains non-unique values
  • :below_minimum (error) -- a column with a minimum constraint contains a value that is below the minimum
  • :above_maximum (error) -- a column with a maximum constraint contains a value that is above the maximum

Other validation options

You can also provide an optional options hash as the fourth argument to Validator#new. Supported options are:

  • :limit_lines -- only check this number of lines of the CSV file. Good for a quick check on huge files.
options = {
  limit_lines: 100
validator = "", nil, nil, options )
  • :lambda -- Pass a block of code to be called when each line is validated, this will give you access to the Validator object. For example, this will return the current line number for every line validated:
    options = {
      lambda: ->(validator) { puts validator.current_line }
    validator = "", nil, nil, options )
    => 1