CSV Functions

The CSV module provides functions for parsing, processing, and converting CSV (Comma-Separated Values) and TSV (Tab-Separated Values) data.

Including the CSV Module

To use the CSV functions, include the module at the top of your mq script:

include "csv"

Functions

csv_parse(input, has_header)

Parses CSV content using a comma as the delimiter.

Parameters:

• input: String containing the CSV data
• has_header: Boolean indicating whether the first row contains headers

Returns:

• If has_header is true: Array of dictionaries where keys are column headers
• If has_header is false: Array of arrays containing raw row data

Example:

include "csv"

# Parse CSV with headers
| "name,age,city\nJohn,30,New York\nJane,25,Boston" | csv_parse(true)
# Returns: [{"name": "John", "age": "30", "city": "New York"}, {"name": "Jane", "age": "25", "city": "Boston"}]

# Parse CSV without headers
| "John,30,New York\nJane,25,Boston" | csv_parse(false)
# Returns: [["John", "30", "New York"], ["Jane", "25", "Boston"]]

csv_parse_with_delimiter(input, delimiter, has_header)

Parses CSV content with a custom delimiter.

Parameters:

• input: String containing the CSV data
• delimiter: String specifying the field delimiter
• has_header: Boolean indicating whether the first row contains headers

Returns:

• If has_header is true: Array of dictionaries where keys are column headers
• If has_header is false: Array of arrays containing raw row data

Example:

include "csv"

# Parse semicolon-separated values
| "name;age;city\nJohn;30;New York\nJane;25;Boston" | csv_parse_with_delimiter(";", true)
# Returns: [{"name": "John", "age": "30", "city": "New York"}, {"name": "Jane", "age": "25", "city": "Boston"}]

tsv_parse(input, has_header)

Parses TSV (Tab-Separated Values) content.

Parameters:

• input: String containing the TSV data
• has_header: Boolean indicating whether the first row contains headers

Returns:

• If has_header is true: Array of dictionaries where keys are column headers
• If has_header is false: Array of arrays containing raw row data

Example:

include "csv"

# Parse TSV with headers
| "name	age	city\nJohn	30	New York\nJane	25	Boston" | tsv_parse(true)
# Returns: [{"name": "John", "age": "30", "city": "New York"}, {"name": "Jane", "age": "25", "city": "Boston"}]

csv_stringify(data, delimiter)

Converts data to a CSV string with a specified delimiter.

Parameters:

• data: Array of dictionaries or array of arrays to convert
• delimiter: String specifying the field delimiter

Returns:

• String containing the formatted CSV data

Example:

include "csv"

# Convert array of dictionaries to CSV
| [{"name": "John", "age": "30"}, {"name": "Jane", "age": "25"}] | csv_stringify(",")
# Returns: "name,age\nJohn,30\nJane,25"

# Convert array of arrays to CSV
| [["name", "age"], ["John", "30"], ["Jane", "25"]] | csv_stringify(",")
# Returns: "name,age\nJohn,30\nJane,25"

csv_to_markdown_table(data)

Converts CSV data to a Markdown table format.

Parameters:

• data: Array of dictionaries or array of arrays

Returns:

• String containing the Markdown table

Example:

include "csv"

# Convert to Markdown table
| [{"name": "John", "age": "30"}, {"name": "Jane", "age": "25"}] | csv_to_markdown_table()
# Returns:
# | name | age |
# | --- | --- |
# | John | 30 |
# | Jane | 25 |

csv_to_json(data)

Converts CSV data to a JSON string.

Parameters:

• data: Array of dictionaries or array of arrays

Returns:

• String containing the JSON representation

Example:

include "csv"

# Convert to JSON
| [{"name": "John", "age": "30"}, {"name": "Jane", "age": "25"}] | csv_to_json()
# Returns: [{"name":"John","age":"30"},{"name":"Jane","age":"25"}]

CSV Format Support

The CSV parser follows RFC 4180 specifications and supports:

• Quoted fields with embedded commas, newlines, and quotes
• Escaped quotes within quoted fields (double quotes)
• Custom delimiters
• Header row processing
• Mixed quoted and unquoted fields