d3-dsv

What is d3-dsv?

The d3-dsv npm package is designed for parsing and formatting delimiter-separated values, such as CSV and TSV files. It provides a simple and efficient way to work with structured data in text format, making it easier to import, analyze, and export data in web applications.

What are d3-dsv's main functionalities?

Parsing CSV strings

This feature allows you to parse a CSV string into an array of objects, where each object represents a row of the CSV, with properties corresponding to column names.

"var d3 = require('d3-dsv');\nvar csvString = 'name,age\nAlice,30\nBob,42';\nvar data = d3.csvParse(csvString);\nconsole.log(data);"

Formatting objects to CSV

This feature enables you to convert an array of objects into a CSV string, where each object in the array represents a row in the CSV and the object properties represent columns.

"var d3 = require('d3-dsv');\nvar data = [{ name: 'Alice', age: 30 }, { name: 'Bob', age: 42 }];\nvar csvString = d3.csvFormat(data);\nconsole.log(csvString);"

Parsing TSV strings

Similar to parsing CSV strings, this feature allows for parsing TSV (Tab-Separated Values) strings into an array of objects, facilitating the handling of TSV formatted data.

"var d3 = require('d3-dsv');\nvar tsvString = 'name\tage\nAlice\t30\nBob\t42';\nvar data = d3.tsvParse(tsvString);\nconsole.log(data);"

Formatting objects to TSV

This feature allows for converting an array of objects into a TSV string, making it easy to generate TSV formatted data from JavaScript objects.

"var d3 = require('d3-dsv');\nvar data = [{ name: 'Alice', age: 30 }, { name: 'Bob', age: 42 }];\nvar tsvString = d3.tsvFormat(data);\nconsole.log(tsvString);"

Other packages similar to d3-dsv

papaparse

Papa Parse is a powerful CSV (Comma Separated Values) parser that can convert CSV files into JSON and back. It is similar to d3-dsv in its ability to handle CSV data but offers a more extensive set of features for handling large files and streaming.

csv-parser

csv-parser is a Node.js module that transforms CSV into JSON at the rate of 90,000 rows per second. It provides a simple and efficient streaming API, making it a good choice for processing large CSV files. It is similar to d3-dsv but focuses more on high-performance streaming.

fast-csv

fast-csv is another npm package for parsing and formatting CSV files. It offers both synchronous and asynchronous APIs and includes features for transforming data. While it shares functionality with d3-dsv, fast-csv emphasizes speed and flexibility in handling CSV data.

README

d3-dsv

This module provides a parser and formatter for delimiter-separated values, most commonly comma- (CSV) or tab-separated values (TSV). These tabular formats are popular with spreadsheet programs such as Microsoft Excel, and are often more space-efficient than JSON. This implementation is based on RFC 4180.

Comma and tab delimiters are built-in. To use a different delimiter, such as “|” for pipe-separated values, use the dsv constructor:

var psv = d3.dsv("|");

console.log(psv.parse("foo|bar\n1|2")); // [{foo: "1", bar: "2"}]

For easy loading of DSV files in a browser, see d3-request’s requestCsv and requestTsv methods.

Installing

If you use NPM, npm install d3-dsv. Otherwise, download the latest release. The released bundle supports AMD, CommonJS, and vanilla environments. Create a custom build using Rollup or your preferred bundler. You can also load directly from d3js.org:

<script src="https://d3js.org/d3-dsv.v0.1.min.js"></script>

In a vanilla environment, a d3_dsv global is exported. Try d3-dsv in your browser.

API Reference

# d3.dsv(delimiter)

Constructs a new DSV parser and formatter for the specified delimiter. The delimiter must be a single character (i.e., a single 16-bit code unit); so, ASCII delimiters are fine, but emoji delimiters are not.

# dsv.parse(string[, row])

Parses the specified string, which must be in the delimiter-separated values format with the appropriate delimiter, returning an array of objects representing the parsed rows.

Unlike dsv.parseRows, this method requires that the first line of the DSV content contains a delimiter-separated list of column names; these column names become the attributes on the returned objects. For example, consider the following CSV file:

Year,Make,Model,Length
1997,Ford,E350,2.34
2000,Mercury,Cougar,2.38

The resulting JavaScript array is:

[
  {"Year": "1997", "Make": "Ford", "Model": "E350", "Length": "2.34"},
  {"Year": "2000", "Make": "Mercury", "Model": "Cougar", "Length": "2.38"}
]

The returned array also exposes a columns property containing the column names in input order (in contrast to Object.keys, whose iteration order is arbitrary). For example:

data.columns; // ["Year", "Make", "Model", "Length"]

Field values are always strings; they will not be automatically converted to numbers, dates, or other types. In some cases, JavaScript may coerce strings to numbers for you automatically (for example, using the + operator), but better is to specify a row conversion function. For each row, the row function is passed an object representing the current row (d), the index (i) starting at zero for the first non-header row, and the array of column names. For example:

var data = d3.csv.parse(string, function(d) {
  return {
    year: new Date(+d.Year, 0, 1), // convert "Year" column to Date
    make: d.Make,
    model: d.Model,
    length: +d.Length // convert "Length" column to number
  };
});

Note: using + rather than parseInt or parseFloat is typically faster, though more restrictive. For example, "30px" when coerced using + returns NaN, while parseInt and parseFloat return 30.

# dsv.parseRows(string[, row])

Parses the specified string, which must be in the delimiter-separated values format with the appropriate delimiter, returning an array of arrays representing the parsed rows.

Unlike dsv.parse, this method treats the header line as a standard row, and should be used whenever DSV content does not contain a header. Each row is represented as an array rather than an object. Rows may have variable length. For example, consider the following CSV file, which notably lacks a header line:

1997,Ford,E350,2.34
2000,Mercury,Cougar,2.38

The resulting JavaScript array is:

[
  ["1997", "Ford", "E350", "2.34"],
  ["2000", "Mercury", "Cougar", "2.38"]
]

Field values are always strings; they will not be automatically converted to numbers. See dsv.parse for details. An optional row conversion function may be specified as the second argument to convert types and filter rows. For example:

var data = d3.csv.parseRows(string, function(d, i) {
  return {
    year: new Date(+d[0], 0, 1), // convert first colum column to Date
    make: d[1],
    model: d[2],
    length: +d[3] // convert fourth column to number
  };
});

The row function is invoked for each row in the DSV content, being passed the current row’s array of field values (d) and index (i) as arguments. The return value of the function replaces the element in the returned array of rows; if the function returns null or undefined, the row is stripped from the returned array of rows. In effect, row is similar to applying a map and filter operator to the returned rows.

# dsv.format(rows[, columns])

Formats the specified array of object rows as delimiter-separated values, returning a string. This operation is the inverse of dsv.parse. Each row will be separated by a newline (\n), and each column within each row will be separated by the delimiter (such as a comma, ,). Values that contain either the delimiter, a double-quote (") or a newline will be escaped using double-quotes.

If columns is not specified, the list of column names that forms the header row is determined by the union of all properties on all objects in rows; the order of columns is nondeterministic. If columns is specified, it is an array of strings representing the column names. For example:

var string = d3.csv.format(data, ["year", "make", "model", "length"]);

All fields on each row object will be coerced to strings. For more control over which and how fields are formatted, first map rows to an array of array of string, and then use dsv.formatRows.

# dsv.formatRows(rows)

Formats the specified array of array of string rows as delimiter-separated values, returning a string. This operation is the reverse of dsv.parseRows. Each row will be separated by a newline (\n), and each column within each row will be separated by the delimiter (such as a comma, ,). Values that contain either the delimiter, a double-quote (") or a newline will be escaped using double-quotes.

To convert an array of objects to an array of arrays while explicitly specifying the columns, use array.map. For example:

var string = d3.csv.formatRows(data.map(function(d, i) {
  return [
    d.year.getFullYear(), // Assuming d.year is a Date object.
    d.make,
    d.model,
    d.length
  ];
}));

If you like, you can also array.concat this result with an array of column names to generate the first row:

var string = d3.csv.formatRows([[
    "year",
    "make",
    "model",
    "length"
  ]].concat(data.map(function(d, i) {
  return [
    d.year.getFullYear(), // Assuming d.year is a Date object.
    d.make,
    d.model,
    d.length
  ];
})));

# d3.csv

A parser and formatter for comma-separated values (CSV), defined as:

var csv = d3.dsv(",");

# d3.tsv

A parser and formatter for tab-separated values (TSV), defined as:

var tsv = d3.dsv("\t");

Content Security Policy

If a content security policy is in place, note that dsv.parse requires unsafe-eval in the script-src directive, due to the (safe) use of dynamic code generation for fast parsing. (See source.) Alternatively, use dsv.parseRows.

Command Line Reference

This module comes with a few binaries to convert DSV files:

• csv2json
• csv2tsv
• tsv2csv
• tsv2json

These programs either take a single file as an argument or read from stdin, and write to stdout. For example, these statements are all equivalent:

csv2json file.csv > file.json
csv2json < file.csv > file.json
cat file.csv | csv2json - > file.json