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.
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);"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 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 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.
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 (CSV) and tab (TSV) delimiters are built-in. For example, to parse:
d3.csvParse("foo,bar\n1,2"); // [{foo: "1", bar: "2"}, columns: ["foo", "bar"]]
d3.tsvParse("foo\tbar\n1\t2"); // [{foo: "1", bar: "2"}, columns: ["foo", "bar"]]
Or to format:
d3.csvFormat([{foo: "1", bar: "2"}]); // "foo,bar\n1,2"
d3.tsvFormat([{foo: "1", bar: "2"}]); // "foo\tbar\n1\t2"
To use a different delimiter, such as “|” for pipe-separated values, use d3.dsvFormat:
var psv = d3.dsvFormat("|");
console.log(psv.parse("foo|bar\n1|2")); // [{foo: "1", bar: "2"}, columns: ["foo", "bar"]]
For easy loading of DSV files in a browser, see d3-fetch’s d3.csv and d3.tsv methods.
If you use NPM, npm install d3-dsv. Otherwise, download the latest release. You can also load directly from d3js.org, either as a standalone library or as part of D3. AMD, CommonJS, and vanilla environments are supported. In vanilla, a d3 global is exported:
<script src="https://d3js.org/d3-dsv.v1.min.js"></script>
<script>
var data = d3.csvParse(string);
</script>
# d3.csvParse(string[, row]) <>
Equivalent to dsvFormat(",").parse.
# d3.csvParseRows(string[, row]) <>
Equivalent to dsvFormat(",").parseRows.
# d3.csvFormat(rows[, columns]) <>
Equivalent to dsvFormat(",").format.
# d3.csvFormatBody(rows[, columns]) <>
Equivalent to dsvFormat(",").formatBody.
Equivalent to dsvFormat(",").formatRows.
Equivalent to dsvFormat(",").formatRow.
Equivalent to dsvFormat(",").formatValue.
# d3.tsvParse(string[, row]) <>
Equivalent to dsvFormat("\t").parse.
# d3.tsvParseRows(string[, row]) <>
Equivalent to dsvFormat("\t").parseRows.
# d3.tsvFormat(rows[, columns]) <>
Equivalent to dsvFormat("\t").format.
# d3.tsvFormatBody(rows[, columns]) <>
Equivalent to dsvFormat("\t").formatBody.
Equivalent to dsvFormat("\t").formatRows.
Equivalent to dsvFormat("\t").formatRow.
Equivalent to dsvFormat("\t").formatValue.
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.
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"]
If a row conversion function is not specified, field values are strings. For safety, there is no automatic conversion 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. See d3.autoType for a convenient row conversion function that infers and coerces common types like numbers and strings.
If a row conversion function is specified, the specified function is invoked for each row, being 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. If the returned value is null or undefined, the row is skipped and will be omitted from the array returned by dsv.parse; otherwise, the returned value defines the corresponding row object. For example:
var data = d3.csvParse(string, function(d) {
return {
year: new Date(+d.Year, 0, 1), // lowercase and convert "Year" to Date
make: d.Make, // lowercase
model: d.Model, // lowercase
length: +d.Length // lowercase and convert "Length" 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"]
]
If a row conversion function is not specified, field values are strings. For safety, there is no automatic conversion 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. See d3.autoType for a convenient row conversion function that infers and coerces common types like numbers and strings.
If a row conversion function is specified, the specified function is invoked for each row, being passed an array representing the current row (d), the index (i) starting at zero for the first row, and the array of column names. If the returned value is null or undefined, the row is skipped and will be omitted from the array returned by dsv.parse; otherwise, the returned value defines the corresponding row object. For example:
var data = d3.csvParseRows(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
};
});
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.csvFormat(data, ["year", "make", "model", "length"]);
All fields on each row object will be coerced to strings. If the field value is null or undefined, the empty string is used. If the field value is a Date, the ECMAScript date-time string format (a subset of ISO 8601) is used: for example, dates at UTC midnight are formatted as YYYY-MM-DD. 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.formatBody(rows[, columns]) <>
Equivalent to dsv.format, but omits the header row. This is useful, for example, when appending rows to an existing file.
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.csvFormatRows(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.csvFormatRows([[
"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
];
})));
Formats a single array row of strings as delimiter-separated values, returning a string. Each column within the 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.
Format a single value or string as a delimiter-separated value, returning a string. A value that contains either the delimiter, a double-quote (") or a newline will be escaped using double-quotes.
Given an object (or array) representing a parsed row, infers the types of values on the object and coerces them accordingly, returning the mutated object. This function is intended to be used as a row accessor function in conjunction with dsv.parse and dsv.parseRows. For example, consider the following CSV file:
Year,Make,Model,Length
1997,Ford,E350,2.34
2000,Mercury,Cougar,2.38
When used with d3.csvParse,
d3.csvParse(string, d3.autoType)
the resulting JavaScript array is:
[
{"Year": 1997, "Make": "Ford", "Model": "E350", "Length": 2.34},
{"Year": 2000, "Make": "Mercury", "Model": "Cougar", "Length": 2.38}
]
Type inference works as follows. For each value in the given object, the trimmed value is computed; the value is then re-assigned as follows:
null."true", then true."false", then false."NaN", then NaN.Values with leading zeroes may be coerced to numbers; for example "08904" coerces to 8904. However, extra characters such as commas or units (e.g., "$1.00", "(123)", "1,234" or "32px") will prevent number coercion, resulting in a string.
Date strings must be in ECMAScript’s subset of the ISO 8601 format. When a date-only string such as YYYY-MM-DD is specified, the inferred time is midnight UTC; however, if a date-time string such as YYYY-MM-DDTHH:MM is specified without a time zone, it is assumed to be local time.
Automatic type inference is primarily intended to provide safe, predictable behavior in conjunction with dsv.format and dsv.formatRows for common JavaScript types. If you need different behavior, you should implement your own row accessor function.
For more, see the d3.autoType notebook.
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.
DSV files sometimes begin with a byte order mark (BOM); saving a spreadsheet in CSV UTF-8 format from Microsoft Excel, for example, will include a BOM. On the web this is not usually a problem because the UTF-8 decode algorithm specified in the Encoding standard removes the BOM. Node.js, on the other hand, does not remove the BOM when decoding UTF-8.
If the BOM is not removed, the first character of the text is a zero-width non-breaking space. So if a CSV file with a BOM is parsed by d3.csvParse, the first column’s name will begin with a zero-width non-breaking space. This can be hard to spot since this character is usually invisible when printed.
To remove the BOM before parsing, consider using strip-bom.
# dsv2dsv [options…] [file]
Converts the specified DSV input file to DSV (typically with a different delimiter or encoding). If file is not specified, defaults to reading from stdin. For example, to convert to CSV to TSV:
csv2tsv < example.csv > example.tsv
To convert windows-1252 CSV to utf-8 CSV:
dsv2dsv --input-encoding windows-1252 < latin1.csv > utf8.csv
Output usage information.
# dsv2dsv -V
# dsv2dsv --version
Output the version number.
# dsv2dsv -o file
# dsv2dsv --out file
Specify the output file name. Defaults to “-” for stdout.
# dsv2dsv -r delimiter
# dsv2dsv --input-delimiter delimiter
Specify the input delimiter character. Defaults to “,” for reading CSV. (You can enter a tab on the command line by typing ⌃V.)
# dsv2dsv --input-encoding encoding
Specify the input character encoding. Defaults to “utf8”.
# dsv2dsv -w delimiter
# dsv2dsv --output-delimiter delimiter
Specify the output delimiter character. Defaults to “,” for writing CSV. (You can enter a tab on the command line by typing ⌃V.)
# dsv2dsv --output-encoding encoding
Specify the output character encoding. Defaults to “utf8”.
# csv2tsv [options…] [file]
Equivalent to dsv2dsv, but the output delimiter defaults to the tab character (\t).
# tsv2csv [options…] [file]
Equivalent to dsv2dsv, but the input delimiter defaults to the tab character (\t).
# dsv2json [options…] [file]
Converts the specified DSV input file to JSON. If file is not specified, defaults to reading from stdin. For example, to convert to CSV to JSON:
csv2json < example.csv > example.json
Or to convert CSV to a newline-delimited JSON stream:
csv2json -n < example.csv > example.ndjson
# dsv2json -h
# dsv2json --help
Output usage information.
# dsv2json -V
# dsv2json --version
Output the version number.
# dsv2json -o file
# dsv2json --out file
Specify the output file name. Defaults to “-” for stdout.
# dsv2json -r delimiter
# dsv2json --input-delimiter delimiter
Specify the input delimiter character. Defaults to “,” for reading CSV. (You can enter a tab on the command line by typing ⌃V.)
# dsv2json --input-encoding encoding
Specify the input character encoding. Defaults to “utf8”.
# dsv2json -r encoding
# dsv2json --output-encoding encoding
Specify the output character encoding. Defaults to “utf8”.
# dsv2json -n
# dsv2json --newline-delimited
Output newline-delimited JSON instead of a single JSON array.
# csv2json [options…] [file]
Equivalent to dsv2json.
# tsv2json [options…] [file]
Equivalent to dsv2json, but the input delimiter defaults to the tab character (\t).
# json2dsv [options…] [file]
Converts the specified JSON input file to DSV. If file is not specified, defaults to reading from stdin. For example, to convert to JSON to CSV:
json2csv < example.json > example.csv
Or to convert a newline-delimited JSON stream to CSV:
json2csv -n < example.ndjson > example.csv
# json2dsv -h
# json2dsv --help
Output usage information.
# json2dsv -V
# json2dsv --version
Output the version number.
# json2dsv -o file
# json2dsv --out file
Specify the output file name. Defaults to “-” for stdout.
# json2dsv --input-encoding encoding
Specify the input character encoding. Defaults to “utf8”.
# json2dsv -w delimiter
# json2dsv --output-delimiter delimiter
Specify the output delimiter character. Defaults to “,” for writing CSV. (You can enter a tab on the command line by typing ⌃V.)
# json2dsv --output-encoding encoding
Specify the output character encoding. Defaults to “utf8”.
# json2dsv -n
# json2dsv --newline-delimited
Read newline-delimited JSON instead of a single JSON array.
# csv2json [options…] [file]
Equivalent to json2dsv.
# tsv2json [options…] [file]
Equivalent to json2dsv, but the output delimiter defaults to the tab character (\t).
FAQs
A parser and formatter for delimiter-separated values, such as CSV and TSV
The npm package d3-dsv receives a total of 3,714,412 weekly downloads. As such, d3-dsv popularity was classified as popular.
We found that d3-dsv demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
Socket researchers have discovered malicious npm packages targeting crypto developers, stealing credentials and wallet data using spyware delivered through typosquats of popular cryptographic libraries.
Security News
Socket's package search now displays weekly downloads for npm packages, helping developers quickly assess popularity and make more informed decisions.
Security News
A Stanford study reveals 9.5% of engineers contribute almost nothing, costing tech $90B annually, with remote work fueling the rise of "ghost engineers."