csv-parser
Streaming csv parser inspired by binary-csv that aims to be faster than everyone else
Top Related Projects
Quick Overview
csv-parser is a fast and lightweight CSV parsing library for Node.js. It provides a streaming interface for efficient processing of large CSV files and offers various options for customizing the parsing behavior.
Pros
- High performance and low memory usage due to its streaming approach
- Simple and intuitive API for easy integration into Node.js projects
- Supports various CSV parsing options and customizations
- Actively maintained with regular updates and bug fixes
Cons
- Limited to Node.js environments, not suitable for browser-based applications
- Lacks advanced features like data validation or transformation out of the box
- May require additional configuration for complex CSV structures or non-standard formats
Code Examples
- Basic usage:
const csv = require('csv-parser')
const fs = require('fs')
fs.createReadStream('input.csv')
.pipe(csv())
.on('data', (row) => {
console.log(row)
})
.on('end', () => {
console.log('CSV file successfully processed')
})
- Custom column names:
const csv = require('csv-parser')
const fs = require('fs')
fs.createReadStream('input.csv')
.pipe(csv({
headers: ['column1', 'column2', 'column3']
}))
.on('data', (row) => {
console.log(row)
})
- Skipping lines and handling quotes:
const csv = require('csv-parser')
const fs = require('fs')
fs.createReadStream('input.csv')
.pipe(csv({
skipLines: 2,
quote: '"'
}))
.on('data', (row) => {
console.log(row)
})
Getting Started
To use csv-parser in your Node.js project, follow these steps:
-
Install the package:
npm install csv-parser
-
Import the library in your JavaScript file:
const csv = require('csv-parser')
-
Use the csv-parser with a readable stream:
const fs = require('fs') fs.createReadStream('input.csv') .pipe(csv()) .on('data', (row) => { // Process each row of data console.log(row) }) .on('end', () => { console.log('CSV parsing completed') })
This setup allows you to start parsing CSV files in your Node.js application. Customize the options as needed for your specific use case.
Competitor Comparisons
Full featured CSV parser with simple api and tested against large datasets.
Pros of node-csv
- More comprehensive CSV functionality (parsing, stringifying, transforming)
- Supports both synchronous and asynchronous operations
- Extensive documentation and examples
Cons of node-csv
- Larger package size and potentially more complex setup
- May have a steeper learning curve for simple use cases
Code Comparison
csv-parser:
const csv = require('csv-parser')
const fs = require('fs')
fs.createReadStream('input.csv')
.pipe(csv())
.on('data', (row) => {
console.log(row)
})
node-csv:
const csv = require('csv')
csv.parse(fs.readFileSync('input.csv'), (err, data) => {
console.log(data)
})
Both libraries offer straightforward ways to parse CSV files, but node-csv provides more options for customization and advanced features. csv-parser focuses on simplicity and performance for parsing, while node-csv offers a broader range of CSV-related functionalities.
csv-parser is ideal for projects that primarily need fast CSV parsing with minimal setup. node-csv is better suited for applications requiring more comprehensive CSV handling, including generation and transformation, albeit with a potentially more complex API.
A simple, blazing-fast CSV parser and encoder. Full RFC 4180 compliance.
Pros of CSV.js
- Supports both parsing and stringifying CSV data
- Provides a more feature-rich API with options for customization
- Handles various CSV formats, including RFC 4180 compliance
Cons of CSV.js
- Less performant for large datasets compared to csv-parser
- More complex setup and usage for simple parsing tasks
- Larger package size due to additional features
Code Comparison
csv-parser:
const csv = require('csv-parser')
const fs = require('fs')
fs.createReadStream('input.csv')
.pipe(csv())
.on('data', (row) => {
console.log(row)
})
CSV.js:
const CSV = require('csv.js')
CSV.parse('a,b,c\n1,2,3', (err, data) => {
console.log(data)
})
Summary
csv-parser is a lightweight and fast CSV parsing library, ideal for processing large CSV files with minimal configuration. It excels in performance and simplicity but lacks advanced features.
CSV.js offers a more comprehensive solution for both parsing and stringifying CSV data, with support for various CSV formats and customization options. However, it may be less performant for large datasets and requires more setup for basic tasks.
Choose csv-parser for simple, high-performance CSV parsing, and CSV.js for more complex CSV manipulation needs or when both parsing and stringifying are required.
Fast and powerful CSV (delimited text) parser that gracefully handles large files and malformed input
Pros of PapaParse
- Browser-based parsing, allowing client-side CSV processing
- More extensive configuration options for parsing
- Built-in support for streaming large files
Cons of PapaParse
- Larger file size, which may impact load times for web applications
- Potentially slower performance for very large datasets
Code Comparison
PapaParse:
Papa.parse(file, {
complete: function(results) {
console.log(results);
}
});
csv-parser:
const csv = require('csv-parser');
const fs = require('fs');
fs.createReadStream('input.csv')
.pipe(csv())
.on('data', (row) => {
console.log(row);
});
Key Differences
- PapaParse is designed for browser use, while csv-parser is Node.js-focused
- csv-parser uses a streaming approach by default, which can be more memory-efficient for large files
- PapaParse offers more built-in features like error handling and data type detection
Use Cases
- Choose PapaParse for client-side parsing or when extensive configuration is needed
- Opt for csv-parser in Node.js environments or when dealing with very large files that require efficient streaming
Both libraries are well-maintained and offer robust CSV parsing capabilities, with the choice depending on specific project requirements and the target environment.
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual CopilotREADME
csv-parser
Streaming CSV parser that aims for maximum speed as well as compatibility with the csv-spectrum CSV acid test suite.
csv-parser
can convert CSV into JSON at at rate of around 90,000 rows per
second. Performance varies with the data used; try bin/bench.js <your file>
to benchmark your data.
csv-parser
can be used in the browser with browserify.
neat-csv can be used if a Promise
based interface to csv-parser
is needed.
Note: This module requires Node v8.16.0 or higher.
Benchmarks
â¡ï¸ csv-parser
is greased-lightning fast
â npm run bench
Filename Rows Parsed Duration
backtick.csv 2 3.5ms
bad-data.csv 3 0.55ms
basic.csv 1 0.26ms
comma-in-quote.csv 1 0.29ms
comment.csv 2 0.40ms
empty-columns.csv 1 0.40ms
escape-quotes.csv 3 0.38ms
geojson.csv 3 0.46ms
large-dataset.csv 7268 73ms
newlines.csv 3 0.35ms
no-headers.csv 3 0.26ms
option-comment.csv 2 0.24ms
option-escape.csv 3 0.25ms
option-maxRowBytes.csv 4577 39ms
option-newline.csv 0 0.47ms
option-quote-escape.csv 3 0.33ms
option-quote-many.csv 3 0.38ms
option-quote.csv 2 0.22ms
quotes+newlines.csv 3 0.20ms
strict.csv 3 0.22ms
latin.csv 2 0.38ms
mac-newlines.csv 2 0.28ms
utf16-big.csv 2 0.33ms
utf16.csv 2 0.26ms
utf8.csv 2 0.24ms
Install
Using npm:
$ npm install csv-parser
Using yarn:
$ yarn add csv-parser
Usage
To use the module, create a readable stream to a desired CSV file, instantiate
csv
, and pipe the stream to csv
.
Suppose you have a CSV file data.csv
which contains the data:
NAME,AGE
Daffy Duck,24
Bugs Bunny,22
It could then be parsed, and results shown like so:
const csv = require('csv-parser')
const fs = require('fs')
const results = [];
fs.createReadStream('data.csv')
.pipe(csv())
.on('data', (data) => results.push(data))
.on('end', () => {
console.log(results);
// [
// { NAME: 'Daffy Duck', AGE: '24' },
// { NAME: 'Bugs Bunny', AGE: '22' }
// ]
});
To specify options for csv
, pass an object argument to the function. For
example:
csv({ separator: '\t' });
API
csv([options | headers])
Returns: Array[Object]
options
Type: Object
As an alternative to passing an options
object, you may pass an Array[String]
which specifies the headers to use. For example:
csv(['Name', 'Age']);
If you need to specify options and headers, please use the the object notation
with the headers
property as shown below.
escape
Type: String
Default: "
A single-character string used to specify the character used to escape strings in a CSV row.
headers
Type: Array[String] | Boolean
Specifies the headers to use. Headers define the property key for each value in
a CSV row. If no headers
option is provided, csv-parser
will use the first
line in a CSV file as the header specification.
If false
, specifies that the first row in a data file does not contain
headers, and instructs the parser to use the column index as the key for each column.
Using headers: false
with the same data.csv
example from above would yield:
[
{ '0': 'Daffy Duck', '1': 24 },
{ '0': 'Bugs Bunny', '1': 22 }
]
Note: If using the headers
for an operation on a file which contains headers on the first line, specify skipLines: 1
to skip over the row, or the headers row will appear as normal row data. Alternatively, use the mapHeaders
option to manipulate existing headers in that scenario.
mapHeaders
Type: Function
A function that can be used to modify the values of each header. Return a String
to modify the header. Return null
to remove the header, and it's column, from the results.
csv({
mapHeaders: ({ header, index }) => header.toLowerCase()
})
Parameters
header String The current column header.
index Number The current column index.
mapValues
Type: Function
A function that can be used to modify the content of each column. The return value will replace the current column content.
csv({
mapValues: ({ header, index, value }) => value.toLowerCase()
})
Parameters
header String The current column header.
index Number The current column index.
value String The current column value (or content).
newline
Type: String
Default: \n
Specifies a single-character string to denote the end of a line in a CSV file.
quote
Type: String
Default: "
Specifies a single-character string to denote a quoted string.
raw
Type: Boolean
If true
, instructs the parser not to decode UTF-8 strings.
separator
Type: String
Default: ,
Specifies a single-character string to use as the column separator for each row.
skipComments
Type: Boolean | String
Default: false
Instructs the parser to ignore lines which represent comments in a CSV file. Since there is no specification that dictates what a CSV comment looks like, comments should be considered non-standard. The "most common" character used to signify a comment in a CSV file is "#"
. If this option is set to true
, lines which begin with #
will be skipped. If a custom character is needed to denote a commented line, this option may be set to a string which represents the leading character(s) signifying a comment line.
skipLines
Type: Number
Default: 0
Specifies the number of lines at the beginning of a data file that the parser should skip over, prior to parsing headers.
maxRowBytes
Type: Number
Default: Number.MAX_SAFE_INTEGER
Maximum number of bytes per row. An error is thrown if a line exeeds this value. The default value is on 8 peta byte.
strict
Type: Boolean
Default: false
If true
, instructs the parser that the number of columns in each row must match
the number of headers
specified or throws an exception.
if false
: the headers are mapped to the column index
less columns: any missing column in the middle will result in a wrong property mapping!
more columns: the aditional columns will create a "_"+index properties - eg. "_10":"value"
Events
The following events are emitted during parsing:
data
Emitted for each row of data parsed with the notable exception of the header row. Please see Usage for an example.
headers
Emitted after the header row is parsed. The first parameter of the event
callback is an Array[String]
containing the header names.
fs.createReadStream('data.csv')
.pipe(csv())
.on('headers', (headers) => {
console.log(`First header: ${headers[0]}`)
})
Readable Stream Events
Events available on Node built-in
Readable Streams
are also emitted. The end
event should be used to detect the end of parsing.
CLI
This module also provides a CLI which will convert CSV to newline-delimited JSON. The following CLI flags can be used to control how input is parsed:
Usage: csv-parser [filename?] [options]
--escape,-e Set the escape character (defaults to quote value)
--headers,-h Explicitly specify csv headers as a comma separated list
--help Show this help
--output,-o Set output file. Defaults to stdout
--quote,-q Set the quote character ('"' by default)
--remove Remove columns from output by header name
--separator,-s Set the separator character ("," by default)
--skipComments,-c Skip CSV comments that begin with '#'. Set a value to change the comment character.
--skipLines,-l Set the number of lines to skip to before parsing headers
--strict Require column length match headers length
--version,-v Print out the installed version
For example; to parse a TSV file:
cat data.tsv | csv-parser -s $'\t'
Encoding
Users may encounter issues with the encoding of a CSV file. Transcoding the source stream can be done neatly with a modules such as:
Or native iconv
if part
of a pipeline.
Byte Order Marks
Some CSV files may be generated with, or contain a leading Byte Order Mark. This may cause issues parsing headers and/or data from your file. From Wikipedia:
The Unicode Standard permits the BOM in UTF-8, but does not require nor recommend its use. Byte order has no meaning in UTF-8.
To use this module with a file containing a BOM, please use a module like strip-bom-stream in your pipeline:
const fs = require('fs');
const csv = require('csv-parser');
const stripBom = require('strip-bom-stream');
fs.createReadStream('data.csv')
.pipe(stripBom())
.pipe(csv())
...
When using the CLI, the BOM can be removed by first running:
$ sed $'s/\xEF\xBB\xBF//g' data.csv
Meta
Top Related Projects
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot