csv-parser vs json2csv vs csvtojson vs json-2-csv
CSV and JSON Conversion Libraries Comparison
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csv-parserjson2csvcsvtojsonjson-2-csvSimilar Packages:
What's CSV and JSON Conversion Libraries?

CSV and JSON conversion libraries are essential tools in web development for handling data interchange formats. They facilitate the conversion between CSV (Comma-Separated Values) and JSON (JavaScript Object Notation), which are widely used for data storage and transmission. These libraries simplify the process of reading, writing, and transforming data, allowing developers to integrate data from various sources seamlessly. They are particularly useful in applications that require data import/export functionalities, such as data analysis tools, reporting systems, and web applications that interact with APIs.

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csv-parser1,205,1681,44229.5 kB56a month agoMIT
json2csv1,200,8412,72551.2 kB172 years agoMIT
csvtojson892,4812,022-1266 years agoMIT
json-2-csv555,67743680.2 kB5a month agoMIT
Feature Comparison: csv-parser vs json2csv vs csvtojson vs json-2-csv

Performance

  • csv-parser:

    csv-parser is optimized for speed and memory efficiency, allowing it to handle large CSV files without significant performance degradation. It processes data in a streaming manner, which means it can start outputting results before the entire file is read, making it suitable for high-performance applications.

  • json2csv:

    json2csv offers good performance for converting JSON to CSV, especially with its ability to handle nested structures. However, performance may vary based on the complexity of the JSON input, so testing with your specific data size is recommended.

  • csvtojson:

    csvtojson is efficient but may have a slightly higher overhead due to its additional features like transformation functions. It is still performant for most use cases, especially when dealing with moderately sized CSV files, but may not be as fast as csv-parser for very large datasets.

  • json-2-csv:

    json-2-csv is lightweight and performs well for straightforward JSON to CSV conversions. It is not designed for handling large datasets but works efficiently for smaller to medium-sized JSON objects, making it suitable for most common use cases.

Ease of Use

  • csv-parser:

    csv-parser has a simple API that is easy to use, especially for developers familiar with Node.js streams. Its row-by-row parsing approach makes it intuitive for processing large files incrementally, which can simplify code structure.

  • json2csv:

    json2csv offers a more feature-rich API that allows for extensive customization, which may require a bit more learning but provides powerful options for those needing detailed control over the conversion process.

  • csvtojson:

    csvtojson provides a user-friendly interface with options for customization, making it easy to convert CSV to JSON with minimal setup. Its ability to handle various CSV formats and transformations adds to its usability.

  • json-2-csv:

    json-2-csv is straightforward and focuses solely on converting JSON to CSV, making it very easy to implement. Its simplicity is a significant advantage for developers looking for a quick solution without complex configurations.

Customization

  • csv-parser:

    csv-parser allows for some customization in terms of how data is parsed, such as specifying custom delimiters and handling headers. However, it is primarily focused on parsing rather than transforming data.

  • json2csv:

    json2csv stands out for its extensive customization capabilities, allowing for detailed control over the output format, including handling nested JSON and specifying field names. This makes it ideal for applications requiring precise CSV output.

  • csvtojson:

    csvtojson excels in customization, offering options for custom delimiters, header mapping, and transformation functions. This makes it a versatile choice for developers needing to manipulate data during conversion.

  • json-2-csv:

    json-2-csv provides basic customization options for the output CSV format, such as specifying headers and formatting. However, it is less flexible than json2csv when it comes to complex JSON structures.

Error Handling

  • csv-parser:

    csv-parser has built-in error handling that allows developers to catch and manage parsing errors effectively. This is crucial when dealing with potentially malformed CSV data, ensuring robustness in applications.

  • json2csv:

    json2csv includes comprehensive error handling options, allowing developers to catch and respond to various issues during the conversion process. This makes it a reliable choice for applications that need to ensure data integrity.

  • csvtojson:

    csvtojson provides error handling features that enable developers to manage issues during the conversion process, such as invalid data formats or parsing errors, enhancing the reliability of data processing.

  • json-2-csv:

    json-2-csv has basic error handling capabilities, but it may not provide as much detail on errors compared to other libraries. It is generally sufficient for straightforward conversions but may require additional checks for complex data.

Community and Support

  • csv-parser:

    csv-parser has a solid community and is actively maintained, providing good support through documentation and community contributions. This ensures that developers can find help and resources when needed.

  • json2csv:

    json2csv has a large user base and robust community support, with extensive documentation and examples available. This makes it a reliable choice for developers looking for help and resources.

  • csvtojson:

    csvtojson benefits from a strong community and extensive documentation, making it easy for developers to find examples and support. Its popularity ensures that it is regularly updated with new features and fixes.

  • json-2-csv:

    json-2-csv has a smaller community compared to others, but it is still maintained and has adequate documentation. Support may be limited, but it is generally sufficient for basic use cases.

How to Choose: csv-parser vs json2csv vs csvtojson vs json-2-csv
  • csv-parser:

    Choose csv-parser if you need a fast, lightweight solution for parsing CSV data into JSON. It is particularly suitable for streaming large CSV files and provides a simple API for handling row-by-row parsing, making it ideal for performance-sensitive applications.

  • json2csv:

    Use json2csv if you are looking for a robust library that offers extensive customization options for converting JSON to CSV. It supports nested JSON structures and allows for fine-tuning of the output format, making it ideal for applications that require detailed control over the CSV output.

  • csvtojson:

    Select csvtojson if you require a comprehensive solution that supports advanced features like custom delimiters, headers, and transformation functions. It is well-suited for converting CSV to JSON with additional processing capabilities, making it great for more complex data manipulation tasks.

  • json-2-csv:

    Opt for json-2-csv when you need to convert JSON data into CSV format easily. This library is straightforward and focuses on the conversion process, making it a good choice for applications that primarily export JSON data to CSV without extensive configuration.

README for csv-parser

csv-parser

tests cover size

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

⚡️ csv-parser is greased-lightning fast

→ 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"

outputByteOffset

Type: Boolean
Default: false

If true, instructs the parser to emit each row with a byteOffset property. The byteOffset represents the offset in bytes of the beginning of the parsed row in the original stream. Will change the output format of stream to be { byteOffset, row }.

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

CONTRIBUTING

LICENSE (MIT)