Tabletojson: Converting Table to JSON objects made easy

Convert local or remote HTML embedded tables into JSON objects.

Table of Contents

• Introduction
• Incompatible changes
  • Conversion from version 1.+ to 2.x
  • Conversion from version 2.0.1 to 3.x
• Installation
• Quickstart
  • esm
  • commonjs
  • Remote (convertUrl)
  • Local (convert)
• Use case examples
  • Duplicate column headings
  • Tables with rowspan
  • Tables with complex rowspan
  • Tables with even more complex rowspans
  • Tables with headings in the first column
  • Tables with HTML
  • Tables with complex headers
• Options
  • fetchOptions (only convertUrl)
  • headers
  • stripHtmlFromHeadings
  • stripHtmlFromCells
  • forceIndexAsNumber
  • countDuplicateHeadings
  • ignoreColumns
  • onlyColumns
  • ignoreHiddenRows
  • headings
  • limitrows
    • Huge Table (see test/tables.html)
    • Example output with limitrows: 5
  • containsClasses
• Known issues and limitations
• Usage
  • Convert an HTML text into an array of all the tables on the page
  • Fetch a URL and parse all it's tables into JSON, using a callback
  • Fetch a URL and parse all it's tables into JSON, using promises
  • Fetch a table from Wikipedia and combine with json2csv to convert to CSV
• Contributing
  • Contributors

Introduction

Tabletojson attempts to convert local or remote HTML tables into JSON with a very low footprint. Can be passed the markup for a single table as a string, a fragment of HTML or an entire page or just a URL (with an optional callback function; promises also supported).

The response is always an array. Every array entry in the response represents a table found on the page (in same the order they were found in HTML).

As of version 2.0 tabletojson is completely written in typescript.

Incompatible changes

• Version 2 on request.js is not used anymore
• Version >=2.1.0 got is not used anymore and got replaced by node internal fetch. more information here...
• Switched from commonjs to module system. Bumped version to 3.0.0
• Providing a "hybrid" library to cope with the needs of both esm and commonjs. Bumped version to 4.0.1.
• Adding support for complex headings as key in the output json object. Bumped version to 4.1.0.

Conversion from version 1.+ to 2.x

• Require must be changed from const tabletojson = require('../lib/tabletojson'); to either const tabletojson = require('../lib/tabletojson').Tabletojson; or const {Tabletojson: tabletojson} = require('../lib/tabletojson');
• Replace request options by fetch options. More information here...

Conversion from version 2.0.1 to 3.x

• Tabletojson now uses esm. Use import {Tabletojson as tabletojson} from 'tabletojson'; or import {tabletojson} from 'tabletojson';
• Added lowercase import import {tabletojson} from 'tabletojson';
• If you are using Node 18 execute examples by calling:

1npm run build:examples
2cd dist/examples
3node --experimental-vm-modules --experimental-specifier-resolution=node example-1.js --prefix=dist/examples

Installation

1npm install tabletojson

Quickstart

esm

1import {tabletojson} from 'tabletojson';
2tabletojson.convertUrl('https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes', function (tablesAsJson) {
3  console.log(tablesAsJson[1]);
4});

commonjs

1const {tabletojson} = require('tabletojson');
2tabletojson.convertUrl('https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes', function (tablesAsJson) {
3  console.log(tablesAsJson[1]);
4});

Remote (convertUrl)

1// example-1.ts
2import {tabletojson} from 'tabletojson';
3tabletojson.convertUrl('https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes', function (tablesAsJson) {
4    console.log(tablesAsJson[1]);
5});

Local (convert)

More examples can be found in examples folder.

1// example-6.ts
2import {tabletojson} from 'tabletojson';
3import * as fs from 'fs';
4import * as path from 'path';
5const html = fs.readFileSync(path.resolve(process.cwd(), '../../test/tables.html'), {
6  encoding: 'utf-8',
7});
8const converted = tabletojson.convert(html);
9console.log(converted);

Use case examples

Duplicate column headings

Tables with duplicate column headings, subsequent headings are suffixed with a count:

1|| PLACE || VALUE || PLACE || VALUE ||
2|--------|--------|--------|---------|
3| abc    |      1 |    def |      2  |

1[
2  {
3    "PLACE": "abc",
4    "VALUE": "1",
5    "PLACE_2": "def",
6    "VALUE_2": "2"
7  }
8]

Tables with rowspan

In tables with rowspan, the content of the spawned cell must be available in the respective object.

Parent	Child	Age
Marry	Sue	15
	Steve	12
	Tom	3

1[
2  {"Parent": "Marry", "Child": "Tom", "Age": "3"},
3  {"Parent": "Marry", "Child": "Steve", "Age": "12"},
4  {"Parent": "Marry", "Child": "Sue", "Age": "15"}
5]

Tables with complex rowspan

In tables with complex rowspans, the content of the spawned cell must be available in the respective object.

Parent	Child	Age
Marry	Sue	15
	Steve	12
	Tom	3
Taylor
	Peter	17

1[
2  {"Parent": "Marry", "Child": "Sue", "Age": "15"},
3  {"Parent": "Marry", "Child": "Steve", "Age": "12"},
4  {"Parent": "Marry", "Child": "Tom", "Age": "3"},
5  {"Parent": "Taylor", "Child": "Tom", "Age": "3"},
6  {"Parent": "Taylor", "Child": "Peter", "Age": "17"}
7]

Tables with even more complex rowspans

In tables with even more complex rowspans, the content of the spawned cell must be available in the respective object.

Department	Major	Class	Instructor	Credit
Engineering	Computer Science	CS101	Kim	3
		CS201	Garcia
		CS303		2
	Electrical Engineering	EE101	Müller	3
Social Science	Economics	EC101	Nguyen	3
		EC401	Smith

1[
2    {
3        "Department": "Engineering",
4        "Major": "Computer Science",
5        "Class": "CS101",
6        "Instructor": "Kim",
7        "Credit": "3"
8    },
9    {
10        "Department": "Engineering",
11        "Major": "Computer Science",
12        "Credit": "3",
13        "Class": "CS201",
14        "Instructor": "Garcia"
15    },
16    {
17        "Department": "Engineering",
18        "Major": "Computer Science",
19        "Instructor": "Garcia",
20        "Class": "CS303",
21        "Credit": "2"
22    },
23    {
24        "Department": "Engineering",
25        "Major": "Electrical Engineering",
26        "Class": "EE101",
27        "Instructor": "Müller",
28        "Credit": "3"
29    },
30    {
31        "Department": "Social Science",
32        "Major": "Economics",
33        "Class": "EC101",
34        "Instructor": "Nguyen",
35        "Credit": "3"
36    },
37    {
38        "Department": "Social Science",
39        "Major": "Economics",
40        "Credit": "3",
41        "Class": "EC401",
42        "Instructor": "Smith"
43    }
44]

Tables with headings in the first column

If a table contains headings in the first column you might get an unexpected result, but you can pass a second argument with options with { useFirstRowForHeadings: true } to have it treat the first column as it would any other cell.

1tabletojson.convertUrl(
2  'https://www.timeanddate.com/holidays/ireland/2017',
3  { useFirstRowForHeadings: true },
4  function(tablesAsJson) {
5    console.log(tablesAsJson);
6  }
7);

Tables with HTML

The following options are true by default, which converts all values to plain text to give you an easier more readable object to work with:

• stripHtmlFromHeadings
• stripHtmlFromCells

If your table contains HTML you want to parse (for example for links) you can set stripHtmlFromCells to false to treat it as raw text.

1tabletojson.convertUrl(
2  'https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes',
3  { stripHtmlFromCells: false },
4  function(tablesAsJson) {
5    //Print out the 1st row from the 2nd table on the above webpage as JSON
6    console.log(tablesAsJson[1][0]);
7  }
8);

Note: This doesn't work with nested tables, which it will still try to parse.

You probably don't need to set stripHtmlFromHeadings to false (and setting it to false can make the results hard to parse), but if you do you can also set both at the same time by setting stripHtml to false.

Tables with complex headers

Use the following rows as keys in the output json object. The values are flattened and concatenated the values with 'concatWith' [default=undefined]

Input:

Industry	Operating income			Operating costs			Total profit
	amount (billion)		Year-over-year growth (%)	amount (billion)		Year-over-year growth (%)	amount (billion)		Year-over-year growth (%)
	$	€*		$	€*		$	€*
total	843280,70	758952,63	-0,30	718255,00	646429,50	0,20	46558,20	41902,38	-11,70
Coal mining and washing industry	22937,30	20643,57	-13,90	14653,70	13188,33	-7,50	5236,90	4713,21	-26,30
Oil and gas extraction	7605,10	6844,59	-9,60	3863,60	3477,24	0,10	2592,70	2333,43	-10,80
Ferrous metal mining and dressing industry	3010,70	2709,63	-4,40	2369,10	2132,19	-4,10	326,60	293,94	-23,80
Non-ferrous metal mining and dressing industry	2180,90	1962,81	-0,30	1394,30	1254,87	-2,50	512,10	460,89	4,00
Non-metallic ore mining and dressing industry	2244,70	2020,23	-5,70	1612,00	1450,80	-5,70	236,10	212,49	-7,00
Mining professional and ancillary activities	1537,30	1383,57	12,70	1453,70	1308,33	11,80	2,60	2,34	-77,00
Note: Some indicators in this table have a situation where the total is not equal to the sum of the sub-items, which is due to rounding of the data and has not been mechanically adjusted. * Conversion rate: 1$=0,9€

Output:

1[
2    {
3        "Industry": "total",
4        "Operating income amount (billion) $": "843280,70",
5        "Operating income amount (billion) €*": "758952,63",
6        "Operating income Year-over-year growth (%)": "-0,30",
7        "Operating costs amount (billion) $": "718255,00",
8        "Operating costs amount (billion) €*": "646429,50",
9        "Operating costs Year-over-year growth (%)": "0,20",
10        "Total profit amount (billion) $": "46558,20",
11        "Total profit amount (billion) €*": "41902,38",
12        "Total profit Year-over-year growth (%)": "-11,70"
13    },
14    {
15        "Industry": "Coal mining and washing industry",
16        "Operating income amount (billion) $": "22937,30",
17        "Operating income amount (billion) €*": "20643,57",
18        "Operating income Year-over-year growth (%)": "-13,90",
19        "Operating costs amount (billion) $": "14653,70",
20        "Operating costs amount (billion) €*": "13188,33",
21        "Operating costs Year-over-year growth (%)": "-7,50",
22        "Total profit amount (billion) $": "5236,90",
23        "Total profit amount (billion) €*": "4713,21",
24        "Total profit Year-over-year growth (%)": "-26,30"
25    },
26    {
27        "Industry": "Oil and gas extraction",
28        "Operating income amount (billion) $": "7605,10",
29        "Operating income amount (billion) €*": "6844,59",
30        "Operating income Year-over-year growth (%)": "-9,60",
31        "Operating costs amount (billion) $": "3863,60",
32        "Operating costs amount (billion) €*": "3477,24",
33        "Operating costs Year-over-year growth (%)": "0,10",
34        "Total profit amount (billion) $": "2592,70",
35        "Total profit amount (billion) €*": "2333,43",
36        "Total profit Year-over-year growth (%)": "-10,80"
37    },
38    {
39        "Industry": "Ferrous metal mining and dressing industry",
40        "Operating income amount (billion) $": "3010,70",
41        "Operating income amount (billion) €*": "2709,63",
42        "Operating income Year-over-year growth (%)": "-4,40",
43        "Operating costs amount (billion) $": "2369,10",
44        "Operating costs amount (billion) €*": "2132,19",
45        "Operating costs Year-over-year growth (%)": "-4,10",
46        "Total profit amount (billion) $": "326,60",
47        "Total profit amount (billion) €*": "293,94",
48        "Total profit Year-over-year growth (%)": "-23,80"
49    },
50    {
51        "Industry": "Non-ferrous metal mining and dressing industry",
52        "Operating income amount (billion) $": "2180,90",
53        "Operating income amount (billion) €*": "1962,81",
54        "Operating income Year-over-year growth (%)": "-0,30",
55        "Operating costs amount (billion) $": "1394,30",
56        "Operating costs amount (billion) €*": "1254,87",
57        "Operating costs Year-over-year growth (%)": "-2,50",
58        "Total profit amount (billion) $": "512,10",
59        "Total profit amount (billion) €*": "460,89",
60        "Total profit Year-over-year growth (%)": "4,00"
61    },
62    {
63        "Industry": "Non-metallic ore mining and dressing industry",
64        "Operating income amount (billion) $": "2244,70",
65        "Operating income amount (billion) €*": "2020,23",
66        "Operating income Year-over-year growth (%)": "-5,70",
67        "Operating costs amount (billion) $": "1612,00",
68        "Operating costs amount (billion) €*": "1450,80",
69        "Operating costs Year-over-year growth (%)": "-5,70",
70        "Total profit amount (billion) $": "236,10",
71        "Total profit amount (billion) €*": "212,49",
72        "Total profit Year-over-year growth (%)": "-7,00"
73    },
74    {
75        "Industry": "Mining professional and ancillary activities",
76        "Operating income amount (billion) $": "1537,30",
77        "Operating income amount (billion) €*": "1383,57",
78        "Operating income Year-over-year growth (%)": "12,70",
79        "Operating costs amount (billion) $": "1453,70",
80        "Operating costs amount (billion) €*": "1308,33",
81        "Operating costs Year-over-year growth (%)": "11,80",
82        "Total profit amount (billion) $": "2,60",
83        "Total profit amount (billion) €*": "2,34",
84        "Total profit Year-over-year growth (%)": "-77,00"
85    },
86    {
87        "Industry": "Note: Some indicators in this table have a situation where the total is not equal to the sum of the sub-items, which is due to rounding of the data and has not been mechanically adjusted.\n            * Conversion rate: 1$=0,9€"
88    }
89]

Options

fetchOptions (only convertUrl)

Tabletojson is using fetch api which is available in node from version 17.5.0 onwards to fetch remote HTML pages. See mdn web docs on fetch for more information. The usage of Tabletojson should now be the same in node as in browsers. So if you need to get data from a remote server you can call tabletojson.convertUrl and pass any fetch-options (proxy, headers,...) by adding a RequestInit object to the options passed to convertUrl. For more information on how to configure request please have a look at Browser Syntax or Node fetch

1tabletojson.convertUrl('https://www.timeanddate.com/holidays/ireland/2017', {
2    useFirstRowForHeadings: true,
3    fetchOptions: {
4        ...
5    }
6});

headers

Define the rows to be used as keys in the output json object. The values will be concatenated with the given concatWith value. [default=undefined]

1const html = fs.readFileSync(path.resolve(process.cwd(), '../test/tables.html'), {
2    encoding: 'utf-8',
3});
4const converted = tabletojson.convert(html, {
5    id: ['table15'],
6
7    // from (Optional) Start row [default=0]
8    // to End row
9    // concatWith Concatenate the values with this string
10
11    headers: {
12        to: 2,
13        concatWith: ' ',
14    },
15});
16console.log(JSON.stringify(converted, null, 2));

stripHtmlFromHeadings

Strip any HTML from heading cells. Default is true.

1||KEY || <b>VALUE</b><||
2|-----|----------------|
3| abc |              1 |
4| dev |              2 |

Example output with stripHtmlFromHeadings:true

1[
2    {
3        "KEY": "abc", "VALUE": "1"
4    },
5    {
6        "KEY": "dev", "VALUE": "2"
7    }
8]

Example output with stripHtmlFromHeadings:false

1[
2    {
3        "KEY": "abc", "<b>VALUE</b>": "1"
4    },
5    {
6        "KEY": "dev", "<b>VALUE</b>": "2"
7    }
8]

stripHtmlFromCells

Strip any HTML from tableBody cells. Default is true.

1||KEY ||    VALUE||
2|-----|-----------|
3|abc  |   <i>1</i>|
4|dev  |   <i>2</i>|

Example output with stripHtmlFromHeadings:true

1[
2    {
3        "KEY": "abc", "VALUE": "1"
4    },
5    {
6        "KEY": "dev", "VALUE": "2"
7    }
8]

Example output with stripHtmlFromHeadings:false

1[
2    {
3        "KEY": "abc", "VALUE": "<i>1</i>"
4    },
5    {
6        "KEY": "dev", "VALUE": "<i>2</i>"
7    }
8]

forceIndexAsNumber

Instead of using column text (that sometime re-order the data), force an index as a number (string number).

1{
2  "0": "",
3  "1": "Ａ会",
4  "2": "Ｂ会",
5  "3": "Ｃ会",
6  "4": "Something",
7  "5": "Else",
8  "6": ""
9}

countDuplicateHeadings

Default is true. If set to false, duplicate headings will not get a trailing number. The value of the field will be the last value found in the table row:

1||PLACE || VALUE || PLACE || VALUE||
2|-------|--------|--------|--------|
3|   abc |      1 |    def |     2  |
4|   ghi |      3 |    jkl |     4  |

Example output with countDuplicateHeadings:false

1[
2    {
3        "PLACE": "def", "VALUE": "2"
4    },
5    {
6        "PLACE": "jkl", "VALUE": "4"
7    }
8]

ignoreColumns

Array of indexes to be ignored, starting with 0. Default is 'null/undefined'.

1|| NAME || PLACE || WEIGHT || SEX || AGE ||
2|-------|--------|---------|------|-------|
3| Mel   |      1 |      58 |    W |  23   |
4| Bill  |      3 |      92 |    M |  31   |
5| Tom   |      2 |      78 |    M |  54   |

Example output with ignoreColumns: [2, 3]

1[
2    {
3        "NAME": "Mel", "PLACE": "1", "AGE": "23"
4    },
5    {
6        "NAME": "Tom", "PLACE": "2", "AGE": "54"
7    },
8    {
9        "NAME": "Bill", "PLACE": "3", "AGE": "31"
10    }
11]

onlyColumns

Array of indexes that are taken, starting with 0. Default is 'null/undefined'. If given, this option overrides ignoreColumns.

1|| NAME || PLACE || WEIGHT || SEX || AGE ||
2|-------|--------|---------|------|-------|
3| Mel   |      1 |      58 |    W |     23|
4| Tom   |      2 |      78 |    M |     54|
5| Bill  |      3 |      92 |    M |     31|

Example output with onlyColumns: [0, 4]

1[
2    {
3        "NAME": "Mel", "AGE": "23"
4    },
5    {
6        "NAME": "Tom", "AGE": "54"
7    },
8    {
9        "NAME": "Bill", "AGE": "31"
10    }
11]

ignoreHiddenRows

Indicates if hidden rows (display:none) are ignored. Default is true:

1|| NAME || PLACE || WEIGHT || SEX || AGE ||
2|-------|--------|---------|------|-------|
3| Mel   |      1 |      58 |    W |     23|
4| Tom   |      2 |      78 |    M |     54|
5| Bill  |      3 |      92 |    M |     31|
6|* Cat  |      4 |       4 |    W |     2*|

Example output with ignoreHiddenRows:true

1[
2  {
3    "NAME":"Mel",
4    "PLACE":"1",
5    "WEIGHT":"58",
6    "SEX":"W",
7    "AGE":"23"
8  },
9  {
10    "NAME":"Tom",
11    "PLACE":"2",
12    "WEIGHT":"78",
13    "SEX":"M",
14    "AGE":"54"
15  },
16  {
17    "NAME":"Bill",
18    "PLACE":"3",
19    "WEIGHT":"92",
20    "SEX":"M",
21    "AGE":"31"
22  }
23]

Example output with ignoreHiddenRows:false

1[
2  {
3    "NAME":"Mel",
4    "PLACE":"1",
5    "WEIGHT":"58",
6    "SEX":"W",
7    "AGE":"23"
8  },
9  {
10    "NAME":"Tom",
11    "PLACE":"2",
12    "WEIGHT":"78",
13    "SEX":"M",
14    "AGE":"54"
15  },
16  {
17    "NAME":"Bill",
18    "PLACE":"3",
19    "WEIGHT":"92",
20    "SEX":"M",
21    "AGE":"31"
22  },
23  {
24    "NAME":"Cat",
25    "PLACE":"4",
26    "WEIGHT":"4",
27    "SEX":"W",
28    "AGE":"2"
29  }
30]

headings

Array of Strings to be used as headings. Default is null/undefined.

If more headings are given than columns exist the overcounting ones will be ignored. If less headings are given than existing values the overcounting values are ignored.

1|| NAME || PLACE || WEIGHT || SEX || AGE ||
2|-------|--------|---------|------|-------|
3| Mel   |      1 |      58 |    W |     23|
4| Tom   |      2 |      78 |    M |     54|
5| Bill  |      3 |      92 |    M |     31|
6|* Cat  |      4 |       4 |    W |     2*|

Example output with headings: ['A','B','C','D','E']

1[
2  {
3    "A":"Mel",
4    "B":"1",
5    "C":"58",
6    "D":"W",
7    "E":"23"
8  },
9  {
10    "A":"Tom",
11    "B":"2",
12    "C":"78",
13    "D":"M",
14    "E":"54"
15  },
16  {
17    "A":"Bill",
18    "B":"3",
19    "C":"92",
20    "D":"M",
21    "E":"31"
22  }
23]

Example output with headings: ['A','B','C']

1[
2  {
3    "A":"Mel",
4    "B":"1",
5    "C":"58"
6  },
7  {
8    "A":"Tom",
9    "B":"2",
10    "C":"78"
11  },
12  {
13    "A":"Bill",
14    "B":"3",
15    "C":"92"
16  }
17]

Example output with headings: ['A','B','C','D','E','F','G','H']

1[
2  {
3    "A":"Mel",
4    "B":"1",
5    "C":"58",
6    "D":"W",
7    "E":"23"
8  },
9  {
10    "A":"Tom",
11    "B":"2",
12    "C":"78",
13    "D":"M",
14    "E":"54"
15  },
16  {
17    "A":"Bill",
18    "B":"3",
19    "C":"92",
20    "D":"M",
21    "E":"31"
22  }
23]

Example output with headings: ['A','B','C'] && ignoreColumns: [2, 3]

1[
2  {
3    "A":"Mel",
4    "B":"W",
5    "C":"23"
6  },
7  {
8    "A":"Tom",
9    "B":"M",
10    "C":"54"
11  },
12  {
13    "A":"Bill",
14    "B":"M",
15    "C":"31"
16  }
17]

limitrows

Number of rows to which the resulting object should be limited to. Default is null/undefined.

Huge Table (see test/tables.html)

1|| Roleplayer Number || Name            || Text to say||
2|--------------------|------------------|--------------|
3| 0                  | Raife Parkinson  | re dolor in hendrerit in vulputate ve|
4| 1                  | Hazel Schultz    | usto duo dolores et ea rebum. Ste|
5| 2                  | Montana Delgado  | psum dolor sit amet. Lorem ipsum dolor|
6| 3                  | Dianne Mcbride   | sit ame olor sit amet. Lorem ipsum|
7| 4                  | Xena Lynch       | us est Lorem ipsum dol|
8| 5                  | Najma Holding    | akimata sanctus est Lorem ipsum dolor sit|
9| 6                  | Kiki House       | ame nvidunt ut|
10|...|
11|197                 | Montana Delgado  | lores et ea rebum. Stet clita kasd gu a|
12|198                 | Myrtle Conley    | rebum. Stet clita kasd gubergren, no sea|
13|199                 | Hanna Ellis      | kimata sanctus est Lorem ipsum dolor si|

Example output with limitrows: 5

1[
2  {
3    "Roleplayer Number":"0",
4    "Name":"Raife Parkinson",
5    "Text to say":"re dolor in hendrerit in vulputate ve"
6  },
7  {
8    "Roleplayer Number":"1",
9    "Name":"Hazel Schultz",
10    "Text to say":"usto duo dolores et ea rebum. Ste"
11  },
12  {
13    "Roleplayer Number":"2",
14    "Name":"Montana Delgado",
15    "Text to say":"psum dolor sit amet. Lorem ipsum dolor sit ame"
16  },
17  {
18    "Roleplayer Number":"3",
19    "Name":"Dianne Mcbride",
20    "Text to say":"olor sit amet. Lorem ipsum"
21  },
22  {
23    "Roleplayer Number":"4",
24    "Name":"Xena Lynch",
25    "Text to say":"us est Lorem ipsum dol"
26  }
27]

containsClasses

Array of classes to find a specific table using this class. Default is null/ undefined.

Known issues and limitations

• Tables needs to be "well formatted" to be convertable.
• Tables in tables are not processed.
• Only supports parsing basic tables with horizontal set of <th></th> headings and corresponding <td></td> cells, but also taking care of rowspan and colspan. It can give useless or weird results on tables that have complex structures (such as nested tables) or multiple headers (such as on both X and Y axis).
• You'll need to handle tables to parse and (in most cases) clean up the data. You might want to combine it with modules like json2csv or CsvToMarkdownTable.
• You might want to use it with a module like 'cheerio' if you want to parse specific tables identified by id or class (i.e. select them with cheerio and pass the HTML of them as a string).

Usage

Find here a list of examples on how to use the library. More examples can be found in the examples folder or in the test folder

Convert an HTML text into an array of all the tables on the page

1import {tabletojson} from 'tabletojson';
2const tablesAsJson = tabletojson.convert(html);
3const firstTableAsJson = tablesAsJson[0];
4const secondTableAsJson = tablesAsJson[1];

Fetch a URL and parse all it's tables into JSON, using a callback

1import {tabletojson} from 'tabletojson';
2tabletojson.convertUrl('https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes', function (tablesAsJson) {
3  console.log(tablesAsJson[1]);
4});

Fetch a URL and parse all it's tables into JSON, using promises

1import {tabletojson} from 'tabletojson';
2const url = 'http://en.wikipedia.org/wiki/List_of_countries_by_credit_rating';
3tabletojson.convertUrl(url)
4.then(function(tablesAsJson) {
5  const standardAndPoorRatings = tablesAsJson[1];
6  const fitchRatings = tablesAsJson[2];
7});

Fetch a table from Wikipedia and combine with json2csv to convert to CSV

1import {tabletojson} from 'tabletojson';
2import {Parser} from 'json2csv';
3const url = 'http://en.wikipedia.org/wiki/List_of_countries_by_credit_rating';
4tabletojson.convertUrl(url).then(function (tablesAsJson) {
5  const fitchRatings = tablesAsJson[2];
6  const json2csvParser = new Parser({
7    fields: ['Country/Region', 'Outlook'],
8  });
9  const csv = json2csvParser.parse(fitchRatings);
10  console.log(csv);
11  /* Example output
12        "Country/Region","Outlook"
13        "Abu Dhabi, UAE","Stable"
14        "Albania","Stable"
15        "Andorra","Negative"
16        "Angola","Stable"
17        "Argentina","Negative"
18        "Aruba","Stable"
19        "Australia","Stable"
20        "Austria","Negative"
21        "Azerbaijan","Positive"
22        ...
23      */
24});

Contributing

Improvements, fixes and suggestions are welcome.

You can find basic tests in the test folder. The library has been implemented to be used straight forward way. Nonetheless there are some edge cases that need to be tested and I would like to ask for support here. Feel free to fork and create PRs here. Every bit of help is appreciated.

For more usage examples have a look in the examples folder that shows usage and would be a good start.

If you submit a pull request, please add an example for your use case, so I can understand what you want it to do (as I want to get around to writing tests for this and want to understand the sort of use cases people have).

Contributors

• Marius Augenstein (@maugenst)
• Iain Collins (@iaincollins)
• Rory O'Kelly (@roryok)
• Nordes Ménard-Lamarre (@Nordes)
• Max Thyen (@maxthyen)
• Thor Jacobsen (@twjacobsen)
• Michael Keller (@mhkeller)
• Jesús Leganés-Combarro (@piranna)
• João Otávio Ferreira Barbosa (@joaobarbosa)
• Stefano Fadda (@StefanoF)
• Michael Telatynski (@t3chguy)
• @mherzhoff
• Dooho Hyung (@dooho-h)