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tag:? license:mit build:? coverage:? codecov.io
npm: dependencies:? devDependency Status

Convert Excel Files to JSON

What

Parse Excel xlsx files into a list of javascript objects and optionally write that list as a JSON encoded file.

You may organize Excel data by columns or rows where the first column or row contains object key names and the remaining columns/rows contain object values.

Expected use is offline translation of Excel data to JSON files, although all methods are exported for other uses.

Install

$ npm install excel-as-json --save-dev

Use

convertExcel = require('excel-as-json').processFile;
convertExcel(src, dst, options, callback);
  • src: path to source Excel file (xlsx only)
  • dst: path to destination JSON file. If null, simply return the parsed object tree
  • options: an object containing
    • sheet: 1 based sheet index as text - default '1'
    • isColOriented: are object values in columns with keys in column A - default false
    • omitEmptyFields: omit empty Excel fields from JSON output - default false
    • convertTextToNumber: if text looks like a number, convert it to a number - default true
  • callback(err, data): callback for completion notification

NOTE If options are not specified, defaults are used.

With these arguments, you can:

  • convertExcel(src, dst)
    will write a row oriented xlsx sheet 1 to dst as JSON with no notification
  • convertExcel(src, dst, {isColOriented: true})
    will write a col oriented xlsx sheet 1 to file with no notification
  • convertExcel(src, dst, {isColOriented: true}, callback)
    will write a col oriented xlsx to file and notify with errors and parsed data
  • convertExcel(src, null, null, callback)
    will parse a row oriented xslx using default options and return errors and the parsed data in the callback

Convert a row/col oriented Excel file to JSON as a development task and log errors:

convertExcel = require('excel-as-json').processFile

options = 
    sheet:'1'
    isColOriented: false
    omitEmtpyFields: false

convertExcel 'row.xlsx', 'row.json', options, (err, data) ->
	if err then console.log "JSON conversion failure: #{err}"

options = 
    sheet:'1'
    isColOriented: true
    omitEmtpyFields: false

convertExcel 'col.xlsx', 'col.json', options, (err, data) ->
	if err then console.log "JSON conversion failure: #{err}"

Convert Excel file to an object tree and use that tree. Note that properly formatted data will convert to the same object tree whether row or column oriented.

convertExcel = require('excel-as-json').processFile

convertExcel 'row.xlsx', undefined, undefined, (err, data) ->
	if err throw err
	doSomethingInteresting data
	
convertExcel 'col.xlsx', undefined, {isColOriented: true}, (err, data) ->
	if err throw err
	doSomethingInteresting data

Why?

  • Your application serves static data obtained as Excel reports from another application
  • Whoever manages your static data finds Excel more pleasant than editing JSON
  • Your data is the result of calculations or formatting that is more simply done in Excel

What's the challenge?

Excel stores tabular data. Converting that to JSON using only a couple of assumptions is straight-forward. Most interesting JSON contains nested lists and objects. How do you map a flat data square that is easy for anyone to edit into these nested lists and objects?

Solving the challenge

  • Use a key row to name JSON keys
  • Allow data to be stored in row or column orientation.
  • Use javascript notation for keys and arrays
    • Allow dotted key path notation
    • Allow arrays of objects and literals

Excel Data

What is the easiest way to organize and edit your Excel data? Lists of simple objects seem a natural fit for a row oriented sheets. Single objects with more complex structure seem more naturally presented as column oriented sheets. Doesn't really matter which orientation you use, the module allows you to speciy a row or column orientation; basically, where your keys are located: row 0 or column 0.

Keys and values:

  • Row or column 0 contains JSON key paths
  • Remaining rows/columns contain values for those keys
  • Multiple value rows/columns represent multiple objects stored as a list
  • Within an object, lists of objects have keys like phones[1].type
  • Within an object, flat lists have keys like aliases[]

Examples

A simple, row oriented key

firstName
Jihad

produces

[{
  "firstName": "Jihad"
}]

A dotted key name looks like

address.street
12 Beaver Court

and produces

[{
  "address": {
    "street": "12 Beaver Court"
    }
}]

An indexed array key name looks like

phones[0].number
123.456.7890

and produces

[{
  "phones": [{
      "number": "123.456.7890"
    }]
}]

An embedded array key name looks like this and has ';' delimited values

aliases[]
stormagedden;bob

and produces

[{
  "aliases": [
    "stormagedden",
    "bob"
  ]
}]

A more complete row oriented example

firstName lastName address.street address.city address.state address.zip
Jihad Saladin 12 Beaver Court Snowmass CO 81615
Marcus Rivapoli 16 Vail Rd Vail CO 81657

would produce

[{
    "firstName": "Jihad",
    "lastName": "Saladin",
    "address": {
      "street": "12 Beaver Court",
      "city": "Snowmass",
      "state": "CO",
      "zip": "81615"
    }
  },
  {
    "firstName": "Marcus",
    "lastName": "Rivapoli",
    "address": {
      "street": "16 Vail Rd",
      "city": "Vail",
      "state": "CO",
      "zip": "81657"
    }
  }]

You can do something similar in column oriented sheets. Note that indexed and flat arrays are added.

firstName Jihad Marcus
lastName Saladin Rivapoli
address.street 12 Beaver Court 16 Vail Rd
address.city Snowmass Vail
address.state CO CO
address.zip 81615 81657
phones[0].type home home
phones[0].number 123.456.7890 123.456.7891
phones[1].type work work
phones[1].number 098.765.4321 098.765.4322
aliases[] stormagedden;bob mac;markie

would produce

[
  {
    "firstName": "Jihad",
    "lastName": "Saladin",
    "address": {
      "street": "12 Beaver Court",
      "city": "Snowmass",
      "state": "CO",
      "zip": "81615"
    },
    "phones": [
      {
        "type": "home",
        "number": "123.456.7890"
      },
      {
        "type": "work",
        "number": "098.765.4321"
      }
    ],
    "aliases": [
      "stormagedden",
      "bob"
    ]
  },
  {
    "firstName": "Marcus",
    "lastName": "Rivapoli",
    "address": {
      "street": "16 Vail Rd",
      "city": "Vail",
      "state": "CO",
      "zip": "81657"
    },
    "phones": [
      {
        "type": "home",
        "number": "123.456.7891"
      },
      {
        "type": "work",
        "number": "098.765.4322"
      }
    ],
    "aliases": [
      "mac",
      "markie"
    ]
  }
]

Data Conversions

All values from the 'excel' package are returned as text. This module detects numbers and booleans and converts them to javascript types. Booleans must be text 'true' or 'false'. Excel FALSE and TRUE are provided from 'excel' as 0 and 1 - just too confusing.

Caveats

During install (mac), you may see compiler warnings while installing the excel dependency - although questionable, they appear to be benign.

Running tests

You can run tests after GitHub clone and npm install with:

ᐅ npm run-script test

> [email protected] test /Users/starver/code/makara/excel-as-json
> tools/test.sh

  assign
    ✓ should assign first level properties
    ✓ should assign second level properties
    ✓ should assign third level properties
#...

Bug Reports

To investigate bugs, we need to recreate the failure. In each bug report, please include:

  • Title: A succinct description of the failure
  • Body:
    • What is expected
    • What happened
    • What you did
    • Environment:
      • operating system and version
      • node version
      • npm version
      • excel-as-json version
    • Attach a small worksheet and code snippet that reproduces the error

Contributing

This project is small and simple and intends to remain that way. If you want to add functionality, please raise an issue as a place we can discuss it prior to doing any work.

You are always free to fork this repo and create your own version to do with as you will, or include this functionality in your projects and modify it to your heart's content.

TODO

  • provide processSync - using 'async' module
  • Detect and convert dates
  • Make 1 column values a single object?

Change History

2.0.2

  • Fix #23 Embedded arrays contain empty string. Flaw in code inserted empty string when no text values were provided for a key like aliases[].
  • Fix #30 not able to force numbers as strings. Added option convertTextToNumber defaulting to true. If set to false, cells containing text that looks like a number are not converted to a numeric type.

2.0.1

  • Fix creating missing destination directories to complete prior to writing file

2.0.0

  • Breaking changes to most function signatures
  • Replace single option isColOriented with an options object to try to stabilize the processFile signature allowing future non-breaking feature additions.
  • Add sheet option to specify a 1-based index into the Excel sheet collection - all of your data in a single Excel workbook.
  • Add omitEmptyFields option that removes an object key-value if the corresponding Excel cell is empty.

1.0.0

  • Changed process() to processFile() to avoid name collision with node's process object
  • Automatically convert text numbers and booleans to native values
  • Create destination directory if it does not exist