Read small to medium *.xlsx
files in a browser or Node.js. Parse to JSON with a strict schema.
Also check out write-excel-file
for writing simple *.xlsx
files.
npm install read-excel-file --save
If you're not using a bundler then use a standalone version from a CDN.
<input type="file" id="input" />
import readXlsxFile from 'read-excel-file'
// File.
const input = document.getElementById('input')
input.addEventListener('change', () => {
readXlsxFile(input.files[0]).then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
})
// Blob.
fetch('https://example.com/spreadsheet.xlsx')
.then(response => response.blob())
.then(blob => readXlsxFile(blob))
.then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
// ArrayBuffer.
// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer
//
// Could be obtained from:
// * File
// * Blob
// * Base64 string
//
readXlsxFile(arrayBuffer).then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
Note: Internet Explorer 11 requires a Promise
polyfill. Example.
const readXlsxFile = require('read-excel-file/node')
// File path.
readXlsxFile('/path/to/file').then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
// Readable Stream.
readXlsxFile(fs.createReadStream('/path/to/file')).then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
// Buffer.
readXlsxFile(Buffer.from(fs.readFileSync('/path/to/file'))).then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
})
const worker = new Worker('web-worker.js')
worker.onmessage = function(event) {
// `event.data` is an array of rows
// each row being an array of cells.
console.log(event.data)
}
worker.onerror = function(event) {
console.error(event.message)
}
const input = document.getElementById('input')
input.addEventListener('change', () => {
worker.postMessage(input.files[0])
})
import readXlsxFile from 'read-excel-file/web-worker'
onmessage = function(event) {
readXlsxFile(event.data).then((rows) => {
// `rows` is an array of rows
// each row being an array of cells.
postMessage(rows)
})
}
To read spreadsheet data and then convert it to an array of JSON objects, pass a schema
option when calling readXlsxFile()
. In that case, instead of returning an array of rows of cells, it will return an object of shape { rows, errors }
where rows
is gonna be an array of JSON objects created from the spreadsheet data according to the schema
, and errors
is gonna be an array of errors encountered while converting spreadsheet data to JSON objects.
Each property of a JSON object should be described by an "entry" in the schema
. The key of the entry should be the column's title in the spreadsheet. The value of the entry should be an object with properties:
-
property
— The name of the object's property. -
required
— (optional) Required properties of the object could be marked as such.-
required: boolean
—true
orfalse
. -
required: (object) => boolean
— A function returningtrue
orfalse
depending on some other properties of the object.
-
-
validate(value)
— (optional) Cell value validation function. Is only called on non-empty cells. If the cell value is invalid, it should throw an error with the error message set to the error code. -
type
— (optional) The type of the value. Defines how the cell value will be parsed. If notype
is specified then the cell value is returned "as is": as a string, number, date or boolean. Atype
could be a:- Built-in type:
String
Number
Boolean
Date
- "Utility" type exported from the library:
Integer
Email
URL
- Custom type:
- A function that receives a cell value and returns a parsed value. If the value is invalid, it should throw an error with the error message set to the error code.
- Built-in type:
When converting cell values to object properties, by default, it skips any missing columns or empty cells, which means that property values for such cells will be undefined
. To be more specific, first it interprets any missing columns as if those columns existed but had empty cells, and then it interprets all empty cells as undefined
s in the output objects.
In some cases thought that default behavior is not appropriate.
For example, spreadsheet data might be used to update an SQL database using Sequelize ORM library, and Sequelize completely ignores any undefined
values. In order for Sequelize to set a certain field value to NULL
in the database, it must be passed as null
rather than undefined
.
So for Sequelize use case, property values for any missing columns should stay undefined
but property values for any empty cells should be null
. That could be achieved by passing two parameters to read-excel-file
: schemaPropertyValueForMissingColumn: undefined
and schemaPropertyValueForEmptyCell: null
.
An additional option that could be passed in that case would be schemaPropertyShouldSkipRequiredValidationForMissingColumn: (column, { object }) => true
: it would skip required
validation for columns that're missing from the spreadsheet.
There's also a legacy parameter includeNullValues: true
that could be replaced with the following combination of parameters:
schemaPropertyValueForMissingColumn: null
schemaPropertyValueForEmptyCell: null
getEmptyObjectValue = () => null
If there were any errors while converting spreadsheet data to JSON objects, the errors
property returned from the function will be a non-empty array. An element of the errors
property contains properties:
-
error: string
— The error code. Examples:"required"
,"invalid"
.- If a custom
validate()
function is defined and it throws anew Error(message)
then theerror
property will be the same as themessage
value. - If a custom
type()
function is defined and it throws anew Error(message)
then theerror
property will be the same as themessage
value.
- If a custom
-
reason?: string
— An optional secondary error code providing more details about the error. Currently, it's only returned for "built-in"type
s. Example:{ error: "invalid", reason: "not_a_number" }
fortype: Number
means that "the cell value is invalid because it's not a number". -
row: number
— The row number in the original file.1
means the first row, etc. -
column: string
— The column title. -
value?: any
— The cell value. -
type?: any
— The schematype
for this column.
// An example *.xlsx document:
// -----------------------------------------------------------------------------------------
// | START DATE | NUMBER OF STUDENTS | IS FREE | COURSE TITLE | CONTACT | STATUS |
// -----------------------------------------------------------------------------------------
// | 03/24/2018 | 10 | true | Chemistry | (123) 456-7890 | SCHEDULED |
// -----------------------------------------------------------------------------------------
const schema = {
'START DATE': {
// JSON object property name.
prop: 'date',
type: Date
},
'NUMBER OF STUDENTS': {
prop: 'numberOfStudents',
type: Number,
required: true
},
// Nested object example.
// 'COURSE' here is not a real Excel file column name,
// it can be any string — it's just for code readability.
'COURSE': {
// Nested object path: `row.course`
prop: 'course',
// Nested object schema:
type: {
'IS FREE': {
prop: 'isFree',
type: Boolean
},
'COURSE TITLE': {
prop: 'title',
type: String
}
}
},
'CONTACT': {
prop: 'contact',
required: true,
// A custom `type` can be defined.
// A `type` function only gets called for non-empty cells.
type: (value) => {
const number = parsePhoneNumber(value)
if (!number) {
throw new Error('invalid')
}
return number
}
},
'STATUS': {
prop: 'status',
type: String,
oneOf: [
'SCHEDULED',
'STARTED',
'FINISHED'
]
}
}
readXlsxFile(file, { schema }).then(({ rows, errors }) => {
// `errors` list items have shape: `{ row, column, error, reason?, value?, type? }`.
errors.length === 0
rows === [{
date: new Date(2018, 2, 24),
numberOfStudents: 10,
course: {
isFree: true,
title: 'Chemistry'
},
contact: '+11234567890',
status: 'SCHEDULED'
}]
})
The function for converting input data rows to JSON objects using a schema is exported independently as read-excel-file/map
, if anyone's interested.
import convertToJson from "read-excel-file/map"
const { rows, errors } = convertToJson(data, schema, options)
Maps a list of rows — data
— into a list of objects — rows
— using a schema
as a mapping specification.
-
data
— An array of rows, each row being an array of cells. The first row should be the list of column headers and the rest of the rows should be the data. -
schema
— A "to JSON" convertion schema (see above). -
options
— (optional) Schema conversion parameters ofread-excel-file
:-
schemaPropertyValueForMissingColumn
— By default, when some of theschema
columns are missing in the inputdata
, those properties are set toundefined
in the output objects. PassschemaPropertyValueForMissingColumn: null
to set such "missing column" properties tonull
in the output objects. -
schemaPropertyValueForNullCellValue
— By default, when it encounters anull
value in a cell in inputdata
, it sets it toundefined
in the output object. PassschemaPropertyValueForNullCellValue: null
to make it set such values asnull
s in output objects. -
schemaPropertyValueForUndefinedCellValue
— By default, when it encounters anundefined
value in a cell in inputdata
, it it sets it toundefined
in the output object. PassschemaPropertyValueForUndefinedCellValue: null
to make it set such values asnull
s in output objects. -
schemaPropertyShouldSkipRequiredValidationForMissingColumn: (column: string, { object }) => boolean
— By default, it does applyrequired
validation toschema
properties for which columns are missing in the inputdata
. One could pass a customschemaPropertyShouldSkipRequiredValidationForMissingColumn(column, { object })
to disablerequired
validation for missing columns in some or all cases. -
getEmptyObjectValue(object, { path? })
— By default, it returnsnull
for an "empty" resulting object. One could override that value usinggetEmptyObjectValue(object, { path })
parameter. The value applies to both top-level object and any nested sub-objects in case of a nested schema, hence the additional (optional)path?: string
parameter. -
getEmptyArrayValue(array, { path })
— By default, it returnsnull
for an "empty" array value. One could override that value usinggetEmptyArrayValue(array, { path })
parameter.
-
Returns a list of "mapped objects".
When parsing a schema property value, in case of an error, the value of that property is gonna be undefined
.
When a "mapped object" is empty, i.e. when all property values of it are null
or undefined
, it is returned as null
rather than an object.
Custom type
example.
{
'COLUMN_TITLE': {
// This function will only be called for a non-empty cell.
type: (value) => {
try {
return parseValue(value)
} catch (error) {
console.error(error)
throw new Error('invalid')
}
}
}
}
Ignoring empty rows.
By default, it ignores any empty rows. To disable that behavior, pass ignoreEmptyRows: false
option.
readXlsxFile(file, {
schema,
ignoreEmptyRows: false
})
How to fix spreadsheet data before schema
parsing. For example, how to ignore irrelevant rows.
Sometimes, a spreadsheet doesn't exactly have the structure required by this library's schema
parsing feature: for example, it may be missing a header row, or contain some purely presentational / irrelevant / "garbage" rows that should be removed. To fix that, one could pass an optional transformData(data)
function that would modify the spreadsheet contents as required.
readXlsxFile(file, {
schema,
transformData(data) {
// Add a missing header row.
return [['ID', 'NAME', ...]].concat(data)
// Remove irrelevant rows.
return data.filter(row => row.filter(column => column !== null).length > 0)
}
})
A React component for displaying errors that occured during schema parsing/validation.
import { parseExcelDate } from 'read-excel-file'
function ParseExcelError({ children }) {
const { type, value, error, reason, row, column } = children
// Error summary.
return (
<div>
<code>"{error}"</code>
{reason && ' '}
{reason && <code>("{reason}")</code>}
{' for value '}
<code>{stringifyValue(value)}</code>
{' in column '}
<code>"{column}"</code>
{' in row '}
<code>{row}</code>
{' of spreadsheet'}
</div>
)
}
function stringifyValue(value) {
// Wrap strings in quotes.
if (typeof value === 'string') {
return '"' + value + '"'
}
return String(value)
}
Same as above, but simpler: without any parsing or validation.
Sometimes, a developer might want to use some other (more advanced) solution for schema parsing and validation (like yup
). If a developer passes a map
option instead of a schema
option to readXlsxFile()
, then it would just map each data row to a JSON object without doing any parsing or validation. Cell values will remain "as is": as a string, number, date or boolean.
// An example *.xlsx document:
// ------------------------------------------------------------
// | START DATE | NUMBER OF STUDENTS | IS FREE | COURSE TITLE |
// ------------------------------------------------------------
// | 03/24/2018 | 10 | true | Chemistry |
// ------------------------------------------------------------
const map = {
'START DATE': 'date',
'NUMBER OF STUDENTS': 'numberOfStudents',
'COURSE': {
'course': {
'IS FREE': 'isFree',
'COURSE TITLE': 'title'
}
}
}
readXlsxFile(file, { map }).then(({ rows }) => {
rows === [{
date: new Date(2018, 2, 24),
numberOfStudents: 10,
course: {
isFree: true,
title: 'Chemistry'
}
}]
})
By default, it reads the first sheet in the document. If you have multiple sheets in your spreadsheet then pass either a sheet number (starting from 1
) or a sheet name in the options
argument.
readXlsxFile(file, { sheet: 2 }).then((data) => {
...
})
readXlsxFile(file, { sheet: 'Sheet1' }).then((data) => {
...
})
By default, options.sheet
is 1
.
To get the names of all sheets, use readSheetNames()
function:
readSheetNames(file).then((sheetNames) => {
// sheetNames === ['Sheet1', 'Sheet2']
})
XLSX format originally had no dedicated "date" type, so dates are in almost all cases stored simply as numbers (the count of days since 01/01/1900
) along with a "format" description (like "d mmm yyyy"
) that instructs the spreadsheet viewer software to format the date in the cell using that certain format.
When using readXlsx()
with a schema
parameter, all schema columns having type Date
are automatically parsed as dates. When using readXlsx()
without a schema
parameter, this library attempts to guess whether a cell contains a date or just a number by examining the cell's "format" — if the "format" is one of the built-in date formats then such cells' values are automatically parsed as dates. In other cases, when date cells use a non-built-in format (like "mm/dd/yyyy"
), one can pass an explicit dateFormat
parameter to instruct the library to parse numeric cells having such "format" as dates:
readXlsxFile(file, { dateFormat: 'mm/dd/yyyy' })
By default, it automatically trims all string values. To disable this feature, pass trim: false
option.
readXlsxFile(file, { trim: false })
By default, it parses numeric cell values from strings. In some rare cases though, javascript's inherently limited floating-point number precision might become an issue. An example might be finance and banking domain. To work around that, this library supports passing a custom parseNumber(string)
function option.
// Arbitrary-precision numbers in javascript.
import Decimal from 'decimal.js'
readXlsxFile(file, {
parseNumber: (string) => new Decimal(string)
})
Sometimes, a spreadsheet doesn't exactly have the structure required by this library's schema
parsing feature: for example, it may be missing a header row, or contain some purely presentational / empty / "garbage" rows that should be removed. To fix that, one could pass an optional transformData(data)
function that would modify the spreadsheet contents as required.
readXlsxFile(file, {
schema,
transformData(data) {
// Add a missing header row.
return [['ID', 'NAME', ...]].concat(data)
// Remove empty rows.
return data.filter(row => row.filter(column => column !== null).length > 0)
}
})
There have been some reports about performance issues when reading very large *.xlsx
spreadsheets using this library. It's true that this library's main point have been usability and convenience, and not performance when handling huge datasets. For example, the time of parsing a file with 2000 rows / 20 columns is about 3 seconds. So, for reading huge datasets, perhaps use something like xlsx
package instead. There're no comparative benchmarks between the two, so if you'll be making one, share it in the Issues.
Dynamically calculated cells using formulas (SUM
, etc) are not supported.
I'm not a TypeScript expert, so the community has to write the typings (and test those). See example index.d.ts
.
One can use any npm CDN service, e.g. unpkg.com or jsdelivr.net
<script src="https://unpkg.com/read-excel-file@5.x/bundle/read-excel-file.min.js"></script>
<script>
var input = document.getElementById('input')
input.addEventListener('change', function() {
readXlsxFile(input.files[0]).then(function(rows) {
// `rows` is an array of rows
// each row being an array of cells.
})
})
</script>
This library comes with TypeScript "typings". If you happen to find any bugs in those, create an issue.
Uses xmldom
for parsing XML.
On March 9th, 2020, GitHub, Inc. silently banned my account (erasing all my repos, issues and comments, even in my employer's private repos) without any notice or explanation. Because of that, all source codes had to be promptly moved to GitLab. The GitHub repo is now only used as a backup (you can star the repo there too), and the primary repo is now the GitLab one. Issues can be reported in any repo.