Bidirectional OneToMany and ManyToOne returns “NULL not allowed for column” on save

This is a shortened version of the entities where I only show the relevant parts.

    @Entity
    @Data
    public class Wrapper {
        @Id
        @GeneratedValue(strategy = GenerationType.IDENTITY)
        private Integer id
    @OneToOne(mappedBy = "wrapper", cascade = CascadeType.ALL, fetch = FetchType.EAGER, orphanRemoval = true)
    private Application application;

    public Wrapper(Application application) {
        this.application = application;
        application.setWrapper(this);
    }
}

@Data
@Entity
@EqualsAndHashCode(exclude = "wrapper")
public class Application {
    @Id
    private Integer id;

    @JsonIgnore
    @OneToOne
    @JoinColumn(name = "id")
    @MapsId
    private Wrapper wrapper;

    @OneToMany(mappedBy = "application", cascade = CascadeType.ALL, orphanRemoval = true, fetch = FetchType.EAGER)
    @SortNatural
    private SortedSet<Apartement> ownedApartements = new TreeSet<>();
}

@Entity
@Data
public class Apartement {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Integer id;

    @ManyToOne(fetch = FetchType.LAZY, optional = false)
    @JoinColumn(name = "application_id", insertable = false, updatable = false)
    private Application application;
}

@Repository

public interface WrapperRepository extends JpaRepository<Wrapper, Integer> {

}

The above entities generates the following create table statements:

create table Wrapper (

id int identity not null,

primary key (id)

)

create table Application (

id int not null,

primary key (id)

)
create table Apartement (
   id int identity not null,
    application_id int not null,
    primary key (id)
)

 alter table Apartement 
   add constraint FKsrweh1i1p29mdjfp03or318od 
   foreign key (application_id) 
   references Application

   alter table Application
   add constraint FKgn7j3pircupa2rbqn8yte6kyc 
   foreign key (id) 
   references Wrapper

Given the follow entities and the following code:

Apartement apartement1 = new Apartement()

Apartement apartement2 = new Apartement()

Wrapper wrapper = new Wrapper(new Application());
Application application = wrapper.getApplication();

application.getOwnedApartements().addAll(Arrays.asList(apartement1, apartement2));

apartement1.setApplication(application);

apartement2.setApplication(application);
WrapperRepository.saveAndFlush(wrapper);

I see three inserts in the log. First wrapper, then application, and finally apartement. But for some reason application_id is null on the first save. But I know it has a bi-directional relationship.

The error I get is:

Caused by: org.h2.jdbc.JdbcSQLException: NULL not allowed for column “APPLICATION_ID”; SQL statement:

insert into Apartement (id) values (null) [23502-197]

Why does this happen? Do I need to store everything in the correct order? Do I need to first store wrapper and application, then finally store the apartement once I have application ID? Cannot hibernate store all three in one go? Or figure this out it self?

#java #spring #hibernate #jpa

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Buddha Community

PySpark Cheat Sheet: Spark in Python

This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning.

Apache Spark is generally known as a fast, general and open-source engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It allows you to speed analytic applications up to 100 times faster compared to technologies on the market today. You can interface Spark with Python through "PySpark". This is the Spark Python API exposes the Spark programming model to Python. 

Even though working with Spark will remind you in many ways of working with Pandas DataFrames, you'll also see that it can be tough getting familiar with all the functions that you can use to query, transform, inspect, ... your data. What's more, if you've never worked with any other programming language or if you're new to the field, it might be hard to distinguish between RDD operations.

Let's face it, map() and flatMap() are different enough, but it might still come as a challenge to decide which one you really need when you're faced with them in your analysis. Or what about other functions, like reduce() and reduceByKey()? 

Even though the documentation is very elaborate, it never hurts to have a cheat sheet by your side, especially when you're just getting into it.

This PySpark cheat sheet covers the basics, from initializing Spark and loading your data, to retrieving RDD information, sorting, filtering and sampling your data. But that's not all. You'll also see that topics such as repartitioning, iterating, merging, saving your data and stopping the SparkContext are included in the cheat sheet. 

Note that the examples in the document take small data sets to illustrate the effect of specific functions on your data. In real life data analysis, you'll be using Spark to analyze big data.

PySpark is the Spark Python API that exposes the Spark programming model to Python.

Initializing Spark 

SparkContext 

>>> from pyspark import SparkContext
>>> sc = SparkContext(master = 'local[2]')

Inspect SparkContext 

>>> sc.version #Retrieve SparkContext version
>>> sc.pythonVer #Retrieve Python version
>>> sc.master #Master URL to connect to
>>> str(sc.sparkHome) #Path where Spark is installed on worker nodes
>>> str(sc.sparkUser()) #Retrieve name of the Spark User running SparkContext
>>> sc.appName #Return application name
>>> sc.applicationld #Retrieve application ID
>>> sc.defaultParallelism #Return default level of parallelism
>>> sc.defaultMinPartitions #Default minimum number of partitions for RDDs

Configuration 

>>> from pyspark import SparkConf, SparkContext
>>> conf = (SparkConf()
     .setMaster("local")
     .setAppName("My app")
     . set   ("spark. executor.memory",   "lg"))
>>> sc = SparkContext(conf = conf)

Using the Shell 

In the PySpark shell, a special interpreter-aware SparkContext is already created in the variable called sc.

$ ./bin/spark-shell --master local[2]
$ ./bin/pyspark --master local[s] --py-files code.py

Set which master the context connects to with the --master argument, and add Python .zip..egg or.py files to the

runtime path by passing a comma-separated list to  --py-files.

Loading Data 

Parallelized Collections 

>>> rdd = sc.parallelize([('a',7),('a',2),('b',2)])
>>> rdd2 = sc.parallelize([('a',2),('d',1),('b',1)])
>>> rdd3 = sc.parallelize(range(100))
>>> rdd = sc.parallelize([("a",["x","y","z"]),
               ("b" ["p","r,"])])

External Data 

Read either one text file from HDFS, a local file system or any Hadoop-supported file system URI with textFile(), or read in a directory of text files with wholeTextFiles(). 

>>> textFile = sc.textFile("/my/directory/•.txt")
>>> textFile2 = sc.wholeTextFiles("/my/directory/")

Retrieving RDD Information 

Basic Information 

>>> rdd.getNumPartitions() #List the number of partitions
>>> rdd.count() #Count RDD instances 3
>>> rdd.countByKey() #Count RDD instances by key
defaultdict(<type 'int'>,{'a':2,'b':1})
>>> rdd.countByValue() #Count RDD instances by value
defaultdict(<type 'int'>,{('b',2):1,('a',2):1,('a',7):1})
>>> rdd.collectAsMap() #Return (key,value) pairs as a dictionary
   {'a': 2, 'b': 2}
>>> rdd3.sum() #Sum of RDD elements 4950
>>> sc.parallelize([]).isEmpty() #Check whether RDD is empty
True

Summary 

>>> rdd3.max() #Maximum value of RDD elements 
99
>>> rdd3.min() #Minimum value of RDD elements
0
>>> rdd3.mean() #Mean value of RDD elements 
49.5
>>> rdd3.stdev() #Standard deviation of RDD elements 
28.866070047722118
>>> rdd3.variance() #Compute variance of RDD elements 
833.25
>>> rdd3.histogram(3) #Compute histogram by bins
([0,33,66,99],[33,33,34])
>>> rdd3.stats() #Summary statistics (count, mean, stdev, max & min)

Applying Functions 

#Apply a function to each RFD element
>>> rdd.map(lambda x: x+(x[1],x[0])).collect()
[('a' ,7,7, 'a'),('a' ,2,2, 'a'), ('b' ,2,2, 'b')]
#Apply a function to each RDD element and flatten the result
>>> rdd5 = rdd.flatMap(lambda x: x+(x[1],x[0]))
>>> rdd5.collect()
['a',7 , 7 ,  'a' , 'a' , 2,  2,  'a', 'b', 2 , 2, 'b']
#Apply a flatMap function to each (key,value) pair of rdd4 without changing the keys
>>> rdds.flatMapValues(lambda x: x).collect()
[('a', 'x'), ('a', 'y'), ('a', 'z'),('b', 'p'),('b', 'r')]

Selecting Data

Getting

>>> rdd.collect() #Return a list with all RDD elements 
[('a', 7), ('a', 2), ('b', 2)]
>>> rdd.take(2) #Take first 2 RDD elements 
[('a', 7),  ('a', 2)]
>>> rdd.first() #Take first RDD element
('a', 7)
>>> rdd.top(2) #Take top 2 RDD elements 
[('b', 2), ('a', 7)]

Sampling

>>> rdd3.sample(False, 0.15, 81).collect() #Return sampled subset of rdd3
     [3,4,27,31,40,41,42,43,60,76,79,80,86,97]

Filtering

>>> rdd.filter(lambda x: "a" in x).collect() #Filter the RDD
[('a',7),('a',2)]
>>> rdd5.distinct().collect() #Return distinct RDD values
['a' ,2, 'b',7]
>>> rdd.keys().collect() #Return (key,value) RDD's keys
['a',  'a',  'b']

Iterating 

>>> def g (x): print(x)
>>> rdd.foreach(g) #Apply a function to all RDD elements
('a', 7)
('b', 2)
('a', 2)

Reshaping Data 

Reducing

>>> rdd.reduceByKey(lambda x,y : x+y).collect() #Merge the rdd values for each key
[('a',9),('b',2)]
>>> rdd.reduce(lambda a, b: a+ b) #Merge the rdd values
('a', 7, 'a' , 2 , 'b' , 2)

Grouping by

>>> rdd3.groupBy(lambda x: x % 2) #Return RDD of grouped values
          .mapValues(list)
          .collect()
>>> rdd.groupByKey() #Group rdd by key
          .mapValues(list)
          .collect() 
[('a',[7,2]),('b',[2])]

Aggregating

>> seqOp = (lambda x,y: (x[0]+y,x[1]+1))
>>> combOp = (lambda x,y:(x[0]+y[0],x[1]+y[1]))
#Aggregate RDD elements of each partition and then the results
>>> rdd3.aggregate((0,0),seqOp,combOp) 
(4950,100)
#Aggregate values of each RDD key
>>> rdd.aggregateByKey((0,0),seqop,combop).collect() 
     [('a',(9,2)), ('b',(2,1))]
#Aggregate the elements of each partition, and then the results
>>> rdd3.fold(0,add)
     4950
#Merge the values for each key
>>> rdd.foldByKey(0, add).collect()
[('a' ,9), ('b' ,2)]
#Create tuples of RDD elements by applying a function
>>> rdd3.keyBy(lambda x: x+x).collect()

Mathematical Operations 

>>>> rdd.subtract(rdd2).collect() #Return each rdd value not contained in rdd2
[('b' ,2), ('a' ,7)]
#Return each (key,value) pair of rdd2 with no matching key in rdd
>>> rdd2.subtractByKey(rdd).collect()
[('d', 1)1
>>>rdd.cartesian(rdd2).collect() #Return the Cartesian product of rdd and rdd2

Sort 

>>> rdd2.sortBy(lambda x: x[1]).collect() #Sort RDD by given function
[('d',1),('b',1),('a',2)]
>>> rdd2.sortByKey().collect() #Sort (key, value) ROD by key
[('a' ,2), ('b' ,1), ('d' ,1)]

Repartitioning 

>>> rdd.repartition(4) #New RDD with 4 partitions
>>> rdd.coalesce(1) #Decrease the number of partitions in the RDD to 1

Saving 

>>> rdd.saveAsTextFile("rdd.txt")
>>> rdd.saveAsHadoopFile("hdfs:// namenodehost/parent/child",
               'org.apache.hadoop.mapred.TextOutputFormat')

Stopping SparkContext 

>>> sc.stop()

Execution 

$ ./bin/spark-submit examples/src/main/python/pi.py

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #python

PySpark Cheat Sheet: Spark DataFrames in Python

This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples.

You'll probably already know about Apache Spark, the fast, general and open-source engine for big data processing; It has built-in modules for streaming, SQL, machine learning and graph processing. Spark allows you to speed analytic applications up to 100 times faster compared to other technologies on the market today. Interfacing Spark with Python is easy with PySpark: this Spark Python API exposes the Spark programming model to Python. 

Now, it's time to tackle the Spark SQL module, which is meant for structured data processing, and the DataFrame API, which is not only available in Python, but also in Scala, Java, and R.

Without further ado, here's the cheat sheet:

This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'll also see that this cheat sheet also on how to run SQL Queries programmatically, how to save your data to parquet and JSON files, and how to stop your SparkSession.

Spark SGlL is Apache Spark's module for working with structured data.

Initializing SparkSession 
 

A SparkSession can be used create DataFrame, register DataFrame as tables, execute SGL over tables, cache tables, and read parquet files.

>>> from pyspark.sql import SparkSession
>>> spark a SparkSession \
     .builder\
     .appName("Python Spark SQL basic example") \
     .config("spark.some.config.option", "some-value") \
     .getOrCreate()

Creating DataFrames
 

Fromm RDDs

>>> from pyspark.sql.types import*

Infer Schema

>>> sc = spark.sparkContext
>>> lines = sc.textFile(''people.txt'')
>>> parts = lines.map(lambda l: l.split(","))
>>> people = parts.map(lambda p: Row(nameap[0],ageaint(p[l])))
>>> peopledf = spark.createDataFrame(people)

Specify Schema

>>> people = parts.map(lambda p: Row(name=p[0],
               age=int(p[1].strip())))
>>>  schemaString = "name age"
>>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
>>> schema = StructType(fields)
>>> spark.createDataFrame(people, schema).show()

From Spark Data Sources
JSON

>>>  df = spark.read.json("customer.json")
>>> df.show()

>>>  df2 = spark.read.load("people.json", format="json")

Parquet files

>>> df3 = spark.read.load("users.parquet")

TXT files

>>> df4 = spark.read.text("people.txt")

Filter 

#Filter entries of age, only keep those records of which the values are >24
>>> df.filter(df["age"]>24).show()

Duplicate Values 

>>> df = df.dropDuplicates()

Queries 
 

>>> from pyspark.sql import functions as F

Select

>>> df.select("firstName").show() #Show all entries in firstName column
>>> df.select("firstName","lastName") \
      .show()
>>> df.select("firstName", #Show all entries in firstName, age and type
              "age",
              explode("phoneNumber") \
              .alias("contactInfo")) \
      .select("contactInfo.type",
              "firstName",
              "age") \
      .show()
>>> df.select(df["firstName"],df["age"]+ 1) #Show all entries in firstName and age, .show() add 1 to the entries of age
>>> df.select(df['age'] > 24).show() #Show all entries where age >24

When

>>> df.select("firstName", #Show firstName and 0 or 1 depending on age >30
               F.when(df.age > 30, 1) \
              .otherwise(0)) \
      .show()
>>> df[df.firstName.isin("Jane","Boris")] #Show firstName if in the given options
.collect()

Like 

>>> df.select("firstName", #Show firstName, and lastName is TRUE if lastName is like Smith
              df.lastName.like("Smith")) \
     .show()

Startswith - Endswith 

>>> df.select("firstName", #Show firstName, and TRUE if lastName starts with Sm
              df.lastName \
                .startswith("Sm")) \
      .show()
>>> df.select(df.lastName.endswith("th"))\ #Show last names ending in th
      .show()

Substring 

>>> df.select(df.firstName.substr(1, 3) \ #Return substrings of firstName
                          .alias("name")) \
        .collect()

Between 

>>> df.select(df.age.between(22, 24)) \ #Show age: values are TRUE if between 22 and 24
          .show()

Add, Update & Remove Columns 

Adding Columns

 >>> df = df.withColumn('city',df.address.city) \
            .withColumn('postalCode',df.address.postalCode) \
            .withColumn('state',df.address.state) \
            .withColumn('streetAddress',df.address.streetAddress) \
            .withColumn('telePhoneNumber', explode(df.phoneNumber.number)) \
            .withColumn('telePhoneType', explode(df.phoneNumber.type)) 

Updating Columns

>>> df = df.withColumnRenamed('telePhoneNumber', 'phoneNumber')

Removing Columns

  >>> df = df.drop("address", "phoneNumber")
 >>> df = df.drop(df.address).drop(df.phoneNumber)
 

Missing & Replacing Values 
 

>>> df.na.fill(50).show() #Replace null values
 >>> df.na.drop().show() #Return new df omitting rows with null values
 >>> df.na \ #Return new df replacing one value with another
       .replace(10, 20) \
       .show()

GroupBy 

>>> df.groupBy("age")\ #Group by age, count the members in the groups
      .count() \
      .show()

Sort 
 

>>> peopledf.sort(peopledf.age.desc()).collect()
>>> df.sort("age", ascending=False).collect()
>>> df.orderBy(["age","city"],ascending=[0,1])\
     .collect()

Repartitioning 

>>> df.repartition(10)\ #df with 10 partitions
      .rdd \
      .getNumPartitions()
>>> df.coalesce(1).rdd.getNumPartitions() #df with 1 partition

Running Queries Programmatically 
 

Registering DataFrames as Views

>>> peopledf.createGlobalTempView("people")
>>> df.createTempView("customer")
>>> df.createOrReplaceTempView("customer")

Query Views

>>> df5 = spark.sql("SELECT * FROM customer").show()
>>> peopledf2 = spark.sql("SELECT * FROM global_temp.people")\
               .show()

Inspect Data 
 

>>> df.dtypes #Return df column names and data types
>>> df.show() #Display the content of df
>>> df.head() #Return first n rows
>>> df.first() #Return first row
>>> df.take(2) #Return the first n rows >>> df.schema Return the schema of df
>>> df.describe().show() #Compute summary statistics >>> df.columns Return the columns of df
>>> df.count() #Count the number of rows in df
>>> df.distinct().count() #Count the number of distinct rows in df
>>> df.printSchema() #Print the schema of df
>>> df.explain() #Print the (logical and physical) plans

Output

Data Structures 
 

 >>> rdd1 = df.rdd #Convert df into an RDD
 >>> df.toJSON().first() #Convert df into a RDD of string
 >>> df.toPandas() #Return the contents of df as Pandas DataFrame

Write & Save to Files 

>>> df.select("firstName", "city")\
       .write \
       .save("nameAndCity.parquet")
 >>> df.select("firstName", "age") \
       .write \
       .save("namesAndAges.json",format="json")

Stopping SparkSession 

>>> spark.stop()

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #dataframes #python #bigdata

Reform: Form Objects Decoupled From Models In Ruby

Reform

Form objects decoupled from your models.

Reform gives you a form object with validations and nested setup of models. It is completely framework-agnostic and doesn't care about your database.

Although reform can be used in any Ruby framework, it comes with Rails support, works with simple_form and other form gems, allows nesting forms to implement has_one and has_many relationships, can compose a form from multiple objects and gives you coercion.

Full Documentation

Reform is part of the Trailblazer framework. Full documentation is available on the project site.

Reform 2.2

Temporary note: Reform 2.2 does not automatically load Rails files anymore (e.g. ActiveModel::Validations). You need the reform-rails gem, see Installation.

Defining Forms

Forms are defined in separate classes. Often, these classes partially map to a model.

class AlbumForm < Reform::Form
  property :title
  validates :title, presence: true
end

Fields are declared using ::property. Validations work exactly as you know it from Rails or other frameworks. Note that validations no longer go into the model.

The API

Forms have a ridiculously simple API with only a handful of public methods.

1. #initialize always requires a model that the form represents.
2. #validate(params) updates the form's fields with the input data (only the form, not the model) and then runs all validations. The return value is the boolean result of the validations.
3. #errors returns validation messages in a classic ActiveModel style.
4. #sync writes form data back to the model. This will only use setter methods on the model(s).
5. #save (optional) will call #save on the model and nested models. Note that this implies a #sync call.
6. #prepopulate! (optional) will run pre-population hooks to "fill out" your form before rendering.

In addition to the main API, forms expose accessors to the defined properties. This is used for rendering or manual operations.

Setup

In your controller or operation you create a form instance and pass in the models you want to work on.

class AlbumsController
  def new
    @form = AlbumForm.new(Album.new)
  end

This will also work as an editing form with an existing album.

def edit
  @form = AlbumForm.new(Album.find(1))
end

Reform will read property values from the model in setup. In our example, the AlbumForm will call album.title to populate the title field.

Rendering Forms

Your @form is now ready to be rendered, either do it yourself or use something like Rails' #form_for, simple_form or formtastic.

= form_for @form do |f|
  = f.input :title

Nested forms and collections can be easily rendered with fields_for, etc. Note that you no longer pass the model to the form builder, but the Reform instance.

Optionally, you might want to use the #prepopulate! method to pre-populate fields and prepare the form for rendering.

Validation

After form submission, you need to validate the input.

class SongsController
  def create
    @form = SongForm.new(Song.new)

    #=> params: {song: {title: "Rio", length: "366"}}

    if @form.validate(params[:song])

The #validate method first updates the values of the form - the underlying model is still treated as immutuable and remains unchanged. It then runs all validations you provided in the form.

It's the only entry point for updating the form. This is per design, as separating writing and validation doesn't make sense for a form.

This allows rendering the form after validate with the data that has been submitted. However, don't get confused, the model's values are still the old, original values and are only changed after a #save or #sync operation.

Syncing Back

After validation, you have two choices: either call #save and let Reform sort out the rest. Or call #sync, which will write all the properties back to the model. In a nested form, this works recursively, of course.

It's then up to you what to do with the updated models - they're still unsaved.

Saving Forms

The easiest way to save the data is to call #save on the form.

if @form.validate(params[:song])
  @form.save  #=> populates album with incoming data
              #   by calling @form.album.title=.
else
  # handle validation errors.
end

This will sync the data to the model and then call album.save.

Sometimes, you need to do saving manually.

Default values

Reform allows default values to be provided for properties.

class AlbumForm < Reform::Form
  property :price_in_cents, default: 9_95
end

Saving Forms Manually

Calling #save with a block will provide a nested hash of the form's properties and values. This does not call #save on the models and allows you to implement the saving yourself.

The block parameter is a nested hash of the form input.

  @form.save do |hash|
    hash      #=> {title: "Greatest Hits"}
    Album.create(hash)
  end

You can always access the form's model. This is helpful when you were using populators to set up objects when validating.

  @form.save do |hash|
    album = @form.model

    album.update_attributes(hash[:album])
  end

Nesting

Reform provides support for nested objects. Let's say the Album model keeps some associations.

class Album < ActiveRecord::Base
  has_one  :artist
  has_many :songs
end

The implementation details do not really matter here, as long as your album exposes readers and writes like Album#artist and Album#songs, this allows you to define nested forms.

class AlbumForm < Reform::Form
  property :title
  validates :title, presence: true

  property :artist do
    property :full_name
    validates :full_name, presence: true
  end

  collection :songs do
    property :name
  end
end

You can also reuse an existing form from elsewhere using :form.

property :artist, form: ArtistForm

Nested Setup

Reform will wrap defined nested objects in their own forms. This happens automatically when instantiating the form.

album.songs #=> [<Song name:"Run To The Hills">]

form = AlbumForm.new(album)
form.songs[0] #=> <SongForm model: <Song name:"Run To The Hills">>
form.songs[0].name #=> "Run To The Hills"

Nested Rendering

When rendering a nested form you can use the form's readers to access the nested forms.

= text_field :title,         @form.title
= text_field "artist[name]", @form.artist.name

Or use something like #fields_for in a Rails environment.

= form_for @form do |f|
  = f.text_field :title

  = f.fields_for :artist do |a|
    = a.text_field :name

Nested Processing

validate will assign values to the nested forms. sync and save work analogue to the non-nested form, just in a recursive way.

The block form of #save would give you the following data.

@form.save do |nested|
  nested #=> {title:  "Greatest Hits",
         #    artist: {name: "Duran Duran"},
         #    songs: [{title: "Hungry Like The Wolf"},
         #            {title: "Last Chance On The Stairways"}]
         #   }
  end

The manual saving with block is not encouraged. You should rather check the Disposable docs to find out how to implement your manual tweak with the official API.

Populating Forms

Very often, you need to give Reform some information how to create or find nested objects when validateing. This directive is called populator and documented here.

Installation

Add this line to your Gemfile:

gem "reform"

Reform works fine with Rails 3.1-5.0. However, inheritance of validations with ActiveModel::Validations is broken in Rails 3.2 and 4.0.

Since Reform 2.2, you have to add the reform-rails gem to your Gemfile to automatically load ActiveModel/Rails files.

gem "reform-rails"

Since Reform 2.0 you need to specify which validation backend you want to use (unless you're in a Rails environment where ActiveModel will be used).

To use ActiveModel (not recommended because very out-dated).

require "reform/form/active_model/validations"
Reform::Form.class_eval do
  include Reform::Form::ActiveModel::Validations
end

To use dry-validation (recommended).

require "reform/form/dry"
Reform::Form.class_eval do
  feature Reform::Form::Dry
end

Put this in an initializer or on top of your script.

Compositions

Reform allows to map multiple models to one form. The complete documentation is here, however, this is how it works.

class AlbumForm < Reform::Form
  include Composition

  property :id,    on: :album
  property :title, on: :album
  property :songs, on: :cd
  property :cd_id, on: :cd, from: :id
end

When initializing a composition, you have to pass a hash that contains the composees.

AlbumForm.new(album: album, cd: CD.find(1))

More

Reform comes many more optional features, like hash fields, coercion, virtual fields, and so on. Check the full documentation here.

Reform is part of the Trailblazer project. Please buy my book to support the development and learn everything about Reform - there's two chapters dedicated to Reform!

Security And Strong_parameters

By explicitly defining the form layout using ::property there is no more need for protecting from unwanted input. strong_parameter or attr_accessible become obsolete. Reform will simply ignore undefined incoming parameters.

This is not Reform 1.x!

Temporary note: This is the README and API for Reform 2. On the public API, only a few tiny things have changed. Here are the Reform 1.2 docs.

Anyway, please upgrade and report problems and do not simply assume that we will magically find out what needs to get fixed. When in trouble, join us on Gitter.

Full documentation for Reform is available online, or support us and grab the Trailblazer book. There is an Upgrading Guide to help you migrate through versions.

Attributions!!!

Great thanks to Blake Education for giving us the freedom and time to develop this project in 2013 while working on their project.

Author: trailblazer
Source code: https://github.com/trailblazer/reform
License:  MIT license

#ruby  #ruby-on-rails

Calculates Edit Distance using Damerau-Levenshtein Algorithm

damerau-levenshtein

The damerau-levenshtein gem allows to find edit distance between two UTF-8 or ASCII encoded strings with O(N*M) efficiency.

This gem implements pure Levenshtein algorithm, Damerau modification of it (where 2 character transposition counts as 1 edit distance). It also includes Boehmer & Rees 2008 modification of Damerau algorithm, where transposition of bigger than 1 character blocks is taken in account as well (Rees 2014).

require "damerau-levenshtein"
DamerauLevenshtein.distance("Something", "Smoething") #returns 1

It also returns a diff between two strings according to Levenshtein alrorithm. The diff is expressed by tags <ins>, <del>, and <subst>. Such tags make it possible to highlight differnce between strings in a flexible way.

require "damerau-levenshtein"
differ = DamerauLevenshtein::Differ.new
differ.run("corn", "cron")
# output: ["c<subst>or</subst>n", "c<subst>ro</subst>n"]

Dependencies

sudo apt-get install build-essential libgmp3-dev

Installation

gem install damerau-levenshtein

Examples

require "damerau-levenshtein"
dl = DamerauLevenshtein

• compare using Damerau Levenshtein algorithm

dl.distance("Something", "Smoething") #returns 1

• compare using Levensthein algorithm

dl.distance("Something", "Smoething", 0) #returns 2

• compare using Boehmer & Rees modification

dl.distance("Something", "meSothing", 2) #returns 2 instead of 4

• comparison of words with UTF-8 characters should work fine:

dl.distance("Sjöstedt", "Sjostedt") #returns 1

• compare two arrays

dl.array_distance([1,2,3,5], [1,2,3,4]) #returns 1

• return diff between two strings

differ = DamerauLevenshtein::Differ.new
differ.run("Something", "smthg")

• return diff between two strings in raw format

differ = DamerauLevenshtein::Differ.new
differ.format = :raw
differ.run("Something", "smthg")

API Description

Methods

DamerauLevenshtein.version

DamerauLevenshtein.version
#returns version number of the gem

DamerauLevenshtein.distance

DamerauLevenshtein.distance(string1, string2, block_size, max_distance)
#returns edit distance between 2 strings

DamerauLevenshtein.string_distance(string1, string2, block_size, max_distance)
# an alias for .distance

DamerauLevenshtein.array_distance(array1, array2, block_size, max_distance)
# returns edit distance between 2 arrays of integers

DamerauLevenshtein.distance and .array_distance take 4 arguments:

• string1 (array1 for .array_distance)
• string2 (array2 for .array_distance)
• block_size (default is 1)
• max_distance (default is 10)

block_size determines maximum number of characters in a transposition block:

block_size = 0
(transposition does not count -- it is a pure Levenshtein algorithm)

block_size = 1
(transposition between 2 adjustent characters --
it is pure Damerau-Levenshtein algorithm)

block_size = 2
(transposition between blocks as big as 2 characters -- so abcd and cdab
counts as edit distance 2, not 4)

block_size = 3
(transposition between blocks as big as 3 characters --
so abcdef and defabc counts as edit distance 3, not 6)

etc.

max_distance -- is a threshold after which algorithm gives up and returns max_distance instead of real edit distance.

Levenshtein algorithm is expensive, so it makes sense to give up when edit distance is becoming too big. The argument max_distance does just that.

DamerauLevenshtein.distance("abcdefg", "1234567", 0, 3)
# output: 4 -- it gave up when edit distance exceeded 3

DamerauLevenshtein::Differ

differ = DamerauLevenshtein::Differ.new creates an instance of new differ class to return difference between two strings

differ.format shows current format for diff. Default is :tag format

differ.format = :raw changes current format for diffs. Possible values are :tag and :raw

differ.run("String1", "String2") returns difference between two strings.

For example:

differ = DamerauLevenshtein::Differ.new
differ.run("Something", "smthng")
# output: ["<ins>S</ins><subst>o</subst>m<ins>e</ins>th<ins>i</ins>ng",
#          "<del>S</del><subst>s</subst>m<del>e</del>th<del>i</del>ng"]

Or with parsing:

require "damerau-levenshtein"
require "nokogiri"

differ = DamerauLevenshtein::Differ.new
res = differ.run("Something", "Smothing!")
nodes = Nokogiri::XML("<root>#{res.first}</root>")

markup = nodes.root.children.map do |n|
  case n.name
  when "text"
    n.text
  when "del"
    "~~#{n.children.first.text}~~"
  when "ins"
    "*#{n.children.first.text}*"
  when "subst"
    "**#{n.children.first.text}**"
  end
end.join("")

puts markup

Output

S*o*m**e**thing~~!~~

Contributing to damerau-levenshtein

• Check out the latest master to make sure the feature hasn't been implemented or the bug hasn't been fixed yet
• Check out the issue tracker to make sure someone already hasn't requested it and/or contributed it
• Fork the project
• Start a feature/bugfix branch
• Commit and push until you are happy with your contribution
• Make sure to add tests for it. This is important so I don't break it in a future version unintentionally.
• Please try not to mess with the Rakefile, version, or history. If you want to have your own version, or is otherwise necessary, that is fine, but please isolate to its own commit so I can cherry-pick around it.

Versioning

This gem is following practices of Semantic Versioning

Download Details: 

Author: GlobalNamesArchitecture
Source Code: https://github.com/GlobalNamesArchitecture/damerau-levenshtein 
License: MIT license

#ruby #algorithm 

SheetJS Community Edition -- Spreadsheet Data Toolkit

SheetJS

The SheetJS Community Edition offers battle-tested open-source solutions for extracting useful data from almost any complex spreadsheet and generating new spreadsheets that will work with legacy and modern software alike.

SheetJS Pro offers solutions beyond data processing: Edit complex templates with ease; let out your inner Picasso with styling; make custom sheets with images/graphs/PivotTables; evaluate formula expressions and port calculations to web apps; automate common spreadsheet tasks, and much more!    

Getting Started

Installation

Standalone Browser Scripts

Each standalone release script is available at https://cdn.sheetjs.com/.

The current version is 0.18.6 and can be referenced as follows:

<!-- use version 0.18.6 -->
<script lang="javascript" src="https://cdn.sheetjs.com/xlsx-0.18.6/package/dist/xlsx.full.min.js"></script>

The latest tag references the latest version and updates with each release:

<!-- use the latest version -->
<script lang="javascript" src="https://cdn.sheetjs.com/xlsx-latest/package/dist/xlsx.full.min.js"></script>

For production use, scripts should be downloaded and added to a public folder alongside other scripts.

Browser builds (click to show)

The complete single-file version is generated at dist/xlsx.full.min.js

dist/xlsx.core.min.js omits codepage library (no support for XLS encodings)

A slimmer build is generated at dist/xlsx.mini.min.js. Compared to full build:

• codepage library skipped (no support for XLS encodings)
• no support for XLSB / XLS / Lotus 1-2-3 / SpreadsheetML 2003 / Numbers
• node stream utils removed

These scripts are also available on the CDN:

<!-- use xlsx.mini.min.js from version 0.18.6 -->
<script lang="javascript" src="https://cdn.sheetjs.com/xlsx-0.18.6/package/dist/xlsx.mini.min.js"></script>

Bower will pull the entire repo:

$ bower install js-xlsx

Bower will place the standalone scripts in bower_components/js-xlsx/dist/

Internet Explorer and ECMAScript 3 Compatibility (click to show)

For broad compatibility with JavaScript engines, the library is written using ECMAScript 3 language dialect as well as some ES5 features like Array#forEach. Older browsers require shims to provide missing functions.

To use the shim, add the shim before the script tag that loads xlsx.js:

<!-- add the shim first -->
<script type="text/javascript" src="shim.min.js"></script>
<!-- after the shim is referenced, add the library -->
<script type="text/javascript" src="xlsx.full.min.js"></script>

Due to SSL certificate compatibility issues, it is highly recommended to save the Standalone and Shim scripts from https://cdn.sheetjs.com/ and add to a public directory in the site.

The script also includes IE_LoadFile and IE_SaveFile for loading and saving files in Internet Explorer versions 6-9. The xlsx.extendscript.js script bundles the shim in a format suitable for Photoshop and other Adobe products.

ECMAScript Modules

Browser ESM

The ECMAScript Module build is saved to xlsx.mjs and can be directly added to a page with a script tag using type="module":

<script type="module">
import { read, writeFileXLSX } from "https://cdn.sheetjs.com/xlsx-0.18.6/package/xlsx.mjs";

/* load the codepage support library for extended support with older formats  */
import { set_cptable } from "https://cdn.sheetjs.com/xlsx-0.18.6/package/xlsx.mjs";
import * as cptable from 'https://cdn.sheetjs.com/xlsx-0.18.6/package/dist/cpexcel.full.mjs';
set_cptable(cptable);
</script>

Frameworks (Angular, VueJS, React) and Bundlers (webpack, etc)

The NodeJS package is readily installed from the tarballs:

$ npm  install --save https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # npm
$ pnpm install --save https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # pnpm
$ yarn add     --save https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # yarn

Once installed, the library can be imported under the name xlsx:

import { read, writeFileXLSX } from "xlsx";

/* load the codepage support library for extended support with older formats  */
import { set_cptable } from "xlsx";
import * as cptable from 'xlsx/dist/cpexcel.full.mjs';
set_cptable(cptable);

Deno

xlsx.mjs can be imported in Deno:

// @deno-types="https://cdn.sheetjs.com/xlsx-0.18.6/package/types/index.d.ts"
import * as XLSX from 'https://cdn.sheetjs.com/xlsx-0.18.6/package/xlsx.mjs';

/* load the codepage support library for extended support with older formats  */
import * as cptable from 'https://cdn.sheetjs.com/xlsx-0.18.6/package/dist/cpexcel.full.mjs';
XLSX.set_cptable(cptable);

NodeJS

Tarballs are available on https://cdn.sheetjs.com.

Each individual version can be referenced using a similar URL pattern. https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz is the URL for 0.18.6

https://cdn.sheetjs.com/xlsx-latest/xlsx-latest.tgz is a link to the latest version and will refresh on each release.

Installation

Tarballs can be directly installed using a package manager:

$ npm  install https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # npm
$ pnpm install https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # pnpm
$ yarn add     https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz # yarn

For general stability, "vendoring" modules is the recommended approach:

Download the tarball (xlsx-0.18.6.tgz) for the desired version. The current version is available at https://cdn.sheetjs.com/xlsx-0.18.6/xlsx-0.18.6.tgz

Create a vendor subdirectory at the root of your project and move the tarball to that folder. Add it to your project repository.

Install the tarball using a package manager:

$ npm  install --save file:vendor/xlsx-0.18.6.tgz # npm
$ pnpm install --save file:vendor/xlsx-0.18.6.tgz # pnpm
$ yarn add            file:vendor/xlsx-0.18.6.tgz # yarn

The package will be installed and accessible as xlsx.

Usage

By default, the module supports require and it will automatically add support for streams and filesystem access:

var XLSX = require("xlsx");

The module also ships with xlsx.mjs for use with import. The mjs version does not automatically load native node modules:

import * as XLSX from 'xlsx/xlsx.mjs';

/* load 'fs' for readFile and writeFile support */
import * as fs from 'fs';
XLSX.set_fs(fs);

/* load 'stream' for stream support */
import { Readable } from 'stream';
XLSX.stream.set_readable(Readable);

/* load the codepage support library for extended support with older formats  */
import * as cpexcel from 'xlsx/dist/cpexcel.full.mjs';
XLSX.set_cptable(cpexcel);

Photoshop and InDesign

dist/xlsx.extendscript.js is an ExtendScript build for Photoshop and InDesign. https://cdn.sheetjs.com/xlsx-0.18.6/package/dist/xlsx.extendscript.js is the current version. After downloading the script, it can be directly referenced with a #include directive:

#include "xlsx.extendscript.js"

Usage

Most scenarios involving spreadsheets and data can be broken into 5 parts:

Acquire Data: Data may be stored anywhere: local or remote files, databases, HTML TABLE, or even generated programmatically in the web browser.

Extract Data: For spreadsheet files, this involves parsing raw bytes to read the cell data. For general JS data, this involves reshaping the data.

Process Data: From generating summary statistics to cleaning data records, this step is the heart of the problem.

Package Data: This can involve making a new spreadsheet or serializing with JSON.stringify or writing XML or simply flattening data for UI tools.

Release Data: Spreadsheet files can be uploaded to a server or written locally. Data can be presented to users in an HTML TABLE or data grid.

A common problem involves generating a valid spreadsheet export from data stored in an HTML table. In this example, an HTML TABLE on the page will be scraped, a row will be added to the bottom with the date of the report, and a new file will be generated and downloaded locally. XLSX.writeFile takes care of packaging the data and attempting a local download:

// Acquire Data (reference to the HTML table)
var table_elt = document.getElementById("my-table-id");

// Extract Data (create a workbook object from the table)
var workbook = XLSX.utils.table_to_book(table_elt);

// Process Data (add a new row)
var ws = workbook.Sheets["Sheet1"];
XLSX.utils.sheet_add_aoa(ws, [["Created "+new Date().toISOString()]], {origin:-1});

// Package and Release Data (`writeFile` tries to write and save an XLSB file)
XLSX.writeFile(workbook, "Report.xlsb");

This library tries to simplify steps 2 and 4 with functions to extract useful data from spreadsheet files (read / readFile) and generate new spreadsheet files from data (write / writeFile). Additional utility functions like table_to_book work with other common data sources like HTML tables.

This documentation and various demo projects cover a number of common scenarios and approaches for steps 1 and 5.

Utility functions help with step 3.

"Acquiring and Extracting Data" describes solutions for common data import scenarios.

"Packaging and Releasing Data" describes solutions for common data export scenarios.

"Processing Data" describes solutions for common workbook processing and manipulation scenarios.

"Utility Functions" details utility functions for translating JSON Arrays and other common JS structures into worksheet objects.

The Zen of SheetJS

Data processing should fit in any workflow

The library does not impose a separate lifecycle. It fits nicely in websites and apps built using any framework. The plain JS data objects play nice with Web Workers and future APIs.

JavaScript is a powerful language for data processing

The "Common Spreadsheet Format" is a simple object representation of the core concepts of a workbook. The various functions in the library provide low-level tools for working with the object.

For friendly JS processing, there are utility functions for converting parts of a worksheet to/from an Array of Arrays. The following example combines powerful JS Array methods with a network request library to download data, select the information we want and create a workbook file:

Get Data from a JSON Endpoint and Generate a Workbook (click to show)

The goal is to generate a XLSB workbook of US President names and birthdays.

Acquire Data

Raw Data

https://theunitedstates.io/congress-legislators/executive.json has the desired data. For example, John Adams:

{
  "id": { /* (data omitted) */ },
  "name": {
    "first": "John",          // <-- first name
    "last": "Adams"           // <-- last name
  },
  "bio": {
    "birthday": "1735-10-19", // <-- birthday
    "gender": "M"
  },
  "terms": [
    { "type": "viceprez", /* (other fields omitted) */ },
    { "type": "viceprez", /* (other fields omitted) */ },
    { "type": "prez", /* (other fields omitted) */ } // <-- look for "prez"
  ]
}

Filtering for Presidents

The dataset includes Aaron Burr, a Vice President who was never President!

Array#filter creates a new array with the desired rows. A President served at least one term with type set to "prez". To test if a particular row has at least one "prez" term, Array#some is another native JS function. The complete filter would be:

const prez = raw_data.filter(row => row.terms.some(term => term.type === "prez"));

Lining up the data

For this example, the name will be the first name combined with the last name (row.name.first + " " + row.name.last) and the birthday will be the subfield row.bio.birthday. Using Array#map, the dataset can be massaged in one call:

const rows = prez.map(row => ({
  name: row.name.first + " " + row.name.last,
  birthday: row.bio.birthday
}));

The result is an array of "simple" objects with no nesting:

[
  { name: "George Washington", birthday: "1732-02-22" },
  { name: "John Adams", birthday: "1735-10-19" },
  // ... one row per President
]

Extract Data

With the cleaned dataset, XLSX.utils.json_to_sheet generates a worksheet:

const worksheet = XLSX.utils.json_to_sheet(rows);

XLSX.utils.book_new creates a new workbook and XLSX.utils.book_append_sheet appends a worksheet to the workbook. The new worksheet will be called "Dates":

const workbook = XLSX.utils.book_new();
XLSX.utils.book_append_sheet(workbook, worksheet, "Dates");

Process Data

Fixing headers

By default, json_to_sheet creates a worksheet with a header row. In this case, the headers come from the JS object keys: "name" and "birthday".

The headers are in cells A1 and B1. XLSX.utils.sheet_add_aoa can write text values to the existing worksheet starting at cell A1:

XLSX.utils.sheet_add_aoa(worksheet, [["Name", "Birthday"]], { origin: "A1" });

Fixing Column Widths

Some of the names are longer than the default column width. Column widths are set by setting the "!cols" worksheet property.

The following line sets the width of column A to approximately 10 characters:

worksheet["!cols"] = [ { wch: 10 } ]; // set column A width to 10 characters

One Array#reduce call over rows can calculate the maximum width:

const max_width = rows.reduce((w, r) => Math.max(w, r.name.length), 10);
worksheet["!cols"] = [ { wch: max_width } ];

Note: If the starting point was a file or HTML table, XLSX.utils.sheet_to_json will generate an array of JS objects.

Package and Release Data

XLSX.writeFile creates a spreadsheet file and tries to write it to the system. In the browser, it will try to prompt the user to download the file. In NodeJS, it will write to the local directory.

XLSX.writeFile(workbook, "Presidents.xlsx");

Complete Example

// Uncomment the next line for use in NodeJS:
// const XLSX = require("xlsx"), axios = require("axios");

(async() => {
  /* fetch JSON data and parse */
  const url = "https://theunitedstates.io/congress-legislators/executive.json";
  const raw_data = (await axios(url, {responseType: "json"})).data;

  /* filter for the Presidents */
  const prez = raw_data.filter(row => row.terms.some(term => term.type === "prez"));

  /* flatten objects */
  const rows = prez.map(row => ({
    name: row.name.first + " " + row.name.last,
    birthday: row.bio.birthday
  }));

  /* generate worksheet and workbook */
  const worksheet = XLSX.utils.json_to_sheet(rows);
  const workbook = XLSX.utils.book_new();
  XLSX.utils.book_append_sheet(workbook, worksheet, "Dates");

  /* fix headers */
  XLSX.utils.sheet_add_aoa(worksheet, [["Name", "Birthday"]], { origin: "A1" });

  /* calculate column width */
  const max_width = rows.reduce((w, r) => Math.max(w, r.name.length), 10);
  worksheet["!cols"] = [ { wch: max_width } ];

  /* create an XLSX file and try to save to Presidents.xlsx */
  XLSX.writeFile(workbook, "Presidents.xlsx");
})();

For use in the web browser, assuming the snippet is saved to snippet.js, script tags should be used to include the axios and xlsx standalone builds:

<script src="https://cdn.sheetjs.com/xlsx-latest/package/dist/xlsx.full.min.js"></script>
<script src="https://unpkg.com/axios/dist/axios.min.js"></script>
<script src="snippet.js"></script>

File formats are implementation details

The parser covers a wide gamut of common spreadsheet file formats to ensure that "HTML-saved-as-XLS" files work as well as actual XLS or XLSX files.

The writer supports a number of common output formats for broad compatibility with the data ecosystem.

To the greatest extent possible, data processing code should not have to worry about the specific file formats involved.

JS Ecosystem Demos

The demos directory includes sample projects for:

Frameworks and APIs

• angularjs
• angular and ionic
• knockout
• meteor
• react, react-native, next
• vue 2.x, weex, nuxt
• XMLHttpRequest and fetch
• nodejs server
• databases and key/value stores
• typed arrays and math

Bundlers and Tooling

• browserify
• fusebox
• parcel
• requirejs
• rollup
• systemjs
• typescript
• webpack 2.x

Platforms and Integrations

• deno
• electron application
• nw.js application
• Chrome / Chromium extensions
• Download a Google Sheet locally
• Adobe ExtendScript
• Headless Browsers
• canvas-datagrid
• x-spreadsheet
• react-data-grid
• vue3-table-light
• Swift JSC and other engines
• "serverless" functions
• internet explorer

Other examples are included in the showcase.

https://sheetjs.com/demos/modify.html shows a complete example of reading, modifying, and writing files.

https://github.com/SheetJS/sheetjs/blob/HEAD/bin/xlsx.njs is the command-line tool included with node installations, reading spreadsheet files and exporting the contents in various formats.

Acquiring and Extracting Data

Parsing Workbooks

API

Extract data from spreadsheet bytes

var workbook = XLSX.read(data, opts);

The read method can extract data from spreadsheet bytes stored in a JS string, "binary string", NodeJS buffer or typed array (Uint8Array or ArrayBuffer).

Read spreadsheet bytes from a local file and extract data

var workbook = XLSX.readFile(filename, opts);

The readFile method attempts to read a spreadsheet file at the supplied path. Browsers generally do not allow reading files in this way (it is deemed a security risk), and attempts to read files in this way will throw an error.

The second opts argument is optional. "Parsing Options" covers the supported properties and behaviors.

Examples

Here are a few common scenarios (click on each subtitle to see the code):

Local file in a NodeJS server (click to show)

readFile uses fs.readFileSync under the hood:

var XLSX = require("xlsx");

var workbook = XLSX.readFile("test.xlsx");

For Node ESM, the readFile helper is not enabled. Instead, fs.readFileSync should be used to read the file data as a Buffer for use with XLSX.read:

import { readFileSync } from "fs";
import { read } from "xlsx/xlsx.mjs";

const buf = readFileSync("test.xlsx");
/* buf is a Buffer */
const workbook = read(buf);

Local file in a Deno application (click to show)

readFile uses Deno.readFileSync under the hood:

// @deno-types="https://deno.land/x/sheetjs/types/index.d.ts"
import * as XLSX from 'https://deno.land/x/sheetjs/xlsx.mjs'

const workbook = XLSX.readFile("test.xlsx");

Applications reading files must be invoked with the --allow-read flag. The deno demo has more examples

User-submitted file in a web page ("Drag-and-Drop") (click to show)

For modern websites targeting Chrome 76+, File#arrayBuffer is recommended:

// XLSX is a global from the standalone script

async function handleDropAsync(e) {
  e.stopPropagation(); e.preventDefault();
  const f = e.dataTransfer.files[0];
  /* f is a File */
  const data = await f.arrayBuffer();
  /* data is an ArrayBuffer */
  const workbook = XLSX.read(data);

  /* DO SOMETHING WITH workbook HERE */
}
drop_dom_element.addEventListener("drop", handleDropAsync, false);

For maximal compatibility, the FileReader API should be used:

function handleDrop(e) {
  e.stopPropagation(); e.preventDefault();
  var f = e.dataTransfer.files[0];
  /* f is a File */
  var reader = new FileReader();
  reader.onload = function(e) {
    var data = e.target.result;
    /* reader.readAsArrayBuffer(file) -> data will be an ArrayBuffer */
    var workbook = XLSX.read(data);

    /* DO SOMETHING WITH workbook HERE */
  };
  reader.readAsArrayBuffer(f);
}
drop_dom_element.addEventListener("drop", handleDrop, false);

https://oss.sheetjs.com/sheetjs/ demonstrates the FileReader technique.

User-submitted file with an HTML INPUT element (click to show)

Starting with an HTML INPUT element with type="file":

<input type="file" id="input_dom_element">

For modern websites targeting Chrome 76+, Blob#arrayBuffer is recommended:

// XLSX is a global from the standalone script

async function handleFileAsync(e) {
  const file = e.target.files[0];
  const data = await file.arrayBuffer();
  /* data is an ArrayBuffer */
  const workbook = XLSX.read(data);

  /* DO SOMETHING WITH workbook HERE */
}
input_dom_element.addEventListener("change", handleFileAsync, false);

For broader support (including IE10+), the FileReader approach is recommended:

function handleFile(e) {
  var file = e.target.files[0];
  var reader = new FileReader();
  reader.onload = function(e) {
    var data = e.target.result;
    /* reader.readAsArrayBuffer(file) -> data will be an ArrayBuffer */
    var workbook = XLSX.read(e.target.result);

    /* DO SOMETHING WITH workbook HERE */
  };
  reader.readAsArrayBuffer(file);
}
input_dom_element.addEventListener("change", handleFile, false);

The oldie demo shows an IE-compatible fallback scenario.

Fetching a file in the web browser ("Ajax") (click to show)

For modern websites targeting Chrome 42+, fetch is recommended:

// XLSX is a global from the standalone script

(async() => {
  const url = "http://oss.sheetjs.com/test_files/formula_stress_test.xlsx";
  const data = await (await fetch(url)).arrayBuffer();
  /* data is an ArrayBuffer */
  const workbook = XLSX.read(data);

  /* DO SOMETHING WITH workbook HERE */
})();

For broader support, the XMLHttpRequest approach is recommended:

var url = "http://oss.sheetjs.com/test_files/formula_stress_test.xlsx";

/* set up async GET request */
var req = new XMLHttpRequest();
req.open("GET", url, true);
req.responseType = "arraybuffer";

req.onload = function(e) {
  var workbook = XLSX.read(req.response);

  /* DO SOMETHING WITH workbook HERE */
};

req.send();

The xhr demo includes a longer discussion and more examples.

http://oss.sheetjs.com/sheetjs/ajax.html shows fallback approaches for IE6+.

Local file in a PhotoShop or InDesign plugin (click to show)

readFile wraps the File logic in Photoshop and other ExtendScript targets. The specified path should be an absolute path:

#include "xlsx.extendscript.js"

/* Read test.xlsx from the Documents folder */
var workbook = XLSX.readFile(Folder.myDocuments + "/test.xlsx");

The extendscript demo includes a more complex example.

Local file in an Electron app (click to show)

readFile can be used in the renderer process:

/* From the renderer process */
var XLSX = require("xlsx");

var workbook = XLSX.readFile(path);

Electron APIs have changed over time. The electron demo shows a complete example and details the required version-specific settings.

Local file in a mobile app with React Native (click to show)

The react demo includes a sample React Native app.

Since React Native does not provide a way to read files from the filesystem, a third-party library must be used. The following libraries have been tested:

• react-native-file-access

The base64 encoding returns strings compatible with the base64 type:

import XLSX from "xlsx";
import { FileSystem } from "react-native-file-access";

const b64 = await FileSystem.readFile(path, "base64");
/* b64 is a base64 string */
const workbook = XLSX.read(b64, {type: "base64"});

• react-native-fs

The ascii encoding returns binary strings compatible with the binary type:

import XLSX from "xlsx";
import { readFile } from "react-native-fs";

const bstr = await readFile(path, "ascii");
/* bstr is a binary string */
const workbook = XLSX.read(bstr, {type: "binary"});

NodeJS Server File Uploads (click to show)

read can accept a NodeJS buffer. readFile can read files generated by a HTTP POST request body parser like formidable:

const XLSX = require("xlsx");
const http = require("http");
const formidable = require("formidable");

const server = http.createServer((req, res) => {
  const form = new formidable.IncomingForm();
  form.parse(req, (err, fields, files) => {
    /* grab the first file */
    const f = Object.entries(files)[0][1];
    const path = f.filepath;
    const workbook = XLSX.readFile(path);

    /* DO SOMETHING WITH workbook HERE */
  });
}).listen(process.env.PORT || 7262);

The server demo has more advanced examples.

Download files in a NodeJS process (click to show)

Node 17.5 and 18.0 have native support for fetch:

const XLSX = require("xlsx");

const data = await (await fetch(url)).arrayBuffer();
/* data is an ArrayBuffer */
const workbook = XLSX.read(data);

For broader compatibility, third-party modules are recommended.

request requires a null encoding to yield Buffers:

var XLSX = require("xlsx");
var request = require("request");

request({url: url, encoding: null}, function(err, resp, body) {
  var workbook = XLSX.read(body);

  /* DO SOMETHING WITH workbook HERE */
});

axios works the same way in browser and in NodeJS:

const XLSX = require("xlsx");
const axios = require("axios");

(async() => {
  const res = await axios.get(url, {responseType: "arraybuffer"});
  /* res.data is a Buffer */
  const workbook = XLSX.read(res.data);

  /* DO SOMETHING WITH workbook HERE */
})();

Download files in an Electron app (click to show)

The net module in the main process can make HTTP/HTTPS requests to external resources. Responses should be manually concatenated using Buffer.concat:

const XLSX = require("xlsx");
const { net } = require("electron");

const req = net.request(url);
req.on("response", (res) => {
  const bufs = []; // this array will collect all of the buffers
  res.on("data", (chunk) => { bufs.push(chunk); });
  res.on("end", () => {
    const workbook = XLSX.read(Buffer.concat(bufs));

    /* DO SOMETHING WITH workbook HERE */
  });
});
req.end();

Readable Streams in NodeJS (click to show)

When dealing with Readable Streams, the easiest approach is to buffer the stream and process the whole thing at the end:

var fs = require("fs");
var XLSX = require("xlsx");

function process_RS(stream, cb) {
  var buffers = [];
  stream.on("data", function(data) { buffers.push(data); });
  stream.on("end", function() {
    var buffer = Buffer.concat(buffers);
    var workbook = XLSX.read(buffer, {type:"buffer"});

    /* DO SOMETHING WITH workbook IN THE CALLBACK */
    cb(workbook);
  });
}

ReadableStream in the browser (click to show)

When dealing with ReadableStream, the easiest approach is to buffer the stream and process the whole thing at the end:

// XLSX is a global from the standalone script

async function process_RS(stream) {
  /* collect data */
  const buffers = [];
  const reader = stream.getReader();
  for(;;) {
    const res = await reader.read();
    if(res.value) buffers.push(res.value);
    if(res.done) break;
  }

  /* concat */
  const out = new Uint8Array(buffers.reduce((acc, v) => acc + v.length, 0));

  let off = 0;
  for(const u8 of arr) {
    out.set(u8, off);
    off += u8.length;
  }

  return out;
}

const data = await process_RS(stream);
/* data is Uint8Array */
const workbook = XLSX.read(data);

More detailed examples are covered in the included demos

Processing JSON and JS Data

JSON and JS data tend to represent single worksheets. This section will use a few utility functions to generate workbooks.

Create a new Workbook

var workbook = XLSX.utils.book_new();

The book_new utility function creates an empty workbook with no worksheets.

Spreadsheet software generally require at least one worksheet and enforce the requirement in the user interface. This library enforces the requirement at write time, throwing errors if an empty workbook is passed to write functions.

API

Create a worksheet from an array of arrays of JS values

var worksheet = XLSX.utils.aoa_to_sheet(aoa, opts);

The aoa_to_sheet utility function walks an "array of arrays" in row-major order, generating a worksheet object. The following snippet generates a sheet with cell A1 set to the string A1, cell B1 set to B1, etc:

var worksheet = XLSX.utils.aoa_to_sheet([
  ["A1", "B1", "C1"],
  ["A2", "B2", "C2"],
  ["A3", "B3", "C3"]
]);

"Array of Arrays Input" describes the function and the optional opts argument in more detail.

Create a worksheet from an array of JS objects

var worksheet = XLSX.utils.json_to_sheet(jsa, opts);

The json_to_sheet utility function walks an array of JS objects in order, generating a worksheet object. By default, it will generate a header row and one row per object in the array. The optional opts argument has settings to control the column order and header output.

"Array of Objects Input" describes the function and the optional opts argument in more detail.

Examples

"Zen of SheetJS" contains a detailed example "Get Data from a JSON Endpoint and Generate a Workbook"

x-spreadsheet is an interactive data grid for previewing and modifying structured data in the web browser. The xspreadsheet demo includes a sample script with the xtos function for converting from x-spreadsheet data object to a workbook. https://oss.sheetjs.com/sheetjs/x-spreadsheet is a live demo.

Records from a database query (SQL or no-SQL) (click to show)

The database demo includes examples of working with databases and query results.

Numerical Computations with TensorFlow.js (click to show)

@tensorflow/tfjs and other libraries expect data in simple arrays, well-suited for worksheets where each column is a data vector. That is the transpose of how most people use spreadsheets, where each row is a vector.

When recovering data from tfjs, the returned data points are stored in a typed array. An array of arrays can be constructed with loops. Array#unshift can prepend a title row before the conversion:

const XLSX = require("xlsx");
const tf = require('@tensorflow/tfjs');

/* suppose xs and ys are vectors (1D tensors) -> tfarr will be a typed array */
const tfdata = tf.stack([xs, ys]).transpose();
const shape = tfdata.shape;
const tfarr = tfdata.dataSync();

/* construct the array of arrays */
const aoa = [];
for(let j = 0; j < shape[0]; ++j) {
  aoa[j] = [];
  for(let i = 0; i < shape[1]; ++i) aoa[j][i] = tfarr[j * shape[1] + i];
}
/* add headers to the top */
aoa.unshift(["x", "y"]);

/* generate worksheet */
const worksheet = XLSX.utils.aoa_to_sheet(aoa);

The array demo shows a complete example.

Processing HTML Tables

API

Create a worksheet by scraping an HTML TABLE in the page

var worksheet = XLSX.utils.table_to_sheet(dom_element, opts);

The table_to_sheet utility function takes a DOM TABLE element and iterates through the rows to generate a worksheet. The opts argument is optional. "HTML Table Input" describes the function in more detail.

Create a workbook by scraping an HTML TABLE in the page

var workbook = XLSX.utils.table_to_book(dom_element, opts);

The table_to_book utility function follows the same logic as table_to_sheet. After generating a worksheet, it creates a blank workbook and appends the spreadsheet.

The options argument supports the same options as table_to_sheet, with the addition of a sheet property to control the worksheet name. If the property is missing or no options are specified, the default name Sheet1 is used.

Examples

Here are a few common scenarios (click on each subtitle to see the code):

HTML TABLE element in a webpage (click to show)

<!-- include the standalone script and shim.  this uses the UNPKG CDN -->
<script src="https://cdn.sheetjs.com/xlsx-latest/package/dist/shim.min.js"></script>
<script src="https://cdn.sheetjs.com/xlsx-latest/package/dist/xlsx.full.min.js"></script>

<!-- example table with id attribute -->
<table id="tableau">
  <tr><td>Sheet</td><td>JS</td></tr>
  <tr><td>12345</td><td>67</td></tr>
</table>

<!-- this block should appear after the table HTML and the standalone script -->
<script type="text/javascript">
  var workbook = XLSX.utils.table_to_book(document.getElementById("tableau"));

  /* DO SOMETHING WITH workbook HERE */
</script>

Multiple tables on a web page can be converted to individual worksheets:

/* create new workbook */
var workbook = XLSX.utils.book_new();

/* convert table "table1" to worksheet named "Sheet1" */
var sheet1 = XLSX.utils.table_to_sheet(document.getElementById("table1"));
XLSX.utils.book_append_sheet(workbook, sheet1, "Sheet1");

/* convert table "table2" to worksheet named "Sheet2" */
var sheet2 = XLSX.utils.table_to_sheet(document.getElementById("table2"));
XLSX.utils.book_append_sheet(workbook, sheet2, "Sheet2");

/* workbook now has 2 worksheets */

Alternatively, the HTML code can be extracted and parsed:

var htmlstr = document.getElementById("tableau").outerHTML;
var workbook = XLSX.read(htmlstr, {type:"string"});

Chrome/Chromium Extension (click to show)

The chrome demo shows a complete example and details the required permissions and other settings.

In an extension, it is recommended to generate the workbook in a content script and pass the object back to the extension:

/* in the worker script */
chrome.runtime.onMessage.addListener(function(msg, sender, cb) {
  /* pass a message like { sheetjs: true } from the extension to scrape */
  if(!msg || !msg.sheetjs) return;
  /* create a new workbook */
  var workbook = XLSX.utils.book_new();
  /* loop through each table element */
  var tables = document.getElementsByTagName("table")
  for(var i = 0; i < tables.length; ++i) {
    var worksheet = XLSX.utils.table_to_sheet(tables[i]);
    XLSX.utils.book_append_sheet(workbook, worksheet, "Table" + i);
  }
  /* pass back to the extension */
  return cb(workbook);
});

Server-Side HTML Tables with Headless Chrome (click to show)

The headless demo includes a complete demo to convert HTML files to XLSB workbooks. The core idea is to add the script to the page, parse the table in the page context, generate a base64 workbook and send it back for further processing:

const XLSX = require("xlsx");
const { readFileSync } = require("fs"), puppeteer = require("puppeteer");

const url = `https://sheetjs.com/demos/table`;

/* get the standalone build source (node_modules/xlsx/dist/xlsx.full.min.js) */
const lib = readFileSync(require.resolve("xlsx/dist/xlsx.full.min.js"), "utf8");

(async() => {
  /* start browser and go to web page */
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto(url, {waitUntil: "networkidle2"});

  /* inject library */
  await page.addScriptTag({content: lib});

  /* this function `s5s` will be called by the script below, receiving the Base64-encoded file */
  await page.exposeFunction("s5s", async(b64) => {
    const workbook = XLSX.read(b64, {type: "base64" });

    /* DO SOMETHING WITH workbook HERE */
  });

  /* generate XLSB file in webpage context and send back result */
  await page.addScriptTag({content: `
    /* call table_to_book on first table */
    var workbook = XLSX.utils.table_to_book(document.querySelector("TABLE"));

    /* generate XLSX file */
    var b64 = XLSX.write(workbook, {type: "base64", bookType: "xlsb"});

    /* call "s5s" hook exposed from the node process */
    window.s5s(b64);
  `});

  /* cleanup */
  await browser.close();
})();

Server-Side HTML Tables with Headless WebKit (click to show)

The headless demo includes a complete demo to convert HTML files to XLSB workbooks using PhantomJS. The core idea is to add the script to the page, parse the table in the page context, generate a binary workbook and send it back for further processing:

var XLSX = require('xlsx');
var page = require('webpage').create();

/* this code will be run in the page */
var code = [ "function(){",
  /* call table_to_book on first table */
  "var wb = XLSX.utils.table_to_book(document.body.getElementsByTagName('table')[0]);",

  /* generate XLSB file and return binary string */
  "return XLSX.write(wb, {type: 'binary', bookType: 'xlsb'});",
"}" ].join("");

page.open('https://sheetjs.com/demos/table', function() {
  /* Load the browser script from the UNPKG CDN */
  page.includeJs("https://cdn.sheetjs.com/xlsx-latest/package/dist/xlsx.full.min.js", function() {
    /* The code will return an XLSB file encoded as binary string */
    var bin = page.evaluateJavaScript(code);

    var workbook = XLSX.read(bin, {type: "binary"});
    /* DO SOMETHING WITH workbook HERE */

    phantom.exit();
  });
});

NodeJS HTML Tables without a browser (click to show)

NodeJS does not include a DOM implementation and Puppeteer requires a hefty Chromium build. jsdom is a lightweight alternative:

const XLSX = require("xlsx");
const { readFileSync } = require("fs");
const { JSDOM } = require("jsdom");

/* obtain HTML string.  This example reads from test.html */
const html_str = fs.readFileSync("test.html", "utf8");
/* get first TABLE element */
const doc = new JSDOM(html_str).window.document.querySelector("table");
/* generate workbook */
const workbook = XLSX.utils.table_to_book(doc);

Processing Data

The "Common Spreadsheet Format" is a simple object representation of the core concepts of a workbook. The utility functions work with the object representation and are intended to handle common use cases.

Modifying Workbook Structure

API

Append a Worksheet to a Workbook

XLSX.utils.book_append_sheet(workbook, worksheet, sheet_name);

The book_append_sheet utility function appends a worksheet to the workbook. The third argument specifies the desired worksheet name. Multiple worksheets can be added to a workbook by calling the function multiple times. If the worksheet name is already used in the workbook, it will throw an error.

Append a Worksheet to a Workbook and find a unique name

var new_name = XLSX.utils.book_append_sheet(workbook, worksheet, name, true);

If the fourth argument is true, the function will start with the specified worksheet name. If the sheet name exists in the workbook, a new worksheet name will be chosen by finding the name stem and incrementing the counter:

XLSX.utils.book_append_sheet(workbook, sheetA, "Sheet2", true); // Sheet2
XLSX.utils.book_append_sheet(workbook, sheetB, "Sheet2", true); // Sheet3
XLSX.utils.book_append_sheet(workbook, sheetC, "Sheet2", true); // Sheet4
XLSX.utils.book_append_sheet(workbook, sheetD, "Sheet2", true); // Sheet5

List the Worksheet names in tab order

var wsnames = workbook.SheetNames;

The SheetNames property of the workbook object is a list of the worksheet names in "tab order". API functions will look at this array.

Replace a Worksheet in place

workbook.Sheets[sheet_name] = new_worksheet;

The Sheets property of the workbook object is an object whose keys are names and whose values are worksheet objects. By reassigning to a property of the Sheets object, the worksheet object can be changed without disrupting the rest of the worksheet structure.

Examples

Add a new worksheet to a workbook (click to show)

This example uses XLSX.utils.aoa_to_sheet.

var ws_name = "SheetJS";

/* Create worksheet */
var ws_data = [
  [ "S", "h", "e", "e", "t", "J", "S" ],
  [  1 ,  2 ,  3 ,  4 ,  5 ]
];
var ws = XLSX.utils.aoa_to_sheet(ws_data);

/* Add the worksheet to the workbook */
XLSX.utils.book_append_sheet(wb, ws, ws_name);

Modifying Cell Values

API

Modify a single cell value in a worksheet

XLSX.utils.sheet_add_aoa(worksheet, [[new_value]], { origin: address });

Modify multiple cell values in a worksheet

XLSX.utils.sheet_add_aoa(worksheet, aoa, opts);

The sheet_add_aoa utility function modifies cell values in a worksheet. The first argument is the worksheet object. The second argument is an array of arrays of values. The origin key of the third argument controls where cells will be written. The following snippet sets B3=1 and E5="abc":

XLSX.utils.sheet_add_aoa(worksheet, [
  [1],                             // <-- Write 1 to cell B3
  ,                                // <-- Do nothing in row 4
  [/*B5*/, /*C5*/, /*D5*/, "abc"]  // <-- Write "abc" to cell E5
], { origin: "B3" });

"Array of Arrays Input" describes the function and the optional opts argument in more detail.

Examples

Appending rows to a worksheet (click to show)

The special origin value -1 instructs sheet_add_aoa to start in column A of the row after the last row in the range, appending the data:

XLSX.utils.sheet_add_aoa(worksheet, [
  ["first row after data", 1],
  ["second row after data", 2]
], { origin: -1 });

Modifying Other Worksheet / Workbook / Cell Properties

The "Common Spreadsheet Format" section describes the object structures in greater detail.

Packaging and Releasing Data

Writing Workbooks

API

Generate spreadsheet bytes (file) from data

var data = XLSX.write(workbook, opts);

The write method attempts to package data from the workbook into a file in memory. By default, XLSX files are generated, but that can be controlled with the bookType property of the opts argument. Based on the type option, the data can be stored as a "binary string", JS string, Uint8Array or Buffer.

The second opts argument is required. "Writing Options" covers the supported properties and behaviors.

Generate and attempt to save file

XLSX.writeFile(workbook, filename, opts);

The writeFile method packages the data and attempts to save the new file. The export file format is determined by the extension of filename (SheetJS.xlsx signals XLSX export, SheetJS.xlsb signals XLSB export, etc).

The writeFile method uses platform-specific APIs to initiate the file save. In NodeJS, fs.readFileSync can create a file. In the web browser, a download is attempted using the HTML5 download attribute, with fallbacks for IE.

Generate and attempt to save an XLSX file

XLSX.writeFileXLSX(workbook, filename, opts);

The writeFile method embeds a number of different export functions. This is great for developer experience but not amenable to tree shaking using the current developer tools. When only XLSX exports are needed, this method avoids referencing the other export functions.

The second opts argument is optional. "Writing Options" covers the supported properties and behaviors.

Examples

Local file in a NodeJS server (click to show)

writeFile uses fs.writeFileSync in server environments:

var XLSX = require("xlsx");

/* output format determined by filename */
XLSX.writeFile(workbook, "out.xlsb");

For Node ESM, the writeFile helper is not enabled. Instead, fs.writeFileSync should be used to write the file data to a Buffer for use with XLSX.write:

import { writeFileSync } from "fs";
import { write } from "xlsx/xlsx.mjs";

const buf = write(workbook, {type: "buffer", bookType: "xlsb"});
/* buf is a Buffer */
const workbook = writeFileSync("out.xlsb", buf);

Local file in a Deno application (click to show)

writeFile uses Deno.writeFileSync under the hood:

// @deno-types="https://deno.land/x/sheetjs/types/index.d.ts"
import * as XLSX from 'https://deno.land/x/sheetjs/xlsx.mjs'

XLSX.writeFile(workbook, "test.xlsx");

Applications writing files must be invoked with the --allow-write flag. The deno demo has more examples

Local file in a PhotoShop or InDesign plugin (click to show)

writeFile wraps the File logic in Photoshop and other ExtendScript targets. The specified path should be an absolute path:

#include "xlsx.extendscript.js"

/* output format determined by filename */
XLSX.writeFile(workbook, "out.xlsx");
/* at this point, out.xlsx is a file that you can distribute */

The extendscript demo includes a more complex example.

Download a file in the browser to the user machine (click to show)

XLSX.writeFile wraps a few techniques for triggering a file save:

• URL browser API creates an object URL for the file, which the library uses by creating a link and forcing a click. It is supported in modern browsers.
• msSaveBlob is an IE10+ API for triggering a file save.
• IE_FileSave uses VBScript and ActiveX to write a file in IE6+ for Windows XP and Windows 7. The shim must be included in the containing HTML page.

There is no standard way to determine if the actual file has been downloaded.

/* output format determined by filename */
XLSX.writeFile(workbook, "out.xlsb");
/* at this point, out.xlsb will have been downloaded */

Download a file in legacy browsers (click to show)

XLSX.writeFile techniques work for most modern browsers as well as older IE. For much older browsers, there are workarounds implemented by wrapper libraries.

FileSaver.js implements saveAs. Note: XLSX.writeFile will automatically call saveAs if available.

/* bookType can be any supported output type */
var wopts = { bookType:"xlsx", bookSST:false, type:"array" };

var wbout = XLSX.write(workbook,wopts);

/* the saveAs call downloads a file on the local machine */
saveAs(new Blob([wbout],{type:"application/octet-stream"}), "test.xlsx");

Downloadify uses a Flash SWF button to generate local files, suitable for environments where ActiveX is unavailable:

Downloadify.create(id,{
  /* other options are required! read the downloadify docs for more info */
  filename: "test.xlsx",
  data: function() { return XLSX.write(wb, {bookType:"xlsx", type:"base64"}); },
  append: false,
  dataType: "base64"
});

The oldie demo shows an IE-compatible fallback scenario.

Browser upload file (ajax) (click to show)

A complete example using XHR is included in the XHR demo, along with examples for fetch and wrapper libraries. This example assumes the server can handle Base64-encoded files (see the demo for a basic nodejs server):

/* in this example, send a base64 string to the server */
var wopts = { bookType:"xlsx", bookSST:false, type:"base64" };

var wbout = XLSX.write(workbook,wopts);

var req = new XMLHttpRequest();
req.open("POST", "/upload", true);
var formdata = new FormData();
formdata.append("file", "test.xlsx"); // <-- server expects `file` to hold name
formdata.append("data", wbout); // <-- `data` holds the base64-encoded data
req.send(formdata);

PhantomJS (Headless Webkit) File Generation (click to show)

The headless demo includes a complete demo to convert HTML files to XLSB workbooks using PhantomJS. PhantomJS fs.write supports writing files from the main process but has a different interface from the NodeJS fs module:

var XLSX = require('xlsx');
var fs = require('fs');

/* generate a binary string */
var bin = XLSX.write(workbook, { type:"binary", bookType: "xlsx" });
/* write to file */
fs.write("test.xlsx", bin, "wb");

Note: The section "Processing HTML Tables" shows how to generate a workbook from HTML tables in a page in "Headless WebKit".

The included demos cover mobile apps and other special deployments.

Writing Examples

• http://sheetjs.com/demos/table.html exporting an HTML table
• http://sheetjs.com/demos/writexlsx.html generates a simple file

Streaming Write

The streaming write functions are available in the XLSX.stream object. They take the same arguments as the normal write functions but return a NodeJS Readable Stream.

• XLSX.stream.to_csv is the streaming version of XLSX.utils.sheet_to_csv.
• XLSX.stream.to_html is the streaming version of XLSX.utils.sheet_to_html.
• XLSX.stream.to_json is the streaming version of XLSX.utils.sheet_to_json.

nodejs convert to CSV and write file (click to show)

var output_file_name = "out.csv";
var stream = XLSX.stream.to_csv(worksheet);
stream.pipe(fs.createWriteStream(output_file_name));

nodejs write JSON stream to screen (click to show)

/* to_json returns an object-mode stream */
var stream = XLSX.stream.to_json(worksheet, {raw:true});

/* the following stream converts JS objects to text via JSON.stringify */
var conv = new Transform({writableObjectMode:true});
conv._transform = function(obj, e, cb){ cb(null, JSON.stringify(obj) + "\n"); };

stream.pipe(conv); conv.pipe(process.stdout);

Exporting NUMBERS files (click to show)

The NUMBERS writer requires a fairly large base. The supplementary xlsx.zahl scripts provide support. xlsx.zahl.js is designed for standalone and NodeJS use, while xlsx.zahl.mjs is suitable for ESM.

Browser

<meta charset="utf8">
<script src="xlsx.full.min.js"></script>
<script src="xlsx.zahl.js"></script>
<script>
var wb = XLSX.utils.book_new(); var ws = XLSX.utils.aoa_to_sheet([
  ["SheetJS", "<3","விரிதாள்"],
  [72,,"Arbeitsblätter"],
  [,62,"数据"],
  [true,false,],
]); XLSX.utils.book_append_sheet(wb, ws, "Sheet1");
XLSX.writeFile(wb, "textport.numbers", {numbers: XLSX_ZAHL, compression: true});
</script>

Node

var XLSX = require("./xlsx.flow");
var XLSX_ZAHL = require("./dist/xlsx.zahl");
var wb = XLSX.utils.book_new(); var ws = XLSX.utils.aoa_to_sheet([
  ["SheetJS", "<3","விரிதாள்"],
  [72,,"Arbeitsblätter"],
  [,62,"数据"],
  [true,false,],
]); XLSX.utils.book_append_sheet(wb, ws, "Sheet1");
XLSX.writeFile(wb, "textport.numbers", {numbers: XLSX_ZAHL, compression: true});

Deno

import * as XLSX from './xlsx.mjs';
import XLSX_ZAHL from './dist/xlsx.zahl.mjs';

var wb = XLSX.utils.book_new(); var ws = XLSX.utils.aoa_to_sheet([
  ["SheetJS", "<3","விரிதாள்"],
  [72,,"Arbeitsblätter"],
  [,62,"数据"],
  [true,false,],
]); XLSX.utils.book_append_sheet(wb, ws, "Sheet1");
XLSX.writeFile(wb, "textports.numbers", {numbers: XLSX_ZAHL, compression: true});

https://github.com/sheetjs/sheetaki pipes write streams to nodejs response.

Generating JSON and JS Data

JSON and JS data tend to represent single worksheets. The utility functions in this section work with single worksheets.

The "Common Spreadsheet Format" section describes the object structure in more detail. workbook.SheetNames is an ordered list of the worksheet names. workbook.Sheets is an object whose keys are sheet names and whose values are worksheet objects.

The "first worksheet" is stored at workbook.Sheets[workbook.SheetNames[0]].

API

Create an array of JS objects from a worksheet

var jsa = XLSX.utils.sheet_to_json(worksheet, opts);

Create an array of arrays of JS values from a worksheet

var aoa = XLSX.utils.sheet_to_json(worksheet, {...opts, header: 1});

The sheet_to_json utility function walks a workbook in row-major order, generating an array of objects. The second opts argument controls a number of export decisions including the type of values (JS values or formatted text). The "JSON" section describes the argument in more detail.

By default, sheet_to_json scans the first row and uses the values as headers. With the header: 1 option, the function exports an array of arrays of values.

Examples

x-spreadsheet is an interactive data grid for previewing and modifying structured data in the web browser. The xspreadsheet demo includes a sample script with the stox function for converting from a workbook to x-spreadsheet data object. https://oss.sheetjs.com/sheetjs/x-spreadsheet is a live demo.

Previewing data in a React data grid (click to show)

react-data-grid is a data grid tailored for react. It expects two properties: rows of data objects and columns which describe the columns. For the purposes of massaging the data to fit the react data grid API it is easiest to start from an array of arrays.

This demo starts by fetching a remote file and using XLSX.read to extract:

import { useEffect, useState } from "react";
import DataGrid from "react-data-grid";
import { read, utils } from "xlsx";

const url = "https://oss.sheetjs.com/test_files/RkNumber.xls";

export default function App() {
  const [columns, setColumns] = useState([]);
  const [rows, setRows] = useState([]);
  useEffect(() => {(async () => {
    const wb = read(await (await fetch(url)).arrayBuffer(), { WTF: 1 });

    /* use sheet_to_json with header: 1 to generate an array of arrays */
    const data = utils.sheet_to_json(wb.Sheets[wb.SheetNames[0]], { header: 1 });

    /* see react-data-grid docs to understand the shape of the expected data */
    setColumns(data[0].map((r) => ({ key: r, name: r })));
    setRows(data.slice(1).map((r) => r.reduce((acc, x, i) => {
      acc[data[0][i]] = x;
      return acc;
    }, {})));
  })(); });

  return <DataGrid columns={columns} rows={rows} />;
}

Previewing data in a VueJS data grid (click to show)

vue3-table-lite is a simple VueJS 3 data table. It is featured in the VueJS demo.

Populating a database (SQL or no-SQL) (click to show)

The database demo includes examples of working with databases and query results.

Numerical Computations with TensorFlow.js (click to show)

@tensorflow/tfjs and other libraries expect data in simple arrays, well-suited for worksheets where each column is a data vector. That is the transpose of how most people use spreadsheets, where each row is a vector.

A single Array#map can pull individual named rows from sheet_to_json export:

const XLSX = require("xlsx");
const tf = require('@tensorflow/tfjs');

const key = "age"; // this is the field we want to pull
const ages = XLSX.utils.sheet_to_json(worksheet).map(r => r[key]);
const tf_data = tf.tensor1d(ages);

All fields can be processed at once using a transpose of the 2D tensor generated with the sheet_to_json export with header: 1. The first row, if it contains header labels, should be removed with a slice:

const XLSX = require("xlsx");
const tf = require('@tensorflow/tfjs');

/* array of arrays of the data starting on the second row */
const aoa = XLSX.utils.sheet_to_json(worksheet, {header: 1}).slice(1);
/* dataset in the "correct orientation" */
const tf_dataset = tf.tensor2d(aoa).transpose();
/* pull out each dataset with a slice */
const tf_field0 = tf_dataset.slice([0,0], [1,tensor.shape[1]]).flatten();
const tf_field1 = tf_dataset.slice([1,0], [1,tensor.shape[1]]).flatten();

The array demo shows a complete example.

Generating HTML Tables

API

Generate HTML Table from Worksheet

var html = XLSX.utils.sheet_to_html(worksheet);

The sheet_to_html utility function generates HTML code based on the worksheet data. Each cell in the worksheet is mapped to a <TD> element. Merged cells in the worksheet are serialized by setting colspan and rowspan attributes.

Examples

The sheet_to_html utility function generates HTML code that can be added to any DOM element by setting the innerHTML:

var container = document.getElementById("tavolo");
container.innerHTML = XLSX.utils.sheet_to_html(worksheet);

Combining with fetch, constructing a site from a workbook is straightforward:

Vanilla JS + HTML fetch workbook and generate table previews (click to show)

<body>
  <style>TABLE { border-collapse: collapse; } TD { border: 1px solid; }</style>
  <div id="tavolo"></div>
  <script src="https://cdn.sheetjs.com/xlsx-latest/package/dist/xlsx.full.min.js"></script>
  <script type="text/javascript">
(async() => {
  /* fetch and parse workbook -- see the fetch example for details */
  const workbook = XLSX.read(await (await fetch("sheetjs.xlsx")).arrayBuffer());

  let output = [];
  /* loop through the worksheet names in order */
  workbook.SheetNames.forEach(name => {

    /* generate HTML from the corresponding worksheets */
    const worksheet = workbook.Sheets[name];
    const html = XLSX.utils.sheet_to_html(worksheet);

    /* add a header with the title name followed by the table */
    output.push(`<H3>${name}</H3>${html}`);
  });
  /* write to the DOM at the end */
  tavolo.innerHTML = output.join("\n");
})();
  </script>
</body>

React fetch workbook and generate HTML table previews (click to show)

It is generally recommended to use a React-friendly workflow, but it is possible to generate HTML and use it in React with dangerouslySetInnerHTML:

function Tabeller(props) {
  /* the workbook object is the state */
  const [workbook, setWorkbook] = React.useState(XLSX.utils.book_new());

  /* fetch and update the workbook with an effect */
  React.useEffect(() => { (async() => {
    /* fetch and parse workbook -- see the fetch example for details */
    const wb = XLSX.read(await (await fetch("sheetjs.xlsx")).arrayBuffer());
    setWorkbook(wb);
  })(); });

  return workbook.SheetNames.map(name => (<>
    <h3>name</h3>
    <div dangerouslySetInnerHTML={{
      /* this __html mantra is needed to set the inner HTML */
      __html: XLSX.utils.sheet_to_html(workbook.Sheets[name])
    }} />
  </>));
}

The react demo includes more React examples.

VueJS fetch workbook and generate HTML table previews (click to show)

It is generally recommended to use a VueJS-friendly workflow, but it is possible to generate HTML and use it in VueJS with the v-html directive:

import { read, utils } from 'xlsx';
import { reactive } from 'vue';

const S5SComponent = {
  mounted() { (async() => {
    /* fetch and parse workbook -- see the fetch example for details */
    const workbook = read(await (await fetch("sheetjs.xlsx")).arrayBuffer());
    /* loop through the worksheet names in order */
    workbook.SheetNames.forEach(name => {
      /* generate HTML from the corresponding worksheets */
      const html = utils.sheet_to_html(workbook.Sheets[name]);
      /* add to state */
      this.wb.wb.push({ name, html });
    });
  })(); },
  /* this state mantra is required for array updates to work */
  setup() { return { wb: reactive({ wb: [] }) }; },
  template: `
  <div v-for="ws in wb.wb" :key="ws.name">
    <h3>{{ ws.name }}</h3>
    <div v-html="ws.html"></div>
  </div>`
};

The vuejs demo includes more React examples.

Generating Single-Worksheet Snapshots

The sheet_to_* functions accept a worksheet object.

API

Generate a CSV from a single worksheet

var csv = XLSX.utils.sheet_to_csv(worksheet, opts);

This snapshot is designed to replicate the "CSV UTF8 (.csv)" output type. "Delimiter-Separated Output" describes the function and the optional opts argument in more detail.

Generate "Text" from a single worksheet

var txt = XLSX.utils.sheet_to_txt(worksheet, opts);

This snapshot is designed to replicate the "UTF16 Text (.txt)" output type. "Delimiter-Separated Output" describes the function and the optional opts argument in more detail.

Generate a list of formulae from a single worksheet

var fmla = XLSX.utils.sheet_to_formulae(worksheet);

This snapshot generates an array of entries representing the embedded formulae. Array formulae are rendered in the form range=formula while plain cells are rendered in the form cell=formula or value. String literals are prefixed with an apostrophe ', consistent with Excel's formula bar display.

"Formulae Output" describes the function in more detail.

Interface

XLSX is the exposed variable in the browser and the exported node variable

XLSX.version is the version of the library (added by the build script).

XLSX.SSF is an embedded version of the format library.

Parsing functions

XLSX.read(data, read_opts) attempts to parse data.

XLSX.readFile(filename, read_opts) attempts to read filename and parse.

Parse options are described in the Parsing Options section.

Writing functions

XLSX.write(wb, write_opts) attempts to write the workbook wb

XLSX.writeFile(wb, filename, write_opts) attempts to write wb to filename. In browser-based environments, it will attempt to force a client-side download.

XLSX.writeFileAsync(filename, wb, o, cb) attempts to write wb to filename. If o is omitted, the writer will use the third argument as the callback.

XLSX.stream contains a set of streaming write functions.

Write options are described in the Writing Options section.

Utilities

Utilities are available in the XLSX.utils object and are described in the Utility Functions section:

Constructing:

• book_new creates an empty workbook
• book_append_sheet adds a worksheet to a workbook

Importing:

• aoa_to_sheet converts an array of arrays of JS data to a worksheet.
• json_to_sheet converts an array of JS objects to a worksheet.
• table_to_sheet converts a DOM TABLE element to a worksheet.
• sheet_add_aoa adds an array of arrays of JS data to an existing worksheet.
• sheet_add_json adds an array of JS objects to an existing worksheet.

Exporting:

• sheet_to_json converts a worksheet object to an array of JSON objects.
• sheet_to_csv generates delimiter-separated-values output.
• sheet_to_txt generates UTF16 formatted text.
• sheet_to_html generates HTML output.
• sheet_to_formulae generates a list of the formulae (with value fallbacks).

Cell and cell address manipulation:

• format_cell generates the text value for a cell (using number formats).
• encode_row / decode_row converts between 0-indexed rows and 1-indexed rows.
• encode_col / decode_col converts between 0-indexed columns and column names.
• encode_cell / decode_cell converts cell addresses.
• encode_range / decode_range converts cell ranges.

Common Spreadsheet Format

SheetJS conforms to the Common Spreadsheet Format (CSF):

General Structures

Cell address objects are stored as {c:C, r:R} where C and R are 0-indexed column and row numbers, respectively. For example, the cell address B5 is represented by the object {c:1, r:4}.

Cell range objects are stored as {s:S, e:E} where S is the first cell and E is the last cell in the range. The ranges are inclusive. For example, the range A3:B7 is represented by the object {s:{c:0, r:2}, e:{c:1, r:6}}. Utility functions perform a row-major order walk traversal of a sheet range:

for(var R = range.s.r; R <= range.e.r; ++R) {
  for(var C = range.s.c; C <= range.e.c; ++C) {
    var cell_address = {c:C, r:R};
    /* if an A1-style address is needed, encode the address */
    var cell_ref = XLSX.utils.encode_cell(cell_address);
  }
}

Cell Object

Cell objects are plain JS objects with keys and values following the convention:

Built-in export utilities (such as the CSV exporter) will use the w text if it is available. To change a value, be sure to delete cell.w (or set it to undefined) before attempting to export. The utilities will regenerate the w text from the number format (cell.z) and the raw value if possible.

The actual array formula is stored in the f field of the first cell in the array range. Other cells in the range will omit the f field.

Data Types

The raw value is stored in the v value property, interpreted based on the t type property. This separation allows for representation of numbers as well as numeric text. There are 6 valid cell types:

Error values and interpretation (click to show)

Type n is the Number type. This includes all forms of data that Excel stores as numbers, such as dates/times and Boolean fields. Excel exclusively uses data that can be fit in an IEEE754 floating point number, just like JS Number, so the v field holds the raw number. The w field holds formatted text. Dates are stored as numbers by default and converted with XLSX.SSF.parse_date_code.

Type d is the Date type, generated only when the option cellDates is passed. Since JSON does not have a natural Date type, parsers are generally expected to store ISO 8601 Date strings like you would get from date.toISOString(). On the other hand, writers and exporters should be able to handle date strings and JS Date objects. Note that Excel disregards timezone modifiers and treats all dates in the local timezone. The library does not correct for this error.

Type s is the String type. Values are explicitly stored as text. Excel will interpret these cells as "number stored as text". Generated Excel files automatically suppress that class of error, but other formats may elicit errors.

Type z represents blank stub cells. They are generated in cases where cells have no assigned value but hold comments or other metadata. They are ignored by the core library data processing utility functions. By default these cells are not generated; the parser sheetStubs option must be set to true.

Dates

Excel Date Code details (click to show)

By default, Excel stores dates as numbers with a format code that specifies date processing. For example, the date 19-Feb-17 is stored as the number 42785 with a number format of d-mmm-yy. The SSF module understands number formats and performs the appropriate conversion.

XLSX also supports a special date type d where the data is an ISO 8601 date string. The formatter converts the date back to a number.

The default behavior for all parsers is to generate number cells. Setting cellDates to true will force the generators to store dates.

Time Zones and Dates (click to show)

Excel has no native concept of universal time. All times are specified in the local time zone. Excel limitations prevent specifying true absolute dates.

Following Excel, this library treats all dates as relative to local time zone.

Epochs: 1900 and 1904 (click to show)

Excel supports two epochs (January 1 1900 and January 1 1904). The workbook's epoch can be determined by examining the workbook's wb.Workbook.WBProps.date1904 property:

!!(((wb.Workbook||{}).WBProps||{}).date1904)

Sheet Objects

Each key that does not start with ! maps to a cell (using A-1 notation)

sheet[address] returns the cell object for the specified address.

Special sheet keys (accessible as sheet[key], each starting with !):

sheet['!ref']: A-1 based range representing the sheet range. Functions that work with sheets should use this parameter to determine the range. Cells that are assigned outside of the range are not processed. In particular, when writing a sheet by hand, cells outside of the range are not included

Functions that handle sheets should test for the presence of !ref field. If the !ref is omitted or is not a valid range, functions are free to treat the sheet as empty or attempt to guess the range. The standard utilities that ship with this library treat sheets as empty (for example, the CSV output is empty string).

When reading a worksheet with the sheetRows property set, the ref parameter will use the restricted range. The original range is set at ws['!fullref']

sheet['!margins']: Object representing the page margins. The default values follow Excel's "normal" preset. Excel also has a "wide" and a "narrow" preset but they are stored as raw measurements. The main properties are listed below:

Page margin details (click to show)

/* Set worksheet sheet to "normal" */
ws["!margins"]={left:0.7, right:0.7, top:0.75,bottom:0.75,header:0.3,footer:0.3}
/* Set worksheet sheet to "wide" */
ws["!margins"]={left:1.0, right:1.0, top:1.0, bottom:1.0, header:0.5,footer:0.5}
/* Set worksheet sheet to "narrow" */
ws["!margins"]={left:0.25,right:0.25,top:0.75,bottom:0.75,header:0.3,footer:0.3}

Worksheet Object

In addition to the base sheet keys, worksheets also add:

ws['!cols']: array of column properties objects. Column widths are actually stored in files in a normalized manner, measured in terms of the "Maximum Digit Width" (the largest width of the rendered digits 0-9, in pixels). When parsed, the column objects store the pixel width in the wpx field, character width in the wch field, and the maximum digit width in the MDW field.

ws['!rows']: array of row properties objects as explained later in the docs. Each row object encodes properties including row height and visibility.

ws['!merges']: array of range objects corresponding to the merged cells in the worksheet. Plain text formats do not support merge cells. CSV export will write all cells in the merge range if they exist, so be sure that only the first cell (upper-left) in the range is set.

ws['!outline']: configure how outlines should behave. Options default to the default settings in Excel 2019:

• ws['!protect']: object of write sheet protection properties. The password key specifies the password for formats that support password-protected sheets (XLSX/XLSB/XLS). The writer uses the XOR obfuscation method. The following keys control the sheet protection -- set to false to enable a feature when sheet is locked or set to true to disable a feature:

Worksheet Protection Details (click to show)

• ws['!autofilter']: AutoFilter object following the schema:

type AutoFilter = {
  ref:string; // A-1 based range representing the AutoFilter table range
}

Chartsheet Object

Chartsheets are represented as standard sheets. They are distinguished with the !type property set to "chart".

The underlying data and !ref refer to the cached data in the chartsheet. The first row of the chartsheet is the underlying header.

Macrosheet Object

Macrosheets are represented as standard sheets. They are distinguished with the !type property set to "macro".

Dialogsheet Object

Dialogsheets are represented as standard sheets. They are distinguished with the !type property set to "dialog".

Workbook Object

workbook.SheetNames is an ordered list of the sheets in the workbook

wb.Sheets[sheetname] returns an object representing the worksheet.

wb.Props is an object storing the standard properties. wb.Custprops stores custom properties. Since the XLS standard properties deviate from the XLSX standard, XLS parsing stores core properties in both places.

wb.Workbook stores workbook-level attributes.

Workbook File Properties

The various file formats use different internal names for file properties. The workbook Props object normalizes the names:

File Properties (click to show)

For example, to set the workbook title property:

if(!wb.Props) wb.Props = {};
wb.Props.Title = "Insert Title Here";

Custom properties are added in the workbook Custprops object:

if(!wb.Custprops) wb.Custprops = {};
wb.Custprops["Custom Property"] = "Custom Value";

Writers will process the Props key of the options object:

/* force the Author to be "SheetJS" */
XLSX.write(wb, {Props:{Author:"SheetJS"}});

Workbook-Level Attributes

wb.Workbook stores workbook-level attributes.

Defined Names

wb.Workbook.Names is an array of defined name objects which have the keys:

Defined Name Properties (click to show)

Excel allows two sheet-scoped defined names to share the same name. However, a sheet-scoped name cannot collide with a workbook-scope name. Workbook writers may not enforce this constraint.

Workbook Views

wb.Workbook.Views is an array of workbook view objects which have the keys:

Miscellaneous Workbook Properties

wb.Workbook.WBProps holds other workbook properties:

Document Features

Even for basic features like date storage, the official Excel formats store the same content in different ways. The parsers are expected to convert from the underlying file format representation to the Common Spreadsheet Format. Writers are expected to convert from CSF back to the underlying file format.

Formulae

The A1-style formula string is stored in the f field. Even though different file formats store the formulae in different ways, the formats are translated. Even though some formats store formulae with a leading equal sign, CSF formulae do not start with =.

Formulae File Format Support (click to show)

Since Excel prohibits named cells from colliding with names of A1 or RC style cell references, a (not-so-simple) regex conversion is possible. BIFF Parsed formulae and Lotus Parsed formulae have to be explicitly unwound. OpenFormula formulae can be converted with regular expressions.

Shared formulae are decompressed and each cell has the formula corresponding to its cell. Writers generally do not attempt to generate shared formulae.

Single-Cell Formulae

For simple formulae, the f key of the desired cell can be set to the actual formula text. This worksheet represents A1=1, A2=2, and A3=A1+A2:

var worksheet = {
  "!ref": "A1:A3",
  A1: { t:'n', v:1 },
  A2: { t:'n', v:2 },
  A3: { t:'n', v:3, f:'A1+A2' }
};

Utilities like aoa_to_sheet will accept cell objects in lieu of values:

var worksheet = XLSX.utils.aoa_to_sheet([
  [ 1 ], // A1
  [ 2 ], // A2
  [ {t: "n", v: 3, f: "A1+A2"} ] // A3
]);

Cells with formula entries but no value will be serialized in a way that Excel and other spreadsheet tools will recognize. This library will not automatically compute formula results! For example, the following worksheet will include the BESSELJ function but the result will not be available in JavaScript:

var worksheet = XLSX.utils.aoa_to_sheet([
  [ 3.14159, 2 ], // Row "1"
  [ { t:'n', f:'BESSELJ(A1,B1)' } ] // Row "2" will be calculated on file open
}

If the actual results are needed in JS, SheetJS Pro offers a formula calculator component for evaluating expressions, updating values and dependent cells, and refreshing entire workbooks.

Array Formulae

Assign an array formula

XLSX.utils.sheet_set_array_formula(worksheet, range, formula);

Array formulae are stored in the top-left cell of the array block. All cells of an array formula have a F field corresponding to the range. A single-cell formula can be distinguished from a plain formula by the presence of F field.

For example, setting the cell C1 to the array formula {=SUM(A1:A3*B1:B3)}:

// API function
XLSX.utils.sheet_set_array_formula(worksheet, "C1", "SUM(A1:A3*B1:B3)");

// ... OR raw operations
worksheet['C1'] = { t:'n', f: "SUM(A1:A3*B1:B3)", F:"C1:C1" };

For a multi-cell array formula, every cell has the same array range but only the first cell specifies the formula. Consider D1:D3=A1:A3*B1:B3:

// API function
XLSX.utils.sheet_set_array_formula(worksheet, "D1:D3", "A1:A3*B1:B3");

// ... OR raw operations
worksheet['D1'] = { t:'n', F:"D1:D3", f:"A1:A3*B1:B3" };
worksheet['D2'] = { t:'n', F:"D1:D3" };
worksheet['D3'] = { t:'n', F:"D1:D3" };

Utilities and writers are expected to check for the presence of a F field and ignore any possible formula element f in cells other than the starting cell. They are not expected to perform validation of the formulae!

Dynamic Array Formulae

Assign a dynamic array formula

XLSX.utils.sheet_set_array_formula(worksheet, range, formula, true);

Released in 2020, Dynamic Array Formulae are supported in the XLSX/XLSM and XLSB file formats. They are represented like normal array formulae but have special cell metadata indicating that the formula should be allowed to adjust the range.

An array formula can be marked as dynamic by setting the cell's D property to true. The F range is expected but can be the set to the current cell:

// API function
XLSX.utils.sheet_set_array_formula(worksheet, "C1", "_xlfn.UNIQUE(A1:A3)", 1);

// ... OR raw operations
worksheet['C1'] = { t: "s", f: "_xlfn.UNIQUE(A1:A3)", F:"C1", D: 1 }; // dynamic

Localization with Function Names

SheetJS operates at the file level. Excel stores formula expressions using the English (United States) function names. For non-English users, Excel uses a localized set of function names.

For example, when the computer language and region is set to French (France), Excel interprets =SOMME(A1:C3) as if SOMME is the SUM function. However, in the actual file, Excel stores SUM(A1:C3).

Prefixed "Future Functions"

Functions introduced in newer versions of Excel are prefixed with _xlfn. when stored in files. When writing formula expressions using these functions, the prefix is required for maximal compatibility:

// Broadest compatibility
XLSX.utils.sheet_set_array_formula(worksheet, "C1", "_xlfn.UNIQUE(A1:A3)", 1);

// Can cause errors in spreadsheet software
XLSX.utils.sheet_set_array_formula(worksheet, "C1", "UNIQUE(A1:A3)", 1);

When reading a file, the xlfn option preserves the prefixes.

Functions requiring `_xlfn.` prefix (click to show)

This list is growing with each Excel release.

ACOT
ACOTH
AGGREGATE
ARABIC
BASE
BETA.DIST
BETA.INV
BINOM.DIST
BINOM.DIST.RANGE
BINOM.INV
BITAND
BITLSHIFT
BITOR
BITRSHIFT
BITXOR
BYCOL
BYROW
CEILING.MATH
CEILING.PRECISE
CHISQ.DIST
CHISQ.DIST.RT
CHISQ.INV
CHISQ.INV.RT
CHISQ.TEST
COMBINA
CONFIDENCE.NORM
CONFIDENCE.T
COT
COTH
COVARIANCE.P
COVARIANCE.S
CSC
CSCH
DAYS
DECIMAL
ERF.PRECISE
ERFC.PRECISE
EXPON.DIST
F.DIST
F.DIST.RT
F.INV
F.INV.RT
F.TEST
FIELDVALUE
FILTERXML
FLOOR.MATH
FLOOR.PRECISE
FORMULATEXT
GAMMA
GAMMA.DIST
GAMMA.INV
GAMMALN.PRECISE
GAUSS
HYPGEOM.DIST
IFNA
IMCOSH
IMCOT
IMCSC
IMCSCH
IMSEC
IMSECH
IMSINH
IMTAN
ISFORMULA
ISOMITTED
ISOWEEKNUM
LAMBDA
LET
LOGNORM.DIST
LOGNORM.INV
MAKEARRAY
MAP
MODE.MULT
MODE.SNGL
MUNIT
NEGBINOM.DIST
NORM.DIST
NORM.INV
NORM.S.DIST
NORM.S.INV
NUMBERVALUE
PDURATION
PERCENTILE.EXC
PERCENTILE.INC
PERCENTRANK.EXC
PERCENTRANK.INC
PERMUTATIONA
PHI
POISSON.DIST
QUARTILE.EXC
QUARTILE.INC
QUERYSTRING
RANDARRAY
RANK.AVG
RANK.EQ
REDUCE
RRI
SCAN
SEC
SECH
SEQUENCE
SHEET
SHEETS
SKEW.P
SORTBY
STDEV.P
STDEV.S
T.DIST
T.DIST.2T
T.DIST.RT
T.INV
T.INV.2T
T.TEST
UNICHAR
UNICODE
UNIQUE
VAR.P
VAR.S
WEBSERVICE
WEIBULL.DIST
XLOOKUP
XOR
Z.TEST

Row and Column Properties

Format Support (click to show)

Row Properties: XLSX/M, XLSB, BIFF8 XLS, XLML, SYLK, DOM, ODS

Column Properties: XLSX/M, XLSB, BIFF8 XLS, XLML, SYLK, DOM

Row and Column properties are not extracted by default when reading from a file and are not persisted by default when writing to a file. The option cellStyles: true must be passed to the relevant read or write function.

Column Properties

The !cols array in each worksheet, if present, is a collection of ColInfo objects which have the following properties:

type ColInfo = {
  /* visibility */
  hidden?: boolean; // if true, the column is hidden

  /* column width is specified in one of the following ways: */
  wpx?:    number;  // width in screen pixels
  width?:  number;  // width in Excel's "Max Digit Width", width*256 is integral
  wch?:    number;  // width in characters

  /* other fields for preserving features from files */
  level?:  number;  // 0-indexed outline / group level
  MDW?:    number;  // Excel's "Max Digit Width" unit, always integral
};

Row Properties

The !rows array in each worksheet, if present, is a collection of RowInfo objects which have the following properties:

type RowInfo = {
  /* visibility */
  hidden?: boolean; // if true, the row is hidden

  /* row height is specified in one of the following ways: */
  hpx?:    number;  // height in screen pixels
  hpt?:    number;  // height in points

  level?:  number;  // 0-indexed outline / group level
};

Outline / Group Levels Convention

The Excel UI displays the base outline level as 1 and the max level as 8. Following JS conventions, SheetJS uses 0-indexed outline levels wherein the base outline level is 0 and the max level is 7.

Why are there three width types? (click to show)

There are three different width types corresponding to the three different ways spreadsheets store column widths:

SYLK and other plain text formats use raw character count. Contemporaneous tools like Visicalc and Multiplan were character based. Since the characters had the same width, it sufficed to store a count. This tradition was continued into the BIFF formats.

SpreadsheetML (2003) tried to align with HTML by standardizing on screen pixel count throughout the file. Column widths, row heights, and other measures use pixels. When the pixel and character counts do not align, Excel rounds values.

XLSX internally stores column widths in a nebulous "Max Digit Width" form. The Max Digit Width is the width of the largest digit when rendered (generally the "0" character is the widest). The internal width must be an integer multiple of the the width divided by 256. ECMA-376 describes a formula for converting between pixels and the internal width. This represents a hybrid approach.

Read functions attempt to populate all three properties. Write functions will try to cycle specified values to the desired type. In order to avoid potential conflicts, manipulation should delete the other properties first. For example, when changing the pixel width, delete the wch and width properties.

Implementation details (click to show)

Row Heights

Excel internally stores row heights in points. The default resolution is 72 DPI or 96 PPI, so the pixel and point size should agree. For different resolutions they may not agree, so the library separates the concepts.

Even though all of the information is made available, writers are expected to follow the priority order:

1. use hpx pixel height if available
2. use hpt point height if available

Column Widths

Given the constraints, it is possible to determine the MDW without actually inspecting the font! The parsers guess the pixel width by converting from width to pixels and back, repeating for all possible MDW and selecting the MDW that minimizes the error. XLML actually stores the pixel width, so the guess works in the opposite direction.

Even though all of the information is made available, writers are expected to follow the priority order:

1. use width field if available
2. use wpx pixel width if available
3. use wch character count if available

Number Formats

The cell.w formatted text for each cell is produced from cell.v and cell.z format. If the format is not specified, the Excel General format is used. The format can either be specified as a string or as an index into the format table. Parsers are expected to populate workbook.SSF with the number format table. Writers are expected to serialize the table.

Custom tools should ensure that the local table has each used format string somewhere in the table. Excel convention mandates that the custom formats start at index 164. The following example creates a custom format from scratch:

New worksheet with custom format (click to show)

var wb = {
  SheetNames: ["Sheet1"],
  Sheets: {
    Sheet1: {
      "!ref":"A1:C1",
      A1: { t:"n", v:10000 },                    // <-- General format
      B1: { t:"n", v:10000, z: "0%" },           // <-- Builtin format
      C1: { t:"n", v:10000, z: "\"T\"\ #0.00" }  // <-- Custom format
    }
  }
}

The rules are slightly different from how Excel displays custom number formats. In particular, literal characters must be wrapped in double quotes or preceded by a backslash. For more info, see the Excel documentation article Create or delete a custom number format or ECMA-376 18.8.31 (Number Formats)

Default Number Formats (click to show)

The default formats are listed in ECMA-376 18.8.30:

Format 14 (m/d/yy) is localized by Excel: even though the file specifies that number format, it will be drawn differently based on system settings. It makes sense when the producer and consumer of files are in the same locale, but that is not always the case over the Internet. To get around this ambiguity, parse functions accept the dateNF option to override the interpretation of that specific format string.

Hyperlinks

Format Support (click to show)

Cell Hyperlinks: XLSX/M, XLSB, BIFF8 XLS, XLML, ODS

Tooltips: XLSX/M, XLSB, BIFF8 XLS, XLML

Hyperlinks are stored in the l key of cell objects. The Target field of the hyperlink object is the target of the link, including the URI fragment. Tooltips are stored in the Tooltip field and are displayed when you move your mouse over the text.

For example, the following snippet creates a link from cell A3 to https://sheetjs.com with the tip "Find us @ SheetJS.com!":

ws['A1'].l = { Target:"https://sheetjs.com", Tooltip:"Find us @ SheetJS.com!" };

Note that Excel does not automatically style hyperlinks -- they will generally be displayed as normal text.

Remote Links

HTTP / HTTPS links can be used directly:

ws['A2'].l = { Target:"https://docs.sheetjs.com/#hyperlinks" };
ws['A3'].l = { Target:"http://localhost:7262/yes_localhost_works" };

Excel also supports mailto email links with subject line:

ws['A4'].l = { Target:"mailto:ignored@dev.null" };
ws['A5'].l = { Target:"mailto:ignored@dev.null?subject=Test Subject" };

Local Links

Links to absolute paths should use the file:// URI scheme:

ws['B1'].l = { Target:"file:///SheetJS/t.xlsx" }; /* Link to /SheetJS/t.xlsx */
ws['B2'].l = { Target:"file:///c:/SheetJS.xlsx" }; /* Link to c:\SheetJS.xlsx */

Links to relative paths can be specified without a scheme:

ws['B3'].l = { Target:"SheetJS.xlsb" }; /* Link to SheetJS.xlsb */
ws['B4'].l = { Target:"../SheetJS.xlsm" }; /* Link to ../SheetJS.xlsm */

Relative Paths have undefined behavior in the SpreadsheetML 2003 format. Excel 2019 will treat a ..\ parent mark as two levels up.

Internal Links

Links where the target is a cell or range or defined name in the same workbook ("Internal Links") are marked with a leading hash character:

ws['C1'].l = { Target:"#E2" }; /* Link to cell E2 */
ws['C2'].l = { Target:"#Sheet2!E2" }; /* Link to cell E2 in sheet Sheet2 */
ws['C3'].l = { Target:"#SomeDefinedName" }; /* Link to Defined Name */

Cell Comments

Cell comments are objects stored in the c array of cell objects. The actual contents of the comment are split into blocks based on the comment author. The a field of each comment object is the author of the comment and the t field is the plain text representation.

For example, the following snippet appends a cell comment into cell A1:

if(!ws.A1.c) ws.A1.c = [];
ws.A1.c.push({a:"SheetJS", t:"I'm a little comment, short and stout!"});

Note: XLSB enforces a 54 character limit on the Author name. Names longer than 54 characters may cause issues with other formats.

To mark a comment as normally hidden, set the hidden property:

if(!ws.A1.c) ws.A1.c = [];
ws.A1.c.push({a:"SheetJS", t:"This comment is visible"});

if(!ws.A2.c) ws.A2.c = [];
ws.A2.c.hidden = true;
ws.A2.c.push({a:"SheetJS", t:"This comment will be hidden"});

Threaded Comments

Introduced in Excel 365, threaded comments are plain text comment snippets with author metadata and parent references. They are supported in XLSX and XLSB.

To mark a comment as threaded, each comment part must have a true T property:

if(!ws.A1.c) ws.A1.c = [];
ws.A1.c.push({a:"SheetJS", t:"This is not threaded"});

if(!ws.A2.c) ws.A2.c = [];
ws.A2.c.hidden = true;
ws.A2.c.push({a:"SheetJS", t:"This is threaded", T: true});
ws.A2.c.push({a:"JSSheet", t:"This is also threaded", T: true});

There is no Active Directory or Office 365 metadata associated with authors in a thread.

Sheet Visibility

Excel enables hiding sheets in the lower tab bar. The sheet data is stored in the file but the UI does not readily make it available. Standard hidden sheets are revealed in the "Unhide" menu. Excel also has "very hidden" sheets which cannot be revealed in the menu. It is only accessible in the VB Editor!

The visibility setting is stored in the Hidden property of sheet props array.

More details (click to show)

With https://rawgit.com/SheetJS/test_files/HEAD/sheet_visibility.xlsx:

> wb.Workbook.Sheets.map(function(x) { return [x.name, x.Hidden] })
[ [ 'Visible', 0 ], [ 'Hidden', 1 ], [ 'VeryHidden', 2 ] ]

Non-Excel formats do not support the Very Hidden state. The best way to test if a sheet is visible is to check if the Hidden property is logical truth:

> wb.Workbook.Sheets.map(function(x) { return [x.name, !x.Hidden] })
[ [ 'Visible', true ], [ 'Hidden', false ], [ 'VeryHidden', false ] ]

VBA and Macros

VBA Macros are stored in a special data blob that is exposed in the vbaraw property of the workbook object when the bookVBA option is true. They are supported in XLSM, XLSB, and BIFF8 XLS formats. The supported format writers automatically insert the data blobs if it is present in the workbook and associate with the worksheet names.

Custom Code Names (click to show)

The workbook code name is stored in wb.Workbook.WBProps.CodeName. By default, Excel will write ThisWorkbook or a translated phrase like DieseArbeitsmappe. Worksheet and Chartsheet code names are in the worksheet properties object at wb.Workbook.Sheets[i].CodeName. Macrosheets and Dialogsheets are ignored.

The readers and writers preserve the code names, but they have to be manually set when adding a VBA blob to a different workbook.

Macrosheets (click to show)

Older versions of Excel also supported a non-VBA "macrosheet" sheet type that stored automation commands. These are exposed in objects with the !type property set to "macro".

Detecting macros in workbooks (click to show)

The vbaraw field will only be set if macros are present, so testing is simple:

function wb_has_macro(wb/*:workbook*/)/*:boolean*/ {
    if(!!wb.vbaraw) return true;
    const sheets = wb.SheetNames.map((n) => wb.Sheets[n]);
    return sheets.some((ws) => !!ws && ws['!type']=='macro');
}

Parsing Options

The exported read and readFile functions accept an options argument:

• Even if cellNF is false, formatted text will be generated and saved to .w
• In some cases, sheets may be parsed even if bookSheets is false.
• Excel aggressively tries to interpret values from CSV and other plain text. This leads to surprising behavior! The raw option suppresses value parsing.
• bookSheets and bookProps combine to give both sets of information
• Deps will be an empty object if bookDeps is false
• bookFiles behavior depends on file type:
  • keys array (paths in the ZIP) for ZIP-based formats
  • files hash (mapping paths to objects representing the files) for ZIP
  • cfb object for formats using CFB containers
• sheetRows-1 rows will be generated when looking at the JSON object output (since the header row is counted as a row when parsing the data)
• By default all worksheets are parsed. sheets restricts based on input type:
  • number: zero-based index of worksheet to parse (0 is first worksheet)
  • string: name of worksheet to parse (case insensitive)
  • array of numbers and strings to select multiple worksheets.
• bookVBA merely exposes the raw VBA CFB object. It does not parse the data. XLSM and XLSB store the VBA CFB object in xl/vbaProject.bin. BIFF8 XLS mixes the VBA entries alongside the core Workbook entry, so the library generates a new XLSB-compatible blob from the XLS CFB container.
• codepage is applied to BIFF2 - BIFF5 files without CodePage records and to CSV files without BOM in type:"binary". BIFF8 XLS always defaults to 1200.
• PRN affects parsing of text files without a common delimiter character.
• Currently only XOR encryption is supported. Unsupported error will be thrown for files employing other encryption methods.
• Newer Excel functions are serialized with the _xlfn. prefix, hidden from the user. SheetJS will strip _xlfn. normally. The xlfn option preserves them.
• WTF is mainly for development. By default, the parser will suppress read errors on single worksheets, allowing you to read from the worksheets that do parse properly. Setting WTF:true forces those errors to be thrown.

Input Type

Strings can be interpreted in multiple ways. The type parameter for read tells the library how to parse the data argument:

Guessing File Type

Implementation Details (click to show)

Excel and other spreadsheet tools read the first few bytes and apply other heuristics to determine a file type. This enables file type punning: renaming files with the .xls extension will tell your computer to use Excel to open the file but Excel will know how to handle it. This library applies similar logic:

DBF files are detected based on the first byte as well as the third and fourth bytes (corresponding to month and day of the file date)

Works for Windows files are detected based on the BOF record with type 0xFF

Plain text format guessing follows the priority order:

• HTML tags include: html, table, head, meta, script, style, div

Why are random text files valid? (click to show)

Excel is extremely aggressive in reading files. Adding an XLS extension to any display text file (where the only characters are ANSI display chars) tricks Excel into thinking that the file is potentially a CSV or TSV file, even if it is only one column! This library attempts to replicate that behavior.

The best approach is to validate the desired worksheet and ensure it has the expected number of rows or columns. Extracting the range is extremely simple:

var range = XLSX.utils.decode_range(worksheet['!ref']);
var ncols = range.e.c - range.s.c + 1, nrows = range.e.r - range.s.r + 1;

Writing Options

The exported write and writeFile functions accept an options argument:

• bookSST is slower and more memory intensive, but has better compatibility with older versions of iOS Numbers
• The raw data is the only thing guaranteed to be saved. Features not described in this README may not be serialized.
• cellDates only applies to XLSX output and is not guaranteed to work with third-party readers. Excel itself does not usually write cells with type d so non-Excel tools may ignore the data or error in the presence of dates.
• Props is an object mirroring the workbook Props field. See the table from the Workbook File Properties section.
• if specified, the string from themeXLSX will be saved as the primary theme for XLSX/XLSB/XLSM files (to xl/theme/theme1.xml in the ZIP)
• Due to a bug in the program, some features like "Text to Columns" will crash Excel on worksheets where error conditions are ignored. The writer will mark files to ignore the error by default. Set ignoreEC to false to suppress.
• Due to the size of the data, the NUMBERS data is not included by default. The included xlsx.zahl.js and xlsx.zahl.mjs scripts include the data.

Supported Output Formats

For broad compatibility with third-party tools, this library supports many output formats. The specific file type is controlled with bookType option:

• compression only applies to formats with ZIP containers.
• Formats that only support a single sheet require a sheet option specifying the worksheet. If the string is empty, the first worksheet is used.
• writeFile will automatically guess the output file format based on the file extension if bookType is not specified. It will choose the first format in the aforementioned table that matches the extension.

Output Type

The type argument for write mirrors the type argument for read:

• For compatibility with Excel, csv output will always include the UTF-8 byte order mark.

Utility Functions

The sheet_to_* functions accept a worksheet and an optional options object.

The *_to_sheet functions accept a data object and an optional options object.

The examples are based on the following worksheet:

XXX| A | B | C | D | E | F | G |
---+---+---+---+---+---+---+---+
 1 | S | h | e | e | t | J | S |
 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
 3 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

Array of Arrays Input

XLSX.utils.aoa_to_sheet takes an array of arrays of JS values and returns a worksheet resembling the input data. Numbers, Booleans and Strings are stored as the corresponding styles. Dates are stored as date or numbers. Array holes and explicit undefined values are skipped. null values may be stubbed. All other values are stored as strings. The function takes an options argument:

Examples (click to show)

To generate the example sheet:

var ws = XLSX.utils.aoa_to_sheet([
  "SheetJS".split(""),
  [1,2,3,4,5,6,7],
  [2,3,4,5,6,7,8]
]);

XLSX.utils.sheet_add_aoa takes an array of arrays of JS values and updates an existing worksheet object. It follows the same process as aoa_to_sheet and accepts an options argument:

origin is expected to be one of:

Examples (click to show)

Consider the worksheet:

XXX| A | B | C | D | E | F | G |
---+---+---+---+---+---+---+---+
 1 | S | h | e | e | t | J | S |
 2 | 1 | 2 |   |   | 5 | 6 | 7 |
 3 | 2 | 3 |   |   | 6 | 7 | 8 |
 4 | 3 | 4 |   |   | 7 | 8 | 9 |
 5 | 4 | 5 | 6 | 7 | 8 | 9 | 0 |

This worksheet can be built up in the order A1:G1, A2:B4, E2:G4, A5:G5:

/* Initial row */
var ws = XLSX.utils.aoa_to_sheet([ "SheetJS".split("") ]);

/* Write data starting at A2 */
XLSX.utils.sheet_add_aoa(ws, [[1,2], [2,3], [3,4]], {origin: "A2"});

/* Write data starting at E2 */
XLSX.utils.sheet_add_aoa(ws, [[5,6,7], [6,7,8], [7,8,9]], {origin:{r:1, c:4}});

/* Append row */
XLSX.utils.sheet_add_aoa(ws, [[4,5,6,7,8,9,0]], {origin: -1});

Array of Objects Input

XLSX.utils.json_to_sheet takes an array of objects and returns a worksheet with automatically-generated "headers" based on the keys of the objects. The default column order is determined by the first appearance of the field using Object.keys. The function accepts an options argument:

• All fields from each row will be written. If header is an array and it does not contain a particular field, the key will be appended to the array.
• Cell types are deduced from the type of each value. For example, a Date object will generate a Date cell, while a string will generate a Text cell.
• Null values will be skipped by default. If nullError is true, an error cell corresponding to #NULL! will be written to the worksheet.

Examples (click to show)

The original sheet cannot be reproduced using plain objects since JS object keys must be unique. After replacing the second e and S with e_1 and S_1:

var ws = XLSX.utils.json_to_sheet([
  { S:1, h:2, e:3, e_1:4, t:5, J:6, S_1:7 },
  { S:2, h:3, e:4, e_1:5, t:6, J:7, S_1:8 }
], {header:["S","h","e","e_1","t","J","S_1"]});

Alternatively, the header row can be skipped:

var ws = XLSX.utils.json_to_sheet([
  { A:"S", B:"h", C:"e", D:"e", E:"t", F:"J", G:"S" },
  { A: 1,  B: 2,  C: 3,  D: 4,  E: 5,  F: 6,  G: 7  },
  { A: 2,  B: 3,  C: 4,  D: 5,  E: 6,  F: 7,  G: 8  }
], {header:["A","B","C","D","E","F","G"], skipHeader:true});

XLSX.utils.sheet_add_json takes an array of objects and updates an existing worksheet object. It follows the same process as json_to_sheet and accepts an options argument:

origin is expected to be one of:

Examples (click to show)

Consider the worksheet:

XXX| A | B | C | D | E | F | G |
---+---+---+---+---+---+---+---+
 1 | S | h | e | e | t | J | S |
 2 | 1 | 2 |   |   | 5 | 6 | 7 |
 3 | 2 | 3 |   |   | 6 | 7 | 8 |
 4 | 3 | 4 |   |   | 7 | 8 | 9 |
 5 | 4 | 5 | 6 | 7 | 8 | 9 | 0 |

This worksheet can be built up in the order A1:G1, A2:B4, E2:G4, A5:G5:

/* Initial row */
var ws = XLSX.utils.json_to_sheet([
  { A: "S", B: "h", C: "e", D: "e", E: "t", F: "J", G: "S" }
], {header: ["A", "B", "C", "D", "E", "F", "G"], skipHeader: true});

/* Write data starting at A2 */
XLSX.utils.sheet_add_json(ws, [
  { A: 1, B: 2 }, { A: 2, B: 3 }, { A: 3, B: 4 }
], {skipHeader: true, origin: "A2"});

/* Write data starting at E2 */
XLSX.utils.sheet_add_json(ws, [
  { A: 5, B: 6, C: 7 }, { A: 6, B: 7, C: 8 }, { A: 7, B: 8, C: 9 }
], {skipHeader: true, origin: { r: 1, c: 4 }, header: [ "A", "B", "C" ]});

/* Append row */
XLSX.utils.sheet_add_json(ws, [
  { A: 4, B: 5, C: 6, D: 7, E: 8, F: 9, G: 0 }
], {header: ["A", "B", "C", "D", "E", "F", "G"], skipHeader: true, origin: -1});

HTML Table Input

XLSX.utils.table_to_sheet takes a table DOM element and returns a worksheet resembling the input table. Numbers are parsed. All other data will be stored as strings.

XLSX.utils.table_to_book produces a minimal workbook based on the worksheet.

Both functions accept options arguments:

Examples (click to show)

To generate the example sheet, start with the HTML table:

<table id="sheetjs">
<tr><td>S</td><td>h</td><td>e</td><td>e</td><td>t</td><td>J</td><td>S</td></tr>
<tr><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td></tr>
<tr><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td><td>8</td></tr>
</table>

To process the table:

var tbl = document.getElementById('sheetjs');
var wb = XLSX.utils.table_to_book(tbl);

Note: XLSX.read can handle HTML represented as strings.

XLSX.utils.sheet_add_dom takes a table DOM element and updates an existing worksheet object. It follows the same process as table_to_sheet and accepts an options argument:

origin is expected to be one of:

Examples (click to show)

A small helper function can create gap rows between tables:

function create_gap_rows(ws, nrows) {
  var ref = XLSX.utils.decode_range(ws["!ref"]);       // get original range
  ref.e.r += nrows;                                    // add to ending row
  ws["!ref"] = XLSX.utils.encode_range(ref);           // reassign row
}

/* first table */
var ws = XLSX.utils.table_to_sheet(document.getElementById('table1'));
create_gap_rows(ws, 1); // one row gap after first table

/* second table */
XLSX.utils.sheet_add_dom(ws, document.getElementById('table2'), {origin: -1});
create_gap_rows(ws, 3); // three rows gap after second table

/* third table */
XLSX.utils.sheet_add_dom(ws, document.getElementById('table3'), {origin: -1});

Formulae Output

XLSX.utils.sheet_to_formulae generates an array of commands that represent how a person would enter data into an application. Each entry is of the form A1-cell-address=formula-or-value. String literals are prefixed with a ' in accordance with Excel.

Examples (click to show)

For the example sheet:

> var o = XLSX.utils.sheet_to_formulae(ws);
> [o[0], o[5], o[10], o[15], o[20]];
[ 'A1=\'S', 'F1=\'J', 'D2=4', 'B3=3', 'G3=8' ]

Delimiter-Separated Output

As an alternative to the writeFile CSV type, XLSX.utils.sheet_to_csv also produces CSV output. The function takes an options argument:

• strip will remove trailing commas from each line under default FS/RS
• blankrows must be set to false to skip blank lines.
• Fields containing the record or field separator will automatically be wrapped in double quotes; forceQuotes forces all cells to be wrapped in quotes.
• XLSX.write with csv type will always prepend the UTF-8 byte-order mark for Excel compatibility. sheet_to_csv returns a JS string and omits the mark. Using XLSX.write with type string will also skip the mark.

Examples (click to show)

For the example sheet:

> console.log(XLSX.utils.sheet_to_csv(ws));
S,h,e,e,t,J,S
1,2,3,4,5,6,7
2,3,4,5,6,7,8
> console.log(XLSX.utils.sheet_to_csv(ws, {FS:"\t"}));
S    h    e    e    t    J    S
1    2    3    4    5    6    7
2    3    4    5    6    7    8
> console.log(XLSX.utils.sheet_to_csv(ws,{FS:":",RS:"|"}));
S:h:e:e:t:J:S|1:2:3:4:5:6:7|2:3:4:5:6:7:8|

UTF-16 Unicode Text

The txt output type uses the tab character as the field separator. If the codepage library is available (included in full distribution but not core), the output will be encoded in CP1200 and the BOM will be prepended.

XLSX.utils.sheet_to_txt takes the same arguments as sheet_to_csv.

HTML Output

As an alternative to the writeFile HTML type, XLSX.utils.sheet_to_html also produces HTML output. The function takes an options argument:

Examples (click to show)

For the example sheet:

> console.log(XLSX.utils.sheet_to_html(ws));
// ...

JSON

XLSX.utils.sheet_to_json generates different types of JS objects. The function takes an options argument:

• raw only affects cells which have a format code (.z) field or a formatted text (.w) field.
• If header is specified, the first row is considered a data row; if header is not specified, the first row is the header row and not considered data.
• When header is not specified, the conversion will automatically disambiguate header entries by affixing _ and a count starting at 1. For example, if three columns have header foo the output fields are foo, foo_1, foo_2
• null values are returned when raw is true but are skipped when false.
• If defval is not specified, null and undefined values are skipped normally. If specified, all null and undefined points will be filled with defval
• When header is 1, the default is to generate blank rows. blankrows must be set to false to skip blank rows.
• When header is not 1, the default is to skip blank rows. blankrows must be true to generate blank rows

range is expected to be one of:

header is expected to be one of:

• If header is not 1, the row object will contain the non-enumerable property __rowNum__ that represents the row of the sheet corresponding to the entry.
• If header is an array, the keys will not be disambiguated. This can lead to unexpected results if the array values are not unique!

Examples (click to show)

For the example sheet:

> XLSX.utils.sheet_to_json(ws);
[ { S: 1, h: 2, e: 3, e_1: 4, t: 5, J: 6, S_1: 7 },
  { S: 2, h: 3, e: 4, e_1: 5, t: 6, J: 7, S_1: 8 } ]

> XLSX.utils.sheet_to_json(ws, {header:"A"});
[ { A: 'S', B: 'h', C: 'e', D: 'e', E: 't', F: 'J', G: 'S' },
  { A: '1', B: '2', C: '3', D: '4', E: '5', F: '6', G: '7' },
  { A: '2', B: '3', C: '4', D: '5', E: '6', F: '7', G: '8' } ]

> XLSX.utils.sheet_to_json(ws, {header:["A","E","I","O","U","6","9"]});
[ { '6': 'J', '9': 'S', A: 'S', E: 'h', I: 'e', O: 'e', U: 't' },
  { '6': '6', '9': '7', A: '1', E: '2', I: '3', O: '4', U: '5' },
  { '6': '7', '9': '8', A: '2', E: '3', I: '4', O: '5', U: '6' } ]

> XLSX.utils.sheet_to_json(ws, {header:1});
[ [ 'S', 'h', 'e', 'e', 't', 'J', 'S' ],
  [ '1', '2', '3', '4', '5', '6', '7' ],
  [ '2', '3', '4', '5', '6', '7', '8' ] ]

Example showing the effect of raw:

> ws['A2'].w = "3";                          // set A2 formatted string value

> XLSX.utils.sheet_to_json(ws, {header:1, raw:false});
[ [ 'S', 'h', 'e', 'e', 't', 'J', 'S' ],
  [ '3', '2', '3', '4', '5', '6', '7' ],     // <-- A2 uses the formatted string
  [ '2', '3', '4', '5', '6', '7', '8' ] ]

> XLSX.utils.sheet_to_json(ws, {header:1});
[ [ 'S', 'h', 'e', 'e', 't', 'J', 'S' ],
  [ 1, 2, 3, 4, 5, 6, 7 ],                   // <-- A2 uses the raw value
  [ 2, 3, 4, 5, 6, 7, 8 ] ]

File Formats

Despite the library name xlsx, it supports numerous spreadsheet file formats:

Features not supported by a given file format will not be written. Formats with range limits will be silently truncated:

Excel 2003 SpreadsheetML range limits are governed by the version of Excel and are not enforced by the writer.

File Format Details (click to show)

Core Spreadsheet Formats

• Excel 2007+ XML (XLSX/XLSM)

XLSX and XLSM files are ZIP containers containing a series of XML files in accordance with the Open Packaging Conventions (OPC). The XLSM format, almost identical to XLSX, is used for files containing macros.

The format is standardized in ECMA-376 and later in ISO/IEC 29500. Excel does not follow the specification, and there are additional documents discussing how Excel deviates from the specification.

• Excel 2.0-95 (BIFF2/BIFF3/BIFF4/BIFF5)

BIFF 2/3 XLS are single-sheet streams of binary records. Excel 4 introduced the concept of a workbook (XLW files) but also had single-sheet XLS format. The structure is largely similar to the Lotus 1-2-3 file formats. BIFF5/8/12 extended the format in various ways but largely stuck to the same record format.

There is no official specification for any of these formats. Excel 95 can write files in these formats, so record lengths and fields were determined by writing in all of the supported formats and comparing files. Excel 2016 can generate BIFF5 files, enabling a full suite of file tests starting from XLSX or BIFF2.

• Excel 97-2004 Binary (BIFF8)

BIFF8 exclusively uses the Compound File Binary container format, splitting some content into streams within the file. At its core, it still uses an extended version of the binary record format from older versions of BIFF.

The MS-XLS specification covers the basics of the file format, and other specifications expand on serialization of features like properties.

• Excel 2003-2004 (SpreadsheetML)

Predating XLSX, SpreadsheetML files are simple XML files. There is no official and comprehensive specification, although MS has released documentation on the format. Since Excel 2016 can generate SpreadsheetML files, mapping features is pretty straightforward.

• Excel 2007+ Binary (XLSB, BIFF12)

Introduced in parallel with XLSX, the XLSB format combines the BIFF architecture with the content separation and ZIP container of XLSX. For the most part nodes in an XLSX sub-file can be mapped to XLSB records in a corresponding sub-file.

The MS-XLSB specification covers the basics of the file format, and other specifications expand on serialization of features like properties.

• Delimiter-Separated Values (CSV/TXT)

Excel CSV deviates from RFC4180 in a number of important ways. The generated CSV files should generally work in Excel although they may not work in RFC4180 compatible readers. The parser should generally understand Excel CSV. The writer proactively generates cells for formulae if values are unavailable.

Excel TXT uses tab as the delimiter and code page 1200.

Like in Excel, files starting with 0x49 0x44 ("ID") are treated as Symbolic Link files. Unlike Excel, if the file does not have a valid SYLK header, it will be proactively reinterpreted as CSV. There are some files with semicolon delimiter that align with a valid SYLK file. For the broadest compatibility, all cells with the value of ID are automatically wrapped in double-quotes.

Miscellaneous Workbook Formats

Support for other formats is generally far behind XLS/XLSB/XLSX support, due in part to a lack of publicly available documentation. Test files were produced in the respective apps and compared to their XLS exports to determine structure. The main focus is data extraction.

• Lotus 1-2-3 (WKS/WK1/WK2/WK3/WK4/123)

The Lotus formats consist of binary records similar to the BIFF structure. Lotus did release a specification decades ago covering the original WK1 format. Other features were deduced by producing files and comparing to Excel support.

Generated WK1 worksheets are compatible with Lotus 1-2-3 R2 and Excel 5.0.

Generated WK3 workbooks are compatible with Lotus 1-2-3 R9 and Excel 5.0.

• Quattro Pro (WQ1/WQ2/WB1/WB2/WB3/QPW)

The Quattro Pro formats use binary records in the same way as BIFF and Lotus. Some of the newer formats (namely WB3 and QPW) use a CFB enclosure just like BIFF8 XLS.

• Works for DOS / Windows Spreadsheet (WKS/XLR)

All versions of Works were limited to a single worksheet.

Works for DOS 1.x - 3.x and Works for Windows 2.x extends the Lotus WKS format with additional record types.

Works for Windows 3.x - 5.x uses the same format and WKS extension. The BOF record has type FF

Works for Windows 6.x - 9.x use the XLR format. XLR is nearly identical to BIFF8 XLS: it uses the CFB container with a Workbook stream. Works 9 saves the exact Workbook stream for the XLR and the 97-2003 XLS export. Works 6 XLS includes two empty worksheets but the main worksheet has an identical encoding. XLR also includes a WksSSWorkBook stream similar to Lotus FM3/FMT files.

• Numbers 3.0+ / iWork 2013+ Spreadsheet (NUMBERS)

iWork 2013 (Numbers 3.0 / Pages 5.0 / Keynote 6.0) switched from a proprietary XML-based format to the current file format based on the iWork Archive (IWA). This format has been used up through the current release (Numbers 11.2).

The parser focuses on extracting raw data from tables. Numbers technically supports multiple tables in a logical worksheet, including custom titles. This parser will generate one worksheet per Numbers table.

The writer currently exports a small range from the first worksheet.

• OpenDocument Spreadsheet (ODS/FODS)

ODS is an XML-in-ZIP format akin to XLSX while FODS is an XML format akin to SpreadsheetML. Both are detailed in the OASIS standard, but tools like LO/OO add undocumented extensions. The parsers and writers do not implement the full standard, instead focusing on parts necessary to extract and store raw data.

• Uniform Office Spreadsheet (UOS1/2)

UOS is a very similar format, and it comes in 2 varieties corresponding to ODS and FODS respectively. For the most part, the difference between the formats is in the names of tags and attributes.

Miscellaneous Worksheet Formats

Many older formats supported only one worksheet:

• dBASE and Visual FoxPro (DBF)

DBF is really a typed table format: each column can only hold one data type and each record omits type information. The parser generates a header row and inserts records starting at the second row of the worksheet. The writer makes files compatible with Visual FoxPro extensions.

Multi-file extensions like external memos and tables are currently unsupported, limited by the general ability to read arbitrary files in the web browser. The reader understands DBF Level 7 extensions like DATETIME.

• Symbolic Link (SYLK)

There is no real documentation. All knowledge was gathered by saving files in various versions of Excel to deduce the meaning of fields. Notes:

Plain formulae are stored in the RC form.

Column widths are rounded to integral characters.

Lotus Formatted Text (PRN)

There is no real documentation, and in fact Excel treats PRN as an output-only file format. Nevertheless we can guess the column widths and reverse-engineer the original layout. Excel's 240 character width limitation is not enforced.

• Data Interchange Format (DIF)

There is no unified definition. Visicalc DIF differs from Lotus DIF, and both differ from Excel DIF. Where ambiguous, the parser/writer follows the expected behavior from Excel. In particular, Excel extends DIF in incompatible ways:

Since Excel automatically converts numbers-as-strings to numbers, numeric string constants are converted to formulae: "0.3" -> "=""0.3""

DIF technically expects numeric cells to hold the raw numeric data, but Excel permits formatted numbers (including dates)

DIF technically has no support for formulae, but Excel will automatically convert plain formulae. Array formulae are not preserved.

HTML

Excel HTML worksheets include special metadata encoded in styles. For example, mso-number-format is a localized string containing the number format. Despite the metadata the output is valid HTML, although it does accept bare & symbols.

The writer adds type metadata to the TD elements via the t tag. The parser looks for those tags and overrides the default interpretation. For example, text like <td>12345</td> will be parsed as numbers but <td t="s">12345</td> will be parsed as text.

• Rich Text Format (RTF)

Excel RTF worksheets are stored in clipboard when copying cells or ranges from a worksheet. The supported codes are a subset of the Word RTF support.

• Ethercalc Record Format (ETH)

Ethercalc is an open source web spreadsheet powered by a record format reminiscent of SYLK wrapped in a MIME multi-part message.

Testing

Node

(click to show)

make test will run the node-based tests. By default it runs tests on files in every supported format. To test a specific file type, set FMTS to the format you want to test. Feature-specific tests are available with make test_misc

$ make test_misc   # run core tests
$ make test        # run full tests
$ make test_xls    # only use the XLS test files
$ make test_xlsx   # only use the XLSX test files
$ make test_xlsb   # only use the XLSB test files
$ make test_xml    # only use the XML test files
$ make test_ods    # only use the ODS test files

To enable all errors, set the environment variable WTF=1:

$ make test        # run full tests
$ WTF=1 make test  # enable all error messages

flow and eslint checks are available:

$ make lint        # eslint checks
$ make flow        # make lint + Flow checking
$ make tslint      # check TS definitions

Browser

(click to show)

The core in-browser tests are available at tests/index.html within this repo. Start a local server and navigate to that directory to run the tests. make ctestserv will start a server on port 8000.

make ctest will generate the browser fixtures. To add more files, edit the tests/fixtures.lst file and add the paths.

To run the full in-browser tests, clone the repo for oss.sheetjs.com and replace the xlsx.js file (then open a browser window and go to stress.html):

$ cp xlsx.js ../SheetJS.github.io
$ cd ../SheetJS.github.io
$ simplehttpserver # or "python -mSimpleHTTPServer" or "serve"
$ open -a Chromium.app http://localhost:8000/stress.html

Tested Environments

(click to show)

• NodeJS 0.8, 0.10, 0.12, 4.x, 5.x, 6.x, 7.x, 8.x
• IE 6/7/8/9/10/11 (IE 6-9 require shims)
• Chrome 24+ (including Android 4.0+)
• Safari 6+ (iOS and Desktop)
• Edge 13+, FF 18+, and Opera 12+

Tests utilize the mocha testing framework.

• https://saucelabs.com/u/sheetjs for XLS* modules using Sauce Labs

The test suite also includes tests for various time zones. To change the timezone locally, set the TZ environment variable:

$ env TZ="Asia/Kolkata" WTF=1 make test_misc

Test Files

Test files are housed in another repo.

Running make init will refresh the test_files submodule and get the files. Note that this requires svn, git, hg and other commands that may not be available. If make init fails, please download the latest version of the test files snapshot from the repo

Latest Snapshot (click to show)

Latest test files snapshot: http://github.com/SheetJS/test_files/releases/download/20170409/test_files.zip

(download and unzip to the test_files subdirectory)

Contributing

Due to the precarious nature of the Open Specifications Promise, it is very important to ensure code is cleanroom. Contribution Notes

File organization (click to show)

At a high level, the final script is a concatenation of the individual files in the bits folder. Running make should reproduce the final output on all platforms. The README is similarly split into bits in the docbits folder.

Folders:

After cloning the repo, running make help will display a list of commands.

OSX/Linux

(click to show)

The xlsx.js file is constructed from the files in the bits subdirectory. The build script (run make) will concatenate the individual bits to produce the script. Before submitting a contribution, ensure that running make will produce the xlsx.js file exactly. The simplest way to test is to add the script:

$ git add xlsx.js
$ make clean
$ make
$ git diff xlsx.js

To produce the dist files, run make dist. The dist files are updated in each version release and should not be committed between versions.

Windows

(click to show)

The included make.cmd script will build xlsx.js from the bits directory. Building is as simple as:

> make

To prepare development environment:

> make init

The full list of commands available in Windows are displayed in make help:

make init -- install deps and global modules
make lint -- run eslint linter
make test -- run mocha test suite
make misc -- run smaller test suite
make book -- rebuild README and summary
make help -- display this message

As explained in Test Files, on Windows the release ZIP file must be downloaded and extracted. If Bash on Windows is available, it is possible to run the OSX/Linux workflow. The following steps prepares the environment:

# Install support programs for the build and test commands
sudo apt-get install make git subversion mercurial

# Install nodejs and NPM within the WSL
wget -qO- https://deb.nodesource.com/setup_8.x | sudo bash
sudo apt-get install nodejs

# Install dev dependencies
sudo npm install -g mocha voc blanket xlsjs

Tests

(click to show)

The test_misc target (make test_misc on Linux/OSX / make misc on Windows) runs the targeted feature tests. It should take 5-10 seconds to perform feature tests without testing against the entire test battery. New features should be accompanied with tests for the relevant file formats and features.

For tests involving the read side, an appropriate feature test would involve reading an existing file and checking the resulting workbook object. If a parameter is involved, files should be read with different values to verify that the feature is working as expected.

For tests involving a new write feature which can already be parsed, appropriate feature tests would involve writing a workbook with the feature and then opening and verifying that the feature is preserved.

For tests involving a new write feature without an existing read ability, please add a feature test to the kitchen sink tests/write.js.

References

OSP-covered Specifications (click to show)

• MS-CFB: Compound File Binary File Format
• MS-CTXLS: Excel Custom Toolbar Binary File Format
• MS-EXSPXML3: Excel Calculation Version 2 Web Service XML Schema
• MS-ODATA: Open Data Protocol (OData)
• MS-ODRAW: Office Drawing Binary File Format
• MS-ODRAWXML: Office Drawing Extensions to Office Open XML Structure
• MS-OE376: Office Implementation Information for ECMA-376 Standards Support
• MS-OFFCRYPTO: Office Document Cryptography Structure
• MS-OI29500: Office Implementation Information for ISO/IEC 29500 Standards Support
• MS-OLEDS: Object Linking and Embedding (OLE) Data Structures
• MS-OLEPS: Object Linking and Embedding (OLE) Property Set Data Structures
• MS-OODF3: Office Implementation Information for ODF 1.2 Standards Support
• MS-OSHARED: Office Common Data Types and Objects Structures
• MS-OVBA: Office VBA File Format Structure
• MS-XLDM: Spreadsheet Data Model File Format
• MS-XLS: Excel Binary File Format (.xls) Structure Specification
• MS-XLSB: Excel (.xlsb) Binary File Format
• MS-XLSX: Excel (.xlsx) Extensions to the Office Open XML SpreadsheetML File Format
• XLS: Microsoft Office Excel 97-2007 Binary File Format Specification
• RTF: Rich Text Format
• ISO/IEC 29500:2012(E) "Information technology — Document description and processing languages — Office Open XML File Formats"
• Open Document Format for Office Applications Version 1.2 (29 September 2011)
• Worksheet File Format (From Lotus) December 1984

Browser Test and Support Matrix

Supported File Formats

Author: SheetJS
Source Code: https://github.com/SheetJS/sheetjs 
License: Apache-2.0 License

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