/ x / sheetjs@v0.18.3 / demos / array

Typed Arrays and Math

ECMAScript version 6 introduced Typed Arrays, array-like objects designed for low-level optimizations and predictable operations. They are supported in most modern browsers and form the basis of various APIs, including NodeJS Buffers, WebGL buffers, WebAssembly, and tensors in linear algebra and math libraries.

This demo covers conversions between worksheets and Typed Arrays. It also tries to cover common numerical libraries that work with data arrays.

Excel supports a subset of the IEEE754 Double precision floating point numbers, but many libraries only support Float32 Single precision values. Math.fround rounds Number values to the nearest single-precision floating point value.

Working with Data in Typed Arrays

Typed arrays are not true Array objects. The array of array utility functions like aoa_to_sheet will not handle arrays of Typed Arrays.

Exporting Typed Arrays to a Worksheet

A single typed array can be converted to a pure JS array with Array.from:

var column = Array.from(dataset_typedarray);

aoa_to_sheet expects a row-major array of arrays. To export multiple data sets, "transpose" the data:

/* assuming data is an array of typed arrays */
var aoa = [];
for(var i = 0; i < data.length; ++i) {
  for(var j = 0; j < data[i].length; ++j) {
    if(!aoa[j]) aoa[j] = [];
    aoa[j][i] = data[i][j];
/* aoa can be directly converted to a worksheet object */
var ws = XLSX.utils.aoa_to_sheet(aoa);

Importing Data from a Spreadsheet

sheet_to_json with the option header:1 will generate a row-major array of arrays that can be transposed. However, it is more efficient to walk the sheet manually:

/* find worksheet range */
var range = XLSX.utils.decode_range(ws['!ref']);
var out = []
/* walk the columns */
for(var C = range.s.c; C <= range.e.c; ++C) {
  /* create the typed array */
  var ta = new Float32Array(range.e.r - range.s.r + 1);
  /* walk the rows */
  for(var R = range.s.r; R <= range.e.r; ++R) {
    /* find the cell, skip it if the cell isn't numeric or boolean */
    var cell = ws[XLSX.utils.encode_cell({r:R, c:C})];
    if(!cell || cell.t != 'n' && cell.t != 'b') continue;
    /* assign to the typed array */
    ta[R - range.s.r] = cell.v;

If the data set has a header row, the loop can be adjusted to skip those rows.


Each example focuses on single-variable linear regression. Sample worksheets will start with a label row. The first column is the x-value and the second column is the y-value. A sample spreadsheet can be generated randomly:

var aoo = [];
for(var i = 0; i < 100; ++i) aoo.push({x:i, y:2 * i + Math.random()});
var ws = XLSX.utils.json_to_sheet(aoo);
var wb = XLSX.utils.book_new(); XLSX.utils.book_append_sheet(wb, ws, "Sheet1");
XLSX.writeFile(wb, "linreg.xlsx");

Some libraries provide utility functions that work with plain arrays of numbers. When possible, they should be preferred over manual conversion.

Reshaping raw float arrays and exporting to a worksheet is straightforward:

function array_to_sheet(farray, shape, headers) {
  /* generate new AOA from the float array */
  var aoa = [];
  for(var j = 0; j < shape[0]; ++j) {
    aoa[j] = [];
    for(var i = 0; i < shape[1]; ++i) aoa[j][i] = farray[j * shape[1] + i];

  /* add headers and generate worksheet */
  if(headers) aoa.unshift(headers);
  return XLSX.utils.aoa_to_sheet(aoa);

Tensor Operations with Propel ML

Propel ML tensor objects can be transposed:

var tensor = pr.tensor(aoa).transpose();
var col1 = tensor.slice(0, 1);
var col2 = tensor.slice(1, 1);

To export to a worksheet, dataSync generates a Float32Array that can be re-shaped in JS:

/* extract shape and float array */
var tensor = pr.concat([col1, col2]).transpose();
var shape = tensor.shape;
var farray = tensor.dataSync();
var ws = array_to_sheet(farray, shape, ["header1", "header2"]);

The demo generates a sample dataset and uses Propel to calculate the OLS linear regression coefficients. Afterwards, the tensors are exported to a new file.


TensorFlow tensor objects can be created from arrays of arrays:

var tensor = tf.tensor2d(aoa).transpose();
var col1 = tensor.slice([0,0], [1,tensor.shape[1]]).flatten();
var col2 = tensor.slice([1,0], [1,tensor.shape[1]]).flatten();

stack should be used to create the 2-d tensor for export:

var tensor = tf.stack([col1, col2]).transpose();
var shape = tensor.shape;
var farray = tensor.dataSync();
var ws = array_to_sheet(farray, shape, ["header1", "header2"]);

The demo generates a sample dataset and uses a simple linear predictor with least-squares scoring to calculate regression coefficients. The tensors are exported to a new file.


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Tagged at
11 months ago