⚠️ NOTE: ⚠️ This project is being merged into a monorepo here. This repo will soon be archived.

---

Introduction

js-math-tools is a little library of (you guessed it) math tools for JS. It was built completely from scratch and has no other dependencies (though it does have some dependencies for bundling, linting, testing, etc.).

Installation

For client-side use, attach the dist/js-math-tools.js file to your web page.

Using a CDN:

<script src="https://unpkg.com/@jrc03c/js-math-tools/dist/js-math-tools.js"></script>

Or installing via NPM:

npm install --save https://github.com/jrc03c/js-math-tools

<script src="node_modules/@jrc03c/js-math-tools/dist/js-math-tools.js"></script>

To use in Node, install via NPM, and then import it:

const tools = require("js-math-tools")

Usage

You can either pull individual functions out, like:

const { add } = require("js-math-tools")
add(3, 4) // 7

Or, for easier access, you can "dump" all of the functions into the global scope:

require("js-math-tools").dump()
add(3, 4) // 7

API

abs(x)

Returns the absolute value(s) of x.

add(a, b, c, ...)

Returns the sum of the given values. See the note below sum to read about the differences between add and sum.

apply(x, fn)

Applies the function fn to x.

Note that (when x is an array, Series, or DataFrame) this function uses slightly different behavior than Array.prototype.map. When a function is passed into an array's map method, that function is applied to every item at the shallowest level of the array. So, for example, if the array is 2-dimensional, then the map method would apply a function to each child array in the parent array. But the apply function doesn't quite work that way; instead, it applies a function on each item in an arbitrarily nested array, regardless of depth. In that sense, the function passed into the apply function will never be given an array as an argument; it can be passed any other data type, but not an array.

For example, when using an array's map method, we can get information about each child array, like its length.

const x = [[100], [200, 300], [400, 500, 600]]
const lengths = x.map(row => row.length)
console.log(lengths)
// [1, 2, 3]

But this won't work when using apply because child arrays will never be passed into the given function. If we try to run the same thing again using the apply function, we'll get results that are perhaps unexpected:

const x = [[100], [200, 300], [400, 500, 600]]
const lengths = apply(x, row => row.length)
console.log(lengths)
// [
//   [undefined],
//   [undefined, undefined],
//   [undefined, undefined, undefined],
// ]

That's because apply is trying to pass numbers into the given function, not arrays. Here's an example that uses apply correctly:

const x = [[100], [200, 300], [400, 500, 600]]
const y = apply(x, v => v * 2)
console.log(y)
// [
//   [200],
//   [400, 600],
//   [800, 1000, 1200],
// ]

arccos(x)

Returns the inverse cosine(s) x.

arcsin(x)

Returns the inverse sine(s) of x.

arctan(x)

Returns the inverse tangent(s) of x.

argmax(x)

Returns the index of the maximum value in x. If x is 1-dimensional, then a whole number will be returned. If, however, x is an arbitrarily nested array, then the returned value will be an array of whole numbers representing indices at each dimension. For example:

const a = [1, 5, 3]
console.log(argmax(a))
// 1

const b = [
  [1, 5],
  [3, 4],
  [9, 2],
]

console.log(argmax(b))
// [2, 0]
// i.e., row 2, item 0

const c = [1, [2, 3, [4, 5, 6, [7, 8, 9, 10]]]]
console.log(argmax(c))
// [1, 2, 3, 3]
// i.e., row 1, sub-row 2, sub-sub-row 3, item 3

If x is a Series, then the returned value will be a string from the object's index (i.e., something akin to a "row" name, though a Series doesn't actually have rows). If x is a DataFrame, then the returned value will be an array of the form [rowName, colName]. To obtain numerical indices in either of these cases, just pass x.values into the argmax function rather than x itself.

argmin(x)

Returns the index of the minimum value in x. See argmax for examples and more information about the returned values.

assert(condition, message)

Does nothing if condition is true; otherwise, it throws an error with the (optional) given message string.

cast(x, type)

Casts the given value x into the type type. Valid type values are:

• "boolean"
• "date"
• "null"
• "number"
• "object"
• "string"

If x is an array, then the values within the array are cast into the given type; i.e., the array itself is not cast. Also note that trying to cast a non-number as a number will return NaN; but for all other types, failure to cast will return null. For example, cast("hello", "number") will return NaN, but cast("hello", "boolean") will return null.

ceil(x)

Returns the ceiling(s) of x.

chop(x, threshold=1e-10)

Returns 0(s) if the absolute value(s) of x is less than the threshold; otherwise, it returns x.

clamp(x, min, max)

Returns min if x is less than min; returns max if x is greater than max; otherwise, returns x.

combinations(x, r)

Returns all possible combinations of r items from x. Note that any nesting of x will be ignored — i.e., x will be "flattened" into a 1-dimensional array before getting the combinations — so it won't be possible with this function to get combinations of arrays.

copy(x)

Returns a copy of x. The only exception is if x is an instance of a custom class. In such a case, a plain JavaScript Object will be returned, though bearing the same members as x but not an instance of the same class. Also, this function handles circular references by replacing them with strings like "<reference to '/some/path/down/into/the/object'>". If you need a copy of x using a custom class, use structuredClone.

correl(a, b)

Returns the correlation of a and b, which are 1-dimensional arrays or Series instances.

cos(x)

Returns the cosine(s) of x.

count(x, matcher)

Returns the number(s) of times that certain values appear in x. If matcher is a single value (like a number), then the returned value will be a single number (indicating the number of times that matcher appears in x). If matcher is an array, then an array of the same size will be returned containing objects with value and count properties. If matcher is a function, then a single number will be returned based on how many times the function returned true when presented with each value in x. Finally, if matcher is undefined, then all of the items in x will be counted and returned as an array of objects with value and count properties.

covariance(a, b)

Returns the covariance between a and b, which are 1-dimensional arrays or Series instances.

---

DataFrame(x)

The DataFrame class is similar to pandas' DataFrame. They at least represent the same kind of data (2-dimensional arrays with row and column names), though they probably differ in many of their members.

The constructor for a DataFrame can optionally receive a value x, which can be any of these:

• another DataFrame
• a 2-dimensional array
• an object whose key-value pairs represent column names and column values, respectively

NOTE: Unlike the pandas DataFrame class, this DataFrame class doesn't have any methods for reading from or writing to CSV files. I recommend using papaparse if you need that functionality because it's very robust.

DataFrame.values

A 2-dimensional array containing the values held by the DataFrame. Technically, values is a getter-setter pair that stores its data in a hidden _values property. While it's possible to set the _values property directly, this is strongly discouraged because it bypasses sanity checks on the data, like checking that new data is 2-dimensional, etc.

DataFrame.columns

An array of column names. Technically, columns is a getter-setter pair that stores its data in a hidden _columns property. While it's possible to set the _columns property directly, this is strongly discouraged because it bypasses sanity checks on the data, like checking to make sure that the length of a new columns array is the same length as the rows in the values array, etc.

DataFrame.index

An array of row names. Technically, index is a getter-setter pair that stores its data in a hidden _index property. While it's possible to set the _index property directly, this is strongly discouraged because it bypasses sanity checks on the data, like checking to make sure that the length of the new rows array is the same as the length of the values array, etc.

DataFrame.rows

Identical to DataFrame.index.

DataFrame.shape

A read-only array with 2 values representing the number of rows and number of columns, respectively, in the values array.

DataFrame.length

A read-only value representing the number of rows in the DataFrame.

DataFrame.width

A read-only value representing the number of columns in the DataFrame.

DataFrame.isEmpty

A read-only boolean value that is true if the DataFrame contains no data or false otherwise.

DataFrame.T

Identical to DataFrame.transpose except that T is a getter.

DataFrame.append(x, axis=0)

Returns a copy of the original DataFrame with x appended to it. Possible axis values are 0 and 1, where 0 indicates that the row(s) of x should be stacked beneath the rows of the original DataFrame, and 1 indicates that the row(s) of x should be placed to the right of the rows of the original DataFrame. For example:

const x = new DataFrame({ foo: [2, 3, 4], bar: [5, 6, 7] })
x.print()
// ┌─────────┬─────┬─────┐
// │ (index) │ foo │ bar │
// ├─────────┼─────┼─────┤
// │  row0   │  2  │  5  │
// │  row1   │  3  │  6  │
// │  row2   │  4  │  7  │
// └─────────┴─────┴─────┘
// Shape: [ 3, 2 ]

// append a vector with axis = 0
x.append(["a", "b", "c"], 0).print()
// ┌─────────┬─────┬─────┬───────────┐
// │ (index) │ foo │ bar │   col2    │
// ├─────────┼─────┼─────┼───────────┤
// │  row0   │  2  │  5  │ undefined │
// │  row1   │  3  │  6  │ undefined │
// │  row2   │  4  │  7  │ undefined │
// │  row3   │ 'a' │ 'b' │    'c'    │
// └─────────┴─────┴─────┴───────────┘
// Shape: [ 4, 3 ]

// append a vector with axis = 1
x.append(["a", "b", "c"], 1).print()
// ┌─────────┬─────┬─────┬──────┐
// │ (index) │ foo │ bar │ col2 │
// ├─────────┼─────┼─────┼──────┤
// │  row0   │  2  │  5  │ 'a'  │
// │  row1   │  3  │  6  │ 'b'  │
// │  row2   │  4  │  7  │ 'c'  │
// └─────────┴─────┴─────┴──────┘
// Shape: [ 3, 3 ]

So, if x is a vector, then it gets treated as a row if the axis is 0 or as a column if the axis is 1.

But working with matrices is slightly different:

const x = new DataFrame({ foo: [2, 3, 4], bar: [5, 6, 7] })
x.print()
// ┌─────────┬─────┬─────┐
// │ (index) │ foo │ bar │
// ├─────────┼─────┼─────┤
// │  row0   │  2  │  5  │
// │  row1   │  3  │  6  │
// │  row2   │  4  │  7  │
// └─────────┴─────┴─────┘
// Shape: [ 3, 2 ]

// append a matrix with axis = 0
x.append([
  ["a", "b", "c"],
  ["d", "e", "f"],
]).print()
// ┌─────────┬─────┬─────┬───────────┐
// │ (index) │ foo │ bar │   col2    │
// ├─────────┼─────┼─────┼───────────┤
// │  row0   │  2  │  5  │ undefined │
// │  row1   │  3  │  6  │ undefined │
// │  row2   │  4  │  7  │ undefined │
// │  row3   │ 'a' │ 'b' │    'c'    │
// │  row4   │ 'd' │ 'e' │    'f'    │
// └─────────┴─────┴─────┴───────────┘
// Shape: [ 5, 3 ]

// append a matrix with axis = 1
x.append(
  [
    ["a", "b", "c"],
    ["d", "e", "f"],
  ],
  1
).print()
// ┌─────────┬─────┬─────┬───────────┬───────────┬───────────┐
// │ (index) │ foo │ bar │   col2    │   col3    │   col4    │
// ├─────────┼─────┼─────┼───────────┼───────────┼───────────┤
// │  row0   │  2  │  5  │    'a'    │    'b'    │    'c'    │
// │  row1   │  3  │  6  │    'd'    │    'e'    │    'f'    │
// │  row2   │  4  │  7  │ undefined │ undefined │ undefined │
// └─────────┴─────┴─────┴───────────┴───────────┴───────────┘
// Shape: [ 3, 5 ]

So, when appending vectors, the vector either gets treated as a row or transposed and treated as a column; but that transposition does not occur with matrices: matrices either get stacked directly below or directly to the right, and in neither case are they transposed.

Finally, when appending Series or DataFrame objects, the method will try to place values in the correct column (if axis is 0) or row (if axis is 1) before tacking on new columns or rows. For example:

const x = new DataFrame({ foo: [2, 3, 4], bar: [5, 6, 7] })
x.print()
// ┌─────────┬─────┬─────┐
// │ (index) │ foo │ bar │
// ├─────────┼─────┼─────┤
// │  row0   │  2  │  5  │
// │  row1   │  3  │  6  │
// │  row2   │  4  │  7  │
// └─────────┴─────┴─────┘
// Shape: [ 3, 2 ]

const y = new DataFrame({ bar: [10, 20, 30, 40], baz: [50, 60, 70, 80] })
y.print()
// ┌─────────┬─────┬─────┐
// │ (index) │ bar │ baz │
// ├─────────┼─────┼─────┤
// │  row0   │ 10  │ 50  │
// │  row1   │ 20  │ 60  │
// │  row2   │ 30  │ 70  │
// │  row3   │ 40  │ 80  │
// └─────────┴─────┴─────┘
// Shape: [ 4, 2 ]

// note that `x` and `y` both have a "bar" column; so the values in "bar" in
// `x` will be inserted below the "bar" values in the original `DataFrame`
x.append(y).print()
// ┌─────────┬───────────┬─────┬───────────┐
// │ (index) │    foo    │ bar │    baz    │
// ├─────────┼───────────┼─────┼───────────┤
// │  row0   │     2     │  5  │ undefined │
// │  row1   │     3     │  6  │ undefined │
// │  row2   │     4     │  7  │ undefined │
// │  row3   │ undefined │ 10  │    50     │
// │  row4   │ undefined │ 20  │    60     │
// │  row5   │ undefined │ 30  │    70     │
// │  row6   │ undefined │ 40  │    80     │
// └─────────┴───────────┴─────┴───────────┘
// Shape: [ 7, 3 ]

The same sort of thing happens when the axis is 1, except that in that case, the rows of the DataFrame objects are matched up as one is appended to the other.

DataFrame.apply(fn, axis=0)

Returns a copy of the original DataFrame in which fn has been applied to each column Series (if axis is 0) or row Series (if axis is 1).

DataFrame.assign(name, values) or DataFrame.assign(obj)

Returns a copy of the original DataFrame to which new values have been assigned in new columns. In the first form, a column name and a corresponding list of values are passed into the method, and the returned DataFrame will contain the original data plus the new column. In the second form, the object passed as obj should contain key-value pairs representing column names and their corresponding values. The second form, therefore, is more convenient when assigning multiple columns at once.

DataFrame.clear()

Returns a copy of the original DataFrame in which all of the values have been replaced with undefined (but the shape is still the same).

DataFrame.copy()

Returns a copy of the original DataFrame.

DataFrame.dropColumns(columns)

Returns a copy of the original DataFrame from which the given columns have been dropped. A whole number, a string, or an array of whole numbers or strings can be passed as columns.

DataFrame.dropRows(rows)

Returns a copy of the original DataFrame from which the given rows have been dropped. A whole number, a string, or an array of whole numbers or strings can be passed as rows.

DataFrame.dropMissing(axis=0, condition="any", threshold=0)

Returns a copy of the original DataFrame from which rows or columns containing missing values (i.e., undefined or null values) have been dropped if condition is met or the threshold is exceeded. The condition isn't a boolean as you might expect; instead, it's a string from ["any", "all", "none"]. If the condition is "any", then any missing values in a row or column will cause that row or column to be dropped. If the condition is "all", then a row or column will be dropped only if all of its values are missing. In the above two cases, the threshold value isn't considered. But if the threshold is set to a value greater than 0, then condition will automatically be set to "none", and then a row or column will be dropped only of the number of missing values it contains exceeds the threshold. If axis is 0, then rows are dropped; and if axis is 1, then columns are dropped.

DataFrame.dropNaN(axis=0, condition="any", threshold=0)

Returns a copy of the original DataFrame from which rows or columns containing NaN values have been dropped if condition is met or the threshold is exceeded. The condition isn't a boolean as you might expect; instead, it's a string from ["any", "all", "none"]. If the condition is "any", then any missing values in a row or column will cause that row or column to be dropped. If the condition is "all", then a row or column will be dropped only if all of its values are missing. In the above two cases, the threshold value isn't considered. But if the threshold is set to a value greater than 0, then condition will automatically be set to "none", and then a row or column will be dropped only of the number of missing values it contains exceeds the threshold. If axis is 0, then rows are dropped; and if axis is 1, then columns are dropped.

DataFrame.drop(rows, columns)

Returns of a copy of the original DataFrame from which the given rows and columns have been dropped. If you don't want to drop any rows, then pass null as that argument; and the same applies for columns. The rows and columns values can be whole numbers, strings, or arrays of whole numbers or strings.

DataFrame.filter(fn, axis=0)

Returns a copy of the original DataFrame with rows or columns filtered out by fn. If axis is 0, then row Series objects will be passed into fn; and if axis is 1, then column Series objects will be passed into fn. If fn returns false for any input, then that input will be filtered out.

DataFrame.getDummies(columns)

Returns a DataFrame containing one-hot encodings of the given columns in the original DataFrame. Note that in most applications of one-hot encodings, if a column contains n unique values, then (n - 1) columns will be returned. But this implementation returns n columns just in case you have other uses for it. But dropping the extra column is easy with the drop method.

DataFrame.getSubsetByIndices(rowIndices, colIndices)

Returns a copy of the original DataFrame only containing the rows and columns specified by rowIndices and colIndices, where those values are one of null, whole numbers, or arrays of whole numbers. This method is mostly used internally, though you can use it if you want; the easier way is just to use the get method.

DataFrame.getSubsetByNames(rowNames, colNames)

Returns a copy of the original DataFrame only containing the rows and columns specified by rowNames and colNames, where those values are one of null, strings, or arrays of strings. This method is mostly used internally, though you can use it if you want; the easier way is just to use the get method.

DataFrame.get(rows, columns)

Returns a copy of the original DataFrame only containing the rows and columns specified by rows and columns, where those values are one of null, whole numbers, strings, or arrays of whole numbers or strings.

DataFrame.join(x, axis=0)

Same as DataFrame.append.

DataFrame.onHotEncode(columns)

Identical to DataFrame.getDummies.

DataFrame.print()

Prints the DataFrame to the console in a pretty way and then returns the DataFrame.

DataFrame.resetIndex()

Returns a copy of the original DataFrame in which the list of row names have been reverted to their original values, like "row0", "row1", etc.

DataFrame.shuffle(axis=0)

Returns a copy of the original DataFrame in which the rows (if axis is 0) or columns (if axis is 1) have been put in a random order.

DataFrame.sort(columns, directions) or DataFrame.sort(fn, directions)

Returns a copy of the original DataFrame sorted by the given columns or fn. The columns argument can be a whole number, string, or array of whole numbers or strings can be given. The fn argument can be a comparison function that will compare Series against each other. By default, all of the columns will be sorted in ascending order; but to override this behavior, pass a boolean value, array of boolean values, or array of "ascending" / "descending" string values as directions.

DataFrame.toJSONString()

Returns a stringified copy of the original DataFrame in JSON format. By default, the JSON object will have a structure like this:

{
  "row0": {
    "col0": 5,
    "col1": 7,
    "col2": 9,
    ...
  },

  "row1": {
    ...
  },

  "row2": {
    ...
  },

  ...
}

However, the nesting can be reversed (putting the column names at the shallowest level and the row names at the next level) by setting axis to 1.

DataFrame.saveAsJSON(path, axis=0)

Writes the DataFrame to disk at path in JSON format. See the DataFrame.toJSONString method for more info about the structure of the object written to disk and the meaning of the axis value. In a browser, only a filename need be passed as path since the file will just be downloaded in whatever way the browser usually downloads files. In Node, however, a filesystem path (relative or absolute) must be passed as path.

DataFrame.toDetailedObject(axis=0)

Returns an object in the format described above in the DataFrame.toJSONString method. See that method for more info about the structure of the returned object and the meaning of the axis value.

DataFrame.toObject()

Returns an object in which they keys are column names and the values are the arrays of values associated with each column name.

DataFrame.transpose()

Returns a copy of the original DataFrame in which the values (and row names and column names) have been flipped across the main diagonal (from top left to bottom right).

---

decycle(x)

Returns a copy of x in which cyclic references are replaced with strings indicating the path through the object to the referenced value. In this context, the path "/" refers to the root object (x), and a path like "/foo/bar" refers to x.foo.bar.

For example:

const person = { name: "Josh" }
person.self = person
console.log(person)
// <ref *1> { name: 'Josh', self: [Circular *1] }

const { decycle } = require("@jrc03c/js-math-tools")
console.log(decycle(person))
// { name: 'Josh', self: '<reference to "/">' }

Here's an example of what the path might look like in a more deeply nested circular reference:

const util = require("util")
const x = { foo: { bar: { baz: { hello: "world" } } } }
x.foo.bar.baz.parent = x.foo.bar

console.log(util.inspect(x, { depth: Infinity, compact: false }))
// {
//   foo: {
//     bar: <ref *1> {
//       baz: {
//         hello: 'world',
//         parent: [Circular *1]
//       }
//     }
//   }
// }

console.log(util.inspect(decycle(x), { depth: Infinity, compact: false }))
// {
//   foo: {
//     bar: {
//       baz: {
//         hello: 'world',
//         parent: '<reference to "/foo/bar">'
//       }
//     }
//   }
// }

---

diff(a, b)

Returns the difference between set(a) and set(b); i.e., the set of values that are included in a and not included in b. Note that the order of the arguments matters. If a and b aren't identical, then diff(a, b) won't necessarily produce the same results as diff(b, a). For example:

const a = [2, 3, 4]
const b = [4, 5, 6]

console.log(diff(a, b))
// [2, 3]

console.log(diff(b, a))
// [5, 6]

distance(a, b)

Returns the 2-norm (i.e., the Euclidean distance) between a and b. And though a and b can have any shape, they must have the same shape as each other (unless either is an individual numbers). For example:

console.log(distance([3, 4], 0))
// 5

console.log(distance([3, 4], [5, 6, 7]))
// error!

divide(a, b)

Returns the result of a divided by b.

dot(a, b)

Returns the dot product of a and b, which can be 1- or 2-dimensional arrays, Series instances, or DataFrame instances.

dropMissing(x)

Returns a copy of x without any undefined or null values. Note that dropping values from nested arrays and DataFrame instances may result in jagged arrays.

dropMissingPairwise(a, b)

Returns copies of a and b without any undefined or null values. Note that a and b must have the same shape. Also note that dropping values from nested arrays and DataFrame instances may result in jagged arrays.

dropNaN(x)

Returns a copy of x without any non-numerical values. Note that dropping values from nested arrays and DataFrame instances may result in jagged arrays.

dropNaNPairwise(a, b)

Returns copies of arbitrarily nested arrays a and b without any non-numerical values. Note that a and b must have the same shape. Also note that dropping values from nested arrays and DataFrame instances may result in jagged arrays.

dropUndefined(x)

Identical to dropMissing.

exp(x)

Returns e to the power(s) of x.

factorial(x)

Returns the factorial(s) of x.

find(x, fn)

Returns the first value that causes the fn function to evaluate to true when evaluated on every item in x. Note that x can be an arbitrarily nested array (or Series or DataFrame) or an object. All of those types are searched to any depth.

findAll(x, fn)

Returns all of the values that cause the fn function to evaluate to true when evaluated on every item in x. Note that x can be an arbitrarily nested array (or Series or DataFrame) or an object. All of those types are searched to any depth.

flatten(x)

Returns a 1-dimensional copy of x.

float(x)

Returns x converted to floating point number(s).

floor(x)

Returns the floor(s) of x.

identity(n)

Returns an identity matrix of size n ✕ n.

indexOf(x, matcher)

Returns the index of the first value that matches the matcher, where matcher is either a function or any other value type. If matcher is a function, then indexOf returns the first value for which the matcher function returns true when evaluated on all values of x. If matcher is a value of any other type, then the indexOf function returns the index of the first value that is equal to matcher (where equality is tested using the isEqual function, meaning that even non-identical objects can be considered to be equal if all of their members are equal). Note that x can be an arbitrarily nested array (or Series or DataFrame) or an object. All of those types are searched to any depth. If x is an object, then the returned index represents the path down through the keys and values of the object to the relevant value. Also note that keys of objects are not evaluated; only an object's values are evaluated.

inferType(x)

Given a vector x, returns an object with these properties:

• type = one of these:
  • boolean
  • date
  • null
  • number
  • object
  • string
• values = all of the values in x cast into the inferred type

Although you can pass DataFrame instances or other (>1)-dimensional arrays into this function, be aware that any data will be flattened into a vector before its type is inferred.

Inferrable types

The inferrable types listed above correspond roughly to the main JS data types, but there are a few exceptions.

First, "date" is not a data type in JS. There are Date objects, of course, but typeof new Date() returns "object". Since dates are commonly stored in datasets and because they come with their own particular set of challenges, I've set these apart as their own data type so that they won't be conflated with other kinds of objects.

Second, arrays are not among the inferrable types because x is assumed to be a vector, and allowing x to be potentially nested makes it difficult to determine whether x is supposed to be a vector, matrix, tensor, mixed data structure, etc. Thus any arrays with more than 1 dimension (or DataFrame instances) that are passed into this function will be flattened before their types are inferred.

Alternate values

It's pretty common for datasets to contain boolean-ish and null-ish values, by which I mean string values like "yes", "no", "NaN", "NONE", "undefined", "NA", etc., or even empty strings. Those values are sort of like boolean or null values, but they're not always consistent or suitable for immediate inference by something like the JSON.parse function. Therefore, the inferType function tries to look for such values. For example, if it encounters a string value like "YES", it counts that value as a boolean, not as a string! In fact, counting a value as a string is the function's very last resort since it's so common for values to be included accidentally as strings. For example, if you use a library like papaparse to read a CSV file from disk, then the returned data may just be a matrix of strings and nothing more; i.e., it's probably pretty common for such libraries to avoid making inferences about the data, leaving such work up to the user. So, when the inferType function encounters a string value, it does its best to cast it into any other data type first; but if it fails to find any suitable type, it gives up and assumes that the value is just a plain ol' string. Here are the lists of boolean-ish and null-ish values that are parsed as booleans and nulls respectively (accounting for case sensitivity, of course):

Nulls:

• ""
• "n/a"
• "na"
• "nan"
• "none"
• "null"
• "undefined"

Booleans:

• "true"
• "false"
• "yes"
• "no"

This function doesn't cover every possible edge case, of course; it should probably only be expected to work on an average dataset. If your data is especially unusual, please consider manually inferring types some other way.

int(x)

Returns x converted to integer(s).

intersect(a, b, c, ...)

Returns the intersection of the given arrays, Series instances, or DataFrame instances; i.e., the set of values that are in all of the given items.

inverse(x)

Returns the inverse of a square matrix or DataFrame x.

isArray(x)

Returns true if x is an array; otherwise, returns false.

isBoolean(x)

Returns true if x is a boolean value; otherwise, returns false.

isBrowser()

Returns true if called in a browser environment (whether in the main thread or in a Web Worker) or false in a Node environment. It's definitely not foolproof and can probably easily be fooled by configuring certain built-in global variables. But as long as you're not modifying those variables (e.g. window in the browser), it should be fairly accurate.

Also, this obviously isn't a math function, but I'm including it here because (1) it's a super useful utility function, and (2) I needed it for this library anyway, so why not add it to the public API?

isDataFrame(x)

Returns true if x is a DataFrame; otherwise, returns false.

isEqual(a, b)

Returns true if a and b are equal; otherwise, returns false. Equality in the context of this function means that the two items are functionally the same, even if they're not literally the same object in memory. For example:

const a = { hello: "world" }
const b = { hello: "world" }
console.log(isEqual(a, b))
// true

In the above example, a and b are not literally the same object in memory, but they are nevertheless functionally equivalent; i.e., they have all the same properties, methods, values, etc. Note that there may be some ways in which this function can be tricked, especially as regards non-enumerable properties. But generally speaking, if an object has the same enumerable properties, methods, values, etc., then isEqual will return true.

isFunction(x)

Returns true if x is a function; otherwise, returns false.

isJagged(x)

Returns true if x is a jagged array; otherwise, returns false.

isNested(x)

Returns true if x is a nested array; otherwise, returns false.

isNumber(x)

Returns true if x is a number; otherwise, returns false.

isObject(x)

Returns true if x is an object; otherwise, returns false. Weirdly, in JS, null is considered an object (which you can see for yourself with typeof null). But for the purposes of this function, null is not considered to be an object in the usual sense; i.e., isObject(null) will return false.

isSeries(x)

Returns true if x is a Series; otherwise, returns false.

isString(x)

Returns true if x is a string; otherwise, returns false.

isUndefined(x)

Returns true if x is undefined or null; otherwise, returns false. Note that NaN values are considered to be defined.

lerp(a, b, f)

Returns the linear interpolation from a to b at fraction f.

log(x)

Returns the natural log(s) of x.

MathError(message)

This class only exists because (1) I wanted to make it clear when errors where coming specifically from this library, and (2) I wanted to color-code the errors in the command line. Those are the only two ways in which MathError differs from Error.

max(x)

Returns the maximum value in x.

mean(x)

Returns the average value in x.

median(x)

Returns the median value in x.

min(x)

Returns the minimum value in x.

mode(x)

Returns the mode(s) of x. Note that an array will always be returned since there can be potentially be multiple modes in x.

multiply(a, b, c, ...)

Returns the product of the given values. See the note under product for a description of how multiply and scale differ from product.

ndarray(shape)

Returns an n-dimensional array where shape is an array of whole numbers. For example, ndarray([5, 10]) would return a 5 ✕ 10 matrix.

normal(shape)

Returns an n-dimensional array of normally-distributed random numbers where shape is undefined, null, or an array of whole numbers. If shape is undefined or null, then a single number will be returned; otherwise, an array will be returned.

ones(shape)

Returns an n-dimensional array of 1s where shape is an array of whole numbers.

permutations(x, r)

Given an arbitrarily nested array x, returns all possible permutations of r items from x. Note that any nesting of x will be ignored — i.e., x will be "flattened" into a 1-dimensional array before getting the permutations — so it won't be possible with this function to get permutations of arrays.

product(x)

Returns the product of all of the values in arbitrarily nested array x. Note that product differs slightly in functionality from multiply and scale in that product only accepts arrays, Series instances, and DataFrame instances. Just as you might want to get the sum of values in an array, so you might also want to get the product of values in an array. If you want to multiply values by each other (whether those values are numbers, arrays, Series instances, or DataFrame instances), you'll want to use the multiply or scale functions.

pow(a, b)

Returns a to the power(s) of b.

print(x)

Prints x to the console. For the most part, this function is basically the same as console.log. The only additional functionality it provides is printing DataFrame and Series objects nicely (most of the time).

random(shape)

Returns an n-dimensional array of random numbers in the range [0, 1] where shape is undefined, null, or an array of whole numbers. If shape is undefined or null, then a single number will be returned; otherwise, an array will be returned.

range(a, b, step=1)

Returns an array of numbers in the range [a, b) incremented by step.

remap(x, a, b, c, d)

Returns x remapped from the range [a, b] to the range [c, d]. For example, remap(2, 0, 10, 0, 100) would return 20.

reshape(x, shape)

Returns x reshaped into shape shape.

reverse(x)

Returns a reversed copy of x. Only reverses at the shallowest level.

round(x)

Returns the next lowest or highest integer(s) when x.

scale(a, b, c, ...)

Identical to multiply.

seed(n)

Seeds the PRNG with n, an integer.

---

Series(x)

The Series class is similar to pandas' Series. They at least represent the same kind of data (named 1-dimensional arrays), though they probably differ in many of their members.

The constructor for a Series can optionally receive a value x, which can be any of these:

• another Series
• a 1-dimensional array
• an object whose lone key-value pair represents the name and values, respectively

Series.values

A 1-dimensional array containing the values held by the Series. Technically, values is a getter-setter pair that stores its data in a hidden _values property. While it's possible to set the _values property directly, this is strongly discouraged because it bypasses sanity checks on the data, like checking that new data is 1-dimensional, etc.

Series.index

An array of names for each value. If you like, you can think of them as "row" or "column" names, even though there technically aren't any rows or columns in a Series. If you get a single row or column from a DataFrame, then the returned value will be a Series whose index represents the column names or row names, respectively, of the originating DataFrame.

Technically, index is a getter-setter pair that stores its data in a hidden _index property. While it's possible to set the _index property directly, this is strongly discouraged because it bypasses sanity checks on the data, like checking to make sure that the length of the new index array is the same as the length of the values array, etc.

Series.name

The name of the Series object. If you get a single row or column out of a DataFrame, then the returned value will be a Series whose name represents the row name or column name, respectively, of the values in the originating DataFrame.

Series.shape

A read-only array containing only a single value: the length of the values array.

Series.length

A read-only value representing the length of the values array.

Series.isEmpty

A read-only boolean value that is true if the Series contains no data or false otherwise.

Series.append(x)

Returns a copy of the original Series with x appended to it. A single value, an array of values, or another Series can be passed as x.

Series.apply(fn)

Returns a copy of the original Series with fn applied to every value.

Series.concat(x)

Same as Series.append.

Series.dropMissing()

Returns a copy of the original Series without null or undefined values.

Series.dropNaN()

Returns a copy of the original Series without NaN values.

Series.filter(fn)

Returns a copy of the original Series with only those values that return true when passed into function fn.

Series.getSubsetByIndices(indices)

Returns a copy of the original Series containing only the values indicated by indices. A single whole number or an array of whole numbers can be passed as indices. This method is mostly used internally, though you can use it if you want; the easier way is just to use the get method.

Series.getSubsetByNames(names)

Returns a copy of the original Series containing only the values indicated by names. A single string or an array of strings can be passed as indices. This method is mostly used internally, though you can use it if you want; the easier way is just to use the get method.

Series.get(selectors)

Returns a copy of the original Series containing only the values indicated by selectors. A single whole number, a single string, or an array of whole numbers or strings can be passed as selectors.

Series.print()

Prints the Series to the console in a pretty way, and then returns the Series.

Series.sortByIndex()

Returns a copy of the original Series sorted by its index values.

Series.sort(fn, ascending=true)

Returns a sorted copy of the original Series. If fn is undefined, then the returned copy will be sorted by its values; otherwise, the copy will be sorted by fn.

Series.toObject()

Returns an object with this form:

{
  [Series.name]: {
    [Series.index[0]]: Series.values[0],
    [Series.index[1]]: Series.values[1],
    ...
  }
}

---

set(x)

Returns the (unsorted) unique values in x.

shape(x)

Returns the shape of x. If x is "smooth" (i.e., non-jagged), then the returned shape will be a 1-dimensional array; but if x is jagged, then the returned shape will be an array with a mix of numbers and sub-arrays. For example:

const smooth = [
  [2, 3, 4],
  [5, 6, 7],
]

console.log(shape(smooth))
// [ 2, 3 ]

const jagged = [2, [3, 4], 5]
console.log(shape(jagged))
// [ 3, [ undefined, 2, undefined ] ]

In the case of smooth above, the returned shape represents the number of rows and columns respectively; i.e., there are 2 rows and 3 columns. But instead of thinking of this shape as [2 rows, 3 columns], we could also think of it as [outer array length is 2, inner array length is 3]. This way of thinking about it will hopefully clarify what's going on in the case of jagged.

In the case of jagged, the first part of the shape, 3, represents the length of the outer array. You can also think of it as having 3 "rows", but that might be a little confusing since we can see that not all of the items are actually rows; so thinking of 3 as the length of the outer array makes the most sense here, I think. If it helps, you can think of jagged as looking like this: [?, ?, ?]. Now, to get the second part of the shape, we need to figure out how long each inner array in jagged is. Well, the first item in jagged is 2, which isn't an array, and thus has a length of undefined; but the second item is [3, 4], which is an array with length of 2; and the third item is 5, which isn't an array, and thus has a length of undefined.

If all of the items in jagged were arrays with length 2, then its shape would be [3, 2]. But because the inner "array" lengths don't all match up, a single number won't capture enough information about what jagged looks like on the inside; so instead we place an array in the second slot of the shape to indicate that each item has a different length.

shuffle(x)

Returns a shuffled copy of x. Note that only the shallowest level of x is shuffled.

sign(x)

Returns -1(s), 0(s), or 1(s) if x is less than 0, equal to 0 or greater than 0, respectively.

sin(s)

Returns the sine(s) of x.

sort(x, fn)

Sorts x by function fn. This function is identical to Array.prototype.sort except that it does not sort x in-place; instead it returns a sorted copy of x.

sqrt(x)

Returns the square root(s) of x.

std(x)

Returns the standard deviation of the values in x.

stdev(x)

Identical to std.

subtract(a, b)

Returns the difference of a and b.

sum(x)

Returns the sum of all values in x. The difference between add and sum is that sum only accepts arrays. In other words, use add when you want to add up multiple distinct values passed as arguments (where those arguments can be numbers, arrays, Series instances, or DataFrame instances); and use sum when you want to add up all of the values in a single array.

tan(x)

Returns the tangent(s) of x.

time(fn)

Identical to timeSync.

timeSync(fn)

Returns the time in milliseconds that it takes for synchronous function fn to run.

timeAsync(fn)

Returns a Promise that resolves to the time in milliseconds that it takes for asynchronous function fn to run.

transpose(x)

Returns the transpose of a 1- or 2-dimensional array (or Series or DataFrame) x.

union(a, b, c, ...)

Returns the union of the sets of values in the given items.

variance(x)

Returns the variance of the values in x.

vectorize(fn)

Returns a function that operates on individual values, arrays, Series instances, or DataFrame instances. It's a little like numpy's vectorize function except that numpy probably has a bunch of fancy optimizations that make vectorized operations very fast. In this library, though, no optimizations are applied; this function merely makes it easier for individual functions to operate on multiple types of data containers.

For example, the Math.sin function only accepts a single value. But by using the vectorize function, we can create a function that accepts either single values or arrays of values:

const sin = vectorize(Math.sin)

console.log(sin(0))
// 0

const angles = [0, Math.PI / 4, Math.PI / 2]
console.log(sin(angles))
// [ 0, 0.7071067811865475, 1 ]

This also works when the function requires multiple arguments. For example, the add function in this library accepts two arguments and has been passed through the vectorize function so that it accepts individual values or arrays of values:

console.log(add(2, 3))
// 5

console.log(add([2, 3, 4], [5, 6, 7]))
// [ 7, 9, 11 ]

console.log(add(2, [5, 6, 7]))
// [ 7, 8, 9 ]

console.log(add([2, 3, 4], 5))
// [ 7, 8, 9 ]

At the moment, though, the function is pretty naive about the shapes of the arrays; e.g., it'll throw an error in the add function if both arguments are arrays of differing shapes.

Finally, a vectorized function can also accept Series and DataFrame instances. When this happens, the function will try to return an object of the same type, if possible. For example, using the add function above to add a Series and a single number will result in a new Series with the same name and index as the original. But if two Series instances are passed into add, then a new Series will be returned that bears a default name and default index (since there's no obvious way to choose which Series name or index to prefer).

zeros(shape)

Returns an n-dimensional array of 0s where shape is an array of whole numbers.

zip(a, b, c, ...)

Returns a new array or new DataFrame in which the shallowest values of the given arrays (a, b, c, etc.) are stacked side-by-side. For example:

const a = [2, 3, 4]
const b = [5, 6, 7, 8]
const c = zip(a, b)
console.log(c)
// [
//   [ 2, 5 ],
//   [ 3, 6 ],
//   [ 4, 7 ],
//   [ undefined, 8 ]
// ]

Notes

Jagged arrays

Note that for all of the above, "arbitrarily nested array" typically means a non-jagged array. Jagged arrays (AKA "ragged" arrays) are arrays in which nested arrays have inconsistent lengths. For example, this — [[1], [2, 3], [4, 5, 6]] — would be a jagged array because the sub-arrays have lengths 1, 2, and 3 respectively. Many of the above functions expect non-jagged arrays. (Is there a technical term for non-jagged arrays? Maybe "even" arrays? Or "smooth" arrays? I'll go with "smooth" for now.) Some of them may not throw an error when passed a jagged array, though; they may quietly do their work and return an unexpected result. For example, the dropNaN function will happily drop NaN values from nested arrays, potentially leaving them jagged as a result. I've tried to let the functions operate this way when it's not strictly necessary for them to operate on smooth arrays. When a smooth array is required, an error should be thrown if the function receives a jagged array instead.

Random numbers

The PRNG (pseudo-random number generator) implemented in this library uses the xoroshiro256++ algorithm, in case that matters to you. To seed the PRNG, pass a number into the seed function. Large integers tend to do better than small ones. The random, normal, and shuffle functions can all be seeded. For example:

const { random, seed } = require("js-math-tools")

seed(230498349)
random(5)
// [
//   0.018838884276985594,
//   0.5304929121766935,
//   0.7364885210604148,
//   0.005920131518888056,
//   0.8434281063536071
// ]

seed(230498349)
random(5)
// [
//   0.018838884276985594,
//   0.5304929121766935,
//   0.7364885210604148,
//   0.005920131518888056,
//   0.8434281063536071
// ]

Do be aware, though, that there's no such thing in this library as having multiple PRNGs at the same time, each with different seeds. Instead, all of the randomization functions share the same seeding because they all share the same core random function.

Troubleshooting

Note that in certain build setups, errors may be thrown when you try to import this library. The error message is usually: "Big integer literals are not available in the configured target environment". To be really honest, I have only the haziest of ideas about what this means or why it happens, but this solution works for (e.g.) Vite and perhaps Svelte — though I haven't tried the latter. If you're not using Vite or Svelte, perhaps that solution will guide you in the right direction for whatever build setup you have. If you do find solutions for other build setups, please let me know and I'll add them to this section.

To do

• Add a method that makes it easy to merge DataFrames along a certain key. For example, it'd be nice to be able to merge multiple datasets that have a unique ID column with values that match across the sets.
• Add a simplex noise function.
• Convert to TS?
• Organize the files a little better? Right now, they're just in a big heap. It might be better, though, to classify them as randomization functions, statistics functions, etc.
• Work out a more coherent theory of when to return false / NaN / undefined / null values versus when to throw errors.
• Keep documentation up-to-date!