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nodejs-polars

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nodejs-polars - npm Package Compare versions

Comparing version 0.8.2 to 0.8.3

bin/html.d.ts
bin/html.js

28

bin/dataframe.d.ts

@@ -8,5 +8,5 @@ /// <reference types="node" />

import { Writable } from "stream";
import { WriteCsvOptions, WriteIPCOptions, WriteParquetOptions, WriteAvroOptions, FillNullStrategy, JoinOptions } from "./types";
import { FillNullStrategy, JoinOptions, WriteAvroOptions, WriteCsvOptions, WriteIPCOptions, WriteParquetOptions } from "./types";
import { DataType } from "./datatypes";
import { ColumnSelection, ColumnsOrExpr, ValueOrArray, ExprOrString } from "./utils";
import { ColumnSelection, ColumnsOrExpr, ExprOrString, ValueOrArray } from "./utils";
import { Arithmetic, Deserialize, GroupByOps, Sample, Serialize } from "./shared_traits";

@@ -73,3 +73,2 @@ declare const inspect: unique symbol;

*
*
* > df.writeJSON({format:"json"})

@@ -934,3 +933,2 @@ * `[ {"foo":1.0,"bar":"a"}, {"foo":2.0,"bar":"b"}, {"foo":3.0,"bar":"c"}]`

/**
*
* Create a spreadsheet-style pivot table as a DataFrame.

@@ -1284,3 +1282,2 @@ *

/**
*
* @example

@@ -1358,2 +1355,10 @@ * ```

/**
* Converts dataframe object into a {@link TabularDataResource}
*/
toDataResource(): TabularDataResource;
/**
* Converts dataframe object into HTML
*/
toHTML(): string;
/**
* Converts dataframe object into column oriented javascript objects

@@ -1613,3 +1618,16 @@ * @example

}
type DataResourceField = {
name: string;
type: string;
};
/**
* Tabular Data Resource from https://specs.frictionlessdata.io/schemas/tabular-data-resource.json,
*/
type TabularDataResource = {
data: any[];
schema: {
fields: DataResourceField[];
};
};
/**
* @ignore

@@ -1616,0 +1634,0 @@ */

77

bin/dataframe.js

@@ -17,4 +17,6 @@ "use strict";

const utils_1 = require("./utils");
const html_1 = require("./html");
const functions_2 = require("./lazy/functions");
const inspect = Symbol.for("nodejs.util.inspect.custom");
const jupyterDisplay = Symbol.for("Jupyter.display");
function prepareOtherArg(anyValue) {

@@ -31,2 +33,26 @@ if (series_1.Series.isSeries(anyValue)) {

}
function mapPolarsTypeToJSONSchema(colType) {
const typeMapping = {
Null: "null",
Bool: "boolean",
Int8: "integer",
Int16: "integer",
Int32: "integer",
Int64: "integer",
UInt8: "integer",
UInt16: "integer",
UInt32: "integer",
UInt64: "integer",
Float32: "number",
Float64: "number",
Date: "string",
Datetime: "string",
Utf8: "string",
Categorical: "string",
List: "array",
Struct: "object",
};
const dataType = colType.variant;
return typeMapping[dataType] || "string";
}
/**

@@ -78,2 +104,20 @@ * @ignore

},
/**
* Return back text/html and application/vnd.dataresource+json representations
* of the DataFrame. This is intended to be a simple view of the DataFrame
* inside of notebooks.
*
* @returns Media bundle / mimetype keys for Jupyter frontends
*/
[jupyterDisplay]() {
let rows = 50;
if (process.env.POLARS_FMT_MAX_ROWS) {
rows = parseInt(process.env.POLARS_FMT_MAX_ROWS);
}
const limited = this.limit(rows);
return {
"application/vnd.dataresource+json": limited.toDataResource(),
"text/html": limited.toHTML(),
};
},
get schema() {

@@ -334,3 +378,3 @@ return this.getColumns().reduce((acc, curr) => {

sample(opts, frac, withReplacement = false, seed) {
// rome-ignore lint/style/noArguments: <explanation>
// biome-ignore lint/style/noArguments: <explanation>
if (arguments.length === 0) {

@@ -437,2 +481,30 @@ return wrap("sampleN", 1, withReplacement, false, seed);

},
toHTML() {
let htmlTable = "<table>";
// Add table headers
htmlTable += "<thead><tr>";
this.getColumns().forEach((field) => {
htmlTable += `<th>${(0, html_1.escapeHTML)(field.name)}</th>`;
});
htmlTable += "</tr></thead>";
// Add table data
htmlTable += "<tbody>";
this.toRecords().forEach((row) => {
htmlTable += "<tr>";
this.getColumns().forEach((field) => {
htmlTable += `<td>${(0, html_1.escapeHTML)(String(row[field.name]))}</td>`;
});
htmlTable += "</tr>";
});
htmlTable += "</tbody></table>";
return htmlTable;
},
toDataResource() {
const data = this.toRecords();
const fields = this.getColumns().map((column) => ({
name: column.name,
type: mapPolarsTypeToJSONSchema(column.dtype),
}));
return { data, schema: { fields } };
},
toObject() {

@@ -624,2 +696,5 @@ return this.getColumns().reduce((acc, curr) => {

has(target, p) {
if (p === jupyterDisplay) {
return true;
}
return target.columns.includes(p);

@@ -626,0 +701,0 @@ },

37

bin/functions.d.ts

@@ -27,7 +27,7 @@ import { Series } from "./series";

* - Horizontal: Stacks Series horizontally and fills with nulls if the lengths don't match.
*
* - Diagonal: Finds a union between the column schemas and fills missing column values with ``null``.
* @example
* > const df1 = pl.DataFrame({"a": [1], "b": [3]})
* > const df2 = pl.DataFrame({"a": [2], "b": [4]})
* > pl.concat([df1, df2])
* > const df1 = pl.DataFrame({"a": [1], "b": [3]});
* > const df2 = pl.DataFrame({"a": [2], "b": [4]});
* > pl.concat([df1, df2]);
* shape: (2, 2)

@@ -43,2 +43,31 @@ * ┌─────┬─────┐

* └─────┴─────┘
*
* > const a = pl.DataFrame({ a: ["a", "b"], b: [1, 2] });
* > const b = pl.DataFrame({ c: [5, 6], d: [7, 8], e: [9, 10]});
* > pl.concat([a, b], { how: "horizontal" });
*
* shape: (2, 5)
* ┌─────┬─────┬─────┬─────┬──────┐
* │ a ┆ b ┆ c ┆ d ┆ e │
* │ --- ┆ --- ┆ --- ┆ --- ┆ --- │
* │ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
* ╞═════╪═════╪═════╪═════╪══════╡
* │ a ┆ 1.0 ┆ 5.0 ┆ 7.0 ┆ 9.0 │
* │ b ┆ 2.0 ┆ 6.0 ┆ 8.0 ┆ 10.0 │
* └─────┴─────┴─────┴─────┴──────┘
*
* > const df_d1 = pl.DataFrame({"a": [1], "b": [3]});
* > const df_d2 = pl.DataFrame({"a": [2], "c": [4]});
* > pl.concat([df_d1, df_d2], { how: "diagonal" });
*
* shape: (2, 3)
* ┌─────┬──────┬──────┐
* │ a ┆ b ┆ c │
* │ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ i64 │
* ╞═════╪══════╪══════╡
* │ 1 ┆ 3 ┆ null │
* │ 2 ┆ null ┆ 4 │
* └─────┴──────┴──────┘
*
*/

@@ -45,0 +74,0 @@ export declare function concat(items: Array<DataFrame>, options?: ConcatOptions): DataFrame;

17

bin/functions.js

@@ -41,8 +41,15 @@ "use strict";

let df;
if (how === "vertical") {
df = items.reduce((acc, curr) => acc.vstack(curr));
switch (how) {
case "vertical":
df = items.reduce((acc, curr) => acc.vstack(curr));
break;
case "horizontal":
df = (0, dataframe_1._DataFrame)(polars_internal_1.default.horizontalConcat(items.map((i) => i.inner())));
break;
case "diagonal":
df = (0, dataframe_1._DataFrame)(polars_internal_1.default.diagonalConcat(items.map((i) => i.inner())));
break;
default:
throw new TypeError("unknown concat how option");
}
else {
df = (0, dataframe_1._DataFrame)(polars_internal_1.default.horizontalConcat(items.map((i) => i.inner())));
}
return rechunk ? df.rechunk() : df;

@@ -49,0 +56,0 @@ }

3

bin/index.d.ts

@@ -93,2 +93,5 @@ import * as series from "./series";

export import anyHorizontal = lazy.anyHorizontal;
export import minHorizontal = lazy.minHorizontal;
export import maxHorizontal = lazy.maxHorizontal;
export import sumHorizontal = lazy.sumHorizontal;
export import spearmanRankCorr = lazy.spearmanRankCorr;

@@ -95,0 +98,0 @@ export import tail = lazy.tail;

7

bin/index.js

@@ -76,7 +76,7 @@ "use strict";

pl.List = datatypes_1.DataType.List;
// rome-ignore lint/suspicious/noShadowRestrictedNames: pl.Date
// biome-ignore lint/suspicious/noShadowRestrictedNames: pl.Date
pl.Date = datatypes_1.DataType.Date;
pl.Datetime = datatypes_1.DataType.Datetime;
pl.Time = datatypes_1.DataType.Time;
// rome-ignore lint/suspicious/noShadowRestrictedNames: pl.Object
// biome-ignore lint/suspicious/noShadowRestrictedNames: pl.Object
pl.Object = datatypes_1.DataType.Object;

@@ -130,2 +130,5 @@ pl.Null = datatypes_1.DataType.Null;

pl.anyHorizontal = lazy.anyHorizontal;
pl.minHorizontal = lazy.minHorizontal;
pl.maxHorizontal = lazy.maxHorizontal;
pl.sumHorizontal = lazy.sumHorizontal;
pl.spearmanRankCorr = lazy.spearmanRankCorr;

@@ -132,0 +135,0 @@ pl.tail = lazy.tail;

4

bin/internals/construction.js

@@ -169,2 +169,4 @@ "use strict";

let orient = options?.orient;
const schema = options?.schema;
const inferSchemaLength = options?.inferSchemaLength;
let dataSeries;

@@ -184,3 +186,3 @@ if (!data.length) {

else if (data[0].constructor.name === "Object") {
const df = polars_internal_1.default.fromRows(data, options);
const df = polars_internal_1.default.fromRows(data, schema, inferSchemaLength);
if (columns) {

@@ -187,0 +189,0 @@ df.columns = columns;

6

bin/io.d.ts

@@ -24,3 +24,3 @@ /// <reference types="node" />

commentChar: string;
quotChar: string;
quoteChar: string;
nullValues: string | Array<string> | Record<string, string>;

@@ -73,3 +73,3 @@ chunkSize: number;

* @param options.commentChar - character that indicates the start of a comment line, for instance '#'.
* @param options.quotChar -character that is used for csv quoting, default = ''. Set to null to turn special handling and escaping of quotes off.
* @param options.quoteChar -character that is used for csv quoting, default = ''. Set to null to turn special handling and escaping of quotes off.
* @param options.nullValues - Values to interpret as null values. You can provide a

@@ -323,3 +323,3 @@ * - `string` -> all values encountered equal to this string will be null

* @param options.commentChar - character that indicates the start of a comment line, for instance '#'.
* @param options.quotChar -character that is used for csv quoting, default = ''. Set to null to turn special handling and escaping of quotes off.
* @param options.quoteChar -character that is used for csv quoting, default = ''. Set to null to turn special handling and escaping of quotes off.
* @param options.nullValues - Values to interpret as null values. You can provide a

@@ -326,0 +326,0 @@ * - `string` -> all values encountered equal to this string will be null

2

bin/lazy/dataframe.d.ts

@@ -397,3 +397,3 @@ import { DataFrame } from "../dataframe";

*/
toJSON(): String;
toJSON(): string;
/**

@@ -400,0 +400,0 @@ * Drop duplicate rows from this DataFrame.

4

bin/lazy/expr/index.d.ts

@@ -12,3 +12,3 @@ import * as dt from "./datetime";

import { Series } from "../../series";
import { Arithmetic, Comparison, Cumulative, Deserialize, Rolling, Round, Sample, Serialize } from "../../shared_traits";
import { Arithmetic, Comparison, Cumulative, Deserialize, Rolling, Round, Sample, Serialize, EwmOps } from "../../shared_traits";
import { FillNullStrategy, RankMethod } from "../../types";

@@ -18,3 +18,3 @@ /**

*/
export interface Expr extends Rolling<Expr>, Arithmetic<Expr>, Comparison<Expr>, Cumulative<Expr>, Sample<Expr>, Round<Expr>, Serialize {
export interface Expr extends Rolling<Expr>, Arithmetic<Expr>, Comparison<Expr>, Cumulative<Expr>, Sample<Expr>, Round<Expr>, EwmOps<Expr>, Serialize {
/** @ignore */

@@ -21,0 +21,0 @@ _expr: any;

39

bin/lazy/expr/index.js

@@ -180,2 +180,41 @@ "use strict";

},
ewmMean(opts, adjust, minPeriods, bias, ignoreNulls) {
if (opts) {
if (typeof opts === "number") {
return wrap("ewmMean", opts, adjust ?? true, minPeriods ?? 1, bias ?? false, ignoreNulls ?? true);
}
else {
return wrap("ewmMean", opts.alpha ?? 0.5, opts.adjust ?? true, opts.minPeriods ?? 1, opts.bias ?? false, opts.ignoreNulls ?? true);
}
}
else {
return wrap("ewmMean", 0.5, true, 1, false, true);
}
},
ewmStd(opts, adjust, minPeriods, bias, ignoreNulls) {
if (opts) {
if (typeof opts === "number") {
return wrap("ewmStd", opts, adjust ?? true, minPeriods ?? 1, bias ?? false, ignoreNulls ?? true);
}
else {
return wrap("ewmStd", opts.alpha ?? 0.5, opts.adjust ?? true, opts.minPeriods ?? 1, opts.bias ?? false, opts.ignoreNulls ?? true);
}
}
else {
return wrap("ewmStd", 0.5, true, 1, false, true);
}
},
ewmVar(opts, adjust, minPeriods, bias, ignoreNulls) {
if (opts) {
if (typeof opts === "number") {
return wrap("ewmVar", opts, adjust ?? true, minPeriods ?? 1, bias ?? false, ignoreNulls ?? true);
}
else {
return wrap("ewmVar", opts.alpha ?? 0.5, opts.adjust ?? true, opts.minPeriods ?? 1, opts.bias ?? false, opts.ignoreNulls ?? true);
}
}
else {
return wrap("ewmVar", 0.5, true, 1, false, true);
}
},
exclude(...columns) {

@@ -182,0 +221,0 @@ return (0, exports._Expr)(_expr.exclude(columns.flat(2)));

194

bin/lazy/functions.d.ts

@@ -146,7 +146,22 @@ import { Expr } from "./expr";

/**
* __Find the indexes that would sort the columns.__
* ___
* Argsort by multiple columns. The first column will be used for the ordering.
* If there are duplicates in the first column, the second column will be used to determine the ordering
* and so on.
* Return the row indices that would sort the columns.
* @param exprs Column(s) to arg sort by. Accepts expression input.
* @param *more_exprs Additional columns to arg sort by, specified as positional arguments.
* @param descending Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.
* @example
* ```
* const df = pl.DataFrame({"a": [0, 1, 1, 0], "b": [3, 2, 3, 2],});
* df.select(pl.argSortBy(pl.col("a")));
* shape: (4, 1)
* ┌─────┐
* │ a │
* │ --- │
* │ u32 │
* ╞═════╡
* │ 0 │
* │ 3 │
* │ 1 │
* │ 2 │
* └─────┘
* ```
*/

@@ -354,33 +369,144 @@ export declare function argSortBy(exprs: Expr[] | string[], descending?: boolean | boolean[]): Expr;

export declare function element(): Expr;
/** Compute the bitwise AND horizontally across columns.
Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.
Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [false, false, true, true],
... "b": [false, true, null, true],
... "c": ["w", "x", "y", "z"],
... }
... )
>>> df.withColumns(pl.allHorizontal(["a", "b"]))
shape: (4, 4)
┌───────┬───────┬─────┬───────┐
│ a ┆ b ┆ c ┆ all │
│ --- ┆ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ str ┆ bool │
╞═══════╪═══════╪═════╪═══════╡
│ false ┆ false ┆ w ┆ false │
│ false ┆ true ┆ x ┆ false │
│ true ┆ null ┆ y ┆ null │
│ true ┆ true ┆ z ┆ true │
└───────┴───────┴─────┴───────┘
/**
* Compute the bitwise AND horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
*
* @example
* ```
* >>> const df = pl.DataFrame(
* {
* "a": [false, false, true, true],
* "b": [false, true, null, true],
* "c": ["w", "x", "y", "z"],
* }
* )
* >>> df.withColumns(pl.allHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ all │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ false │
* │ true ┆ null ┆ y ┆ null │
* │ true ┆ true ┆ z ┆ true │
* └───────┴───────┴─────┴───────┘
* ```
*/
export declare function allHorizontal(exprs: ExprOrString | ExprOrString[]): Expr;
/**
* Compute the bitwise OR horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [false, false, true, null],
* ... "b": [false, true, null, null],
* ... "c": ["w", "x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.anyHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ any │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ true │
* │ true ┆ null ┆ y ┆ true │
* │ null ┆ null ┆ z ┆ null │
* └───────┴───────┴─────┴───────┘
* ```
*/
export declare function anyHorizontal(exprs: ExprOrString | ExprOrString[]): Expr;
/**
* Get the maximum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.maxHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ max │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 4 │
* │ 8 ┆ 5 ┆ y ┆ 8 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/
export declare function maxHorizontal(exprs: ExprOrString | ExprOrString[]): Expr;
/**
* Get the minimum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.minHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ min │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 1 │
* │ 8 ┆ 5 ┆ y ┆ 5 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/
export declare function minHorizontal(exprs: ExprOrString | ExprOrString[]): Expr;
/**
* Sum all values horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.sumHorizontal(pl.col("a"), ol.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬──────┐
* │ a ┆ b ┆ c ┆ sum │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪══════╡
* │ 1 ┆ 4 ┆ x ┆ 5 │
* │ 8 ┆ 5 ┆ y ┆ 13 │
* │ 3 ┆ null ┆ z ┆ null │
* └─────┴──────┴─────┴──────┘
* ```
*/
export declare function sumHorizontal(exprs: ExprOrString | ExprOrString[]): Expr;

242

bin/lazy/functions.js

@@ -6,3 +6,3 @@ "use strict";

Object.defineProperty(exports, "__esModule", { value: true });
exports.anyHorizontal = exports.allHorizontal = exports.element = exports.struct = exports.list = exports.tail = exports.spearmanRankCorr = exports.select = exports.quantile = exports.pearsonCorr = exports.nUnique = exports.median = exports.mean = exports.last = exports.head = exports.groups = exports.format = exports.first = exports.exclude = exports.cov = exports.count = exports.concatString = exports.concatList = exports.avg = exports.argSortBy = exports.all = exports.intRanges = exports.intRange = exports.lit = exports.cols = exports.col = void 0;
exports.sumHorizontal = exports.minHorizontal = exports.maxHorizontal = exports.anyHorizontal = exports.allHorizontal = exports.element = exports.struct = exports.list = exports.tail = exports.spearmanRankCorr = exports.select = exports.quantile = exports.pearsonCorr = exports.nUnique = exports.median = exports.mean = exports.last = exports.head = exports.groups = exports.format = exports.first = exports.exclude = exports.cov = exports.count = exports.concatString = exports.concatList = exports.avg = exports.argSortBy = exports.all = exports.intRanges = exports.intRange = exports.lit = exports.cols = exports.col = void 0;
const expr_1 = require("./expr");

@@ -170,7 +170,22 @@ const series_1 = require("../series");

/**
* __Find the indexes that would sort the columns.__
* ___
* Argsort by multiple columns. The first column will be used for the ordering.
* If there are duplicates in the first column, the second column will be used to determine the ordering
* and so on.
* Return the row indices that would sort the columns.
* @param exprs Column(s) to arg sort by. Accepts expression input.
* @param *more_exprs Additional columns to arg sort by, specified as positional arguments.
* @param descending Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.
* @example
* ```
* const df = pl.DataFrame({"a": [0, 1, 1, 0], "b": [3, 2, 3, 2],});
* df.select(pl.argSortBy(pl.col("a")));
* shape: (4, 1)
* ┌─────┐
* │ a │
* │ --- │
* │ u32 │
* ╞═════╡
* │ 0 │
* │ 3 │
* │ 1 │
* │ 2 │
* └─────┘
* ```
*/

@@ -437,31 +452,30 @@ function argSortBy(exprs, descending = false) {

exports.element = element;
/** Compute the bitwise AND horizontally across columns.
Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.
Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [false, false, true, true],
... "b": [false, true, null, true],
... "c": ["w", "x", "y", "z"],
... }
... )
>>> df.withColumns(pl.allHorizontal(["a", "b"]))
shape: (4, 4)
┌───────┬───────┬─────┬───────┐
│ a ┆ b ┆ c ┆ all │
│ --- ┆ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ str ┆ bool │
╞═══════╪═══════╪═════╪═══════╡
│ false ┆ false ┆ w ┆ false │
│ false ┆ true ┆ x ┆ false │
│ true ┆ null ┆ y ┆ null │
│ true ┆ true ┆ z ┆ true │
└───────┴───────┴─────┴───────┘
/**
* Compute the bitwise AND horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
*
* @example
* ```
* >>> const df = pl.DataFrame(
* {
* "a": [false, false, true, true],
* "b": [false, true, null, true],
* "c": ["w", "x", "y", "z"],
* }
* )
* >>> df.withColumns(pl.allHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ all │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ false │
* │ true ┆ null ┆ y ┆ null │
* │ true ┆ true ┆ z ┆ true │
* └───────┴───────┴─────┴───────┘
* ```
*/

@@ -474,33 +488,30 @@ function allHorizontal(exprs) {

exports.allHorizontal = allHorizontal;
/*
Compute the bitwise OR horizontally across columns.
Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.
Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [false, false, true, null],
... "b": [false, true, null, null],
... "c": ["w", "x", "y", "z"],
... }
... )
>>> df.withColumns(pl.anyHorizontal([pl.col("a"), pl.col("b")]))
shape: (4, 4)
┌───────┬───────┬─────┬───────┐
│ a ┆ b ┆ c ┆ any │
│ --- ┆ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ str ┆ bool │
╞═══════╪═══════╪═════╪═══════╡
│ false ┆ false ┆ w ┆ false │
│ false ┆ true ┆ x ┆ true │
│ true ┆ null ┆ y ┆ true │
│ null ┆ null ┆ z ┆ null │
└───────┴───────┴─────┴───────┘
*/
/**
* Compute the bitwise OR horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [false, false, true, null],
* ... "b": [false, true, null, null],
* ... "c": ["w", "x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.anyHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ any │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ true │
* │ true ┆ null ┆ y ┆ true │
* │ null ┆ null ┆ z ┆ null │
* └───────┴───────┴─────┴───────┘
* ```
*/
function anyHorizontal(exprs) {

@@ -512,2 +523,101 @@ exprs = Array.isArray(exprs) ? exprs : [exprs];

exports.anyHorizontal = anyHorizontal;
/**
* Get the maximum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.maxHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ max │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 4 │
* │ 8 ┆ 5 ┆ y ┆ 8 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/
function maxHorizontal(exprs) {
exprs = Array.isArray(exprs) ? exprs : [exprs];
exprs = (0, utils_1.selectionToExprList)(exprs);
return (0, expr_1._Expr)(polars_internal_1.default.maxHorizontal(exprs));
}
exports.maxHorizontal = maxHorizontal;
/**
* Get the minimum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.minHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ min │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 1 │
* │ 8 ┆ 5 ┆ y ┆ 5 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/
function minHorizontal(exprs) {
exprs = Array.isArray(exprs) ? exprs : [exprs];
exprs = (0, utils_1.selectionToExprList)(exprs);
return (0, expr_1._Expr)(polars_internal_1.default.minHorizontal(exprs));
}
exports.minHorizontal = minHorizontal;
/**
* Sum all values horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.sumHorizontal(pl.col("a"), ol.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬──────┐
* │ a ┆ b ┆ c ┆ sum │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪══════╡
* │ 1 ┆ 4 ┆ x ┆ 5 │
* │ 8 ┆ 5 ┆ y ┆ 13 │
* │ 3 ┆ null ┆ z ┆ null │
* └─────┴──────┴─────┴──────┘
* ```
*/
function sumHorizontal(exprs) {
exprs = Array.isArray(exprs) ? exprs : [exprs];
exprs = (0, utils_1.selectionToExprList)(exprs);
return (0, expr_1._Expr)(polars_internal_1.default.sumHorizontal(exprs));
}
exports.sumHorizontal = sumHorizontal;
// // export function collect_all() {}

@@ -514,0 +624,0 @@ // // export function all() {} // fold

4

bin/series/index.d.ts

@@ -7,3 +7,3 @@ import { DataType, Optional } from "../datatypes";

import { SeriesStructFunctions } from "./struct";
import { Arithmetic, Comparison, Cumulative, Deserialize, Rolling, Round, Sample, Serialize } from "../shared_traits";
import { Arithmetic, Comparison, Cumulative, Deserialize, Rolling, Round, Sample, Serialize, EwmOps } from "../shared_traits";
import { InterpolationMethod, RankMethod } from "../types";

@@ -14,3 +14,3 @@ declare const inspect: unique symbol;

*/
export interface Series extends ArrayLike<any>, Rolling<Series>, Arithmetic<Series>, Comparison<Series>, Cumulative<Series>, Round<Series>, Sample<Series>, Serialize {
export interface Series extends ArrayLike<any>, Rolling<Series>, Arithmetic<Series>, Comparison<Series>, Cumulative<Series>, Round<Series>, Sample<Series>, EwmOps<Series>, Serialize {
inner(): any;

@@ -17,0 +17,0 @@ name: string;

11

bin/series/index.js

@@ -225,2 +225,11 @@ "use strict";

},
ewmMean(...args) {
return expr_op("ewmMean", ...args);
},
ewmStd(...args) {
return expr_op("ewmStd", ...args);
},
ewmVar(...args) {
return expr_op("ewmVar", ...args);
},
explode() {

@@ -532,3 +541,3 @@ return wrap("explode");

sample(opts, frac, withReplacement = false, seed) {
// rome-ignore lint/style/noArguments: <explanation>
// biome-ignore lint/style/noArguments: <explanation>
if (arguments.length === 0) {

@@ -535,0 +544,0 @@ return wrap("sampleN", 1, withReplacement, false, seed);

132

bin/shared_traits.d.ts

@@ -295,6 +295,9 @@ /// <reference types="node" />

* @param center - Set the labels at the center of the window
* @param ddof
* "Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements.
* By default ddof is 1.
* @category Rolling
*/
rollingStd(options: RollingOptions): T;
rollingStd(windowSize: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean): T;
rollingStd(windowSize: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean, ddof?: number): T;
/**

@@ -332,6 +335,9 @@ * __Apply a rolling sum (moving sum) over the values in this Series.__

* @param center - Set the labels at the center of the window
* @param ddof
* "Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements.
* By default ddof is 1.
* @category Rolling
*/
rollingVar(options: RollingOptions): T;
rollingVar(windowSize: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean): T;
rollingVar(windowSize: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean, ddof?: number): T;
/**

@@ -356,3 +362,3 @@ * Compute a rolling median

rollingQuantile(options: RollingQuantileOptions): T;
rollingQuantile(quantile: number, interpolation?: InterpolationMethod, windowSize?: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean, by?: String, closed?: ClosedWindow): T;
rollingQuantile(quantile: number, interpolation?: InterpolationMethod, windowSize?: number, weights?: Array<number>, minPeriods?: Array<number>, center?: boolean, by?: string, closed?: ClosedWindow): T;
/**

@@ -1139,1 +1145,121 @@ * Compute a rolling skew

}
/***
* Exponentially-weighted operations that can be applied to a Series and Expr
*/
export interface EwmOps<T> {
/**
* Exponentially-weighted moving average.
*
* @param alpha Specify smoothing factor alpha directly, :math:`0 < \alpha \leq 1`.
* @param adjust Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings
* - When ``adjust: true`` the EW function is calculated using weights :math:`w_i = (1 - \alpha)^i`
* - When ``adjust=false`` the EW function is calculated recursively
* @param bias When ``bias: false``, apply a correction to make the estimate statistically unbiased.
* @param minPeriods Minimum number of observations in window required to have a value (otherwise result is null).
* @param ignoreNulls Ignore missing values when calculating weights.
* - When ``ignoreNulls: false`` (default), weights are based on absolute positions.
* - When ``ignoreNulls: true``, weights are based on relative positions.
* @returns Expr that evaluates to a float 64 Series.
* @examples
* ```
* > const df = pl.DataFrame({a: [1, 2, 3]});
* > df.select(pl.col("a").ewmMean())
* shape: (3, 1)
* ┌──────────┐
* │ a │
* | --- │
* │ f64 │
* ╞══════════╡
* │ 1.0 │
* │ 1.666667 │
* │ 2.428571 │
* └──────────┘
* ```
*/
ewmMean(): T;
ewmMean(alpha?: number, adjust?: boolean, minPeriods?: number, bias?: boolean, ignoreNulls?: boolean): T;
ewmMean(opts: {
alpha?: number;
adjust?: boolean;
minPeriods?: number;
bias?: boolean;
ignoreNulls?: boolean;
}): T;
/**
* Exponentially-weighted standard deviation.
*
* @param alpha Specify smoothing factor alpha directly, :math:`0 < \alpha \leq 1`.
* @param adjust Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings
* - When ``adjust: true`` the EW function is calculated using weights :math:`w_i = (1 - \alpha)^i`
* - When ``adjust: false`` the EW function is calculated recursively
* @param minPeriods Minimum number of observations in window required to have a value (otherwise result is null).
* @param bias When ``bias: false``, apply a correction to make the estimate statistically unbiased.
* @param ignoreNulls Ignore missing values when calculating weights.
* - When ``ignoreNulls: false`` (default), weights are based on absolute positions.
* For example, the weights of :math:`x_0` and :math:`x_2` used in calculating the final weighted average of
* - When ``ignoreNulls: true``, weights are based on relative positions.
* @returns Expr that evaluates to a float 64 Series.
* @examples
* ```
* > const df = pl.DataFrame({a: [1, 2, 3]});
* > df.select(pl.col("a").ewmStd())
* shape: (3, 1)
* ┌──────────┐
* │ a │
* | --- │
* │ f64 │
* ╞══════════╡
* │ 0.0 │
* │ 0.707107 │
* │ 0.963624 │
* └──────────┘
* ```
*/
ewmStd(): T;
ewmStd(alpha?: number, adjust?: boolean, minPeriods?: number, bias?: boolean, ignoreNulls?: boolean): T;
ewmStd(opts: {
alpha?: number;
adjust?: boolean;
minPeriods?: number;
bias?: boolean;
ignoreNulls?: boolean;
}): T;
/**
* Exponentially-weighted variance.
*
* @param alpha Specify smoothing factor alpha directly, :math:`0 < \alpha \leq 1`.
* @param adjust Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings
* - When ``adjust: true`` the EW function is calculated using weights :math:`w_i = (1 - \alpha)^i`
* - When ``adjust: false`` the EW function is calculated recursively
* @param minPeriods Minimum number of observations in window required to have a value (otherwise result is null).
* @param bias When ``bias: false``, apply a correction to make the estimate statistically unbiased.
* @param ignoreNulls Ignore missing values when calculating weights.
* - When ``ignoreNulls: false`` (default), weights are based on absolute positions.
* - When ``ignoreNulls=true``, weights are based on relative positions.
* @returns Expr that evaluates to a float 64 Series.
* @examples
* ```
* > const df = pl.DataFrame({a: [1, 2, 3]});
* > df.select(pl.col("a").ewmVar())
* shape: (3, 1)
* ┌──────────┐
* │ a │
* | --- │
* │ f64 │
* ╞══════════╡
* │ 0.0 │
* │ 0.5 │
* │ 0.928571 │
* └──────────┘
* ```
*/
ewmVar(): T;
ewmVar(alpha?: number, adjust?: boolean, minPeriods?: number, bias?: boolean, ignoreNulls?: boolean): T;
ewmVar(opts: {
alpha?: number;
adjust?: boolean;
minPeriods?: number;
bias?: boolean;
ignoreNulls?: boolean;
}): T;
}

3

bin/types.d.ts

@@ -18,3 +18,3 @@ /**

rechunk?: boolean;
how?: "vertical" | "horizontal";
how?: "vertical" | "horizontal" | "diagonal";
}

@@ -153,2 +153,3 @@ /**

center?: boolean;
ddof?: number;
}

@@ -155,0 +156,0 @@ /**

26

package.json
{
"name": "nodejs-polars",
"version": "0.8.2",
"version": "0.8.3",
"repository": "https://github.com/pola-rs/nodejs-polars.git",

@@ -58,9 +58,9 @@ "license": "MIT",

"devDependencies": {
"@biomejs/biome": "^1.0.0",
"@biomejs/biome": "^1.1.2",
"@napi-rs/cli": "^2.16.3",
"@types/chance": "^1.1.4",
"@types/jest": "^29.5.4",
"@types/node": "^20.5.9",
"@types/node": "^20.6.0",
"chance": "^1.1.11",
"jest": "^29.6.4",
"jest": "^29.7.0",
"source-map-support": "^0.5.21",

@@ -77,12 +77,12 @@ "ts-jest": "^29.1.1",

"optionalDependencies": {
"nodejs-polars-win32-x64-msvc": "0.8.2",
"nodejs-polars-darwin-x64": "0.8.2",
"nodejs-polars-linux-x64-gnu": "0.8.2",
"nodejs-polars-darwin-arm64": "0.8.2",
"nodejs-polars-linux-arm64-gnu": "0.8.2",
"nodejs-polars-linux-arm64-musl": "0.8.2",
"nodejs-polars-android-arm64": "0.8.2",
"nodejs-polars-win32-ia32-msvc": "0.8.2",
"nodejs-polars-linux-x64-musl": "0.8.2"
"nodejs-polars-win32-x64-msvc": "0.8.3",
"nodejs-polars-darwin-x64": "0.8.3",
"nodejs-polars-linux-x64-gnu": "0.8.3",
"nodejs-polars-darwin-arm64": "0.8.3",
"nodejs-polars-linux-arm64-gnu": "0.8.3",
"nodejs-polars-linux-arm64-musl": "0.8.3",
"nodejs-polars-android-arm64": "0.8.3",
"nodejs-polars-win32-ia32-msvc": "0.8.3",
"nodejs-polars-linux-x64-musl": "0.8.3"
}
}

41

README.md
# Polars
Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R and SQL
[![rust docs](https://docs.rs/polars/badge.svg)](https://docs.rs/polars/latest/polars/)

@@ -66,3 +68,3 @@ [![Build and test](https://github.com/pola-rs/polars/workflows/Build%20and%20test/badge.svg)](https://github.com/pola-rs/polars/actions)

... pl.lit("fruits").alias("literal_string_fruits"),
... pl.col("B").filter(pl.col("cars").eq(lit("beetle"))).sum(),
... pl.col("B").filter(pl.col("cars").eq(pl.lit("beetle"))).sum(),
... pl.col("A").filter(pl.col("B").gt(2)).sum().over("cars").alias("sum_A_by_cars"),

@@ -119,2 +121,39 @@ ... pl.col("A").sum().over("fruits").alias("sum_A_by_fruits"),

## Deno
In Deno modules you can import polars straight from `npm`:
```typescript
import pl from "npm:nodejs-polars";
```
With Deno 1.37, you can use the `display` function to display a `DataFrame` in the notebook:
```typescript
import pl from "npm:nodejs-polars";
import { display } from "https://deno.land/x/display@v1.1.1/mod.ts";
let response = await fetch(
"https://cdn.jsdelivr.net/npm/world-atlas@1/world/110m.tsv",
);
let data = await response.text();
let df = pl.readCSV(data, { sep: "\t" });
await display(df)
```
With Deno 1.38, you only have to make the dataframe be the last expression in the cell:
```typescript
import pl from "npm:nodejs-polars";
let response = await fetch(
"https://cdn.jsdelivr.net/npm/world-atlas@1/world/110m.tsv",
);
let data = await response.text();
let df = pl.readCSV(data, { sep: "\t" });
df
```
<img width="510" alt="image" src="https://github.com/pola-rs/nodejs-polars/assets/836375/90cf7bf4-7478-4919-b297-f8eb6a16196f">
___

@@ -121,0 +160,0 @@

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