# @rapidsai/cudf

> cuDF - NVIDIA RAPIDS GPU DataFrame Library

Latest version **22.12.1** (published 2023-06-05) · Apache-2.0 license · 0 weekly downloads

## Install

```sh
npm install @rapidsai/cudf
pnpm add @rapidsai/cudf
yarn add @rapidsai/cudf
bun add @rapidsai/cudf
```

## Health

**Score 25/100 (F)** — status: abandoned.

Positive: has types; no vulnerabilities; high quality score.

Warnings: low downloads; no esm support.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 22.12.1 |
| Published | 2023-06-05 |
| First published | 2022-08-16 |
| Weekly downloads | 0 |
| License | Apache-2.0 |
| TypeScript types | bundled |
| Module format | CommonJS |
| Dependencies | 3 |
| Unpacked size | 1.7 MB |
| Known vulnerabilities | 0 |
| Install scripts | yes |
| Author | NVIDIA, Inc. |
| Maintainers | rapidsai-owner, raydouglass, jacobtomlinson |

## Links

- npm: https://www.npmjs.com/package/@rapidsai/cudf
- Repository: https://github.com/rapidsai/node
- Homepage: https://github.com/rapidsai/node/tree/main/modules/cudf#readme
- Issues: https://github.com/rapidsai/node/issues
- npm.io page: https://npm.io/package/@rapidsai/cudf

## Dependencies (3)

- [apache-arrow](https://npm.io/package/apache-arrow.md) ^9.0.0
- [@rapidsai/rmm](https://npm.io/package/@rapidsai/rmm.md) ~22.12.1
- [@rapidsai/cuda](https://npm.io/package/@rapidsai/cuda.md) ~22.12.1

## Recent versions

- 22.12.1 (latest) — 2023-06-05
- 22.12.0 — 2023-05-23
- 22.8.2 — 2023-02-28
- 22.8.1 — 2022-09-20
- 22.8.0 — 2022-09-02
- 22.6.2 — 2022-08-17
- 22.6.1 — 2022-08-16
- 22.6.0 — 2022-08-16

## README

# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp; node-rapids cuDF - GPU DataFrames</div>

### Installation

`npm install @rapidsai/cudf`

### About

The js bindings for [cuDF](https://github.com/rapidsai/cudf) provides an API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows in a Javascript runtime environment, without going into the details of CUDA programming.

For example, the following snippet creates a series, then uses the GPU to run some calculations:

```javascript
var { Series, Int32 } = require("@rapidsai/cudf");

var series1 = Series.new({ type: new Int32(), data: [1, 2, 3] });
console.log(series1.mean()); // 2
console.log(series1.max()); // 3
```

The following snippet creates a DataFrame, then uses the GPU to to run some calculations:

```javascript
var {
  DataFrame,
  DataType,
  Float64,
  GroupBy,
  Int32,
  Series,
} = require("@rapidsai/cudf");

var a = Series.new({ type: new Int32(), data: [5, 4, 3, 2, 1, 0] });
var b = Series.new({ type: new Int32(), data: [0, 0, 1, 1, 2, 2] });
var df = new DataFrame({ a: a, b: b });
var grp = new GroupBy({ obj: df, by: ["a"] });

var groups = grp.getGroups();

console.log(...groups["keys"].get("a")); // [0,1,2,3,4,5]
console.log(...groups.values?.get("b")); // [2,2,1,1,0,0]
console.log(...groups["offsets"]); // [0,1,2,3,4,5,6]
```

For detailed node-cuDF API, follow our [API Documentation](https://rapidsai.github.io/node/modules/cudf_src.html).

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_Source: https://npm.io/package/@rapidsai/cudf · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._