https://github.com/wesm/feather

Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow
https://github.com/wesm/feather

Last synced: 29 days ago
JSON representation

Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow

• Host: GitHub
• URL: https://github.com/wesm/feather
• Owner: wesm
• License: apache-2.0
• Created: 2016-01-27T22:59:51.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-11-19T16:21:56.000Z (almost 3 years ago)
• Last Synced: 2024-06-11T17:11:12.083Z (5 months ago)
• Language: JavaScript
• Homepage:
• Size: 398 KB
• Stars: 2,720
• Watchers: 97
• Forks: 169
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
## Feather Development is in Apache Arrow now

Feather development lives on in [Apache

Arrow](https://github.com/apache/arrow). The `arrow` R package includes [a much

faster implementation of

Feather](http://arrow.apache.org/blog/2019/08/08/r-package-on-cran/),

i.e. `arrow::read_feather`. The Python package `feather` is now a wrapper

around `pyarrow.feather`.

## Feather: fast, interoperable data frame storage

Feather provides binary columnar serialization for data frames. It is designed to

make reading and writing data frames efficient, and to make sharing data across

data analysis languages easy. This initial version comes with bindings for

[python](python/) (written by [Wes McKinney](https://github.com/wesm)) and [R](R/)

(written by [Hadley Wickham](https://github.com/hadley/)).

Feather uses the [Apache Arrow](https://arrow.apache.org) columnar memory

specification to represent binary data on disk. This makes read and write

operations very fast. This is particularly important for encoding null/NA values

and variable-length types like UTF8 strings.

Feather is a part of the broader Apache Arrow project. Feather defines its own

simplified schemas and metadata for on-disk representation.

Feather currently supports the following column types:

* A wide range of numeric types (int8, int16, int32, int64, uint8, uint16,

  uint32, uint64, float, double).

* Logical/boolean values.

* Dates, times, and timestamps.

* Factors/categorical variables that have fixed set of possible values.

* UTF-8 encoded strings.

* Arbitrary binary data.

All column types support NA/null values.

## Installation

### Python

`pip install feather-format`

### R

`install.packages("feather")`

## Julia

```

julia> using Pkg

julia> Pkg.add("Feather")

```

## License and Copyrights

This library is released under the [Apache License, Version 2.0](LICENSE.txt).

See [`NOTICE`](NOTICE) for details about the library's copyright holders.

## Getting started

### Python

To get started with the python bindings, see the [python feather documentation](https://arrow.apache.org/docs/python/feather.html)

### R

To get started with the R bindings, see the [R feather documentation](https://cran.r-project.org/web/packages/feather/feather.pdf)

### Julia

To get started with the Julia bindings see [Feather.jl](https://github.com/JuliaStats/Feather.jl)