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rover

(★ 387)

Simple, powerful data frames for Ruby

파일 탐색기

최종 버전 다운로드 (.zip)

- - build.yml
- - data_frame.rb
  - group.rb
  - vector.rb
  - version.rb
- rover-df.rb
- rover.rb
- - columns.csv
  - data.csv
  - data.parquet
  - empty.csv
  - null.parquet
  - types.csv
  - types.parquet
  - unnamed.csv
- active_record_test.rb
- csv_test.rb
- data_frame_test.rb
- group_test.rb
- join_test.rb
- parquet_test.rb
- plot_test.rb
- test_helper.rb
- types_test.rb
- vector_test.rb
.gitignore
CHANGELOG.md
Gemfile
LICENSE.txt
Rakefile
README.md
rover-df.gemspec

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/ankane/rover

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd rover

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Ruby

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Ruby bundle 명령어를 쓰려면 Ruby가 필요합니다.

bundle install

Gemfile에 명시된 루비 라이브러리를 설치합니다.

bundle exec rake test

이 명령어를 터미널에 그대로 입력해 실행하세요.

✅ bundle install이 에러 없이 끝나면 README에 안내된 실행 명령어(예: rails server)로 마저 실행해보세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# Rover

Simple, powerful data frames for Ruby

:mountain: Designed for data exploration and machine learning, and powered by [numo-narray-alt](https://github.com/yoshoku/numo-narray-alt)

:evergreen_tree: Uses [Vega](https://github.com/ankane/vega) for visualization

[![Build Status](https://github.com/ankane/rover/actions/workflows/build.yml/badge.svg)](https://github.com/ankane/rover/actions)

## Installation

Add this line to your application’s Gemfile:

```ruby
gem "rover-df"
```

## Intro

A data frame is an in-memory table. It’s a useful data structure for data analysis and machine learning. It uses columnar storage for fast operations on columns.

## Creating Data Frames

From an array

```ruby
Rover::DataFrame.new([
  {a: 1, b: "one"},
  {a: 2, b: "two"},
  {a: 3, b: "three"}
])
```

From a hash

```ruby
Rover::DataFrame.new({
  a: [1, 2, 3],
  b: ["one", "two", "three"]
})
```

From Active Record

```ruby
Rover::DataFrame.new(User.all)
```

From a CSV

```ruby
Rover.read_csv("file.csv")
# or
Rover.parse_csv("CSV,data,string")
```

From Parquet (requires the [red-parquet](https://github.com/apache/arrow/tree/master/ruby/red-parquet) gem)

```ruby
Rover.read_parquet("file.parquet")
# or
Rover.parse_parquet("PAR1...")
```

## Attributes

Get number of rows

```ruby
df.count
```

Get column names

```ruby
df.keys
```

Check if a column exists

```ruby
df.include?(name)
```

## Selecting Data

Select a column

```ruby
df[:a]
```

Select multiple columns

```ruby
df[[:a, :b]]
```

Select first rows

```ruby
df.head
# or
df.first(5)
```

Select last rows

```ruby
df.tail
# or
df.last(5)
```

Select rows by index

```ruby
df[1]
# or
df[1..3]
# or
df[[1, 4, 5]]
```

Iterate over rows

```ruby
df.each_row { |row| ... }
```

Iterate over a column

```ruby
df[:a].each { |item| ... }
# or
df[:a].each_with_index { |item, index| ... }
```

## Filtering

Filter on a condition

```ruby
df[df[:a] == 100]
df[df[:a] != 100]
df[df[:a] > 100]
df[df[:a] >= 100]
df[df[:a] < 100]
df[df[:a] <= 100]
```

In

```ruby
df[df[:a].in?([1, 2, 3])]
df[df[:a].in?(1..3)]
df[df[:a].in?(["a", "b", "c"])]
```

Not in

```ruby
df[!df[:a].in?([1, 2, 3])]
```

And, or, and exclusive or

```ruby
df[(df[:a] > 100) & (df[:b] == "one")] # and
df[(df[:a] > 100) | (df[:b] == "one")] # or
df[(df[:a] > 100) ^ (df[:b] == "one")] # xor
```

## Operations

Basic operations

```ruby
df[:a] + 5
df[:a] - 5
df[:a] * 5
df[:a] / 5
df[:a] % 5
df[:a] ** 2
df[:a].sqrt
df[:a].cbrt
df[:a].abs
```

Rounding

```ruby
df[:a].round
df[:a].ceil
df[:a].floor
```

Logarithm

```ruby
df[:a].ln # or log
df[:a].log(5)
df[:a].log10
df[:a].log2
```

Exponentiation

```ruby
df[:a].exp
df[:a].exp2
```

Trigonometric functions

```ruby
df[:a].sin
df[:a].cos
df[:a].tan
df[:a].asin
df[:a].acos
df[:a].atan
```

Hyperbolic functions

```ruby
df[:a].sinh
df[:a].cosh
df[:a].tanh
df[:a].asinh
df[:a].acosh
df[:a].atanh
```

Error function

```ruby
df[:a].erf
df[:a].erfc
```

Summary statistics

```ruby
df[:a].count
df[:a].sum
df[:a].mean
df[:a].median
df[:a].percentile(90)
df[:a].min
df[:a].max
df[:a].std
df[:a].var
```

Count occurrences

```ruby
df[:a].tally
```

Cross tabulation

```ruby
df[:a].crosstab(df[:b])
```

## Grouping

Group

```ruby
df.group(:a).count
```

Works with all summary statistics

```ruby
df.group(:a).max(:b)
```

Multiple groups

```ruby
df.group(:a, :b).count
```

## Visualization

Add [Vega](https://github.com/ankane/vega) to your application’s Gemfile:

```ruby
gem "vega"
```

And use:

```ruby
df.plot(:a, :b)
```

Specify the chart type (`line`, `pie`, `column`, `bar`, `area`, or `scatter`)

```ruby
df.plot(:a, :b, type: "pie")
```

Group data

```ruby
df.plot(:a, :b, group: :c)
```

Stacked columns or bars

```ruby
df.plot(:a, :b, group: :c, stacked: true)
```

## Updating Data

Add a new column

```ruby
df[:a] = 1
# or
df[:a] = [1, 2, 3]
```

Update a single element

```ruby
df[:a][0] = 100
```

Update multiple elements

```ruby
df[:a][0..2] = 1
# or
df[:a][0..2] = [1, 2, 3]
```

Update all elements

```ruby
df[:a] = df[:a].map { |v| v.gsub("a", "b") }
# or
df[:a].map! { |v| v.gsub("a", "b") }
```

Update elements matching a condition

```ruby
df[:a][df[:a] > 100] = 0
```

Clamp

```ruby
df[:a].clamp!(0, 100)
```

Delete columns

```ruby
df.delete(:a)
# or
df.except!(:a, :b)
```

Rename columns

```ruby
df.rename(a: :new_a, b: :new_b)
# or
df[:new_a] = df.delete(:a)
```

Sort rows

```ruby
df.sort_by! { |r| r[:a] }
```

Clear all data

```ruby
df.clear
```

## Combining Data Frames

Add rows

```ruby
df.concat(other_df)
```

Add columns

```ruby
df.merge!(other_df)
```

Inner join

```ruby
df.inner_join(other_df)
# or
df.inner_join(other_df, on: :a)
# or
df.inner_join(other_df, on: [:a, :b])
# or
df.inner_join(other_df, on: {df_col: :other_df_col})
```

Left join

```ruby
df.left_join(other_df)
```

## Encoding

One-hot encoding

```ruby
df.one_hot
```

Drop a variable in each category to avoid the dummy variable trap

```ruby
df.one_hot(drop: true)
```

## Conversion

Array of hashes

```ruby
df.to_a
```

Hash of arrays

```ruby
df.to_h
```

Numo array

```ruby
df.to_numo
```

CSV

```ruby
df.to_csv
```

Parquet (requires the [red-parquet](https://github.com/apache/arrow/tree/master/ruby/red-parquet) gem)

```ruby
df.to_parquet
```

## Types

You can specify column types when creating a data frame

```ruby
Rover::DataFrame.new(data, types: {"a" => :int64, "b" => :float64})
```

Or

```ruby
Rover.read_csv("data.csv", types: {"a" => :int64, "b" => :float64})
```

Supported types are:

- boolean - `:bool`
- float - `:float64`, `:float32`
- integer - `:int64`, `:int32`, `:int16`, `:int8`
- unsigned integer - `:uint64`, `:uint32`, `:uint16`, `:uint8`
- object - `:object`

Get column types

```ruby
df.types
```

For a specific column

```ruby
df[:a].type
```

Change the type of a column

```ruby
df[:a].to!(:int32)
```

## History

View the [changelog](https://github.com/ankane/rover/blob/master/CHANGELOG.md)

## Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

- [Report bugs](https://github.com/ankane/rover/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/rover/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features

To get started with development:

```sh
git clone https://github.com/ankane/rover.git
cd rover
bundle install
bundle exec rake test
```