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https://github.com/ankane/polars-ruby

Blazingly fast DataFrames for Ruby
https://github.com/ankane/polars-ruby

Last synced: 4 months ago
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Blazingly fast DataFrames for Ruby

Host: GitHub
URL: https://github.com/ankane/polars-ruby
Owner: ankane
License: mit
Created: 2022-11-22T05:59:48.000Z (over 1 year ago)
Default Branch: master
Last Pushed: 2024-03-05T22:13:33.000Z (4 months ago)
Last Synced: 2024-03-05T23:24:56.794Z (4 months ago)
Language: Ruby
Size: 1.77 MB
Stars: 721
Watchers: 9
Forks: 24
Open Issues: 3
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt

Lists

awesome-polars - polars for Ruby - [Ruby](https://www.ruby-lang.org/en/) `polars-df` gems to use Polars with Ruby. (Libraries/Packages/Scripts / Ruby)
awesome-dataframes - polars-ruby - Blazingly fast DataFrames for Ruby. (Libraries)
awesome-stars - ankane/polars-ruby - Blazingly fast DataFrames for Ruby (Ruby)

README

        # Polars Ruby

:fire: Blazingly fast DataFrames for Ruby, powered by [Polars](https://github.com/pola-rs/polars)

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

## Installation

Add this line to your application’s Gemfile:

```ruby

gem "polars-df"

```

## Getting Started

This library follows the [Polars Python API](https://pola-rs.github.io/polars/py-polars/html/reference/index.html).

```ruby

Polars.read_csv("iris.csv")

  .lazy

  .filter(Polars.col("sepal_length") > 5)

  .group_by("species")

  .agg(Polars.all.sum)

  .collect

```

You can follow [Polars tutorials](https://pola-rs.github.io/polars-book/user-guide/) and convert the code to Ruby in many cases. Feel free to open an issue if you run into problems.

## Reference

- [Series](https://www.rubydoc.info/gems/polars-df/Polars/Series)

- [DataFrame](https://www.rubydoc.info/gems/polars-df/Polars/DataFrame)

- [LazyFrame](https://www.rubydoc.info/gems/polars-df/Polars/LazyFrame)

## Examples

### Creating DataFrames

From a CSV

```ruby

Polars.read_csv("file.csv")

# or lazily with

Polars.scan_csv("file.csv")

```

From Parquet

```ruby

Polars.read_parquet("file.parquet")

# or lazily with

Polars.scan_parquet("file.parquet")

```

From Active Record

```ruby

Polars.read_database(User.all)

# or

Polars.read_database("SELECT * FROM users")

```

From JSON

```ruby

Polars.read_json("file.json")

# or

Polars.read_ndjson("file.ndjson")

# or lazily with

Polars.scan_ndjson("file.ndjson")

```

From Feather / Arrow IPC

```ruby

Polars.read_ipc("file.arrow")

# or lazily with

Polars.scan_ipc("file.arrow")

```

From Avro

```ruby

Polars.read_avro("file.avro")

```

From a hash

```ruby

Polars::DataFrame.new({

  a: [1, 2, 3],

  b: ["one", "two", "three"]

})

```

From an array of hashes

```ruby

Polars::DataFrame.new([

  {a: 1, b: "one"},

  {a: 2, b: "two"},

  {a: 3, b: "three"}

])

```

From an array of series

```ruby

Polars::DataFrame.new([

  Polars::Series.new("a", [1, 2, 3]),

  Polars::Series.new("b", ["one", "two", "three"])

])

```

## Attributes

Get number of rows

```ruby

df.height

```

Get column names

```ruby

df.columns

```

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

```

Select last rows

```ruby

df.tail

```

## Filtering

Filter on a condition

```ruby

df[Polars.col("a") == 2]

df[Polars.col("a") != 2]

df[Polars.col("a") > 2]

df[Polars.col("a") >= 2]

df[Polars.col("a") < 2]

df[Polars.col("a") <= 2]

```

And, or, and exclusive or

```ruby

df[(Polars.col("a") > 1) & (Polars.col("b") == "two")] # and

df[(Polars.col("a") > 1) | (Polars.col("b") == "two")] # or

df[(Polars.col("a") > 1) ^ (Polars.col("b") == "two")] # 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"].abs

```

Rounding

```ruby

df["a"].round(2)

df["a"].ceil

df["a"].floor

```

Logarithm

```ruby

df["a"].log # natural log

df["a"].log(10)

```

Exponentiation

```ruby

df["a"].exp

```

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

```

Summary statistics

```ruby

df["a"].sum

df["a"].mean

df["a"].median

df["a"].quantile(0.90)

df["a"].min

df["a"].max

df["a"].std

df["a"].var

```

## Grouping

Group

```ruby

df.group_by("a").count

```

Works with all summary statistics

```ruby

df.group_by("a").max

```

Multiple groups

```ruby

df.group_by(["a", "b"]).count

```

## Combining Data Frames

Add rows

```ruby

df.vstack(other_df)

```

Add columns

```ruby

df.hstack(other_df)

```

Inner join

```ruby

df.join(other_df, on: "a")

```

Left join

```ruby

df.join(other_df, on: "a", how: "left")

```

## Encoding

One-hot encoding

```ruby

df.to_dummies

```

## Conversion

Array of hashes

```ruby

df.rows(named: true)

```

Hash of series

```ruby

df.to_h

```

CSV

```ruby

df.to_csv

# or

df.write_csv("file.csv")

```

Parquet

```ruby

df.write_parquet("file.parquet")

```

Numo array

```ruby

df.to_numo

```

## Types

You can specify column types when creating a data frame

```ruby

Polars::DataFrame.new(data, schema: {"a" => Polars::Int32, "b" => Polars::Float32})

```

Supported types are:

- boolean - `Boolean`

- float - `Float64`, `Float32`

- integer - `Int64`, `Int32`, `Int16`, `Int8`

- unsigned integer - `UInt64`, `UInt32`, `UInt16`, `UInt8`

- string - `String`, `Binary`, `Categorical`

- temporal - `Date`, `Datetime`, `Time`, `Duration`

- nested -  `List`, `Struct`, `Array`

- other - `Object`, `Null`

Get column types

```ruby

df.schema

```

For a specific column

```ruby

df["a"].dtype

```

Cast a column

```ruby

df["a"].cast(Polars::Int32)

```

## Visualization

Add [Vega](https://github.com/ankane/vega-ruby) 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.group_by("c").plot("a", "b")

```

Stacked columns or bars

```ruby

df.group_by("c").plot("a", "b", stacked: true)

```

## History

View the [changelog](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/polars-ruby/issues)

- Fix bugs and [submit pull requests](https://github.com/ankane/polars-ruby/pulls)

- Write, clarify, or fix documentation

- Suggest or add new features

To get started with development:

```sh

git clone https://github.com/ankane/polars-ruby.git

cd polars-ruby

bundle install

bundle exec rake compile

bundle exec rake test

```