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Tried Polars in Ruby

#ruby #polars

Polars is a data frame in the Rust language using Apache Arrow Columnar Format. polars-ruby is the Ruby binding for Polars created by Andrew Kane.

Several members of the Ruby community have been deeply involved in the development of Apache Arrow.

Fast data processing with Ruby and Apache Arrow - rubykaigi2022

So while the Arrow C binding for the Ruby language is relatively well-developed, polars-df is not an Arrow C binding, but a binding to Polars implemented in Rust. magnus is used for the connection between Ruby and Rust. In fact, there is also a Ruby data frame that uses the Arrow binding, which is called RedAmber. But we are not talking about that now.

Please note that this post is incomplete and polars-df is still in the development phase, so the API is subject to change.

Documentation

RubyDoc.info

Chapter 1 Getting started in Ruby

Installation

Ruby gem

gem install polars
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From source code

git clone
https://github.com/ankane/polars-ruby
cd polars-ruby
bundle
bundle exec rake compile
bundle exec rake install
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Quick start

Below we show a simple snippet that parses a CSV file, filters it, and finishes with a groupby operation. This example is presented in python only, as the "eager" API is not the preferred model in Rust.

require 'polars'
require 'uri'

df = Polars.read_csv(URI('https://j.mp/iriscsv'))
df.filter(Polars.col('sepal_length') > 5)
  .groupby('species')
  .agg(Polars.all.sum)
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The snippet above will output:

shape: (3, 5)
┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐
│ species    ┆ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width │
│ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │
│ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │
╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡
│ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │
└────────────┴──────────────┴─────────────┴──────────────┴─────────────┘
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As we can see, Polars pretty-prints the output object, including the column name and datatype as headers.

Lazy quick start

If we want to run this query in lazy Polars we'd write:

require 'polars'

Polars.read_csv(URI('https://j.mp/iriscsv'))
      .lazy
      .filter(Polars.col('sepal_length') > 5)
      .groupby('species')
      .agg(Polars.all.sum)
      .collect
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shape: (3, 5)
┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐
│ species    ┆ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width │
│ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │
│ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │
╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡
│ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │
└────────────┴──────────────┴─────────────┴──────────────┴─────────────┘
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Chapter 2 Polars cheat sheet in Ruby

Creating / reading DataFrames

Create DataFrame

df = Polars::DataFrame.new({
  nrs: [1, 2, 3, nil, 5],
  names: ["foo", "ham", "spam", "egg", nil],
  random: [0.3, 0.7, 0.1, 0.9, 0.6],
  groups: %w[A A B C B],
})
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shape: (5, 4)                           
┌──────┬───────┬────────┬────────┐      
│ nrs  ┆ names ┆ random ┆ groups │      
│ ---  ┆ ---   ┆ ---    ┆ ---    │      
│ i64  ┆ str   ┆ f64    ┆ str    │      
╞══════╪═══════╪════════╪════════╡      
│ 1    ┆ foo   ┆ 0.3    ┆ A      │      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤      
│ 2    ┆ ham   ┆ 0.7    ┆ A      │      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3    ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ null ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5    ┆ null  ┆ 0.6    ┆ B      │
└──────┴───────┴────────┴────────┘
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Read CSV

iris = Polars.read_csv(URI('https://j.mp/iriscsv'),
                     has_header: true)
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shape: (150, 5)                             
┌──────────────┬─────────────┬──────────────┬─────────────┬───────────┐
│ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width ┆ species   │
│ ---          ┆ ---         ┆ ---          ┆ ---         ┆ ---       │
│ f64          ┆ f64         ┆ f64          ┆ f64         ┆ str       │
╞══════════════╪═════════════╪══════════════╪═════════════╪═══════════╡
│ 5.1          ┆ 3.5         ┆ 1.4          ┆ 0.2         ┆ setosa    │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 4.9          ┆ 3.0         ┆ 1.4          ┆ 0.2         ┆ setosa    │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 4.7          ┆ 3.2         ┆ 1.3          ┆ 0.2         ┆ setosa    │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 4.6          ┆ 3.1         ┆ 1.5          ┆ 0.2         ┆ setosa    │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ ...          ┆ ...         ┆ ...          ┆ ...         ┆ ...       │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 6.3          ┆ 2.5         ┆ 5.0          ┆ 1.9         ┆ virginica │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 6.5          ┆ 3.0         ┆ 5.2          ┆ 2.0         ┆ virginica │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 6.2          ┆ 3.4         ┆ 5.4          ┆ 2.3         ┆ virginica │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ 5.9          ┆ 3.0         ┆ 5.1          ┆ 1.8         ┆ virginica │
└──────────────┴─────────────┴──────────────┴─────────────┴───────────┘
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Read parquet

Polars.read_parquet('file.parquet')
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Expressions 

df.filter(Polars.col('nrs') < 4) # symbols not work?
  .groupby('groups')
  .agg(Polars.all.sum)
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shape: (2, 4)
┌────────┬─────┬───────┬────────┐
│ groups ┆ nrs ┆ names ┆ random │
│ ---    ┆ --- ┆ ---   ┆ ---    │
│ str    ┆ i64 ┆ str   ┆ f64    │
╞════════╪═════╪═══════╪════════╡
│ A      ┆ 3   ┆ null  ┆ 1.0    │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ B      ┆ 3   ┆ null  ┆ 0.1    │
└────────┴─────┴───────┴────────┘
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Subset Observations - rows

Filter: Extract rows that meet logical criteria

df.filter(Polars.col('random') > 0.5)
df.filter(
  (Polars.col('groups') == 'B') &
  (Polars.col('random') > 0.5)
)
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shape: (1, 4)
┌─────┬───────┬────────┬────────┐
│ nrs ┆ names ┆ random ┆ groups │
│ --- ┆ ---   ┆ ---    ┆ ---    │
│ i64 ┆ str   ┆ f64    ┆ str    │
╞═════╪═══════╪════════╪════════╡
│ 5   ┆ null  ┆ 0.6    ┆ B      │
└─────┴───────┴────────┴────────┘
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Randomly select fraction of rows.

df.sample(frac: 0.5)
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# Results are random.
shape: (1, 4)
┌─────┬───────┬────────┬────────┐
│ nrs ┆ names ┆ random ┆ groups │
│ --- ┆ ---   ┆ ---    ┆ ---    │
│ i64 ┆ str   ┆ f64    ┆ str    │
╞═════╪═══════╪════════╪════════╡
│ 2   ┆ ham   ┆ 0.7    ┆ A      │
└─────┴───────┴────────┴────────┘
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Randomly select n rows.

df.sample(n: 2)
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# Results are random.
shape: (2, 4)
┌──────┬───────┬────────┬────────┐
│ nrs  ┆ names ┆ random ┆ groups │
│ ---  ┆ ---   ┆ ---    ┆ ---    │
│ i64  ┆ str   ┆ f64    ┆ str    │
╞══════╪═══════╪════════╪════════╡
│ 3    ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ null ┆ egg   ┆ 0.9    ┆ C      │
└──────┴───────┴────────┴────────┘
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select first n rows.

df.head(2)
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shape: (2, 4)                                       
┌─────┬───────┬────────┬────────┐                   
│ nrs ┆ names ┆ random ┆ groups │                   
│ --- ┆ ---   ┆ ---    ┆ ---    │                   
│ i64 ┆ str   ┆ f64    ┆ str    │                   
╞═════╪═══════╪════════╪════════╡                   
│ 1   ┆ foo   ┆ 0.3    ┆ A      │                   
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                   
│ 2   ┆ ham   ┆ 0.7    ┆ A      │                   
└─────┴───────┴────────┴────────┘
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select last n rows.

df.tail(2)
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shape: (2, 4)                                       
┌──────┬───────┬────────┬────────┐                  
│ nrs  ┆ names ┆ random ┆ groups │                  
│ ---  ┆ ---   ┆ ---    ┆ ---    │                  
│ i64  ┆ str   ┆ f64    ┆ str    │                  
╞══════╪═══════╪════════╪════════╡                  
│ null ┆ egg   ┆ 0.9    ┆ C      │                  
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                  
│ 5    ┆ null  ┆ 0.6    ┆ B      │                  
└──────┴───────┴────────┴────────┘
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Subset Observations - columns

Select multiple columns with specific names

df.select(["nrs", "names"])
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shape: (5, 2)                                       
┌──────┬───────┐                                    
│ nrs  ┆ names │
│ ---  ┆ ---   │
│ i64  ┆ str   │
╞══════╪═══════╡
│ 1    ┆ foo   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ ham   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ spam  │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ egg   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 5    ┆ null  │
└──────┴───────┘
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Select columns whose name matches regex

df.select(Polars.col("^n.*$"))
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shape: (5, 2)
┌──────┬───────┐
│ nrs  ┆ names │
│ ---  ┆ ---   │
│ i64  ┆ str   │
╞══════╪═══════╡
│ 1    ┆ foo   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ ham   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ spam  │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ egg   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 5    ┆ null  │
└──────┴───────┘
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Subsets - rows and columns

Select rows 2-4

? # Yet Range support appears to be limited
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Select columns in positions 1 and 3 (first column is 0)

?
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Select rows meeting logical condition, and only the specific columns

?
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Reshaping Data – Change layout, sorting, renaming

Append rows of DataFrames

Polars.concat([df, df2])
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Append columns of DataFrames

Polars.concat([df, df3], how: "horizontal")
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Gather columns into rows

df.melt(
  id_vars: 'nrs',
  value_vars: %w[names groups]
)
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shape: (10, 3)
┌──────┬──────────┬───────┐
│ nrs  ┆ variable ┆ value │
│ ---  ┆ ---      ┆ ---   │
│ i64  ┆ str      ┆ str   │
╞══════╪══════════╪═══════╡
│ 1    ┆ names    ┆ foo   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ names    ┆ ham   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ names    ┆ spam  │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ names    ┆ egg   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ ...  ┆ ...      ┆ ...   │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ groups   ┆ A     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ groups   ┆ B     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ groups   ┆ C     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 5    ┆ groups   ┆ B     │
└──────┴──────────┴───────┘
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Spread rows into columns

df.pivot(values: 'nrs', index: 'groups',
         columns: 'names')
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shape: (3, 6)
┌────────┬──────┬──────┬──────┬──────┬──────┐
│ groups ┆ foo  ┆ ham  ┆ spam ┆ egg  ┆ null │
│ ---    ┆ ---  ┆ ---  ┆ ---  ┆ ---  ┆ ---  │
│ str    ┆ i64  ┆ i64  ┆ i64  ┆ i64  ┆ i64  │
╞════════╪══════╪══════╪══════╪══════╪══════╡
│ A      ┆ 1    ┆ 2    ┆ null ┆ null ┆ null │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ B      ┆ null ┆ null ┆ 3    ┆ null ┆ 5    │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ C      ┆ null ┆ null ┆ null ┆ null ┆ null │
└────────┴──────┴──────┴──────┴──────┴──────┘
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Order rows by values of a column

# low to high
df.sort("random")
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shape: (5, 4)                                                           
┌──────┬───────┬────────┬────────┐                                      
│ nrs  ┆ names ┆ random ┆ groups │                                      
│ ---  ┆ ---   ┆ ---    ┆ ---    │                                      
│ i64  ┆ str   ┆ f64    ┆ str    │                                      
╞══════╪═══════╪════════╪════════╡                                      
│ 3    ┆ spam  ┆ 0.1    ┆ B      │                                      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                                      
│ 1    ┆ foo   ┆ 0.3    ┆ A      │                                      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                                      
│ 5    ┆ null  ┆ 0.6    ┆ B      │                                      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                                      
│ 2    ┆ ham   ┆ 0.7    ┆ A      │                                      
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤                                      
│ null ┆ egg   ┆ 0.9    ┆ C      │
└──────┴───────┴────────┴────────┘
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# high to low
df.sort("random", reverse: true)
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shape: (5, 4)
┌──────┬───────┬────────┬────────┐
│ nrs  ┆ names ┆ random ┆ groups │
│ ---  ┆ ---   ┆ ---    ┆ ---    │
│ i64  ┆ str   ┆ f64    ┆ str    │
╞══════╪═══════╪════════╪════════╡
│ null ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2    ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5    ┆ null  ┆ 0.6    ┆ B      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1    ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3    ┆ spam  ┆ 0.1    ┆ B      │
└──────┴───────┴────────┴────────┘
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Rename the columns of a DataFrame

df.rename({"nrs" => "idx"})
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shape: (5, 4)
┌──────┬───────┬────────┬────────┐
│ idx  ┆ names ┆ random ┆ groups │
│ ---  ┆ ---   ┆ ---    ┆ ---    │
│ i64  ┆ str   ┆ f64    ┆ str    │
╞══════╪═══════╪════════╪════════╡
│ 1    ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2    ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3    ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ null ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5    ┆ null  ┆ 0.6    ┆ B      │
└──────┴───────┴────────┴────────┘
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Drop columns from DataFrame

df.drop(["names", "random"])
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shape: (5, 2)
┌──────┬────────┐
│ nrs  ┆ groups │
│ ---  ┆ ---    │
│ i64  ┆ str    │
╞══════╪════════╡
│ 1    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2    ┆ A      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3    ┆ B      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ null ┆ C      │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5    ┆ B      │
└──────┴────────┘
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Summarize Data

Count number of rows with each unique value of variable

df["groups"].value_counts
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shape: (3, 2)
┌────────┬────────┐
│ groups ┆ counts │
│ ---    ┆ ---    │
│ str    ┆ u32    │
╞════════╪════════╡
│ B      ┆ 2      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ A      ┆ 2      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ C      ┆ 1      │
└────────┴────────┘
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Number of rows in DataFrame

df.height
# => 5
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Tuple of number of rows, number of columns in DataFrame

df.shape
# => [5, 4]
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Number of distinct values in a column

df["groups"].n_unique
# => 3
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Basic descriptive and statistics for each column

df.describe
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shape: (7, 5)                                       
┌────────────┬──────────┬───────┬──────────┬────────┐
│ describe   ┆ nrs      ┆ names ┆ random   ┆ groups │
│ ---        ┆ ---      ┆ ---   ┆ ---      ┆ ---    │
│ str        ┆ f64      ┆ str   ┆ f64      ┆ str    │
╞════════════╪══════════╪═══════╪══════════╪════════╡
│ count      ┆ 5.0      ┆ 5     ┆ 5.0      ┆ 5      │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ null_count ┆ 1.0      ┆ 1     ┆ 0.0      ┆ 0      │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ mean       ┆ 2.75     ┆ null  ┆ 0.52     ┆ null   │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ std        ┆ 1.707825 ┆ null  ┆ 0.319374 ┆ null   │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ min        ┆ 1.0      ┆ egg   ┆ 0.1      ┆ A      │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ max        ┆ 5.0      ┆ spam  ┆ 0.9      ┆ C      │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ median     ┆ 2.5      ┆ null  ┆ 0.6      ┆ null   │
└────────────┴──────────┴───────┴──────────┴────────┘
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Aggregation functions

sum min max std median mean quantile first

df.select(
  [
    # Sum values
    Polars.sum('random').alias('sum'),
    # Minimum value
    Polars.min('random').alias('min'),
    # Maximum value
    Polars.max('random').alias('max'),
    # or
    Polars.col('random').max.alias('other_max'),
    # Standard deviation
    Polars.std('random').alias('std dev'),
    # Variance
    Polars.var('random').alias('variance'),
    # Median
    Polars.median('random').alias('median'),
    # Mean
    Polars.mean('random').alias('mean'),
    # Quantile
    Polars.quantile('random', 0.75).alias('quantile_0.75'),
    # or
    Polars.col('random').quantile(0.75).alias('other_quantile_0.75'),
    # First value
    Polars.first('random').alias('first')
  ]
)
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┌─────┬─────┬─────┬───────────┬─────┬──────┬────────────┬──────────────┬───────┐
│ sum ┆ min ┆ max ┆ other_max ┆ ... ┆ mean ┆ quantile_0 ┆ other_quanti ┆ first │
│ --- ┆ --- ┆ --- ┆ ---       ┆     ┆ ---  ┆ .75        ┆ le_0.75      ┆ ---   │
│ f64 ┆ f64 ┆ f64 ┆ f64       ┆     ┆ f64  ┆ ---        ┆ ---          ┆ f64   │
│     ┆     ┆     ┆           ┆     ┆      ┆ f64        ┆ f64          ┆       │
╞═════╪═════╪═════╪═══════════╪═════╪══════╪════════════╪══════════════╪═══════╡
│ 2.6 ┆ 0.1 ┆ 0.9 ┆ 0.9       ┆ ... ┆ 0.52 ┆ 0.7        ┆ 0.7          ┆ 0.3   │
└─────┴─────┴─────┴───────────┴─────┴──────┴────────────┴──────────────┴───────┘
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Group Data

Group by values in column named "col", returning a GroupBy object

df.groupby("groups")
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All of the aggregation functions from above can be applied to a group as well

df.groupby(by = 'groups').agg(
  [
    # Sum values
    Polars.sum('random').alias('sum'),
    # Minimum value
    Polars.min('random').alias('min'),
    # Maximum value
    Polars.max('random').alias('max'),
    # or
    Polars.col('random').max.alias('other_max'),
    # Standard deviation
    Polars.std('random').alias('std_dev'),
    # Variance
    Polars.var('random').alias('variance'),
    # Median
    Polars.median('random').alias('median'),
    # Mean
    Polars.mean('random').alias('mean'),
    # Quantile
    Polars.quantile('random', 0.75).alias('quantile_0.75'),
    # or
    Polars.col('random').quantile(0.75).alias('other_quantile_0.75'),
    # First value
    Polars.first('random').alias('first')
  ]
)
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shape: (3, 12)
┌────────┬─────┬─────┬─────┬─────┬──────┬───────────────┬──────────────┬───────┐
│ groups ┆ sum ┆ min ┆ max ┆ ... ┆ mean ┆ quantile_0.75 ┆ other_quanti ┆ first │
│ ---    ┆ --- ┆ --- ┆ --- ┆     ┆ ---  ┆ ---           ┆ le_0.75      ┆ ---   │
│ str    ┆ f64 ┆ f64 ┆ f64 ┆     ┆ f64  ┆ f64           ┆ ---          ┆ f64   │
│        ┆     ┆     ┆     ┆     ┆      ┆               ┆ f64          ┆       │
╞════════╪═════╪═════╪═════╪═════╪══════╪═══════════════╪══════════════╪═══════╡
│ C      ┆ 0.9 ┆ 0.9 ┆ 0.9 ┆ ... ┆ 0.9  ┆ 0.9           ┆ 0.9          ┆ 0.9   │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ A      ┆ 1.0 ┆ 0.3 ┆ 0.7 ┆ ... ┆ 0.5  ┆ 0.7           ┆ 0.7          ┆ 0.3   │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ B      ┆ 0.7 ┆ 0.1 ┆ 0.6 ┆ ... ┆ 0.35 ┆ 0.6           ┆ 0.6          ┆ 0.1   │
└────────┴─────┴─────┴─────┴─────┴──────┴───────────────┴──────────────┴───────┘
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Additional GroupBy functions

??
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Handling Missing Data

Drop rows with any column having a null value

df.drop_nulls
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shape: (3, 4)
┌─────┬───────┬────────┬────────┐
│ nrs ┆ names ┆ random ┆ groups │
│ --- ┆ ---   ┆ ---    ┆ ---    │
│ i64 ┆ str   ┆ f64    ┆ str    │
╞═════╪═══════╪════════╪════════╡
│ 1   ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2   ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3   ┆ spam  ┆ 0.1    ┆ B      │
└─────┴───────┴────────┴────────┘
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Replace null values with given value

df.fill_null(42)
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shape: (5, 4)
┌─────┬───────┬────────┬────────┐
│ nrs ┆ names ┆ random ┆ groups │
│ --- ┆ ---   ┆ ---    ┆ ---    │
│ i64 ┆ str   ┆ f64    ┆ str    │
╞═════╪═══════╪════════╪════════╡
│ 1   ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2   ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3   ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 42  ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5   ┆ 42    ┆ 0.6    ┆ B      │
└─────┴───────┴────────┴────────┘
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Replace null values using forward strategy

df.fill_null(strategy: "forward")
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shape: (5, 4)
┌─────┬───────┬────────┬────────┐
│ nrs ┆ names ┆ random ┆ groups │
│ --- ┆ ---   ┆ ---    ┆ ---    │
│ i64 ┆ str   ┆ f64    ┆ str    │
╞═════╪═══════╪════════╪════════╡
│ 1   ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2   ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3   ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3   ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5   ┆ egg   ┆ 0.6    ┆ B      │
└─────┴───────┴────────┴────────┘
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Other fill strategies are "backward", "min", "max", "mean", "zero" and "one"

Replace floating point Nan values with given value

dfn = Polars::DataFrame.new(
  {
    "a" => [1.5, 2, Float::NAN, 4],
    "b" => [0.5, 4, Float::NAN, 13]
  }
)
dfn.fill_nan(99)
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Make New Columns

Add a new columns to the DataFrame

df.with_column(
  (Polars.col('random') * Polars.col('nrs'))
  .alias('product')
)
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shape: (5, 5)
┌──────┬───────┬────────┬────────┬─────────┐
│ nrs  ┆ names ┆ random ┆ groups ┆ product │
│ ---  ┆ ---   ┆ ---    ┆ ---    ┆ ---     │
│ i64  ┆ str   ┆ f64    ┆ str    ┆ f64     │
╞══════╪═══════╪════════╪════════╪═════════╡
│ 1    ┆ foo   ┆ 0.3    ┆ A      ┆ 0.3     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 2    ┆ ham   ┆ 0.7    ┆ A      ┆ 1.4     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 3    ┆ spam  ┆ 0.1    ┆ B      ┆ 0.3     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ null ┆ egg   ┆ 0.9    ┆ C      ┆ null    │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 5    ┆ null  ┆ 0.6    ┆ B      ┆ 3.0     │
└──────┴───────┴────────┴────────┴─────────┘
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Add several new columns to the DataFrame

df.with_columns(
  [
    (Polars.col('random') * Polars.col('nrs'))
    .alias('product'),
    Polars.col('names').str.lengths
    .alias('names_lengths')
  ]
)
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shape: (5, 6)
┌──────┬───────┬────────┬────────┬─────────┬───────────────┐
│ nrs  ┆ names ┆ random ┆ groups ┆ product ┆ names_lengths │
│ ---  ┆ ---   ┆ ---    ┆ ---    ┆ ---     ┆ ---           │
│ i64  ┆ str   ┆ f64    ┆ str    ┆ f64     ┆ u32           │
╞══════╪═══════╪════════╪════════╪═════════╪═══════════════╡
│ 1    ┆ foo   ┆ 0.3    ┆ A      ┆ 0.3     ┆ 3             │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2    ┆ ham   ┆ 0.7    ┆ A      ┆ 1.4     ┆ 3             │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3    ┆ spam  ┆ 0.1    ┆ B      ┆ 0.3     ┆ 4             │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ null ┆ egg   ┆ 0.9    ┆ C      ┆ null    ┆ 3             │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 5    ┆ null  ┆ 0.6    ┆ B      ┆ 3.0     ┆ null          │
└──────┴───────┴────────┴────────┴─────────┴───────────────┘
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Add a column at index 0 that counts the rows

df.with_row_count
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shape: (5, 5)
┌────────┬──────┬───────┬────────┬────────┐
│ row_nr ┆ nrs  ┆ names ┆ random ┆ groups │
│ ---    ┆ ---  ┆ ---   ┆ ---    ┆ ---    │
│ u32    ┆ i64  ┆ str   ┆ f64    ┆ str    │
╞════════╪══════╪═══════╪════════╪════════╡
│ 0      ┆ 1    ┆ foo   ┆ 0.3    ┆ A      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1      ┆ 2    ┆ ham   ┆ 0.7    ┆ A      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2      ┆ 3    ┆ spam  ┆ 0.1    ┆ B      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 3      ┆ null ┆ egg   ┆ 0.9    ┆ C      │
├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 4      ┆ 5    ┆ null  ┆ 0.6    ┆ B      │
└────────┴──────┴───────┴────────┴────────┘
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Rolling Functions

The following rolling functions are available

df.select(
  [
    # Rolling maximum value
    Polars.col('random')
          .rolling_max(2)
          .alias('rolling_max'),
    # Rolling mean value
    Polars.col('random')
          .rolling_mean(2)
          .alias('rolling_mean'),
    # Rolling median value
    Polars.col('random')
          .rolling_median(2, min_periods: 2)
          .alias('rolling_median'),
    # Rolling minimum value
    Polars.col('random')
          .rolling_min(2)
          .alias('rolling_min'),
    # Rolling standard deviation
    Polars.col('random')
          .rolling_std(2)
          .alias('rolling_std'),
    # Rolling sum values
    Polars.col('random')
          .rolling_sum(2)
          .alias('rolling_sum'),
    # Rolling variance
    Polars.col('random')
          .rolling_var(2)
          .alias('rolling_var'),
    # Rolling quantile
    Polars.col('random')
          .rolling_quantile(
            0.75,
            window_size: 2,
            min_periods: 2
          )
          .alias('rolling_quantile'),
    # Rolling skew
    Polars.col('random')
          .rolling_skew(2)
          .alias('rolling_skew')
    # Rolling custom function
    # Polars.col('random')
    # .rolling_apply(
    #   function = np.nanstd, window_size = 2
    # )
    # .alias('rolling_apply')
  ]
)
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shape: (5, 9)
┌───────────┬────────────┬────────────┬───────────┬─────┬───────────┬───────────┬────────────┬────────────┐
│ rolling_m ┆ rolling_me ┆ rolling_me ┆ rolling_m ┆ ... ┆ rolling_s ┆ rolling_v ┆ rolling_qu ┆ rolling_sk │
│ ax        ┆ an         ┆ dian       ┆ in        ┆     ┆ um        ┆ ar        ┆ antile     ┆ ew         │
│ ---       ┆ ---        ┆ ---        ┆ ---       ┆     ┆ ---       ┆ ---       ┆ ---        ┆ ---        │
│ f64       ┆ f64        ┆ f64        ┆ f64       ┆     ┆ f64       ┆ f64       ┆ f64        ┆ f64        │
╞═══════════╪════════════╪════════════╪═══════════╪═════╪═══════════╪═══════════╪════════════╪════════════╡
│ null      ┆ null       ┆ null       ┆ null      ┆ ... ┆ null      ┆ null      ┆ null       ┆ null       │
├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0.7       ┆ 0.5        ┆ 0.5        ┆ 0.3       ┆ ... ┆ 1.0       ┆ 0.08      ┆ 0.7        ┆ -4.3368e-1 │
│           ┆            ┆            ┆           ┆     ┆           ┆           ┆            ┆ 6          │
├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0.7       ┆ 0.4        ┆ 0.4        ┆ 0.1       ┆ ... ┆ 0.8       ┆ 0.18      ┆ 0.7        ┆ 3.8549e-16 │
├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0.9       ┆ 0.5        ┆ 0.5        ┆ 0.1       ┆ ... ┆ 1.0       ┆ 0.32      ┆ 0.9        ┆ 0.0        │
├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 0.9       ┆ 0.75       ┆ 0.75       ┆ 0.6       ┆ ... ┆ 1.5       ┆ 0.045     ┆ 0.9        ┆ 0.0        │
└───────────┴────────────┴────────────┴───────────┴─────┴───────────┴───────────┴────────────┴────────────┘
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Window Functions

Window functions allow to group by several columns simultaneously

df.select(
  [
    'names',
    'groups',
    Polars.col('random').sum.over('names')
    .alias('sum_by_names'),
    Polars.col('random').sum.over('groups')
    .alias('sum_by_groups')
  ]
)
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Combine Data Sets 

df_1 = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df_2 = Polars::DataFrame.new(
  {
    "apple" => ["x", "y", "z"],
    "ham" => ["a", "b", "d"]
  }
)
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shape: (3, 3)                           
┌─────┬─────┬─────┐                     
│ foo ┆ bar ┆ ham │                     
│ --- ┆ --- ┆ --- │                     
│ i64 ┆ f64 ┆ str │                     
╞═════╪═════╪═════╡                     
│ 1   ┆ 6.0 ┆ a   │                     
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤                     
│ 2   ┆ 7.0 ┆ b   │                     
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤                     
│ 3   ┆ 8.0 ┆ c   │                     
└─────┴─────┴─────┘
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shape: (3, 2)                           
┌───────┬─────┐                         
│ apple ┆ ham │                         
│ ---   ┆ --- │                         
│ str   ┆ str │
╞═══════╪═════╡
│ x     ┆ a   │
├╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ y     ┆ b   │
├╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ z     ┆ d   │
└───────┴─────┘
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Inner Join

Retains only rows with a match in the other set.

df_1.join(df_2, on: "ham")
df_1.join(df_2, on: "ham", how: "inner")
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shape: (2, 4)
┌─────┬─────┬─────┬───────┐                           
│ foo ┆ bar ┆ ham ┆ apple │                           
│ --- ┆ --- ┆ --- ┆ ---   │                           
│ i64 ┆ f64 ┆ str ┆ str   │                           
╞═════╪═════╪═════╪═══════╡                           
│ 1   ┆ 6.0 ┆ a   ┆ x     │                           
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤                           
│ 2   ┆ 7.0 ┆ b   ┆ y     │                           
└─────┴─────┴─────┴───────┘
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Left Join

Retains each row from "left" set (df).

df_1.join(df_2, on: "ham", how: "left")
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shape: (3, 4)
┌─────┬─────┬─────┬───────┐
│ foo ┆ bar ┆ ham ┆ apple │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ f64 ┆ str ┆ str   │
╞═════╪═════╪═════╪═══════╡
│ 1   ┆ 6.0 ┆ a   ┆ x     │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2   ┆ 7.0 ┆ b   ┆ y     │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3   ┆ 8.0 ┆ c   ┆ null  │
└─────┴─────┴─────┴───────┘
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Outer Join

Retains each row, even if no other matching row exists.

df_1.join(df_2, on: "ham", how: "outer")
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shape: (4, 4)
┌──────┬──────┬─────┬───────┐
│ foo  ┆ bar  ┆ ham ┆ apple │
│ ---  ┆ ---  ┆ --- ┆ ---   │
│ i64  ┆ f64  ┆ str ┆ str   │
╞══════╪══════╪═════╪═══════╡
│ 1    ┆ 6.0  ┆ a   ┆ x     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ 7.0  ┆ b   ┆ y     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ null ┆ d   ┆ z     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ 8.0  ┆ c   ┆ null  │
└──────┴──────┴─────┴───────┘
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Anti Join

Contains all rows from df that do not have a match in other_df

df_1.join(df_2, on: "ham", how: "anti")
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┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 3   ┆ 8.0 ┆ c   │
└─────┴─────┴─────┘
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