I just released an update to my joinable crate (source code here) as well as a new irisdata crate (source code) well-known in the data science field.

This update to joinable renames the Joinable trait to JoinableGrouped to reflect that the results (at least of inner- and outer-joins) group the right-hand side. It also adds a new trait with the Joinable name that behaves perhaps more intuitively – each left-hand record can be yielded multiple times (as matches are found).

Joinable only defines inner_join and outer_join methods. JoinableGrouped defines inner_join_grouped, outer_join_grouped, semi_join, and anti_join.

use std::cmp::Ordering;

use irisdata::{Species, IRIS_DATA};
use joinable::{JoinableGrouped, RHS};

struct IrisData {
    species: Species,
    common_name: &'static str,
    average_sepal_length: f32,
    average_sepal_width: f32,
    average_petal_length: f32,
    average_petal_width: f32,

fn main() {
    let common_names = [
        (Species::IrisVersicolor, "blue flag"),
        (Species::IrisVersicolor, "harlequin blueflag"),
        (Species::IrisVersicolor, "larger blue flag"),
        (Species::IrisVersicolor, "northern blue flag"),
        (Species::IrisVersicolor, "poison flag"),
        (Species::IrisVirginica, "Virginia blueflag"),
        (Species::IrisVirginica, "Virginia iris"),
        (Species::IrisVirginica, "great blue flag"),
        (Species::IrisVirginica, "southern blue flag"),

    let joined = common_names
        .inner_join_grouped(RHS::new_unsorted(&IRIS_DATA[..]), |(lhs_species, _), r| {
            if *lhs_species == r.species {
            } else {
        .map(|(lhs, grp)| IrisData {
            species: lhs.0,
            common_name: lhs.1,
            average_sepal_length: grp.iter().map(|i| i.sepal_length).sum::<f32>() / grp.len() as f32,
            average_sepal_width: grp.iter().map(|i| i.sepal_width).sum::<f32>() / grp.len() as f32,
            average_petal_length: grp.iter().map(|i| i.petal_length).sum::<f32>() / grp.len() as f32,
            average_petal_width: grp.iter().map(|i| i.petal_width).sum::<f32>() / grp.len() as f32,